EcoFlow has launched a Mother’s Day Sale with up to 64% discounts that will be running past the upcoming holiday, and with its launch, the brand is also offering a 24-hour flash sale window with up to 49% savings on four units. Starting at the lowest price among the bunch, you can find EcoFlow’s DELTA 2 Max Portable Power Station down at $829 shipped, beating out its Amazon pricing by $70. While you may see it carrying a $1,599 MSRP tag, you can more often find it at Amazon only climbing as high as $1,399 since fall 2025, with discounts only having gone as low as $849 up until today. Now, you’re getting a 49% markdown for $570 off the going rate (and $770 off the MSRP), landing it at a new all-time low price. Head below to check out the other 24-hour flash offers while they’re still around. One of EcoFlow’s more popular backup power solutions, the DELTA 2 Max power station brings along an ample 2,048Wh LiFePO4 capacity to your out-of-home adventures alongside at-home needs. There are 15 ports on this unit (6x ACs, 4x USB-As, 2x USB-Cs, 2x DCs, and a car port) for a versatile array of connection options for devices and appliances, delivering up to 2,400W of steady power while also surging as high as 3,400W. It also brings along four primary recharging methods. There’s the usual AC outlet charging that you would expect that can have it back to 80% in around 68 minutes, as well as a maximum 1,000W solar panel input that can top off the battery in around 2.3 hours with ideal sunlight for the entire period. You can also charge as you drive via the carport, with the last option being dual AC and solar charging simultaneously. You can find a bunch more full sales on backup power solutions in our dedicated power stations hub here.
Contract manufacturer American Steel and Aluminum (ASA) has opened a new 50,000 sq-ft facility in Syracuse, New York, to meet growing demand from markets including renewable energy. This is ASA’s second facility in Syracuse. The company hopes to shorten product lead times and introduce new processing capabilities in this new plant. “This expansion is a…
Latest report from the International Energy Agency’s (IEA) Photovoltaic Power Systems Programme (PVPS) finds measurable advancements in PV module recycling performance compared to its prior studies, including higher material recovery rates, improved process yields and higher output purity. Image: Soren H, Unsplash PV module recycling is making “meaningful advancement” across both commercial and pilot-scale technologies, according to a new report from the IEA-PVPS. The latest Task 12 report presents new and updated life cycle inventory (LCI) data. Its sources include two U.S.-based commercial crystalline silicon (c-Si) module recyclers, Solarcycle and SPR, Italian pilot-scale c-Si module recycler 9-Tech, the EU-funded Photorama project and updated global LCI data on cadmium telluride (CdTe) modules from U.S.-based thin-film solar module specialist First Solar. The report says that, in comparison to prior Task 12 studies, the research found measurable advancements in PV module recycling performance across higher material recovery rates, improved process yields and higher output purity. It says recovery rates for high-value materials have “improved significantly” compared to a 2024 study, when the pure-mechanical benchmark recycling technology did not recover silicon or silver. “In the current study, SPR reports recovery of 98 weight percent (wt. %) of input silicon using a pure-mechanical process at commercial scale, while 9-Tech achieves 95 wt. % silicon recovery in a pilot-scale system that employs mechanical, thermal and chemical recycling processes,” the report explains. IEA-PVPS also highlights the recovery of non-ferrous metals, including silver, aluminum, and copper, which the report says represents “a new capability for mechanical processes at scale.” “Solarcycle reports recovery of nearly 92 wt. % for silver and approximately 95 wt.% for copper, while SPR reports 99% copper recovery,” the report continues. “In its pilot-scale system, 9-Tech achieves recovery rates of 95 wt. % for copper, 90 wt. % for silver, and 90 wt. % for aluminum. First Solar reports recovery of more than 90 wt. % for the semiconductor material and more than 90 wt. % of metals beyond the semiconductor materials.” The report then notes developments in output purity, further enhancing the value of recovering materials. “In the current study, Photorama achieves 5N purity for silicon and greater than 2N purity for silver,” IEA-PVPS’ results add. “SPR reports 99% purity for recovered copper and other trace metals through mechanical processing, while 9-Tech achieves up to 95% purity for copper and silver in recovered metallic powders.” Glass recovery has progressed on 2024 levels, IEA-PVPS’ report continues, explaining that advances in mechanical, thermal and other separation approaches, such as flash lamp separation and water jet cleaning, can achieve high glass yield and purity but may require more energy than pure mechanical processes. IEA-PVPS’ report outlines that applications for the reuse of recovered materials is expanding. It says recovered silicon is being used for battery anodes, sputter targets, and metallurgical grade applications while non-ferrous metals are sent to metal recyclers, smelters, and refineries, helping to reduce reliance on new resources. The report also finds evidence of glass recovery being reused in flat glass production. Despite the overall progress, the report stresses that there are persistent gaps in material quality reporting, system boundary harmonization and energy-use characterization. It also suggests that additional information on downstream use and treatment pathways would help future efforts to quantify material recovery, energy recovery and landfill disposal, in turn improving assessment of reuse pathways in future updates. “Continued collaboration among recyclers, researchers, policymakers, and standard-setting bodies will be essential to improve data consistency, guide research and development priorities and support the development of circular, high-value pathways for PV materials,” IEA-PVPS’ report concludes. “A forthcoming Task 12 study will develop life cycle assessment-based analyses to assess life-cycle implications across different PV recycling pathways.” This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Patrick Jowett Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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HERZILIYA PITUACH, Israel(AP) — HERZILIYA PITUACH, Israel (AP) — SolarEdge Technologies Inc. (SEDG) on Wednesday reported a loss of $57.4 million in its first quarter. On a per-share basis, the Herziliya Pituach, Israel-based company said it had a loss of 95 cents. Losses, adjusted for stock option expense and non-recurring costs, were 43 cents per share. The results did not meet Wall Street expectations. The average estimate of nine analysts surveyed by Zacks Investment Research was for a loss of 23 cents per share. The photovoltaic products maker posted revenue of $310.5 million in the period, which topped Street forecasts. Nine analysts surveyed by Zacks expected $303.4 million. For the current quarter ending in June, SolarEdge said it expects revenue in the range of $325 million to $355 million. _____ This story was generated by Automated Insights (http://automatedinsights.com/ap) using data from Zacks Investment Research. Access a Zacks stock report on SEDG at https://www.zacks.com/ap/SEDG Copyright 2025 Associated Press. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed. To stream KENS 5 on your phone, you need the KENS 5 app. Next up in 5 Example video title will go here for this video Next up in 5 Example video title will go here for this video
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 15, Article number: 43600 (2025) Cite this article 1138 Accesses Metrics details In recent years, the growing interest in renewable energy has increased attention on photovoltaic systems. While traditional photovoltaic systems are typically built on the ground, floating photovoltaic power generation involves placing photovoltaic panels on floating platforms in water. When these platforms are on the sea surface, the solar radiation received by the photovoltaic array changes periodically due to ocean waves, leading to fluctuations in the maximum power point temperature and light radiation. This results in varying power generation capacities and an unbalanced energy supply at different locations. The current fixed DC bus voltage further exacerbates this issue, reducing power generation efficiency. A new multi-winding permanent magnet synchronous motor (MWPMSM) system for distributed energy is proposed to address these challenges. This system features multiple independent DC buses, each operating at a different voltage level to ensure compatibility with the energy absorbed by each part of the photovoltaic array. The MWPMSM system is designed with a specific structure and mathematical model, and its structure parameters are carefully chosen and verified through finite element analysis. A control strategy for the MWPMSM is then established, and an experimental platform is used to demonstrate that the system maintains high efficiency and unchanged output power while ensuring a balanced power ratio (the Maximum error of winding power ratio is 6.7%). With the increasing energy demand, the siting of photovoltaic power stations worldwide is mainly based on land. However, land resources are extremely precious, which poses a significant barrier to the promotion of photovoltaic power stations. Floating photovoltaic systems, due to their low cost, ease of installation and maintenance, and high-power generation efficiency, have become a hot topic of research. Many countries and regions have begun to apply this technology on a large scale1,2. The PV characteristic curve is shown in Fig. 1, The blue dashed line represents the MPPT curve. To enhance the value of photovoltaic utilization, maximum power point tracking (MPPT) technology is very important3. The V-I curve of photovoltaic cells is similar to a parabola. When the voltage increases from zero, the current remains constant, and when it increases to the maximum power point (MPP), the current begins to decrease as the voltage increases, so it is very necessary to find the maximum power point4. PV characteristic curve under temperature and light intensity variations. Figure 2 shows the offshore work platform is powered by different offshore floating photovoltaic arrays, and the offshore floating power supply at different locations. When the floating body swings under the action of ocean waves, the equivalent light intensity on the surface of each photovoltaic cell changes accordingly, and with the change of light intensity, the V-I of the photovoltaic array also changes, and the maximum power point also changes5,6,7,8,9. Since the light and wave floating conditions are different, as shown in Fig. 3, the tradition is to import the power supply into the same voltage busbar, adopt a single busbar voltage, and then use it for the work platform water pump motor10. The problem with this form is due to adopting a single bus voltage, where different floating photovoltaic arrays cannot operate at their respective maximum power points11. This situation complicates energy scheduling and collaborative motor control, ultimately reducing the overall efficiency of the energy management system12. Offshore floating photovoltaic power generation system. Traditional offshore work platform powered from floating photovoltaic arrays. The solution is to adopt the idea of power distribution and management of distributed generation and nearby conversion13, with energy balance and energy flow efficiency as the optimization goals, split the control model of the energy management system into multiple subsystems, and implement the distributed collaborative control method to make each module work together to maintain power balance and improve energy utilization efficiency. Since the energy management system uses distributed generation, the motor should also be in the form of multiple modules, as shown in Fig. 4(such as division into three parts). Multiple modules powered from floating photovoltaic arrays. In Fig. 4, the DC bus voltage is no longer fixed at a single value as shown in Fig. 3. The photovoltaic arrays in different regions can track their own MPP due to changes in irradiance caused by factors such as waves and shading, The power-distribution controller allocates power to each winding based on the conditions of different areas. Thereby improving the overall efficiency of the system. Permanent magnet synchronous motors (PMSM) have many advantages, such as energy saving, high power density, high efficiency, and fast dynamic response. It is very suitable for offshore working platforms with high requirements for energy saving and fast response. Sun P. et al. proposed that the two sets of windings are with double winding hybrid exciters; these exciters are on the stator and rotor, respectively. In this drive system, two Converters and heat dissipation-related challenges require that the inner rotor winding cannot be ignored. Jiang X et al. proposed double-winding Fault-tolerant machines, in which double windings were backed up to have the same polar pair numbering as each other. Reliability, however, the drive system is improved at the expense of torque density. The motor is not conducive to a floating photovoltaic distributed power generation system. Hui‑Min Wang proposed the single‑winding arrangement for arbitrary multiphase bearingless permanent magnet synchronous motor14,15. Most of these motors adopt a multiphase structure. Inaccurate position detection can easily lead to torque imbalance16. Shaoshuai Wang et al. proposed the flux-modulated multi-winding PM machine (FMM-PMM) for electric vehicles, by adopting two stator windings with different pole-pairs aiming to achieve multiple harmonics utilization with high efficiency. Due to the technical features of harmonic utilization and magnetic gear effect, the torque density of the proposed PM machine can be improved without worsening the power factor17. Furthermore, increasing the number of phases will bring complexity to the design of stator and rotor structures, which is not conducive to multi-modular design. In this paper, a new type of outer rotor permanent magnet synchronous motor is proposed to reduce the unbalanced torque caused by multiple modules, and it can well meet the requirements of distributed power supply. Considering the floating photovoltaic distributed power generation system application scenario, the motor has a disc-type outer rotor structure, as shown in Fig. 5, and the winding adopts a design scheme of the same phase18. At the same time, due to its redundant structural design, three sets of accumulators can be independently powered and generated using a motor controller PWM The signal directly drives three sets of inverters in parallel, ensuring that the three sets of windings of the MWPMSM have three times the output power of the conventional permanent magnet synchronous motor when driven by stator current at the same frequency and phase. In this way, the MWPMSM reduces the number of turns per set of stator windings compared to conventional permanent magnet motors of equal power by 1/3, while the total number of turns of the stator winding remains unchanged. Moreover, the working characteristics of the MWPMSM are better when analyzed from the point of view of the actual driving capability. Each of the three sets of stator windings of the motor is arranged in a phase-free angle difference arrangement and is not electrically connected Y.Y. Type connection. The schematic diagram of the stator structure is shown in Fig. 5b. So, the motor has a total of nine outlet ends, which need the controller to provide each phase stator current, and at the same time, the control strategy must be used to ensure the same frequency and phase. At the same time, from the perspective of motor design and operation, this structure can ensure the balance of forces on the motor shaft, reduce the uneven rotation of the motor rotor, and reduce the vibration and noise generated by the motor. The winding motor winding electromagnetic wire is in the form of multiple strands and is insulated between each strand. The motor wire turns and outlet ends are evenly divided according to the number of strands 3 Sections, each of which may be used as a separate motor winding, A1B1C1 Composition of motor windings 1, A2B2C2 Composition of motor windings 2, A3B3C3 Composition of motor windings 3, are all controlled by an independent controller19. Each part of the power is designed for 100W, the motor operating voltage is set at 35V ~ 100V. Overall structure diagram of MWPMSM. The MWPMSM is a non-linear system with multi-variable, multi-input features. To facilitate the analysis and the establishment of the model, the following ideal assumptions are made20: 1. The three-phase windings are symmetrically distributed, and the windings are arranged symmetrically in spatial position. 2. Ignoring core saturation, excluding eddies and hysteresis phenomena. 3. The magnetic potential generated by the stator winding current in the air gap is sinusoidally distributed, ignoring the higher harmonics. Mathematical model of MWPMSM under a stationary coordinate system. Voltage Eq. (1): Magnetic flux Eq. (2): where: where, uai, ubi, and uci (i = 1,2,3) are the three-phase stator voltages of windings, respectively. iai, ibi, ici, (i = 1,2,3)are three-phase stator currents of windings; Rs is the winding resistance of the stator per phase; ψai, ψbi, ψci, (i = 1,2,3) are three-phase stator fluxes of windings, respectively. The flux coefficient matrix γ represents the position distribution relationship of each phase winding; θ represents the electrical angle between the rotor pole position and the winding shaft of the stator phase A; Ls represents the motor inductance coefficient matrix, respectively. Lm1, Lm2, and Lm3 are the mutual inductance coefficients between winding 1 and winding 2, winding 1 and winding 3, and winding 2 and winding 3, respectively. Lr1, Lr2, and Lr3 are the leakage inductance coefficients of windings 1,2, and 3. ψf is the flux of a permanent magnet. Electromagnetic Torque Eq. (9): Since the motors designed herein are three sets of windings, all in phase Y. Shift 0° The electrical angles are arranged. At the same time, the electromagnetic coupling between the windings of this motor is small, and the difference between the self-induction coefficient, mutual induction coefficient, and leakage induction coefficient is minimal, so coordinate transformation and establishment are being carried out d-q Influences such as winding mutual inductance are ignored during analysis of coordinate systems20. The electrical characteristics of each set of motor windings during operation are the same as those of ordinary PMSM21. Thus, only one set of windings needs to be mathematically modeled for subsequent analysis, such as coordinate transformation. Furthermore, the total electromagnetic torque Te of the MWPMSM is the vector sum of the three sets of windings superimposed on each other, so: To control the motor more accurately, first of all, we need to consider the design structure of the motor, simulate the electromagnetic field, and analyze the finite element model of the motor according to the structural parameters and basic performance of the motor. This section mainly discusses the relevant technical indicators of multi-winding motor design and the boundary conditions of motor design, to study the calculation method of the motor magnetic circuit, and establish the finite element model of the motor. The basic requirement of MWPMSM is to use a total of 2 motors on the left and right as the propulsion device, each motor includes 3 sets of windings, and each set of windings and its corresponding drive are a motor module. According to the project requirements, the motor system design basic requirement is shown in Table 1: The MWPMSM is mainly composed of four parts: motor stator, rotor, winding, and permanent magnet. This section mainly discusses the design methods of these four parameters. The stator parameters of the motor mainly include the parameters of the stator core and the parameters of the slot. The parameters of the stator core mainly involve the inner diameter of the stator punch, the outer diameter of the stator, and the effective length of the core. The size of the inner diameter of the stator and the effective length of the core are inextricably linked to the performance of the motor. Therefore, an important step in the design of a motor is to determine the main dimensions of the motor. The parameters of the groove include the selection of the number of grooves and the determination of the size of the groove. The selection of the number of slots is closely related to the design of the motor winding. In terms of winding design, there are usually single- or double-layer windings. Single-layer winding refers to a slot in only one group of windings, only connected with the winding of another slot, forming a closed loop. For single-layer winding, the selection of the number of slots should meet the number of phases and the number of poles of the motor at the same time. Double-layer winding refers to a slot with two layers of windings, the windings are separated by insulating materials. For multi-layer windings, the selection of the number of slots should meet the Eq. (11) 22. where Q1 indicates the number of stator slots for the motor, k indicates a value greater than or equal to 1, a represents the number of winding layers. In terms of motor rotor parameters, two aspects are mainly considered: the outer diameter of the rotor and the length of the rotor core. The selection of the outer diameter of the rotor should not be too small, as the outer diameter of the rotor is too small, there will be insufficient space to achieve the installation of the motor poles and rotating shafts. Motor winding parameters: where NФ1 represents the number of series conductors per phase of the motor, NS1 represents the number of conductors per slot of the motor, and α1 represents the number of parallel branches of the motor. When designing PMSM, the magnetic field of PMSM is usually converted into a magnetic circuit for research and analysis, and a preliminary design is carried out to achieve the purpose of simplifying the calculation. Through the calculation of the magnetic circuit, the key parameters such as the magnetic flux of the motor and the magnetic density of the motor can be calculated. The following is a study of the magnetic circuit calculation in the case of no load of the motor. The expression for the calculated length of the stator tooth magnetic circuit is Eq. (13). where h′t1 represents the calculated length of the stator tooth magnetic circuit; hs1, hs2, and r represent the specific size parameters of the cogging. The magnetic pressure drop of the stator tooth can be obtained by calculating the length of the stator tooth magnetic circuit, and its expression is Eq. (14). where Ft1 represents the magnetic pressure drop of the stator teeth; Ht10 can query the DC magnetization characteristic table of stator material to obtain the parameter value, which indicates the degree of magnetization. After a series of parameters about the stator teeth have been calculated, the parameters about the stator yoke need to be calculated. The stator yoke calculates the height expression in Eq. (15). where h′j1 denotes the calculated height of the stator yoke; D1 denotes the outer diameter of the stator. Before calculating the magnetic circuit, it is necessary to calculate the pole distance and relative recovery permeability of the motor: The no-load working point of the permanent magnet that will be obtained bm0, with the no-load work point set at the beginning, b′m0 Compare and calculate, if the error value between the two Δbm0 Less than 1%, it can be proved that the no-load working point set at the beginning meets the current requirements. If the current requirements are not met, one unloaded working point needs to be reassumed and a series of calculations performed. It’s error value Δbm0 the expression in Eq. (17) 23. The above calculation of the magnetic circuit of the motor was carried out, based on the above formula, and at the same time, concerning the design of multi-winding motors at home and abroad, the main dimensions of each part of the motor and the materials used in each part were established, the basic parameters of the motor The numbers are as shown in Table 2. According to the above motor design parameters and design requirements, the surface-mounted external rotor structure of the MWPMSM was finally established, and the finite element model was established by design software. Through the finite element software, you can quickly check whether the design parameters of each part of the motor meet the requirements, play a role in verification, and optimize and modify the motor in combination with the simulation results24. After modeling the motor, the design is first analyzed for a no-load simulation. The no-load state refers to no current excitation source, and only permanent magnets are excited separately25. The following are the simulation results: Figure 6 shows that the no-load air gap magnetic dense waveform has good waveform smoothness, the flux density waveform has two zero crossings within each cycle, which is a typical characteristic of PMSM and helps to produce stable torque, and the cogging torque has little influence, so the design is reasonable. The amplitude of the air-gap magnetic dense waveform is about 0.8 T when the motor is no-load, which meets the performance requirements of the motor26. Relationship between the magnetic density of the air gap and the angle of the rotor position. Figure 7 is the vector magnetic potential distribution diagram at the 45-degree rotor position angle, from which it can be seen that the magnetic field distribution is relatively uniform, the magnetic field is not saturated, and the motor still has a large adjustment range27. Vector potential distribution at 45 degrees. Figure 8 shows the relationship between the flux and the current of the DQ axis when the stator current of the motor changes, the flux value is within a reasonable range, the core has not reached saturation, and the torque and speed of the motor can be accurately controlled by controlling the current of the D axis and the Q axis. By adjusting the magnitude and phase of the current in the D-axis and Q-axis, the magnetic field-oriented control of the motor can be realized, thereby improving the efficiency and performance of the motor28. Relationship between DQ axis flux and DQ current. Figure 9 shows the relationship between the DQ axis inductance and the stator current of the motor in a permanent magnet synchronous motor, the DQ axis inductance is a parameter that describes the internal inductance of the motor, and in a permanent magnet synchronous motor, the DQ axis inductance is usually nonlinear and related to the current magnitude and magnetic field distribution, it is a linear relationship within a certain range. By controlling the current in the D-axis and Q-axis, the inductance of the motor can be affected, and the performance and control characteristics of the motor can be affected29. Relationship between DQ axis inductance and DQ current. Figure 10 shows the relationship between the control angle and the torque, and marks the working point of the motor at 30A. The control angle refers to the relative position between the magnetic field of the rotor and the magnetic field of the stator, controlling the motor. A reasonable selection of control angle can improve the efficiency, response speed, and stability of the motor; while reducing energy loss and vibration noise, As can be seen in Fig. 10, the torque at the working point is about 9.5Nm to meet the design requirements30. Relationship between control angle V.S. torque. Figure 11 shows the relationship between the air gap magnetic field and the rotor position angle, and it can be seen from the air gap magnetic field curve that the magnetic field density amplitude meets the design requirements, which is about 0.9T, and there is a slight deformation at the individual peaks, which can be solved by optimizing the motor cogging in the later stage31. Air gap magnetic field V.S. rotor position angle. Figure 12 shows the relationship between output torque and speed, from which it can be seen that the motor can still maintain the maximum torque of 9.5Nm at 900r/min, and the torque is still greater than 3Nm when the motor reaches 2600r/min, which meets the design requirements32. Output torque V.S. speed. Figure 13 shows the relationship between the power factor of the motor and the speed, and it can be seen that in the whole speed range, the power factor varies between 0.974–1, the electric energy utilization rate of the motor is higher, the reactive power is smaller, and the motor efficiency is higher33. Motor power factor V.S. speed. How to keep the current phase of the three sets of windings consistent is the core problem, the research on the control strategy of MWPMSM mainly includes the following aspects: first, the comparison and analysis of different controllers selected for multi-motor modules are carried out, and the reasonable parameters of the best controller are determined; Second, based on theoretical analysis, the tracking effect of the winding current of the remaining motor modules on the reference current phase after selecting different controllers is simulated and compared34. The third is to carry out system simulation experiments of multi-winding motors to verify the feasibility of their coordinated operation35. Figure 14 shows the block diagram of the MWPMSM control system and control strategy. Figure 14b gives the control strategy diagram. To simplify control, the (i_{d}^{ * } = 0) control method is adopted to achieve static decoupling control of active and reactive currents36. After the Park transformation, the electromagnetic torque equation is (18): Control system and control strategy diagram. where Ω is motor angular velocity, The electromagnetic torque is proportional to iq , and the electromagnetic power is proportional to Te. Therefore, by proportionally distributing iq , power can be proportionally distributed at a certain speed. KPD1, KPD2 are Power-distribution proportionality coefficients. iA1 is multiplied by KPD1 to serve as the basis for tracking current in winding 2, iA1 is multiplied by KPD2 to serve as the basis for tracking current in winding 3, and so on. Because the reference current iA1 is an AC input signal, the use of PI control cannot improve the phase-locking accuracy, and there is a system steady-state error. Due to the wide speed range of the motor, the corresponding current frequency conversion range is large, so the current should be tracked over a wide range37. As shown in Fig. 15, an SPLL based on an orthogonal signal generator controller can be used to realize the tracking of the current phase of the other two sets of windings to the phase of the reference current iA138,39. Structure diagram of SPLL based on orthogonal signal generator. From Fig. 15, Small signal analysis is done using the network theory, the PLL closed-loop Phase transfer function can be written as follows: Eq. (19) 40. Comparing the closed-loop phase transfer function to the generic second-order system transfer function, the transfer function Eq. (20) can be obtained. where Ti is the sample time. Figure 16 shows the block diagram of Orthogonal signal generator of SPLL. The presented structure is based on a second-order generator integrator (SOGI), which is defined as Eq. (21). Orthogonal signal generator of SPLL. where ωn represents the resonance frequency of the SOGI. When the SPLL based on an orthogonal signal generator control strategy is used, the second-order generalized integrator closed-loop transfer function can be written in Eq. (22) 40. where k affects the bandwidth of the closed-loop system. Once the orthogonal signal is generated Park transform is used to detect the d and q components on the rotating reference frame. This is then fed to the loop filter, which controls the VCO of the PLL. Take the rotational speed 1500r/min with a current frequency is 450Hz as the working point40. Using the proposed method, the input signal is filtered, resulting in two clean orthogonal signals, due to the resonance frequency of the Orthogonal signal generator of SPLL at ωn (grid frequency). The level of filtering can be set from k, as shown in Fig. 17, where there is a higher gain over a wide frequency range near the operating point. By selecting the parameters of k, comprehensively considering the stability performance and anti-interference ability of the SPLL based on the orthogonal signal generator controller system, and continuously optimizing the simulation model, the parameter values of SPLL are finally determined as follows: k = 3. The Bode plot is shown in Fig. 17 to optimize the control performance of the system. Bode plot and step response of the closed-loop transfer function (Hd). The simulated waveform based on the SPLL control current inner loop tracking strategy is shown in Fig. 18, after the current is stable, the tracking current iA2 and the reference current iA1 basically maintain the same phase and amplitude before 0.02s, and after the sudden load torque of 0.02s, the current increase of the two is still sinusoidally distributed, and there is almost no steady-state error, that is, the tracking current can effectively track the AC reference signal without static difference. After adopting the SPLL control strategy, the current phase of the tracking current iA2 can quickly track the current phase of the reference current iA1 with a very small error, and the simulation results are consistent with the theoretical analysis in the previous section, that is, the SPLL based on SOGI control can realize the static tracking of the AC input signal. At the same time, the phase synchronization between multiple winding currents is realized, which solves the problem that the phase needs to be consistent between multiple winding currents and improves the accuracy of phase locking. Waveform diagram of tracking current and reference current under SPLL control. Figure 18 illustrates the tracking current of module 2 following the implementation of the SPLL based on the SOGI control strategy. This method effectively mitigates the high-order harmonics of the output current, significantly enhancing the system’s steady-state accuracy. Additionally, it results in minimal phase shift between the tracking current waveform and the reference current waveform (phase lag of less than 0.005 rad) and enables error-free tracking without static error. Figure 19 shows the speed and torque of the motor. The initial speed is ω = 1200rad/s, the load torque remains unchanged at 10Nm, the speed increases to ω = 1600rad/s at 0.5s, the system simulation time is the same as 1s, and the waveform of the motor speed and torque is observed. MWPMSM speed and torque during acceleration. Based on the simulation results shown in Fig. 19, it is evident that following a sudden change in motor speed at 0.5s, the speed quickly stabilizes at a new set value of 1600 r/min and remains constant. Throughout the motor’s startup, speed adjustment, and stable operation phases, the motor speed remains relatively stable. Although there is a brief fluctuation in the electromagnetic torque at 0.5s, it quickly stabilizes at the initial value of 10Nm. While there is some pulsation in the electromagnetic torque when the speed increases at 0.5s, it is brief and does not impact the system’s stability or dynamic performance. Figure 20 shows the experimental platform of a MWPMSM control system. The basic parameters of the motors used in the experiment are shown in Table 2. The voltage range of the DC bus is 35 ~ 100V, each motor module in the controller corresponds to a set of main control board and power drive board respectively, each power board has six drive signal circuits, and there is a power module on the bottom board, which sup-plies power to the main control board and the power drive board after voltage conversion. Experimental platform for MWPMSM control system. In this experimental platform, the host computer interface is used to simulate the new energy management system, and the speed command and winding power distribution command are issued to the motor controller. When a given DC bus voltage is input to the controller, configure the communication settings of the host computer monitoring system, select the corresponding host COM port, and then enter the operation control, and set the corresponding target speed to the power ratio of multiple windings, as shown in Fig. 21. Host computer monitoring system interface. The motor controller designed in this experiment consists of two sets of main control board and power drive board, because the winding current of module 2 and module 3 is obtained by the same control strategy and the same controller, the winding current of the two is the same, so only two sets of windings in the MWPMSM are used for experimental verification (i.e., winding 1 and winding 2). Each set of the main control board and power driver board can be directly connected with a set of motor windings to form a motor module. An external regulated source provides the DC bus voltage. After power-on, the MWPMSM does not rotate, and the controller forces the magnetic field orientation to ensure that the starting position of the two sets of windings is consistent, and the monitoring interface of the host computer shows that the magnetic field orientation is being carried out. After entering the starting state, the motor will increase the speed until it reaches the set speed and enters the stable operation state, and the power distribution can be adjusted after reaching the stable operation state. The monitoring system of the host computer sets the speed of the MWPMSM to 1000r/rpm. Taking the C phase of the respective windings as an example, the current at the stage from 0 to 400 r/min during motor operation is shown in the Figure 22a, and the current at the stage from 400 r/min to a given speed of 1000 r/min is shown in Figure 22b. where 10mv represents a current of 10A. (a) Current waveform at 0 to 400r/min (b) Current waveform at 400 to 1000r/min. Figure 22 shows the current change, the analysis, and the comparison of the two figures. The winding current of module 1 is synchronized with the winding current of module 2 in the process of motor speed increase, which confirms the feasibility of theoretical verification. At the beginning of the motor, weak magnetic speed increases, when running to 400r/min, it will stay stable for a short time, at this time, the current waveform changes from Fig. 22a, b and the current amplitude decreases slightly, which is because it is in the forced commutation stage at this moment, the excitation current becomes the torque current, and the demagnetization is carried out, to achieve closed-loop torque control and ensure that the motor rises to a given speed. The frequency is gradually increasing, and the rotational speed is gradually increasing. When the given speed is reached at 1000r/min, the monitoring interface of the host computer is displayed as normal operation, and the DC bus voltage of the two windings is set to 63.1V respectively. Figure 23 shows the current waveform when the power ratio of winding 1 and winding 2 is set to 1:1, 2:1, and 3:1, respectively. The monitoring interface of the upper computer can display the power occupied by each winding under different distribution ratios. The power ratio of the remaining windings is shown in Table 3. (a) The current of phase C at 1000r/min with a winding power ratio of 1:1 (b). C-phase current with a 1000r/min winding power ratio of 2:1 (c). C-phase current waveform with a winding power ratio of 3:1 at 1000r/min. In Table 3, the C-phase current was measured at various settings of the winding power ratio. When the motor operates normally and the power ratio of winding 1 to winding 2 is set to 1:1, the amplitude and phase of the current waveform for winding 1 and winding 2 are identical. This demonstrates the feasibility of a multi-winding motor with a 1:1 power ratio. In this configuration, the winding current of motor module 2 can accurately mirror the winding current of motor module 1, maintaining phase synchronization and achieving static tracking of the AC input signal. When the power ratio is set to 2:1, the current amplitude of winding 1 will increase immediately, and the current amplitude of winding 2 will decrease immediately, as shown in Fig. 23. It is estimated to be roughly 2:1 by the internal program, which corresponds to the set ratio of winding power. While changing the winding power ratio, the multi-winding motor maintains a given speed of 1000r/min, and the change of winding power is proportional to the change of torque, and the C-phase current and torque current are measured by the experiment and need to be transformed by Park, which is a nonlinear relationship. Therefore, from the experimental waveform of Fig. 23b above, it is not completely proportional to 2:1, but the current increases when the power of winding 1 increases. When the power of winding 2 is reduced, the current decreases, and the two still maintain the same frequency, and the current error is very small, maintaining the change trend of synchronization to meet the real-time performance of the power ratio. Similarly, when the power ratio is set to 3:1, as shown in Fig. 23c, the current of winding 1 continues to increase, the current of winding 2 decreases correspondingly, and the current still basically maintains the same frequency, and the phase error is small so that the current of winding 1 and the current of winding 2 are controlled and the current phase synchronization is maintained. It solves the difficulty of how to keep the phase synchronization between the currents of multiple windings and improves the phase-locking accuracy. From Table 3, the Maximum error of winding power ratio is 6.7%, and the error is less than 10%. At the same time, under the premise of ensuring the phase between the winding currents, the power of winding 1 and winding 2 can still be reasonably controlled according to the winding power ratio instruction, which proves that the MWPMSM control system has strong robustness, and verifies the feasibility and effectiveness of the design scheme in this paper. In this paper, the structure of the new MWPMSM is analyzed and modeled in detail, the magnetic circuit of the MWPMSM is calculated in detail, its main parameters are determined, and the finite element model of the motor is built for simulation to verify the rationality of the parameter design. The SPLL based on SOGI control strategy is used to track the phase of the reference current iA1, and the current tracking waveforms under SPLL based on SOGI control are given by simulation. The simulation results show that the SPLL based on the SOGI control strategy can accurately track the amplitude and phase of the reference current and prove the coordination between multiple motor modules. On this basis, the overall system simulation of the motor is carried out, and the simulation results show that the dynamic performance of the motor is well under the condition of load change and speed change, which verifies the correctness and feasibility of the scheme that each motor module in the MWPMSM control system can work independently and coordinated. The MWPMSM control system was carried out, an experimental platform was built, and the upper computer simulated the new energy management system to issue the speed and the winding power ratio setting instructions of each motor module. 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IEEE Access8, 156946–156956. https://doi.org/10.1109/ACCESS.2020.3019204 (2020). Article Google Scholar Download references This research is supported by the National Key Research and Development Program of China, Efficient and Safe Electrical System Design and Study on weather resistance of offshore floating photovoltaic (grant number 2022YFB4200703). School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, 300382, China Peng Chen, Qiang Fu & Chunjie Wang Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin, 300382, China Peng Chen, Qiang Fu & Chunjie Wang Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Peng Chen conducted the overall design and writing of the article, Qiang Fu designed the experimental system, and Peng Chen and Chunjie Wang analyzed the experimental data. All authors reviewed the manuscript. Correspondence to Peng Chen, Qiang Fu or Chunjie Wang. The author declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Chen, P., Fu, Q. & Wang, C. Design and performance evaluation of a MWPMSM for distributed floating photovoltaic system. Sci Rep15, 43600 (2025). https://doi.org/10.1038/s41598-025-25152-8 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-025-25152-8 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 15, Article number: 43383 (2025) Cite this article 2699 Accesses 1 Citations Metrics details Solar energy is gaining global prominence and is rapidly becoming a major energy source worldwide. According to reports, Egypt had made significant progress in solar energy installations by September 2022, reaching a total capacity of approximately 3.70 GW and setting renewable energy targets of 42% by 2035. Efficient and accurate PV system design is essential to meet future energy demands. This study presents a novel, cost-effective methodology for designing and validating a stand-alone photovoltaic (PV) system using PVsyst software, with a specific focus on evaluating the load requirements of the Solar Energy Lab at Mansoura University, located in the center of the Nile Delta, Egypt. A 2.64 kWp stand-alone system, integrated with a battery storage unit, is designed using PVsyst. The Lab’s annual energy demand is estimated at approximately 4279.78 kWh, while the system’s simulated generation reaches 4418.01 kWh, achieving a performance ratio (PR) of 0.81. PR analysis reveals seasonal variation, with January recording the highest value of 80% due to lower module temperatures, while June records the lowest at 76% as a result of higher temperatures. The annual average PR stands at 81%, with a levelized cost of energy (LCOE) of $0.082/kWh, indicating an optimized system design. The system’s performance is influenced by losses due to environmental factors such as dust, humidity, and temperature. A solar fraction of 87% reflects high reliability in meeting energy demand. To further enhance system efficiency, this study introduces a dynamic algorithm for system design, validated through simulations and a three-month experimental campaign using Watchpower software. The validated approach offers a scalable framework for academic institutions and facilities seeking to implement reliable, low-cost, off-grid PV systems in data-constrained environments. The economic progress of any nation is dependent on its energy sources. With globalization and industrialization, the depletion of nonrenewable energy sources has become a pressing concern1. Countries worldwide are actively seeking alternative energy options, and among them, solar energy has emerged as a prominent solution2. Motivated by the need for decarbonization and the global energy crisis, the shift towards a green energy sector is of utmost importance3. The PV systems’ utilization plays a crucial role in mitigating global warming and achieving climate change objectives4. These systems have the ability to convert solar energy into electrical energy efficiently. Currently, solar systems are widely adopted as the preferred technology for harnessing solar energy5. One critical consideration is the storage of electrical energy derived from the sun to ensure a continuous supply during periods of low solar irradiation6. Various factors come into play in determining the effective utilization of solar energy, including geographical and weather conditions and the electrical load consumption7. Solar energy utilization is expanding across the globe due to its abundant availability. The sun alone possesses an interceptable potential energy surpassing the current human energy consumption requirements8. Furthermore, solar energy is a sustainable and environmentally friendly source that holds great potential to meet future energy demands7. To address these concerns, proactive measures have been implemented, including the ratification of the Paris Agreement by numerous governments, aimed at curbing (CO_{2}) emissions, a significant contributor to global warming9. Over the past few decades, photovoltaic systems have undergone significant advancements in terms of technical efficiency, maintenance considerations, and grid-forming converters10. The performance of PV arrays is influenced by ambient conditions such as solar irradiance, wind, and temperature11. Consequently, the behavior of solar cells is significantly affected by voltage fluctuations caused by temperature changes and current fluctuations caused by variations in solar irradiation12. In this context, the implementation of effective control techniques is crucial to mitigate the impact of unpredictable weather conditions, ensuring the reliability and security of energy resources13. Egypt has significant potential to harness solar energy due to its geographical location. In 2023, a report released by the New and Renewable Energy Authority (NREA) revealed that Egypt had expanded its renewable energy capacity to 6500 MW for the country’s power generation, comprising 21% of the total peak power demand14. Approximately 35% of this capacity is attributed to private sector initiatives. The total energy production had increased to 25,100 GWh, comprising 15,000 GWh from hydroelectric sources, 5600 GWh from wind power, and 4,500 GWh from solar power15. The Egyptian government has established ambitious goals by 2035 to acquire 42% of the country’s electricity from renewable sources. Solar energy offers a wide range of applications beyond electrical energy, including water heating, room heating, solar pumps, and dryers16. To achieve these goals, two approaches can be taken: utilizing batteries for stand-alone systems or on-grid systems in which the solar power systems are connected to the main grid17. Stand-alone rooftop systems can greatly benefit households and medium-sized enterprises by reducing their peak loads. Evaluating the efficiency of photovoltaic systems involves considering important variables such as the solar yield, performance ratio, and system losses. The performance ratio, which relates the actual and theoretical outputs of solar energy while accounting for various losses, depends on many factors like ambient conditions, mounting systems, and electrical designs18. Utilizing coolants during high-temperature days can enhance the performance of solar cell modules by preventing overheating. Accurate location-specific parameters are crucial for designing, operating, maintaining, and sizing rooftop systems19. Several simulation software, such as PVsyst, INSEL, TRNSYS, PVSOL, and SOLARPRO, along with economic assessment tools like HOMER, Solar Advisor Model (SAM), RETScreen, SOLinvest, and Energy Periscope, are available to calculate energy production and assess the economic viability of PV systems20. Table 1 gives a comparison of the most used software; HOMER and SAM with proposed method for designing the PV stand-alone system. These software tools also aid in determining the performance ratio and minimizing losses. Meteorological databases from sources such as AEMET European Solar Radiation Atlas, NASA, METEONORM, ISPRA-GIS, HELIOS, Solaris, and PV-Design-Pro are used for simulations, with this study utilizing data from METEONORM and PVsyst simulations21,22,23. PVsyst is a widely employed tool for simulating stand-alone photovoltaic system performance. By example, reporting an annual array output energy of 841.31 kWh with 735.84 kWh supplied to the load24. In Odisha, India, with 1156.39 W/(hbox {m}^2) average solar irradiance, a 1 kWp rooftop PV system generates approximately 4.8 kWh/day, offsetting 28 tons of (CO_{2}) over its lifetime. To validate system performance, a case study utilized PVsyst V6.84 to simulate a 2 kWp rooftop PV system for an 8.9 kWh/day residential load25. A PVsyst case study22 analyzed a stand-alone solar system in Bikaner, designed for an annual demand of 1086.24 kWh. Results show that 1143.6 kWh/year generated, with 1068.12 kWh supplied to the load slightly below demand due to various system losses. The annual performance ratio averaged 72.8% and between 64% and 86% monthly. This highlights performance analysis’s importance for optimal energy delivery and reliability in PV system design. This analysis quantified power losses, enabling optimal system sizing and demonstrating PVsyst’s reliability in forecasting energy delivery, which is critical for design optimization and efficiency assessment in PV applications. Despite some advances in PV modeling software, most studies lack experimental validation for systems designed with minimal instrumentation. Also, there have been limited academic studies addressing the problems of PV deployment by using streamlined tools like PVsyst. This study fills this gap by introducing a practical, experimentally verified methodology suited for educational Labs in the developing regions. The key contributions include simulation-based design optimization, low-cost hardware implementation, and real-time performance validation under actual climatic conditions. The main objectives and contributions of this study are: Developing an efficient design method for the PV stand-alone system based on simulation by software PVsyst and experiments. Evaluate the solar energy potential at the selected location (Mansoura University, Egypt) based on actual meteorological data. Identify the minimum load requirements to sustain the daily operation of the Solar Energy Lab as a stand-alone system. Design a photovoltaic system layout using PVsyst software, optimized for local environmental conditions and practical implementation constraints. Introduce a streamlined design methodology that avoids the use of costly pyranometers and multiple current sensors, relying instead on a single current measurement and simulation-based estimation—representing a key novelty of this work. Simulate system performance using PVsyst, including detailed loss analysis, performance ratio (PR), and solar fraction (SF), to estimate the long-term behavior of the system. Demonstrate that a reliable off-grid PV system can be deployed in institutional settings using minimal instrumentation and open-access tools—providing a practical model for similar environments in regions with limited technical infrastructure. Assessing the performance ratio and losses of the PV system through simulation using PVsyst software. Correlating the simulation and the experimental results for the selected site. Experimentally validate the simulation model through real-world measurements of energy output, irradiance, and temperature under operating conditions, confirming the effectiveness of the proposed design approach. The efficiency of a solar system is enhanced by minimizing losses through component optimization, like selecting compatible inverters and ensuring module uniformity. Figure 1 represents the architecture of the proposed system operating under low-voltage (LV), low-power DC comprising the array feeding the DC loads by its own boost converter. The configuration of a solar photovoltaic (PV) system, illustrating the interconnections between PV panels, an inverter, battery storage, various loads, and optional grid/generator inputs, is shown in Fig. 2. The system mainly consists of arrays connected with the battery storage system through a battery charger with its own MPPT, then battery-stored energy is converted to AC for appliances, emphasizing the importance of mitigating losses in cable systems for optimal performance and maximizing power utilization. The system also includes a bypass diode for protection31. Additionally, UV-safe and weather-resistant cables are essential for these open-air applications. Layout of stand-alone system as designed by PVsyst. Configuration of a solar photovoltaic (PV) system, illustrating the interconnections between PV panels, an inverter, battery storage, various loads, and optional grid/generator inputs. This study focuses on analyzing the design and performance of a stand-alone solar photovoltaic system. The investigation explores losses attributed to various factors and closely monitors the plant’s performance using the performance ratio metric. Losses across different aspects are evaluated using PVsyst simulation. It is also used to calculate the performance ratio based on simulated performance. Additionally, the execution of the plant measures energy, solar resources, and the overall impact of the performance ratio and losses. The solar cell can be represented as shown in Fig. 3 with its basic model, which is detailed in32 by: To precisely simulate the practical behavior of arrays consisting of various attached cells, other parameters should be considered. This practical model is characterized by accuracy and simplicity; therefore, it is widely used. Accordingly, the array behavior can be defined by the model given by equations (2), (3), and (4) and as detailled in32. Table 2 lists the parameters of the solar panel as detailed in31,32 by the equivalent circuit of the solar cell as shown in Fig. 3. Equivalent model of solar cell. In some cases, the PV system operates under varying solar irradiance conditions throughout the day. This variation affects the output power, output voltage, and relationship of current under different temperature conditions. The solar module (I-V) and (P-V) characteristics under different solar irradiance and the corresponding maximum power are detailed in31. Selecting the appropriate panel is crucial for designing a system that meets the total load demand efficiently with minimal panel usage. The panel selection is influenced by the following factors: solar irradiance, temperature, voltage, current, and configuration. When designing a photovoltaic system, the geographical location does not constrain the configuration but rather depends on solar irradiance levels. The quality of modules, inverters, and the orientation of solar panels are pivotal factors shaping a system’s design and efficiency, enabling adaptability across diverse settings. The methodology for crafting a PV system involves a comprehensive approach that delves into critical considerations. Solar irradiance levels at a specific site are foundational in gauging the system’s energy potential accurately. The methodology avoids the use of pyranometers and multiple sensors by employing a simulation-based estimation. The design is validated with a single-point current measurement and experimental data by real-time Watchpower based software, representing a novel approach for academic system design. horough comprehension analysis of these levels is crucial for precise performance estimations. Moreover, selecting components of superior quality tailored to site-specific demands is imperative for optimal performance. Additionally, the positioning and tilt of solar panels are pivotal design aspects directly impacting energy generation. Strategic alignment towards the sun and ideal tilt angles serve to maximize sunlight exposure, thereby boosting overall system performance. By intricately weaving together these elements throughout the design process, a meticulously planned methodology ensures the seamless deployment of a dependable and efficient photovoltaic system customized to meet the distinct requirements of each individual project. In designing the PV system, PVsyst V7.4, a PC software package, is configured using real site coordinates, METEONORM weather data, and detailed load profiles for the Solar Energy Lab. It customizes parameters for solar modules, enabling the study, sizing, and analysis of various systems such as grid-connected and stand-alone setups. The software incorporates comprehensive databases for meteorological and component data, along with general solar energy tools. It facilitates the design of system configurations and provides an estimation of the energy generated. Table 3 presents a summary of PVsyst setup and a comparison with HOMER and SAM software. The software relies on geographical information to simulate the system sizing accurately, as shown in Fig. 4. The outcomes of simulations conducted in PVsyst can encompass various variables, which can be presented in monthly, daily, or hourly values. One valuable feature is the “loss diagram,” which identifies potential weaknesses in the system design26. PVsyst offers a range of pre-existing sites and meteorological files in its databases, but users also have the option to create their own projects based on the specific location and meteorological data they intend to use. Design of the system is achieved by two steps; the first step is creating a system variant, and the second step is the simulation. In the first step, users define the calculation version of their project. They have the flexibility to specify the module orientation, system configuration, and loss parameters according to their requirements. The second step is running the simulation process to generate various graphs and reports that provide insights into the performance of the system. The simulation process in PVsyst involves a series of steps. Users can conveniently analyze the results within the PVsyst program, export them to other software for further analysis, or save them for future evaluation. Determining the sunlight availability at a specific location is necessary for better design. It helps in planning and designing solar energy systems. The solar irradiance depends on the geographical location and the time of the year. The monthly values of global horizontal, diffused, extraterrestrial irradiation, clearness index, ambient temperature, wind velocity, etc. have been obtained by PVsyst and described in Table 4 for the selected location: Mansoura City, Egypt. Mansoura City lies between (31.03^{circ })N latitude and (31.38^{circ })E longitude. While temperature also plays a role, its impact is secondary to that of irradiance; cooler temperatures generally improve performance. Factors such as irradiance levels, ambient temperature, and wind speed influence the cell temperature. The available current and output power of a solar array depend directly on the amount of irradiance it receives. Figure 5 illustrates the variation of solar irradiance throughout the day, showing both direct and diffuse components. The solar data are some of the major inputs for an energy yield evaluation. The solar irradiance during the day is composed of direct and diffused irradiance, and its highest value is during midday time with the solar energy of a certain day. The analysis is generated by PVsyst and based on weather records, including temperature and humidity, at the selected locations. Figure 6 represents the annual solar horizon profile for Mansoura city. The accessible solar energy is depicted within the horizon boundary. Two slanted (oblique) blue lines appearing on either side of Fig. 6 represent the sunrise (left) and sunset (right) periods. During these times, due to the panel installation angle (southward and zero azimuth angle for the chosen site: Mansoura), sunlight strikes the rear side of the modules, resulting in zero energy generation36. Although shading losses from nearby and distant objects can range from 1% to 40%, they are not considered here as the system is installed on a building rooftop. Functionality of PVsyst to design stand-alone system. Irradiation components for Mansoura-one day. Sun paths height/azimuth plot for chosen site within PVsyst.. The total number of operating hours is assumed to be 5 hrs, where the peak power rating of the panel is considered to be 330 W. The operating factor is taken to be 0.75, and the peak equivalent is 0.88; i.e., sunlight available in a day is chosen to be only 8 hrs. Theoretical design of the solar system is done in the following few steps: Load calculation: The average daily load consumption required for the Lab and its office and equipment operations is outlined in Table 5 and depicted in Fig. 7. Battery specification: The specifications for the battery set used in the design of the system are detailed in Table 6. Array (module): The specifications for the PV modules used in the system design are provided in Table 7. Charge controllers: The universal controller 50 A MPPT Converter—built into the inverter-of 5 kW and 48 V—is used to design the stand-alone system having maximum charging and minimum discharging current, i.e., 50 A to 10 A. Load profile of Solar Energy Lab (total daily energy = 14.14 kWh, monthly energy = 424,4 kWh). Calculations for the entire load of the Lab are as follows: Total PV power calculation: Since the modules are connected in series and parallel, the total output power of the array is calculated by using the following formula: Total power (W) = number of series modules (times) number of parallel strings (times) module power (W). Where the number of series modules = 2, the number of parallel strings (available at the Lab) = 4, and the module maximum power = 330 W, then the total power = 2 (times) 4 (times) 330 = 2640 W. Daily energy production of the array: The energy produced by the array per day is calculated based on the total power of the array and the average daily solar irradiance hours at the selected location. where the daily energy production (Wh) = total power (W) (times) average daily solar irradiance hours. where total power = 2640 W and average daily solar irradiance = 5 hrs/day. Then, the daily energy production = 2640 (times) 5 = 13200 Wh/day = 13.20 kWh/day. Battery capacity in watt-hours (Wh): The battery capacity, measured in Wh, is determined by converting ampere-hours (Ah) to watt-hours. This calculation serves to quantify the total energy storage capability of the battery as the following: Battery capacity (Wh) = battery capacity (Ah) (times) battery voltage (V), where battery capacity for one string (4 batteries in series) = 200 Ah. Battery capacity for three strings = 600 Ah, and battery voltage (4 batteries in series) = 48 V. Then, battery capacity = 600 (times) 48 = 28.80 kWh. The used battery characteristic is detailed in Fig. 8. System Autonomy Calculation: Autonomy (days) = Battery capacity (Wh) / /average daily energy needs (Wh/day), where total battery capacity = 28.80 kWh and daily energy needs = 14.15 kWh. Then, autonomy =28.80/14.15 (approx) 2.04 days. This value determines how many days the system can operate using only the stored energy in the batteries, without any solar input. It’s a measure of how long the system can sustain the load during periods of no sunlight. Summary of the Results as following: Total power: 2640 W, daily energy production: 13200 Wh/day (or 13.20 kWh/day), battery capacity: 28800 Wh (or 28.80 kWh), and system autonomy: 2.04 days. Battery block characteristics calculation by PVsyst. Obiwulu et al.37 use simulations in identifying optimal tilt angle and radiation levels. Other theoretical models have been proposed to assess tilt angle performance across different latitudes north or south of the Equator. In this study, the PV panel structure is a fixed tilted plane of tilt (31^o) and plane orientation azimuth (true south) (0^o) as shown in Fig. 9. The optimization is done for the whole year with respect to optimum loss of zero percent, and the energy collector on the plane is 2014 kWh/(hbox {m}^2). Solar irradiance calculation for the chosen site by using PVsyst. The reliability of meteorological data, module models, and manufacturing specifications is uncertain in photovoltaic energy production. Properly installed rooftop solar panels, tailored to specific energy demands, offer a pathway to energy self-sufficiency for residential or small industrial use. This research provides valuable insights for future off-grid system design and operation, focusing on factors that influence system efficiency, such as material technology, energy generation methods, and manufacturing processes. Module behavior plays a pivotal role in defining system losses during simulation. The following results depict the distribution of yearly incident irradiation on a global collection plane and present data regarding monthly energy production relative to the system’s capacity. The analysis demonstrates the correlation between energy output and system losses, enabling a comprehensive understanding of the energy production dynamics throughout the year. In this study, PVsyst is employed to simulate the system’s performance, incorporating models for all components of the system to address various sources of losses. In this work, the performance ratio (PR), as an On-site production assessment, is a useful graphical tool for the system to indicate the yearly yield of energy production. The PR is seasonally dependent and must be found on precise irradiance data. To normalize short-term variability, the weather-corrected performance Ratio developed by NREL and implemented in the IEC 61724-1 standard compensates for seasonal temperature impacts, but not for other weather impacts such as irradiance level, wind, and soiling. All the presented results are based on the simulation results for the proposed site. Figure 10 illustrates the monthly energy production data from the solar power system, factoring in energy losses during production. Daily system output energy. The analysis, which incorporates weather records such as temperature and humidity at the selected locations, is depicted in Fig. 11. This figure also displays the monthly energy generation and the associated losses, revealing that the photovoltaic system’s average daily energy output over the year is 5.48 kWh/(hbox {m}^2). The highest energy generation occurs between May and August. On average, 4.44 kWh/kWp/day is supplied to the user, while array and battery charging losses account for 0.64 kWh/kWp/day and 0.38 kWh/kWp/day, respectively. Table 8 provides a summary of the annual energy balance for the off-grid system, indicating that 4279.80 kWh is delivered to the user annually. The simulation consistently demonstrates stable performance ratios throughout the year, as shown in Fig. 11. Graphical analysis of normalized energy distribution over the year. The normalized energy and performance ratio is shown in Fig. 12. The normalized production and performance ratio for every month has been depicted. Whereas the performance ratio is 81%, in which the system is working in good condition. The normalized production gives the three important parameters: 1- Collection loss (PV-array losses) is 0.67 kWh/kWp/day. 2- System loss (inverter, …) is 0.38 kWh/kWp/day. 3- Produced useful energy (inverter output) is 4.44 KWh/KWp/day. The analysis further reveals that the annual energy demand of the Solar Energy Lab is 4915.90 kWh, while the solar panels produce 4279.78 kWh, resulting in a power deficit due to various losses. The performance ratio (PR) and Solar Fraction (SF) of the system, as shown in Fig. 13, provide insights into system performance. The highest PR of 87.60% is recorded in January due to lower module temperatures, whereas the lowest PR of 75.34% occurs in June because of higher temperatures. The system achieves an annual average PR of 81%. Additionally, the solar fraction, which indicates the portion of energy needs met by solar energy, averages 87% annually, as detailed in Table 8. The PR, defined as the ratio of final system yield ((Y_{f})) to reference yield ((Y_{r})), is thoroughly explained in38. The software comprehensively analyzes all system loss factors during simulation, making it a critical tool for this study. Monthly normalized productions with losses. Performance ratio and solar fraction. Figure 14 with the distinct types of field losses within stand-alone photovoltaic systems step by step are illustrated in Fig. 14. The effective irradiation on collectors in this system is 1902 kWh/(hbox {m}^2) (with 8.60% global and incident irradiation in the collector plane) with the efficiency of 19.57%. In which the 1843 kWh/(hbox {m}^2) is falling in the chosen site to the system during one year. Due to array incidence loss and incidence angle modifier, incidence angle modifier (IAM) factor and soiling loss factor. Finally, 5025 kWh for array nominal energy at STC, and the rest of the energy is lost due to light-induced degradation (LID), mismatch loss, inverter loss during operation, and Ohmic loss. Yielding an energy need of the user (load) of approximately 4917 kWh. Arrow loss analysis of the proposed system. Possible causes include undersized system or battery capacity, inaccurate and variable load profiles, high system losses due to the nature of the devices at the educational Lab, suboptimal issues, or unsuitable climate data, especially in this period. Further optimization will be addressed in future work. Additional Insights: While the simulation results indicate promising energy outputs, there remains a shortfall in the energy available for user consumption due to inherent system losses. This highlights the importance of optimizing both array efficiency and energy storage solutions, especially in off-grid systems. In array voltage sizing, there are few conditions, such as the array maximum operating voltage must be below the maximum inverter operating voltage at the MPPT range. Also, the maximum array absolute voltage should not be more than the maximum system voltage. The V−I characteristics of PV systems with different losses is shown in Fig. 15. I–V curves with different losses for installed PV array. Daily energy and power available on inverter side of proposed stand-alone PV system. Variations in global irradiance significantly influence the performance of the solar system. As a result, the array power is shown in Fig. 16. Figure 18 illustrates the global irradiance distribution that resides on the collector plane of the 5 kW solar system, which is installed at Mansoura Solar Energy Lab. The hottest month in Mansoura is August, with an average temperature of the solar cells of approximately 65 (^o)C. The impact of the temperatures of the array throughout the year is shown in Fig. 17. High temperatures are expected to significantly affect the open-circuit voltage, thereby reducing the output power of the array. In the PVsyst model, no losses occurred when horizontal irradiation was converted to global irradiation incidence. However, temperature derating, driven by hot and sunny conditions, resulted in a 5.60% loss. Additionally, the system experienced a 5.30% loss due to the converter, a 3% reduction from battery roundtrip inefficiency, and 13% of missing energy due to the probability of loss of load. The system is designed as an off-grid solution, allowing it to operate independently from the main power grid. The solar panels generate energy based on available sunlight, and while this energy is stored for use, the output can fluctuate depending on irradiance levels. Since solar generation is inherently variable and daily production cannot be precisely predicted, the system is equipped to manage these fluctuations. Figure 19 shows the average state of battery bank charging of 40%, and this is compatible with the above result for the design of the proposed system. Array temperature vs. effective irradiance. Incident irradiation distribution. State of charge daily distribution. For 2.40 kWp, the required PLOL is found to be equal to 5%; the array detailed sizing tool shows the loss of load probability (PLOL) as a function of the installed array power as illustrated in Fig. 20. Figure 21 illustrates that the cumulative global incident irradiance on the collector plane decreases as the global incident irradiance increases. When the global incident irradiance reaches its peak value of 1000 W/(hbox {m}^2), the cumulative irradiance approaches nearly zero. Solar fraction as function of output of installed PV array. Incident irradiance tail distribution of proposed stand-alone PV system. The analyzed location shows high solar energy potential. It is ideal for implementing a photovoltaic system. PVsyst software can optimize the system design by adjusting layout, orientation, and capacity based on available solar resources. The solar irradiance data highlights stable energy conditions, enabling efficient energy production. PVsyst allows fine-tuning parameters like tilt angles and panel orientation to enhance output. This approach ensures high efficiency and promotes sustainable solar energy use. A prototype system is built and tested in real-time conditions, with power generation monitored using WatchPower software. The loads supplied by the stand-alone system at the Lab are the same as indicated by the Table 5. The experimental setup system at the Lab is shown in Fig. 22 which comprises the arrays, inverter, a battery package, connection boxes, PC-based Watchpower, and the Solar Energy Lab load. The inverter is supplied with its own built-in MPPT. The tilt angle of the photovoltaic arrays is set to align with the latitude (31o) to optimize solar energy capture for the chosen site: Mansoura. Block diagram of experimental setup of proposed stand-alone PV system. The received data from the inverter is analyzed by the WatchPower over the USB communication and employed for the research and the educational purposes. WatchPower aids in continuous data collection and optimization. The data confirms the consistent performance and peak load management. Combining the analysis performed with the PVsyst simulations supports the tailored design for local conditions. The pyranometer solar irradiance meter (SPM-1116SD) is employed to measure and record the solar irradiance intensity with an accuracy of (±10 W/m2). Figure 23 represents the natural stochastic solar irradiance during a moderately cloudy day in the summer: August. Besides, the measured solar power is more than 800 W under a partially cloudy day during midday in August on the inclined panel. The reading of power over three months is analyzed in the following section. The figures show only the daytime in which the system generates the power. The night intervals were deleted from all figures for simplification reasons. Experimental validation for solar validation (August measurement). The data presented in Fig. 24 indicates the energy output and performance of the photovoltaic system in July. The reading provides an overview of the photovoltaic system’s performance, highlighting key metrics such as average and maximum output power, as well as total energy generated. The average output power ((P_{pvavg})) is approximately 675.47 W, indicating steady performance, while the system reached a maximum output ((P_{pvmax})) of 2113 W during peak conditions. The total energy produced for the month of July ((P_{energy})) is 1.4962 (times)(10^5) Wh, demonstrating overall efficiency. These values suggest that the system performed reliably, with consistent energy generation and the capacity to handle peak energy demands effectively as indicated in Table 10. The August reading, which is presented in Fig. 25 represents key insights into the photovoltaic system’s performance, with slightly higher values compared to that of July. The average output power ((P_{pvavg})) is approximately 727.63 W, reflecting an improvement in the system’s overall efficiency. The maximum output power ((P_{pvmax})) reached 2317 W, indicating robust performance during peak conditions. Additionally, the total energy generated for the month ((P_{energy})) amounted to 1.7359 (times)(10^5) Wh, showcasing a higher cumulative output compared to the previous month. Overall, the system demonstrated enhanced energy production, maintaining consistent reliability while effectively managing peak power demands in August. July readings showing daily PV output power (blue) and inverter output voltage (magenta). Average PV power for July was 675.47 W, with a maximum of 2113 W and total energy of 14.962 kWh. August readings showing daily PV output power (blue) and inverter output voltage (magenta). Average PV power for August was 727.63 W, with a maximum of 2317 W and total energy of 17.359 kWh. Figure 26 represents September reading. The photovoltaic system’s performance slightly decreased compared to August. The average output power ((P_{pvavg})) is 678.28 W, indicating a modest drop in efficiency. The maximum output power ((P_{pvmax})) reached 1969 W, reflecting a reduction in peak performance compared to the previous month. The total energy generated for September ((P_{energy})) is approximately 1.5617 (times)(10^4) Wh, which is also lower than in August. Overall, the system maintained reliable energy generation but experienced a slight decline in both average and maximum output power during September. The values of power decreased due to the cloudy weather as expected before. September readings showing daily PV output power (blue) and inverter output voltage (magenta). Average PV power for September was 678.28 W, with a maximum of 1969 W and total energy of 15.617 kWh. In modern engineering, economic and environmental evaluations are fundamental aspects of designing efficient and high-performance systems. Economic considerations have become a critical component of nearly every research effort. There are many methods by which the cost of electricity generation can be calculated. One of the most commonly utilized methods is the so-called levelized cost of electricity (LCOE), average lifetime levelized generation cost (ALLGC), and levelized cost of generation (LCG)5. One of the most effective tools for evaluating the economic performance of various power generation systems is LCOE11,39. LCOE provides a comprehensive measure of the cost of electricity by dividing the total lifetime costs—including installation ((C_{text {I}})) and maintenance ((C_{text {M}}))—by the total energy produced over the system’s lifespan, while accounting for energy production degradation ((d)), as given by equation (6). where: (C_{text {In}}): Investment cost in year, (n) (C_{text {Mn}}): Maintenance cost in year, (n) (E_0): energy produced in the first year (kWh), (d): annual degradation rate (e.g., 0.005 for 0.5%), (i): discount rate, (N): number of years (project/system lifetime: 25 years). To assess the economic feasibility of a 5 kW stand-alone system, the following procedures can be applied, assuming a lifetime of 25 years and an annual degradation rate of 0.5%. The financial cost parameters considered in the economic evaluation are presented in Table 9, including the installation and maintenance expenditure for the proposed system throughout its lifetime. By applying 0.5% annual degradation over 25 years, the total energy generated over the lifetime becomes: where: (E_{text {total}}) is the total energy produced over the lifetime (kWh). The produced energy in the first year is about 4,418.01 kWh; then, the system is expected to generate 100,470 kWh over its lifetime, considering the degradation rate of 0.5% and neglecting the discount rate; i. Since the system was installed at the Solar Energy Lab, the operation and maintenance costs are not counted and maintained by the Lab. The battery replacement will be every 7 years, so 3 times in 25 years (original batteries replaced twice). The battery’s future replacement costs are about $1,980 (times) 2 = $3,960. So, the total costs = initial ($4,680) + replacements ($3,960) = $8,640. The levelized cost of energy (LCOE) is calculated as follows: The calculations revealed that the system achieved an LCOE of 0.082 $/kWh, which can be decreased by optimizing the installed system and time schedule of operating hours at the Solar Energy Lab at Mansoura University. The transition to renewable energy sources (RESs) is essential to rescue the world from the negative effects of climate change, which is mainly due to (CO_{2}) and particulate emissions. The photovoltaic systems offer a pollution-free source of power by reducing harmful emissions in electricity production. However, since they are quite inefficient, they require large surfaces in order to meet energy demands. While PV systems reduce emissions during operation, their manufacture and disposal currently emit greenhouse gases. The carbon balance assessment for the photovoltaic system is computed based on Life Cycle Emissions (LCE), the quantity of (CO_{2}) emissions by every component or energy output over its entire life cycle—from production, transportation, and installation to operation, maintenance, and disposal. The emission balance for an off-grid system installed at the Solar Energy Lab at Mansoura University, which is expected to produce approximately 4.42 MWh of electricity per year over a 25 years lifetime, accounting for a 0.5% annual degradation in performance. The system will replace an estimated 55.24 tons of (CO_{2}) that would have otherwise been generated by the local grid, which emits 500 (gCO_{2}) per kWh released to the atmosphere40. This study presents a comprehensive design, simulation, and experimental validation of a stand-alone PV system for the Solar Energy Lab at Mansoura University. The proposed PV system, with a capacity of 2.64 kWp optimized using PVsyst software, demonstrates high efficiency in meeting an annual load demand of 4,279.78 kWh. Simulation results indicate an annual energy yield of 4,418.01 kWh, with a performance ratio (PR) of 81% and a solar fraction of 87%, confirming the system’s robust design. Economically, the system achieves a competitive levelized cost of energy (LCOE) of 0.082$ per kWh over its lifetime, while also contributing to environmental sustainability by offsetting an estimated 55.24 tons of (CO_{2}) emissions compared to conventional grid supply. A critical contribution of this work is the experimental validation of the simulated performance. 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Enhancing pv system modeling accuracy: Comparative analysis of radiation models and data sources. Next Res.2, 100165 (2025). Article Google Scholar Chabook, M. & Tashakor, S. Design of emergency solar energy system adjacent the nuclear power plant to prevent nuclear accidents and increase safety. Nucl. Anal.3(1), 100092 (2024). Article Google Scholar Obiwulu, A. U., Erusiafe, N., Olopade, M. A. & Nwokolo, S. C. Modeling and estimation of the optimal tilt angle, maximum incident solar radiation, and global radiation index of the photovoltaic system. Heliyon8(6), e09598 (2022). ArticlePubMedPubMed Central Google Scholar Eyigün, S. & Güler, Ö. Turkey solar potential and viability of solar photovoltaic power plant in central anatolia. in International Renewable Energy Congress 94–99 (2010). Elminshawy, N. A., Osama, A., Gagliano, A., Oterkus, E. & Tina, G. M. A technical and economic evaluation of floating photovoltaic systems in the context of the water-energy nexus. Energy303, 131904 (2024). Article Google Scholar Abdallah, L. & El-Shennawy, T. Evaluation of co2 emission from Egypt’s future power plants. Euro-Mediterr. J. Environ. Integr.5(3), 49 (2020). Article Google Scholar Download references Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). Electrical Engineering Department, Faculty of Engineering, Mansoura University, 35516, Mansoura, Egypt Ahmed Mashaly, Mohamed Elmadawy, Mohamed Elgohary & Ahmed SHAHIN Faculty of Engineering, Mansoura National University, Dakahlia, Egypt Ahmed Mashaly & Mohamed Elmadawy Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Conception or design of the work: Ahmed Shahin, Mohamed Elgohary, Mohamed Elmadawy, Ahmed Mashaly (25%/ 25%/ 25%/25%). Data collection and tools Ahmed Shahin, Mohamed Elgohary, Mohamed Elmadawy, Ahmed Mashaly (20%/ 30%/20%/ 30%). Data analysis and interpretation: Ahmed Shahin, Mohamed Elgohary, Mohamed Elmadawy, Ahmed Mashaly (20%/25%/25%/ 30%). Methodology: Ahmed Shahin, Mohamed Elgohary, Mohamed Elmadawy, Ahmed Mashaly (30%/ 20/20%/ 30%). Project administration: Ahmed Shahin, Mohamed Elgohary, Mohamed Elmadawy, Ahmed Mashaly (20%/ 30%/ 20%/ 30%). Software: Ahmed Shahin, Mohamed Elgohary, Mohamed Elmadawy, Ahmed Mashaly (20%/ 25%/25%/ 30%). Drafting the article: Ahmed Shahin, Mohamed Elgohary, Mohamed Elmadawy, Ahmed Mashaly (25%/ 20%/20%/ 35%). Critical revision of the article: Ahmed Shahin, Mohamed Elgohary, Mohamed Elmadawy, Ahmed Mashaly (20%/25%/ 25%/ 30%) Correspondence to Mohamed Elmadawy. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Mashaly, A., Elmadawy, M., Elgohary, M. et al. Novel and cost-efficient design of stand-alone PV system with simulation using PVsyst and experimental validation. Sci Rep15, 43383 (2025). https://doi.org/10.1038/s41598-025-28401-y Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-025-28401-y Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Dutch-German quality assurance firm Sinovoltaics has released a free browser-based tool that generates project-specific lab testing strategies for utility-scale solar projects, sorting 19 lab tests by priority and attaching ISO 2859-1 sample sizes to each. Image: Markus Spiske, Unsplash Sinovoltaics has launched an online tool that generates project-specific PV module test plans based on site and technology inputs. The PV Lab Test Advisor, available at labadvisor.sinovoltaics.com, takes seven project inputs – size and module power, climate zone, environmental conditions, cell technology, module configuration, and encapsulant type – and returns a prioritized list of recommended tests with sample sizes and suggested pass/fail criteria. Output is a downloadable PDF intended to be agreed with the supplier before the supply contract is signed. The tool is designed to replace testing scopes copied from project to project regardless of climate, technology, or site conditions. Sinovoltaics said the difference between a generic and a calibrated test scope on a 200 MW project is typically a six-figure swing in lab spend, and said the swing in long-term performance risk is larger still. “A 200 MW tunnel oxide passivated contact (TOPCon) project on the Vietnamese coast and a 50 MW PERC project in Finland face fundamentally different degradation risks,” said Arthur Claire, director of technology at Sinovoltaics. “IEC 61215 and IEC 61730 qualification testing is a necessary minimum, but it will not protect you against any raw material quality variation related to the specific bill of materials used to manufacture your modules, and it is not designed to predict 25 to 30 years of field performance under specific project conditions.” The advisor scores tests against single-factor and multi-factor rules, the latter capturing compounded risk that no individual input would surface – such as large-format bifacial modules in coastal high-wind environments. Each recommendation carries a written justification keyed to the specific input combination. Sample sizes use ISO 2859-1 Special Inspection Levels rather than General Inspection Levels, which Sinovoltaics said would otherwise require 125 to 200 modules per lot. Pass/fail criteria are drawn from governing standards where they exist and from industry practice where they do not. Sinovoltaics cited NREL research finding that UV exposure can cause non-recoverable degradation of 2.3% to 3.2% in TOPCon cells after a one-year equivalent UV dose – losses the company said are severe enough to exceed typical module warranty limits and largely invisible to existing qualification tests. Claire said the current release does not yet take bill-of-materials inputs and works from project conditions rather than the specific bill of materials behind each module. “It supplements IEC qualification testing, it does not replace it. The one limitation in the current release is that it does not yet take bill-of-materials (BOM) inputs,” he said. “It works from project conditions rather than the specific BOM behind each module. And as with any sampling-based approach, what comes out is a risk-calibrated recommendation. It is not a guarantee that every defect in a multi-hundred-megawatt shipment will be caught, and we would not want anyone using it as one.” This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Brian Publicover Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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German research body the Fraunhofer Institute for Solar Energy Systems (ISE) has opened a new lab to support the commercialisation of tandem perovskite-silicon PV technology. The new lab, dubbed “Pero-Si-SCALE”, will provide research and development (R&D) facilities for companies in the German and wider European PV industry. Its goal is to bring industrial tandem PV products to market faster and reduce technological and economic risks. Get Premium Subscription Fraunhofer ISE said the lab would offer solar cell and module manufacturers with industry-standard manufacturing processes to scale up new tandem cell designs to large cell formats, analyse them and integrate them into modules. At the facility’s opening, Fraunhofer ISE highlighted the opportunities presented by combining silicon and perovskite technologies, with a perovskite cell just 500 nanometers thick layered onto a conventional silicon solar cell boosting the cell’s theoretical efficiency limit from 29.4 to 43.3%. “Photovoltaics is far from being ‘fully researched,’” said Stefan Glunz, head of the photovoltaics division at Fraunhofer ISE. “On the contrary, there is still a great deal to be gained here, and tandem solar cells are the key to achieving even greater efficiency. This means more solar energy in a smaller area and with less material usage.” Fraunhofer said the new Pero-Si-SCALE builds on developments from the laboratory and transfers the innovative cell designs to industrial cell formats—up to a wafer size of 210 by 210 square millimetres—using scalable, high-throughput manufacturing processes. In addition to technologies for manufacturing perovskite-silicon PV cells and modules, the Pero-Si-SCALE also provides a comprehensive characterisation and analysis environment. Fraunhofer ISE said its approach focuses on the so-called “hybrid route”, which combines vacuum and wet chemical processes for the manufacturing process of perovskite-silicon tandem solar cells. Using this technology, the institute said it had already achieved peak efficiencies of over 33% on a laboratory scale. An advantage of this process is that “standard”, textured silicon solar cells from the industry can still be used, allowing for direct integration with current solar cell standards and achieving a higher energy yield from the tandem modules. The opening of the lab comes at a critical time for Europe’s PV manufacturing industry, which has been steadily losing market share to China, despite efforts to revive it. “Perovskite-silicon tandem solar cells offer an opportunity for a (re)entry into European industrial PV manufacturing,” said Andreas Bett, institute director of Fraunhofer ISE. Fraunhofer has already notched up some notable milestones in developing perovskite-silicon tandem technologies. In 2024, it revealed details of a full-sized tandem module developed with perovskite specialist Oxford PV with a record efficiency of 25%.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 16, Article number: 3438 (2026) Cite this article 1895 Accesses Metrics details Machine-learning techniques are widely used across many disciplines, including electricity generation forecasting. In this study, the Support Vector Machine (SVM) based models, one of the machine learning techniques, were developed for daily PV power forecasting. To improve model performance, models were tuned with four metaheuristic optimizers, including the Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Daily PV power and temperature data from 2020 to 2023 were obtained for the Stavanger, Oslo, and Kristiansand regions which located in southern Norway. One of the innovative aspects of this study is the investigation of the performance of SVM (Support Vector Machine) combined with various optimization methods across four alternative input configurations. To examine the different feature combinations, four different input configurations were created through the Minimum-Redundancy Maximum-Relevancy (MRMR) method. The analysis results obtained with SVM were further enhanced using all optimization techniques. Among those, the SVM-PSO-M04 (r = 0.7707, NSE = 0.5748, KGE = 0.7092, PI = 0.2964 and RMSE = 0.6513) method produced the most effective results (improving the correlation coefficient (r) to 0.7707 (approximately a 19% increase over the untuned SVM)) among the tested hybrid configurations obtained in our experiments. Moreover, coupling temperature data alongside PV power as model input also tends to improve forecasting skill. Results of this study provide a case-study benchmark for researchers, institutions, and other stakeholders engaged in renewable energy planning and management in high-latitude regions. Solar energy has emerged as a critical frontier of global decarbonisation strategies. Yet, under climate change, the power capacity of these systems remains fundamentally linked to meteorological conditions that are themselves undergoing a transformation1,2,3,4,5. Long term climate model ensembles consistently project regional scale shifts in both surface solar radiation and near-surface air temperature which jointly determine photovoltaic (PV) efficiency6,7,8,9,10. While solar energy is subject to geographic and temporal variations4,6, the relation between rising temperatures and solar energy production introduces a more complex technical challenge. The linear degradation of photovoltaic conversion efficiency as cell temperature rises is a fundamental issue particularly in urban or low-latitude regions which consequently can offset the benefits from increased insolation even with minor warming conditions9,11,12. A case study in Istanbul exemplifies this negative synergy. The study demonstrated that according to RegCM based projections through 2050, a small decrease in incoming radiation levels may pose a direct threat to future PV yields when coupled with simultaneous multi-degree warming13. For ensuring modern energy grids remain resilient in the face of a rapidly shifting climate accurate solar power forecasting is a fundamental necessity. Future climate conditions, intensified by climate change, are expected to increase the complexity of PV system from initial design through to operation, and maintenance. In Europe and parts of East Asia as the reduction in cloud cover is expected to offsets thermal performance losses, photovoltaic yields are expected to experience modest rise, even under aggressive warming scenarios4,5,7,9,10. Conversely regions like West Africa, North Africa, Australasia and parts of Central Asia are likely to face declines linked to solar dimming and rising cell temperatures, although most studies still find decreases remain within a 6% margin of current baselines4,6,8,10,12. While global case for solar remains as a no regrets investment, site specific planning must integrate a consistent climate signal into long-term energy planning to ensure sustained accuracy when projections are considered1,2,3,5,7. Predictive analytics have become from a support tool to a fundamental need to maintain grid stability and manage net loads effectively as solar integration grows14,15. Beyond immediate operations, solar forecasting plays an important role in the economic profile of projects by optimizing battery storage and reducing wasted energy through curtailments16. It is suggested that integrating solar predictions with storage systems not only lowers operational expenses but also decreases overall grid dependency17. While various modeling techniques, apart from traditional regression, such as advanced deep learning, offer reliable short- and long-term outputs18, recent evidence suggests that the latter has demonstrated superior accuracy for irradiance forecasting19.Considering future climate where solar volatility is expected to intensify, the ability to quantify prediction uncertainty will be beneficial for microgrid resilience and the sophisticated management of modern power infrastructures20. Solar power prediction provides consistent grid operation, storage optimization, economic allocation, and integration of renewable sources. Furthermore, it helps in reducing the impacts of climate-induced variability. Ultimately, these forecasting tools serve as the backbone for consistent, climate-resilient power systems. Recent research on solar-irradiance forecasting spans a wide spectrum of time-horizons and data sources, progressing from purely statistical baselines to highly integrated, image-enhanced deep-learning pipelines. Early work demonstrated the value of classic statistical approaches: Paulescu and Paulescu21 showed that an empirical two-state clear-sky model outperformed random-walk, moving-average and Autoregressive integrated moving average (ARIMA) baselines for four-samples-per-minute data from Timișoara, Romania, while Zambrano and Giraldo22 built multidimensional transfer models that dispense with costly on-site training measurements. Parallel semi-empirical efforts for hourly horizons combined extraterrestrial irradiance and clearness-index signals to surpass the Angström-Prescott formula at several Turkish sites23. Surveys and reviews have mapped the methodological landscape, covering statistical, cloud-image and NWP (Numerical Weather Prediction), routes24 and benchmarking time-series, image and hybrid families25. Machine-learning studies have gradually pushed forecast granularity to the minute scale. Image-only pipelines link real-time sky-camera RGB profiles to 1–10 min global horizontal irradiance with competitive the mean absolute percentage error (MAPE) and the root mean square error (RMSE) scores26; all-sky imagers coupled with simultaneous irradiance readings achieved ramp-event detection indices of 43–62% at a Uruguayan test bed27. Satellite/NWP coupling also remains powerful at the 0–3 h range, trimming persistence errors by ≈ 10 W m⁻² across the U.S. SURFRAD network28. Where on-site imagery is unavailable, short-term physics-free predictors such as Artificial Neural Network (ANN)-SFP29 and daily-scale SVM/ANN/k-NN ensembles30 still yield R² values up to 0.94. Deep learning has become the dominant trend for sub-hourly horizons. For example, CNN–LSTM hybrids (convolutional neural networks combined with long short-term memory networks) that fuse wavelet-packet-decomposed sequences with ground imagery have been shown to lower RMSE compared to back-propagation neural networks (BPNN), Support Vector Regression (SVR), and standalone LSTM models on three U.S. stations31, while multi-modal deep clustering aligns cloud-camera frames with the Numerical Weather Prediction (NWP) fields, reaching a 29.4 W m⁻² day-ahead RMSE in California32. Recurrent architectures remain strong: Deep Recurrent Neural Networks (DRNN) surpassed SVR and feed-forward networks in Canada33; LSTM networks delivered R² > 0.9 under complicated weather in Atlanta and Hawaii34; and bidirectional/attention LSTMs benefited from multi-site NASA POWER inputs across India35. Cutting-edge transformer variants now integrate variational-mode-decomposed components, eclipsing Convolutional Neural Network (CNN)-LSTM and vanilla transformer baselines over a 2015–2019 EMAP data set36. Comprehensive Indian reviews confirm that such CNN, LSTM and CNN-LSTM hybrids can lift accuracy by up to 37%37. Collectively these studies highlight three converging insights for solar-radiation forecasting. First, hybridization, whether statistical-empirical, image-plus-NWP, or signal-decomposition-plus-deep-network, consistently boosts performance across climates. Second, model choice and horizon must reflect data availability: transfer learning and clear-sky filters remain valuable when imagery is scarce, whereas minute-ahead dispatch benefits most from sky cameras and CNN-LSTM fusion. Third, the field is shifting toward interpretable, multi-modal deep architectures that can generalize without local retraining, a direction reinforced by the superior accuracy of multi-modal deep clustering (MMDC)32 and Multiple Image Convolutional Long Short-Term Memory Fusion Network (MICNN-L)25. Remaining gaps include systematic cross-climate validation beyond the U.S., Europe and India, and unified uncertainty quantification to complement RMSE-based metrics. In addition to new DL techniques and ML methods, nature-inspired metaheuristics have also emerged as powerful tools for solving complex nonlinear, multimodal optimization problems, particularly in the domains of machine learning model tuning, feature selection, and system design. Among these, the Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA) are widely recognized for their balance of exploration and exploitation strategies. Those optimization algorithms are used both as a single optimization technique or a combination of various alternatives. ABC algorithm, inspired by the foraging behavior of honeybees, has gained attention due to its simplicity and strong global search capabilities. As a population intelligent algorithm, ABC applied in many studies. Oruc et al.38, Zhang et al.39, Gujarathi et al.40 incorporated ABC directly employed with a ML method or hybridized with another optimization algorithm in studies ranging drought forecast to engine optimization and handling with high dimensional datasets. GWO mimics the social leadership and cooperative hunting strategies of grey wolves. It is valued for its algorithmic simplicity and convergence stability. GWO has been successfully applied in time series forecasting and parameter tuning tasks and demonstrated its effectiveness in optimizing41,42,43. GA is a classical evolutionary algorithm based on principles of natural selection, crossover, and mutation44. It has broad application across optimization tasks and is often used as a baseline for performance comparisons with newer methods. Genetic Algorithms remain to be used in hybrid models to boost convergence rates across problem spaces containing either discrete or continuous parameters in diverse application44,45,46,47. The Whale Optimization Algorithm (WOA) inspired from feeding strategy of whale population48. WOA gained recognition for its simplicity and ability to balance intensification and diversification. Tang et al.49 introduced a combined WOA-ABC algorithm to overcome local optima problem and improve solution accuracy across theoretical and practical engineering problems. Although less frequently cited in academic databases, the Coyote Optimization Algorithm (COA) or COATI represents another nature-inspired method that modeled on coyote pack dynamics and social learning50. Despite showing potential across broad domains ranging from energy planning to photovoltaic parameter extraction51,52, additional comparative studies are needed to validate its performance relative to better-established algorithms like ABC, GWO, and WOA. Similar to other algorithms, since its initial development, COA has evolved into several improved forms, including Chaotic COA (CCOA)53 and Multiobjective COA (MOCOA)54 which have been implemented in a broad range of problem domains. As Norway targets toward broadening its energy mix and reduce its dependence on fossil fuels sources, solar power becomes the increasing component of the energy strategy. Norway now recognizes the importance of solar energy as an important complementary source of renewable electricity generation while it has been historically known for its hydropower capabilities55,56,57. The possibility of using solar energy across Norway’s diverse landscapes, such as urban areas, agricultural regions, and industrial sites, has become much more realistic due to the significant drop in solar PV system prices and progress in solar technology in recent years56,58. As solar energy will not be sufficient to supply Norway especially during the winter months, hybrid systems offer a promising solution for integrating solar energy into Norway’s energy landscape to meet the demands for renewable energy59. While Norway is not typically known for its solar energy production, it possesses a number of hidden advantages for PV performance. The favorable effect of low ambient temperatures on solar panel efficiency counterbalance lower irradiance levels by reducing heat-related efficiency losses since PV systems, especially those based on crystalline silicon, are thermally sensitive—their efficiency decreases by ~ 0.4–0.5% per °C increase in module temperature59,60,61,62,63. Moreover, new applications like icephobic nanocoatings64, considering climate variations and orientations affect65 increasing performance of the systems. Rees et al.,66 also built a simple, transparent workflow that turns freely available high-resolution LiDAR into city-wide rooftop-solar potential estimations and concluded that Rooftop solar PV in Tromsø could realistically supply ≈ 20–30% of the city’s annual electricity demand, about 200 GWh yr⁻¹, with residential roofs alone contributing roughly 40% of that total. In addition, new technologies, including tilting systems, bifacial panels, and heat recovery-integrated PV (PVT), present opportunities to improve year-round utilization67,68,69,70. Given the evolving scientific landscape and Norway’s focus on solar energy, there is a need to integrate advanced solar power forecasting methodologies into regional energy planning and grid management. International studies have exhibited the superiority of hybrid deep learning models, especially those combining sky imagery, NWP outputs, and statistical decomposition. Though, local adoption in high-latitude contexts like Norway remains limited. The seasonal extremes in insolation, coupled with complex topography and urban form, demand forecasting frameworks that are both data-adaptive and climatically robust. Moreover, with evidence that rooftop solar in cities like Tromsø could meet up to 30% of local electricity demand, the importance of precise, site-specific irradiance forecasting becomes even more pronounced. Many forecasting studies in environmental sciences and renewable-energy applications focus on a small set of established learning algorithms (e.g., ANN, SVM/SVR, and Random Forest) for which performance and generalization properties have been widely discussed71. In addition, several studies report that hybridization, through data preprocessing and/or metaheuristic hyperparameter tuning, can further improve SVM/SVR performance in related forecasting tasks72,73,74 Accordingly, the present study uses SVM as a controlled base learner to systematically quantify the incremental value of (i) feature selection and lag-structure design and (ii) different metaheuristic optimizers. The aim is not to claim universal superiority over all alternative machine-learning/deep-learning methods, but to provide a transparent, like-for-like comparison within a unified modeling framework. In line with this objective, we develop and evaluate SVM-based hybrid forecasting models for daily PV power in southern Norway (Kristiansand, Stavanger, and Oslo) using PVGIS-ERA5 derived PV output and near-surface air temperature data covering 2020–2023. Four metaheuristic optimizers (ABC, GWO, GA, and PSO) are compared, and four alternative input configurations are defined via the Minimum Redundancy Maximum Relevance (MRMR) feature selection method. Model performance is assessed using multiple goodness-of-fit criteria (r, RMSE, NSE, KGE, and PI) together with visual diagnostics. The main contributions of this work are as follows: A daily PV power forecasting case study for three southern Norway sites using consistent PVGIS-ERA5 input data. Development of a controlled comparison of four metaheuristic optimizers (ABC, GWO, GA, PSO) for tuning an SVM forecasting model. Construction of four lagged input structures via MRMR and evaluation of four lagged input structures combining PV history and temperature signals. Multi-metric evaluation (r, RMSE, NSE, KGE, PI) complemented by visual diagnostics to interpret performance differences and model limitations. This work presents an analysis of PV generation prediction based on the data derived from three distinct locations of Norway. The analysis used SVM with a range of optimization techniques implemented including PSO, GWO, GA and ABC. The methodological framework in this study is summarized graphically in Fig. 1. Methodological framework. This study focuses on three coastal locations across southern Norway to evaluate solar radiation and PV power potential. The selected sites vary in latitude, elevation, and orientation. This diversification provides examination of how geographic and topographic parameters affect solar energy production. Geographically, the study area extends between 58.15° to ~ 59.90°N latitude and 5.73° to ~ 10.74°E longitude which covers a diverse range of coastal terrains. Table 1 presents the key site characteristics including geographic location, elevation above sea level, slope, azimuth, and PV system specifications considered. The PVGIS-ERA5 radiation database was used to ensure climatic input consistency across all locations (PVGIS 5.3)75. The optimal slope angles fall within 46° to 49 range and azimuth values span from − 5° to 0°. These values indicate minor deviations from due south alignment, which is considered the optimal for fixed PV installations at Northern Hemisphere latitudes. A standard monocrystalline silicon PV system with a nominal capacity of 1.0 kWp were assumed for each site. Uniform system losses of 14% are applied to account for thermal, wiring, and inverter-related inefficiencies. The selection of these representative sites and configuration aim to make the results more comparable and transferable to broader PV deployment scenarios across similar latitudes and climates. In this study, hourly PV output and temperature records were first obtained and then aggregated to a daily scale for forecasting. Hourly PV output contains structurally zero values during nighttime; if retained, these long zero sequences can dominate error metrics and encourage trivial predictions dominated by nocturnal conditions rather than the physically informative daylight signal. To focus on daytime generation, nighttime hours were excluded and the daily PV target was computed as the total PV power generated during a representative 7-hour daylight window (selected to consistently capture peak sun hours for the region and to represent daytime generation consistently across the study period). Daily mean air temperature was used as the meteorological predictor. This aggregation inevitably reduces intra-day variability; therefore, sub-daily (hourly) forecasting and uncertainty quantification are recommended directions for future work. Table 1 represents statistics of data used in this study (Fig. 2). Study locations in southern Norway.. The map was generated using ArcMap (version 10.8) from ArcGIS Desktop (https://desktop.arcgis.com). Using different model structures in analyses may either increase or decrease model performance. Numerous studies and applications related to this topic can be found in the literature. Researchers often prefer feature selection methods grounded in statistical or stochastic process theory, and MRMR is one such technique. The Minimum Redundancy Maximum Relevance (MRMR) method is an effective feature selection technique designed to identify the optimal subset of input features for predicting an output. Its primary goal is to select features that are highly relevant to the output while maintaining minimum redundancy among themselves76. By prioritizing crucial, uncorrelated features, MRMR can enhance machine learning model accuracy and significantly reduce the risk of overfitting based on a greedy algorithm and a relevance-redundancy measure, making it particularly effective for high-dimensional datasets. In this study, the MRMR method was used to define the input structure for our models, based on its preference in the literature for yielding significant results77. For more details on the method, see Ding and Peng76. Through the MRMR, lagged input structures from PV output and air temperature were constructed. The significance levels and lagged values of temperature and PV data at different time intervals. Figure 3 shows the feature importance ranking from the MRMR analysis, and the resulting input combinations are summarized in Table 2. Feature importance results from the MRMR analysis. (Pt = PV target value on day t; Pt5 = PV value lagged by 5 days; Tt4 = temperature value lagged by 4 days; etc.). Support Vector Machines (SVMs) and their regression variant SVR rely on kernel functions to map nonlinear relationships in complex datasets. The support vector machine (SVM) is a classifier that belongs to the kernel approaches in machine learning. This learning system is employed to classify and predict the data fitness function, aiming to minimize mistakes in data categorization or the fitness function itself. To advance these methods not only the optimization kernel architectures or refining hyperparameters but also tailoring implementations for particular application contexts were used78,79,80,81. Wang et al.82 demonstrated that the Gaussian kernel’s spread parameter (γ) must fall within a specific range to ensure the model achieve optimal generalization. SVM is extensively utilized for both regression and classification problems. Owing to its adaptability and efficacy it is positioned as a premier method in machine learning. For practical applications, Kusuma and Kudus83 describe SVR as a regression method designed to control overfitting and demonstrate its application on mortgage survival data using a linear kernel. Theoretical foundations connecting SVMs to probabilistic frameworks emerged through Wang et al.‘s84 work linking Gaussian kernel density estimation (GKDE) to kernel-based learning. Their analysis revealed that Gaussian-kernel SVMs operate as probabilistic classifiers, thereby providing Bayesian justification for the algorithm’s empirical success. . The mathematical formulation of SVM is presented in Eq. (1), defining the relationship between input and output variables as follows: where (:varphi:left(xright)) denotes a high dimensional feature space, w represents weight vectore, and b referred as the bias term. For implementation details and an extended reference trail used in closely related hybrid SVM studies the readers can refer to previous studies of Oruc et al.38 and Oruc et al.85 and for foundational SVR/SVM theory and formulation, cite standard SVR/SVM sources. In this study, SVM algorithm was used to predict PV. Optimization methods, which are ABC, GWO, GA, and PSO, were used to enhance model performances. Initially, algorithm learning was performed with 70%, this ratio is frequently preferred in the literature, and it has been reported by many researchers that it yields effective results, of the dataset obtained from the region. Besides, time series data was not shuffled during the analysis because the sequence of data is critically important in time series analysis. The data was separated into training and testing sets without altering this sequence. The split ratios were selected based on proportions commonly found in the literature73,77,85. No random shuffling was applied, in order to simulate real forecasting conditions. Furthermore, preliminary steps or precautions against overfitting were taken using performance metrics like PI. Then, the optimization techniques mentioned above were used to further improve the performance of these algorithms. The results obtained from these models were evaluated according to performance evaluation criteria, RMSE, r, PI, KGE and NSE. All models were trained on the training set, and their performance was evaluated on the test set as described below. Karaboga86 modeled the Artificial Bee Colony (ABC) algorithm based on honeybee foraging behavior. The algorithm divides the colony into three functional groups which exhibit distinct search behaviors. Employed bees exploit known food sources and communicate their quality through waggle dances at the hive. Onlooker bees observe these dances, evaluate potential sources based on profitability, and focus search efforts on those promising locations. Scout bees are responsible for exploring search space randomly and identifying new sources when existing sites are depleted87,88,89. The algorithm maintains a balance between exploration and exploitation through dynamic role transitions. When a particular food source fail to show improvement over successive iterations, the employed bee assigned that location abandons it and transitions into a scout bee. This conversion starts random exploration for new opportunities90. This abandonment mechanism prevents the algorithm from being trapped in suboptimal solutions. The scouts introduce diversity and novelty into the search process, employed and onlooker bees refine and extract benefit from known solutions. Mathematical details and implementation procedures provided in Karaboga86, Li et al.88, and Vitorino et al.89. Mirjalili et al.91 introduced the Grey Wolf Optimizer (GWO) by modeling the hunting dynamics observed in grey wolf packs. The algorithm transforms this behavior into a computational optimization framework. Grey wolves hunt through a hierarchical system of coordinated roles that includes tracking prey, encircling, and attacking. This is a strategy that translates effectively into computational search patterns92,93. The straightforward linear structure of the algorithm simplifies implementation while maintaining performance and has produced successful results in many fields, as mentioned by numerous researchers94. GWO establishes candidate solutions based on the observed wolf pack hierarchy. The alpha (α) represents the current best solution and positions itself as the leader of the search process. Beta (β) and delta (δ) wolves correspond to the second and third-best solutions that guide exploration of potential regions. All remaining solutions function as omega (ω) wolves that represents the pack’s lowest tier that explores the broader search space91,95. This hierarchical structure drives the optimization mechanism forward. Alphas direct the hunt, betas and deltas refine the search direction and omegas ensure diversity in exploration. Mathematically, the three hunting phases translate into iterative position adjustments. These are namely, tracking, encircling, and attacking phases which are guided by the alpha, beta, and delta solutions. Each iteration recalculates and refines wolf positions based on their distance from these leaders. The process progressively tightening the search around optimal regions until the algorithm reaches convergence, that is two criteria are determined in the GWO process: (1) catching the prey (reaching the best solution) and (2) reaching the maximum number of iterations96. Details governing the process can be found in Mirjalili et al.91. Holland introduced the Genetic Algorithm (GA) in 1960 and refined it over the following two decades. The algorithm translates Darwin’s evolutionary principles into computational optimization methods. GA maintains a population of potential solutions that compete, recombine, and randomly mutate across iterative generations. This process directly parallels biological evolution where advantageous characteristics persist through populations while disadvantageous ones disappear44 and97. Industrial applications have validated the effectiveness of the algorithm across diverse application domains, ranging from parameter optimization to complex scheduling problems46,47,98,99. The fundamental strength of the algorithm lies in its gradient-free search mechanism. This feature enables it to handle both continuous and discrete optimization challenges without requiring gradient calculations. Such a requirement constrains many traditional optimization methods44 and97. Bras et al.100 documented GA’s flexibility through applications extending from linguistic analysis to fuzzy network tuning. Their work demonstrates how the same evolutionary mechanisms, namely crossover, mutation, and selection, can be adapted to different problem domains100,101. The algorithm proceeds through three core stages. First it initializes a random population, second applies genetic operators through probabilistic rather than deterministic process, and third evaluates solution quality until convergence criteria trigger termination101,102. When solutions fail to meet established quality thresholds, the algorithm iterates through another generational cycle. This iteration systematically refines and enhances the population through selection pressure and genetic variation101,103. These steps are: Crossover (stochastic): part of two solutions “is swapped” to produce new ones. Mutation (stochastic): part of a new solution “is flipped” to generate a new one and prevent it from converging into local optima. Selection: the new solutions are evaluated according to the objective function, and the best candidates are selected. In certain instances, such as a high mutation rate that could lead to the loss of good solutions, the elitism operator is employed to guarantee that the optimal solutions are transferred to the next generation without modification, ensuring that the best candidates are maintained within the solution set100,103. Kennedy and Eberhart introduced particle swarm optimization (PSO) in 1995. This algorithm conceptually inspires from coordinated and collective animal behaviors observed in natural system such as schools of fish navigating currents and bird flocks foraging collectively. Unlike genetic algorithms and other comparable evolutionary methods, PSO achieves more rapid convergence while requiring fewer computational resources. This advantage becomes particularly evident when addressing nonlinear optimization problems and impact modeling success104,105. The algorithm treats each potential solution as a particle moving through complex search spaces and is to use information of the current position X and velocity V of particles106. Initially, particles are positioned randomly across the solution space. As the algorithm progresses through successive iterations, each particle adjusts its trajectory based on two guiding influences. These are the particle’s best position discovered so far and the globally best position identified by any particle of the entire swarm. This dual-component memory system drives particles toward potential solution regions while simultaneously maintaining exploration capability107,108,109,110,111. The search continues until particles converge on a shared optimal location. This convergence signals that the algorithm has identified the best available solution within the search space112,113,114. The correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and Performance Index (PI), were employed key statistical indicators to assess model performance as specified in Equations (2) through (6). (2), (3), (4), (5), and (6) NSE and KGE are goodness-of-fit measures that attain 1 for perfect prediction. For NSE, a value of 0 indicates performance equivalent to the mean-prediction baseline, whereas KGE uses a benchmark threshold of approximately − 0.41 for comparable interpretation115. In this study, total daily PV power generation data and daily average temperature values obtained from Kristiansand, Stavanger, and Oslo, which are in the southern region of Norway, were used to investigate the performance of forecasting models that were developed through combination of machine learning and optimization techniques. A total of 70% of the data was employed for training, while the remaining 30% was reserved for testing. Support Vector Machines (SVM) were selected as the machine learning algorithm, whereas optimization techniques included the Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) due to one of the primary objectives of the study which is to enhance model performance by hybridizing. Furthermore, in order to examine the effect of model input variables on prediction performance, the MRMR (Minimum Redundancy Maximum Relevance) method was applied to construct four different input structures. The results of all analyses are presented in Table 3. Furthermore, it should be noted that these results and rankings (e.g., PSO-M04 being best) are specific to the data and sites analyzed; generalizing beyond this case study should be done with caution. According to the results presented in Table 3, the performance metrics of the four input structures analyzed with SVM in the Kristiansand region differ from one another. Among these, the lowest performance was observed in M01, while the strongest baseline (untuned) SVM results were obtained with M04 (r = 0.6469, NSE = 0.3587, KGE = 0.3141, PI = 0.2937, and RMSE = 0.8004). The analysis of model structures shows that M01 was built using one input variable (Pt-5), while M04 was built using four input variables (Pt-5, Pt-3, Tt-4, and Tt-1). While both models used Pt as the target variable, integrating power data with temperature improves daily forecasting precision based on the results. Evidence from the analysis also reveals the success of MRMR-based input selection process in identifying superior input combinations. MRMR-based input selection process when coupled with hybridization of SVM with metaheuristic optimizers, also a consistent improvement was obtained in performance metrics (Table 3). Therefore, results also validate the value of hybridizing machine learning with optimization techniques compared to standalone configurations. However, the differences were marginal, which makes it challenging to identify a single superior optimizer for this region. Despite this, a closer look at the performance metrics revealed that SVM-PSO-M04 delivered the best test results for the Kristiansand region (r = 0.7707, NSE = 0.5748, KGE = 0.7092, PI = 0.2964, and RMSE = 0.6513). Among the tested models it can be concluded that metaheuristic hyperparameter tuning can enhance SVM performance for forecasting in this study. Compared to the analysis without any optimization technique (SVM-M04), the performance metrics were improved. Additionally, when the results of the other optimization methods were evaluated, the M04 model consistently demonstrated success across all cases. This finding further validates the effectiveness of the MRMR method in determining the model input structure for this region; while these results should be interpreted as site- and dataset-specific. Stavanger is another station within the study area where analyses were conducted. At this station, the same methods as in the previous case were applied, and the results are presented in Table 3. In the analyses performed solely with SVM, the M04 model achieved the highest scores among the four input structures (r = 0.6091, NSE = 0.3018, KGE = 0.2543, PI = 0.3548, and RMSE = 0.8352). Similar to the previous station, the input structure of M04 also demonstrated effective performance in this part of the analysis. For this station as well, optimization techniques were utilized to improve model performance. In the analyses involving ABC, GWO, GA, and PSO, performance metrics generally improved, with the results being very close to each other. Therefore, it was difficult to identify a single best-performing algorithm for the Stavanger station also. A primary takeaway of the analyses can be the overall performance among the optimization algorithms which produced comparable results. Another key observation is while the highest score metrics were obtained from M03 with the SVM-ABC and SVM-PSO techniques, all other methods achieved their highest scores using M04. These results suggest that the effectiveness of model input structures may vary upon regional environmental characteristics and they are sensitive to those characteristics. An additional finding is that using an optimal number of input variables with expanded data diversity which means balancing input complexity with representativeness, can achieve higher predictive precision. Another station from which data were collected within the study area is Oslo. All of these stations are located in the southern region of Norway, where sunshine duration is considerably higher compared to other regions located in the country. The analysis results obtained from this station are presented in Table 3. As in the other stations, analyses were first performed using only SVM, and optimization techniques were subsequently applied to improve the results. Overall, the analyses incorporating optimization techniques improved the performance metrics of all models. In the analyses performed solely with SVM, the highest scores were obtained with M04 (r = 0.5994, NSE = 0.3043, KGE = 0.2518, PI = 0.3344, and RMSE = 0.8336). Among the optimization-based analyses, SVM-PSO-M04 achieved the highest test scores (r = 0.6861, NSE = 0.4470, KGE = 0.6252, PI = 0.3686, and RMSE = 0.7428), although differences among the top optimizers were modest. Unlike the Stavanger station, the best-performing input structure at this station was obtained with M04. Therefore, it can be concluded that while determining model input structures, not only PV power data but also temperature information can be beneficial for daily PV forecasting. In general, across all regions, in this dataset, the strongest results were most often obtained with M04, while the input structure M01 exhibited the lowest performance metrics. All optimization techniques improved the performance of the models relative to the untuned SVM baseline. However, because performance differences among optimizers were relatively small, PSO should be interpreted as yielding the highest scores in this study rather than as universally superior. In this study, in addition to statistical calculations, visual comparison methods were also employed. For the three different stations within the study area, violin plots were generated and are presented in Fig. 4. These plots were constructed to enable the comparison of the best-performing models within each category. According to the results, the only model that did not visually align with the observed values for Kristiansand was SVM-M04. Almost all of the other plots displayed highly similar distributions. Therefore, the most critical aspects to consider in this context are measures such as the mean, extremes, and median. Violin diagram for all models in different stations on study area where SVM-M04; analysis of baseline SVM with M04 and, SVM-ABC-M04; analysis of SVM coupled ABC (hybridized SVM) with M04 etc. (a) Kristiansand (b) Stavanger (c) Oslo. Although it is rather difficult to distinguish between the models based on visual observations alone, an evaluation of kernel densities, the 3rd quartile, and median values revealed that the model most similar to the observed data was SVM-PSO-M04. In Stavanger, the best-performing model was identified as SVM-PSO-M03, while in Oslo, the most successful model was again SVM-PSO-M04. In determining these successful models, multiple statistical parameters from the violin plots were taken into consideration. In Fig. 5, a box-normal plot is presented to enable a better comparison of the best-performing models. Upon detailed examination of this plot, it appears quite difficult to distinguish the differences between the observed values and the prediction models. This difficulty mainly arises from the fact that all optimization methods produced results that were very close to each other. While the prediction models in the Kristiansand and Stavanger regions yielded highly similar outcomes, a few of the prediction models in the Oslo region could be distinguished more easily. In this region, the SVM-ABC-M03 and SVM-GWO-M04 models visually resembled the observed values more closely than the others; however, in terms of statistical results, these two methods lagged behind. Thus, the visual outcomes and the statistical results did not fully align. In conclusion, when the performance metrics obtained from prediction models are very close to one another, the box-normal method should not be preferred for visual comparison. Box-normal diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. (a) Kristiansand (b) Stavanger (c) Oslo. The ridge plot, another visual comparison method, is also included in this study. Figure 6 shows the ridge plot for all models that were successful in their respective class. Upon examining this plot, a model that visually overlaps with the observation values in the Kristiansand and Oslo regions could not be identified. However, in the Stavanger region, the models were able to predict the peak that occurred in the initial values of the observation data. But this was also not effective in determining the most successful model. Consequently, this visual comparison method cannot be considered successful either. In conclusion, the ridge plot was also unsuccessful in determining the best model, that is, in comparing prediction models whose performance metrics were very close to each other. Ridge diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. (a) Kristiansand (b) Stavanger (c) Oslo. Apart from the Violin plot, the visual comparison methods mentioned above did not yield exceptional results in distinguishing the most successful models. Therefore, to both increase the comparison comprehensiveness of the study and to enable a more qualified distinction of the results, Bland-Altman and Box plots were generated for all regions. All plots, specific to different regions, are shown in Figs. 7 and 8, and 9. Bland-Altman diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. in Kristiansand. In Fig. 7, the differences between the prediction models based on the observed values at 95% limits of agreement level are shown. One of the most important results here is that the differences for the SVM-PSO-M04 model are notably clustered around the zero-line indicating little bias for the SVM-PSO-M04 model. Although the predicted values for the other models also concentrate around zero, the densest region was determined to be SVM-PSO-M04. Therefore, this is an indication that it is the most successful model in that region. Bland-Altman diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. in Stavanger. Figure 8, on the other hand, shows the differences between the prediction models for Stavanger at 95% limits of agreement level, based on the observed values. The key point is that the predicted values for all models failed to cluster around the zero line, meaning their predictions have larger deviations from observations. Therefore, this indicates that no conclusive results were found for Stavanger. However, when looking at the SVM-PSO-M03 model, the predicted values are clustered between the 95% limits of agreement, which suggests that results close to the observed values were obtained. The predicted values are more concentrated between these two lines compared to the other models. Bland-Altman diagram for all models in different stations on study area where SVM-M04; analysis of SVM with M04, SVM-ABC-M04; analysis of SVM and ABC with M04 etc. in Oslo. Figure 9 displays the Bland-Altman plots for the prediction models based on the observed values for the Oslo station. Upon examining the figure, it is observed that the differences in the predicted values for the SVM-GA-M03 and SVM-PSO-M04 models are concentrated around zero. This indicates that these models yield more effective results compared to the others. The results here are consistent with the statistical findings. To ensure the reliability of the results obtained in this study, ANOVA (Analysis of Variance) and Kruskal-Wallis statistical tests were applied to the forecasting models developed for the Stavanger, Kristiansand, and Oslo regions. The findings were examined at a 95% significance level in Table 4. For all stations, p-values were greater than 0.05, indicating that we fail to reject the null hypothesis of no statistically significant differences among the compared groups at 95% confidence. This suggests that the performance differences among the best models are modest and should be interpreted alongside the multi-metric evaluation and visual diagnostics. Also supporting the claim that while PSO-M04 had the highest metrics in general, its edge was not statistically significant. The use of machine learning and optimization techniques in a hybridized manner is among the methods preferred by researchers across many disciplines in literature. Model performance metrics are typically improved using optimization techniques. In this study, consistent but moderate improvements in model performance values were also achieved through optimization techniques. The results obtained in this study, both in terms of the methods used and the optimization techniques applied, are consistent with those of several studies in literature. Some of these include: AlMohimeed et al.116 developed prediction models for the forward-looking estimation of cancer cells by utilizing image processing methods. The SVM-PSO model achieved one of the successful results. Just as this hybrid method was effective in the forward-looking prediction of cancer cells, it has also proven effective in the early diagnosis of hypertension problems117. In addition to their use in the health sector, hybrid methods are also utilized in hydrological studies. Oruc et al.38 investigated the performance of forward-looking drought prediction models in the Norwegian region by utilizing The Adaptive Neuro-Fuzzy Inference System (ANFIS) and SVM. They improved the model performances by hybridizing the machine learning algorithms with GWO, ABC, PSO, and GA. In their results, they emphasized that effective outcomes were obtained in analyses using SVM-PSO. In addition to these, they created 12 different model input structures using cross-correlation. In their conclusions, they stated that the data type and the number of delayed data points in the model input structure should be kept at an optimum level. Results consistent with these findings were also obtained in this study. To broaden the scope of the study, the model input structure in this work was created using the MRMR method instead of cross-correlation. Another study that obtained effective results with SVM-PSO is that of Samantaray et al.118. In their study, they chose Back Propagation Neural Network (BPNN) and SVM, along with PSO from the optimization techniques. Samantaray et al., who aimed to model the forward-looking prediction of floods in the Barak valley by creating 5 different input structures, obtained the most effective results from analyses performed with SVM-PSO. Unlike this study, they did not use any delayed data in their model input structures. Furthermore, they enriched their model input structure with meteorological data. In the results of their work, they also mentioned that effective results were achieved through this enrichment. In many studies in the literature where hybrid machine learning and optimization techniques are preferred, usually only two or three optimization techniques are used. In this study, however, SVM, which is considered a well-established machine learning algorithm, was chosen and hybridized with four different optimization techniques (ABC, GWO, GA, and PSO). While these optimizers have been widely applied across different domains, side-by-side comparisons within a unified high-latitude PV forecasting setup remain relatively limited. Although the combined use of these methods strengthens the comparative aspect of our study, including additional machine learning models (e.g., a neural network or ensemble method) could further broaden the scope of comparison. However, such analyses involving optimization techniques are highly time-consuming and computationally expensive. This would move beyond the scope of this study and potentially become a topic for a separate follow-up work. Researchers interested in hybrid models may consider comparing a small set of learners and optimizers under consistent validation protocols and may also investigate sensitivity to learning rates and hyperparameter ranges. The use of hybrid methodologies has become widespread across many disciplines today. Recently, deep learning techniques such as CNN, and CNN-LSTM have gained increasing visibility in research. These methods are considered highly innovative due to their nature as modifications of ANN architecture119. However, in this study, a key motivation was to benchmark the extent to which an established method (SVM) can benefit from input-structure design and metaheuristic tuning in a high-latitude PV forecasting context before moving to more complex deep-learning approaches. Deep-learning methods remain a promising direction for future work. The models utilized in this study are data-driven models. Although data-driven models have advanced beyond physical-based models, the performance comparison between the two modeling approaches is frequently debated among researchers. This debate arises from the fact that physical-based models incorporate multiple parameters that accurately portray real-life phenomena. In contrast data-driven models can be very powerful but they also rely on heavily the quality and quantity of available data and sometimes may not capture certain physical constraints. Therefore, for critical applications, both approaches should be used to cross-validate results and ensure robustness for the area being examined. Limitations of this study include: (i) daily aggregation of PV output, which smooths intra-day dynamics and ramp events; (ii) the use of a fixed 7-hour daytime window, which may not fully represent seasonal daylight variability at high latitudes; (iii) reliance on PVGIS-ERA5 derived PV output rather than site-measured power, which can introduce modeling biases; (iv) a restricted predictor set (primarily temperature and lagged PV) without additional meteorological drivers (e.g., irradiance, cloud cover, wind); (v) a limited number of sites and years (2020–2023); and (vi) evaluation based on a single chronological split without rolling-origin cross-validation or probabilistic forecasts. These limitations restrict generalization beyond the studied sites and period. Future work should (i) use measured PV data when available, (ii) expand predictors, (iii) benchmark against additional ML/DL baselines (e.g., RF, gradient boosting, LSTM/GRU, CNN-LSTM), (iv) apply rolling-origin evaluation, and (v) report prediction intervals to quantify forecast uncertainty. Addressing these limitations would allow to generalize the findings. This study developed SVM-based hybrid models tuned with ABC, GWO, GA, and PSO to forecast daily PV power for three southern Norway locations (Kristiansand, Stavanger, and Oslo) using PVGIS-ERA5 derived PV output and temperature data covering 2020–2023. A chronological 70/30 train–test split was used to evaluate out-of-sample performance. Across all sites and input configurations, metaheuristic tuning improved test performance relative to the untuned SVM baseline. PSO produced the best mean test metrics, although differences among the top-performing optimizers were generally small. Input configurations that included both lagged PV power and lagged temperature (M03–M04) typically outperformed PV-only structures (M01–M02), confirming the value of incorporating meteorological inputs in PV forecasting. Model skill varied by location, with the highest scores obtained for Kristiansand in this dataset. These results should be interpreted as case-study findings for southern Norway rather than universal performance rankings. Visual error analysis (e.g., Bland–Altman plots) plots were particularly helpful for diagnosing bias and agreement between observations and predictions; however, when model metrics are very close, visual methods alone may be insufficient to clearly distinguish models. However, this study’s scope is limited to daily data from three sites and a specific train-test split; thus, the results should be interpreted as a case study rather than generalized truths. The use of simulated PVGIS data, a restricted set of input features, and absence of cross-validation can be list as key limitations in this study and will be addressed in future work. Overall, the proposed SVM–metaheuristic framework provides a benchmark for high-latitude PV power forecasting. It demonstrates that even established machine learning models can benefit significantly from intelligent input selection and parameter tuning. Future studies can build on this by incorporating additional data sources (e.g., irradiation and cloud cover measurements) and exploring advanced models to further improve forecast reliability. The hyper-parameters used for each technique are shown in Table 5. The original contributions presented in the study are included in the article. The raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request. Bosetti, V., Catenacci, M., Fiorese, G. & Verdolini, E. 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Eng.207, 111300 (2025). Article Google Scholar Samantaray, S., Sahoo, A. & Agnihotri, A. Prediction of flood discharge using hybrid PSO-SVM algorithm in Barak river basin. MethodsX10, 102060 (2023). ArticlePubMedPubMed Central Google Scholar Kaleybar, F. A. & Molavi, A. CNN-LSTM-RF integration for predicting Mississippi river discharge dynamics. Acta Geophys.73, 6005–6020 (2025). ArticleADS Google Scholar Download references Funding: This research received no external funding. APC was supported/funded by UiT, the Arctic University of Norway. Acknowledgments: During the preparation of this work, the authors used ChatGPT and Quillbot in order to improve readability, edit grammar, and language of some parts of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Conflicts of Interest: The authors declare that there is no conflict of interest. Open access funding provided by UiT The Arctic University of Norway (incl University Hospital of North Norway). This research received no external funding. APC was supported/funded by UiT, the Arctic University of Norway. The Center for Sámi Studies, UiT Norges Arktiske Universitet, Tromsø, 9037, Norway Sertaç Oruç Civil Engineering Department, Faculty of Engineering and Natural Sciences, Ankara Yıldırım Beyazıt University, 15 Temmuz Şehitleri Campus, Ankara, 06010, Turkey Sertaç Oruç Civil Engineering Department, Faculty of Engineering, Central Campus, Aksaray University, Aksaray, 68100, Türkiye, Turkey Mehmet Ali Hınıs Civil Engineering Department, Technology Faculty, Central Campus, Gazi University, Ankara, 06560, Turkey Türker Tuğrul Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Conceptualization, S.O. and M.A.H.; Formal analysis, T.T.; Methodology, T.T., S.O. and M.A.H.; Supervision, S.O. and M.A.H.; Visualization, T.T., S.O. and M.A.H.; Writing—original draft, T.T. and S.O.; Writing—review and editing, T.T., S.O., and M.A.H. All authors have read and agreed to the published version of the manuscript. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Oruç, S., Hınıs, M.A. & Tuğrul, T. Forecasting photovoltaic power in high-latitude regions via support vector machine optimized by meta-heuristics. Sci Rep16, 3438 (2026). https://doi.org/10.1038/s41598-025-33415-7 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-025-33415-7 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
A solar farm near Tulia, which also has a herd of over one thousand sheep, recently celebrated its 1-year anniversary and shared how it has benefitted Swisher County and ERCOT grid stability. Hornet Solar, developed by Vesper Energy, announced that in 2025 alone, it contributed more than $5 million to the community and generated 1.5 million megawatt-hours of electricity, according to a company news release. “Texas is growing fast, and so is the demand for reliable power. Hornet Solar has delivered more than 1.5 million megawatt-hours to the ERCOT grid in its first year and over five million dollars in tax revenue flowing directly to local schools, hospitals, and services in Swisher County,” said Juan Suarez, Co-CEO of Vesper Energy. “This is what successful development and power generation looks like, and it is the standard we intend to carry forward.” Solar and sheep: 2,500 sheep are working at a Tulia solar farm. Here’s why One of Vesper’s contributions includes more than $4,000 to the Tulia Volunteer Fire Department, so it could buy a utility trailer for the department’s emergency side-by-side vehicle. “This support improves how we respond to emergencies across the county,” saidChief Johnny Shelburne, Tulia Fire Department. “New equipment, including a trailer that allows us to reach remote and hard-to-access areas, enhances our response times and firefighting capabilities.” This article continues after the photo gallery. Vesper has also given more than $31,000 to local nonprofits, community programs and first responders, which has gone on to support youth sports, food access and emergency services, according to the release. During the Hornet Solar project, it spent more than $1.8 million with regional businesses, including Rock Hard Trucking, Bed Rock Caliche, High Plains Concrete, Swisher Tires, and Ed Harris Lumber. Through that, it created 450 construction jobs, mostly local, and made nine permanent positions. “The economic growth spurred by Hornet Solar is making a real difference in Swisher County,” said Swisher County Commissioner Larry Buske. “These funds directly support the businesses and services our community relies on every day, and we look forward to the benefits that will continue to come from this long-term partnership.” Alana Edgin writes about business for the Lubbock Avalanche-Journal. Got a news tip? Contact her via email at aedgin@lubbockonline.com.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 16, Article number: 5004 (2026) Cite this article 1480 Accesses Metrics details Financial analysis has a long history of capturing the stochasticity of real-world phenomena. For informed investment decisions, it is crucial to understand and quantify uncertainty propagation from financial model input to output. Yet to that end, in the photovoltaics sector one has so far relied on coarse-grained approximations or extensive simulations. Here we present a numerically inexpensive approach that exactly traces uncertainty propagation on the level of probability distributions. It leverages analytic shortcuts through switching between different distribution representations, and only assumes independent input variables. With the financial analysis of a typical photovoltaic system as a case study, we use this approach to compute key financial metrics and demonstrate that their values can differ significantly from those obtained by a standard approximation. Moreover, we show with both frameworks that input uncertainty alone can significantly impact the outcome of financial analysis. A swift and large-scale deployment of renewable energies is essential for limiting global warming1, yet it relies on financing. Respective investment decisions however hinge on the computation of profitability metrics such as the net present value (NPV) or levelized cost of electricity (LCOE)2,3,4. These computations in turn need as an input accurate long-term (20+ year) forecasts of often highly fluctuating random variables describing, among others, meteorological conditions, costs and generated yields. The stochastic character of these input variables propagates to calculated economic target variables, and the challenge is to accurately model this uncertainty propagation to enable more informed investment decisions. As an important pillar for the transition towards renewable energies, photovoltaic (PV) power plants are a popular subject of economic analyses4,5,6,7,8,9,10,11,12,13,14. Relevant work on uncertainty propagation in the PV context focuses primarily on LCOE calculation8,14,15,16,17, but lacks a systematic account of the impact of input uncertainty on the calculation result. One route to achieving the latter is to disentangle the shifting input averages from varying fluctuations around those averages. Our first main contribution presented here is precisely to investigate how input uncertainty alone — while keeping input averages fixed — influences from an investor’s perspective the outcome of LCOE analysis and associated economic metrics. Moreover, we extend this investigation to another key variable, the NPV. A very coarse-grained way to account for uncertainty propagation in computations are scenario18,19 and sensitivity8,13,14,17,20 analysis, which are adequate in the absence of both data on and educated guesses for the random character of input variables. Yet nowadays, the availability of big data more often than not allows for a more detailed characterization of input uncertainty in terms of probability distributions or some of their moments — most notably through averages and standard deviations. In NPV and LCOE computations for PV power plants, random input variables like annual yields or costs are continuous (see Methods) and thus described by probability density functions (PDFs). In case underlying PDFs are unknown, their averages, standard deviations and percentiles can be well approximated using real-world time series, delivering already useful uncertainty quantifiers. Clearly, NPV and LCOE averages are the primary measures of profitability in NPV and LCOE analysis. Standard deviations, variances and coefficients of variation (CVs) can be interpreted as measures of uncertainty in input and target variables, respectively. In the PV sector, percentiles are used to define thresholds for the respective random variable that are exceeded with a given probability, e.g., the 10th percentile yields P90 as a relatively robust lower bound that is exceeded with probability 90%. These deliberate underestimations are a handy metric for investors to assess the bankability of projects. They can also be exactly computed with the random variable’s cumulative distribution function (CDF) F(x), e.g., by solving (F(P90)=1-90%) for P90. Other valuable uncertainty measures can only be computed with full information on underlying PDFs, such as the probability (P_mathrm {textrm{NPV}> 0}) that the NPV is positive, i.e., that a given project is profitable. The guide to the expression of uncertainty in measurement (GUM) lays out the de facto standard of how to define and measure uncertainties, as well as how to trace their propagation from model input to output21. It gives approximate equations relating input and output averages as well as respective variances (see equations 3a–3b). These equations, in the following referred to as the standard approximation (of uncertainty propagation), generally break down for large standard deviations of nonlinear input variables that can result from a highly intermittent character of renewable energies. Moreover, these equations in their standard form do not capture strongly correlated input variables, but can be amended to account for input correlations. Gaussianity of the target variable is often assumed in literature9,15,21 and considered part of the standard approximation here. For a more detailed treatment of uncertainty propagation, GUM proposes a full mapping of input variable PDFs onto target variable PDFs, yielding all uncertainty measures discussed above as a by-product. For such a mapping, it is straightforward to write down the respective — and generally high-dimensional — integral transforms whose solving GUM advises against, arguing it to be too time-consuming without further simplifications. Instead, the use of Monte-Carlo (MC) simulations is recommended22 and indeed pursued in relevant literature for only a handful of input variables7,8,15,16,23,24. These stochastic algorithms (i) sample probability distributions of input variables (ii) compute target variables based on sampled input variables and (iii) repeat steps (i)-(ii) to generate probability distributions of target variables. This allows MC simulations to trace uncertainty propagation also for correlated sets of input variables. However, it is difficult to draw analytic conclusions from computed output statistics. Moreover, in order to obtain reliable output statistics, one relies on extensive sampling of input distributions. Achieving acceptable runtimes for MC simulations with dozens of input variables (as in the scenario definitions further below) is beyond the scope of this work and left for future consideration. Here, as our second main contribution, we extend the aforementioned systematic economic analysis of PV systems to large input uncertainties for which the standard approximation fails. To this end, we present a novel analytic approach that tracks — on the level of entire PDFs — uncertainty propagation in modelling, promising feasible runtimes also for large numbers of input variables. This PDF mapping approach consists of the (mostly numerical) solution of integrals that are of significantly lower dimension than those GUM22 puts forward, with the simplification achieved through appropriate conversions between characteristic functions (CFs) as well as PDFs and CDFs. On the one hand, this Accelerating Conversion of Mapping Equations (ACME) approach sidesteps the long computation times associated with both MC simulations and brute-force integral transforms while still leaving room for further numerical optimization of involved integrals. On the other hand (and unlike the standard approximation), the presented method is valid for arbitrarily large input uncertainties and delivers the propagation of all PDF moments. The only prerequisite is that of the independence of input variables, which is a common modelling assumption due to scarce data on joint PDFs or even just covariances. In order to systematically analyze uncertainty propagation in the economic forecast for a PV system, we apply the novel approach from an investor’s perspective to multiple scenarios that represent different degrees of input uncertainty, yet constant input averages. To that end, we lay out and motivate in the Methods section the scenarios, as well as relevant input and target variables. These variables are then used to formulate the standard approximation equations and to introduce the proposed ACME formalism. Scaling relations are derived for the dependence of model outputs on a crucial model parameter, and consistency checks for the ACME formalism are formulated to ensure proper numerical implementation. In Results and Discussion, we benchmark both methods using proposed scenarios and a sensitivity analysis, assessing when and how differing degrees of input uncertainty impact key metrics for a PV system’s profitability (cf. Fig. 1). A PV plant’s profitability is influenced by its electric yield, which itself is determined by on-site meteorological conditions such as irradiance, ambient temperatures and wind, but also by technical specifications (e.g., module setup and performance ratio) and the degradation of plant components. Economic factors influencing profitability are the selling price of generated electricity, costs for operation and maintenance (O&M) and investment costs. To assess PV plant profitability in a comparative analysis of ACME approach and standard approximation (cf. Fig. 1), we focus on target variables NPV and LCOE and — without loss of generality — on uncertainty in two types of input variables. This is justified from an investor’s perspective with near-definite knowledge on project-specific input for such an economic analysis, but residual uncertainty tied to environmental variability. Workflow of and interaction between standard approximation (dashed boxes) and ACME approach (solid boxes) for the three considered scenarios (dotted boxes). Model output – distributions, scalars and equations – is represented and distinguished through shaded boxes. Assumptions used in the workflows are indicated through shaded arrow labels. In the PV context, we compute the net present value as which incorporates specific (i.e., normalized by nominal capacity) investment costs I, specific time-dependent revenues (scdot Y_t) as well as specific operation and maintenance (O&M) costs (O_textrm{M}+O_{textrm{R},t}) for each year t during a PV plant’s lifetime of T years. Here (Y_t) is the specific yield in year t sold for a fixed selling price s, while O&M costs are split into a constant maintenance-related ((O_textrm{M})) and t-dependent repair-related ((O_{textrm{R},t})) part. The quantity (O_textrm{M}) can be assigned a flat rate because it covers routine tasks like monitoring and inspections. Running costs and revenues are discounted with a rate (r_textrm{DI}) and, for the sake of simplicity, the residual value of decommissioned plants is not considered here. The levelized cost of electricity gives the average cost of electricity generation over a plant’s lifetime T, with the annual specific yields being discounted. With the LCOE, one can rank the competitiveness of different forms of electricity generation independently of electricity monetization, accounting for different plant sizes and cost structures. Unlike the NPV however, it is of limited use when assessing the absolute profitability of PV plants. Due to their complementary character in economic analysis, NPV and LCOE are chosen in the following as metrics whose uncertainty propagation from input variables is tracked in three scenarios. The input quantities in NPV and LCOE computation depend on many factors such as PV plant specifications, its geographical location, regional market dynamics, legislation and the pursued business model (in the NPV case). While the validity of the ACME approach does not depend on such specifications, we still assign to input quantities values describing a typical PV plant (see parameters in Table 1), mainly taken from19 and largely corroborated by25. In the following, we consider — in three main scenarios for NPV and LCOE computation — uncertainty propagation from two sets of random input variables: T annual specific yields ({Y_t}) and T annual specific repair-related O&M costs ({O_{textrm{R},t}}), with (t=1..T). In these scenarios, any uncertainty in computed NPVs and LCOEs arises through uncertainty in these input variables, which are considered on an annual basis t, as this is the temporal resolution on which standard NPV and LCOE operate [see equations (1)-(2)]. However, this input uncertainty does not refer to the random character of inter-annual variability as in other works26. Instead, each year t of operation in equations (1)-(2) is given its own pair ({Y_t,O_{textrm{R},t}}) of random input variables, reflecting respective uncertainties in year t, but not beyond. As in other works, it is assumed here that all random input variables are independent, which implies zero correlations within any pair of input variables. The choice of these two sets of random input variables is motivated by the following observations: (i) In the planning stage for PV plants, investors and project developers usually have specific information on quantities like I, (O_textrm{M}), (r_textrm{DE}) and (r_textrm{DI}) which are typically fixed over the project lifetime. Yet they need to accept and capture the stochasticity of yield generation and failure occurence [cf. (P_{textrm{Y}_t}(x)) and (P_mathrm {O_R}(x)) in Table 1]. (ii) The random character of these latter two processes is quantified in literature. (iii) They have markedly different PDFs, allowing for non-trivial behavior of the calculated target PDFs (see below). Correlations are neglected in all calculations of this work, an assumption that could however be relaxed (see Methods section). To systematically analyze uncertainty propagation in NPV and LCOE calculation, we choose a sequence of scenarios that represents different degrees of uncertainty in the set ({Y_t}) of T annual yields, while keeping uncertainty in ({O_{textrm{R},t}}) as well as all averages and other parameters constant across scenarios. This is to investigate the impact of input uncertainty on the outcome of economic analysis, and to disentangle in the process the contribution of either set {(O_{textrm{R},t})} and {(Y_t)}. In the O-scenario, we assume deterministic (Y_t) and thus set (hat{sigma }_Y=0), but draw T random variables {(O_{textrm{R},t})} ((t=1..T)) from a t-independent exponential distribution (P_mathrm {O_R}(x)) (cf. Table 1). Consequently, both NPV and LCOE are linear combinations of T independent and identically distributed random variables as also frequently assumed in standard literature. This is used as a base scenario to verify derived expressions, as well as to assess the propagation of the non-Gaussianity in ({O_{textrm{R},t}}) to the respective target variable. In the YO-scenario, the T exponentially distributed ({O_{textrm{R},t}}) ((t=1..T)) are combined with another set ({Y_t}) ((t=1..T)) of T random variables drawn from a t-dependent narrow Gamma distribution (P_{textrm{Y}_t}(x)) (cf. Table 1). This incorporation of non-identically distributed variables in linear (NPV) or nonlinear (LCOE) expressions reflects our current understanding of input uncertainties in LCOE and NPV computation. In the wYO-scenario, the same 2T independent random input variables as in the YO-scenario are considered, but the Gamma distribution underlying ({Y_t}) is widened considerably. This describes a strongly fluctuating annual yield (around the same average as in the YO-scenario) induced through pronounced climate volatility. Used input PDFs and their parameters in Table 1 can be motivated both on theoretical and empirical grounds. On the theoretical side, the employed Gamma distribution with shape parameter (alpha) and t-dependent scale parameter (theta _t) is highly versatile, with the exponential and normal distribution as limiting cases for (alpha =1) and (alpha rightarrow infty), respectively. These limiting cases are indeed observed for ({O_{textrm{R},t}}) and ({Y_t}), see below. Moreover, the Gamma distribution has support ([0,infty )), accounting for the fact that both (O_{textrm{R},t}) and (Y_t) are non-negative for all considered t. Lastly, it has a very simple characteristic function (varphi (f)=(1-icdot f cdot theta _t)^{-alpha }) that allows for quick algebraic manipulation and numeric integration in the ACME approach. Experimentally, the choice of the PDF for (O_{textrm{R},t}) is motivated by a recent comprehensive study involving around 80 PV rooftop systems27. There, a t-independent exponential distribution with mean (mu _mathrm {O_R}) and standard deviation (sigma _mathrm {O_R}) of (7 , mathrm {EUR/kW_p}) is found to sufficiently capture data variability, which is reproduced here by setting the Gamma distribution’s shape parameter to (alpha =1) and its scale parameter to (theta =7 , mathrm {EUR/kW_p}). Other studies quantify uncertainty in overall annual O&M costs ((O_{textrm{R}_t}+O_textrm{M})), assigning a normal distribution8,16 or uniform distribution ([17]), with CVs ranging from 0.058 to 0.3316. Yet in those cases, the chosen probability distributions seem to stem from the maximum-entropy principle rather than from sampling real-world cost PDFs. For (Y_t), a normal distribution is commonly assumed16,28,29 or deemed plausible8; but see17 that operates with an exponential shape instead. The YO-scenario approximates this normal distribution, as there the Gamma distribution’s shape parameter (alpha =100) is fairly large, with the associated CV of 0.1 in line with16. The wYO-scenario with its shape parameter of roughly (alpha =1) and CV of 0.9 resembles the setup with an exponentially distributed capacity factor in17, while the O-scenario approximates the very narrow normal distribution with CV=0.03 used in8 (cf. Table 1 and Fig. 1). Note that, to account for module degradation, the Gamma-distributed (Y_t) have a time-dependent mean (mu _textrm{Y}(t)=hat{mu }_textrm{Y}left( 1-r_textrm{DE}cdot tright)) and standard deviation (sigma _textrm{Y}(t)=hat{sigma }_textrm{Y}left( 1-r_textrm{DE}cdot tright)) with the scenario-specific initial annual specific yield (hat{mu }_textrm{Y}) and standard deviation (hat{sigma }_textrm{Y}), as well as the annual linear degradation rate (r_textrm{DE})30 (see Table 1). Both the mean and the standard deviation are set to decrease by the same fraction in each year t. This is because small averages usually entail small variability and, without further data, a constant coefficient of variation in (Y_t) is a sensible guess. Consequently, (alpha) is a constant, while (theta _t) depends on t (cf. Table 1). Formalizing notation in uncertainty propagation, one deals with a vector (textbf{x}=(x_1, x_2,…, x_v)) of input variables — with associated mean values (mathbf {mu _x}=left( mu _1, mu _2,…, mu _vright)) and standard deviations (mathbf {sigma _x}=left( sigma _1, sigma _2,…,sigma _vright)) — feeding into the computation of a target variable (f(x_1, x_2,…,x_v)) (e.g., the LCOE or NPV). If (f=sum _{j=1}^v{a_j x_j}) is a mere linear combination of uncorrelated input variables, then its variance — the squared standard deviation — is (sigma _f^2=sum _{j=1}^v{a_j^2 sigma _j^2}). This is similar to the propagation of averages (mu _f=sum _{j=1}^v{a_j mu _j}) for such a linear function f. If, instead, f factorizes as (f=kprod _{j=1}^v{x_j}) with independent input variables and constant k, then (sigma _f^2=k^2left[ prod _{j=1}^v{left( sigma _j^2+mu _j^2right) -prod _{j=1}^vmu _j^2}right]) since f’s raw moments also factorize. The latter re-declaration of a given output variable f as a product of input variables is a common procedure in literature to simplify the computation of (sigma _f)15,24. However, it shifts the modeller’s efforts towards interpreting the associated input variables (x_j) and quantifying their standard deviations (sigma _j). For general functional forms (f(x_1, x_2,…,x_v)), the standard approximation is to consider their Taylor expansion around (textbf{x}=mathbf {mu _x}), assuming uncorrelated input variables (x_j)21. This delivers output averages and variances as where only terms up to (O(sigma _j^2)) are considered. Note that already the approximate equation for the average yields a counter-intuitive second-order term which can however be motivated through a simple example: consider the function (f(x)=x^2) of a single random variable x with mean (mu _xequiv langle x rangle) and variance (sigma ^2_xequiv langle x^2 rangle -langle x rangle ^2), where (langle cdot rangle) denotes averaging. From (mu _fequiv langle f rangle =langle x^2 rangle) follows (mu _f=f(mu _x)+sigma _x^2), i.e., (mu _f= fleft( mu _xright) +frac{1}{2}frac{partial ^2 f}{partial x^2} biggr |_{{textbf {x}}={mu _{{textbf {x}}}}} sigma _x^2) as an exact equality. The equation for the variance is known as the Gaussian law of error propagation and, for the special case of (f=kprod _{j=1}^v x_j), turns into (sigma ^2_f/f^2approx sum _{j=1}^vsigma ^2_j/x_j^2), directly relating coefficients of variation instead of absolute standard deviations, which is the de-facto standard expression in the PV sector for analytically calculating uncertainty propagation9,15. Reassuringly, these equations are completely agnostic with respect to the shape of underlying PDFs, including the symmetry of the latter. And yet, assuming Gaussian input variables further simplifies calculations: The target variable f is Gaussian if it is a linear combination of independent Gaussian input variables. This is useful, because in the Gaussian case, standard deviations can be quickly converted to percentiles through standard normal tables. These simplifications add to the appeal of using Gaussian variables, but also let some modelers mistake their usefulness for their necessity. For averages and variances of NPVs and LCOEs, equations 3a–3b yield where only equations (4a)-(4b) are exact due to the linearity of the NPV in its considered input variables [cf. equation (1)]. Additionally assuming a Gaussian target distribution parametrized by the calculated averages and variances, the standard approximation equations (4a)-(4d) can be used to estimate (P_mathrm {textrm{NPV}> 0}) as well as P90 values. Note that equations (4a)-(4d) are also valid in the O-scenario (setting (sigma _textrm{Y}=0)) and, in that case, moreover all exact [due to the linear dependence of equations (1)-(2) on (O_{textrm{R},t})]. The proposed PDF mapping is a two-step process, where the second step is necessary only for nonlinear target variable LCOE: In the functional form of the target variable, any linear combination (sum _i a_i X_i) of independent input variables (X_i) (with possibly different PDFs) is expressed as a new composite variable Z. The characteristic function of Z, which is defined as the Fourier transform of Z’s PDF, is then simply (varphi _Z (f)=prod _i{varphi _ileft( a_i fright) }), where (varphi _i(f)) is the CF of input variable (X_i). Introducing such composite variables significantly reduces complexity through replacing multiple integration of PDFs in probability space with mere multiplication of CFs in Fourier space. This already delivers the NPV PDF and CDF through a single numerical integration (with the respective Gil-Pelaez inversion formula) in all considered scenarios and the LCOE PDF in the O-scenario, since — according to equations (1)-(2) — we deal in those cases with just a linear combination of (assumed independent) input variables. In contrast, the brute-force strategy laid out in GUM22 requires — both for obtaining NPV and LCOE PDFs — solving high-dimensional integrals over the PDFs of (T=30) (O-scenario) or (2T=60) (YO- and wYO-scenario) random variables. For the target variable LCOE in the YO- and wYO-scenario that is a nonlinear function of random variables, PDF and CDF cannot be exclusively computed with step 1. Instead, both numerator and denominator are expressed as composite random variables (hat{Z}) and (Z_1), respectively, according to step 1. Both are measurable functions of disjoint sets of independent random variables and thus also independent. This allows to write the CF of LCOE as (varphi _textrm{LCOE}(f)equiv varphi _{hat{Z}/Z_1}(f)=int _{-infty }^infty {textrm{d}z_1,varphi _mathrm {hat{Z}}left( f/z_1right) P_mathrm {Z_1}(z_1)}), where (varphi _mathrm{hat{Z}}(f)), (varphi _mathrm {Z_1}(f)) and (P_mathrm {Z_1}(z_1)) are obtained as in step 1. Finally, the PDF and CDF of the LCOE are computed from (varphi _textrm{LCOE}(f)). Hence in total, two single integrations are necessary in this case to compute LCOE distributions. We first detail the NPV PDF computation for the YO- and wYO-scenario, and then adapt obtained expressions to the O-scenario. According to equation (1), one can write (textrm{NPV}=s cdot Z_1-Z_2-Z_3) with composite variables (Z_1equiv sum _{t=1}^T{Y_t/(1+r_textrm{DI})^t}) and (Z_2equiv sum _{t=1}^T{O_{textrm{R},t}/(1+r_textrm{DI})^t}) as well as constant (Z_3equiv I+O_textrm{M}sum _{t=1}^T{(1+r_textrm{DI})^{-t}}). The CF of the annual yield (Y_t) is (varphi _{textrm{Y}_t}(f)=left[ 1- icdot fcdot hat{sigma }^2_textrm{Y}/hat{mu }_textrm{Y} left( 1-r_textrm{DE}cdot tright) right] ^{-hat{mu }^2_textrm{Y}/hat{sigma }^2_textrm{Y}}), the CF of (O_{textrm{R},t}) is (varphi _{textrm{O}_textrm{R,t}}(f)=left( 1-icdot f cdot sigma _mathrm {O_R} right) ^{-1}), and the CF of any constant c is (varphi _textrm{c}(f)=e^{icdot fcdot c}). Therefore the CFs of (Z_1), (Z_2) and (Z_3) are, according to step 1 of the PDF mapping approach, The CF of the NPV is simply the product again according to equation (1) and the fact that also (Z_1) and (Z_2) are independent, being measurable functions of disjoint sets of independent random variables. The Gil-Pelaez inversion formulas then yield for the NPV PDF and (F_textrm{NPV}(x)=1/2-pi ^{-1}int _textrm{0}^infty {textrm{d}f,f^{-1}operatorname {Im}left[ e^{-icdot fcdot x}varphi _textrm{NPV}(f)right] }) for the NPV CDF. Here (operatorname {Re}[z]) and (operatorname {Im}[z]) are real and imaginary part of complex number z, respectively. For the O-scenario, we set instead and compute PDF and CDF as above. Here (langle . rangle) is the ensemble average, so that (langle Y_{t}rangle =hat{mu }_textrm{y}(1-r_textrm{DE}cdot t)) in (langle Z_1rangle). For the YO- and wYO-scenario, we set (hat{Z}equiv Z_2+Z_3) and observe (textrm{LCOE}=hat{Z}/Z_1) [cf. equation (2]. We then calculate the characteristic functions of (hat{Z}) [delivering (varphi _mathrm {hat{Z}}(f)=varphi _mathrm {Z_2}(f)cdot varphi _mathrm {Z_3}(f))] and (Z_1) [yielding (varphi _mathrm {Z_1}(f))]. Knowing that (P_{textrm{Y}_t}(0)=0) in all scenarios due to (alpha>1) (cf. Table 1), it follows that also (P_mathrm {Z_1}(0)=0), so that For the LCOE PDF computation in the O-scenario, we proceed similarly to the respective NPV calculation, obtaining The LCOE PDF and CDF are then obtained from the CF analogously to the NPV case. In our systematic analysis of uncertainty propagation further below, all computed quantities are subject to an additional sensitivity analysis with respect to the plant lifetime T. This is because uncertainty propagation from T to target variables NPV and LCOE, with T being a discrete model parameter, cannot be traced with ACME or standard approach in their form laid out above. Still, uncertainty in T can be considerable due to environmental or economic hazards, and thus should be accounted for. Here, we qualitatively predict — through approximate scaling relations — benchmarking results in Results and Discussion for the T-dependent behavior of computed quantities. To this end, we make use of the fact that both the discount rate (r_textrm{DI}) and degradation rate (r_textrm{DE}) commonly attain very small values (see Table 1). To assess the T-dependence of computed averages and standard deviations, we use equations (4a)-(4d). For small (r_textrm{DI}) and (r_textrm{DE}), we obtain In these simplified standard approximation equations, the averages’ and variances’ dependence on T can be read off easily and compared to predictions of the ACME approach and the original equations (4a)-(4d) (see Results and Discussion). We further note that the LCOE equations (4c)-(4d) are exact only in the O-scenario and approximate in the YO- and wYO-scenario. To understand how T influences the quality of latter approximations, we first remark that for (r_textrm{DE}rightarrow 0), (P_{textrm{Y}_t}(x)) is t-independent (cf. Table 1). With now all input distributions being t-independent as well as (r_textrm{DI}rightarrow 0), we rewrite equation (2) as Here (langle Y_t rangle _T=T^{-1}sum _{t=1}^T Y_t) and (langle O_{textrm{R},t}rangle _T=T^{-1}sum _{t=1}^T O_{textrm{R},t}) are sample means (with sample size T) of random variables (Y_t) and (O_{textrm{R},t}), respectively. These sample means are themselves random variables drawn from T-dependent distributions with variances (hat{sigma }_textrm{Y}^2/T) and (sigma _mathrm {O_R}^2/T), respectively. Consequently, the LCOE output variable can be approximated as a function of only two random input variables (langle Y_t rangle _T) and (langle O_{textrm{R},t}rangle _T) whose variances decrease with T (with the only other LCOE dependence on T given by a vanishing additive term in the numerator of the LCOE). This suggests that the accuracy of the standard approximation equations for LCOE averages and standard deviations will increase with T. Given independent input variables, the presented ACME approach is exact, but needs careful numerical implementation. This is ensured by the following consistency checks on ACME output: Computed PDFs of NPV and LCOE (as well as of all input and intermediate characteristic functions) must be non-negative on considered intervals. The area under each of these PDF curves must moreover be 1 on considered intervals. NPV averages and variances computed from equation (7) must match those in equations (4a)-(4b). Additionally, LCOE averages and variances computed from the inversion formula applied to equation (10) in the O-scenario should match those in equations (4c). Furthermore, averages and variances computed from (P_mathrm {hat{Z}}(x)) and (P_mathrm {Z_1}(x)) must match those obtained from the numerator and denominator in equation (2), respectively (as both (hat{Z}) and (Z_1) are also linear in considered input variables). ACME averages and variances should qualitatively obey scaling relations given by equations (11a)-(11d). LCOE averages and variances computed in the ACME approach should coincide with their standard approximation counterparts (i) for small input variances (i.e., in the O- and YO-scenario) as well as (ii) for large T (in any scenario). Moreover, for large T, the NPV PDFs must be Gaussian to a high degree of accuracy, since then in equation (1), the central limit theorem approximately holds due to (r_textrm{DI}approx 0) and (r_textrm{DE}approx 0). This also applies to the LCOE PDF in the O-scenario [cf. equation (2)]. Some frameworks competing with ACME can in principle incorporate input covariances, but in practice assume uncorrelated input variables9,15,21,22,23. Like24, the ACME framework relies on the stronger assumption of independent input variables which, given the lack of data on respective joint probabilities, is a sensible approach from an operational perspective. But ACME’s independence assumption translates into strict real-world requirements for the modeled PV system: First, assuming independent annual yields ({Y_t}) implies neglecting inter-annual climate trends. Second, independent repair-related operation and maintenance costs ({O_{textrm{R},t}}) presuppose that PV system failure events appear independently of failures and repairs in previous years. Third, imposing independent ({Y_{t_1},O_{textrm{R},t_2}}) (with (t_1,t_2=1..T)) assumes that losses in (Y_t) (due to failure- and repair-induced PV system downtime) are balanced out by repair-induced gains in (Y_t) due to higher system performance. These requirements are hardly realistic, so it is natural to ask how the ACME approach can be adapted to account for input correlations in case these are known. One obvious strategy targets the summation over t in equations (1)-(2): Instead of binning all summed input variables into a single composite random variable as in the original approach, only those with sufficiently large time lags are pooled together to minimize cross-correlations between them — for example, only those with an even index t into one composite variable and those with an odd index t into another. This results in NPV and LCOE expressions with potentially very few significantly correlated composite variables, with higher chances of proper analytical or numerical treatment than the initial expressions. With both uncertainty propagation frameworks laid out, input and target variables specified as well as scenarios defined, we want to use these to systematically trace uncertainty propagation in the NPV and LCOE analysis of PV plants (cf. Fig. 1). To that end, equations (4a)-(4d) from the standard approximation are used to compute (mostly approximate) quantities that are then contrasted with their exact counterparts obtained from the ACME approach. These are NPV and LCOE averages, standard deviations as well as P90 values. To explore the behaviour of P90 values, we additionally plot cumulative distribution functions. Moreover, we consider (P_mathrm {textrm{NPV}ge 0}) (the probability of profitability) as well as the LCOE coefficient of variation. The respective ACME output in the YO- and wYO-scenario is obtained from equations (6) and (7) (NPV) as well as the Gil-Pelaez inversion of equation (9) (LCOE). In the O-scenario, equations (7) and (8) (NPV) as well as the Gil-Pelaez inversion of equation (10) (LCOE) are used, with all ACME output subject to the consistency checks laid out above. Plotting the underlying ACME PDFs, we assess their degree of Gaussianity to evaluate whether and when Gaussianity is a justified assumption in simplified models like the standard approximation. As shown in Fig. 2a, NPV averages increase roughly linearly with the number of years of operation T [cf. equation (11a)]. Moreover, since the NPV is linear in considered input variables, the latter variables’ variances do not affect computed NPV averages — hence NPV curves for the O-, YO- and wYO-scenario coincide. This linearity furthermore renders equation (4a) exact, letting NPV curves for the standard approximation and for the ACME approach coincide as well. (a) NPV averages and (b) LCOE averages computed with ACME approach and standard approximation in three scenarios. As expected [cf. equation (11c)], LCOE averages decrease with T (Fig. 2b). For large T, LCOE averages converge across scenarios, as then the only scenario-specific term (hat{sigma }^2_textrm{Y}/hat{mu }^2_textrm{Y}/T) in equation (11c) vanishes. For small T however, input uncertainty (in (Y_t)) does have an effect on LCOE averages — it increases the LCOE, especially for small T [cf. equations (4c) and (11c)], while small (Y_t) uncertainty lets the LCOE curves for the O- and YO-scenario largely coincide. Also, the approximate character of equation (4c) becomes most apparent for large (Y_t) uncertainty (i.e., in the wYO-scenario) and very small T. There, the standard approximation underestimates the LCOE. For larger T, we observe a quick convergence to ACME output as anticipated in the Methods section [cf. equation (12)]. As a consequence, the value of the calculated NPV does not hinge on the amount or shape of input uncertainty. In contrast, the projected LCOE is driven up by yield forecast uncertainty, particularly for short project lifetimes. For large yield forecast uncertainties and short project lifetimes, the standard approximation underestimates the LCOE and thus — by this measure — overestimates the economic feasibility of the PV project in question. NPV uncertainty — as given by the NPV standard deviation — increases with T and with input uncertainty (Fig. 3a), as predicted by equation (11b). Values delivered by the ACME approach and the standard approximation moreover match exactly due to the exact character of equation (4b). In contrast, LCOE uncertainty decreases with T (Fig. 3b), in line with the prediction of equation (11d). (a) NPV standard deviations and (b) LCOE standard deviations computed with ACME approach and standard approximation in three scenarios. As in the NPV case, an increased input uncertainty is reflected by increasing LCOE uncertainty, with only the O-scenario yielding a perfect match of standard approximation and ACME predictions due to the linearity of equation (2) in (O_textrm{R,t}). As for LCOE averages, the standard approximation underestimates LCOE uncertainty in the wYO-scenario, but across a fairly large T interval, before convergence to ACME output. Hence both NPV and LCOE forecasts follow intuition in that for them, input uncertainty is a driver of output uncertainty. Yet in contrast to the NPV case, the LCOE forecast accuracy actually increases with project lifetime and is moreover systematically overestimated by the standard approximation, especially for short project lifetimes. With NPV averages increasing faster with T than standard deviations (cf. equations (11a)-(11b) ), the monotonically increasing behaviour of NPV P90 values in Fig. 4 is plausible across all three scenarios of varying input uncertainty. Moreover, the higher the input uncertainty, the smaller the NPV P90 value for fixed T, which is a consequence of increasing NPV uncertainty (Fig. 3a) around constant NPV averages (Fig. 2a). We observe a fairly good agreement between ACME predictions and the standard approximation, with only large input uncertainty triggering a slight underestimation of P90 values by the standard approximation. The good performance of the standard approximation here can be attributed to averages and standard deviations coinciding with ACME predictions in Fig. 2a and Fig. 3a. (a) NPV P90 values and (b) LCOE P90 values computed with ACME approach and standard approximation in three scenarios. For similar reasons as in the NPV case, the monotonically decreasing T-dependency of LCOE P90 values in Fig. 4b follows from equations (11c)-(11d). Again we observe that, for fixed T, increasing the input uncertainty decreases the LCOE P90 value. The excellent fit of standard approximation P90 values and ACME output in Fig. 4b is counterintuitive, especially for small T in the wYO-scenario. This is because in this regime — and unlike in the NPV case — averages and standard deviations obtained from the standard approximation can differ significantly from ACME predictions (cf. Fig. 2b and Fig. 3b), with ACME PDFs being non-Gaussian (cf. Figs. 8c-i). NPV cumulative distribution functions and LCOE cumulative distribution functions computed with ACME approach and standard approximation in the wYO-scenario. This remarkable fit is put into perspective by plotting the cumulative distribution functions for small T in the wYO-scenario. In the resulting Fig. 5, we observe for both the NPV and LCOE case that percentiles of very small and very large rank are underestimated by the standard approximation, while being overestimated for intermediate percentile ranks. At the two intersections of ACME and standard approximation CDFs, computed percentiles coincide — for (T=6) in the wYO-scenario, these are roughly the 20th and 85th percentile (Fig. 5a) or the 10th and 80th percentile (Fig. 5b), explaining the good fit for P90 values in Fig 4. The larger T and the smaller input uncertainty, the less ACME and standard approximation CDFs differ from each other, and the less pronounced are resulting percentile mismatches. An added benefit of considering CDFs is that they directly deliver the probability of the target variable being in a given interval. For instance, in the wYO-scenario with (T=6), the probability of the LCOE being between 0.1 EUR/kWh and 0.2 EUR/kWh is (F_textrm{LCOE}(0.2,mathrm {EUR/kWh})-F_textrm{LCOE}(0.1,mathrm {EUR/kWh})approx 0.38) in the ACME framework and 0.27 in the standard approximation (cf. Fig. 5b). The consequences for investment decisions are multi-faceted: As in the case for NPV and LCOE averages, prolonging the project lifetime increases profitability when instead considering P90 values. Yet in the NPV case, the projected competitiveness decreases with input uncertainty while it increases in the LCOE case. Moreover, the competitiveness in the LCOE case is now underestimated by the standard approximation for large input uncertainty and short project lifetimes. Apart from the classical profitability metrics computed above, we can extend the economic analysis to two other quantities. It is straightforward to compute the probability of profitability (P_mathrm {textrm{NPV}> 0}equiv 1-F_textrm{NPV}(0)). As expected, this probability is close to zero for small T and almost 1 for large T (Fig. 6a). The location of the transition between these two values (the NPV payback year) does not significantly depend on the degree of input certainty (cf. Fig. 2a). However, the transition is steeper for smaller input certainties, in line with Fig. 3a. It is only for large input uncertainties that the quality of the standard approximation’s predictions noticeably worsens. (a) Probabilities of profitability and (b) LCOE coefficients of variation computed with ACME approach and standard approximation in three scenarios. Additionally, we calculate the LCOE’s coefficient of variation (textrm{CV}_textrm{LCOE}=sigma _textrm{LCOE}/mu _textrm{LCOE}), which in our case is a measure of relative uncertainty in the computed LCOE. With both (sigma _textrm{LCOE}) and (mu _textrm{LCOE}) monotonically decreasing with T (cf. Fig. 2b and 3b), the resulting behaviour of the (textrm{CV}_textrm{LCOE}) is not obvious. In Fig. 6b, we observe that (textrm{CV}_textrm{LCOE}) is a monotonically increasing function of T if only input uncertainty in the numerator of the LCOE is involved (i.e., in the O-scenario). For sufficiently large input uncertainty in the LCOE denominator (i.e., in the YO- and wYO-scenario), we note instead a monotonic decrease of (textrm{CV}_textrm{LCOE}) with T. Moreover, (textrm{CV}_textrm{LCOE}) increases with input uncertainty, with the standard approximation significantly underestimating the ACME value for large input uncertainties and smaller T. For PV investors envisioning a concrete business model for electricity monetization, Fig. 6a demonstrates that input uncertainty can significantly smear out the predicted NPV payback year both to earlier and later dates. Contrasting the results of Fig. 3b on absolute LCOE uncertainties, Fig. 3b suggests that the relative LCOE uncertainty actually increases with project lifetime if the yield forecast uncertainty is negligible compared to O&M cost uncertainty. The above discussion of computed NPV and LCOE quantities often invoked the purported shape of underlying PDFs. Here, we show the latter in Fig. 7 and Fig. 8 for the most relevant parameter regimes. Centered ACME NPV probability density functions and normal distributions of same mean and variance. Computed for three scenarios and different lifetimes. Centered ACME LCOE probability density functions and normal distributions of same mean and variance. Computed for three scenarios and different lifetimes. We observe that in the NPV case, PDF widths grow both with input uncertainty and T, whereas they decrease with T in the LCOE case (cf. Fig. 3a and Fig. 3b). The O-scenario (Fig. 7a and Figs. 8a,d and g) illustrates how heavily non-Gaussian input (drawn from a single exponential distribution) propagates in NPV and LCOE calculation. For small T, the resulting PDFs are heavily non-Gaussian, but become Gaussian for larger T when conditions for the central limit theorem are better met. To a weaker extent, these observations also apply to the wYO-scenario (Fig. 7c and Fig. 8c, f and i) that features a more heterogeneous non-Gaussian input drawn from an exponential distribution as well as a broad and time-dependent unimodal distribution. In the YO-scenario, Gaussian-shaped input propagates to Gaussian-shaped output, independently of the value of T (Fig. 7b and Fig. 8b, e and h). The plots show that, in an investor-oriented model setup like ours, the Gaussian assumption for NPV and LCOE PDFs only holds in two cases: if the yield uncertainty is indeed given by a normal distribution or the projected lifetime surpasses 30 years. Consequently, caution should be exercised when leveraging the purported Gaussianity of LCOE and NPV variables for computational shortcuts in economic analysis (cf. Methods section). In this work, we perform a systematic study of uncertainty propagation in the NPV and LCOE analysis of PV plants. To this end, we introduce the ACME approach that traces — on the level of probability distributions — the uncertainty propagation from independent input variables to target variables. This is achieved through leveraging the occurrence of independent random variables in basic arithmetic operations occurring in NPV and LCOE, switching between different representations of probability distributions. The accuracy of the framework is limited only by its numerical implementation (for which we formulate several sanity checks), and its execution speed promises to be often much faster than that of Monte-Carlo simulations of similar accuracy. Computed ACME expressions tend to involve only low-dimensional integrals (relative to the number of considered input variables) that are more amenable to analytic treatment than the brute-force formulation of the problem. We apply the ACME approach to different scenarios that reflect an investor’s perspective in the economic analysis of PV plants. These scenarios feature constant input averages, yet varying degrees of input uncertainty, and compute several quantifiers of the stochastic character of NPVs and LCOEs. This is done for a range of plausible PV plant lifetimes, with results qualitatively predicted by scaling relations and quantitatively compared with the output of a standard approximation framework. The analysis confirms the intuition that increased input uncertainty triggers increased output uncertainty (i.e., decreases forecast accuracy) in both the NPV and the LCOE case. Less intuitively, we observe that the effect of input uncertainty on economic analysis is not clear-cut: In the NPV case, the forecast economic competitiveness is independent of input uncertainty (according to NPV averages) or a decreasing function of it (according to NPV P90 values). In the LCOE case, the forecast competitiveness decreases or increases with (nonlinear) input uncertainty, depending on whether LCOE averages or P90 values are considered. Moreover, we observe that the forecast accuracy increases (NPV) or decreases (LCOE) with the assumed project lifetime. We notice that even for longer project lifetimes, non-Gaussian input can trigger non-Gaussian NPV and LCOE output, with the latter being rather the norm than the exception in our investor-centric analysis. In our study, the standard approximation and ACME approach give matching predictions whenever they should (i.e., for many years of operation as well as for any NPV average and standard deviation), and a fairly good agreement for all other quantities and regimes. The one exception are large uncertainties entering LCOE calculations nonlinearly, i.e., exactly the scenario where the assumptions of the standard approximation are violated. Observed discrepancies should hence also occur for other choices of input PDFs provided that the yield standard deviation is sufficiently large. For a broader picture — beyond our input uncertainty scenarios and sensitivity analysis for T — of how input assumptions influence ACME and standard approximation output, we encourage further computational studies featuring comprehensive robustness checks. Future work could also elaborate on the inclusion of correlated input variables as briefly outlined in the text, and determine whether the relatively good fit between the two discussed frameworks persists in that case. Moreover, ACME and standard approximation could be applied to other contexts like the PV performance modeling chain, with the added difficulty of tracing uncertainty propagation through a sequence of submodels, some of which are only given in implicit form. All data generated during this study is included in this published article. Intergovernmental Panel on Climate Change. Climate Change 2022: Mitigation of Climate Change (Cambridge University Press, 2022). Google Scholar Colla, M., Ioannou, A. & Falcone, G. Critical review of competitiveness indicators for energy projects. Renew. Sustain. Energy Rev.125, 109794. https://doi.org/10.1016/j.rser.2020.109794 (2020). Article Google Scholar Delapedra-Silva, V., Ferreira, P., Cunha, J. & Kimura, H. 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A monte carlo approach to integrating uncertainty into the levelized cost of electricity. The Electricity Journal29, 21–30. https://doi.org/10.1016/j.tej.2016.04.001 (2016). Article Google Scholar Tran, T. T. & Smith, A. D. Incorporating performance-based global sensitivity and uncertainty analysis into lcoe calculations for emerging renewable energy technologies. Appl. Energy216, 157–171. https://doi.org/10.1016/j.apenergy.2018.02.024 (2018). ArticleADS Google Scholar Kost, C. et al. Modeling of persistence, non-acceptance and sufficiency in long-term energy scenarios for germany. Energies14, https://doi.org/10.3390/en14154484 (2021). Solar Bankability Consortium. Best practice guidelines for pv cost calculation: Accounting for technical risks and assumptions in pv lcoe (2016). Horizon 2020 Solar Bankability Project. Bosetti, V. et al. Sensitivity to energy technology costs: A multi-model comparison analysis. 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Download references S.W and U.G. would like to thank Simone Vitale and Jonathan Leloux for fruitful discussions. Open Access funding enabled and organized by Projekt DEAL. S.W. and U.G. acknowledge funding from the European Commission through the SERENDI-PV project (grant number 953016) as well as financial support from the German Federal Ministry for Economic Affairs and Energy (BMWE) through the PV2Float project (grant number 03EE1097A). Fraunhofer Institute for Solar Energy Systems ISE, Heidenhofstr. 2, 79110, Freiburg, Germany Stefan Wieland & Utku Gürsal Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar S.W. devised the project, developed conceptual ideas and wrote the manuscript. Both authors performed the numerical analysis and reviewed the manuscript. Correspondence to Stefan Wieland. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Wieland, S., Gürsal, U. Uncertainty propagation in financial models of photovoltaic systems. Sci Rep16, 5004 (2026). https://doi.org/10.1038/s41598-026-38053-1 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-026-38053-1 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Halifax Police Department senior officer Justin Boyd (left) and Halifax Police Department officer Jeremy King were recognized at Monday evening’s Halifax County Board of Supervisors’ meeting for performing lifesaving actions on Feb. 21 in the line of duty. Members of the Ruritan Club of Halifax County join Larry Roller, chair of the Halifax County Board of Supervisors (pictured second from left in the back), in recognizing May 2026 as “Ruritan Awareness Month” in Halifax County.
An electrical short in a solar panel sparked a brush fire at the Water Strider Solar facility in northern Halifax County. The fire burned 14.7 acres of land. “A few weeks ago, we had a fire at the Water Strider Solar Facility,” Halifax County’s emergency services coordinator Jason Johnson told the board of supervisors at their Monday evening meeting. “There was evidence of an internal electrical short and overheating. Only one panel has damage to it.” Water Strider Solar is an 80-megawatt solar facility on Stagecoach Road in Nathalie. It has been operational since March 2021. Johnson told the supervisors that statistically, solar panel fires are “rare.” North Halifax Volunteer Fire Department reported the “large fire” happened on April 14. “Most incidents are traced to an isolated component, and that’s what this looks like,” he said. “It looks like a single panel had a short in it…and dropped a small flame out and started a 14-acre fire.” The damaged solar panel was sent to the manufacturer for forensic testing to determine the cause of the spark. “Everything is still operational?” Board Chair Larry Roller asked. Johnson confirmed that everything is still operational at the Water Strider Solar facility except the sole panel that sparked the fire. He added there are “thousands” of solar panels at the Water Strider site. The North Halifax, Liberty and Brookneal fire departments responded to the blaze. Roller asked Johnson, “How much fuel was there? Was it 3 inches, 4 inches?” “Most of it (the grass) is knee high,” Johnson responded. “It was a lot of fuel up there for it.” Johnson added he has talked with the county’s planning and zoning administrator Detrick Easley about re-examining conditions set forth in conditional use permits for solar facilities regarding keeping the grass maintained at a certain level to minimize brush fire risk. An ongoing drought in Halifax County creating conditions for the fast spread of fire prompted the county to issue an open burn ban effective April 22, which will remain in place while drought conditions persist. The ban prohibits any form of open-air burning, which includes, but is not limited to, the burning of “leaves, brush and/or other vegetation.” The Water Strider Solar fire also raised questions about the safety of firefighters battling blazes at solar sites. “What we talked about was not allowing anybody to be on the inside and staying on the outside, because water and DC current don’t mix,” Johnson told the board. Election District 3 Supervisor Jeff Oakes also noted that Ronnie Waller, chief of the North Halifax Volunteer Fire Department, brought up concerns about perimeter access to the Water Strider Solar Facility. “That’s a big problem,” Johnson acknowledged. “Not just at that facility but at other facilities, too.” Johnson explained that the road at the Water Strider Solar facility is 12 feet wide, and he recommended that the road should be widened to 25 feet to allow enough room for two fire trucks to meet each other. At Monday’s meeting, Johnson also discussed next steps following a fire and EMS study of the county conducted by the Virginia Fire Services Board last year. The study identified the county’s emergency radio communications as a major area in need of improvement. Johnson told the board that he has met with Motorola, the provider for the county’s emergency radio communications system, and they are working with Motorola to determine how to improve the system. Members of the Ruritan Club of Halifax County join Larry Roller, chair of the Halifax County Board of Supervisors (pictured second from left in the back), in recognizing May 2026 as “Ruritan Awareness Month” in Halifax County. The board of supervisors took the following actions at their Monday evening meeting: • approved advertising the tax rates for a public hearing at their current rate for fiscal year 2027, • approved amending the county ordinance to establish a tourism zone in Halifax County, • approved making an additional $80,000 contribution to the resurfacing of the Tisha Waller Track, taking the county’s total contribution to the project to $271,000, • set a public hearing for their May 19 joint meeting with the planning commission to gain public input on a proposal to relocate three polling precincts in the county: Black Walnut, Mt. Carmel and Virgilina. The proposals are to relocate the Black Walnut precinct to the TJM Community Center, relocate the Mt. Carmel precinct to the Turbeville Ruritan Club, and relocate the Virgilina precinct to the Virgilina Volunteer Association Lodge, • approved the submission of applications for three Virginia Department of Transportation Smart Scale projects – U.S. 58 and Route 751 turn lane improvements, Sinai Road pedestrian project and U.S. 501 and Greens Folly Road intersection improvements, • approved VDOT’s six-year plan for secondary roads in Halifax County, Halifax Police Department senior officer Justin Boyd (left) and Halifax Police Department officer Jeremy King were recognized at Monday evening’s Halifax County Board of Supervisors’ meeting for performing lifesaving actions on Feb. 21 in the line of duty. • approved a recommendation from the policy and personnel committee to adopt a resolution in support of VDOT naming the Highway 92 bridge in memory of Jennifer Nichols. Roller told the board that Nichols, who passed away in February 2021, advocated for the repair of the Highway 92 bridge following its closure and contributed to many causes that benefited her community. • approved a recommendation from the policy and personnel committee to establish a policy that any board member serving three terms is eligible to have his/her portrait displayed in the board room. • approved a resolution establishing the county employees’ health care plans for FY 2027 and approving the county incurring the increased employer costs for health insurance allowing employee premiums to remain the same next year. Miranda Baines is a staff writer for The Gazette-Virginian. Contact her at mbaines@yourgv.com. {{description}} Email notifications are only sent once a day, and only if there are new matching items. Your browser is out of date and potentially vulnerable to security risks. We recommend switching to one of the following browsers: Sorry, an error occurred.
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Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 16, Article number: 10336 (2026) Cite this article 1942 Accesses Metrics details Accurate photovoltaic (PV) power forecasting is essential for grid operation but remains difficult due to nonlinear multi-scale dynamics and seasonal distribution shifts. This work presents MKAN-iTransformer, a cascaded framework that integrates two existing components—the Multi-Scale Kolmogorov–Arnold Network (MKAN) for scale-aware temporal representation learning and iTransformer for variable-wise attention and inter-variable dependency modeling—under a 15-minute single-step setting. Experiments on a real-world 30 MW PV plant dataset from the Chinese State Grid Renewable Energy Generation Forecasting Competition use chronological splits within each season. MKAN-iTransformer achieves the best overall performance in spring, autumn, and winter. In spring, it reaches MSE=2.892, RMSE=1.701, MAE=0.864, and ({R^{2}}=0.947), improving over LSTM by 23.5%/12.5%/20.5% (MSE/RMSE/MAE). In autumn, it attains MSE=2.884, RMSE=1.698, MAE=0.774, and ({R^{2}}=0.962), reducing errors vs. iTransformer by 16.5%/8.7%/12.4%. In winter, it achieves MSE=1.721, RMSE=1.312, MAE=0.443, and ({R^{2}}=0.969), yielding 81.6%/57.1%/71.9% error reductions vs. Transformer. Ablation further confirms the complementarity between MKAN and iTransformer and shows that direct KAN integration can be unstable under winter shifts (KAN-iTransformer: MSE=7.082, ({R^{2}}=0.872)). Amid escalating global climate change, transforming energy structures and accelerating renewable energy adoption have become shared priorities worldwide1. The growing electricity demand drives increasing renewable power requirements2, motivated by the carbon neutrality and eco-friendliness of renewable energy sources (RESs) compared to fossil fuels3. Empirical studies report negative associations between carbon emissions and renewable energy consumption, indicating emissions decrease as per-capita renewable energy use increases4,5. According to the International Energy Agency (IEA), renewables are expected to supply 42% of global electricity generation between 2023 and 2028, with solar and wind contributing 25%6. Photovoltaic (PV) generation, as representative green technology, has experienced rapid expansion. Global PV installed capacity continues growing with steadily rising power system share7. While large-scale PV integration brings substantial environmental benefits, it introduces new operational challenges8. The primary challenge stems from PV output variability. PV generation is highly sensitive to meteorological conditions—irradiance, temperature, humidity, and wind speed—whose nonlinear and time-varying nature creates pronounced fluctuations and uncertainty9. This uncertainty complicates grid dispatch, increases storage and flexibility requirements, and affects electricity market operations10,11. Therefore, accurate PV power forecasting is essential for secure and efficient power system operation12 and supports downstream decision-making including operational management and demand response13. These observations motivate a design that (i) captures multi-scale temporal dynamics of PV series and (ii) models cross-variable dependencies among meteorological inputs and historical power, while supporting transparent analysis. We develop MKAN-iTransformer, integrating Multi-Scale Kolmogorov-Arnold Networks (MKAN)14 with iTransformer15. MKAN provides multi-scale temporal representation learning with explicit functional structure, while iTransformer models inter-variable dependencies through variable-wise attention, together targeting robust and interpretable PV forecasting. Contributions. Our main contributions are: Cascaded forecasting framework. We develop MKAN-iTransformer, cascading multi-scale temporal representation learning with variable-wise attention for 15-minute single-step PV power prediction. Season-wise chronological evaluation. Beyond overall test splits, we evaluate models within each season using chronological splits, making seasonal robustness and failure modes explicit. Comprehensive KAN-enhanced baseline construction and evaluation. We systematically construct KAN/MKAN-augmented variants of recurrent and attention-based baseline architectures and establish unified benchmarking framework with consistent preprocessing, training, and evaluation protocols, enabling fair comparison and demonstrating the broad applicability of interpretable neural components in PV forecasting. Interpretability analysis. We provide multi-scale time-frequency decomposition, learned KAN function inspection, and attention visualization for transparent explanations. PV power forecasting has progressed from physics-driven and classical statistical models to modern machine learning and deep learning pipelines, largely driven by the need to handle nonlinearity, non-stationarity, and regime shifts. Physical and statistical models. Early forecasting relied on physical simulation using meteorological inputs and device characteristics, which can be physically meaningful but often requires high-quality inputs and detailed plant specifications, limiting scalability in practice9,12. Classical statistical models (e.g., ARMA/ARIMA and regression families) exploit temporal correlations and can perform well under relatively stable conditions; for instance, regression combined with numerical weather prediction has shown robust hourly forecasting16. However, abrupt ramps and distribution shifts common in PV generation challenge these assumptions and motivate more flexible nonlinear approaches. Machine learning approaches. Conventional ML methods improved nonlinear mapping from weather variables to PV output, including linear regression and ensemble methods such as random forests and gradient boosting17,18. Support vector regression has also been adopted for high-dimensional nonlinear forecasting17,19. Despite progress, many ML pipelines rely on handcrafted features and can degrade under seasonal and weather-regime shifts, encouraging end-to-end deep architectures with better representation learning. RNN-based models. LSTM and GRU variants have been widely used to capture temporal dependencies in PV forecasting20,21,22. Performance gains have been reported via parallel structures, feature selection, CNN integration, and attention augmentation20,23,24,25. Nevertheless, RNN-based components remain sequential and may become computational bottlenecks for long contexts, while their ability to explicitly model cross-variable interactions is often limited. Hybrid CNN-RNN architectures. CNN-LSTM and related hybrids seek to combine local pattern extraction and temporal modeling26, with variants replacing standard CNN blocks by temporal convolutional networks to improve receptive fields and parallelism27. Attention mechanisms are frequently introduced for feature weighting and fusion; for example, dual-stream CNN-LSTM with self-attention has been reported to improve accuracy on PV datasets28. However, these hybrids may still struggle to represent multi-scale behaviors spanning intra-hour variability to seasonal cycles, and they often treat heterogeneous meteorological variables as homogeneous inputs without an explicit variable-wise dependency mechanism. Transformer-based architectures. Transformers have enabled stronger long-range dependency modeling for time series, with forecasting-oriented variants targeting efficiency and inductive biases. Informer reduces attention complexity via ProbSparse attention with (O(L log L)) behavior29; Autoformer and FEDformer incorporate decomposition and frequency-aware mechanisms to better capture trend/seasonality30,31. In PV-specific contexts, multi-scale and hybrid designs combine Transformers with CNNs/GRUs or decomposition modules32,33,34, and domain-enhanced Transformers inject domain knowledge or nonlinear dependency modeling to improve robustness35,36. The iTransformer introduces an inverted design that treats variables (rather than time steps) as tokens, enabling efficient variable-wise attention for cross-variable dependency modeling15, which is particularly relevant for PV forecasting where meteorological drivers and historical power jointly determine future output. Multi-scale pattern recognition. PV generation exhibits multi-scale dynamics (diurnal cycles, intra-hour fluctuations, and weather-driven ramps), motivating multi-resolution modeling through decomposition, frequency-aware transformations, or multi-scale feature extraction. Interpretable deep learning pipelines have been proposed to disentangle multi-scale solar radiation variations while retaining predictive accuracy (e.g., reporting (R^2=0.97))37. Yet, many approaches increase architectural complexity and do not always provide transparent, component-wise explanations that remain stable across operating regimes. Interpretability requirements in energy systems. For energy applications, interpretability supports operational decision-making and stakeholder trust, but many deep models remain black boxes; moreover, attention weights alone do not guarantee faithful explanations. This motivates exploring model families with more explicit functional forms. Kolmogorov–Arnold Networks (KAN) for interpretable learning. KANs parameterize multivariate mappings via sums of learned univariate functions, often implemented with spline-based learnable functions, offering a potentially more inspectable representation than standard MLP layers38. Recent surveys summarize rapid development of KAN variants and applications (e.g., TKAN, Wav-KAN, DeepOKAN) and discuss their empirical strengths38,39. Theoretical extensions such as KKANs further improve robustness and approximation behavior40. For temporal data, KAN-based time series modeling has been explored, including general demonstrations and targeted work on bridging accuracy and interpretability in time series settings41,42. KAN integration with dynamical systems has also been studied via KAN-ODEs43. More recently, multi-scale KAN variants (MKAN) have been proposed to better capture mixed-frequency behaviors in temporal signals14. Despite these developments, systematic integration of KAN-style multi-scale representations with state-of-the-art variable-wise attention, and task-specific interpretability validation for PV forecasting, remains limited. Many PV forecasting studies emphasize aggregate metrics, which can obscure failure modes under seasonal regime shifts. Seasonal variability changes irradiance, temperature, and daylight duration, making season-wise evaluation important for deployment9,12. However, evaluation protocols and baselines are often inconsistent across model families, hindering fair comparison and limiting insights into robustness under regime transitions. The above literature motivates four gaps addressed in this work: Architectural integration gap: limited evidence on combining multi-scale temporal representations with explicit variable-wise attention for PV forecasting14,15. Interpretability integration gap: insufficient task-specific validation of interpretability when integrating KAN-style components with attention-based architectures38,39. Evaluation methodology gap: limited systematic assessment under seasonal regime shifts9,12. Benchmarking consistency gap: inconsistent protocols across model families impede fair comparison and understanding of when interpretable neural components help21. Building on existing components14,15, our MKAN-iTransformer focuses on principled integration of MKAN-style multi-scale representation learning with variable-wise attention, accompanied by systematic seasonal evaluation and interpretability-oriented analyses to clarify both strengths and limitations under different regimes. The photovoltaic (PV) power forecasting task aims to predict the near-future output power of a PV plant based on historical multivariate time series observations. Let the historical observation sequence be where (x_t in mathbb {R}^d) denotes the d-dimensional feature vector at time step t. In this study, we consider single-step forecasting with a 15-minute horizon (sampling interval = 15 minutes). Therefore, the forecasting horizon is (h=1), and the prediction target is the PV power at the next time step: The input variables include total solar irradiance, direct normal irradiance, global horizontal irradiance, air temperature, atmospheric pressure, relative humidity, and historical PV power. The target variable is the PV plant output power at the next 15-minute step. The Multi-Scale Kolmogorov-Arnold Network (MKAN) module is designed to efficiently capture complex, multi-scale temporal dependencies in multivariate time series forecasting. The overall structure is illustrated in Fig. 1 and consists of the following key components. Overall architecture of the Multi-Scale Kolmogorov-Arnold Network (MKAN) module. The left part shows the hierarchical residual structure with stacked TimeKAN blocks, each extracting features at different scales through multi-scale patching (MSP) modules. The right part details the patching, encoding, KAN-based transformation, decoding, and unpatching process within each MSP block. Cumulative addition and subtraction operations are used to aggregate both local and global temporal features. Multi-scale patching: Given an input sequence (X in mathbb {R}^{T times d}), we divide it into S sets of patches at different temporal scales, where the s-th scale consists of (N_s) patches of length (l_s): Patch encoder: Each patch is mapped to a latent embedding via a learnable encoder: where (operatorname {Enc}_s) denotes the patch encoder for scale s. KAN-based Transformation: Each scale has a dedicated Kolmogorov-Arnold Network (KAN) block to transform the encoded patch embedding: where (operatorname {KAN}_s) is the KAN subnetwork for the s-th scale. Patch decoder: The transformed embeddings are decoded back to the temporal domain: Feature aggregation: The reconstructed patches are reassembled to form multi-scale feature maps, which are then aggregated (e.g., by summation or concatenation) to obtain the final sequence representation: where (operatorname {Agg}) denotes the aggregation operation across scales. Forecasting head: The aggregated features are passed to a forecasting head to generate the final prediction: The overall output of the MKAN module can be summarized as a weighted sum of KAN transformations across all scales: where (phi _{s,n}(cdot )) represents the output of the KAN subnetwork for the n-th patch at scale s, (alpha _{s,n}) are learnable weights, and b is a bias term. A major advantage of the MKAN module is its interpretability. Each KAN block is inherently symbolic and can be visualized or analyzed, allowing for direct inspection of the learned temporal features at each scale. In summary, the MKAN module integrates multi-scale patching with expressive KAN transformations, providing a transparent and effective solution for multivariate time series forecasting. The iTransformer module is designed to efficiently model multivariate time series forecasting by leveraging an inverted Transformer architecture. The overall structure is illustrated in Fig. 2 and consists of the following key components. Overall architecture of the iTransformer module. The framework consists of independent variable-wise embedding, temporal layer normalization, multivariate self-attention, feed-forward transformation, and aggregation for final forecasting. The left and right parts of the figure detail the embedding and feed-forward processes, respectively. Variable-wise embedding: Given a multivariate input sequence (X in mathbb {R}^{T times N}), where T is the sequence length and N is the number of variables, each variable’s time series (X_{:,n}) is independently embedded into a latent representation: where (operatorname {Embedding}) is a learnable mapping from (mathbb {R}^T) to (mathbb {R}^d). Temporal layer normalization: Each variable embedding is normalized along the temporal dimension to reduce scale and distribution discrepancies: where (mu _n) and (sigma _n) are the mean and standard deviation of the n-th variable embedding. Multivariate self-attention: All variable embeddings are jointly processed by a self-attention mechanism to capture inter-variable dependencies: where Q, K, V are linear projections of the variable embeddings. The detailed structure of the multivariate self-attention mechanism is shown in Fig. 3. Detailed structure of the multivariate self-attention mechanism in the iTransformer module. The input is first projected to Q, K, and V, then split into multiple heads for independent attention computation. The results are merged and projected to form the final output. Feed-forward network: Each variable embedding is independently transformed by a shared feed-forward network to extract nonlinear features: where (operatorname {FFN}) denotes a two-layer MLP with activation and dropout. Stacked blocks and aggregation: The above operations are stacked for L layers, and the final output embeddings are aggregated for forecasting: where (operatorname {TrmBlock}) denotes one iTransformer block, and (operatorname {Projection}) maps the final embedding to the prediction space. The overall output of the iTransformer module can be summarized as: where (operatorname {Head}) is typically a linear layer for regression or forecasting. A major advantage of the iTransformer module is its variable-centric design. By treating each variable’s time series as an independent token, the model can explicitly capture inter-variable correlations and global temporal patterns, while maintaining efficient parallel computation and interpretability of learned representations. In summary, the iTransformer module integrates variable-wise embedding, normalization, and attention-based transformation, providing a simple yet powerful backbone for multivariate time series forecasting. The hybrid architecture adopts a cascaded design, where the Multi-Scale Kolmogorov-Arnold Network (MKAN) module first extracts multi-scale temporal features from the input sequence, and the resulting representations are subsequently processed by the iTransformer module to model inter-variable dependencies. The overall structure is illustrated in Fig. 4. Overall architecture of the cascaded MKAN-iTransformer framework. The pipeline consists of sequential MKAN and iTransformer modules, followed by a forecasting head. The left part details the multi-scale patching and KAN transformation, while the right part illustrates variable-wise attention and prediction. Multi-scale feature extraction (MKAN): Given an input sequence (X in mathbb {R}^{T times N}), the MKAN module extracts multi-scale temporal features: where (Z_{text {MKAN}}) encodes rich temporal dependencies across different resolutions. Inter-variable modeling (iTransformer): The multi-scale features (Z_{text {MKAN}}) are fed into the iTransformer module, which captures global dependencies among variables via self-attention mechanisms: where (Z_{text {iTrm}}) denotes the variable-attentive feature representation. Forecasting head: The final representation is passed to a forecasting head to generate the prediction: This cascaded hybrid design enables the model to: Efficiently extract multi-scale temporal patterns using the MKAN module, which models complex dynamics at various time resolutions. Explicitly capture inter-variable relationships through the iTransformer, which leverages attention to integrate information across variables. Produce robust and interpretable representations for accurate multivariate time series forecasting. In summary, the cascaded MKAN-iTransformer architecture unifies multi-scale temporal feature extraction and variable-wise attention modeling, forming a transparent and powerful backbone for multivariate time series forecasting. In this study, real-world operational data from a 30 MW photovoltaic (PV) power plant are utilized for experimental evaluation. The dataset contains records from 2019 and 2020, with a sampling interval of 15 minutes. The input features include total solar irradiance, direct normal irradiance, global horizontal irradiance, air temperature, atmospheric pressure, and relative humidity. The target variable is the output power of the PV power plant. Details are shown in Table 1. The quality of the dataset has a decisive impact on the accuracy of forecasting models. Therefore, it is particularly important to pay attention to missing value handling and dataset partitioning during the process of model optimization. To ensure the overall trend and consistency of the data, this study first employs linear interpolation to impute missing values during the data preprocessing stage. For outliers in each column, reasonable value ranges are defined based on actual physical meanings. Values exceeding these ranges are clipped to the valid interval, thereby improving the reliability of the data and the prediction accuracy of the model. Due to the fact that the operational intensity of photovoltaic systems is almost negligible during nighttime, the dataset contains sparse and uninformative data points for these periods. Such sparsity is detrimental to the performance of forecasting models. To address this issue, all nighttime data points were excluded from the dataset in this study. Specifically, only data collected between 6:00 AM and 8:00 PM were retained for subsequent experiments.For the 15-minute single-step setting, we align inputs and targets by shifting the PV power series by one step: the target at time t is the PV power at (t+1). This alignment is performed after nighttime filtering, and no future information is included in the model inputs. A total of 70,177 sampling points were collected from two years of photovoltaic data. The data were divided into four seasons according to the following scheme: spring (March to May), summer (June to August), autumn (September to November), and winter (December to February). The number of sampling points for each season was 17,666, 17,378, 17,467, and 17,666, respectively. To explore the relationships between meteorological and operational features and photovoltaic (PV) output power, this study employs the Pearson Correlation Coefficient for all numerical variables. The Pearson correlation coefficient measures the degree of linear correlation between two variables, with possible values in the interval ([-1,1]), where a value closer to 1 or (-1) indicates a stronger correlation. A positive value indicates a positive correlation, while a negative value indicates a negative correlation. Note that the correlation analysis is conducted for interpretability and exploratory understanding, rather than for feature selection. In particular, we retain all physically meaningful variables to support the subsequent variable-wise attention visualization of the iTransformer and to avoid excluding variables that may contribute through nonlinear interactions. The calculation formula for the Pearson correlation coefficient is as follows: where (x_i) and (y_i) denote the i-th observations of the two variables, (bar{x}) and (bar{y}) are their respective means, and n is the total number of samples. The correlation among features is visualized in the form of a heatmap, as shown in Fig. 5. Furthermore, the Pearson correlation coefficients between the main meteorological features and PV output power are listed in Table 2. Heatmap of Pearson correlation coefficients among main features. As shown in Fig. 5 and Table 2, PV output power (Power, MW) has the strongest correlation with total solar irradiance (Total solar irradiance, W/m(^2)), with a coefficient as high as 0.95. It also shows strong positive correlations with direct normal irradiance (Direct normal irradiance, W/m(^2)) and global horizontal irradiance (Global horizontal irradiance, W/m(^2)), with coefficients of 0.89 and 0.64, respectively. This indicates that irradiance is the dominant factor affecting PV output power. Air temperature ((^circ)C) has a correlation coefficient of 0.26 with output power, indicating a weak positive correlation. Relative humidity (%) shows a negative correlation with output power, with a coefficient of (-0.35). Atmospheric pressure (hPa) exhibits a very low correlation with PV output power, suggesting a limited linear association. Overall, irradiance-related features are the primary factors influencing PV output power, while temperature, humidity, and pressure provide complementary meteorological information. Final input features. In the forecasting experiments, the model inputs include total solar irradiance, direct normal irradiance, global horizontal irradiance, air temperature, atmospheric pressure, relative humidity, and historical PV power, while the prediction target is the PV plant output power at the next 15-minute step. This subsection describes the compared models and the unified hyperparameter tuning protocol used to ensure fair and reproducible evaluation. Compared models. We evaluate multiple forecasting backbones and their KAN/MKAN-augmented variants for 15-minute single-step PV power forecasting. KAN and MKAN are adopted from prior work; we implement their integrations with different backbones to form the compared variants. Specifically, we consider LSTM/GRU/BiLSTM/Transformer/xLSTM/iTransformer and their corresponding KAN- and MKAN-augmented versions (i.e., KAN-LSTM and MKAN-LSTM; KAN-GRU and MKAN-GRU; KAN-BiLSTM and MKAN-BiLSTM; KAN-Transformer and MKAN-Transformer; KAN-xLSTM and MKAN-xLSTM; KAN-iTransformer and MKAN-iTransformer). All models are trained and evaluated under the same input–output setting. Chronological split. To avoid look-ahead bias in time-series forecasting, we split the data in chronological order into a training set (80%), a validation set (10%), and a test set (10%). Specifically, the earliest 80% of samples are used for training, the subsequent 10% for validation, and the latest 10% for testing. The same temporal rule is applied within each seasonal subset. Grid search protocol. Hyperparameters are tuned on the validation set using a grid search with the following candidate values: learning rate in ({1times 10^{-2}, 5times 10^{-3}, 1times 10^{-3}, 5times 10^{-4}}), hidden dimension in ({32, 64, 128}), number of skip connections in ({1, 2, 3}), number of attention heads in ({2, 4, 8}), and convolution kernel size in ({3, 5, 7}). This yields (4times 3times 3times 3times 3 = 324) configurations. For model components where a hyperparameter is not applicable (e.g., attention heads for purely recurrent architectures), we keep that component at its default setting while tuning the remaining applicable parameters. The same tuning criterion (minimum validation loss) and training budget are applied to all models. Training and selection. Each configuration is trained for up to 100 epochs with early stopping based on the validation loss (patience = 10), and the checkpoint with the best validation loss is selected. The best hyperparameter setting is chosen according to the validation loss. Using the selected hyperparameters, we retrain the model on the union of the training and validation sets and report the final performance on the held-out test set. All experiments are conducted with a fixed random seed (seed = 42) to reduce randomness. All models are implemented in PyTorch and trained using the same pipeline to ensure a fair comparison. The input features are standardized using statistics computed on the training split only, and the same transformation is applied to the validation and test splits. The PV power target is kept in its original scale (i.e., no target normalization is applied). We optimize all models using the Adam optimizer and minimize the mean squared error (MSE) on the training set. The initial learning rate and other hyperparameters are selected via the validation-based grid search described above. We use mini-batch training with a batch size of 64. To improve training stability, gradient clipping is applied with a maximum norm of 1.0. Early stopping is performed based on the validation loss with a patience of 10 epochs, and the checkpoint with the lowest validation loss is selected. After hyperparameter selection, each model is retrained on the combined training and validation sets using the selected configuration, and the final performance is reported on the held-out test set using MSE, RMSE, MAE, and (R^2). All experiments are conducted with a fixed random seed (seed = 42) to reduce randomness. To comprehensively evaluate the prediction performance of the proposed MKAN-iTransformer and baseline models, four commonly used regression metrics are adopted: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination ((R^2)). The definitions are as follows: Mean Squared Error (MSE): Root Mean Squared Error (RMSE): Mean Absolute Error (MAE): Coefficient of determination ((R^2)): where (bar{y}) is the mean of the true values. A lower value of MSE, RMSE, and MAE indicates better model performance, while a higher (R^2) value (closer to 1) implies a better fit between predictions and actual values. In this section, we present and analyze the experimental results of the proposed MKAN-iTransformer model and various baseline methods on photovoltaic power forecasting tasks. The experiments are conducted under different seasonal. The performance of all models is evaluated using the metrics introduced previously. To systematically evaluate the impact of seasonal variations on model performance, we adopted the conventional monthly division method to classify the dataset into four seasons: spring (March–May), summer (June–August), autumn (September–November), and winter (December–February). This classification enables a more detailed analysis of the predictive capabilities of MKAN-iTransformer and baseline models under different seasonal conditions. Typical daily PV power curves for each month in 2019 and 2020. As shown in Fig. 6, the typical daily power curves for each month in 2019 and 2020 exhibit significant seasonal variations. The power output is higher in spring and summer due to abundant sunlight, while it is relatively lower in autumn and winter. These seasonal differences provide a solid foundation for the subsequent model performance analysis based on seasonal classification. To validate the effectiveness of MKAN-iTransformer, we conducted a detailed analysis of model prediction results and error distributions across different seasonal conditions. This section presents a comparative evaluation of MKAN-iTransformer and baseline models, highlighting both the accuracy and robustness of the proposed approach. Spring: single-day prediction curves and prediction error distribution. Summer: single-day prediction curves and prediction error distribution. Autumn: single-day prediction curves and prediction error distribution. Winter: single-day prediction curves and prediction error distribution. Figures 7, 8, 9 and 10 present typical-day forecasting results for spring, summer, autumn, and winter, respectively. For each season, the upper subfigure compares the predicted PV output power with the ground-truth measurements (black curve) over the daytime period (6:00–20:00 at 15-minute intervals), while the lower subfigure summarizes the corresponding prediction error distribution of each model. This season-wise “curve fitting + error distribution” layout allows an intuitive assessment of both temporal tracking ability (shape, peak timing, and ramping behavior) and statistical error characteristics (bias, dispersion, and tail behavior). In the spring case (Fig. 7), most models can capture the overall diurnal pattern, but noticeable deviations appear around rapid ramping segments and local peaks. The proposed MKAN-iTransformer shows a closer alignment with the ground truth during the main rising stage and peak region, and its error histogram is more concentrated around zero, suggesting reduced dispersion and fewer large-magnitude errors. For summer (Fig. 8), PV output typically exhibits a smoother and higher plateau under stronger irradiance conditions, making the dominant daily trend easier to learn. Accordingly, multiple models achieve relatively good tracking performance. Nevertheless, differences remain in reproducing sharp changes (e.g., abrupt drops and recoveries), where MKAN-iTransformer tends to maintain smaller deviations. The error distribution in summer is comparatively narrower for several models, indicating that the forecasting task is less challenging than in transitional or winter conditions. In autumn (Fig. 9), the ground-truth curve shows more frequent fluctuations and irregular ramps, likely due to increased variability in meteorological conditions. Some baseline models display either lagged responses or oversmoothing, leading to larger deviations during abrupt changes. MKAN-iTransformer provides more stable tracking across multiple fluctuation segments, and its error distribution shows reduced spread relative to many baselines, implying improved generalization to more volatile patterns. Winter results (Fig. 10) are the most challenging, as reflected by larger mismatches in several baselines and a visibly broader error spread in the histogram. The seasonal difficulty may be attributed to lower sun angles, shorter effective generation windows, and more frequent rapid variations (e.g., due to clouds and atmospheric conditions), which amplify both bias and variance in predictions. In contrast, MKAN-iTransformer remains closely aligned with the ground truth for most time intervals, and the error distribution remains comparatively concentrated, indicating stronger robustness under adverse seasonal conditions. Overall, across all four seasons, MKAN-iTransformer consistently achieves closer curve fitting and more compact error distributions, demonstrating improved accuracy and robustness. These observations are consistent with the quantitative seasonal metrics reported in Table 3, where MKAN-iTransformer achieves competitive or best performance on MSE/RMSE/MAE and high (R^2) in multiple seasons. Using MKAN-iTransformer as the main benchmark, we compare it with representative baselines (LSTM, GRU, BiLSTM, Transformer, xLSTM, and iTransformer) as well as KAN/MKAN-augmented variants on seasonal datasets. The quantitative results in Table 3 indicate that MKAN-iTransformer achieves the most consistent and competitive performance across seasons. In particular, it attains the best overall results in spring, autumn, and winter (covering MSE, RMSE, MAE, and (R^2)), while in summer it delivers the lowest MSE/RMSE and remains highly competitive in (R^2), although the best MAE is achieved by KAN-GRU. In spring, MKAN-iTransformer achieves the best performance across all four metrics, with MSE = 2.892, RMSE = 1.701, MAE = 0.864, and (R^2) = 0.947. Compared with LSTM, it reduces MSE/RMSE/MAE by 23.5%, 12.5%, and 20.5%, respectively, and improves (R^2) by 1.7%. Relative to GRU, MKAN-iTransformer reduces MSE by 13.7%, RMSE by 7.1%, and MAE by 13.4%, while increasing (R^2) by 0.9%. Against Transformer, the reductions are 7.4% (MSE), 3.7% (RMSE), and 11.5% (MAE), with a 0.4% gain in (R^2). These improvements demonstrate that MKAN-iTransformer better captures springtime ramping and peak behaviors, yielding both lower average error and improved goodness-of-fit. Summer exhibits different characteristics: MKAN-iTransformer achieves the lowest MSE (3.962) and RMSE (1.991) among all compared models, while the best MAE is obtained by KAN-GRU (0.951), and the highest (R^2) is achieved by xLSTM (0.924). Compared with LSTM, MKAN-iTransformer decreases MSE and RMSE by 3.3% and 1.6%, and slightly increases (R^2) (0.921 to 0.923). Compared with iTransformer, it yields a clear reduction in MSE (9.5%) and RMSE (4.8%) and improves (R^2) from 0.915 to 0.923. Although its MAE is not the best in summer, the advantage in MSE/RMSE suggests MKAN-iTransformer is particularly effective at suppressing larger deviations (which are weighted more heavily by MSE), while some models (e.g., KAN-GRU) achieve smaller absolute errors on average. In autumn, MKAN-iTransformer again provides the best results across all metrics (MSE = 2.884, RMSE = 1.698, MAE = 0.774, (R^2) = 0.962). Compared with LSTM, it reduces MSE/RMSE/MAE by 24.9%, 13.4%, and 27.4%, respectively, and improves (R^2) by 1.4%. Relative to GRU, it reduces MSE by 9.4%, RMSE by 4.8%, and MAE by 11.6%, with (R^2) increasing from 0.958 to 0.962. Compared with iTransformer, MKAN-iTransformer reduces MSE by 16.5%, RMSE by 8.7%, and MAE by 12.4%, while improving (R^2) from 0.954 to 0.962. These results indicate stronger adaptability to autumn’s higher variability and more frequent fluctuations. Winter is the most challenging season for many baselines, yet MKAN-iTransformer achieves the strongest overall performance with MSE = 1.721, RMSE = 1.312, MAE = 0.443, and (R^2) = 0.969. Compared with LSTM, it reduces MSE/RMSE/MAE by 71.4%, 46.5%, and 66.6%, respectively, and improves (R^2) from 0.891 to 0.969 (an 8.8% relative increase). Against Transformer, the reductions are 81.6% (MSE), 57.1% (RMSE), and 71.9% (MAE), with (R^2) increasing from 0.831 to 0.969. Compared with iTransformer, MKAN-iTransformer remains slightly better in error-based metrics (e.g., MSE from 1.730 to 1.721 and RMSE from 1.315 to 1.312) while maintaining the same (R^2). Overall, these results demonstrate that MKAN-iTransformer offers strong robustness under winter conditions, substantially reducing both average errors and large-error events relative to most baselines. This work focuses on evaluating the effectiveness of combining an iTransformer backbone with KAN-based modules. Note that KAN and MKAN are borrowed from prior work and are not proposed in this paper; our goal is to investigate whether integrating these modules with iTransformer yields complementary gains and improved robustness across seasonal distributions. Model variants. We compare four variants: (1) iTransformer, the backbone baseline; (2) KAN-iTransformer, which integrates a KAN-based (ekan) module into iTransformer; (3) MKAN-iTransformer, which combines MKAN with iTransformer (our main combination model); and (4) MKAN, the standalone MKAN model without iTransformer, included to distinguish the effect of MKAN alone from the fusion setting. All variants are trained and evaluated under the same experimental protocol. Metrics. We report MSE, RMSE, and MAE (lower is better) as well as (varvec{R^2}) (higher is better). To examine distribution shifts, results are presented for Spring, Summer, Autumn, and Winter. Results and discussion. As shown in Table 4, MKAN-iTransformer delivers the most consistent improvements across seasons. In Spring, it achieves the best results on all metrics, indicating clear complementarity between MKAN and iTransformer. In Autumn, MKAN-iTransformer again obtains the best overall performance, slightly outperforming KAN-iTransformer, suggesting that the multi-scale design provides additional benefit beyond directly integrating KAN. In Summer, MKAN-iTransformer yields the lowest MSE/RMSE and the highest (R^2), while iTransformer attains the lowest MAE. This indicates a trade-off between reducing larger errors (more reflected by squared-error metrics) and minimizing average absolute deviation; nevertheless, the improved RMSE and (R^2) suggest a better overall fit for MKAN-iTransformer. In Winter, iTransformer and MKAN-iTransformer are nearly identical, implying that the iTransformer backbone already captures the dominant winter dynamics and that MKAN integration does not introduce degradation. By contrast, KAN-iTransformer shows a pronounced performance drop in winter (MSE=7.082, (R^2)=0.872), indicating that this integration may be more sensitive to seasonal distribution shifts. Overall, these results support that MKAN-iTransformer is a robust and effective combination, whereas the gains from KAN-iTransformer are less stable across seasons. To explain the seasonal performance differences observed in the previous sections, we conduct an interpretability analysis of MKAN from three complementary perspectives. First, we decompose the PV power signal into hierarchical temporal components to isolate fast ramps, intermediate variations, and slow diurnal trends, and validate the separation in both time and frequency domains. Second, we inspect the learned KAN edge functions to understand how MKAN adapts its nonlinear transformations across seasons. Third, we visualize the inverted attention mechanism over features to quantify seasonal changes in feature importance, attention dispersion, and cross-feature interaction pathways. Together, these analyses form a consistent evidence chain from signal dynamics (multi-scale decomposition), to nonlinear representation (KAN activations), and finally to decision routing (feature-wise attention), clarifying why the model behaves differently under distinct seasonal atmospheric regimes. To capture PV dynamics from fast cloud-induced ramps to slow diurnal trends, the MKAN module decomposes the 15-min PV power series into three additive temporal components using hierarchical moving-average (MA) operators and residual (difference) bands. This formulation yields a physically consistent separation of high-, mid-, and low-frequency behaviors while preserving approximate additivity. Let P(t) denote the normalized PV power at 15-min resolution and let (textrm{MA}_m(cdot )) be an m-step moving average (centered window for analysis/visualization). We define: Thus, where (epsilon (t)) mainly captures boundary effects and minor mismatch. Figure 11 illustrates the multi-scale decomposition of PV power on a representative summer day. The decomposition separates the observed signal into three time-scale components, which helps interpret variability sources and motivates using scale-aware features in forecasting. High-frequency (45 min and below): rapid ramps and short-term fluctuations dominated by transient clouds and local turbulence, critical for short-horizon forecasting. Medium-frequency (90–180 min): intra-day variability related to evolving weather regimes and smooth changes in solar geometry. Low-frequency (180 min trend): slowly varying baseline reflecting the dominant diurnal envelope and seasonal irradiance level. Multi-scale temporal decomposition of PV power on a representative summer day. From top to bottom, the panels show the original signal and its high-, medium-, and low-frequency components. The low-frequency term captures the smooth diurnal envelope, the medium-frequency term reflects intra-day regime changes, and the high-frequency term highlights fast fluctuations. To validate that the proposed multi-scale decomposition indeed separates variability across time scales, we conduct a frequency-domain check using Welch’s power spectral density (PSD). Figure 12 reports the PSD characteristics of the decomposed components: the high-frequency residual (P_{text {high}}), the medium-frequency component (P_{text {mid}}), and the low-frequency trend (P_{text {low}}) for a representative summer day. We partition the frequency axis into three bands (in cycles/hour) to summarize spectral energy: Low-frequency band:(f < 0.1) (dominant diurnal/slow envelope and baseline variations). Mid-frequency band:(0.1 le f le 0.5) (intra-day variability and regime transitions). High-frequency band:(f > 0.5) (fast ramps and short-term fluctuations). For each component, the band energy percentages are computed by integrating its PSD over the corresponding frequency band and normalizing by the component’s total spectral energy: where denotes the Welch PSD estimate and is one of the three bands above. As shown in Fig. 12, (P_{text {high}}) allocates a larger portion of energy to higher frequencies, while (P_{text {low}}) concentrates energy in the low-frequency region consistent with the diurnal envelope. The medium-scale component (P_{text {mid}}) mainly captures intermediate-band energy, supporting the intended multi-scale separation. Frequency-domain validation (summer). The left column shows Welch PSD for (P_{text {high}}), (P_{text {mid}}), and (P_{text {low}}). The top-right panel compares PSD curves across scales, and the bottom-right panel summarizes the energy distribution over the predefined low/mid/high frequency bands. Overall, this hierarchical MA residual decomposition provides interpretable temporal bands and supports MKAN’s multi-branch design, reducing interference between fast ramps and slow trends. The seasonal consistency of this separation is further confirmed in Table 5. KAN replaces fixed activation functions (e.g., ReLU, GELU) with learnable univariate edge functions, making nonlinear transformations explicit and interpreable. We analyze learned activation patterns and relate their shapes to PV forecasting behavior across seasonal regimes. For an input feature (x_i) and output node (y_j), KAN learns an edge function (phi _{i,j}(cdot )) using cubic B-splines: where (B_k(x)) are spline basis functions, (c_{i,j,k}) are learnable coefficients, and K denotes the number of spline control points. A KAN layer aggregates edge functions as: This formulation allows each connection to learn a data-driven nonlinear mapping tailored to a specific input-output relation. Figure 13 illustrates representative learned KAN activations and their differences from standard fixed activations. In PV forecasting, asymmetric nonlinear responses are useful: suppressing low-power noise (e.g., dawn/dusk or heavy haze) while preserving sensitivity during normal operating conditions. Comprehensive analysis of KAN activation functions. The figure compares fixed activations with representative learned KAN activations and highlights how learnable nonlinearities adapt to different data regimes. Figure 14 shows season-specific learned activations, indicating that KAN adapts its nonlinearity to seasonal PV dynamics. Seasonal adaptation of learned KAN activation functions. Each panel shows a representative learned activation from seasonal data (colored) compared with a fixed baseline (gray). Shaded regions indicate deviation, highlighting season-specific nonlinear adaptation. We quantify seasonal differences using three metrics over a fixed input range: Table 6 indicates stronger nonlinear adaptation in more challenging regimes, supporting KAN interpretability: learned activation shapes reflect seasonal PV generation characteristics. MKAN adopts an iTransformer-style inverted attention mechanism operating over the feature dimension, enabling dynamic feature-to-feature interaction modeling. We visualize seasonal feature importance, attention distributions, and cross-feature attention pathways to interpret how meteorological variables contribute under different atmospheric conditions. Figure 15 presents normalized feature importance by season. Table 7 reports the corresponding scores (normalized to the maximum within each season), revealing clear seasonal reweighting between irradiance-driven and atmosphere-driven predictors. Seasonal comparison of feature importance scores. Bars show normalized importance of each meteorological feature within a season. Across seasons, DNI dominates in spring and winter, while GHI becomes most important in summer, reflecting stronger scattering/cloud effects. Autumn shifts toward atmospheric pressure and historical power, suggesting increased reliance on synoptic conditions and temporal persistence during transitional weather. Winter vs. summer shift: Compared with summer, winter assigns substantially higher importance to RH (+0.5742) and DNI (+0.4962), and also increases reliance on historical power (+0.3096). In contrast, GHI becomes less dominant in winter (–0.1831), consistent with reduced diffuse-driven regimes and stronger sensitivity to beam irradiance availability. Figure 16 shows attention weight distributions across features and seasons. Wider distributions indicate more frequent reallocation of attention, typically associated with more volatile atmospheric conditions. Seasonal attention weight distributions across features. F1: Total solar irradiance, F2: Direct normal irradiance, F3: Global horizontal irradiance, F4: Air temperature, F5: Atmospheric pressure, F6: Relative humidity, F7: Power. We summarize attention dispersion using entropy computed from mean attention weights: where (bar{w}_i) is the mean attention weight of feature i. Table 8 reports the attention entropy and the seasonal prediction performance (RMSE in MW). Higher entropy indicates more distributed attention (i.e., no single dominant feature), reflecting more frequent reallocation of attention across variables under volatile atmospheric conditions. Figure 17 visualizes seasonal cross-feature attention matrices. To highlight dominant interaction pathways, Table 9 lists the top-3 attention pairs (query (rightarrow) key) per season. Seasonal cross-feature attention matrices. Rows are query features, columns are key features. F1: Total solar irradiance, F2: Direct normal irradiance, F3: Global horizontal irradiance, F4: Air temperature, F5: Atmospheric pressure, F6: Relative humidity, F7: Power. These pathways are physically plausible: summer emphasizes humidity–irradiance coupling (cloud formation and scattering), while winter concentrates multiple queries onto DNI, indicating that beam irradiance penetration becomes a key bottleneck signal under haze/fog conditions. We further quantify attention matrix structure using diagonal dominance: and interaction diversity: Table 10 confirms a seasonal shift between distributed attention (higher I, lower D) and focused attention (higher D, lower I), consistent with changes in atmospheric conditions and feature reliability. This study has several limitations that should be acknowledged when interpreting the results. Single-site evaluation. All experiments are conducted on data from a single PV plant. While the seasonal split provides a meaningful within-site distribution-shift test, the cross-site generalization of MKAN-iTransformer (e.g., different climates, terrains, PV technologies, and sensor configurations) is not verified here. Daytime-only forecasting protocol. Nighttime samples are excluded (06:00–20:00) because PV generation is near-zero and the series becomes sparse and less informative for learning daytime dynamics. This choice improves training stability and focuses the evaluation on operationally relevant generation periods, but it limits applicability to round-the-clock settings. In particular, behavior during dawn/dusk transitions and full-day forecasting is not evaluated. Dataset size and coverage. The dataset covers two years and yields a moderate number of samples after filtering and seasonal partitioning. Although sufficient for 15-minute single-step forecasting, larger multi-year and multi-site datasets may expose additional failure modes, especially rare extreme-weather ramps. Lack of uncertainty quantification. This work reports point forecasting metrics (MSE/RMSE/MAE and ({R^{2}})) only. For grid operation and risk-aware scheduling, probabilistic forecasts (e.g., prediction intervals or quantiles) and calibration analyses are often required. Uncertainty quantification is not addressed in this paper. These limitations motivate future work on cross-site evaluation, round-the-clock and multi-horizon forecasting protocols, and probabilistic forecasting with calibrated uncertainty estimates. This paper studies robust and interpretable PV power forecasting under seasonal regime shifts and proposes MKAN-iTransformer, a cascaded hybrid framework that combines MKAN-based multi-scale temporal representation learning with iTransformer-style variable-wise attention for cross-variable dependency modeling. The model is evaluated under a unified protocol for 15-minute single-step forecasting with consistent preprocessing, hyperparameter tuning, and chronological splits within each seasonal subset. Seasonal accuracy and robustness. Season-wise benchmarking (Table 3) shows that MKAN-iTransformer achieves consistent and competitive performance across all four seasons. It delivers the best overall results in spring, autumn, and winter across MSE/RMSE/MAE and ({R^{2}}), and remains highly competitive in summer with the lowest squared-error metrics. The typical-day prediction curves and error histograms further support these findings by showing closer tracking during ramps and peaks and more concentrated error distributions, indicating fewer large-deviation events under seasonal variability. Component contribution validated by ablation. The ablation study (Table 4) isolates the effects of MKAN and iTransformer and confirms that their combination is beneficial. Comparing iTransformer, MKAN, and MKAN-iTransformer demonstrates that neither multi-scale temporal modeling nor variable-wise dependency modeling alone fully explains the observed improvements; rather, the gains arise from their complementarity. In addition, the inclusion of KAN-iTransformer reveals that not all KAN-style integrations are equally stable: KAN-iTransformer exhibits a pronounced degradation in winter, suggesting sensitivity to seasonal distribution shifts, whereas MKAN-iTransformer remains robust. Interpretability evidence. Beyond performance, we provide a coherent interpretability analysis from three perspectives: (i) a multi-scale temporal decomposition aligned with MKAN branches and validated in the frequency domain, clarifying how fast ramps, intermediate variations, and slow diurnal trends are separated; (ii) inspection and quantification of learned KAN univariate edge/activation functions, showing season-dependent nonlinear adaptations; and (iii) feature-wise attention visualization, demonstrating seasonal reweighting of meteorological drivers and physically plausible cross-feature interaction pathways. Implications. Overall, MKAN-iTransformer offers an effective balance among accuracy, seasonal robustness, and model transparency for short-horizon PV forecasting. The results indicate that coupling scale-aware temporal feature extraction with explicit inter-variable modeling is a practical strategy to mitigate seasonal degradation commonly observed in baseline architectures. Future directions. 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Kan-odes: Kolmogorov–arnold network ordinary differential equations for learning dynamical systems and hidden physics. Comput. Methods Appl. Mech. Eng.432, 117397. https://doi.org/10.1016/j.cma.2024.117397 (2024). ArticleMathSciNet Google Scholar Download references This research was funded by the National Natural Science Foundation of China, grant number 51967004. This research was funded by the National Natural Science Foundation of China, grant number 51967004. College of Electrical Engineering, Guizhou University, Guiyang, China Linjie Liu, Min Liu, Zhuangchou Han & HaiQiang Zhao North Alabama International College of Engineering and Technology, Guizhou University, Guiyang, China Min Liu Guizhou Provincial Key Laboratory of Power System Intelligent Technologies, Guiyang, China Min Liu Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar L.L. (first author) conceived the research idea, developed the MKAN-iTransformer model, implemented all experiments, and wrote the initial draft of the manuscript. M.L. (corresponding author) supervised the entire research process, provided key guidance on model design and result analysis, and substantially revised the manuscript. Z.H. and H.Z. contributed to data preprocessing, experimental support, and manuscript review. All authors have read and approved the final version of the manuscript. Correspondence to Min Liu. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Liu, L., Liu, M., Han, Z. et al. Interpretable ultra-short-term photovoltaic power forecasting with multi-scale temporal modeling and variable-wise attention. Sci Rep16, 10336 (2026). https://doi.org/10.1038/s41598-026-39797-6 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-026-39797-6 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
India’s power grid struggles to handle excess solar energy. ReNew Energy Global Plc faces power curtailment, affecting earnings. The company is investing in battery storage to balance supply and demand. Rising costs from global events add to challenges. Future projects will focus more on solar power.
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Lorenzo Ravelo, right, plugs in his electric tricycle at a charging station in Santa Clara, Cuba, Saturday, May 2, 2026. Credit: AP/Jorge Luis Banos SANTA CLARA, Cuba — Yudelaimys Barrero Muñoz used to spend up to three hours on the side of a highway under the blazing sun waving money at drivers as she attempted to hitch a ride from Cienfuegos, Cuba to Santa Clara, where she buys supplies to resell and support her husband and two children. The 43-mile (70-kilometer) trip was impossible to make on her husband’s bicycle — at one time the family’s only mode of transportation — and later, with a rechargeable, three-wheeled vehicle whose battery didn't have the capacity for the round trip. Then, in early April, a local business owner opened what is believed to be Cuba’s first solar-powered charging station — and it was free. Cubans soon flocked to the solar station — or “solinera” as it's known in Cuba — recharging everything from electric vehicles to UV nail lamps. The Cuban government has stepped up the installation of solar panels in hospitals and other public places and established solar farms in the face of chronic blackouts and in recent months, a severe gas shortage stemming from a U.S. energy blockade. Renewable energy now accounts for some 10% of the island's electricity, up from 3.6% in 2024, but distribution remains limited, and few Cubans can afford such a system. Globally, just over 30% of electricity generation comes from renewable energies like solar, wind and hydropower, according to energy think tank Ember. Get the latest breaking news as it happens.
Voters in Richland County voted in favor of a ban on the development of large solar and wind farms in 11 of the county’s 18 townships. The issue passed with less than 53% of the vote, prohibiting these renewable energy projects in Bloominggrove, Franklin, Jackson, Jefferson, Mifflin, Monroe, Perry, Plymouth, Sharon, Troy and Weller townships. Richland County commissioners voted to ban the solar and wind farms last year, citing concerns over the loss of agricultural land, the lifespan of renewable energy developments and uncertainty if clean energy companies fail. A recent report from The Ohio Newsroom looked into the background of this ballot issue. Ohio Senate Bill 52 passed five years ago. Among other provisions, it gives commissioners the authority to restrict big renewable energy developments in a county’s unincorporated areas. “So, we requested from all 18 of our townships, what would you like us to do?” said Darrell Banks, one of Richland County’s commissioners. “Eleven townships sent us back the resolution asking us to ban large wind and solar in the unincorporated areas of their townships, and seven did not. So, we did that.” While Senate Bill 52 gives county commissioners the power to restrict large renewable energy projects in unincorporated areas, it also gives citizens the right to petition for a referendum if commissioners do, preventing a ban from taking effect until citizens have the chance to vote on it. Some residents have raised concerns about government overreach and a possible negative effect on economic development in the county. Benefits of renewable energy projects include diversifying energy supply and reducing dependence on imported fuel, along with reducing air pollution and creating jobs through manufacturing and installation, according to the Environmental Protection Agency. County-level bans are on the rise nationwide, according to the Sabin Center for Climate Change Law at Columbia University with more than 450 counties across 44 states issuing some sort of restriction or ban on renewable energy development. About one-third of Ohio’s 88 counties have issued some sort of restriction on renewable energy projects. The vote Tuesday makes Richland County the 28th county to issue its own ban.
Wednesday, May 6, 2026 at 3:00 AM By Kathy Bottorff
During Monday's meeting, the County Commissioners gave final approval to two significant ordinances: one restricting the size of solar-field projects and another officially cleaning up language to permanently prohibit carbon capture within the county. Both measures were approved on their second and third readings. County Attorney Sean Surrisi explained that the new Farm Scale Solar Ordinance strictly prohibits the development of large-scale solar panel farms spanning multiple properties and acres across any zoning district. However, the ordinance includes provisions for private use, allowing citizens or businesses to install up to five acres of solar panels on a single parcel of property. With no public comments presented, the commissioners voted 2-0 to pass the Farm Scale Solar Energy System ordinance. Commissioner Bohannon was absent from the meeting. The commissioners also approved an ordinance regarding carbon capture. According to Surrisi, this action served as a necessary "cleanup" of an ordinance initially passed last fall that prohibited carbon capture projects in the county. At the beginning of 2025, the commissioners enacted a two-year moratorium on carbon capture. When the outright prohibition was passed later in the fall, the original moratorium language was inadvertently left in the county code. The newly approved ordinance removes the outdated moratorium language, solidifying the permanent prohibition. Previously, the county commissioners passed ordinances to increase setbacks for Battery Energy Storage Systems to 1,320 feet. They also enacted an ordinance prohibiting data centers in the county.
The Australian government has announced the results of Capacity Investment Scheme (CIS) Tenders 5 and 6 for Western Australia, awarding contracts to 10 projects that will deliver 1.886GW of renewable energy generation and 3.683GWh of standalone battery energy storage to the state’s Wholesale Electricity Market (WEM). The tenders represent AU$5 billion (US$3.5 billion) in new energy infrastructure investment. Get Premium Subscription They are also expected to support more than 7,000 construction jobs and 500 operations and maintenance positions annually across their lifetime, according to a joint statement from federal energy and climate minister Chris Bowen, assistant minister Josh Wilson, and Western Australia’s energy and decarbonisation minister Amber-Jade Sanderson. Tender 5, the first generation-focused tender in the WEM, awarded contracts to six wind farms and one solar-battery hybrid project, totalling 1.536GW of wind capacity, 350MW of solar, and 2.1GWh of battery storage. Neoen Australia secured two wind projects: the 420MW Yathroo Wind Farm and the 168MW Narrogin Wind Farm. Other successful wind projects include Shell Energy and Foresight’s 130MW Kondinin Wind Farm, SynergyRED’s 240MW Tathra Wind Farm, Tilt Renewables’ 108MW Waddi Wind Farm, and Zephyr Energy’s 470MW Parron Maam Marang Wind Farm. Trina Solar’s 350MW/2,100MWh Killawarra Hybrid Project was the sole solar-battery hybrid awarded under Tender 5. You can read the full article, which includes more information on CIS Tender 6, on Energy-Storage.news.
German inverter and battery manufacturer SMA Solar Technology has confirmed its withdrawal from the Australian residential and commercial market, with CEO Jürgen Reinert declaring the company will instead focus on large-scale infrastructure, grid-forming technology and hybrid integration. Image: SMA Australia SMA Solar Technology Chief Executive Officer Jürgen Reinert has declared the German-headquartered inverter and battery manufacturer remains committed to the Australian market after confirming the company will exit the residential and commercial sector. “Australia represents enormous potential in the global energy transition, and SMA is committed to this market for the long term,” he said. “The decisions we are making now, focusing on large-scale infrastructure, grid-forming technology and hybrid integration, are not a response to short-term market conditions. They reflect a considered view of where the energy transition is heading and where SMA can make the most meaningful contribution.” Reinert explains the company’s new focus and maps out what lays ahead for SMA in Australia. Reports indicate that SMA is withdrawing from the Australian residential and commercial market. Can you explain the reasoning behind the decision and what that means for SMA in the Australian market going forward? JR: The residential and commercial inverter segments have experienced rapid commoditisation globally. Massive oversupply, aggressive pricing and state-subsidised manufacturing, particularly from China, have fundamentally altered the competitive dynamics. Competing sustainably in those segments requires cost structures that are increasingly difficult to sustain as a premium technology manufacturer without compromising the long-term integrity of what we do. In this context, our decision to exit the home and business segment in Australia is market-specific, with SMA continuing to operate in this segment in other regions globally. Australia remains one of our top three global markets for our large-scale division, and our commitment here is long-standing. SMA supplied technology to the Greenough River Solar Farm, one of Australia’s first utility-scale solar farms, 15 years ago. In 2018, we introduced locally integrated solutions in partnership with Wilson Transformer Company, setting a benchmark for local collaboration. We have supported more than 10GW of solar and storage entering operation in this country. We are continuing to invest in and develop locally relevant solutions by partnering with Australian developers, EPCs and utilities. The home and business exit is a strategic decision to concentrate SMA’s resources where we can have the greatest impact, in grid-forming capability, hybridisation, system integration and large-scale project expertise. Our commitment to Australia’s energy infrastructure is unchanged, and in many respects, deepening. Image: SMA Solar Technologies What are the key existing and emerging challenges in the Australian market, and what can be done to alleviate them? JR: One of the most pressing challenges is maintaining grid stability. As renewable penetration increases, the system is losing the inertia traditionally provided by synchronous generation. Managing frequency, voltage and system strength in this environment is technically complex—and Australia is navigating this transition faster than almost any other market globally. At the same time, there is an ongoing industry discussion around the role of synchronous machines versus inverter-based resources in providing these services. While synchronous condensers continue to play an important role today, grid-forming inverter technology is rapidly advancing and increasingly demonstrating its capability to deliver system strength and stability services. Battery integration presents both a major opportunity and a challenge. While deployment is accelerating rapidly, the real value lies in integrating storage in a way that delivers essential grid services—not just energy shifting or backup capacity. This requires advanced inverter capabilities and sophisticated plant control. Grid connection complexity is another key challenge. The requirements in Australia are among the most stringent in the world, which adds time and cost to project delivery. While these high standards are critical to maintaining system security – and something we have strong experience navigating—it’s important to recognise that the regulators and industry stakeholders are continuously evolving technical frameworks and requirements, particularly as grid-forming capabilities become more relevant. At the same time, Australia is ahead of many other markets in addressing these challenges. The experience and learnings gained here provide a strong foundation that can be applied globally, as other regions move towards higher shares of renewable and inverter-based generation. Underlying all of this is an energy security imperative. Supply chain resilience, the origin of critical components, and sovereign capability in infrastructure are increasingly front of mind. The answer isn’t protectionism, but it does require a more strategic approach to where critical technology comes from and who controls it over the long term. Addressing these challenges will require continued investment in grid-forming technologies, smarter hybrid system integration, and deeper collaboration between technology providers, network operators and government. Australia is already leading in many of these areas, the key now is to maintain momentum and scale these solutions effectively. 3. Where is the potential for growth in the Australian renewable energy transition and why? JR: The greatest growth potential sits at the intersection of scale and complexity, which is where SMA’s Large Scale division operates. Utility-scale solar and storage are still in an expansive phase in Australia, and the pipeline remains strong. But the more interesting growth is in hybrid systems, solar paired with battery storage and grid-forming capability, which are becoming essential as the grid evolves. Australia commissioned its first solar and storage hybrid with a single point of connection in the National Electricity Market at Quorn Park last year, and that model is expected to become increasingly common. Beyond individual projects, there is significant growth potential in the services and intelligence layer, the software, controls and integration capability that make large-scale renewable assets perform reliably within a complex grid. As penetration increases, project developers and utilities will increasingly value technology partners who can deliver that intelligence, not just hardware. Australia also has a strong pipeline in sectors adjacent to traditional solar, such as green hydrogen, industrial decarbonisation, and behind-the-meter solutions at an industrial scale. These are areas where SMA’s expertise in power conversion and system integration is directly relevant. 4. Battery energy storage is emerging as essential infrastructure in Australia, what does that mean for SMA? JR: The greatest growth potential sits at the intersection of scale and complexity – this is exactly where SMA’s large-scale division operates. Utility-scale solar and storage continue to see strong growth in Australia, supported by a robust project pipeline. However, the more meaningful shift is towards hybrid systems – combining solar, battery storage, and increasingly grid-forming capability. As the grid evolves, these types of systems are moving from optional to essential, enabling stability, system strength, and more flexible operation. Beyond individual assets, a significant area of growth lies in the intelligence layer – plant controls, system integration, and advanced inverter capabilities. As renewable penetration increases, the ability to actively manage and support the grid becomes a key differentiator. Utilities and developers are placing greater value on partners who can deliver not just hardware, but fully integrated, grid-supporting solutions. This is where SMA is strongly positioned, with proven expertise in power conversion, grid-forming technologies, and large-scale system integration across global markets. In addition, there is growing momentum in adjacent areas such as green hydrogen, industrial decarbonisation, and large-scale behind-the-meter applications. These segments further expand the role of renewable generation and storage – and align closely with SMA’s capabilities in delivering flexible, high-performance energy systems. 5. What lessons does Australia’s energy transition provide for other markets? JR: Australia has consistently been ahead of the curve, and other markets are watching closely. One of the key lessons is that high renewable penetration—particularly in relatively weak grid environments—fundamentally changes how power systems need to operate. System strength, frequency response, voltage management and overall resilience cannot be addressed by generation capacity alone – they require advanced inverter capabilities, sophisticated controls, and intelligent system integration. Australia has also highlighted the importance of robust grid connection frameworks. While the requirements are among the most advanced globally, they have driven a high level of technical rigor and capability across the industry, supporting long-term system stability. The growing focus on energy security is another important lesson. Themes such as grid resilience, supply chain transparency and sovereign capability are becoming more prominent in Australia and are now emerging across other major markets, including the US, the UK and Europe. What happens in Australia today often plays out in other high-penetration markets – such as Chile, Texas, the UK and parts of Europe – within a few years. This is why Australia is such an important market for SMA. It is where solutions are refined and proven in practice, particularly in areas like grid-forming and hybrid system integration, before being applied more broadly across global markets. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from David Carroll Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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WEST LAFAYETTE, IN — Hours after Tippecanoe County commissioners unanimously approved contentious new restrictions on solar energy developments, the West Lafayette city council followed suit on an ordinance that represents a compromise months in the making. Monday’s county approval ended an 11-month moratorium county commissioners placed on large-scale solar developments in June 2025. The conversation around industrial-scale solar projects has since fallen between two poles; on one end are farmers who shudder at the thought of many hundreds of acres of prime farmland becoming unusable, and on the other are renewable energy supporters who fear that tight regulations will push out new solar development entirely. The new ordinance represents something of a middle ground. West Lafayette is too dense to house a large solar development; a 400-acre project allowed by the new law would be more than three times the size of the land on which SK Hynix’s semiconductor chip facility will be built. But that didn’t stop councilors and residents from debating the new ordinance’s wisdom Monday night. “It’s better than nothing, right?” said District 5 Rep. Kathy Parker, who also serves on the county Area Plan Commission and recommended against the ordinance when that body voted to advance it, 13 to three, in April. “Maybe we made this too restrictive,” she said. “I thought we made it too restrictive. I voted no at APC, but yes here in the city because it’s not going to happen in the city.” Parker said the county can now proceed with solar ground rules while it waits to see whether the fears of detractors will come true, and large solar development will be stymied. “If nobody comes for a whole year,” she said, “then maybe we reexamine … It’s a starting place.” The approved ordinance limits new commercial solar developments to 400 acres and stipulates at least one mile in distance between them, as well as a minimum of 500 feet from neighboring property lines. It also limits total commercial solar acreage in the county to 6,000 acres. The council passed the ordinance five to one. At-large Rep. James Blanco voted against it. APC Executive Director Ryan O’Gara told the council that the new code is structured so “with a few tweaks, it could be very different,” meaning officials could amend the size limit to, say, 600 acres without needing to make substantial change to the ordinance. O’Gara said the county has yet to receive interest from a solar project that would fit within the new restrictions. The only development to advance beyond speculation was the 1,700-acre Rainbow Trout solar farm, planned for northwest Tippecanoe County, which ran aground in the zoning approval stage. Developers are suing the county in an attempt to reverse its rejection of the project. At county meetings, supporters of the restrictions have pointed to dozens of industrial solar projects they say are underway across the state at 400 acres or fewer. “Projects of that scale are certainly being done,” O’Gara said. “We just not have not had any of those entities come to us proposing one. Before Rainbow Trout came to town, I believe I had at least two companies knock on our door. They never submitted anything; they just chatted. But their projects were of a similar scope to Rainbow Trout, hundreds of acres.” At-large representative David Sanders asked O’Gara if, as he had heard from detractors of the new code, these restrictions amounted to an effective solar ban. “I’m being honest when I say it’s not prohibitive,” O’Gara said. He then laid out the process a prospective company would need to follow. “If you want to do a project of this scope, 400 acres, perhaps less, and you have a site that’s zoned properly, or perhaps you petition the commissioners to rezone a site to the correct zone, and you win them over, and all the neighbors are happy, then there are remedies in our code to achieve these projects.” Nicole Duttlinger, a West Lafayette resident and an advocate for much smaller-scale, community solar investment — like solar panels on individual homes — said during public comment that industrial-scale solar projects could power 15,000 houses. But, she said, that power does not stay local; it goes to the power grid. She referred to efforts by the city to install solar panels on the five public buildings able to house them. Mayor Erin Easter said the panels, on the public library and wellness center, among others, help to partially offset electricity needs. The city’s sustainability website says 84% of city rooftops have the potential for solar panel installation, citing Google’s Environmental Insights Explorer. That energy potential could power about 20,000 average U.S. homes. Contact Israel Schuman at ieschuma@purdue.edu or on X @ischumanwrites.Contact Meagan Hipsky at mmhipsky@gmail.com.
The Chinese manufacturer has launched an “Australia specific” variant of its Vertex S+ modules featuring a power output of 515 W and a maximum efficiency of 24.65%. Its lower-voltage design reportedly enables more flexible string sizing, allowing installers to optimize system layouts across a range of inverter configurations. Image: Trina Solar From pv magazine Australia Trina Solar has unveiled a new variant of its Vertex S+ module series, designed to deliver higher output within standard rooftop constraints and tailored for Australia’s residential and commercial and industrial (C&I) market. The company said the Australia-specific module supports systems of up to 100 kW under the Small-scale Renewable Energy Scheme (SRES), where higher wattage and efficiency per module enable installers to optimize system size and maximize Small-scale Technology Certificate (STC) returns within limited roof space. The monofacial NEG10R.28Z module offers up to 515 W output with a maximum conversion efficiency of 24.65%, within a standard module footprint of 1,842 mm × 1,134 mm × 30 mm. Based on Trina’s n-type i-TOPCon cell architecture, the module incorporates zero-busbar and zero-gap technologies to improve efficiency and reduce electrical losses. Trina said the higher power density allows installers to reach target system capacities with fewer modules, increasing system capacity without expanding the footprint. This can reduce balance-of-system (BOS) requirements and improve the levelized cost of electricity (LCOE). The module features an open-circuit voltage of 38.3 V and a short-circuit current of 12.85 A. According to the company, its lower-voltage design enables more flexible string sizing across a range of inverter configurations, supporting optimized system layouts where roof design or electrical limits apply. The module is designed for Australian conditions, with a temperature coefficient of -0.26%/C to support performance in high temperatures, and a dual-glass structure to enhance durability. It is rated to withstand mechanical loads of up to 5,400 Pa (snow) and 4,000 Pa (wind). The product comes with a 25-year product warranty and a 30-year power output guarantee. End-of-life output is guaranteed at no less than 88.85% of nominal power, with first-year degradation limited to 1%. Edison Zhou, Trina Solar’s head of operations for Australia and Asia Pacific, said the product reflects a shift toward system optimization in the Australian rooftop market. He said the 510–515 W range represents a practical “sweet spot” for rooftop systems, as installers increasingly seek higher-wattage, higher-efficiency modules within standard dimensions, particularly where roof size and configuration constrain system design. The Vertex S+ 515 W module is available for preorder and is expected to be launched in Australia in early Q3 2026, subject to final certification and listing requirements. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Ev Foley Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Communicationsvolume 16, Article number: 6742 (2025) Cite this article 14k Accesses 14 Altmetric Metrics details Tripling renewable energy capacity by 2030 requires increasing technology production capacity, including solar photovoltaics (PV). Current supply chains rely heavily on Chinese production; however, this situation is not aligned with regions aiming to increase self-sufficiency, decrease supply chain emissions, and increase local job opportunities. Here, we apply a supply chain optimization model to perform scenario analysis of the PV supply chain development through 2021-2030 considering various European economic and job creation goals. Irrespective of regional goals, we find that China is poised to remain a globally dominant supplier through 2030, especially in terms of lower-value PV components, given that future demand requires increasing global production capacity by a factor of at least 1.5. We find that some regional supply chain goals can be co-beneficial, for example in terms of joint job gains and increased regional self-sufficiency. However, pursuing highly isolationist policies can introduce cost-significant inefficiencies. Our results highlight that an open trade policy is key to minimizing costs, even when considering security and environmental supply chain objectives. Achieving a net-zero energy transition relies heavily upon the deployment of solar photovoltaic (PV) systems. Countries have pinned much of their ambitions for renewables expansion on solar PV, with the technology expected to account for 50% of new renewable capacity and be the most important renewable technology in terms of total capacity by 20301. Solar PV’s appeal can be attributed to several factors, including the technology’s cost-effectiveness, high social acceptance, and potential for global deployment2,3. Another factor in solar PV’s popularity as a low-carbon technology is its market potential. The total value of global solar PV trade is valued at ~40 billion United States dollars (USD) per year—but to meet net-zero goals, the rate of annual solar PV additions must more than quadruple4. Chinese production accounts for over 80% of the current market4, a position obtained with help from large government subsidies for research and development5,6 and investments in primary production7,8. Lower-cost energy and labor support the cost-competitiveness of Chinese solar PV9. Besides supplying most of the world’s demand for solar PV, China’s support for solar PV has also helped drive down costs quickly, saving the global economy at least USD 67 billion between 2008 and 20207. The global benefits, past and future, of Chinese solar PV manufacturing capacity to global deployment are tightly linked to open trade policies, which facilitate global technology learning and making the most of existing capacities7,10. However, Chinese supply chain dominance is not viewed entirely favorably for several reasons. First, developing low-carbon technologies is an attractive and future-oriented market development opportunity. Countries are considering how they can also share financial, innovation, and job creation benefits, instead of concentrating on a single supplier. Solar PV’s relatively assured importance to the transition, comparatively low reliance upon critical minerals11, and comparatively low design complexity and customization requirements12 help increase the attractiveness of building up solar PV manufacturing capacity compared to other low-carbon technologies. Moreover, since producing a solar PV panel involves several intermediate products—namely, polysilicon, ingots, wafers, cells, and modules4—there is an opportunity for countries to contribute to and benefit from at least some part of the supply chain. For example, countries in Southeast Asia such as Malaysia, Thailand, and Vietnam have developed manufacturing capacity for module assembly4. Second, relying primarily on a single supplier can lead to a clear supply chain dependency. Such a dependency raises concerns for supply chain resilience and, in some cases, for geopolitical reasons. The transition to a low-carbon energy system holds the potential to reshape global energy markets and move away from existing fossil-fuel-based dependencies to more autonomous and diversified supply13; moving from dependency on one country to another is, therefore, undesirable. The desire to reduce trade with single countries may also stem from broader political shifts, such as within American federal politics14. Policymakers have two primary strategies for increasing self-sufficiency in the PV supply chain, namely, limiting the availability of foreign products and introducing supportive measures for local manufacturers. Both strategies for increasing PV self-supply have pros and cons. The appeal of trade policies, like tariffs, is that they protect local manufacturers; however, such policies can lead to price surges, hinder adoption rates, and impede technology learning7,10,15,16. Directly supporting local manufacturers is meanwhile appealing because subsidies, tax incentives, and grants for research and development can boost domestic production capabilities and foster innovation. However, these direct support measures require substantial upfront capital commitment, especially in the early stage of PV development17. In practice, both strategies are frequently used. For example, in May 2024, the United States (US) has proposed a 50% tariff on Chinese solar cells to protect the local industry18. On the supportive side, the US Inflation Reduction Act allocates 370 billion USD to energy security and climate spending, aiming to enhance domestic manufacturing, which had an impact on the decision-making of companies’ relocation19. China also relied on a direct support approach to build its solar PV supply chain: government support included fiscal support20, energy incentives15, research and development (R&D) funding5, tax rebates20, land use incentives15, and infrastructure investments20. In the European Union (EU), developing local PV manufacturing capacity fits into the bloc’s wider strategic plans for developing its low-carbon economy21,22. Besides capitalizing on the potential financial benefits and supply chain independence, European policymakers hope that local low-carbon technology production can stimulate high-quality, low-carbon jobs21 and reduce the environmental burden of the manufacturing process itself. The environmental benefits of producing solar PV in Europe come from Europe’s lower-carbon energy supply23,24, avoided emissions linked to transportation23, and mitigating the risk of carbon leakage25. Building up the solar PV supply chain can also be seen as a chance for Europe to regain a missed opportunity: the initial wave of solar PV adoption was led by European demand and, for a time, Germany’s manufacturing was a competitive supplier4. The EU has goals to reach 30 gigawatts (GW) of operational solar PV manufacturing and 40% self-production of net-zero technologies, including solar PV, by 203026. Several policies are in place to achieve these goals, including providing production27 and purchase subsidies28, upgrading skills29, implementing a carbon tariff on goods entering the EU25, and supporting industry networks like the European Solar Photovoltaic Industry Alliance30. Despite the extensive and varied policy support, it is unclear whether European policymakers will be able to achieve their ambitions for localizing PV supply chains. Building up low-carbon manufacturing is a complex task, requiring a supportive regulatory environment12 and access to skilled labor31. Supplier preferences32 and cost differences33 also influence the industry’s willingness to alter supply chain patterns. Singular events and policies can disrupt global supply PV chains, as did the global COVID pandemic15 and American trade policies targeting Chinese production34. Recent work suggests that relocating PV manufacturing outside of China could help reduce supply chain emissions35, but that systemic financial support is required to overcome financial barriers23. As such, the full scope of the opportunities, trade-offs, and impacts of European policy action is yet unclear. To address these gaps, we examine how European policy actions aimed at building a local solar PV supply chain affect global trade flows and quantify the associated environmental and social impacts. We test different European policy scenarios using a bi-objective optimization model considering costs and job creation, and analyze the corresponding impacts over the period from 2021 to 2030 (Methods—Modeling framework). Relying on an optimization model allows us to test “what-if” scenarios associated with strong policy action (see Methods— Scenarios). We find that setting regional policy goals can help reshape and diversify the global PV supply chain; however, China remains a dominant supplier in all scenarios. We furthermore find that supply chain development goals can be cohesive, i.e., aiming to localize the European supply chain also supports local job growth and decarbonization. Our research contributes to the literature in three ways. First, we demonstrate the impact of regional policy action on future global supply chain networks, with and without coordinated action of partner regions. By doing so, we overcome the limitations of past work that takes only a retrospective perspective7,15 or focus only on local policy impacts without considering knock-on supply chain effects33,35. Second, our results reinforce existing literature in suggesting that open trade policy is key to minimizing costs and creating jobs, even when considering global and local security and environmental impacts. Finally, we provide an in-depth analysis of the specific trade-offs facing European policymakers, thereby providing region-specific decision-making input that has hereto been lacking. To analyze the global PV supply chain and identify potential transformations, we develop an optimization-based supply chain model for PV production (Methods). This model follows the real-world trend of cost-driven decision-making in production and trade, while also supporting policies that promote local supply and job creation. The model encompasses 12 regions to provide a comprehensive view of the global solar PV market. We consider five steps of PV production: polysilicon, ingots, wafers, cells, and modules (Supplementary Fig. 1)4. Figure 1a shows the schematics of the model based on the demand and manufacturing capacity data in 2021. To begin, each region is assigned product manufacturing capacity based on industry reports and statistics4,36. For example, Germany can produce products other than ingots, while China owns the manufacturing capacity for all five products. To satisfy the regional demand for PV modules, the regions can either acquire PV via trade or produce components locally, shown as the dashed lines linking the products among regions in Fig. 1a. Each region’s demand for PV modules changes over time, and they can expand the manufacturing capacity and store products, if needed (Fig. 1b). The model covers the global PV supply chain from 2021 to 2030, using weighted multi-objective optimization for total industry costs and job creation (details in Methods: Modeling framework) to analyze the impact and pathways of the supply chain transition. Figure 1c shows the module production in 2030 relative to 2022, considering the dual goals of minimizing industry costs and maximizing job creation, indicating a common increase in local production across the regions. For example, module production in China and the US can become 1.5 times and 3.8 times the level in 2022, respectively; while for European regions (EUR), the production is modeled to become over 6.5 times the level in 2022. This result is roughly in line with the estimates provided by the International Energy Agency37. Panel a shows the model framework with 12 regions participating in five components’ supply, based on the demand and manufacturing capacity data in 2021. Point size shows the relative module demand and production capacity. Panel b shows the options for meeting increasing demand within a region, with time series from year t to several years’ lead time (L) beyond year t. The process is shown for a sample region. Panel c shows the relative increase in module production from 2022 to 2030 for the modeled baseline scenario. We investigate the structure of potential global PV supply chains in 2030 considering different European development priorities. We consider five scenarios with different sets of objectives and constraints (Methods and Data: Scenarios). In the baseline scenario, the model finds supply chains that achieve the lowest industry costs while maximizing job creation objectives over the 2021–2030 period, without considering European strategic interests. In the second scenario, the model finds supply chains that are low-cost and that maximize European job creation (MEJ scenario), while in the third scenario, Europe is set to achieve at least 40% self-sufficiency with a focus on maximizing job creation26. The fourth and fifth scenarios consider additional European policy intervention, respectively considering that European regions stop importing from China starting in 2024 and that European regions subsidize module production by 20%. Throughout, we assign the costs of building up manufacturing capacity to industry, while government subsidies are excluded from the industry costs. Figure 2 shows the trade flows of the five solar PV component products in 2022 and the five scenarios’ results in 2030. In 2022, mainland China produced over 81% of the products, with the share for ingots and wafers reaching 98% (Fig. 2; purple arrows). Compared to other components, the supply of PV modules is the most diverse in terms of both the number of suppliers and total market share. While polysilicon and modules see more diverse global production than other products, China’s low production costs—up to 35% lower than in Europe4—make the country the most cost-competitive, attracting worldwide buyers. The results of the optimization model suggest that the key product flows resemble the current supply chain, but there are still opportunities for the supply chain transition to be more cost-effective and job-supporting (Supplementary Fig. 2). Outer rings compound production and demand for the ten indicated regions. Arrows indicate regional trade flows and show the share of supply and demand for each outer ring; for example, the black arrow in (a) emphasizes that the majority of China’s polysilicon supply is used to meet the domestic national demand and the remaining demand is mainly supplied from Europe and Malaysia. a shows 2022 trade flows and b shows the 2030 trade flows according to the tested scenarios. Trade flows for individual products are shown left-to-right following the PV value chain, with modules being the products sold to end-consumers. China retains a dominant market position into 2030 (Fig. 2b), hosting as much as ten times manufacturing capacity as the second largest supplier (Supplementary Fig. 3). However, compared to the 2022 market, the 2030 scenario results suggest that building a supply chain aiming to minimize industry costs and maximize job creation would lead to a more diverse manufacturing market, as the suppliers participate with higher market shares in 2030. All scenarios show a trend towards reduced reliance on imports and increased self-sufficiency. In all scenarios, Europe is completely self-sufficient in module production, due to the relatively low cost of building manufacturing capacity and the goal of maximizing jobs. Europe is also able to achieve its 40% self-sufficiency targets by 2030 for all PV products in three different scenarios: when maximizing European jobs while being forced to meet the self-sufficiency goals, when introducing trade barriers against China, and when providing subsidies for European PV modules (MEJ + Europe >40%, European trade barrier, and European module subsidies in Fig. 2b). Other regions also increase their market shares. For example, the Rest of Asia (ROA) becomes more important in supplying polysilicon, ingots, and wafers across all 2030 scenarios. Increasing European self-sufficiency and job creation drive the supply chain’s relocation to Europe (blue arrows in Fig. 2; see scenarios MEJ and MEJ+ Europe >40% self-supply). This effect is strongest for upstream components, as Europe is more self-sufficient in downstream products to begin with. Imposing a European trade barrier with China shifts the Europe’s dependence from China to Malaysia, thus not totally achieving the goal of increased self-sufficiency but merely transferring dependency to another country. Similar effects can be expected if both the USA and Europe implement trade barriers against China—the unmet demand turns to India, Malaysia, Vietnam, and ROA (Supplementary Fig. 4). Banning products along the entire supply chain has greater impacts in rerouting supply chains than banning individual products (Supplementary Fig. 4). Moreover, banning the trade of downstream products simply shifts the dependence of upstream products on other regions, i.e., banning module imports leads to more imports of cells (Supplementary Fig. 4). Subsidizing module production in Europe has little impact on upstream products, as Europe is already highly self-sufficient in module production, even in the baseline scenario for 2030. Increasing market shares requires regions to increase their production across the PV supply chain. For example, China needs to increase its production of polysilicon, ingots, wafers, and cells; ROA needs to expand its production of polysilicon, ingots, and wafers; Europe requires growth in ingots, wafers, cells, and modules; and India needs to focus on increasing its cell production (Supplementary Fig. 3). One risk that emerges is the potential of oversupply in PV production capacity, already anticipated (Supplementary Fig. 5)38. The global efficiency of manufacturing capacity, measured as the share of actual production relative to total capacity, falls in the range of 58–79% (Supplementary Fig. 6). Even lower efficiencies are observed for specific national-level production cases, such as for module production in Malaysia and Vietnam and wafer production in Germany and the USA, with less than 10% efficiency. In contrast, the efficiency of ingots capacity exceeds 96% in ROE, India, and ROA (Supplementary Fig. 6). This uneven efficiency reveals regional imbalances since regions prioritize low industry costs and local supply; this results in underutilized capacity. Pursuing regional supply chain goals can clearly affect global supply chains (Fig. 2) and, by extension, supply chain impacts in terms of cost, carbon dioxide emissions, job creation, and regional self-sufficiency. We next investigate how these four impacts differ according to policy scenario and the associated tradeoffs. Figure 3a–d compare the global industry costs, carbon dioxide emissions, jobs, and average regional self-sufficiency across four scenarios. These scenarios consider factors such as job creation location, trade policies, and subsidy policies without imposing additional self-supply constraints. Adopting a Euro-centric focus for building global PV supply chains has clear effects on all four impact categories. For example, aiming to maximize European job creation provides 44.5% more jobs in Europe (but 0.4% less jobs globally), reduces global emissions by 1.6%, and increases global self-sufficiency by 4%. Likewise, if Europe stops importing PV products from China, total industry costs and emissions increase by about 5%, global self-sufficiency increases by 0.3%, and global job creation decreases by 0.1%. Finally, government subsidies in Europe, amounting to 7.1% of the baseline industry costs, help share the financial burden on European industry (0.8% globally) but lower the global average self-sufficiency by 0.3%. This effect occurs because localizing European production reduces dependence on other regions and even reduces the need for other regions to build up their manufacturing capacity. In turn, this also reduces the ability of other regions to meet their local demand with local supply. These results collectively indicate the power of regional (e.g., European) policies on global supply chains and their resulting global socioeconomic and environmental impacts. From the perspective of components, polysilicon is the most expensive product, while module supply creates the most jobs with the highest self-sufficiency (Supplementary Fig. 7). Panels a–d show the scenario-by-scenario results on global a cumulative costs, b cumulative emissions, c cumulative job creation, and d average self-sufficiency across years. The circles show the median and the error bars show the min-max range. Panels e–g show the cumulative cost-jobs trade-offs according to policy actions aiming to e maximize European jobs (MEJ), f maximize European jobs and provide a production subsidy, and g maximize European jobs and impose a trade barrier on all Chinese PV products. The point shading in panels e–g corresponds to different European self-sufficiency targets, which are modeled as increasingly stringent optimization constraints. Manufacturing location entails critical social and environmental trade-offs, particularly between carbon emissions and job creation, which are heavily influenced by regional differences in industrial production processes. Manufacturers in developed regions, such as Europe, benefit from lower-carbon energy sources but generate fewer jobs due to higher labor costs4 (see Supplementary Data 1). This results in the scenario where maximizing European jobs leads to the lowest carbon emissions but also the least global job creation (Fig. 3b, c). In contrast, trade barriers between Europe and China shift production to other Asian regions with even higher carbon intensity and more variable labor conditions than in China, increasing global CO₂ emissions while only marginally reducing job creation (by 0.1%). These results highlight a key tension: policies favoring localized production in developed economies may reduce emissions but limit global employment opportunities, whereas shifting supply chains to lower-cost regions can expand job creation at the expense of higher environmental impact. Considering the scenarios aiming to maximize European jobs in more detail reveals the cost-job trade-offs. The scatter plots shown in Fig. 3e–g summarize the total jobs created versus total European PV supply chain industry costs from 2021 to 2030. In all three scenarios, achieving more jobs and higher self-sufficiency in Europe correlate with higher cost. Between 2021 and 2030, transforming the PV supply chain could create 850–870 thousand full-time manufacturing jobs in Europe; this represents an increase of 45-48% compared to the number of European jobs in the baseline scenario. Correspondingly, the cumulative industry costs increase by 94–119 billion USD compared to the baseline scenario. Trade disruption leads to more job creation but at a higher cost: creating 1000 more jobs in Europe requires 1.76 billion USD, which is about 30% higher costs than the other two scenarios. Overall, these results suggest that strictly aiming to avoid imports and increase jobs without considering other supply chain impacts would be a costly strategy for European policymakers. Governments can stimulate local PV production to reduce the dependence on foreign supply by investing in manufacturing capacity and subsidizing production. However, significant capacity expansion and associated costs are required to achieve those goals. Figure 4a shows the global capacity expansion and corresponding expansion costs needed to bridge the gap between 2022 and 2030 for the scenarios considered. According to the baseline scenario, global manufacturing capacity in 2030 must increase by over 3.4 TW (over 1.5 times) compared to 2022 values. In Europe, a 6.1-fold capacity increase is needed compared to 2022 values for the baseline scenario and an 11-fold capacity increase for the “Maximize European jobs” scenario, focusing on expanding module, cell, and wafer production. Within Europe, Germany is expected to build 20% of the capacity, while ROE is expected to expand up to 80% of the manufacturing capacity especially for products other than modules (Supplementary Fig. 8). Chinese manufacturing capacity must increase by around 1.3 times its 2022 capacity to supply domestic and other regions’ demand, especially for polysilicon. a shows capacity expansions and capacity expansion costs for global total, China, and Europe, for the year 2022 as reference (labeled as “Current capacity 2022”), and for cumulative from 2023 to 2030 in the four scenarios, by product in colors. b shows Europe cumulative industry costs and self-sufficiency requirements for each product by 2030, by scenario and cost type in colors. The highest capacity increases occur in scenarios that maximize European jobs, showing that regional goals can introduce significant trade-offs to a globally efficient energy transition. Similarly to the results for local impacts of the transformation, we find that job creation objectives and trade disruptions are linked to higher capacity expansion and industry costs, while subsidies in Europe incur capacity expansion and costs to industry compared to other policy scenarios. The cost for global capacity expansion across all scenarios is over 1 trillion USD (Fig. 4a); more than 900 billion of this is set to be invested in China, which is nearly three times the amount the country invested in solar manufacturing and power generation in 202339. We estimate that capital costs for European supply chain development to be in the range of 50–120 billion USD. Our cost estimates for the baseline scenario align with those suggested by the European Solar Industry Alliance40 in terms of the financing required to increase downstream wafer, cell, and module capacity. However, our financing estimates for developing a fully European PV value chain imply far greater efforts than currently called for in other proposals9, namely, developing polysilicon and ingot manufacturing requires an additional 20–52 billion USD. Capacity expansion costs account for only 12.4% (as median) of the total industry costs, and the actions needed for supply chain transformation are more than building sufficient capacity for local manufacturing. The cumulative industry costs include costs for capacity expansion, production, trade, and managing surplus stock. Figure 4b shows cumulative industry costs and self-sufficiency constraints for the baseline and the maximum European job creation scenario. In both cases, increasing the requirements for European self-sufficiency increases the supply costs for polysilicon, ingots, wafers, and cells. The cost increase is primarily due to Europe’s relatively higher industry costs in capacity expansion, production, and import expenses41,42,43. Production costs remain the predominant expense for modules. When compared to the global cost-and-job-optimal scenario (baseline case), pursuing increased employment in Europe (MEJ) leads to a nearly one-third (32.2%) rise in cumulative industry costs for the European PV industry. Maximizing European job creation also indirectly contributes to increased self-sufficiency, providing 14% higher self-sufficiency in module production. Additional self-sufficiency requirements on European PV only have a mild cost impact on the local industry: increasing self-sufficiency constraints from 10 to 90% only increases total costs to European industry by 6.3%. The same trend holds when considering additional trade barriers and subsidies (see Supplementary Figs. 9, 10). Developing local manufacturing capacity in low-carbon technologies, including solar PV, offers the potential benefits of supporting self-sufficiency, job creation, and lower environmental impacts. Here, we show that regional European policy goals targeting these supply chain objectives can significantly reshape supply chain dynamics. Moreover, we find that achieving self-sufficiency and job creation goals could increase industry costs by up to 34% higher than those of a globally optimized supply chain. However, irrespective of European regional goals, China will maintain a predominant role in the solar PV supply chain due to the advantages of manufacturing capacity and costs, and the need to expand global capacity by over 1.5 times. As such, the goal for diversifying the global supply chain can only be partially fulfilled, but still presents notable benefits to countries and regions that apply concerted efforts to build up their local capacities. Our findings agree with existing work that an open trade policy is key to achieving a low-cost energy transition7,10. Implementing trade barriers can raise the cost of job creation by up to 30% and increase global emissions by shifting production to countries with low production costs and carbon-intensive energy systems. Moreover, trade barriers may simply shift rather than resolve trade dependency. Our model results suggest that subsidizing European module production would both support the growth of the local PV industry and reduce local supply chain emissions. Subsidies for producers are conducive to increasing the self-sufficiency in module production by shifting some costs from companies to the government. In Europe, supporting local solar PV manufacturing through subsidies or investment can improve competitiveness, create jobs, and increase self-reliance. Compared to trade barriers on China, subsidies can cut industry expenses by 23.6%, create jobs 27.5% more cost-efficiently, and provide a similar gain in self-sufficiency. Performing a Monte Carlo analysis (Methods—Sensitivity analysis) shows that our results are robust against the uncertainty in cost data and expected demand. Global regional production shares remain similar throughout the sensitivity analysis, particularly in scenarios prioritizing global objectives. Demand levels directly influence the total manufacturing capacity. For example, a ±50% change in total global demand leads to corresponding changes of −50% to +67% in 2030 capacity (Supplementary Fig. 11). Meanwhile, changing cost assumptions amplifies the differences in national-level market shares between scenarios (Supplementary Fig. 12). For example, aiming to increase job production in Europe leads to greater variability in European production shares as compared to other scenarios (see ROE and Germany in Supplementary Fig. 12). Cost fluctuations in individual products do not significantly affect regional market shares; however, market shares are more sensitive to variations in downstream costs than upstream costs (Supplementary Figs. 13, 14). The total job creation potential also varies strongly depending on the regional demand data that drive the model. Accordingly, while our model relies on similar labor intensities (jobs/GW) as other studies, the total job creation estimates may vary. For example, our results suggest that Europe could host over 125,000 full-time equivalent (FTE) jobs and 74 GW of module production by 2028, whereas industry reports44 suggest roughly 60,000 FTE for 30 GW of module production. Differences in input data (e.g., the demand data that drives the model) and methodology lead to the gaps between our results and other reports; differences in output highlight the uncertainty surrounding future supply chain planning. Efforts to boost self-sufficiency must consider what parts of the supply chain to try to localize. Subsidizing module production is often the most cost-effective and feasible focus, with capacity expansion for other parts of the value chain costing up to ten times more per kW of end product (Supplementary Table 1). In any case, funding for PV manufacturing must be justified against other public priorities and energy policy goals. For example, although the EU seeks greater self-sufficiency in PV manufacturing, it is also committed to being fully independent of Russian oil and gas45. Policymakers considering subsidies must also account for subsidy timing to best support their markets: governmental subsidies are most effective at the early exploratory stage rather than when local industry is mature46, presenting different strategies for developed economies and emerging markets. From the perspective of industrial costs and job creation, our results align with the European Commission’s recommendation that a “mixed strategy”—retaining necessary imports and diversifying suppliers—is essential for decarbonization, particularly when Europe is at a cost disadvantage9,16,41. International trade supports global renewable deployment goals in several ways. First, a global PV market helps regional capacity development and utilization by distributing global demand towards the least-cost suppliers and available manufacturing capacity. By extension, a global market also facilitates a fast transition by allowing regions immediate access to clean energy technologies even as they strive to build up their local production capacity. Second, global collaboration enables technology transfer, standardizes regulations, and maintains controllable costs (as modeled through assumed technology learning rates)47. These developments also support human capacity growth, as building up local manufacturing capacity requires not only new capacity investments but also technology and skill training. Finally, a global supply chain is more robust against shocks, e.g., suddenly imposed trade barriers, which could derail the global energy transition. Achieving globally diversified supply chains requires both international cooperation and local efforts. One key requirement is common manufacturing and quality standards, such that components have larger global markets; stable and efficient communication channels between producers and purchasers would also help to build a resilient supply chain. Companies and governments must also provide the skills and technology training needed to build up a local workforce. Achieving a global transition requires all regions to increase the size of their workforces, but regional efforts may differ considerably. Our results suggest that European and American policymakers need to increase their workforces by more than 120% each year to meet supply chain targets, while Asian regions require annual job growth below 30% (see Supplementary Table 2). Overall, localizing and maintaining PV supply chains will depend not only on investment, but also on rapidly expanding the available workforce. Global supply chains also feature strong environmental and social trade-offs. For instance, localizing production in developed economies cuts emissions but reduces global jobs and opportunities for developing economies to benefit from the low-carbon industry. Accounting for regional labor and environmental policies could further increase the global social and environmental trade-offs between local and international supply chains. For example, accounting for the EU’s Carbon Border Adjustment Mechanism25 would reduce the cost advantages of importing PV made in carbon-intensive economies and further strengthen the argument for localizing the PV industry. However, a more self-reliant Europe also reduces job opportunities and low-carbon technology development in Southeast Asia. Nonetheless, the trade-offs offered by globalized supply chains would change over time. Global cooperation could help reduce regional differences in labor intensities, and global decarbonization will reduce regional differences in the carbon intensity of manufacturing. Engaging in global cooperation paradoxically also increases regions’ competitive positions as they acquire infrastructure, technological expertise, and a skilled workforce. As a cornerstone of the net-zero emissions energy system, installing solar PV requires a stable and reliable supply, and transparent assessments of costs, carbon emissions, and employment impacts. This research lays the groundwork for understanding the dynamics between producers and consumers and the consequences of price-led supply chain fluctuations on global climate objectives by providing a comprehensive mapping of the global PV supply chain’s transformation and its associated impacts. However, our work has some limitations that ought to be addressed in future work. First, the model simplifies regional strategies to achieve a globally optimized solution, which enables us to explore cost-and job-optimal supply chain transitions under policy scenarios and assess trade-offs. However, it does not capture the full complexity of individual companies’ actions or country-specific policies, and our results should not be seen as projections. Real-world development is strongly influenced by political will1, infrastructure and capacity availability1,9, and investment risk48. Future work could account for these factors by incorporating their consideration into the optimization process or by investigating likely development pathways49,50. Additionally, our research focuses on the regional level and uses yearly data, thereby not capturing within-region trade or sub-yearly trade fluctuations. Future studies with higher temporal and spatial resolution would deepen the understanding of renewable energy technology supply chains, better distribute the supply chain benefits, and identify supply chain links most vulnerable to political or natural disruptions. Finally, resolving data gaps in terms of product demand, trade flows, and quality would provide a fuller picture of the current supply chains and facilitate more nuanced analysis in terms of actors, space, and time (see Data sources). Solving these data gaps requires updating trade accounting standards to more precisely capture the international trade of low-carbon technology. Combined, this future work would help characterize current supply chains and help governments identify the supply chain strategies needed to achieve a fast, low-cost, and resilient low-carbon energy transition. A linear programming optimization model is developed for the solar PV global supply chain analysis. Unlike other possible modelling approaches10,49, our optimization model explicitly seeks out solutions that maximize a specified goal. As such, the results help establish the boundaries on future possibilities rather than establish what is per se likely to occur. Here, the global market is separated into 12 regions, including the top eight producers of solar PV (mainland China – CHN, Vietnam – VNM, United States – USA, Malaysia – MYS, Germany – DEU, Thailand – THA, Korea – KOR, and India – IND)36, three aggregated regions (Rest of Europe – ROE, Rest of Asia – ROA, and Rest of World – ROW), and Switzerland (CHE) as a typical region that only possesses manufacturing capacity of one step in the supply chain (i.e., for one product). In the main text, Europe (EUR) indicates the region that includes Germany, Switzerland, and ROE. Each region is modeled as a node with PV demand, production capacity, and production costs. The supply chain itself considers the production of solar PV’s five main components: polysilicon, ingots, wafers, cells, and modules. Producing each component requires input from lower-value components; namely, producing modules requires cells, producing cells requires wafers, and so on (as shown in Fig. 1a and Supplementary Fig. 1). Each of the 12 regions can fulfill their per-component demand by manufacturing the products themselves, using reserve stocks, or importing components from other regions. Similarly, the regions can export components to other regions. Each region can invest in local manufacturing by paying an upfront capital cost. This cost depends on the amount of new capacity built and the regional cost of expanding capacity. Production costs are based on the actual output and per-unit production cost, but decline with global cumulative PV manufacturing capacity, reflecting the technology’s learning rate7. Inter-regional trade costs include transportation costs, communication costs, and costs to comply with foreign regulations51. These costs are modeled using a unified method that estimates trade costs and their effects on economic agents43,51. Decision variables in the model include capacity expansion, production levels, trade flows, and inventory management. For the product p in region i on year T, capacity investment (({{mathrm{CapInv}}}_{i}^{p,T})) determines how much new production capacity to build in each region and time period. This decision is informed by lead times, historical expansion trends, and projected demand. Capacity (({{mathrm{Cap}}}_{i}^{p,T})) reflects the cumulative effect of past investments, accounting for delays before new facilities come online. Production (({y}_{i}^{p,T})) is determined to meet demand while respecting capacity limits and job creation targets. Trade flows (({x}_{i,j}^{p,T})) allocate products across regions (from i to j), balancing cost-effective shipping with demand fulfillment. Inventory (({{mathrm{Stock}}}_{i}^{p,T})) acts as a buffer, smoothing supply-demand gaps across time periods. The supply chain is calibrated using five types of parameters. Cost parameters include export costs (({{mathrm{EC}}}_{i}^{p,T})), import costs (({{mathrm{IC}}}_{i}^{p,T})), production costs (({{mathrm{ProdCost}}}_{i}^{p,T})), capacity expansion expenses (({{mathrm{CapCost}}}_{i}^{p})), and stock costs (({{mathrm{StockCost}}}_{i}^{p,T})). Subsidies (({{mathrm{Subsidy}}}_{i}^{p,T})) is enabled in some scenarios to reduce industrial producers’ production costs. These cost parameters directly influence the affordability of each purchase and production decision. Employment factors (({{mathrm{Job}}}_{i}^{p})) tie production levels to job creation and are measured in full-time jobs per unit production of p. Technical constraints, like lead times (({{Tl}}_{i}^{p})) and historical expansion ceilings (({{mathrm{CapLim}}}^{p})), ensure solutions are actionable. Material conversion factors capture demand relationships. Specifically, the conversion factors (({{mathrm{Conversion}}}^{p1,p2})) capture the ratio of material (p1) needed to produce product (p2) and are used for the material demand (({{mathrm{Demand}}}_{i}^{p1,T})) projections. For example, polysilicon demand can be calculated by multiplying the amount needed to produce one unit of ingot by the total ingot production. Emission factors for production (({{mathrm{ProdEF}}}_{i}^{p})) and trade (({{mathrm{TradeEF}}}_{i,j}^{p})), are applied to the production4 and trade52 of each component. We focus on one example of social and environmental outputs—jobs and carbon emissions—and their variation between locations. Other impacts of real-world production, such as labor standards and pollution control, are beyond the scope of this study. We consider two types of objectives, minimizing costs and maximizing jobs, which are considered in bi-objective optimizations (Eq. (1)). First, we optimize the global total costs (Eq. (2)), where the model is tasked with finding the optimal solution by varying trade flows (({x}_{i,j}^{p,T})) and production (({y}_{i}^{p,T})). The results from this optimization (Eq. (2)) provide the reference values for cumulative costs (({{mathrm{Costs}}}_{0})) and associated job creation (({{mathrm{Jobs}}}_{0})) used in Eq. (1). We then take the reference values for the bi-objective optimization (calculated by Eqs. (1–3)) with weights from 0 to 1 by a step of 0.1 each, which led to 59 unique possible supply chains. Here, normalized cost and job metrics are weighed (with ({w}_{a}) for cost and ({w}_{b}) for jobs), allowing decision-makers to explore trade-offs. For instance, a higher ({w}_{b}) would favor job-rich but potentially costlier production strategies. To reconcile these competing objectives, the model combines them into a single bi-objective function: In the baseline case, the maximum job creation is found for all 12 regions. In the Maximize European Jobs (MEJ) policy scenarios, only the jobs created in Europe are considered within ({Z}_{b}), i.e., (iin {mathrm{European}}{mathrm{regions}}). The first objective, affordability (({Z}_{a})), aims to minimize total system costs (Eq. (2)). Costs include export and import expenses for inter-region trade (({{EC}}_{i}^{p,T}) and ({{IC}}_{j}^{p,T})), production costs to industry, inventory holding costs, and capital expenditures for expanding manufacturing capacity. The second objective, job creation (({Z}_{b})), aims to maximize employment by prioritizing production activities that generate the most jobs per unit output (Eq. (3)). The number of full-time jobs created depends on production levels (({y}_{i}^{p,T})) and the region and product-specific employment factor (({{mathrm{Job}}}_{i}^{p}))4. Finally, we select all scenarios with the supply chains costs no more than 110% of the lowest-cost solution53. By considering supply chains that are nearly cost-optimal, our analysis reveals the potential trade-offs between economic costs and job creation, thereby facilitating policy discussions. The model enforces several critical constraints to ensure realistic and feasible solutions. Supply-demand balance is maintained through two key equations: one linking intermediate product demand to production via conversion factors (({{mathrm{Conversion}}}^{p1,p2}); Eq. (4)) and another ensuring final demand is met through local production ((,{y}_{j}^{,p,T})), imports (({x}_{i,j}^{p,T})), exports (({x}_{j,k}^{p,T})), and available stock (({{mathrm{Stock}}}_{j}^{p,T}); Eq. (5)). Capacity expansion follows a lead time-delayed process: investments announced at time (T) only become operational after the product-specific lead time (({{Tl}}_{i}^{p}); Eq. (6)). Expansions are capped at 95% of historical rates (({{mathrm{CapLim}}}^{p}); Eq. (7)) to reflect challenges of scaling up new technologies54. Production and trade limits prevent infeasible outcomes. Production must be positive and cannot exceed available capacity (Eq. (8)) and regional exports cannot exceed its production (Eq. (9)). The analysis considers two main objectives and three types of industry support policies (Table 1), which we investigate in a set of scenarios. The main policy goals are minimizing system cost and maximizing job creation, which we consider in two main scenarios. The baseline scenario optimizes the global supply chain to minimize industry costs and maximize job creation. The second main scenario is the Maximize European Jobs scenario, where we shift the emphasis from global to European job creation while still aiming to minimize global costs as the secondary objective. In addition to the two main scenarios, we consider three sets of policy cases. The first policy case is “limits on trade”. We consider two trade policy cases: the first case is free trade (no trade restrictions), and the second case is Europe stops importing all the components directly from China. These trade scenarios respectively mimic the free trade promoted by the World Trade Organization and the trade defense driven by geopolitical tensions. The trade disruptions begin in 2024 and limit the trade of product (p) between a given trade pair, region (i) and region j (i ≠ j) on year T (T >2024) by constraining the trade flow to zero, as per Eq. (10). The second policy case is “manufacturing subsidies”. Specifically, we consider subsidies to decrease production costs in Europe and stimulate domestic module production. The subsidies are worth 20% of production costs, which is approximately the relative cost variance between Europe-made and imported PV from literature4,15. We do not consider subsidies for the other, lower-value PV components (i.e., polysilicon, ingots, wafers, and cells). The competing subsidies are valid starting from 2024, which means that the government in region i subsidizes the production of PV modules (T >2023) to producers. The final policy case is “European self-sufficiency targets for 2030”. Here, self-sufficiency is defined as the share of local production relative to local demand (Eq. (11)), with a maximum value of 100% when local production exceeds local demand. This scenario mimics Europe’s self-production targets proposed in European Net-Zero Industry Act26. We implement linear constraints that gradually increase from 0% to the target self-sufficiency between 2025 and 2030 (Eq. (12)). For example, if the European self-sufficiency target is 40%, the constraints will increase incrementally by 8% each year: 0% in 2025, 8% in 2026, 16% in 2027, 24% in 2028, 32% in 2029, and 40% in 2030. This policy case is modeled for scenarios with maximum job creation in Europe as an objective, since these two goals are linked in actual policy documents26. The model results about self-sufficiency in Europe are shown as Supplementary Fig. 15. We conduct a Monte Carlo simulation to assess the impacts of uncertain demand and costs on manufacturing capacity, regional global production shares, and cumulative costs needed to achieve self-sufficiency in Europe. We assume that the demand for PV modules across 12 regions varies independently between 50 and 150% of the baseline data, following a uniform distribution. Similarly, cost data, including capacity expansion, production, trade, and stock costs, vary uniformly from 50 to 150% of the model’s baseline assumptions. After 100 random samples of uncertain demand and costs, we executed the bi-objective optimization model with 51 unique weights (down from 59 after rounding and redundancy checks, ensuring efficient but comprehensive coverage of the cost-employment trade-off space) across the four main scenarios—baseline, maximize European jobs, subsidies in Europe, and trade barrier between Europe and China. Then, we compare all scenarios with the supply chain costs no more than 110% of the lowest-cost solution. The results of sensitivity in regions’ market shares and manufacturing capacity are shown in Supplemental Information (Supplementary Figs. 11–13). The analysis relies on a combination of open privately held data relating to current PV production patterns and forecast trends. We next present the data sources in the order in which they were integrated within the analysis. First, existing demand and production capacity by country are collected from the BloombergNEF database36, while future demand estimates correspond to the BloombergNEF high-demand net-zero scenario. The model’s capacity expansion constraint in the model is the 95-percentile of the historical (2008–2020) national ratio between annual announced capacity and annual commissioned capacity based on this database. This information is privately held but can be accessed through the purchase of a user license. Next, capacity expansion costs by region are collected from research by the National Renewable Energy Laboratory42 and The European House55. PV production costs are collected from reports from the Department of Energy, Solar Energy Technologies Office of the United States42,56,57, the International Energy Agency (IEA)4, and the International Renewable Energy Agency’s (IRENA)58. The production costs data covered major producers, including China, the United States, the Association of Southeast Asian Nations (ASEAN), India, Korea, and Europe. Future production costs are estimated assuming a decline with cumulative capacity and learning rates collected from literature7 (details in Supplementary Data 1). Trade cost indices by country, year, and sector, including export cost index (ECI) and import cost index (ICI), are collected from WTO research43,51. The trade cost index includes transportation costs, trade policy barriers, costs to comply with foreign regulations, communication costs, transaction costs, or information costs43. The COVID pandemic increased global trade costs in multiple ways: transport and travel costs have witnessed an increase due to the global logistics crisis, including port congestion, increasing shipping times, and the scarcity of containers, increased border controls, as well as the insufficient resilience of trade policies59,60. Therefore, we assume that the import and export cost indices in 2022 increased relative to 2018 levels, following existing literature59. Conversion factors between segments in PV supply chain, stocks of modules, lead time for manufacturing investment by region and product, and job creation of the manufacturing by product are collected from the Special Report for Solar PV Global Supply Chain from IEA4. These values are based on production efficiencies from the year 2020–2021 (details in Supplementary Data 1). Finally, we estimate the manufacturing impact by considering carbon dioxide emission factors collected from the IEA Special Report for Solar PV Global Supply Chain4. Emission factors of trade by sector are calculated based on the carbon emissions data from CEADs datasets61, IEA greenhouse gas emissions data62, and the EMERGING global multi-regional input-output model63,64. Our analysis relies on single, secondary sources for demand, production, cost, and conversion factors. Our main sources include BloombergNEF, IEA reports, and WTO, which provide relatively reliable and broad coverage of global regions in the PV supply chain. Relying on secondary sources always risks introducing bias linked to the market interests of the data providers and a focus on dominant technologies and regions. However, relying on secondary sources is a necessary compromise given the limited availability of primary data. While our main sources are globally trusted institutions, their data and projections may differ in definition and scope from alternative sources, which can contribute to discrepancies between our model results and those featured in other reports. Mitigating these risks would require a comparative analysis between all input data, an activity that remains outside the scope of the present work. The data used to develop the model in this study has been made available in Supplementary Data 1 as an electronic spreadsheet. 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Earth Syst. Sci. Data15, 1317–1328 (2023). ArticleADS Google Scholar International Energy Agency. CO2 emissions from fuel combustion (detailed estimates). (2023). Li, S. et al. The heterogeneous driving forces behind carbon emissions change in 30 selective emerging economies. Patterns4, 100760 (2023). ArticlePubMedPubMed Central Google Scholar Huo, J. et al. Full-scale, near real-time multi-regional input–output table for the global emerging economies (EMERGING). J. Ind. Ecol.26, 1218–1232 (2022). Article Google Scholar Cui, C. Potential transformation of the solar PV global supply chain and resulting impacts. Zenodohttps://doi.org/10.5281/zenodo.14260389 (2024). Download references We want to thank Paolo Gabrielli and the Reliability and Risk Engineering lab for discussions on this topic. C.C. and G.S. are part of the SPEED2ZERO, a Joint Initiative co-financed by the ETH Board. Open access funding provided by Swiss Federal Institute of Technology Zurich. Institute of Energy and Process Engineering, ETH Zurich, 8092, Zurich, Switzerland Can Cui, Katherine Emma Lonergan & Giovanni Sansavini Reliability and Risk Engineering, ETH Zurich, 8092, Zurich, Switzerland Can Cui, Katherine Emma Lonergan & Giovanni Sansavini Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar C.C. and G.S. designed the research. C.C. collected data, developed the model, and conducted the analyses with input from K.E.L. and G.S.. C.C. wrote the manuscript with input from K.E.L. and G.S. Correspondence to Giovanni Sansavini. The authors declare no competing interests. Nature Communications thanks Tao Cao, Fabian Aponte, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Cui, C., Lonergan, K.E. & Sansavini, G. Policy-driven transformation of global solar PV supply chains and resulting impacts. Nat Commun16, 6742 (2025). https://doi.org/10.1038/s41467-025-61979-5 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41467-025-61979-5 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Fraunhofer Institute for Solar Energy Systems ISE has opened a dedicated lab in Freiburg, Germany, to scale perovskite-silicon tandem cell designs to large wafer formats using industry-standard processes. Image: pv magazine Fraunhofer ISE has opened a new lab in Freiburg, Germany, to move perovskite‑silicon tandem PV closer to industrial production. The institute has already exceeded 33% efficiency on a laboratory scale using a hybrid manufacturing route combining vacuum and wet chemical deposition. Its new Pero-Si-SCALE lab extends that work by scaling the technology to wafer formats of 210 mm x 210 mm using high-throughput, industry-standard processes. It aims to give German and European PV manufacturers access to independent R&D infrastructure for perovskite-silicon tandem cells and modules. Adding a perovskite layer just 500 nanometers thick to a conventional silicon cell raises the theoretical efficiency limit from 29.4% to 43.3%. Fraunhofer ISE said its hybrid route retains compatibility with standard textured silicon cells already in industrial use, achieving a higher energy yield from finished tandem modules. Prof. Dr. Stefan Glunz, head of the Photovoltaics Division at Fraunhofer ISE, said tandem solar cells are key to achieving higher efficiency, enabling more solar energy in a smaller area with less material usage. “Photovoltaics is far from being ‘fully researched,’” he said. The Pero-Si-SCALE builds on Fraunhofer ISE’s Photovoltaic Technology Evaluation Center (PV-TEC), a 20-year-old industry-oriented silicon PV platform. PV-TEC will supply optimized silicon bottom cells to the new lab and maintain continuity with current production processes, said Priv.-Doz. Dr. Ralf Preu, also head of the Photovoltaics Division. Von Ardenne GmbH, whose vacuum coating systems are installed at the lab, said its equipment is being used in the new facility. Fraunhofer ISE Director Andreas Bett said funding from the German Federal Ministry for Economic Affairs and Energy (BMWE) has enabled Fraunhofer ISE to maintain an internationally competitive research position that supports local industry partners. Perovskite-silicon tandem cells have been advancing rapidly on efficiency. Fraunhofer ISE recently recorded 30.6% for a perovskite-silicon tandem built on an industry-standard tunnel oxide passivated contact (TOPCon) bottom cell, while JinkoSolar pushed the benchmark to 34.76% using n-type TOPCon wafers. The Pero-Si-SCALE opening also connects to broader European manufacturing ambitions – a joint SolarPower Europe and Fraunhofer ISE report found that targeted policy intervention is essential to closing the cost gap with Chinese producers and reaching the European Union’s 30 GW annual manufacturing target by 2030. At the research infrastructure level, the EU-funded Laperitivo project, which includes Fraunhofer ISE among its partners, is separately advancing large-area perovskite module pilot production through February 2028. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Brian Publicover Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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onurdongel/iStock via Getty Images I previously rated First Solar, Inc. (FSLR) as a Hold in February 2026, attributed to the cooling demand and the mixed booking trends. In this article, I shall discuss why FSLR remains a Hold here, pending further This article was written by Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
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Researchers in Brazil found that retrofitting commercial PV panels into PVT systems can boost total efficiency to around 46–50%, but added thermal resistance limits heat extraction and slightly reduces electrical performance. Their experiments showed performance is capped by interface quality and system design, requiring improved heat extraction capacity. The PV module retrofitted with thermosyphons Image: Federal University of Technology-Parana, Energy Conversion and Management, CC BY 4.0 Researchers at the Federal University of Paraná (UFPR) in Brazil have assessed the potential for retrofitting commercial photovoltaic modules into photovoltaic-thermal (PVT) panels, identifying practical constraints that currently limit the approach’s technical and economic viability. The scientists explained that previous research focused on custom-designed PV/T collectors with optimized integration, rather than standard commercial PV modules, with only a few studies having explored retrofitting existing panels. The literature indicates that effective performance depends not only on component optimization but also on overcoming inherent thermal limitations of commercial modules. “Our work provides a design-oriented, experimental assessment of PVT retrofitting, identifying the key structural and operational parameters that govern system performance and establishing minimum requirements for heat extraction capacity to achieve effective thermal regulation under real operating conditions,” the academics stressed, noting that their analysis focused on PVT modules using heat pipes and thermosyphons, which stand out for their high efficiency, passive operation, and ability to transfer heat via phase-change mechanisms with minimal temperature gradients. Thermosyphons, in particular, rely on gravity-driven circulation between evaporator and condenser sections, enabling effective heat removal without capillary structures. Their performance depends on factors such as filling ratio, inclination angle, working fluid, and system geometry. The experimental setup consisted of a standard 60 W polycrystalline photovoltaic panel with four rear-mounted thermosyphons under real outdoor conditions. The thermosyphons, made of copper and filled with distilled water, were designed with distinct evaporator, adiabatic, and condenser sections to enable passive heat transfer. Aluminum absorber bars were used to ensure thermal contact between the panel and the thermosyphons, while the condenser sections were integrated into a water-cooled manifold acting as a heat sink. A closed-loop circulation system, including a thermal reservoir, pump, expansion vessel, and flow meter, was used to manage water flow and heat recovery. The setup allowed continuous recirculation and storage of heated water for further use, according to the research team. The PVT system was installed alongside a reference PV panel to enable direct performance comparison under identical environmental conditions. Both panels were mounted at a 25° tilt and oriented north to maximize solar exposure. Temperature measurements were taken using thermocouples placed on the panel surface and within the water circuit, while solar irradiance was recorded with a pyranometer. An Arduino-based I–V curve tracer was developed to measure electrical performance, including voltage, current, and power output. Image: Federal University of Technology-Parana, Energy Conversion and Management, CC BY 4.0 The two systems were experimentally evaluated under real outdoor conditions, using different water flow rates and weather scenarios. Tests were conducted on sunny and cloudy days at 6.5 L/min and on a sunny day at 1.5 L/min, with controlled inlet water temperatures to ensure consistency. The results showed that the PVT module consistently operated at higher temperatures than the reference panel due to added thermal resistance and reduced natural convection at the rear, which resulted in a slight electrical efficiency drop, highlighting a thermal penalty associated with the retrofit design. However, the PVT system achieved significantly higher total energy efficiency, reaching about 45.75% under sunny conditions, driven by effective heat recovery. The system also exhibited strong thermal inertia, smoothing temperature fluctuations and enabling continued heat transfer even when solar irradiance decreased. On cloudy days, this thermal inertia improved performance further, increasing total efficiency to over 50% due to delayed heat release from stored energy. However, heat extraction rates showed a plateau, indicating an upper limit imposed by thermal resistances and thermosyphon capacity. Flow rate, meanwhile proved to be a critical parameter. At 6.5 L/min, efficient cooling maintained better electrical and thermal performance, while at 1.5 L/min, reduced convection led to overheating, significantly lowering electrical efficiency, down to 10.93%, and overall efficiency, down to 19.02%. Further findings confirmed that increasing flow rate alone cannot fully overcome the limitations, as heat extraction is ultimately capped by interface quality and system design. Overall, the results demonstrate that PVT retrofit performance depends on balancing heat extraction capacity with inherent thermal resistances. They also highlight the existence of a maximum heat removal threshold and the need for optimized design parameters, such as flow rate and thermosyphon configuration. “The current four-thermosyphon configuration is undersized for achieving thermal parity with a standard PV module,” the academics emphasized. “An increase of approximately 60% in heat extraction capacity is required, which can be achieved by increasing the number of thermosyphons (to six or seven) or enhancing the effective thermal contact area. Despite these limitations, the system demonstrated stable operation and consistent heat recovery under variable environmental conditions, supporting its applicability for low-grade thermal energy utilization.” Looking forward, the scientists are planning to prioritize enhancing heat extraction capacity by optimizing thermosyphon design, spatial configuration, and thermal interface quality, as well as exploring alternative working fluids and geometries tailored to retrofit constraints. Moreover, they want to address long-term performance, system integration in real applications such as building-integrated PV, and comprehensive economic assessments to evaluate cost-effectiveness and scalability. Their findings were presented in the paper “Experimental assessment of thermal performance limits in a thermosyphon-based PV/T retrofit of a commercial photovoltaic panel,” published in Energy Conversion and Management. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Emiliano Bellini Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement volume 7, Article number: 325 (2026) Cite this article 3220 Accesses 10 Altmetric Metrics details Photovoltaic energy is expected to lead renewable energy growth, but rising solar energy penetration increases vulnerability to climate-driven intermittency. Here, we examine how the El Niño-Southern Oscillation, the dominant source of seasonal-to-interannual climate variability, affects photovoltaic power output. Using four decades of reanalysis data, we show that El Niño events reduce surface solar irradiance, causing sustained solar energy deficits in regions with growing solar energy penetration, including California, the southern Atacama Desert, the Chaco Basin, the Middle East, and East China. These impacts are especially pronounced during rare Super El Niño events, of which only three have occurred since the early 1980s. Our analysis indicates that future Super El Niño events could significantly lower photovoltaic generation, increase reliance on fossil fuel backup, and temporarily raise carbon dioxide emissions by tens of millions of tons. Photovoltaic (PV) power remains the primary driver of the renewable energy transition, accounting for over 75% of new renewable capacity installed in 2023 and nearly 60% of the electricity generated from newly added renewables worldwide1. Global cumulative PV capacity rose from 1.1 TW in 2022 to approximately 1.5 TW in 20232. Rapid PV expansion in key regions (Fig. 1a, b) has pushed PV penetration to around 10% in China and the European Union (EU)1. While PV meets over 8% of global electricity demand, solar power has supplied 100% of electricity for several hours in parts of Australia and Chile1. PV is expected to remain the primary driver of renewable energy growth. Under a low-emission scenario, global PV generation could increase 60-fold by mid-century3. a Cumulative installed photovoltaic (PV) capacity (upper panel) and the cumulative number of PV power plants with a capacity >100 MW (lower panel) in China, the EU, and the USA. China’s PV installations surged in 2023, reaching a record annual growth of ~215 GW—over 60% of new global capacity built that year—and bringing China’s cumulative PV capacity to more than 600 GW. The European Union (EU) also installed a robust ~50 GW in 2023, and the USA installed >30 GW that year. Collectively, these regions (China, the EU, and the USA) accounted for two-thirds of global PV generation in 2023 and are expected to drive PV expansion in the coming decades. b Total utility-scale photovoltaic (PV) power plants existing in 2024. Each dot represents a power plant with a capacity >20 MW. China, the EU, the USA, Japan, and India exhibit the largest density of utility-scale PV power plants. The key Niño 3.4 region (5°N–5°S, 170°W–120°W) is also highlighted in the plot. c Annual mean of the photovoltaic potential (PVPOT) computed over the period 1982–2024. Data on PV capacity in plot (a) (upper panel) are sourced from the International Renewable Energy Agency (IRENA) renewable capacity statistics2, available at https://www.irena.org/Publications/2024/Mar/Renewable-capacity-statistics-2024. Data on PV power plants in plots (a) (lower panel) and b are sourced from the Global Energy Monitor75, available at https://globalenergymonitor.org/projects/global-solar-power-tracker/. PVPOT data in plot c were calculated using the ERA5 reanalysis dataset77, available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. Plots were generated using Python’s Matplotlib library, version 3.4.3, available at https://matplotlib.org/3.4.3/contents.html. As PV systems reach higher penetration levels in more countries, managing PV intermittency becomes increasingly challenging. These fluctuations in PV power output complicate grid balancing by causing mismatches between resource availability and electricity demand, thereby impacting system reliability4,5. While diurnal and seasonal cycles drive intermittency on timescales from hours to months5,6, weather and climate variability influence PV power outputs on timescales from seconds to years7. By altering surface shortwave (SW) irradiance, both weather and climate variability play a crucial role in PV intermittency8,9,10. Climate change is expected to exacerbate PV intermittency in some regions through extreme weather and enhanced climate variability11,12. Climate-induced deficits in PV power output, often called energy droughts, can last from days to months12,13,14,15,16,17 and may trigger forced oscillations, thermal runaway, frequency and voltage disturbances, and heightened grid instability risks7. While recent research has focused on short-term (hour-to-day) solar resource variability18,19, less attention has been given to the broader impacts of seasonal-to-interannual variability driven by large-scale climate modes. Enhanced climate variability is not always man-made. Natural climate modes, such as the El Niño–Southern Oscillation (ENSO), can also amplify PV intermittency by modulating regional climate patterns worldwide20. ENSO is a fluctuation in sea surface temperature (SST) and atmospheric pressure across the equatorial Pacific21. During El Niño, weakened trade winds cause warm water to accumulate in the tropical Pacific. In contrast, La Niña strengthens trade winds, increasing upwelling and bringing cold, nutrient-rich water to the surface21. Through atmospheric teleconnections, ENSO phases influence seasonal-to-interannual solar resource variability in South America22,23, Australia24, Africa25, Texas26, California15,27, and even Europe, where ENSO has been linked to fluctuations in the EU renewable energy stock market28. While low PV penetration has so far limited ENSO-driven disruptions to the energy grid, this situation is poised to change rapidly. As PV systems reach higher penetration levels, electricity grids are becoming more vulnerable to disruptions from El Niño and La Niña events, especially during rare Super El Niño occurrences, only three of which have been recorded since the early 1980s29. Super El Niño events are typically defined by SST anomalies of at least 2 °C in the Niño 3.4 Region (5°N–5°S, 170°–120°W, Fig. 1b)30,31, as identified by the National Oceanic and Atmospheric Administration (NOAA)32. While Super El Niño events are well known for their severe socioeconomic consequences33,34,35,36, their impact on increasingly PV-dependent energy grids remains largely unexamined. During the last Super El Niño in 2015–2016, global installed PV capacity was nearly ten times lower than today. Here, we used reanalysis datasets from 1982 to 2024 to reconstruct the PV power response to El Niño and La Niña events (i.e., positive and negative SST anomalies in the equatorial Pacific, respectively). As a proxy for PV power output, we used the PV potential (PVPOT), defined as the ratio of a PV module’s power output under standard test conditions to its actual output in the field12,37,38,39,40,41. PVPOT primarily depends on surface SW irradiance, which is influenced by aerosols42,43,44 and cloud cover45,46. PVPOT is also affected by air temperature (cooler conditions generally improve PV cell performance47) and surface wind speed (stronger airflow enhances module cooling48). In spite of these other factors, SW irradiance remains the dominant factor, and that explains why annual mean PVPOT (Fig. 1c) closely mirrors annual mean surface SW irradiance (Fig. S1). Our findings show that El Niño (La Niña) negatively (positively) affects surface solar irradiance, leading to persistent PV energy deficits (surpluses) in regions with increasing PV penetration, including parts of China, the USA, and South America. These results underscore the importance of accounting for ENSO-driven variability when developing climate-resilient PV-based grids. El Niño and La Niña events significantly affect factors that influence PV cell performance, including solar irradiance, air temperature, and surface wind speed. While NOAA’s Climate Prediction Center (CPC) monitors several equatorial Pacific regions, including Niño 3 (eastern Pacific), Niño 4 (central Pacific), and Niño 1 + 2 (off the Peruvian coast), El Niño and La Niña events are defined based on SST anomalies in the Niño 3.4 region32. During El Niño events, SST in the tropical central Pacific (specifically, the Niño 3.4 region) can rise by several degrees Celsius, even exceeding 2 °C during Super El Niño events (Fig. S2). This warming is accompanied by an annual average temperature increase of up to 1 °C over the basin’s warmest waters (Fig. 2a, upper panel) and increased cloud cover. During Super El Niño events, annual average surface SW irradiance over the Niño 3.4 region can decrease by more than 15 W m−2 (Fig. 2a, lower panel). During La Niña, the Niño 3.4 region cools, and the basin’s warmest waters shift closer to Indonesia and the western Pacific. This redistribution disrupts atmospheric circulation21, altering climate patterns, including air temperature (Fig. 2b), surface wind speed (Fig. S3), and solar irradiance (Fig. 2c) across many regions. a Surface air temperature (upper panel) and surface shortwave (SW) irradiance (lower panel) in the Niño 3.4 Region relative to the 1982–2024 mean. The red-shaded columns highlight the Super El Niño events of 1982–1983, 1997–1998, and 2015–2016. Pearson correlation between the 12-month average sea surface temperature (SST) anomalies in the Niño 3.4 region and the corresponding 12-month average of the: b surface air temperature, and c surface irradiance. Data for the period 1982–2024 were analyzed. Stippling in b and c indicates statistical significance. The 12-month averages of surface air temperature, surface irradiance, and SST in plots b and c are calculated from September to August of the following year. Surface irradiance and surface temperature data come from the ERA5 reanalysis77 available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. SST anomalies in the Niño Regions come from the https://www.cpc.ncep.noaa.gov/data/indices/wksst9120.for74. Plots were generated using Python’s Matplotlib library (version 3.4.3), available at https://matplotlib.org/3.4.3/contents.html. The interannual variability of air temperature, surface SW irradiance, and surface wind speed around the world is influenced by year-to-year changes in SST in the tropical central Pacific. We found a strong correlation between SST anomalies in the Niño 3.4 Region and the air temperature across large areas, including tropical South America (particularly across the Amazon Basin), southern Africa, Australia, and Southeast Asia (Fig. 2b). This correlation indicates that El Niño events are associated with spikes in air temperature in these regions, while La Niña events generally bring cooler conditions. We also found a significant anticorrelation between SST anomalies in the Niño 3.4 Region and the surface SW irradiance across large parts of southern South America (including the southern Atacama Desert and the Chaco Basin), North America, the Middle East, and the Sahara (Fig. 2c). This suggests that La Niña events typically result in sunnier conditions, whereas El Niño events are linked to substantial decreases in surface SW irradiance in these regions. As shown in Fig. S3, anomalies in SST in the Niño 3.4 Region also significantly affect surface wind speed over vast areas of the planet. The impacts of El Niño and La Niña events are typically strongest during December–January–February (DJF). El Niño usually peaks in December, explaining the strong correlation between DJF SST anomalies in the Niño 3.4 Region and DJF air temperatures across much of the globe in both hemispheres. While the effects are particularly pronounced in tropical South America, most regions also experience warmer conditions during the austral summer (DJF) under El Niño events (Fig. S4). In tropical South America, the correlation between SST anomalies and air temperatures remains strong during March–April–May (MAM) but weakens in other seasons (Fig. S4). Similarly, correlations between DJF SST anomalies in the Niño 3.4 Region and DJF SW irradiance are significant in most of the Amazon Basin, southern Africa, western Australia, and Southeast Asia (Fig. S5). However, El Niño’s effects on SW irradiance show more regional variation than its effects on air temperature. For instance, during DJF, some areas (e.g., the Amazon Basin) experience sunnier conditions, while others (e.g., the Horn of Africa) become cloudier (Fig. S5d). The complexity of the climate system is further underlined by the fact that correlations can weaken or even reverse during different seasons. For example, in the Horn of Africa, El Niño events can lead to cloudier conditions during DJF (Fig. S5d) and sunnier conditions during boreal summer (June–July–August, JJA) (Fig. S5b). The atmospheric response to ENSO events is driven by circulation anomalies that ultimately modulate temperature (Fig. 2b), cloudiness and surface solar radiation (Fig. 2c). During El Niño, the Walker circulation weakens, generating an anomalous anticyclone over the western North Pacific that enhances southerly moisture transport into East China and strengthens subsidence-induced cloud formation along its western flank49. The combined effect is a marked increase in low-level cloudiness and a corresponding reduction in surface solar irradiance over East China. El Niño conditions also shift the Pacific jet stream southward and intensify subtropical westerlies, steering more frequent extratropical cyclones toward California21. This increases both deep and low-level cloud cover, especially in boreal winter, reducing the occurrence of clear-sky conditions. A similar mechanism operates along the southeast Pacific margin: weakening of the South Pacific subtropical anticyclone, combined with enhanced zonal flow, leads to increased storm-track activity over southern South America33. These changes produce cloudier and wetter conditions during winter and spring, directly suppressing surface solar radiation. While El Niño also enhances cloudiness and storm activity over the Levant and the Middle East50, convection over the western Pacific weakens substantially, producing anomalous subsidence over eastern Australia51. This downward motion suppresses cloud formation, particularly reducing deep convective clouds associated with the Australian monsoon and subtropical rainfall systems. The interannual variability of PV potential exhibits the signature of El Niño and La Niña events. In regions that include northern California and southern Brazil, PVPOT values have dropped substantially during El Niño events, particularly during the Super El Niño events of 1982–1983, 1997–1998, and 2015–2016. These drops are most noticeable during the austral summer (DJF), as the largest anomalies associated with El Niño events occur around the end of the year and the beginning of the following year (Fig. 3a). In northern California, DJF PVPOT values dropped by more than 10% during Super El Niño events (Fig. 3a, upper panel). In contrast, northern California experienced sharp increases in PVPOT during strong La Niña events such as those in 1984–1985, 1988–1989, 2011–2012, and 2021–2022 (Fig. 3a, upper panel). Similar, but less pronounced, drops and spikes in DJF PVPOT values occurred in southern Brazil (Fig. 3a, lower panel). The impacts of El Niño and La Niña events on PVPOT are also relevant in many other regions. a DJF PV potential (PVPOT) relative to the 1982–2024 mean in northern California (upper panel) and southern Brazil (lower panel). The red-shaded columns highlight the Super El Niño events of 1982–1983, 1997–1998, and 2015–2016. Southern Brazil includes the states of Rio Grande do Sul, Santa Catarina, Paraná, São Paulo, and Rio de Janeiro. Northern California includes the counties of Del Norte, Humboldt, Siskiyou, Modoc, Trinity, Shasta, Lassen, Mendocino, Tehama, Plumas, Lake, and Sierra. Pearson correlation between the 12-month average PV potential (PVPOT) and the corresponding 12-month average sea surface temperature (SST) anomalies in: b the Niño 3.4 region (5°N–5°S, 170°W–120°W), and c the Niño 1 + 2 region (0–10°S, 90°W–80°W). Data for the period 1982–2024 were analyzed. Stippling in plots b and c indicates statistical significance. The 12-month averages of PVPOT and SST in plots b and c are calculated from September to August of the following year. The PV potential (PVPOT) was calculated using data from the ERA5 reanalysis77 available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. SST anomalies in the Niño Regions come from the Climate Prediction Center (CPC), part of the National Oceanic and Atmospheric Administration (NOAA), available at https://www.cpc.ncep.noaa.gov/data/indices/wksst9120.for74. Plots were generated using Python’s Matplotlib library (version 3.4.3), available at https://matplotlib.org/3.4.3/contents.html. Year-to-year changes in PV potential are influenced by the interannual variability of tropical Pacific SST. Across areas with high solar potential, there are significant correlations (either positive or negative) between SST anomalies in the Niño 3.4 region and PVPOT values (Fig. 3b). Regions with positive correlations are expected to see increases in PVPOT during El Niño events, likely due to sunnier conditions resulting from significant reductions in cloud cover. This is the case of southern Africa, eastern Australia, Southeast Asia, and the Amazon Basin (Fig. 3b). These increases are expected despite the fact that these regions will also experience significant spikes in air temperature during El Niño events that negatively affect PV cell performance (Fig. 2b). The strong positive correlations in the case of southern Africa, eastern Australia, Southeast Asia, and the Amazon Basin (Fig. 3b) highlight that the reduction in cloudiness and the resulting increase in solar irradiance during El Niño events have a much stronger influence on PVPOT than the temperature rise. Regions with negative correlations are expected to see increases in PVPOT during La Niña events. This is the case of southern Brazil, the Middle East, and southern and western USA (Fig. 3b). Conversely, these same regions are likely to experience decreases in PVPOT during El Niño events. The PV potential is influenced by changes in SST in various Niño regions, including the Niño 1 + 2 region (off the western coast of Peru), the Niño 3 region (eastern Pacific), and the Niño 4 region (central Pacific). In most countries, correlations between SST anomalies in these regions and PVPOT values (Fig. 3c and S6a, b) are slightly weaker compared to the Niño 3.4 region (Fig. 3b). A notable exception is East China, where the correlation between SST anomalies in the Niño 1 + 2 region PVPOT values (Fig. 3c) becomes slightly stronger compared to the Niño 3.4 region (Fig. 3b). Nevertheless, regardless of which Niño region is used in the comparison, the areas showing the strongest correlations (whether positive or negative) are located in North and South America, particularly California, the southern Atacama Desert, central Chile, the Amazon Basin, and the Chaco region. As shown in Fig. 3c, the Amazon Basin is expected to see increases in PVPOT during warming events in the Niño 1 + 2 region, typically associated with eastern Pacific (EP) El Niño events33. Conversely, California, the southern Atacama Desert, Central Chile, and the Chaco Basin (including southern Brazil and northern Argentina) are expected to experience declines in PVPOT during EP El Niño events. While SST anomalies in the Niño regions are often coupled (Fig. S2), fluctuations in the Niño 1 + 2 region tend to be stronger and more frequent than those in the other Niño regions52. For instance, the 2023–2024 El Niño was a strong event, particularly in the Niño 1 + 2 region53 (Fig. S2). However, the associated atmospheric response (specifically, changes in atmospheric pressure) was substantially weaker than those observed during the canonical Super El Niño events of 1982–1983, 1997–1998, and 2015–2016. The concurrent strong warming in the Atlantic and Indian Oceans was crucial in dampening the atmospheric response during the 2023 El Niño54, clearly distinguishing its impacts from those of the other three strong El Niño events. Nevertheless, the strong correlations in (Fig. 3c and S6a, b) underscore the influence of ENSO-driven fluctuations in tropical Pacific SSTs on the PV intermittency across large portions of the globe. In vast regions of the planet, the effects of El Niño and La Niña events on PV potential are significant regardless of the season. For the austral spring (SON) and summer (DJF), we found relatively strong correlations (either negative or positive) between PVPOT and SST anomalies in the Niño 3.4 Region across parts of South America (in particular across the Amazon Basin and the Chaco Basin), the Horn of Africa, Australia, Southeast China, and mainland Southeast Asia (Fig. S7). The correlations weaken across the Chaco Basin, Horn of Africa, Australia, Southeast China, but strengthen in mainland Southeast Asia during the austral fall (MAM) (Fig. S7a). The complexity of the climate system is further highlighted by shifts in correlations across regions and changes in sign during different seasons. For instance, the effects of SST fluctuations in the Niño 3.4 Region shift from eastern Australia in the austral spring (Fig. S7c) to western Australia in the austral summer (Fig. S7d). In the Horn of Africa, El Niño events can reduce PVPOT during the austral summer (DJF) (Fig. S7d) and increase it during the boreal summer (JJA) (Fig. S7b). Regional patterns remain similar in the case of correlations between PVPOT and SST anomalies in the Niño 1 + 2 Region (Fig. S8). Our assessment of the impacts of the next Super El Niño event is based on the effects observed during past canonical Super El Niño episodes, under the assumption that the next event will likely exhibit similar large-scale characteristics. Relative to the 1982–2024 period, 12-month PVPOT anomalies during the three most recent Super El Niño events (1982–1983, 1997–1998, and 2015–2016) averaged over +5% in parts of the Amazon Basin and approached −10% in parts of East China and the Chaco Basin (including southern Brazil and northern Argentina) (Fig. 4a). Positive anomalies indicate PV power surpluses (i.e., energy oversupply), while negative anomalies signal PV deficits (i.e., energy undersupply). Both can amplify intermittency, leading to grid congestion in the case of oversupply or increasing the need for backup and stabilization services during undersupply. In East China, negative anomalies were particularly intense in the provinces of Hunan, Guangdong, and Fujian, as well as in the Guangxi Zhuang Autonomous Region. Although less severe than in East China, negative anomalies also dominate in California, the southern Atacama Desert, and Central Chile (Fig. 4a). In parts of California and Central Chile, regions with high PV penetration and where the influence of El Nino is well established, 12-month PVPOT anomalies during the three most recent Super El Niño events approached −5% (Fig. 4a). a A 12-month PV Potential (PVPOT) anomalies averaged during the Super El Niño events (1982–1983, 1997–1998, and 2015–2016), relative to the 1982–2024 period. A 12-month positive anomalies suggest an increase in PV power output (i.e., energy over-production), while a 12-month negative anomalies indicate a decrease in PV power output (i.e., energy under-production). b 12-month PVPOT droughts derived from the 12-month PVPOT anomalies in (a). A mild, moderate, or severe drought is defined as a 12-month period in which PV potential (PVPOT) falls below the 30th, 20th, or 10th percentile, respectively, of the historical distribution for the same calendar period. We used data from 1982 to 2024 to compute these percentile thresholds. Stippling in plot a indicates statistical significance according to the two-sided Welch’s t-test. The 12-month PVPOT anomalies in plot (a) and the 12-month PVPOT droughts in plot (b) are calculated from August to July of the following year. Accordingly, we used the Super El Niño years 1982, 1997, and 2015 for the ASOND months and the Super El Niño years 1983, 1998, and 2016 for the JFMAMJJ months. PVPOT values were calculated using data from the ERA5 reanalysis77 available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. Expected Super El Niño–induced PVPOT anomalies are substantial enough to trigger multi-month solar energy droughts in vast regions. Seasonal and multi-month droughts in solar energy remain an emerging concept, with definitions varying widely across studies55,56,57. Traditional production-based metrics16 define energy droughts as uninterrupted sequences of days with anomalously low power production; however, these definitions were originally developed to characterize short-term (hour-to-day) fluctuations in solar resource availability. Here, we adopt the multi-month energy-drought thresholds proposed by Allen & Otero55, which classify moderate (severe) droughts as periods when energy potential falls below roughly the 20th (10th) percentile of the historical distribution for that same period. Using these thresholds and relative to the 1982–2024 climatology, our results indicate that the next Super El Niño event could produce 12-month PV droughts ranging from moderate to severe across large regions of East China, the Chaco Basin (including southern Brazil and northern Argentina), California, the southern Atacama Desert and Central Chile, as well as in the southern Arabian Peninsula (Fig. 4b). The impact of Super El Niño events on PV power output varies by season. During the three most recent Super El Niño events (1982–1983, 1997–1998, and 2015–2016), PVPOT in some provinces of East China dropped by up to −15% during boreal fall (SON) (Fig. S9). During the boreal summer (JJA), when solar yield typically peaks in the Northern Hemisphere, the declines in PVPOT in East China were less pronounced, yet still significant (~−5%) in the southeastern provinces (Fig. S9b). In California, PVPOT anomalies remain negative year-round during Super El Niño events, with declines ranging from around −3% during the boreal summer (Fig. S9b) to about −10% during the boreal winter (Fig. S9d). In the southern Atacama Desert, Central Chile, and the Chaco Basin (including southern Brazil and northern Argentina), PVPOT anomalies approached −10% during the austral fall (MAM) (Fig. S9a) and the austral winter (JJA) (Fig. S9b) but considerably weakened during the austral summer (DJF) (Fig. S9d). The intensity of a Super Niño’s effects is not always the same. In some provinces of East China, 12-month PVPOT anomalies during the 1997–1998 Super El Niño approached −15% (Fig. S10), whereas they were much weaker (around −5%) during the most recent Super El Niño event. In California and the Chaco Basin, the decline in the 12-month PVPOT was markedly deeper during the 1982–1983 and 1997–1998 events than during the 2015–2016 event (Fig. S10). Regarding positive anomalies, Super El Niño events consistently led to the largest spikes in 12-month PVPOT values (up to +10%) in the Amazon Basin (Fig. S10). While significant in many regions, the anomalies from the three most recent Super El Niño events (1982–1983, 1997–1998, and 2015–2016) had minimal impact on the electrical grid due to low PV penetration at the time. When the 2015–2016 event occurred, global installed PV capacity was nearly ten times lower than today. As PV systems continue to expand, the energy grid is becoming increasingly susceptible to disruptions associated with Super El Niño events. The expected rise in PV penetration will increase the grid’s vulnerability to Super El Niño events. Under a low-emission scenario, global PV generation is projected to grow 60-fold by mid-century compared to current levels3. By 2035, PV generation in East China, California, Argentina, and Chile is expected to increase by at least tenfold compared to 2023 levels (Fig. 5a, upper panel). At the same time, rising PV penetration will reduce carbon intensity, the amount of CO₂ emitted per unit of electricity generated. The International Energy Agency (IEA) projects significant reductions in carbon intensity worldwide in the coming years58. Based on announced pledges and net-zero scenarios, East China’s electricity carbon intensity is expected to fall by half, while in California, Argentina, and Chile, it is projected to approach zero within the next decade (Fig. 5a, lower panel). Among major PV-producing regions (China, the EU, the USA, India, South Korea, Australia, Brazil, Chile, and Japan) only China and India have not committed to carbon neutrality by 2050 or earlier58. Tables S1 and S2 provide data on 2023 levels and future projections for PV generation and carbon intensity in key regions and countries. a PV generation (upper panel) and carbon intensity (lower panel) in East China, California, Chile, and Argentina. In these regions with high PV penetration, the influence of El Niño is well established. In this study, East China includes the provinces of Anhui, Fujian, Guangdong, Guizhou, Hainan, Hebei, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Jiangxi, Jilin, Liaoning, Shandong, Shanxi, Yunnan, and Zhejiang; the autonomous region of Guangxi Zhuang; the direct-controlled municipalities of Beijing, Chongqing, Shanghai, and Tianjin; as well as the special administrative region of Hong Kong. Data for other regions and countries of interest are shown in Tables S1 and S2. b Expected impacts of next Super El Niño event on PV Potential (upper panel) and on CO2 Emissions (lower panel) in East China, California, Chile, and Argentina. The range of values in the upper panel is defined by the maximum and minimum anomalies observed in these countries and regions during the Super El Niño events of 1982–1983, 1997–1998, and 2015–2016 (Fig. S10). For Chile, the expected impacts were averaged over the area between 23°S and 37°S, which includes the southern Atacama Desert and Central Chile and where the country’s PV capacity is located. For Argentina, the estimates are averaged over the region north of 35°S, which contains the vast majority of its PV capacity. Data for other regions and countries of interest are shown in Tables S3 and S4. Boxplots in the lower panel are based on the simulations shown in Fig. S11. In each box, the central mark (white stripe) indicates the median, and the edges indicate the 25th and 75th percentiles. The whiskers extend to the maximum and minimum data, excluding outliers. Results for other regions and countries of interest are shown in Table S5. Carbon intensity and PV generation data for 2023 come from Ember´s Yearly Electricity Data (https://ember-energy.org/data/yearly-electricity-data/). In the case of East China, PV generation was derived from provincial-scale PV generation, sourced from the Chinese National Energy Administration (https://www.nea.gov.cn/2024-02/28/c_1310765696.htm). Projections of the carbon intensity are based on announced pledges and net-zero scenarios58. Projections of the PV generation are derived from the REMIND_EU 2.0 model, assuming the optimistic NewPl_1.5scenario3. Plots were generated using Python’s Matplotlib library (version 3.4.3), available at https://matplotlib.org/3.4.3/contents.html. The next Super El Niño event will likely reduce PV generation in key regions. While such events can lead to increases in PV output (i.e., energy oversupply) in some areas, they also lead to significant decrease (i.e., energy undersupply) in others, such as East China, California, the southern Atacama Desert, Central Chile, and the Chaco Basin (including southern Brazil and northern Argentina) (Fig. 4a). Although the frequency of Super El Niño events is expected to increase in the 21 st century59, they have historically occurred approximately every 15–20 years, suggesting that the next event could occur before 2035. While the impacts of the next Super El Niño on PV generation remain uncertain, insights can be drawn from the three most recent events (1982–1983, 1997–1998, and 2015–2016). For instance, the maximum and minimum PVPOT anomalies observed during these events (Tables S3 and S4) can be used to estimate the likely range of PVPOT reductions for the next Super El Niño. While the expected 12-month PV generation reductions can reach up to approximately 5% in California (Table S3), the 12-month PVPOT can decline by nearly 10% in some southeastern Chinese provinces, such as Guangdong, Jiangxi, and Fujian (Table S4). Figure 4c (upper panel) shows the expected range of 12-month PV generation declines during the next Super El Niño for East China, California, Argentina (averaged for the region north of 35°S), and Chile (averaged over the area between 23°S and 37°S, which includes the southern Atacama Desert and Central Chile). While the next Super El Niño could reduce the 12-month PVPOT by more than 8% in northern Argentina (Fig. 5b, upper panel), the effects may be particularly consequential in China. In the provinces of East China—home to nearly 90% of the country’s population—a Super El Niño could collectively reduce the 12-month PVPOT by more than 4% (Fig. 5b, upper panel), potentially leading to temporary energy deficits and increased emissions from backup energy sources. Super El Niño events can temporarily increase carbon emissions by reducing PV generation in key regions. The impact on CO₂ emissions can be estimated by multiplying the expected decreases in PV generation by the carbon intensity of the affected country or region. This estimate assumes that ENSO-driven PV undersupply will be compensated by a mix of available backup sources rather than exclusively by carbon-intensive power plants (e.g., coal-fired plants). However, this assessment involves significant uncertainties. The exact timing of the next Super El Niño is unknown, as are the actual PV generation and carbon intensity levels at the time of the event. To account for these uncertainties, here we conducted Monte Carlo simulations to estimate the potential CO₂ emissions impact (Fig. S11). These simulations involved recursively computing the expected additional CO₂ emissions using large sets of previously generated values for carbon intensity and ENSO-driven PV undersupply (Fig. S11). Our simulations suggest that the next Super El Niño could temporarily increase CO₂ emissions by tens of millions of tons in regions with growing PV penetration, such as East China, California, Argentina, and Chile (Fig. 5b, lower panel). Most of these additional emissions are expected in East China, not only because it is one of the world’s most ENSO-sensitive regions but also because its carbon intensity is projected to decline more slowly than in California, Argentina, and Chile (Fig. 5a, lower panel). To compensate for the energy gap left by the next Super El Niño, China will likely still need to rely on carbon-intensive power plants as part of its backup energy mix. El Niño and La Niña significantly amplify PV intermittency across vast regions of the planet. Our findings illuminate the impact of ENSO phases (i.e., El Niño and La Niña) on the seasonal-to-interannual variability of solar resources and PV power output. Using reanalysis datasets spanning four decades, we found that El Niño (La Niña) negatively (positively) affects surface solar irradiance, leading to season-spanning PV energy deficits (surpluses) in regions with increasing PV penetration, such as California, the southern Atacama Desert, the Chaco Basin, the Middle East, and East China. From both a reliability and financial perspective, persistent energy deficits (surpluses) are critical, as they can drive spot market prices up (down)26. Super El Niño events can cause prolonged and severe PV deficits in key regions. As PV penetration rates rise, energy grids are becoming more vulnerable to ENSO-induced disruptions, particularly during Super El Niño events. Our analyses indicate that Super El Niño events can lead to season-spanning PV energy deficits of up to 10% across parts of East China, one of the world’s most populated and PV-intensive regions. While low PV penetration previously shielded the grid from ENSO-induced disruptions, the situation is rapidly changing. Since the most recent Super El Niño event in 2015–2016, China’s installed PV capacity has grown nearly fifteenfold. As PV penetration continues to increase, ENSO-driven PV intermittency will have greater implications for energy security, grid stability, and carbon emissions. Super El Niño events can temporarily increase carbon emissions. By reducing PV generation in key regions, ENSO events can increase dependence on backup power, often from fossil fuels. We found that the next Super El Niño event could temporarily boost CO₂ emissions by dozens of millions of tons. Most of these additional emissions will likely occur in East China, where PV penetration is growing rapidly, but coal is expected to remain a major part of the energy mix for at least the next two decades58. While Super El Niño events may also reduce PV generation in regions of Europe and the U.S., the impact on emissions is expected to rapidly diminish as these regions transition to lower-carbon backup solutions. In affected regions, mitigating emissions from ENSO-driven PV intermittency will require enhanced energy storage and greater capacity redundancy. The effect of eventual feedback loops between climate change and ENSO on solar resources remains uncertain. While climate change is expected to increase PV intermittency in some regions due to enhanced climate variability and extreme weather11,12, how ENSO itself may change under future greenhouse warming is still unclear60. Climate models suggest that under a likely emissions scenario, extreme El Niño frequency increases linearly with global mean temperature, doubling at 1.5 °C warming61. The frequency of Super El Niño events is projected to double in the 21st century, potentially occurring once every 10 years instead of every twenty59. Beyond the 21st century, however, climate-driven ENSO amplification may weaken or even reverse due to a collapse in equatorial Pacific upwelling62. Under high-emission scenarios, ENSO variability after 2100 may decrease from its earlier enhanced state to amplitudes smaller than those of the 20th century63. These projected climate-driven changes in ENSO highlight the importance of explicitly incorporating ENSO-related seasonal-to-interannual variability into the planning and design of climate-resilient PV-dominated electricity systems. Overcoming ENSO-driven disruptions will require policies and investment. Recognizing the need to integrate climate extremes and extreme weather into energy planning and management is becoming increasingly widespread64. Our findings emphasize that managing ENSO-driven PV intermittency in high-penetration regions will likely require proactive curtailment65, demand response66, and policies promoting energy storage67; China’s mandatory coupling of storage with solar has already led to record deployment volumes1. While technological advances can help mitigate ENSO-driven PV intermittency, wide geographical distribution remains the simplest way to counteract the effects of enhanced climate variability. Distributed generation and geographical diversity offer a feasible approach to accommodating high PV penetration and enhanced intermittency. As the energy system transitions toward greater reliance on PV, ensuring energy security requires overcoming local variability. When solar power is deployed over a large geographical area with significant time zone differences, intermittency is significantly reduced68, along with electricity market balancing costs69. By mapping the spatial footprint of ENSO-driven disruptions, we provide essential insights for energy planners to develop climate-resilient PV deployment strategies. The strong regional dependence of ENSO-induced variability highlights the need for location-specific climate risk assessments in energy planning and the entwinement of energy systems with knowledge infrastructures. Future research should focus on improving seasonal and interannual climate forecasting to anticipate ENSO-driven PV disruptions and understanding how ENSO interacts with other climate modes, such as regional monsoons and large-scale circulation patterns. Additionally, strengthening the role of low-carbon dispatchable energy sources to mitigate PV undersupply will be crucial for minimizing excess emissions in regions negatively affected by ENSO events. A deeper integration of climate science into energy planning is essential for ensuring the long-term stability and sustainability of solar-powered energy systems. The PV potential (PVPOT) is defined as the ratio of the power output under standard test conditions to the power output a PV module can achieve in the field12,37,38,39,40,41: where ISTC represents the shortwave (SW) irradiance under standard test conditions (1000 W m−2), I is the SW irradiance reaching the PV module in the field, and PR is the performance ratio, which accounts for the impact of the cell temperature (Tcell) on the module’s efficiency. According to earlier studies12, PR can be calculated as follows: where TSTC is typically 25 °C, and γ is 0.005 °C−1 for monocrystalline silicon cells70,71. According to Eq. (2), higher cell temperatures reduce the performance ratio. The cell temperature, Tcell, depends on air temperature (T) and surface wind speed (v). Following prior efforts41,48, Tcell can be estimated as: where c1 = 4.3 °C, c2 = 0.943, c3 = 0.028 °C W−1 m2 and c4 = 1.528 °C m−1s. Equation (3) shows that increased wind speed enhances PV module cooling, leading to a lower cell temperature and, consequently, a higher performance ratio. Although I, v and T change during the day, here we used monthly averages from the ERA5 reanalysis dataset (available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) to compute monthly PVPOT values. Specifically, in Eq. (3), I corresponds to the monthly average of downwelling SW irradiance, v to the monthly average of surface wind speed, and T to the monthly average of surface air temperature. Our results are constrained by the spatial resolution of the ERA5 reanalysis data. Regional estimates (e.g., Northern California and East China) are based on spatial averages within those regions. Despite its limitations, PVPOT is a widely used and well-established proxy for PV power output12,37,38,39,40,41. The absolute magnitude of PVPOT depends on the accuracy of the inputs in Eq. (1) and on the empirical coefficients in Eqs. (2) and (3), which may differ slightly from those used in PV modules deployed in various regions. These differences can introduce small biases in absolute values. However, they affect all calculations in a systematic manner and therefore have only a minor influence on anomalies or relative deviations computed with respect to a reference period. Because the ENSO-induced PV response arises from changes in irradiance and temperature rather than from module-specific characteristics, PVPOT is well-suited to assess the relative PV potential changes that are the focus of this study. This is consistent with previous research where PVPOT has been shown to reliably capture the climate-driven variability in PV output12,37,38,39,40,41. Monitoring of ENSO conditions by NOAA’s Climate Prediction Center (CPC) primarily focuses on sea surface temperature (SST) in several geographic regions of the equatorial Pacific including the Niño 1 + 2 Region (right in front of the western coast of Peru), the Niño 3 Region (eastern Pacific), the Niño 4 Region (central Pacific), and the Niño 3.4 Region. In this study, we analyzed SST anomalies in El Niño regions produced by NOAA’s CPC, available at https://www.cpc.ncep.noaa.gov/data/indices/wksst9120.for. According to NOAA’s CPC, SST anomalies equal to or greater than 0.5 °C in the Niño 3.4 Region are indicative of ENSO warm phase (El Niño) conditions, while anomalies less than or equal to −0.5 °C are associated with cool phase (La Niña) conditions32. Super El Niño events are typically defined by SST anomalies of at least 2 °C in the Niño 3.4 Region30,31. The expected CO₂ emissions (ΔCO₂) resulting from the next Super El Niño event were estimated using the following equation: where PVEnergy represents the annual PV energy generation, CO2-intesity is the carbon intensity of electricity generation, and ΔPVPOT is the expected impact of the next Super El Niño event on PV potential in the country or region of interest. This estimation assumes that the energy shortfall caused by El Niño will be compensated using a mix of backup energy sources available in the country or region of interest, rather than relying exclusively on carbon-intensive power plants (e.g., coal power plants). In other words, for substantial but still moderate ENSO-driven deviations in solar generation (typically within ±10%), we assume most power systems compensate by proportionally increasing the dispatch of the available backup-energy mix. While the exact relationship between solar deficits and emissions may deviate from the strict linearity assumed in Eq. (4) under certain system configurations, we argue that such deviations will lead to uncertainties likely smaller than those introduced by the rapidly evolving energy mix itself. Evaluating Eq. (4) requires accounting for significant sources of uncertainty. For instance, while a Super El Niño event may occur at any point within the next decade, the exact timing remains unknown. Additionally, the future values of PVEnergy, CO2-intesity, and ΔPVPOT at the time of the event are uncertain. To account for these uncertainties, Monte Carlo simulations72,73 were conducted for each country or region of interest. To estimate the expected impact on CO₂ emissions if a Super El Niño event occurs within the next decade, we recursively applied Eq. (4) using large sets of previously generated values for PVEnergy, CO2-intesity, and ΔPVPOT. For PVEnergy and CO2-intesity, values were randomly generated within the range defined by observed data from 2023 and projections for 2035 (Tables S1 and S2, respectively). While the former represents a worst-case scenario (with no progress over the next decade), the latter represents a best-case scenario, with PV generation estimates derived from the REMIND_EU 2.0 model, assuming the optimistic NewPl_1.5 scenario3, and carbon intensity values based on announced pledges and net-zero scenarios58. For ΔPVPOT, values were randomly generated within the range defined by the maximum and minimum 12-month average PVPOT anomalies observed during past Super El Niño events (Tables S3 and S4). Positive anomalies indicate PV power surpluses (i.e., energy oversupply), while negative anomalies signal PV deficits (i.e., energy undersupply). The randomly generated values were then used as inputs in Eq. (4). Simulation results for some key regions with high PV penetration (and where the influence of El Niño is well established) are shown in Fig. S11. Results for other regions and countries of potential interest are summarized in Table S5. Note that our projections for the expected impacts of the next Super El Niño event already account for an exceptionally wide range of possible future backup-energy mixes (Tables S1 and S2). The effect introduced by these possible future energy mixes is presumably much larger than any arising from residual non-linearities in our simplified linear model (Eq. 4). We conducted Pearson correlation tests to evaluate the dependence between SST anomalies in the Niño regions and selected variables of interest (PVPOT, for example). A low p-value (lower than 0.05) indicates statistical significance, suggesting a dependent relationship between the two time series. The tests were conducted using both annual averages and seasonal averages, with the latter based on meteorological seasons: DJF, MAM, JJA, and SON. For correlation maps, regions with statistically significant correlations are highlighted with stippling. We used the two-sided Welch’s t-test to assess the significance of PVPOT anomalies during the Super El Niño events (1982–1983, 1997–1998, and 2015–2016), relative to the 1982–2024 period. The two-sided Welch’s t-test is a variation of the Student’s t-test, but it is more reliable when the two samples have different variances and/or unequal sample sizes. The tests were conducted using both 12-month averages and seasonal averages, with the latter based on meteorological seasons: DJF, MAM, JJA, and SON. Regions with statistically significant anomalies in Figs. 4a and S10 are highlighted with stippling. As EU countries, we considered in this study Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden. In this study, East China includes the provinces of Anhui, Fujian, Guangdong, Guizhou, Hainan, Hebei, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Jiangxi, Jilin, Liaoning, Shandong, Shanxi, Yunnan, and Zhejiang; the autonomous region of Guangxi Zhuang; and the direct-controlled municipalities of Beijing, Chongqing, Shanghai, and Tianjin. SST anomalies in the Niño regions come from the Climate Prediction Center (CPC)74 available at https://www.cpc.ncep.noaa.gov/data/indices/wksst9120.for. Data on PV capacity in plot (1a) (upper panel) are from the International Renewable Energy Agency (IRENA) renewable capacity statistics2, available at: https://www.irena.org/Publications/2024/Mar/Renewable-capacity-statistics-2024. Data on PV power plants in plots (1a) (lower panel) and (1b) are from the Global Energy Monitor75, available at: https://globalenergymonitor.org/projects/global-solar-power-tracker/. PV generation and carbon intensity of electricity generation in selected regions and countries for 2023 (Table S1) come from Ember´s Yearly Electricity Data (https://ember-energy.org/data/yearly-electricity-data/). In the case of China, provincial-scale PV generation was calculated by multiplying the installed PV capacity per province and the corresponding capacity factor. While the latter comes from He & Kammen76, the former was quoted from the Chinese National Energy Administration (https://www.nea.gov.cn/2024-02/28/c_1310765696.htm). Expected changes in the carbon intensity of electricity generation in selected regions and countries over the coming decades (Table S2) are based on announced pledges and net-zero scenarios. Data for China, the EU, the USA, and India are sourced from the IEA58 available at https://www.iea.org/data-and-statistics/charts/carbon-intensity-of-electricity-generation-in-selected-regions-in-the-announced-pledges-and-net-zero-scenarios-2000-2040. Estimates for France, South Korea, Italy, Australia, Spain, Brazil, Chile, Argentina, Germany, and Japan assume pathways similar to those expected in the EU. All of these countries have pledged to achieve carbon neutrality by 2050 or earlier. PV generation estimates for 2030 to 2050 (Table S2) for China, the EU, the USA, India, Japan, Germany, and France are derived from the REMIND_EU 2.0 model, assuming the optimistic NewPl_1.5scenario3. 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Download references The support of FONDECYT 1231904 and USACH DICYT Vicerrectoría de Investigación, Desarrollo e Innovación is gratefully acknowledged. University of Groningen, Leeuwarden, The Netherlands Sarah Feron, Richard Bintanja & Anne Beaulieu Universidad de Santiago de Chile, Santiago, Chile Raúl R. Cordero & Jaime Pizarro Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Kanagawa, Japan Alessandro Damiani Integrated Research on Energy, Environment and Society (IREES), Energy and Sustainability Research Institute Groningen (ESRIG), University of Groningen, Groningen, the Netherlands Paul Upham & Xin Sun Royal Netherlands Meteorological Institute (KNMI), Department of Weather and Climate Modelling (RDWK), De Bilt, The Netherlands Richard Bintanja School of Meteorology, University of Oklahoma, Norman, OK, USA Chenghao Wang Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK, USA Chenghao Wang College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL, USA Zutao Ouyang Global Adaptation Center (GCA), Rotterdam, The Netherlands Xun Sun Department of Earth System Science, Stanford University, Stanford, CA, USA Robert B. Jackson Woods Institute for the Environment and Precourt Institute for Energy, Stanford University, Stanford, CA, USA Robert B. Jackson Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar S.F., R.R.C., A.D., P.U., R.B., X.S., J.P., C.W., Z.O., Xu. S., A.B., and R.B.J. wrote the text. R.R.C. and S.F. contributed materials. S.F., R.R.C., A.D., and Xu S. analyzed the data. All authors reviewed the paper. Correspondence to Raúl R. Cordero. The authors declare no competing interests. This research did not involve human participants or animal subjects. Ethical approval was therefore not required. The authors support inclusive, collaborative, and reproducible research practices. Communications Earth and Environment thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: I-Yun Hsieh and Nandita Basu. A peer review file is available. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. 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Indonesia’s PLN has launched a tender for a 1,225 MW solar project that will be spread across several regions of the country. The state-owned utility has not publicly announced a closing date. Image: mz romadhoni/Unsplash Indonesian state-owned electric utility company PLN has opened a tender for a solar project with a total capacity of 1,225 MW. The Mentari Nusantara I solar power project will be developed across multiple regions of Indonesia, with 35 MW planned in Sumatra, 340 MW in Kalimantan, 600 MW in Java, 50 MW in Sulawesi, 80 MW in West Nusa Tenggara and 120 MW in Maluku and Papua. The tender is being run through an integrated procurement scheme titled ‘Giga One’, which the utility explains promotes economies of scale and provides measurable project certainty for investors by bundling several projects into one package. PLN kicked off the tender process last week (April 30). The utility has not yet published a closing date for the tender but has given the projects a targeted commercial operation date of 2029. Suroso Isnandar, Director of Project Management and New and Renewable Energy at PLN, said the Mentari Nusantra project is a key initial driver in supporting the Indonesian government’s target of building 100 GW of solar. Isnandar also said Giga One is “a new blueprint for renewable energy procurement in Indonesia and an important milestone in the national energy transition journey,” while advising that the procurement strategy will be replicated in future hydropower, wind power and battery energy storage system tenders. Earlier this year, the Institute for Essential Services Reform and Indonesia’s Coordinating Ministry for Economic Affairs published a study exploring how Indonesia can work towards its 100 GW solar target, which targets 80 GW of decentralised, small-scale solar systems alongside 20 GW of centralised solar. Indonesia surpassed 1 GW of solar capacity last year, with total capacity reaching 1.49 GW. From pv magazine Gobal This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Patrick Jowett Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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MNRE has updated the ALMM List-I, adding 20 GW of solar module capacity in May 2026, taking the total enlisted capacity to 193,144 MW. Sudarshan Saur Shakti, Nav-Yug Solar and Silver Pumps and Motors are the new entrants. Waaree, Reliance Industries, Novasys Greenergy, Rayzon Solar, Luminous, PIXON Green Energy, Redren Energy have increased capacities. May 04, 2026. By Mrinmoy Dey Mufin Green Finance's Gunjan Jain Bets on Premium Financing as India’s Next Credit Opportunity Grid Modernisation, Storage, and Hydrogen to Shape India’s Energy Future: Advait's Rutvi Sheth Energy Security Has Evolved into a Strategic Imperative for India: Hartek Singh Geopolitics Reshaping Solar Strategy, Says Hindustan Power's Chairman Ratul Puri Solar Shifts Farming from Constraint to Opportunity, Says Solarsure’s Bhavesh Patidar
A German research team has developed a nondestructive, on-site method to quantify water ingress in photovoltaic modules using near-infrared absorption (NIRA) spectroscopy calibrated with Karl–Fischer titration (KFT). The approach enables precise measurement of absolute moisture content in sealed modules without disassembly, improving inspection, failure analysis, and lifetime prediction. Image: Fraunhofer Center for Silicon Photovoltaics (CSP), Progress in Photovoltaics: Research and Applications, CC BY 4.0
A German research group has developed a novel, nondestructive method to quantify water ingress in solar modules on site. The technique uses near-infrared absorption (NIRA) spectroscopy calibrated against absolute water content measured via Karl–Fischer titration (KFT), enabling inspectors to determine moisture levels inside modules without opening them. “The methodology is noninvasive, requires no bill-of-material modifications such as additional sensors, and is broadly applicable to field-deployed modules, provided prior calibration has been conducted,” corresponding author Anton Mordvinkin told pv magazine. “Unlike conventional approaches, it does not rely on assumptions such as Henry’s law or on approximations of evolving barrier properties or uncertainties related to a module’s internal microclimate.” Mordvinkin said the approach lays the groundwork for more precise modeling of moisture ingress and improves the reliability of module lifetime predictions. “It provides actionable insights for manufacturers to optimize the design and qualification of products resistant to moisture-induced degradation mechanisms, including moisture-induced degradation (MID) and potential-induced degradation (PID), particularly in challenging environments such as floating PV systems and tropical climates, as well as for emerging technologies like tandem cells,” he added. He also noted that the method enhances solar park inspection by enabling the identification of modules with insulation deficiencies, supporting targeted mitigation measures. “These advances contribute directly to improved asset bankability and provide a robust technical basis for future warranty and reclamation processes,” he said. Image: Fraunhofer Center for Silicon Photovoltaics (CSP), Progress in Photovoltaics: Research and Applications, CC BY 4.0
The novel method involves exposing polymer materials commonly used in PV modules to varying moisture levels through damp-heat testing. Each sample is then measured using near-infrared absorption (NIRA) spectroscopy, in which water is detected by its strong absorption of infrared light. However, as NIRA provides only a relative signal, the same samples are subsequently analyzed using Karl–Fischer titration (KFT), a technique that heats the material and precisely quantifies the amount of water released. By correlating the NIRA signal with the absolute water content determined by KFT, the researchers establish calibration curves for each material. The materials tested include encapsulants such as ethylene-vinyl acetate (EVA), polyolefin elastomer (POE), thermoplastic polyolefin (TPO), and thermoplastic polyurethane (TPU), as well as backsheets such as polyethylene terephthalate (PET), polypropylene (PP), polyamide-aluminum-polyamide (AAA), polyvinylidene fluoride (PVDF), and fluorinated-coated PET. Image: Fraunhofer Center for Silicon Photovoltaics (CSP), Progress in Photovoltaics: Research and Applications, CC BY 4.0
Once calibrated, a handheld NIRA spectroscopy device can be used directly on installed the modules. To demonstrate this capability, the research team tested minimodules with PET- and PP-based backsheets under damp-heat conditions, polymer coupons exposed to accelerated ultraviolet (UV) radiation and humidity aging, rooftop modules exhibiting backsheet cracking and snail trails, and field-retrieved modules with both cracked and intact AAA backsheets to compare real-world moisture ingress and degradation behavior. The tests showed that PET-based modules absorbed more water than PP-based modules. In field studies, modules with backsheet and cell cracking exhibited up to 50% higher water content, while modules with cracked AAA backsheets absorbed water up to ten times faster than intact reference modules. “In this work, it was found that the improved barrier performance of PP is primarily governed by its lower water solubility, whereas the diffusion coefficients of both materials are comparable,” said Mordvinkin. “This provides a more detailed mechanistic explanation for the previously observed differences and is consistent with trends reported in the literature.” “Another particularly insightful observation is the presence of a non-homogeneous water-content distribution in modules with severely degraded backsheets after extended outdoor exposure of over 7 years,” he added. “Localized moisture accumulation was significantly enhanced in regions with cell microcracks, which correlate with visually observable snail trail patterns. This finding points to a coupling between mechanical degradation and localized moisture ingress behavior.” The new method was presented in “Nondestructive Quantification of Water Ingress in PV Modules via Spectroscopic and Chemical Analysis for Enhanced Quality Assurance and On-Site Inspection,” published in Progress in Photovoltaics: Research and Applications. Researchers from Germany’s Fraunhofer Center for Silicon Photovoltaics (CSP), Fraunhofer Institute for Microstructure and Systems (IMWS), and Forschungszentrum Jülich have contributed to the study. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Lior Kahana Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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Brazil’s Aneel has revoked 3.57 GW of photovoltaic project authorizations across four resolutions, mainly at the request of developers citing insufficient grid evacuation capacity and rising curtailment. Image: Ilanwet, Pixabay From pv magazine Brazil Through four resolutions published in recent days, Brazil’s National Electric Energy Agency (Aneel) revoked authorization for photovoltaic projects totaling 3,572 MW. The cancellations were requested by the project developers themselves, with the most frequent justification being insufficient grid evacuation capacity to connect plants to the transmission and distribution system. Generation curtailment was also cited. Auren relinquished the largest volume of projects among the revocations published last week. Other companies, including Solatio and Enel Green Power, also requested the cancellation of more than 500 MW each. By state, Piauí and Minas Gerais account for most of the revoked capacity, with 1,747 MW and 1,265 MW, respectively. Additional projects were cancelled in Bahia, Tocantins, and Rio Grande do Norte. To qualify for revocation, projects must not have sold energy in the regulated market, meaning the authorizations applied to projects intended for the free market. Despite recurring cancellations – another 2.8 GW were revoked in March – solar remains the leading technology in Brazil’s utility-scale expansion pipeline. Aneel’s tracking system, Ralie, currently lists 79 GW of projects at different stages of development, followed by 14.7 GW of wind capacity. Distributed generation is also facing similar grid connection constraints. Alongside transmission expansion through auctions, increased battery storage deployment is being considered as a potential solution to alleviate bottlenecks.
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This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Lívia Neves Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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