Solar Now

© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.
“The Central Government is placing the highest priority on solar and hydro power.”
Photo Credit: iStock
Tripura is turning to rooftop solar as it faces two mounting pressures at once: rising electricity demand and a dwindling supply of natural gas that has long powered much of the state’s grid.
State officials said that shift could be especially meaningful for lower-income households, since it may help keep energy bills from becoming even more difficult to manage.
During a special council meeting in Agartala on Aug. 17, 2026, Tripura Power Minister Ratan Lal Nath said the state needs broader rooftop solar adoption to meet future electricity needs without leaving vulnerable residents behind, according to Northeast Today. The session at the Agartala Municipal Corporation Conference Hall centered on rooftop solar installations under the “ULA Model.”
According to Nath, Tripura’s electricity consumer base has grown to around 10.68 lakh (about 1.07 million), up from roughly 7 lakh before the current government took office. He added that more than four lakh consumers use no more than 50 units a month, which he said suggests many belong to economically weaker sections.
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers in your area.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
“When the demand for something increases, its price goes up,” Nath said.
Nath said the challenge is not only rising demand, but also mounting pressure on the state’s conventional power system. He said a limited gas supply has reduced the Palatana plant’s output to about 500 MW, even though it was designed for around 700 MW, while Ramchandranagar is generating only 50 to 60 MW instead of roughly 100 MW.
As electricity demand rises, solar is one of the best ways to save money on home energy. Homeowners who want to explore rooftop systems can use EnergySage to get free solar installation estimates and compare quotes.
Reliable electricity is needed across the state, Nath said, including for industry, hospitals, schools, colleges, and waste-recycling facilities.
FROM OUR PARTNER
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers that can help you save as much as $10k on installation.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best local options for your needs, and their expert advisers can help you compare quotes and pick a winner.
He described solar, wind, and hydropower as greener options than gas- and coal-based generation, which he characterized as more polluting.
Tripura is also working to advance rooftop solar through coordination between the municipal corporation and power authorities. Nath said Agartala Mayor Dipak Majumder, Agartala Municipal Corporation Commissioner Saju Vaheed A, Deputy Mayor Manika Das Datta, and other officials took part in the discussion.
For households elsewhere, the economics of solar can be easier to understand with free comparison tools. With EnergySage’s help, the average person can save up to $10,000 on solar purchases and installations. EnergySage’s solar map shows the average cost of a home solar panel system on a state-by-state basis, along with solar panel incentives for each state. Together, these resources can help readers get the best price for rooftop solar panels and access available incentives.
Additionally, adding battery storage to a solar setup is one of the best ways to protect your home during outages. It can also save money on energy and make going off-grid more realistic, and readers can explore EnergySage for information about home battery storage options, including competitive installation estimates.
💡Go deep on the latest news and trends shaping the residential solar landscape
Nath said, “Gas reserves are depleting; the supply is dwindling,” and “The Central Government is placing the highest priority on solar and hydro power.”
Tripura’s rooftop solar push reflects a broader shift as households and governments look for cleaner, cheaper power while conventional fuel supplies come under strain. Across India and in nearby Pakistan, wider access to solar is already changing monthly bills, grid planning, and the pace of renewable investment.
• Across India, rooftop solar installations surged 125% as PM Surya Ghar accelerated household adoption.
• India saves billions by prioritizing solar, showing why states are leaning beyond gas.
• In Gujarat, a vast solar-wind project underscores how quickly India’s renewable buildout is scaling.
• In Pakistan, rooftop solar is shielding consumers from shocks and easing import costs.
• In Colorado, plug-in solar panels are widening home-energy access for residents shut out.
Rooftop solar is becoming a bigger piece of a much wider energy transition. These projects also show how much policy design matters when the goal is getting cleaner, lower-cost power to households that need relief most.
Get TCD’s free newsletters for easy tips, smart advice, and a chance to earn $5,000 toward home upgrades. To see more stories like this one, change your Google preferences here.
© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.

source

Action News Now reporter Bella Barbosa reports from Butte County on new details about a large solar project that is expected to be proposed in unincorporated Butte County between the Skyway and Neal Road.
A Southern California energy company is looking to build a 300-megawatt solar facility in Butte County between the Skyway and Neal Road. The company, Cenergy Power, is planning to submit a proposal for the solar farm on 1,700 acres in unincorporated Butte County.
Currently in Chico
Your browser is out of date and potentially vulnerable to security risks.
We recommend switching to one of the following browsers:

source

0
Powered by :
Bangladesh has launched an incentive programme for rooftop solar systems paired with battery storage. The programme aims to ease the country’s worst power shortages in years and reduce its dependence on imported fuels. Consumers installing eligible systems by February 28, 2027, will receive BDT 10.50 ($0.086) per kWh for surplus electricity fed into the national grid, for three years. The government has capped the generation cost of rooftop solar power, including battery storage, at BDT 8 ($0.065) per unit. The incentive payment includes a 20% profit margin and an additional premium, while consumers installing systems below the benchmark cost can retain the savings. Bangladesh currently has about 1,559 MW of renewable capacity, mostly solar, and aims to reach 20% renewable generation, roughly 5,500 MW, by 2030. 
SOLARbytes brings you the latest news in solar world in bite size; essentially a gist of important solar news in few sentences.
Subscribe to our Newsletter!

Quick Links
© 2026 SOLARbytes | SOLARbytes is a publication of Wat(t) Bytes Media Pvt. Ltd. All rights reserved.

source

India proposes mandatory battery storage for new renewable projects from July 2027  Reuters
source

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 Reports volume 13, Article number: 19051 (2023) Cite this article
11k Accesses
34 Citations
10 Altmetric
Metrics details
Partial shading of solar panels diminishes their operating efficiency and energy synthesized as it disrupts the uniform absorption of sunlight. To tackle the issue of partial shading in photovoltaic (PV) systems, this article puts forward a comprehensive control strategy that takes into account a range of contributing factors. The proposed control approach is based on using multi-string PV system configuration in place of a central-type PV inverter for all PV modules with a single DC-DC converter. This adaptation enhances overall efficiency across varying radiation levels. Also, the proposed technique minimizes the overall system cost by reducing the required sensors number by utilizing a radiation estimation strategy. The converter switching strategy is synthesized considering direct duty-cycle control method to establish the maximum power point (MPP) location on the P–V curve. The direct duty-cycle tracking approach simplifies the control system and improves the system’s response during sudden partial shading restrictions. To validate the effectiveness of the suggested MPPT method, two system configurations were constructed using MATLAB/SIMULINK software and assessed under various partial shading scenarios. Additionally, a multi-string system was subjected to real irradiance conditions. The sensor-less MPPT algorithm proposed achieved an impressive system efficiency of 99.81% with a peak-to-peak ripple voltage of 1.3V. This solution offers clear advantages over alternative approaches by reducing tracking time and enhancing system efficiency. The system findings undoubtedly support the theoretical scrutiny of the intended technique.
Renewable energy sources (RESs) are gaining prominence worldwide as a response to urgent environmental challenges caused by fossil fuels. For instance, global warming, the nature destruction, and water contamination that lead to worst climate impacts^1,2,3. Solar photovoltaic (PV) systems have emerged as a feasible answer to address the increasing global electricity demands. The combined installed capacity of the solar PV market stands at 892.6 GW and is projected to experience a compound annual growth rate (CAGR) exceeding 15% from 2021 to 2030⁴. The enhancement of solar energy efficiency has gained a significant alertness from scholars, owing to its environmentally friendly nature, consumer expenses reduction, and encourage economic expansion⁵. However, PV-based generation systems undergo numerous encounters, involving excessive installation expenditures, low efficiency, and climate reliance^6,7. The stochastic nature of the solar PV system under continuous varying weather conditions leads to voltagefrequency fluctuation at the coupling point with the utility network, which leads to an intermittent and unstable consumer power service⁸. Many factors influence the operation of the solar PV system. One of the most familiar issues related to weather dependence is partial shading condition due to existence of clouds, dust on the surface of the panel, etc. The performance of the entire PV system is negatively impacted by even light shade that may leads to PV system destruction^9,10. In such conditions, Shaded PV modules generate lower electrical current compared to the unshaded surroundings, and a larger passing current that would heat them up and causing a hot-spot problem that could harm those arrays¹¹. This issue can be resolved by connecting a bypass diode in shunt, which will cause the current to go through a forward-biased diode^12,13. In fact, the existence of partial shading condition (PSC) results in multiple local maximum power points (LMPPs) and a unique global maximum power point (GMPP). Therefore, many maximum power point tracking (MPPT) techniques have been recommended to track the GMPPT of the PV module to impose the system to operate at a specific unique GMPP, where the system can achieve its maximum power. In addition, power electronic circuits are applied with control circuits to attain GMPP operation^14,15.
Many tracking approaches have been, recently, introduced to attain the optimal power of the PV system, which has drawn considerable interest from researchers^16,17. Two MPPT categories are broadly classified; conventional and soft computing MPPT algorithms as depicted in Fig. 1. Owing to their low cost and ease of installation, numerous applications typically use the conventional MPPT algorithms, including; fractional open-circuit voltage (FOCV), fractional short-circuit current (FSCC), Hill climbing (HC) and perturb and observe (P&O). These techniques’ primary shortcomings include their poor tracking speed and significant steady-state oscillations, which lower system efficiency^18,19,20. To overcome the previous common shortcomings, many computation-based MPPT algorithms have been suggested such as; fuzzy logic control (FLC), sliding mode control (SMC), artificial neural network (ANN), and meta heuristic-based algorithm. However, the main drawbacks of former algorithms, compared to conventional ones, are the long computation-burden and control-system-complexity^21,22. To lessen the former shortcomings and the impact of partial shade, a number of static and dynamic reconfiguration approaches and control algorithms were suggested^23,24. The dynamic reconfiguration technique (DRT) calls for a large number of hardware structures, a supervising reconfiguration algorithm to determine the ideal configuration, sensors, and switch matrices to connect PV modules, which increases control complexity and decreases system reliability²⁵. In contrast to DRT, static reconfiguration technique (SRT) does not dynamically change the electrical interconnections^26,27,28.
MPPT categories.
Following the completion of the connection, which remains unchanged, the placements of the PV arrays are selected using a certain puzzle. Moreover, switches and other auxiliary circuits are not required while using SRT, which lowers the cost and complexity of installation²⁹. In general, there are some essential drawbacks to SRT that must be considered. Long connectivity wires and a considerable number of steps are needed to complete the final reconfiguration matrix. In ref.^30,31, the Su Do Ku approach has a wide variety of physical PV array reconfigurations. However, it still has a slight drawback that the first column’s value is constant, thus submatrix formation and execution consume long time. Regarding the magic-square proposition in²⁸, the mismatch losses are greatly reduced. However, as the array reconfiguration process moves toward its final iterations, this methodology becomes time-consuming and difficult. It also calls for several analyses and assumes a lack of the necessary reconfiguration abilities. Moreover, the reliability of dominance square topology (DS) has been proven for many PSC, but conducting it is more challenging³². Also, the competence square method (CS) is rarely used as they require complex implementations of switching and sensors^33,34,35. In ref.³⁶, a varying step size ANN-based MPPT technique is used to mitigate the effect of PSCs on PV systems. In ref.^37,38, FLC-based on dynamic safety margin (DSM) as an MPPT method to overwhelmed the restrictions of conventional FLC in shading conditions. The limitations of the last two approaches lie in their reliance on human experience and the need for a high-performance controller. In ref.³⁹, A novel optimization algorithm called the “θ-modified krill herd (θ-MKH) method” has been introduced to identify the parameters of the SMC. The primary handicap of this type is the discontinuity of the control signal along the sliding surface due to the presence of the sign function. To achieve duty cycle control, the switching frequency must be sufficiently high, leading to the occurrence of chattering⁴⁰. The genetic algorithm presented in⁴¹ uses solutions to represent the chosen process routes for remanufacturing jobs. To assess the quality of a solution, Monte Carlo simulation is employed, generating a production schedule based on the specified process routes. However, it is difficult to implement and slow computation capability. In ref.^42,43, A new MPPT technique, utilizing the Particle Swarm Optimization (PSO) algorithm, is introduced. This technique instantly evaluates the duty cycle, eliminating the necessity for PI control loops. It effectively addresses the limitations of conventional direct control methods, especially under partial shading conditions. However, the approach does have a drawback in that it requires significant time delays for the particles to gather towards the MPP, resulting in longer computation times⁴⁴. References^45,46 propose a MPPT technique which uses Gray Wolf Optimization (GWO). Drawing inspiration from grey wolves, this approach mimics the leadership hierarchy and hunting procedure observed in wildlife. It demonstrates remarkable accuracy in finding high-quality solutions. However, it suffers from its complexity and difficulty of implementation.
A global flexible power point tracking (GFPPT) approach is introduced, which includes the conventional search-skip-judge global MPP method and a vital strategic scheme⁴⁷. Additionally, an adaptive P&O algorithm with enhanced skipping features is employed⁴⁸. To mitigate the drawbacks of previous methodologies, the utilization of analog-to-digital converter (ADC) samples, shared between the MPPT algorithm and the PI controller in⁴⁹. However, challenges associated with being stuck in a local maximum power point (LMPP) within partial shading conditions (PSC) are often encountered by these approaches, which make use of P&O and INC methods. Furthermore, over-scanning behavior is exhibited, with more than 70% of the parameter space being explored by many of them before finally converging to the Global Maximum Power Point (GMPP)⁵⁰.
To mitigate the previous MPPT controller’s limitations, a direct duty-cycle control (DDCC) strategy is proposed. The DDCC technique boosts the overall system efficiency by the steady-state oscillations elimination, hardware simplification, and ease of implementation. Additionally, the DDCC has a fast-tracking speed for GMPPT extraction during PSCs⁵¹.
Motivated by the former literature survey, this paper’s contributions can be concluded as follows:
The solar PV tracking efficiency is investigated for using multiple and a single MPPT DC-DC converter under various PSC patterns with constant and realistic radiation data.
Implementation of a DDCC for MPPT and extraction the GMPP
Proposing a novel irradiance estimation strategy to reduce the required sensors number.
The residue of the paper is presented as follows; Section “Proposed system modelling” demonstrates the mathematical modeling of the system. Section “Partial shading causes and effects” presents partial shading causes and effects. The proposed methodology of the article is given in Sect. “Proposed methodology”. While Sect. “Efficiency and power compare” describes efficiency calculation and power compare. The proposed system results and discussion are represented in Sect. “Comparison with counterpart approaches”. Finally, Sect. “Proposed system results and discussion” gives a conclusion of the paper and summarizes the article’s outcomes.
In this paper, two systems are proposed. The first one consists of a PV station connected to a single DC-DC converter. While in another system, each string is connected to a separated DC-DC converter.
The main components of PV generation systems that convert sunlight to electrical energy for end users are photovoltaic cells. These cells are electrically connected in cascaded and shunt to achieve the desired voltage and current at STC^52,53. Circuitry modelling of solar cells enables the seamless integration of larger PV systems, including power converters, grid connectivity, etc., using computer aided software⁵⁴. Despite the numerous presented solar cell models, single-diode model (SDM) is widely used owing to its simplicity⁵⁵. Figure 2 presents the single-diode model equivalent circuit^56,57.
SDM of PV solar cell.
The correlation between voltage and current in solar PV is explained as follows:
where i_pv is the solar PV-array generated-current (A), v_pv is the solar PV array terminal voltage (V), N_s—N_p are number of cascaded and shunt modules, I_ph is the PV-cell light-generated current (A), I_sat is the reverse saturation current (A), R_s—R_sh are series and shunt resistances, respectively, K is Boltzmann constant (1.38×10^–23 J/K), q is the electron charge (1.6022 × 10^–19 C), A Is the p–n junction ideality factor, T Is the PV cell surface temperature.
The current caused by a photovoltaic module is influenced by several factors, including solar radiation, ambient temperature, cell temperature, and temperature-dependent current coefficient. This formula can be mathematically represented as follows:
where I_sc is the actual short-circuit current (SCC) (A), G is the ambient solar emission (W/m²), ({G}_{0}) is the reference solar emission (1000 W/m²), T_i is the reference temperature (298 K), K_i is the temperature coefficient of SCC, T is the ambient temperature.
The ambient temperature can significantly affect the effectiveness of the solar PV system. Hence, accurate modeling of the temperature dependence of the solar cells is imperative for the optimal design and implementation of the PV system. The reverse-saturation-current of PV-cell is affected by the surrounding temperature, as shown in the following equation:
where V_o is the actual open circuit voltage (OCV) of solar PV cell (V), K_v is the OCV coefficient, V_th is the thermal voltage (({V}_{th}=K.frac{T}{q})).
Additionally, precise measurement and modelling of the (SCC) are essential for correctly forecasting how well the photovoltaic system would operate under various environmental conditions. The SCC is defined in terms of temperature and irradiation (T, G) as follows;
Also, the diode current of the SDM can be expressed as;
where, V_D is diode voltage terminals.
This section analyses the anticipated OCV estimation approach considering the impact of temperature and irradiance on the MPP voltage. Hence, an appropriate mathematical formulation is required.
The OCV of PV modules can be formulated as a function of the array’s temperature⁵⁸, as follows;
where V_oc(STC) is the OCV at STC, T_(STC) is the temperature of the PV module at STC (25°C), µ_Vov is the thermal-coefficient for OCV for PV cell (V/^oC).
According to (6), the OCV can be determined concerning the solar cell temperature, as develops:
where ({mu }_{{V}_{oc}}) is the OCV thermal-coefficient.
The effect of irradiance on the OCV can be determined as follows;
In the open-circuit test, V_D and I_D are counterpart to V_oc and I_ph, respectively. Consequently, the photo-produced current conveyed in (5) can be symbolized as develops;
For simplicity, the OCV can be determined as results;
As, I_ph >  > I_sat, then the OCV can be expressed as develops;
The OCV at STC may be determined as arises;
where I_sat = I_sat(STC).
According to Eqs. (10, 11);
Thus,
By recognizing the effect of I_ph from (13) into (7);
Also, the PV current is directly proportional to the radiation intensity (G). Therefore, the OCV of PV modules can be formulated as a function of PV-array’s radiation. Hence, Eq. (13) can be expressed, based on system irradiation, as follows;
while ({mathrm{G}}_{0}) is the irradiance level at STC (1000 W/m²).
The MPP-voltage estimation forms a critical task in optimizing the performance of PV-systems, as it affects the MPP efficient extraction from the PV array.
According to Fig. 3, the distribution of MPPs spacing resembles that of open-circuit points. Hence, the MPP can be defined as follows¹;
P–V characteristics at various radiation level.
Additionally, there is a linear relevance between the temperature and the OCVs¹. Since the MPP voltages are seen similarly, hence, the MPP voltage can be expressed as follows;
where ({v}_{mppt}) is the voltage at MPP due to temperature variant.
Considering (16, 17) and for the complete influence of the environmental factors (radiation and temperature) on the values of MPP voltage, the following conclusion can be reached;
where ({v}_{mppn}) is the MPP voltage at any atmospheric condition. Since the PV array’s operating temperature is identical to the temperature at STC, the term referring to temperature variation may be ignored, leading to the equation;
where n is the diode’s ideality factor. For simplicity; Eq. (19) can be expressed as follows;
where (A= frac{{nN}_{s}{V}_{th}}{{v}_{mppleft(STCright)}}.)
In the PV-system design, a reduction in the system’s total number of sensors is necessary because the realistic budget cost is a critical factor. Hence, accurate solar-irradiation estimation is essential for MPP-voltage determination to enhance system reliability and reduce the required number of sensors. Many studies have proposed different strategies for solar irradiance estimation by using the PV-system existing voltage/current sensors¹. In line with international standards such as IEC 60,891, a new technique has been proposed for irradiance estimation, which can be expressed as follows⁵.
where (Delta i,Delta v) are the variation due to climate change in the PV cell current and voltage, respectively.
The variation in current and voltage of the PV system can be represented as follows:
By substituting the value of the estimated irradiance to evaluate the voltage at MPP, Eq. (20) can be stated as follows;
DC-DC converters are electronic circuits designed to transform one DC voltage level into another DC voltage level with a specific voltage conversion ratio. A DC-DC boost converter (BC), represented in Fig. 4, controls the PV array’s low and erratic voltage and serves as an mediator between the grid-connected inverter and the PV module circuit^54,59. With the help of the DC-DC boost converter, one can apply the MPPT controller to confirm the system operation at the MPP⁵. Also, the BC duty-cycle controls the converter’s output voltage. Thus, when the switch is opened, the chopper switching state (w) is defined as zero (0), and adjusted to one (1) when the switch is closed⁶⁰, as clearly expressed in the following equation;
Boost converter model.
The BC dynamic model can be deduced using voltage and current Kirchhoff’s laws as follows⁶¹;
where ({V}_{out}) is the BC ‘s terminal-voltage, ({i}_{L}) is the inverter input current.
The DDCC is employed in PV systems to regulate the produced power of the system by adapting the duty-cycle of the DC-DC converter. It forms a simple and efficient way to handle the power output from the PV systems, which is often used in small-scale and low-cost PV systems⁶². In DDCC, the duty-cycle of the DC-DC converter is adjusted based on the difference between the required and actual output-powers of the PV system. By adjusting the duty cycle, the DC-DC converter can regulate the output voltage and current to accord the required output-power⁶³. Using DDCC can help improving the system overall efficiency, which can result in greater energy production and reduced costs over time⁶⁴. The BC voltage transfer-ratio can be expressed in terms of the duty-cycle (D), as follows;
where ({V}_{DC}) is the DC-link voltage at the front end of the utility side converter (GSC). Hence, the duty-cycle of the BC can be obtained directly as follows;
The GSC function is to regulate and control the flow of power between the PV generation system and utility grid. The grid-side converter works by transforming the DC-power produced by the PV generation system into AC-power that can be supplied into the utility grid. In addition, it ensures that the PV-system output is synchronized with the utility frequency and voltage to allow seamless integration. Moreover, the GSC regulates the power factor (PF) of the grid-integrated PV-system for unity PF operation of the proposed system. By maintaining an improved PF, the grid-side converter ensures that the PV system runs at optimal productivity and reduces the risk of system instability and voltage fluctuations^49,65.
To control the DC-link voltage and maintain its functional status, the GSC employs a PI-based outer control loop, while the dual PI-based inner control loops are used to adjust the d-q axis currents to its reference ones. A control block diagram illustrating this process is presented in Fig. 5.
DC-AC converter control scheme.
Assuming a balanced grid-integrated system, hence, the voltages of three-phase grid can be expressed as develops;
where ({e}_{r}), ({e}_{s}) and ({e}_{t}) are the GSC line voltages, ({i}_{r}), ({i}_{s}) and ({i}_{t}) are the GSC line currents, ({V}_{gr}), ({V}_{gs}) and ({V}_{gt}) are the GSC phase voltages, ({R}_{f}) is the filter resistance, ({L}_{f}) is the filter inductance.
The GSC d-q axis voltages or the grid voltages’ rotating reference-frame can be stated as follows;
where v_gd, v_gq, e_d, and e_q are the inverter d and q axes voltage components of the utility voltages, respectively, ({omega }_{0}) is the angular frequency of the grid (rad/s).
The GSC exchanges no reactive power with the utility to achieve the unity PF. The two-power component can be expressed instantaneously in the d-q axis representation as follows;
When one or more panels in a series string are shaded, the output-current of the whole string decreases, which constraints the current flow in the entire string and reduces the overall output-power of the PV-system^66,67. Utilizing bypass diodes, which enable current to pass through the unshaded cells in the string, is one method of addressing PS. Bypass diodes are connected in shunt with each solar cell or group of them, and are designed to activate when the voltage across the cells drops below a certain threshold. By redirecting the current around the shaded cells, bypass diodes reduce the harmful effects of PS on the performance of the PV system. This enables the rest of the module to operate at higher efficiency and avoid the PV-system overheating^8,9. This article studies the performance of the PV-system under various PSCs. Figure 6 depicts PV module’s characteristic curve, which exhibits a unique MPP as it exposed to a uniform irradiance⁶⁸.
P–V characteristic curve under PSC.
However, when the module is subjected to different levels of radiation, it exhibits multiple LMPPs based on the shading patterns. Among these LMPPs, there is a unique GMPP that offer optimal operating power of the shaded system^69,70,71. Also, understanding the behaviour of PV modules under PS is quite important to enhance the system performance and operating efficiency. There are two types of partial shading that are possible to exists on the PV array: static shading, and dynamic shading. The static shading refers to a specific shadow that remains on the PV array for a period of time, where the dynamic shading is the varying shades on the surface of PV array that results from a moving cloud or swaying tree branches caused by the wind^19,72,73,74.
In this study proposes a reconfiguration of the PV-system to suppress the negative impact of PS on the PV-system performance by dividing the PV-system into multiple parallel strings. In this work, the proposed system comprises four strings, with each string comprises three paralleled sub-strings. Each sub-string involves of five cascaded-connected modules. The PV-station studied in this work consists of 60 Canadian CS5P-220 M panels, with a total power of 13.2 kW. Table 1 lists the PV-module specifications at STC.
Figure 7 depicts the PV system’s single-MPPT (SMPPT) arrangement, in which a single DC-DC converter is connected to four parallel strings of solar panels. The usage of an SMPPT in PV systems has a number of drawbacks that may reduce the system’s overall performance and efficiency such as;
First off, an SMPPT can only track the MPP of a single PV module or string at a time. Hence, the system will only function at the MPP of the weakest module or string if numerous modules or strings are linked in parallel that diminish the system efficiency.
Second, the usage of a SMPPT makes the PV-system vulnerable to shading, as even a small shading area on single module/string causes the entire system to operate at a sub-optimal MPP, leading to a reduction in system output power.
Third, the configuration of SMPPT encounters difficulties in locating the system’s global MPP. Consequently, there is a need to identify a controller capable of rectifying this issue, which increases the system complexity and the required controller’s specifications. For instance, if a cloud passes over the PV system, the output voltage and current of each module or string may change, and a SMPPT may not be able to adjust to these changes quickly enough, leading to reduced power output^22,25,75,76.
Single-MPPT system configuration.
To overcome the limitations of SMPPT configuration, Multi-MPPT (MMPPT) approach offers optimal MPPT under different levels of shading conditions, as shown in Fig. 8. Each string is tied to a DC-DC converter, which enables optimal power extraction of each individual string of PV solar panels.
Multi-MPPT system configuration.
Hence, results in increased energy conversion efficiency. In comparison to SMPPT, MMPPT offers more dependability by enabling sustained operation even in the case of a problem with one of the PV solar panel strings. Additionally, it provides more design flexibility for PV systems because it can support a variety of solar panel counts and configurations and is simple to adapt to changing power needs. Additionally, by maximizing the power output of each string of solar panels, MMPPT arrangement lowers the cost of the PV solar system and enables the use of smaller, more affordable power converters^32,77. Table 2 lists the parameters of the DC-DC converters.
Figure 9 depicts the flow chart of the projected control approach. The first step is to measure the voltage and current of PV module, then calculate the difference between these values and voltage and current of MPP at any shading condition. We use this subtraction product to estimate the radiation. The estimated radiation is used to evaluate the estimated voltage, hence, the duty cycle of the converter.
Proposed control strategy.
This section provides comparison between SMPPT and MMPPT configurations under varying irradiance profiles. The primary factors considered in this comparison are the average output power and voltage ripples. The tracking efficiency of each system can be readily determined through the following formula:
where ({P}_{act.}) and ({P}_{th.}) are the actual and theoretical PV output power, respectively⁷⁸.
This section compares the proposed MPPT method with some recent approaches. The comparative analysis is established considering the system capability to track the GMPP under the partial shading condition, the system tracking response, system efficiency, and the contained steady-state oscillations (SSO). Evidently, Table 3 presents a comparative analysis between the proposed MPPT method and recent approaches. This analysis highlights the efficiency of the proposed method in accurately tracking the GMPP during Partial Shading Conditions. Furthermore, it demonstrates the fast tracking speed of the proposed methodology. In addition, the proposed strategy diminishes the system high-frequency steady-state oscillations, which enhances the control system design and its reliability for practical use. Despite the parallel operation of Multi-MPPT that may enquires many calculations, the proposed system shows a simple MPPT control system compared to most of the recent topologies, which lowers the required specifications of the controller for real system implementation.
As previously illustrated, the dual-stage grid-connected PV-system has been studied considering two configurations. The two configurations are constructed using MATLAB/Simulink computer-aided software. In addition, three solar irradiance patterns, see Fig. 10, are applied to the former PV-system configurations, i.e. SMPPT and MMPPT, for a fair comparison between the selected configurations. The purpose of this study is to know which configuration has reliable functioning under various PSCs. The following results show the estimated solar radiation, PV output-voltage, PV output-power and the converter duty-cycle of the converter for MPPT.
Studied shading pattern on the proposed system.
In shading pattern-1, all the PV panels are subjected to a uniform irradiance of 1000 W/m², which is considered as the STC. Under these conditions, the optimal output power at STC is 13.2 kW. First of all, the proposed dual-stage system is tested under STC conditions to illustrate the system performance characteristics and to ensure the control system robustness. Also, by conducting the STC tests, the results obtained can be used as a benchmark to evaluate the system’s performance under other operating conditions. Therefore, these findings are vital for the design and optimization of PV-systems, which provide valuable insights into the system’s efficiency and performance under ideal conditions.
Figure 11 shows the PV-side results of the proposed grid-tied system considering both SMPPT as well as MMPPT configurations. As shown in Fig. 11a, the SMPPT-based estimated radiation profile exhibits steady-state oscillations between 970 W/m² and 1020 W/m², while displays steady-state oscillations between 985 W/m² and 998 W/m² for the MMPPT PV-system configuration. Obviously, it confirms the accurate operation of the MMPPT-based DDC control-loop with low peak-to-peak (PTP) steady-state oscillations compared to SMPPT-structure. The irradiance is estimated in order to decrease the number of sensors used in the system installation, hence diminishing the cost of operation. Besides, the PV output voltages of the intended system, according to system specifications listed in Table 1, are depicted in Fig. 11b. According to the characteristics of the PV panels in Table 1, the voltage at which optimal power point is extracted at STC is 241.5 V. The SMPPT-based PV-system shows a PTP steady-state voltage oscillations of 5.3 V, where the system exhibits only 0.5 PTP steady-state voltage for the MMPPT system configuration. Hence, the reduced oscillations level of the proposed PV-system enhances the system power quality, elements voltage and current stresses, power loss, and improve its overall efficiency. Figure 11c shows the PV generated power of the two configurations. The average generated power of SMPPT is 13.15 kW, with an efficiency of 99.7%, while MMPPT’s average power is 13.18, with efficiency of 99.85%.
Results of pattern 1.
The shift from the optimal voltage and the oscillation levels plays a crucial role in contributing to difference in the output power values. Also, the duty-cycles are illustrated in Fig. 11d. The settling time and the oscillation level in MMPPT are smaller than that of SMPPT.
In this scenario, the PV-system is subdivided into two levels of irradiations: the first level forms a constant irradiation profile of 1000 W/m² for first two strings (1st & 2nd strings). Second irradiance level is exposed to the remaining two strings (3rd & 4th strings), in which a uniform irradiance profile of 1000 W/m² is applied for 0.5 s and then stepped down to 500 W/m² at t = 0.5 s for another 0.5 s, as depicted in Fig. 12.
Results of pattern 2.
This pattern protests the performance of the two configurations and the anticipated control strategy. Figure 12a shows the estimated irradiance profiles of the SMPPT and MMPPT configurations. The SMPPT-based estimated irradiance does not correctly track the actual radiation profile, as the irradiance sensor is installed at the first string, which is always exposed to 1000 W/m². However, the MMPPT-based estimated irradiance profile performs as an online tracking strategy for irradiance level variation at any location of the PV-system. Moreover, the effect of irradiance variation on the PV-system performance, power quality, and efficiency is less compared to the SMPPT configuration. Figure 12b shows the PV-system estimated-voltage for both configurations. In the SMPPT configuration, the PV-system estimated-voltage remains unchanged following the same behaviour of the wrong estimated radiation. Also, the average value of the estimated-voltage at the MPP is 242.7 V and the oscillation level is around 4 V PTP. In MMPPT configuration, the estimated output-voltage from the first two strings is depicted in Fig. 12b with the blue colour. The average output-voltage is 240.5 V, and the oscillation level is 0.5 V PTP. The estimated output voltage of the last two strings is shown in green colour. The average output voltage is 225.29 V with an oscillation level of 1.3 V, whereas the MPP-voltage is 230.7 V at 500 W/m².
The PV generated-powers of the two PV-system configurations are depicted in Fig. 12c. At the time interval between (0–0.5 s), it shows a performance similar to pattern-1. At the second interval (0.5–1 s), the PV-system output-powers are decreased due to the effect of radiation. The theoretical optimal output-power is 9.62 kW under radiation of 500 w/m². For the SMPPT structure, the average output-power is 9.51 kW with 98.85% system efficiency. In the other side, the average output-power of the MMPPT configuration is 9.6 kW with a tracking efficiency of 99.79%. In addition, the converter duty-cycles for both SMPPT and MMPPT configurations are portrayed in Fig. 12d. Obviously, the duty-cycles of both cases remain unchanged during the first 0.5 s due to the uniform applied irradiance profiles.
However, the duty-cycles of the SMPPT remain unchanged during the time interval (0.5–1 s) despite the irradiance change, which reveals the tracking system weakness under PCSs. On the other side, the duty-cycle of the 3rd and 4th strings of the MMPPT changed for voltage boosting, which is portrayed with green color in Fig. 12d. Moreover, Fig. 12 confirms the effectiveness of the MMPPT for shaded and unshaded PV-system, compared to the SMPPT configuration. These results show that the voltage stability in MMPPT configuration is better than that of SMPPT.
In this pattern, the radiation on the first two strings is a uniform irradiance profile of 1000 W/m². On the other two strings, the radiation has two step-changes. First, the radiation starts with 1000 W/m² at all strings for 0.3 s (0–0.3 s), changes to 500 W/m² for 0.3 s (0.3–0.6 s) and decreased to 250 w/m² for 0.4 s (0.6–1 s) as represented in Fig. 13. Figure 13a shows the estimated radiation of the SMPPT and MMPPT configurations. In SMPPT configuration, the results remain similar to the mistaken results of the previous pattern-2 as the irradiance sensor is installed at the first string. In the MMPPT, the estimated radiation always tracks the actual radiation as cleared in the green color of Fig. 13a. Similarly, the estimated output-voltage of two configurations is illustrated in Fig. 13b. The average estimated voltage of SMPPT is 242.7 V with an oscillation level of 5.3 V, as depicted with red color. Also, the estimated voltage of the first two strings is depicted in blue color, which has a uniform irradiance level of 1000 w/m². This voltage has an average value of 240.6 V with an oscillation value of 1.3 V PTP. The other voltage tracks the changes in the radiation, exposed to the third and fourth strings, illustrated in green color. For 500 w/m² radiation level, this voltage has an average value of 225.29 V with a PTP oscillation level of 1.3 V. For irradiation level of 250 w/m², the estimated voltage is 210.13 V, while the optimal voltage at MPP is 217.7 V and the PTP oscillation level is 1.3 V. Figure 13c shows the output power of the two PV-system configurations. The optimal power in the case of 250 W/m² is 7.92 kW. Obviously, the power obtained from the MMPPT is higher than the power generated from the SMPPT configuration. In the time interval from 0.7 to 1 s under 250 w/m².
Results of pattern 3.
The average output-power in from SMPPT and MMPPT configurations are 7.65 and 7.9, respectively, and the percentage efficiencies of the two configurations are 96.92 and 99.7, respectively. Figure 13d shows the duty-cycle generated by the controller. Duty cycle does not change in case of SMPPT. In MMPPT configuration, the duty-cycle tracks the estimated-irradiance profile and achieves estimated voltages close to the V_MPP at different radiation levels. However, it remains unchanged for the SMPPT configuration.
This section provides a comparative view between the two proposed systems. Tables 4 and 5 summarize all results of SMPPT and MMPPT, respectively. The first column shows the pattern number as explained previously. The second column declares the time of each period in the specified pattern. The next two columns illustrate the operating voltage and the duty cycle of each DC-DC converter, respectively. The following column shows the output power of the system under pattern, the last column exhibits the efficiency of the approach under any studied pattern to make a comparison between two approaches.
As the estimated radiation is provided for the first string, values of estimated voltages and duty cycle do not change with pattern variation. These values have an average value of 242.7 V and 0.676, respectively. The constancy of this value can be attributed to the fact that the SMMPT estimation is exclusively conducted for the first string within the system. In this initial pattern, the voltage closely approximates Vmpp. However, as we move to the subsequent two patterns, the voltage differential between them expands, resulting in decreased system efficiency, as evidenced by the power and efficiency columns. This configuration leads to misalliances and reduction in the generated power. The system with SMPPT configuration cannot withstand under severe weather condition change. By comparing the output power extracted from the system and the optimal power, more shading runs less efficiency. At radiation values of 1000, 500 and 250 W/m², the PV out power is 13.15, 9.51 and 7.68 kW, respectively. Also, the system efficiency for the aforementioned radiation is 99.77, 9.85 and 96.92%, respectively. The system efficiency has distorted with alteration of radiation level, which is not appropriate operation under PSCs.
On the other hand, Table 5 clarifies the results of MMPPT configuration. The MMPPT strategy extracts the MPPs of each string individually under any weather condition. The values of voltages and duty cycle changed with radiation deviation, and be more nearer from the V_MPP than that of SMPPT configuration. At radiation levels of 1000, the PV voltage is 240.6 V while the voltage at MPP is 241.5 V. In the second level of radiation, the PV output voltage is 225.29 V while V_MPP is 230.7 V, compared this value with the unchanged value of the SMMPPT of 242.7 V. At the lowest radiation, the PV output voltage and V_MPP are 210.13 V and 217.7 V, respectively. The PV generated power and efficiency from the MMPPT configuration at the three radiation levels are; 13.16 kW and 99.85%, 9.6 kW and 99.79% and 7.9 kW and 99.7%, respectively.
The efficiency of MMPPT configuration is almost constant over all shading patterns which demonstrates the effectiveness and validation of the proposed methodology. The assessment has been accomplished by observing the performance of the systems under different radiation patterns. The efficiency is computed across all time periods to assess the dynamic performance of the proposed scheme.
To ensure the validity of the intended dual-stage PV-system, the following study conducts the system investigation considering a real irradiance profile of Benban city, which located in the south of Egypt⁸², see Fig. 14. Two irradiance profiles are employed in this study; the first profile represents the normal radiation that is applied to the first three strings as detected in Fig. 14a. Figure 14b provides a visual representation of the estimated irradiance for the fourth string. The controller accurately tracks the actual radiation profile and provides the estimated radiation. Figure 14c illustrates the PV-output voltage, which includes fluctuations due to non-uniform irradiance. In addition, the DDCC is used to generate duty-cycle for the converters. Figure 14d shows each duty-cycle waveform that corresponds to its voltage value in Fig. 14c. Obviously, the DDCC successfully adapts the rapid changes in solar radiation profile. Also, the PV output-power from the PV-module, shown in Fig. 14e, closely follows the irradiance curve as expected. At zero radiation, the net output power is almost zero and gradually increases to a maximum value, and then decays again to zero at 2.1 s. In Fig. 14f, the PV output-currents of normal irradiance strings are identical as shown in the blue color, while the current generated by the fourth string is presented in the green color with the same pattern as the irradiance profile. In summary of PV side results, the presented results demonstrate the effectiveness of the controller to accurately track the actual radiation profile even under tough and real irradiance profiles with reduced contained error. Also, the PV output power closely follows the irradiance curve, and the system can adapt to non-uniform irradiance and changing weather conditions over time. Grid side results illustrate the DC-link voltage, grid active and reactive powers, grid d-q axes currents, and the grid-voltages and currents. Figure 14g displays the reference DC-link voltage, fixed to 750 V, and the actual voltage measured at the inverter’s front-end terminals. As seen in Fig. 14g, the system displays a reduced oscillation level, even under rapid irradiance variations. Figure 14h illustrates the d-q axes currents of the system. The d-axis current typically tracks the irradiance profile and power curve, while the q-axis remains at zero that confirms the unity PF operation of the grid-tied system. Also, Fig. 14i depicts the active and reactive power obtained by the system, in which the active power follows the irradiation patterns, and the reactive power is almost zero. Furthermore, Fig. 14j shows the grid phase-voltage and current, which are in-phase that approves the unity PF operation of the system.
Results of real data.
The results demonstrate the PV-system transient and steady-state stability, and reliability for grid-integrated applications. The presented findings provide precious insights into the performance and behavior of the dual-stage PV-system considering both SMPPT and MMPPT configurations. These results are of significant importance for selecting PV-system configuration and design of grid-connected systems in the future.
In this paper, an investigation into the use of a MMPPT configuration as an alternative to SMPPT configuration to address the issue of partial shading in photovoltaic systems. The contributions are twofold: firstly, implementation of radiation estimation technique to reduce system costs by minimizing the required number of sensors. Secondly, using a direct duty cycle method to determine the converters’ duty cycles, simplifying the control system. This paper proposes and simulates two systems using MATLAB-Simulink, comparing the SMPPT and MMPPT systems under varying radiation profiles. Under these conditions, the average efficiency of the SMPPT system is found to be 98.98%, while the MMPPT system achieves an efficiency of 99.81%. These findings validate the proposed approach. A real radiation dataset from Benban, a location in southern Egypt, is used in MMPPT configuration. The results demonstrate that the proposed control system enhances overall system effectiveness while reducing installation costs. These findings hold significant importance as they offer a practical solution to mitigate the challenges of partial shading and enhance the efficiency of photovoltaic systems. The proposed methodology can be readily applied to future PV system designs, offering improved performance and cost savings. Additionally, the approach enables the system to operate at higher efficiency levels. It is worth noting that the proposed control system is characterized by its simplicity, adaptability to rapid climate changes, and robust performance during operation.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Ahmed, M., Harbi, I., Kennel, R., Rodriguez, J. & Abdelrahem, M. Model-based maximum power point tracking algorithm with constant power generation capability and fast DC-link dynamics for two-stage PV systems. IEEE Access 10, 48551–48568 (2022).
Article  Google Scholar 
Ismeil, M. A., Omar, M. M., Yousef, A.-R. & Ismail, A. Review of distributed power electronics solutions for PV generation systems. SVU Int. J. Eng. Sci. Appl. 4(1), 85–90 (2023).
Google Scholar 
Pathak, P. K., Yadav, A. K., Padmanaban, S. & Alvi, P. Design of robust multi-rating battery charger for charging station of electric vehicles via solar PV system. Electr. Power Compon. Syst. 50(14–15), 751–761 (2022).
Article  Google Scholar 
Raj, A. & Praveen, R. Highly efficient DC-DC boost converter implemented with improved MPPT algorithm for utility level photovoltaic applications. Ain Shams Eng. J. 13(3), 101617 (2022).
Article  Google Scholar 
Ali, A. I., Sayed, M. A. & Mohamed, E. E. Modified efficient perturb and observe maximum power point tracking technique for grid-tied PV system. Int. J. Electr. Power Energy Syst. 99, 192–202 (2018).
Article  Google Scholar 
Ahmed, J. & Salam, Z. A critical evaluation on maximum power point tracking methods for partial shading in PV systems. Renew. Sustain. Energy Rev. 47, 933–953 (2015).
Article  Google Scholar 
Husain, M. A., Jain, A. & Tariq, A. A novel fast mutable duty (FMD) MPPT technique for solar PV system with reduced searching area. J. Renew. Sustain. Energy https://doi.org/10.1063/1.4963314 (2016).
Article  Google Scholar 
Celikel, R., Yilmaz, M. & Gundogdu, A. A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions. Renew. Energy 184, 361–373 (2022).
Article  Google Scholar 
Patel, H. & Agarwal, V. Maximum power point tracking scheme for PV systems operating under partially shaded conditions. IEEE Trans. Ind. Electron. 55(4), 1689–1698 (2008).
Article  Google Scholar 
Jalil, M. F., Sharma, D. & Bansal, R. Cross kit reconfiguration algorithm for enhanced output power of PV array during shading mismatch conditions. Optik 288, 171218 (2023).
Article  ADS  Google Scholar 
Murtaza, A., Chiaberge, M., Spertino, F., Boero, D. & De Giuseppe, M. A maximum power point tracking technique based on bypass diode mechanism for PV arrays under partial shading. Energy Build. 73, 13–25 (2014).
Article  Google Scholar 
Pathak, P. K., Yadav, A. K. & Alvi, P. A state-of-the-art review on shading mitigation techniques in solar photovoltaics via meta-heuristic approach. Neural Comput. Appl. 34, 1–39 (2022).
Article  Google Scholar 
Ali, A. I. M. & Mohamed, H. R. A. Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation. Int. J. Electr. Power Energy Syst. 137, 107805 (2022).
Article  Google Scholar 
Rezazadeh, S. et al. Photovoltaic array reconfiguration under partial shading conditions for maximum power extraction: A state-of-the-art review and new solution method. Energy Convers. Manag. 258, 115468 (2022).
Article  Google Scholar 
Cherukuri, S. K. et al. A novel array configuration technique for improving the power output of the partial shaded photovoltaic system. IEEE Access 10, 15056–15067 (2022).
Article  Google Scholar 
Chang, C. C. W. et al. Nature-inspired optimization algorithms in solving partial shading problems: A systematic review. Arch. Comput. Methods Eng. 30(1), 223–249 (2023).
Article  Google Scholar 
Ali, A. I. M. et al. An enhanced P&O MPPT algorithm with concise search area for grid-tied PV systems. IEEE Access 11, 79408–79421 (2023).
Article  Google Scholar 
Mamun, M. A. A., Hasanuzzaman, M. & Selvaraj, J. Experimental investigation of the effect of partial shading on photovoltaic performance. IET Renew. Power Gen. 11(7), 912–921 (2017).
Article  Google Scholar 
Osmani, K. et al. Mitigating the effects of partial shading on PV system’s performance through PV array reconfiguration: A review. Therm. Sci. Eng. Prog. 31, 101280 (2022).
Article  Google Scholar 
Pathak, P. K., Padmanaban, S., Yadav, A. K., Alvi, P. & Khan, B. Modified incremental conductance MPPT algorithm for SPV-based grid-tied and stand-alone systems. IET Gen. Transm. Distrib. 16(4), 776–791 (2022).
Article  Google Scholar 
Li, C. et al. A novel methodology for partial shading diagnosis using the electrical parameters of photovoltaic strings. IEEE J. Photovolta. 12(4), 1027–1035 (2022).
Article  CAS  Google Scholar 
Jeong, H., Lee, H., Liu, Y.-C. & Kim, K. A. Review of differential power processing converter techniques for photovoltaic applications. IEEE Trans. Energy Convers. 34(1), 351–360 (2018).
Article  ADS  Google Scholar 
Krishna, G. S. & Moger, T. Reconfiguration strategies for reducing partial shading effects in photovoltaic arrays: State of the art. Solar Energy 182, 429–452 (2019).
Article  ADS  Google Scholar 
Yang, B. et al. PV arrays reconfiguration for partial shading mitigation: Recent advances, challenges and perspectives. Energy Convers. Manag. 247, 114738 (2021).
Article  Google Scholar 
Ramli, M. Z. & Salam, Z. Performance evaluation of dc power optimizer (DCPO) for photovoltaic (PV) system during partial shading. Renew. energy 139, 1336–1354 (2019).
Article  Google Scholar 
Sharma, D., Jalil, M. F., Ansari, M. S. & Bansal, R. A review of PV array reconfiguration techniques for maximum power extraction under partial shading conditions. Optik 275, 170559 (2023).
Article  ADS  Google Scholar 
Kamran, M. et al. Implementation of improved Perturb & Observe MPPT technique with confined search space for standalone photovoltaic system. J. King Saud Univ. Eng. Sci. 32(7), 432–441 (2020).
Google Scholar 
Chavan, V. C., Mikkili, S. & Senjyu, T. Experimental validation of novel shade dispersion PV reconfiguration technique to enhance maximum power under PSCs. CPSS Trans. Power Electron. Appl. https://doi.org/10.24295/CPSSTPEA.2023.00014 (2023).
Article  Google Scholar 
Ajmal, A. M., Babu, T. S., Ramachandaramurthy, V. K., Yousri, D. & Ekanayake, J. B. Static and dynamic reconfiguration approaches for mitigation of partial shading influence in photovoltaic arrays. Sustain. Energy Technol. Assess. 40, 100738 (2020).
Google Scholar 
Horoufiany, M. & Ghandehari, R. Optimization of the Sudoku based reconfiguration technique for PV arrays power enhancement under mutual shading conditions. Solar Energy 159, 1037–1046 (2018).
Article  ADS  Google Scholar 
Srinivasa Rao, P., Dinesh, P., Saravana Ilango, G. & Nagamani, C. Optimal Su-Do-Ku based interconnection scheme for increased power output from PV array under partial shading conditions. Front. Energy 9, 199–210 (2015).
Article  Google Scholar 
Pendem, S. R. & Mikkili, S. Assessment of cross-coupling effects in PV string-integrated-converters with P&O MPPT algorithm under various partial shading patterns. CSEE J. Power Energy Syst. 8(4), 1013–1028 (2020).
Google Scholar 
Dhanalakshmi, B. & Rajasekar, N. A novel competence square based PV array reconfiguration technique for solar PV maximum power extraction. Energy Convers. Manag. 174, 897–912 (2018).
Article  Google Scholar 
Dhanalakshmi, B. & Rajasekar, N. Dominance square based array reconfiguration scheme for power loss reduction in solar PhotoVoltaic (PV) systems. Energy Convers. Manag. 156, 84–102 (2018).
Article  Google Scholar 
Rezazadeh, S. et al. A novel prime numbers-based PV array reconfiguration solution to produce maximum energy under partial shade conditions. Sustain. Energy Technol. Assess. 47, 101498 (2021).
Google Scholar 
Kiran, S. R. et al. Reduced simulative performance analysis of variable step size ANN based MPPT techniques for partially shaded solar PV systems. IEEE Access 10, 48875–48889 (2022).
Article  Google Scholar 
Ali, M. N., Mahmoud, K., Lehtonen, M. & Darwish, M. M. An efficient fuzzy-logic based variable-step incremental conductance MPPT method for grid-connected PV systems. IEEE Access 9, 26420–26430 (2021).
Article  Google Scholar 
Zou, Y., Yan, F., Wang, X. & Zhang, J. An efficient fuzzy logic control algorithm for photovoltaic maximum power point tracking under partial shading condition. J. Frankl. Inst. 357(6), 3135–3149 (2020).
Article  MATH  Google Scholar 
Ali, Z. M., Quynh, N. V., Dadfar, S. & Nakamura, H. Variable step size perturb and observe MPPT controller by applying θ-modified krill herd algorithm-sliding mode controller under partially shaded conditions. J. Clean. Prod. 271, 122243 (2020).
Article  Google Scholar 
Ahmad, F. F., Ghenai, C., Hamid, A. K. & Bettayeb, M. Application of sliding mode control for maximum power point tracking of solar photovoltaic systems: A comprehensive review. Annu. Rev. Control 49, 173–196 (2020).
Article  MathSciNet  Google Scholar 
Zhang, R., Ong, S. & Nee, A. Y. A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Appl. Soft Comput. 37, 521–532 (2015).
Article  Google Scholar 
Li, H., Yang, D., Su, W., Lü, J. & Yu, X. An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading. IEEE Trans. Ind. Electron. 66(1), 265–275 (2018).
Article  Google Scholar 
Koad, R. B., Zobaa, A. F. & El-Shahat, A. A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems. IEEE Trans. Sustain. Energy 8(2), 468–476 (2016).
Article  ADS  Google Scholar 
Sundareswaran, K., Sankar, P., Nayak, P. S. R., Simon, S. P. & Palani, S. Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony. IEEE Trans. Sustain. Energy 6(1), 198–209 (2014).
Article  ADS  Google Scholar 
Mohanty, S., Subudhi, B. & Ray, P. K. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Trans. Sustain. Energy 7(1), 181–188 (2015).
Article  ADS  Google Scholar 
Motahhir, S., Chtita, S., Chouder, A. & El Hammoumi, A. Enhanced energy output from a PV system under partial shaded conditions through Grey wolf optimizer. Clean. Eng. Technol. 9, 100533 (2022).
Article  Google Scholar 
Xing, X., Jia, L. & Yang, X. A novel global FPPT strategy for PV system under partial shading condition. IET Renew. Power Gen. 17, 3403–3423 (2023).
Article  Google Scholar 
Ahmed, S., Mekhilef, S., Mubin, M., Tey, K. S. & Kermadi, M. An adaptive perturb and observe algorithm with enhanced skipping feature for fast global maximum power point tracking under partial shading conditions. IEEE Trans. Power Electron. 262, 111817 (2023).
Google Scholar 
Deboucha, H., Kermadi, M., Mekhilef, S. & Belaid, S. L. Ultra-fast and accurate MPPT control structure for mobile PV system under fast-changing atmospheric conditions. IEEE Trans. Sustain. Energy 14, 2168–2176 (2023).
Article  ADS  Google Scholar 
H. M. Ashraf, M. Elahi, and C. H. Kim, A novel technique using stretching, repulsion, and chimp optimization algorithm to find global maximum power point considering complex partial shading conditions. Int. J. Circuit Theory Appl.
Belkaid, A., Colak, I., Kayisli, K. & Bayindir, R. Design and implementation of a cuk converter controlled by a direct duty cycle INC-MPPT in PV battery system. Int. J. Smart Grid-ijSmartGrid 3(1), 19–25 (2019).
Google Scholar 
Gu, Z., Xiong, G. & Fu, X. Parameter extraction of solar photovoltaic cell and module models with metaheuristic algorithms: A review. Sustainability 15(4), 3312 (2023).
Article  Google Scholar 
Chauhan, S., Vashishtha, G. & Kumar, A. Approximating parameters of photovoltaic models using an amended reptile search algorithm. J. Ambient Intell. Humaniz. Comput. 14(7), 9073–9088 (2023).
Article  Google Scholar 
Daryaei, M., Esteki, M. & Khajehoddin, S. A. High efficiency and full MPPT range partial power processing PV module-integrated converter. IEEE Trans. Power Electron. 38(5), 6627–6641 (2023).
Article  ADS  Google Scholar 
Ibrahim, M. H. et al. Optimizing step-size of perturb & observe and incremental conductance MPPT techniques using PSO for grid-tied PV system. IEEE Access 11, 13079–13090 (2023).
Article  Google Scholar 
Senthilkumar, S., Mohan, V. & Krithiga, G. Brief review on solar photovoltaic parameter estimation of single and double diode model using evolutionary algorithms. Int. J. Eng. Technol. Manag. Res. 10(1), 64–78 (2023).
Article  Google Scholar 
Xu, J., Zhou, C. & Li, W. Photovoltaic single diode model parameter extraction by dI/dV-assisted deterministic method. Solar Energy 251, 30–38 (2023).
Article  ADS  Google Scholar 
Pavković, D., Kasać, J., Krznar, M. & Cipek, M. Adaptive constant-current/constant-voltage charging of a battery cell based on cell open-circuit voltage estimation. World Electr. Veh. J. 14(6), 155 (2023).
Article  Google Scholar 
Firpo, P., Ravera, A., Oliveri, A., Lodi, M. & Storace, M. Use of a partially saturating inductor in a boost converter with model predictive control. Electronics 12(14), 3013 (2023).
Article  Google Scholar 
Reddy, B. R. S., Reddy, V. V. & Kumar, M. V. Modelling and analysis of DC-DC converters with AI based MPP tracking approaches for grid-tied PV-fuel cell system. Electr. Power Syst. Res. 216, 109053 (2023).
Article  Google Scholar 
Shaikh, A. M. et al. Comparative analysis of different MPPT techniques using boost converter for photovoltaic systems under dynamic shading conditions. Sustain. Energy Technol. Assess. 57, 103259 (2023).
Google Scholar 
Katche, M. L., Makokha, A. B., Zachary, S. O. & Adaramola, M. S. A comprehensive review of maximum power point tracking (mppt) techniques used in solar pv systems. Energies 16(5), 2206 (2023).
Article  Google Scholar 
Gulzar, M. M., Iqbal, A., Sibtain, D. & Khalid, M. An innovative converterless solar PV control strategy for a grid connected hybrid PV/wind/fuel-cell system coupled with battery energy storage. IEEE Access 11, 23245–23259 (2023).
Article  Google Scholar 
Alharbi, Y., Darwish, A. & Ma, X. A comprehensive review of distributed MPPT for grid-tied PV systems at the sub-module level. Energies 16(14), 5468 (2023).
Article  Google Scholar 
Manna, S. et al. Design and implementation of a new adaptive MPPT controller for solar PV systems. Energy Rep. 9, 1818–1829 (2023).
Article  Google Scholar 
Ragb, O. & Bakr, H. A new technique for estimation of photovoltaic system and tracking power peaks of PV array under partial shading. Energy 268, 126680 (2023).
Article  CAS  Google Scholar 
Jha, V. Generalized modelling of PV module and different PV array configurations under partial shading condition. Sustain. Energy Technol. Assess. 56, 103021 (2023).
Google Scholar 
Sajid, I. et al. Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions. Energy 283, 129169 (2023).
Article  Google Scholar 
Nassef, A. M., Houssein, E. H., Helmy, B.E.-D. & Rezk, H. Modified honey badger algorithm based global MPPT for triple-junction solar photovoltaic system under partial shading condition and global optimization. Energy 254, 124363 (2022).
Article  Google Scholar 
Javed, S. & Ishaque, K. A comprehensive analyses with new findings of different PSO variants for MPPT problem under partial shading. Ain Shams Eng. J. 13(5), 101680 (2022).
Article  Google Scholar 
Naseem, M. et al. Assessment of meta-heuristic and classical methods for GMPPT of PV system. Trans. Electr. Electron. Mater. 22, 217–234 (2021).
Article  Google Scholar 
Kermadi, M. et al. Recent developments of MPPT techniques for PV systems under partial shading conditions: A critical review and performance evaluation. IET Renew. Power Gen. 14(17), 3401–3417 (2020).
Article  Google Scholar 
Rezk, H. et al. A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system. Renew. Sustain. Energy Rev. 115, 109372 (2019).
Article  Google Scholar 
Naseem, M. et al. A Spider monkey optimization based global maximum power point tracking technique for photovoltaic systems. In 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), pp 1–6 (IEEE, 2022).
Nobile, G. et al. Study on mismatch losses in large PV plants: Data analysis of a case study and modeling approach. In 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), pp 858–864 (IEEE, 2020).
Ram, J. P. & Rajasekar, N. A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC). Energy 118, 512–525 (2017).
Article  Google Scholar 
Gaafar, M. A., Ibrahim, E. A. & Orabi, M. Multi-input transformer-less four-wire microinverter with distributed MPPT for PV systems. Int. J. Circuit Theory Appl. 49(6), 1704–1725 (2021).
Article  Google Scholar 
Rizzo, S. A. & Scelba, G. ANN based MPPT method for rapidly variable shading conditions. Appl. Energy 145, 124–132 (2015).
Article  Google Scholar 
Ahmed, S., Mekhilef, S., Mubin, M., Tey, K. S. & Kermadi, M. An enhanced scanning technique for flexible power point tracking under partial shading condition. Solar Energy 262, 111817 (2023).
Article  ADS  Google Scholar 
Xu, S., Gao, Y., Zhou, G. & Mao, G. A global maximum power point tracking algorithm for photovoltaic systems under partially shaded conditions using modified maximum power trapezium method. IEEE Trans. Ind. Electron. 68(1), 370–380 (2020).
Article  Google Scholar 
Peng, B.-R., Ho, K.-C. & Liu, Y.-H. A novel and fast MPPT method suitable for both fast changing and partially shaded conditions. IEEE Trans. Ind. Electron. 65(4), 3240–3251 (2017).
Article  Google Scholar 
Youssef, A.-R., Mallah, M., Ali, A., Shaaban, M. F. & Mohamed, E. E. Enhancement of microgrid frequency stability based on the combined power-to-hydrogen-to-power technology under high penetration renewable units. Energies 16(8), 3377 (2023).
Article  CAS  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, South Valley University, Qena, 83523, Egypt
Abdel-Raheem Youssef, Mostafa M. Hefny & Ahmed Ismail M. Ali
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
A.-R.Y. and A.I.M.A. revised the language and contributions and verified the simulation. M.M.H. wrote the main manuscript text. All authors reviewed the manuscript.
Correspondence to Mostafa M. Hefny.
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
Youssef, AR., Hefny, M.M. & Ali, A.I.M. Investigation of single and multiple MPPT structures of solar PV-system under partial shading conditions considering direct duty-cycle controller. Sci Rep 13, 19051 (2023). https://doi.org/10.1038/s41598-023-46165-1
Download citation
Received: 04 September 2023
Accepted: 28 October 2023
Published: 03 November 2023
Version of record: 03 November 2023
DOI: https://doi.org/10.1038/s41598-023-46165-1
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.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Scientific Reports (Sci Rep)
ISSN 2045-2322 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

source

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 Data volume 11, Article number: 198 (2024) Cite this article
15k Accesses
71 Citations
34 Altmetric
Metrics details
We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated from publicly available Sentinel-2 imagery. Meanwhile, topographic features consisting of slope and aspect that are sensitive to PV locations are also included, aiming to construct a multi-dimensional and discriminative feature space. Finally, the trained random forest model is adopted to predict PV power stations of China parallelly on GEE. Technical validation has been carefully performed across China which achieved a satisfactory accuracy over 89%. Above all, as the first publicly released 10-m national-scale distribution dataset of China’s ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.
As an indispensable part of renewable energy sources, photovoltaic (PV) power has drawn increasingly more attention around the globe nowadays^1,2. The total global capacity of PV power has been reached 115 GW in 2019², justifying the significance of PV power in energy industries, especially in the context of fossil fuel shortage and overexploitation. Meanwhile, PV power belongs to an environmentally friendly way for energy utilization with few carbon emissions^3,4, which shows a positive effect against global warming.
Due to the above advantages, PV power stations were firstly welcomed in developed countries such as the United Kingdom⁵ and the United States⁶. Both large-scale ground-mounted PV power stations and distributed roof-mounted PV panels emerged with great speed. Meanwhile, PV power has gradually raised huge concerns in China. According to statistics⁷, the installed capacity of PV power in China was only 100 MW in 2007, but grew rapidly to 205,000 MW in 2019, with an average growth of 17,075 MW per year. It should be noted that China’s central government released the Carbon Peak and Carbon Neutrality strategy in 2020, which committed that China’s carbon emissions would reach the peak by 2030 and achieve carbon neutrality by 2060⁸. Therefore, it is predictable that PV power would play an increasingly essential role in the near future. The monitoring of PV power stations would be meaningful for both researchers and government officials.
As mentioned above, the last decade has witnessed the widespread of PV power stations in China, where much previous gobi, grassland, water bodies and mountain land have now been covered by newly-built PV power stations (Fig. 1). When looking into the publicly released scientific data of China’s PV power stations, only the statistical data of PV’s installed capacity for each province could be achieved, lacking the spatial distribution data that could provide more details of China’s PV power industry. Although some researchers released several PV power station maps, most only met a medium resolution of 30 meters^9,10. There thus still lacks a national map of China’s PV power stations with a higher spatial resolution (i.e., 10 meters) that could provide a global understanding of PV’s spatial deployment patterns. Considering that the large-scale grounded-mounted PV power stations almost cover more than 90% of the total PV capacity in China, we attempt to provide the first publicly available 10-m national map of ground-mounted PV power stations in this dataset.
Examples of PV power stations in China. The land used for PV power stations includes gobi (left), grassland (top), water bodies (right), mountain land (bottom), etc.
As for PV power station mapping, previous methods mainly focused on field survey and visual inspection, where manual annotation was performed to delineate the locations or boundaries based on the remote sensing imagery. However, this approach is time-consuming with low efficiency, especially when facing the task of national-scale or continental-scale mapping. To tackle this issue, several researchers resorted to automatic mapping methods such as machine learning^11,12,13 or deep learning^{14,15,16,17,18}. A general pipeline includes sample selection, feature extraction, model training and validation, accuracy assessment, etc. For instance, Plakman et al.¹¹ designed an object-based random forest classification method for solar park detection in Netherlands and achieved a user accuracy of 92.39%. Costa et al.¹⁵ utilized several deep semantic segmentation models to map the PV solar plants in Brazil with an IoU (intersection of union) of 91%.
Although deep learning has been popular nowadays, the training of a deep neural network needs a huge number of labelled samples¹⁹. Otherwise, the deep learning model would be easily overfitted on limited training samples and show poor performance when predicting the new unseen datasets. Under this context, classic machine learning methods such as random forest (RF)²⁰ together with powerful cloud computing platforms like Google Earth Engine (GEE, https://code.earthengine.google.com/)²¹ would be a better choice for the task of large-scale mapping from remote sensing data.
The objective of this study is to provide the first publicly released 10-m national map of ground-mounted PV power stations of China in 2020. Specifically, Sentinel-2 multi-spectral imagery²² was used as data sources, from which random forest classifier was utilized to predict these PV power stations via GEE cloud computing platform. Both partition modelling and active learning strategy were adopted to tackle the data unbalance problem in large-scale mapping. In addition, the fine-scale classification data of PV power stations could also provide a huge amount of labelled samples, making it possible to train a sophisticated deep learning model in the near future.
Above all, we provide a 10-m national-scale map for PV power stations in China of 2020, which would be of particular interest to the following research areas.
Based on the fine-scaled national map of PV power stations, it would be possible to estimate and predict the accurate generating capacity, when considering both solar radiation and weather conditions above these PV power stations²³.
It would be much easier for the site selection of future PV power stations in China^24,25 according to the dataset provided in this study.
According to previous land use land cover (LULC) data and the PV power station map²⁶, it would be interesting to study where, how, and why the other LULC changes into PV power stations.
The detailed map of PV power stations would provide more clues to test the effect of China’s newly established energy policies⁸.
This dataset could also provide a large number of PV power station samples within China with high quality, which makes it possible to train a robust deep learning model¹⁷.
The overall workflow is depicted in Fig. 2, including study area partition, feature extraction, PV power station classification based on random forest and active learning, post-processing and statistical analysis.
Overall workflow of this study. Both GEE cloud platform and local processing (QGIS) are used. The methods consist of data partition, feature extraction, random forest classification & active learning and zonal analysis.
Specifically, when performing national-scale PV power station mapping, if only one classification model was built, it would be difficult to yield high accuracy due to the samples’ distribution variation across large regions. To tackle this issue, we adopt the partition methodology, where a series of RF classifiers have been built for each province of China to increase the model’s sensitivity to local samples. Afterwards, a multi-dimensional feature space could be constructed, which consists of spectral features, texture features and topographical features, aiming to increase the inter-class separability. Next, active learning is synthesized with RF classifier to perform a coarse-to-fine procedure for PV power station mapping. Finally, post-processing is adopted to remove the isolated noises while spatial statistics (i.e., zonal analysis) are performed for subsequent analysis.
As an important part of European Space Agency’s (ESA) Copernicus mission, Sentinel-2 satellite provides a high resolution (10-m) multi-spectral remote sensing data with a global coverage²². Compared with NASA’s Landsat data, Sentinel-2 shows two major advantages, making it a better choice for national-scale PV power station mapping. Firstly, Sentinel-2 has a finer spatial resolution (10-m) than that of Landsat (30-m), which could show more details of PV power stations. Besides, the mixed pixel would also be relieved for Sentinel-2 data due to the high resolution, which could increase the quality of classification results, especially at the boundaries. Secondly, Sentinel-2 has a shorter revisit period (5 days) than Landsat (15 days), which makes it easier for GEE to prepare the cloud-free data for subsequent PV power station classification. Since Sentinel-2 consists of two satellites, Sentinel-2A and Sentinel-2B, it could be operated in a collaborative way to shorten the revisit time to provide denser sequential observation data than Landsat. When performing the national-scale or continental-scale classification, the cloud-free remote sensing data is of great significance. These two reasons stated above make Sentinel-2 a better choice for PV power station mapping across China.
Meanwhile, as for the period of Sentinel-2 images, the spring of 2020 was selected from March to May. The reason is as follows. Firstly, winter time was not select because there is a high probability that the snow would cover PV panels, which would lead to classification errors between snow and PV power stations. Summer and autumn were not considered because these two seasons witness a flourishing vegetation. As there are also some vegetation (mostly grass) inside the PV power stations, there would be a high possibility to confuse PV stations and other vegetations. When in spring, most vegetation just turns green, PV power stations and the surroundings show a great difference, making it easier to separate them from each other hence to achieve a high classification accuracy. Besides, due to the existence of clouds, multi-temporal images were used rather than mono-temporal image. The reason is that it is impossible to obtain national-scale cloudless remote sensing images within a single time phase. To tackle this issue, the period from March to May was selected and the image composition method from GEE was used to get the cloudless data.
Moreover, GEE offers an official code for data pre-processing and data composition over large-scale regions. Users just set a starting date and an end date, GEE will automatically stitch the satellite images within the time interval to avoid both data missing and cloud coverage. This official code proves to be effective in large-scale mapping and has been widely used by previous researchers²¹. In this study, we selected the satellite images from March to May of 2020 to shorten the time interval to reduce the possibility of witnessing the inconsistency of satellite images.
Considering that the locations of PV power stations are closely related to terrains, therefore, we also considered the topographic features as input variables for PV power station classification. The reason is simple, as the PV panel should be placed tilted where its normal is coincided with the solar incident angle to get as much solar radiation as possible, most PV power stations would be built on flat ground or sunny side rather than the shady side of the mountain. Therefore, through the inclusion of topographic features such as aspect and slope, it would increase the inter-class separability between PV power stations and other land objects to further improve the mapping performance.
Specifically, the digital terrain data used is ALOS World 3D-30m (AW3D30)²⁷ provided by GEE. According to previous study²⁸, AW3D30 is the most accurate DEM to date among all the free DEMs with a horizontal resolution of 30-m. In this study, to co-register with Sentinel-2 data, AW3D30 data was resampled to 10-m resolution using a bilinear interpolation method, after which both the slope and aspect were derived.
Feature extraction is of great significance for remote sensing based classification. In this study, a series of spectral and textual features are calculated based on Sentinel-2 multi-spectral data to construct a multi-dimensional feature space, aiming to increase the inter-class differences.
Specifically, the following spectral features are utilized, including normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI), and normalized difference building index (NDBI). NDVI is used to enhance the differences between vegetation and PV power stations, while SAVI could further correct the NDVI value influenced by soil brightness in areas where the vegetation coverage is rather low. MNDWI is a widely used water index, which could increase the separability of PV power stations and water bodies. NDBI is for the separation of PV power stations and buildings. In addition, a newly proposed spectral index for PV power station recognition, normalized difference PV index (NDPI)²⁹, has also been introduced as follows.
where ρ(SWIR1) and ρ(SWIR2) refer to the two short wave infrared bands of Sentinel-2, i.e., Band-11 and Band-12, respectively, while ρ(NIR) represents Sentinel-2’s near infrared band (B8). Since the PV panels show an obvious reflectance peak in Band-11 (SWIR1) and an absorption bottom in both Band-8 (NIR) and Band-12 (SWIR2), the design of NDPI would highlight the PV panels against other land objects.
Meanwhile, compared with other natural land covers such as grassland and bare soil, PV power stations have regular boundaries with clear textures observed from satellite. The rectangle shape of PV power stations would result in different texture features with other land covers, therefore, the inclusion of texture features could improve the classification performance. In this study, the popular gray level co-occurrence matrix (GLCM) was used for texture feature calculation, including mean, variance, homogeneity, dissimilarity, entropy, correlation, inverse difference moment and angular second moment. Besides, the GLCM texture window size is 3 × 3, according to our previous studies³⁰. To make the manuscript more readable, a table that contains all the necessary terminologies and their definitions could be found in the Supplementary file (Table S1).
As stated above, during the site selection of large-scale ground-mounted PV power stations, there is an inclination to choose the flat ground or sunny side of the mountains to receive more solar radiation. Therefore, the inclusion of PV location sensitive features such as slope and aspect would help in differentiating PV power stations from other land covers.
To further justify the above hypothesis, we randomly selected a total of 100, 000 PV samples directly from the final released national PV power station maps to calculate the distribution of both aspect and slope.
Figure 3 shows that in terms of slope, most of the PV power stations (about 90%) lie on flat ground and gentle slope that with an aspect of less than 6°. There are only very few PV power stations (about 1.9%) lie in regions with a slope of more than 15°. This is understandable since the construction workload would be greatly decreased in those flattened areas. Furthermore, the maintenance of PV power stations would also be easier. However, Fig. 3 indicates that even in slopes that are larger than 15 degrees, PV panels could still be placed successfully through the fixed or automatic rotated support.
Histogram of slope for PV power stations. Most PV power stations lie in slope under 6°.
Figure 4a indicates that China’s PV power stations witness a rather flattened distribution across all the aspects. It should be noted that the aspect here means the orientation of land where PV power station lies, not the orientation of PV panels installation. In fact, the PV panels installation orientation should be close to south to get more solar radiation. However, the aspect of land where PV lies on has little effect on the PV panels installation orientation, since the latter could be adjusted accordingly.
(a) spider chart of aspect for PV power stations; (b) histogram of slope for PV power stations under each aspect interval.
To further investigate this issue, we also calculated the histogram of land slope in each direction (Fig. 4b). It depicts that most of the PV power stations in the northern parts (i.e., north, northeast, and northwest) have a slope of below 5°, i.e., most lying on the flatten ground instead of the nightside of the mountain. Figure 4b also shows that flattened land with small slope is the ideal location to place PV panels, since the installation and maintenance of PV power stations would be easier in such regions.
Besides, after the extraction of both spectral features, textural features, and topographical features, a multi-dimensional feature space has been constructed by feature concatenation. All the PV power stations would be classified in this feature space based on random forest and active learning strategy, which would be elaborated in the next section.
In this study, a random forest classifier was adopted as the machine learning method to predict PV power stations from multi-dimensional feature space as stated above. The reason why RF was used is according to its effectiveness in modelling multi-linear features²⁰. Up to now, RF has been widely used in various remote sensing applications, such as vegetation mapping, water body extraction, etc^30,31.
Random forest could be viewed as an ensemble classifier that consists of a large number of decision trees²⁰, where the classification results would be determined by the vote of each decision tree. Besides, two random sampling processes are performed in RF. The first one is bootstrap sampling, which belongs to an inherent step of RF method. In specific, all the initial training samples now are re-sampled using bootstrap strategy to train each base classifier (i.e., decision tree) of RF. The other is the randomly selection of features used to train each decision tree. Therefore, the above random process makes RF robust to noises and outliers²⁰. Meanwhile, the parameterization of RF is rather simple than that of other classifiers such as support vector machine. Only a few parameters are to be tuned including the number of decision trees, the max split number of each tree, and the number of features used to train each tree²⁰. All the above parameters have been determined through grid search in this study, where the number of the above three parameters were 200, 10, and 6, respectively.
RF classifier was constructed with the API of GEE, i.e., Classifier.randomForest(). The advantages of using GEE for large-scale mapping are as follows. Firstly, GEE provides the well pre-processed Sentinel-2 data that have been radiometric calibrated, atmospheric corrected, and mosaiced to avoid cloud coverage, which could greatly reduce the burden of remote sensing data downloading and pre-processing³². Secondly, GEE also provides the high performance computing service that makes large-scale classification an easy task. According to the above merits, GEE has been popular in various remote sensing applications, such as aquaculture ponds mapping^32,33,34, crop mapping^35,36,37, etc.
Besides, because the study area is the entire China, the intuitive approach for PV power station mapping is to train a single RF classifier for the entire China instead of partition modelling. However, since the PV power stations (i.e., positive samples) and the surroundings (i.e., negative samples) have high variations in appearance among different provinces of China, which would lead to a high intra-class difference, making it impossible to train one single RF classifier with high accuracy. Therefore, we trained a series of separate RF classifiers for each province of China to make each RF more sensitive to local training samples. By reducing the intra-class variations through partition modelling, it would yield a more accurate local map hence to increase the entire classification performance. Meanwhile, considering that the model’s spatial generalization ability is also very important, therefore, province is selected as the unit of partition modelling rather than city or county. The importance of model’s generalization ability is that model can learn the common and representative features of PV power stations within a large area, making it possible to train a single classifier to achieve a satisfactory accuracy. So how to define the right and reasonable spatial scale to train a single classifier is a key issue. In this study, the PV power stations in the same province of China may share very similar appearances. Besides, in our previous work of mapping China’s agricultural greenhouses³⁸, we also selected province as the basic unit and trained a series of RF classifiers. Although city or county could be used as the unit of partition modelling, the workload for training models would increase dramatically. Therefore, training a single RF for each province of China is a trade-off between model’s accuracy, generalization ability and also workload for model training.
Besides, as for some provinces that are too big (including Xinjiang, Inner Mongolia, and Tibet), partition modelling was also performed to split these provinces into several subregions. The reason behind is that the intra-class differences might still be huge for a large province, which would increase the learning difficulty of RF classifier and degrade the classification accuracy. Therefore, subdivision is used to further reduce the intra-class variations and increase the fitting capability of RF to local samples. The practice of subdivision may lower model’s generalization capability, however, for classic machine learning method such as RF, it is difficult to take into account both generalization capability and local sensitivity. Under this circumstance, subdivision of provinces that are too big is also a trade-off between model’s accuracy and generalization ability.
As for the selection of training samples, a stratified sampling method²⁰ was used to collect both PV and non-PV samples independently in every provincial unit of China. In specific, before visual inspection from remote sensing images, we performed a detailed search for as many as possible the sites of China’s PV power stations from both relevant research reports, academic papers and news reports. We have also added the PV power station sites derived from our previous field surveys. Afterwards, based on these PV power station sites, we have selected training samples using visual inspection from both Sentinel-2 data and very high resolution Google Earth images. Although we have tried our best to obtain the sites of all the PV power stations of China, it should be noted the above sites still could not cover every PV site of China. To compensate for this, we have also spent lots of time in visual inspection of remote sensing images to collect as many PV samples as possible. Even in those provinces that witness very few PV power stations such as Sichuan, Chongqing, Beijing and Shanghai, we still performed the above process to select both PV and non-PV samples. Another issue is that the selection of non-PV samples is also very important since it would assist in the reduction of false positives (i.e., non-PV predicted as PV) in the classification map. After the labelling process, a total of 320,000 PV samples and 320,000 non-PV samples were collected. Meanwhile, considering that non-PV samples consist of several land covers, therefore, we collect these negative samples from finer categories including forest, grassland, farmland, water, bare land, and impervious surface.
During the mapping process, it would be difficult to produce an accurate map with automatic classification once and only once. In this situation, we resorted to active learning³⁹ with a coarse-to-fine strategy to improve the mapping performance iteratively. Actually, active learning originated from machine learning field³⁹, which has been a better choice in remote sensing classification under limited labelled samples. The core idea of active learning is described as follows. Firstly, train a classifier with initial labelled data, then use the trained classifier to predict the entire dataset. Afterwards, the wrongly predicted unlabelled data would be sent to experts for further annotation. Then, the classifier would be re-trained with both the initial and the newly labelled data to avoid potential prediction mistakes. The above process would be run in an iterative way until a satisfactory classification result would be achieved (Fig. 5).
Active learning for re-selecting unlabelled data.
In this study, we combined the active learning strategy with RF classifier to refine the PV power station mapping results (Fig. 5). The wrongly predicted pixels were carefully selected and then re-labelled to re-train the RF model. We mainly focused the following two kinds of prediction errors, including the missed PV pixels and these false positive ones to increase the robustness of RF on these typical errors. The above procedure would perform iteratively until the accuracy of PV classification could be satisfied.
Indeed, active learning strategy is a trade-off between automatic machine learning and expert interpretation, aiming to achieve a satisfactory classification accuracy with as less expert interpretation as possible³⁹. Meanwhile, the limiting case of expert interpretation is the total or overall vectorization or delineation of each PV power station, however, this would bring in a huge workload for annotators or experts. And the limiting case of automatic machine learning is to train the classifier only once with the initial training samples. Nonetheless, this would not always obtain a good classification map since the initial samples may have a low representativeness. On the other hand, active learning is just a trade-off to increase the accuracy of automatic machine learning at the expense of extra work of human interpretation. With the help of newly added samples, the machine learning classification results would be improved in a coarse-to-fine manner. Besides, according to the experiments (Figs. S1, S2 in the Supplementary file) and our previous study³⁸, the number of iterations would be about three times to generate a satisfactory classification result. Therefore, the adoption of active learning and experts’ opinions could increase the classification accuracy with only a small cost of extra annotation work, which could be a good option for large-scale mapping applications.
Note that, since pixel-based classification method has been utilized for satellite remote sensing image, the isolated speckle noises are unavoidable³⁰, leading to the so-called salt-and-pepper effect³⁰ in the classified PV power station map that would degrade the mapping accuracy. After carefully visual inspection of these noises, most of them have a small size with a total of only several pixels, which is far smaller than that of PV power stations. To eliminate these noises, a post-processing method was adopted, where all the connected regions that are less than 9 pixels (3 × 3) would be removed from the classification map. We also used close operation from mathematical morphology to fill the tiny holes of classification results. To better show the performance of the post-processing method, in the Supplementary file (Figs. S3, S4) we show several cases that and justifies that after the post-progressing step, most of the speckle noises (about 90%) are eradicated while the signal to noise ratio (SNR) improves about 2 db.
The national-scale PV power station map⁴⁰ in this study is provided for entire China in 2020 with a fine spatial resolution of 10 meters, which is the highest resolution recorded among all the publicly released PV datasets. The data format is GeoTIFF while the spatial reference is WGS-84. Meanwhile, only two kinds of values are in the PV power station map, where 0 stands for the non-PV regions while 1 represents the PV power stations. In addition, the provided PV dataset could be loaded into GIS software such as ArcGIS and QIS for data visualization and spatial analysis. Besides, we will continue producing China’s PV power station map in other years, and will release these maps in the future. The dataset⁴⁰ of this study is freely available for all users at Science Data Bank (https://doi.org/10.57760/sciencedb.o00121.00001).
In this section, we will describe the method for technical and accuracy validation of the PV power station map. Firstly, a national-scale testing dataset has been carefully constructed to perform the accuracy assessment of the mapping results. Afterwards, we will show several detailed PV classification maps to qualitatively evaluate the given dataset.
Specifically, to maintain the repressiveness of the testing dataset spatially, we have selected a total of 10,000 testing samples around China (Fig. 6), including both 5,000 positive samples (i.e., PV) and 5,000 negative samples (non-PV). It should be noted that all these testing samples and the training samples are independent from each other without any spatial intersection. As for the sampling process of testing samples, to maintain spatial uniformity, we divided the entire China into grids with a size of 50 km × 50 km, resulting in a total of 6069 grids nationally, where a stratified sampling strategy was used in all over all the grids to maintain a spatially-balanced sampling process. Since not every grid contain PV stations, we only selected PV samples in certain grids that witness PV panels. Meanwhile, as for negative samples, there are sampled based on all the grids to maintain the spatial representativeness in around China (i.e., about one negative sample per grid).
Spatial distribution of testing samples across China.
Both confusion matrix and several accuracy metrics^41,42 (including recall, precision, F1-score and mIoU) are calculated for both testing samples (Table 1) and training samples (Table S2 in the Supplementary file). It indicates that the training set has shown a very high accuracy with an OA of 98.09%, recall of 0.9777, precision of 0.9851, F1-score of 0.9814 and mIoU of 0.9635. Although the testing set shows a lower accuracy than training set, whose OA, recall, precision, F1-score and mIoU is 89.13%, 0.9014, 0.8836, 0.8057, 0.8924, respectively. Considering that the testing datasets are distributed around the entire China, the classification performance is good enough to understand the spatial patterns of China’s PV stations.
To better illustrate the PV classification results, we collect several detailed PV maps shown as follows.
Figure 7 depicts that PV power stations could be precisely extracted from various backgrounds such as gobi, water bodies, grassland and mountainous regions. Since the 10-m Sentinel-2 imagery was utilized, a detailed boundary of PV power stations could be obtained, which is a merit over Landsat-derived 30-m PV maps. In addition, based on the provided national-scale PV map and the associated Sentinel-2 imagery, it would be easy to obtain numerous image and mask patches to train a deep learning based semantic segmentation model for PV classification in the near future. Therefore, the PV dataset in this study could also be viewed as a benchmark for deep learning based studies.
Details of PV power stations map across China.
Figure 7 also shows that some roads and related facilities inside the PV power stations are classified into non-PV, this is mainly because we did not select theses regions as PV samples. On the contrary, if we selected roads and related facilities (mainly power transformation equipment) within PV power stations as positive samples, this would lead to the appearance of more false positives in the final classification map. Other land cover types especially the roads and industrial roof tops may be misclassified into PV power stations. The drawback of this study is that roads and other facilities have not been classified, leading to a risk of underestimating the areas of PV power stations. In fact, the discard of roads and other facilities is actually a trade-off to reduce false positives, which is an inevitable issue in large-scale mapping applications. Besides, although roads and other facilities are not recognized, most of the PV panels are classified correctly, which would also reflect the spatial pattern of China’s PV power stations.
In addition, one merit of random forest is that it could yield the importance of input features. To further validate this merit, we have calculated the variable importance of each province and then obtained the top-10 variables based on the average importance values. Meanwhile, we have also trained a single RF classifier for the entire China using the combination of training samples from each province and then calculated the variable importance (Fig. S5 in the Supplementary file). It indicates that the top-10 important variables are the same for both entire modelling and partition modelling, including NDBI, NDPI, Band-8, Band-2, slope, Band-12, elevation, SAVI, NDVI and Band-4 of Sentinel-2. More specifically, among these ten variables, several variables show very high importance in both partition modelling and entire modelling, including NDBI, NDPI Band-2, slope and Band-12. The reason is that both NDBI and NDPI could increase the separability between PV power stations and buildings. Band-2 is blue band and most of PV panels show the colour of dark blue from the satellite image. Slope is an important parameter that determines the suitable positions of PV power stations. While Band-12 is the key parameter in NDPI, which has also a high importance. Above all, these variables have more impact on the classification of PV power stations, which could be paid more attention in future relevant studies. The only exception is elevation, which has a high average importance and high deviation in partition modelling but has a low value in entire modelling. The reason may be that in partition modelling, the variations of elevation in each province is not that high when compared with the entire China, therefore, the correlation between elevation and PV power stations is higher locally (i.e., at provincial scale) than globally (i.e., at national scale).
Besides, we have taken a series of steps during the remote sensing classification to maintain the data’s accuracy and reliability as follows.
A strict training course has been taken for all the annotators for visual interpretation, which could help them recognize PV power stations under various backgrounds. Both variability and correctness of PV samples are required for each annotator. After the initial labelling procedure, annotators are asked to perform a cross-check step to further refine the sample’s quality.
Firstly, the high-resolution Sentinel-2 data are selected to reduce the mixed pixels hence to reserve as much as the details of PV power stations. Secondly, due to the short revisit time of Sentinel-2, it is easier to generate cloud-free images on GEE, which is of great significance to large-scale remote sensing classification.
To increase the inter-class difference between PV power stations and other LULC categories, both spectral features, texture features and topographical features have been extracted.
A difficulty for large-scale remote sensing classification is the huge intra-class variations. To tackle this issue, a partition modelling strategy is utilized to increase the fitting ability of classifiers for the local samples.
Active learning is used to select the typical wrongly predicted pixels to be re-labelled. Afterwards, the RF classifier would be re-trained with the refined labelled data to reduce the classification errors iteratively.
To further refine the classification results, post-processing step is taken to improve the salt-and-pepper effect.
We have released the distribution map of China’s PV power stations in the unit of province. The PV map is in the standard format of GeoTIFF, which could be easily further processed by both GIS software and coding language such as R and Python. The GEE code for generating PV map has also been released for the purpose of reproduction, including the calculation of each feature, random forest classification, etc. Besides, the released code could also provide a useful reference for other large-scale mapping applications such as cropland mapping, flood mapping, etc.
Each pixel in the released PV maps has the area of 100 m² (10 m × 10 m). Meanwhile, because we have used post-processing method to filter the speckle noises that are less than 3 × 3 pixels (i.e., 900 m²), therefore, the smallest PV station that can be mapped is 900 m². Since we are mainly focused on the ground mounted PV stations, whose areas are much more than 900 m² while even the small PV stations in China are about 30,000 m². Therefore, the post-processing step would not lead to the possibility of missing out these small ground-mounted PV stations.
Based on the provided PV dataset, it would be easy to illustrate where these PV power stations lie across entire China. Considering that the classified PV power stations only account for a very small ratio of China, if we directly put these PV pixels on a national-scale map, it would be difficult to show their spatial distribution patterns. To tackle this issue, we first split entire China into a series of grids with a size of 5 km × 5 km, then calculated the percentage of PV pixels and finally put these PV area ratios on a national-scale map (Fig. 8a).
(a) PV power stations density map across China; (b) PV power stations area map for each county of China.
Figure 8a illustrates that most of PV power stations lie in the northern part of China, especially in northwest and northeast China. Interestingly, a large number of PV power stations lie along the Great Wall (including the northern parts of Hebei, Shanxi, Shaanxi, Ningxia and Gansu Province) and the Silk Road (mainly refers to Gansu and Xinjiang Province). Meanwhile, in eastern China, PV power stations mainly locate in Anhui, Jiangsu, Shandong, Henan, Hubei and Jiangxi Province, while in southwestern China, Guizhou, Yunnan and Sichuan witnessed the most PV power stations.
Apart from the grid map, we also calculated the areas of PV power stations in each county of China and put these data on map (Fig. 8b). Compared with the grid map, county-level PV map could provide the panel data of PV power stations of each county, which could facilitate in-depth analysis with socio-economic data, since most socio-economic data in China are in the units of county. In this context, Fig. 8b could also provide policy implications for decision makers.
Finally, based on the provided national-scale PV map, we also calculated the area ratio of each province (Fig. 9). According to our dataset, China has a total of 2467.7 km² ground-mounted PV power stations in 2020. The top three largest provinces refer to Xinjiang, Inner Mongolia and Qinghai, whose PV area ratio are 14.92%, 12.49% and 11.26%, respectively, with a total of nearly 40% of all the PV power stations of China. Among the rest provinces, both Gansu, Ningxia, Hebei and Shaanxi witness a PV area ratio above 5%, which contributes to another 20% of China’s PV power stations in total. It should also be noted that with the rapid development of China’s PV industry, increasingly more eastern provinces built large-scale PV power stations, including Jiangsu, Anhui and Shandong Province.
Areas of PV power stations for each province of China.
The above analysis indicates that China’s PV power stations mainly locate in three regions, including the northern, eastern and southwestern parts of China. The driving forces behind are analysed as follows. First, as for Northern China (especially Xinjiang, Qinghai, Gansu, Ningxia and western part of Inner Mongolia), it has a typical arid and semi-arid climate with very few cloudy and rainy weathers, leading to a long effective sunshine duration. Besides, these regions have large-scale gobi and desert with a rather flat terrain, which is suitable for placing PV power stations. Second, as for Eastern China (especially Shandong, Jiangsu and Zhejiang), it has developed industries which contribute to more carbon emissions than Western China. Therefore, to response to China’s Carbon Peak and Carbon Neutrality strategy, provinces in Eastern China have also constructed PV power stations, aiming to produce more clean energy and reduce the consumption of fossil fuel. Finally, As for Southwestern China (especially the border regions for Sichuan, Guizhou and Yunnan), the placement of PV power stations is for the purpose of reducing poverty. Local residents could be benefited from the land subsidy, making it possible to maintain both clean solar energy production and poverty alleviation, both of which are highly valued by China’s central government.
Besides, the released dataset is mainly focused on ground-mounted PV power stations of China and not yet consider distributed PV stations such as rooftop PV systems. Two reasons may account for this. Firstly, rooftop PVs are more scattered and in small size. Considering that the spatial resolution of Sentinel-2 data is 10 meters, and rooftop PVs do not manifest themselves obviously under this resolution, which will increase the difficulty of extracting the rooftop PVs. Secondly, the grounded-mounted PV power stations almost cover more than 90% of the total PV capacity in China, therefore, even without distributed PV systems, the released dataset could also locate most of the PV industries in China. Moreover, in future studies, we will consider utilizing very high resolution data (i.e., WorldView-3/4) and deep learning models to produce rooftop PV data.
To sum up, we provide a 10-m map for China’s PV power stations to provide reference data to understand the spatial pattern of China’s PV industry. The dataset could also be used for other applications such as prediction of PV’s generating capacity and site selection for newly built PV power stations. Besides, the detailed PV map could also support for policy making of China’s clean energy and provide useful data for studies such as land use and land cover change.
The GEE code for PV power stations classification based on Sentinel-2 imagery and DEM data is available at https://github.com/MrSuperNiu/PV_ScientificData_Classification_Code. The code is written in JavaScript, including all the mentioned steps in this paper, including feature calculation, random forest training, etc.
Mellit, A. & Kalogirou, S. Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions. Renewable and Sustainable Energy Reviews 143, 110889 (2021).
Article  Google Scholar 
Murdock, H. E. et al. Renewables 2020-global status report. (Paris, France, 2020).
Xia, Z. et al. Solar photovoltaic program helps turn deserts green in China: Evidence from satellite monitoring. Journal of Environmental Management 324, 116338 (2022).
Article  PubMed  Google Scholar 
Kruitwagen, L. et al. A global inventory of photovoltaic solar energy generating units. Nature 598, 604–610 (2021).
Article  CAS  PubMed  ADS  Google Scholar 
Stowell, D. et al. A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK. Scientific Data 7, 394 (2020).
Article  PubMed  PubMed Central  Google Scholar 
Bradbury, K. et al. Distributed solar photovoltaic array location and extent dataset for remote sensing object identification. Scientific Data 3, 160106 (2016).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Dong, H., Zeng, B., Wang, Y., Liu, Y. & Zeng, M. China’s solar subsidy policy: Government funding yields to open markets. IEEE Power and Energy Magazine 18, 49–60 (2020).
Article  Google Scholar 
Mallapaty, S. How China could be carbon neutral by mid-century. Nature 586, 482–483 (2020).
Article  CAS  PubMed  ADS  Google Scholar 
Xia, Z. et al. Mapping the rapid development of photovoltaic power stations in northwestern China using remote sensing. Energy Reports 8, 4117–4127 (2022).
Article  Google Scholar 
Zhang, X., Wang, S., Huang, Y., Xie, Z. & Xu, M. Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine. Earth System Science Data Discussions 14, 3743–3755 (2022).
Article  Google Scholar 
Plakman, V., Rosier, J. & van Vliet, J. Solar park detection from publicly available satellite imagery. GIScience & Remote Sensing 59, 462–481 (2022).
Article  Google Scholar 
Zhang, X., Zeraatpisheh, M., Rahman, M. M., Wang, S. & Xu, M. Texture is important in improving the accuracy of mapping photovoltaic power plants: A case study of Ningxia Autonomous Region, China. Remote Sensing 13, 3909 (2021).
Article  ADS  Google Scholar 
Malof, J. M. et al. Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier. In 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA). 799–803 (IEEE, 2016).
Zhang, C., Li, Z., Jiang, H., Luo, Y. & Xu, S. Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China. Applied Energy 283, 116329 (2021).
Article  Google Scholar 
Costa, M. V. C. V. D. et al. Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation. Energies 14, 2960 (2021).
Article  Google Scholar 
Kasmi, G., Dubus, L., Blanc, P. & Saint-Drenan, Y.-M. DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping. Preprint at https://arxiv.org/abs/2207.07466 (2022).
Kausika, B. B., Nijmeijer, D., Reimerink, I., Brouwer, P. & Liem, V. GeoAI for detection of solar photovoltaic installations in the Netherlands. Energy and AI 6, 100111 (2021).
Article  Google Scholar 
Ge, F. et al. A Hierarchical Information Extraction Method for Large-Scale Centralized Photovoltaic Power Plants Based on Multi-Source Remote Sensing Images. Remote Sensing 14, 4211 (2022).
Article  ADS  Google Scholar 
Chen, B. et al. Multi-modal fusion of satellite and street-view images for urban village classification based on a dual-branch deep neural network. International Journal of Applied Earth Observation and Geoinformation 109, 102794 (2022).
Article  Google Scholar 
Breiman, L. Random forests. Machine learning 45, 5–32 (2001).
Article  Google Scholar 
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment 202, 18–27 (2017).
Article  ADS  Google Scholar 
Drusch, M. et al. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote sensing of Environment 120, 25–36 (2012).
Article  ADS  Google Scholar 
Qin, J., Jiang, H., Lu, N., Yao, L. & Zhou, C. Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning. Renewable and Sustainable Energy Reviews 167, 112680 (2022).
Article  Google Scholar 
Jiang, H. et al. Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data. Energy and AI 10, 100185 (2022).
Article  Google Scholar 
Borfecchia, F. et al. Remote Sensing and GIS in planning photovoltaic potential of urban areas. European Journal of Remote Sensing 47, 195–216 (2014).
Article  Google Scholar 
Zhu, Z., Qiu, S. & Ye, S. Remote sensing of land change: A multifaceted perspective. Remote Sensing of Environment 282, 113266 (2022).
Article  Google Scholar 
Tadono, T. et al. Precise global DEM generation by ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2, 71–76 (2014).
Article  Google Scholar 
Caglar, B., Becek, K., Mekik, C. & Ozendi, M. On the vertical accuracy of the ALOS world 3D-30m digital elevation model. Remote Sensing Letters 9, 607–615 (2018).
Article  Google Scholar 
Wang, S. Application of machine learning method in remote sensing extraction of photovoltaic power plants. Jiangsu Normal University (2018).
Feng, Q., Liu, J. & Gong, J. Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—A case of Yuyao, China. Water 7, 1437–1455 (2015).
Article  ADS  Google Scholar 
Belgiu, M. & Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing 114, 24–31 (2016).
Article  ADS  Google Scholar 
Yang, L. et al. Google Earth Engine and artificial intelligence (AI): a comprehensive review. Remote Sensing 14, 3253 (2022).
Article  ADS  Google Scholar 
Ottinger, M., Bachofer, F., Huth, J. & Kuenzer, C. Mapping aquaculture ponds for the coastal zone of Asia with Sentinel-1 and Sentinel-2 time series. Remote Sensing 14, 153 (2021).
Article  ADS  Google Scholar 
Wang, Z. et al. Global mapping of the landside clustering of aquaculture ponds from dense time-series 10 m Sentinel-2 images on Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation 115, 103100 (2022).
Article  Google Scholar 
Qiu, B. et al. From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2. International Journal of Applied Earth Observation and Geoinformation 113, 103006 (2022).
Article  Google Scholar 
Singha, M., Dong, J., Zhang, G. & Xiao, X. High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Scientific data 6, 26 (2019).
Article  PubMed  PubMed Central  Google Scholar 
You, N. et al. The 10-m crop type maps in Northeast China during 2017–2019. Scientific data 8, 41 (2021).
Article  PubMed  PubMed Central  Google Scholar 
Feng, Q. et al. A dataset of remote sensing-based classification for agricultural plastic greenhouses in China in 2019. China Scientific Data 6, 4 (2021).
ADS  Google Scholar 
Settles, B. Active learning literature survey. https://minds.wisconsin.edu/handle/1793/60660 (2009).
Feng, Q. et al. A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020. Science Data Bank. https://doi.org/10.57760/sciencedb.o00121.00001 (2022).
Zhang, Z. et al. Carbon mitigation potential afforded by rooftop photovoltaic in China. Nature Communications 14, 2347 (2023).
Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 
Zhu, R. et al. Deep Solar PV Refiner: An advanced deep learning network for photovoltaic area segmentation from satellite imagery. International Journal of Applied Earth Observation and Geoinformation 116, 103134 (2023).
Article  Google Scholar 
Download references
This study is funded by National Key Research and Development Program of China (2022YFE0197300), National Natural Science Foundation of China (42001367), the Programme of Kezhen-Bingwei Excellent Young Talents of IGSNRR, CAS (2023RC002).
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Quanlong Feng, Bowen Niu, Yan Ren, Shuai Su, Jiudong Wang, Hongda Shi & Jianyu Yang
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Mengyao Han
Centre for Environment, Energy and Natural Resource Governance (C-EENRG), University of Cambridge, Cambridge, CB2 3QZ, United Kingdom
Mengyao Han
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Q.F. designed the study, wrote the code and generated the data and contributed to manuscript writing and revision. B.N. wrote the code and generated the data and contributed to manuscript writing and revision. Y.R. contributed to coding, data generation, technical validation and manuscript revision. S.S. contributed to sampling and manuscript revision. J.W. contributed to sampling and manuscript revision. H.S. contributed to sampling and manuscript revision. J.Y. contributed to manuscript revision. M.H. contributed to study design, manuscript writing and revision.
Correspondence to Mengyao Han.
The authors declare no competing interests.
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
Feng, Q., Niu, B., Ren, Y. et al. A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020. Sci Data 11, 198 (2024). https://doi.org/10.1038/s41597-024-02994-x
Download citation
Received: 17 November 2022
Accepted: 25 January 2024
Published: 13 February 2024
Version of record: 13 February 2024
DOI: https://doi.org/10.1038/s41597-024-02994-x
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.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Scientific Data (Sci Data)
ISSN 2052-4463 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source

By ZHENG XIN | China Daily | Updated: 2026-09-02 09:23
China’s installed solar power capacity has surpassed coal for the first time to become the country’s largest single power source, a historic milestone that fundamentally reshapes the nation’s energy supply landscape and accelerates its ongoing green, carbon-cutting transition.
As of the end of July, China’s total installed solar capacity reached 1.286 billion kilowatts, narrowly edging past the 1.285 billion kW of coal-fired capacity, said the National Energy Administration.
This massive footprint firmly secures China’s position as the world’s largest photovoltaic energy market. The country’s solar power capacity now far exceeds that of the United States, India and Germany, and is significantly higher than the total installed photovoltaic capacity of the entire European Union, said the China Electricity Council.
Liu Zhiqiang, deputy director of the CEC’s planning and development department, said this eclipsing achievement is a landmark event that lays a solid supply-side foundation for the nation’s dual-carbon goals.
“With China rapidly building a new power system dominated by renewable energy, this structural adjustment on the generation side will drastically reduce the power sector’s overall carbon emissions. The vast domestic market has fortified the resilience of China’s photovoltaic supply chain, unleashing economies of scale that drive rapid technological iterations. This, in turn, consolidates the nation’s global leadership in solar manufacturing, technical standards and engineering construction,” Liu said.
Consequently, the role of coal-fired power is undergoing a profound structural shift. Rather than pursuing continuous output growth, coal is accelerating its transition from a foundational baseload provider to a flexible, more auxiliary supportive power source.
During midday peaks in frequent sunshine intensity, coal units now actively throttle down their output to make room for renewable energy absorption, Liu added.
Conversely, at night or during cloudy weather when solar generation plummets, coal plants rely on their rapid adjustability to swiftly ramp up and fill in grid gaps.
“While the average annual utilization hours of coal plants are trending downward to accommodate more renewables, their value in ensuring system security — such as providing rotational inertia and frequency support — has become even more prominent. Coal remains the ‘ballast stone’ of our power system’s safe operation.”
Despite surpassing the historic capacity threshold, experts caution that installed capacity does not necessarily directly translate into actual power generation. Because solar is inherently intermittent and its utilization hours remain much lower than fossil fuels, coal will temporarily remain the primary backbone for grid reliability.
To successfully convert this massive renewable capacity into steady, reliable electricity supplies, Liu suggests that China should scale up flexible grid resources, including pumped hydro, novel long-duration energy storage and next-generation upgrades for coal plants. Smart grid platforms — integrating main grids, distribution networks and micro-grids — are necessary to boost cross-regional and cross-seasonal power-sharing capabilities, he said, adding that through technical innovation, the nation should push solar power from being “weather-dependent” to being highly executable and predictable.
The CEC official also highlighted the critical need to refine and enhance electricity market mechanisms.
Hao Yingjie, secretary-general of the CEC, said this capacity milestone cements the foundation for China’s dual-carbon goals of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060.
“The continued expansion of solar power is steadily lowering the carbon intensity of the power sector, expanding the supply of green electricity and providing robust clean energy support for the electrification of industries and transportation,” Hao said.
zhengxin@chinadaily.com.cn

source

File photo from state court system of courtroom..

File photo from state court system of courtroom..
Hall of Fame running back Emmitt Smith faces a lawsuit in Delaware Chancery Court over claims that his company misused a loan intended to advance a solar farm.
Courthouse News Service reported that a Cherokee tribal group filed a civil action against a company co-owned by Smith, 4 13 Solutions. The company invests in energy and real estate.
The suit claims that 4 13 Solutions used proceeds from a $2.5 billion loan from Kituwah LLC to pay off another loan. The solar farm project has not advanced, according to the suit.
Smith is the NFL’s all-time leading rusher and spent most of his long career with the Dallas Cowboys.
The loan was based on a quick payback once the solar farm began operating. Kituwah LLC also claimed that the company was shut out of management of the venture.
The venture never received private or federal loans, with 4 13 Solutions coming up with multiple excuses on not paying back the loan, according to the suit.
While best known for handling complex corporate disputes, Chancery Court also hears cases over payments and other transactions.
Cases are typically heard by a judge rather than a jury. Should the lawsuit advance, Smith could testify either in person or remotely.
Your comment has been submitted.

Reported
There was a problem reporting this.
Log In
Keep it Clean. Please avoid obscene, vulgar, lewd, racist or sexually-oriented language.
PLEASE TURN OFF YOUR CAPS LOCK.
Don't Threaten. Threats of harming another person will not be tolerated.
Be Truthful. Don't knowingly lie about anyone or anything.
Be Nice. No racism, sexism or any sort of -ism that is degrading to another person.
Be Proactive. Use the 'Report' link on each comment to let us know of abusive posts.
Share with Us. We'd love to hear eyewitness accounts, the history behind an article.
View previous campaigns
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.

Already Subscribed!

Cancel anytime
Account processing issue – the email address may already exist
Must be at least 8 characters, not contain repeating characters (e.g., 111), and not contain sequential numbers (e.g., 123).

Thank you .
Your account has been registered, and you are now logged in.
Check your email for details.
Invalid password or account does not exist
Submitting this form below will send a message to your email with a link to change your password.
An email message containing instructions on how to reset your password has been sent to the email address listed on your account.
No promotional rates found.

Secure & Encrypted
Must be at least 8 characters, not contain repeating characters (e.g., 111), and not contain sequential numbers (e.g., 123).
Secure transaction. Secure transaction. Cancel anytime.

Thank you.
Your gift purchase was successful! Your purchase was successful, and you are now logged in.
A receipt was sent to your email.

source

India proposes mandatory battery storage for new renewable projects from July 2027  reuters.com
source

© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.
Clivet’s range starts with five single-module models offering 25.2 kilowatts to 45 kilowatts of cooling capacity.
Photo Credit: Clivet
Italian heat pump manufacturer Clivet has unveiled a new variable refrigerant flow, or VRF, lineup for homes and businesses that can connect with photovoltaics without relying on a conventional solar inverter, a move that could simplify how buildings use on-site solar electricity for heating and cooling.
For buildings that generate their own solar electricity, Clivet says its latest VRF heat pumps can use that power more directly.
The Italy-based manufacturer, part of Midea, introduced the residential and commercial series with photovoltaic compatibility, eliminating the need for a traditional solar inverter and potentially simplifying on-site solar use for heating and cooling.
According to pv magazine, the new CVT9 outdoor units expand Clivet’s variable refrigerant flow offerings for homes and commercial buildings. Clivet is an Italian manufacturer and a subsidiary of Chinese appliance company Midea.
Rather than depending on a standard PV inverter, the units are meant to pair with a PV Box that delivers a steady 400- to 600-volt DC supply. That is what enables the direct photovoltaic connection.
Clivet’s range starts with five single-module models offering 25.2-45 kilowatts of cooling capacity. Installers can combine two modules for systems rated from 50 kilowatts to 90 kilowatts, and the biggest setup reaches 100 kilowatts at maximum heating output.
💡These best-sellers from Quince deliver affordable, sustainable luxury for all
Starting at $50
Starting at $99
Starting at $60
Starting at $80
According to Clivet, the new series will work with indoor units and control systems from the earlier V8 generation.
Clivet expects its integrated PV control system to become available in 2027.
The technical specifications suggest Clivet is aiming for year-round performance in demanding conditions.
The CVT9 units use R32 refrigerant and DC inverter compressors with enhanced vapor injection and a secondary microchannel heat exchanger. Clivet says that combination allows the systems to operate in outdoor temperatures down to -22 degrees Fahrenheit (-30 C) while still maintaining nominal heating capacity at 23 F (-5 C).
A compressor that can modulate down to 7% of capacity could improve efficiency when full output is not needed, helping reduce wasted electricity while keeping indoor temperatures more stable.
Single-module units are listed with seasonal energy efficiency ratios of 8.00 to 8.58 and seasonal coefficients of performance of 4.78 to 4.89, with similar figures for dual-module systems.
The units come with an integrated digital ammeter for real-time consumption monitoring, Modbus RTU connectivity for building management systems, and a Bluetooth module with a USB Type-C port for diagnostics.
For buildings with limited electrical capacity, the systems are adjustable from 40% to 100% in 1% increments.
Two-module systems can automatically shift the load if one outdoor unit triggers an alarm, and they can alternate operating hours between modules to balance wear.
The units also use the company’s SafeBox2 protection system for electronic components.
Clivet’s inverter-free PV heat pumps land at the intersection of two fast-changing technologies: HVAC and solar.
The articles here show how better air-conditioning systems and more advanced panels could make on-site solar power more useful inside buildings.
• Researchers have unveiled a complete rethinking of air conditioning that could slash building energy use.
• Oxford PV has established its first-ever beachhead in the US for perovskite solar.
• Solar developers are racing to make solar electricity even cheaper as advanced silicon technologies improve.
That progress helps explain why direct solar-powered heating and cooling may become more practical as both parts of the system continue to improve.
Get TCD’s free newsletters for easy tips, smart advice, and a chance to earn $5,000 toward home upgrades. To see more stories like this one, change your Google preferences here.
© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.

source

Cohance Lifesciences Invests $18 Million to Expand ADC Portfolio
GREW Solar Bags Solar Module Order
NRDC and ONGC Partner to Advance Technology Transfer and Innovation
Brazil and India Deepen Cooperation on Green Energy
A New Awakening for Coal Gasification in India
Climate Change and Energy Transition Towards Net Negative Emission – Part 2
Climate Change and Energy Transitions Towards Net Negative Emissions – Part 1
A Visual Tribute to a Living Legend: Prof. M.M. Sharma – Guru of Gurus and The Alchemist of India
NEW 3-in-1 Spectrophotometer – Quality Control for Chemicals
Routine Testing of Balances and Scales
Rapid Investigation of Thermally Hazardous Substances
Gain a Digital Line of Sight of your Plant
Make the Most of Sensor Data
CEO’s Forum | Evolving Role of Sustainability
CEO’s Forum | How Sustainability is Evolving in Chemical Industry
Managing Water Shortage in Chemical Industries
Chhattisgarh Boiler Explosion– A Wake-Up Call for Industrial Safety in High-Tech India
Dmitri Mendeleev: The Man Who Ordered the Elements
Susumu Kitagawa: Nobel Laurate Chemistry 2025
Richard Robson: A Quiet Architect of Molecular Order
Omar Yaghi: From Refugee Camp to Nobel Chemistry Laureate
GREW Solar bagged a repeat order worth Rs. 430 crore from an independent power producer (IPP) in India for the supply of high-efficiency solar photovoltaic (PV) modules. The latest order further strengthens the company’s position in India’s rapidly expanding solar energy market. However, GREW Solar has not disclosed the name of the IPP.
G12R Modules to Power Solar Projects
Under the agreement, GREW Solar will supply its G12R solar modules, which use advanced n-type TOPCon (Tunnel Oxide Passivated Contact) cell technology. This technology enables higher module efficiency and supports the growing demand for high-performance solar solutions as India accelerates its renewable energy transition.
GREW Solar Expands Manufacturing Capacity
GREW Solar, a venture of the Chiripal Group, currently operates a 6.5 GW solar PV module manufacturing facility in Dudu, Rajasthan. Meanwhile, the company is scaling up its manufacturing footprint. It plans to increase its total PV module production capacity to 11 GW by the end of 2026.
In addition, GREW Solar is investing in solar cell manufacturing and backward integration. These investments are expected to strengthen its supply chain, improve manufacturing capabilities and support the company’s long-term growth in India’s renewable energy sector.
Supporting India’s Solar Energy Growth
With the latest Rs. 430 crore order and ongoing capacity expansion, GREW Solar is positioning itself to meet the rising demand for high-efficiency solar PV modules. As reported by pv-magazine-india.com, GREW’s investments across the solar manufacturing value chain align with India’s broader push towards renewable energy and greater domestic manufacturing capacity.

1,true,6,Contact Email,21,false,1,First Name,21,false,1,Last Name,2


Newspaper is your news, entertainment, music fashion website. We provide you with the latest breaking news and videos straight from the entertainment industry.
Contact us: chemindigest@gmail.com
© 2026 ChemInDigest . All Rights Reserved.

source

Aresearch group led by scientists in Saudi Arabia and Greece has developed a stabilizer-free, seed-assisted growth strategy to produce the pure α-phase of formamidinium lead iodide (α-FAPbI₃) perovskite.
FAPbI₃ is one of the leading absorber candidates for single-junction perovskite solar cells. However, it is unstable under ambient conditions, typically requiring chemical stabilizers that can widen the bandgap and limit its photovoltaic potential.
“This research presents an exciting approach to one of the biggest challenges facing FAPbI₃ perovskite solar cells: stabilizing the highly efficient α-phase without relying on compositional additives that compromise the material’s ideal bandgap,” corresponding author Essa A. Alharbi told pv magazine. “Rather than using conventional α-phase stabilizers such as cesium (Cs), rubidium (Rb), or methylammonium (MA), we introduce a seeded-growth strategy in which α-FAPbI₃ seed crystals are incorporated directly into the precursor solution to guide crystallization.”
Alharbi added that “the work combines experimental characterization with multiscale simulations to reveal the underlying mechanism of seeded growth. This provides a scientific explanation for the exceptional efficiency and long-term stability achieved.”
The study consisted of two parts – experimental work and simulations. In the first, the researchers fabricated control and seed-assisted perovskite solar cells with an n-i-p architecture. For the seed-assisted devices, instead of using conventional chemical stabilizers, they incorporated preformed α-FAPbI₃ seeds directly into the PbI₂ precursor to guide crystallization toward the desired α-phase.
The target devices were then produced through a second deposition step using formamidinium iodide (FAI) and methylammonium chloride (MACl), followed by annealing at 150 C for 20 minutes. The control devices were fabricated under the same conditions but without the seeds.
“The pre-existing α-FAPbI₃ seeds lower the nucleation barrier and direct the growth of the desired photoactive α-phase while suppressing the formation of the photoinactive δ-phase. This results in highly crystalline, compact films with larger grains, fewer defects, lower surface roughness, and significantly reduced non-radiative recombination,” Alharbi said.
“As a result, the devices achieve a power conversion efficiency of 23.51%, compared with 15.5% for conventionally processed control devices, while maintaining 99% of their initial performance after 3,000 hours of continuous operation under ambient conditions and one-sun illumination without encapsulation.”
In the second part of the study, the researchers used multiscale simulations to investigate how the seeds influence crystallization and device performance. Density functional theory (DFT) calculations compared the energetics of α- and δ-phase growth on an existing α-FAPbI₃ seed, while molecular dynamics simulations tracked the dissolution of a 10 nm α-FAPbI₃ seed in the precursor solution and compared it with a seed-free solution.
The researchers also used metadynamics to examine nucleation pathways, while separate optical-electrical simulations assessed the effects of carrier mobility, lifetime, and non-radiative recombination on solar cell performance.
“The multiscale simulations showed that dissolving α-FAPbI₃ seeds retain structural motifs that preferentially promote α-phase nucleation while suppressing the competing δ-phase. This reveals that seeded growth not only improves film morphology but fundamentally alters the crystallization pathway, resulting in fewer defects, reduced non-radiative recombination, negligible hysteresis, and more balanced charge transport,” the academics explained.
The research team now plans to extend the seeded-growth strategy from laboratory-scale devices to large-area perovskite solar modules using scalable sequential deposition processes.
“We will optimize seed concentration, size, and processing conditions to ensure uniform crystallization over large substrates while maintaining high efficiency and long-term operational stability,” Alharbi said.
“The seeded-growth strategy is fully compatible with sequential deposition, making it well suited for large-area manufacturing and commercial-scale perovskite photovoltaics,” he concluded. “The approach also has potential to improve other perovskite optoelectronic devices, including light-emitting diodes, where suppressing defect-assisted recombination is essential for high performance.”
The research, “Stabilizer-free pure α-phase FAPbI3 perovskite through seed-assisted growth yields efficient photovoltaics,” was published in Materials Horizons.
Researchers from Saudi Arabia’s King Abdulaziz City for Science and Technology (KACST), Princess Nourah bint Abdulrahman University and Taibah University; Greece’s Foundation for Research and Technology – Hellas (FORTH), Hellenic Mediterranean University (HMU) and University of Ioannina; and the UK’s University College London (UCL) 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: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.
“You’re going to see lower panel output right when the demand is the highest.”
Photo Credit: YouTube
Very hot weather can cut into a home’s solar production, leaving systems less efficient during the same summer stretches when electricity use tends to surge.
That dynamic was highlighted in a YouTube video from Two Steps from Off-Grid (@terrahillfarm), where a homesteader in Central Texas said his off-grid panels reached about 140 degrees Fahrenheit and saw output fall by 12%-20%.
In the video’s caption, he said the drop could amount to “almost 2,500 watts” versus what the panels might produce under cooler conditions. He reported air temperatures of 100-105 degrees in the area, with the panel surfaces running far hotter than the surrounding air.
He further said that the timing is especially problematic because “You’re going to see lower panel output right when the demand is the highest.” For homes that rely on solar, including off-grid setups, that mismatch can put more pressure on power needs during air-conditioning season and increase reliance on batteries or other backup sources.
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers in your area.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Commenters echoed the concern with their own real-world experiences. “Learned this for the first time on record hot days with my extra dark panels,” one wrote. “They derated by half.”
Going solar is one of the best ways to save money on home energy costs, but system design matters if you want significant savings during extreme weather. Homeowners who are considering panels can explore EnergySage to get free solar installation estimates and compare quotes.
The poster said high temperatures can also affect equipment beyond the panels themselves. Batteries and inverters kept in hot garages or sheds may wear out more quickly, and excessive heat can cause those components to perform worse or shut down to protect themselves.
Because batteries and inverters are among the priciest parts of a residential solar setup, any heat-driven loss of lifespan could translate into replacement costs sooner than expected, in addition to the energy lost when overheated panels produce less power.
FROM OUR PARTNER
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers that can help you save as much as $10k on installation.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best local options for your needs, and their expert advisers can help you compare quotes and pick a winner.
To help protect that equipment, he said he added a solar-hybrid mini-split to the room, along with his battery bank and inverters, and has kept the area under 80 degrees even during peak heat. He said, “That one change has made a real difference” by reducing wear and thermal stress.
Among the steps he recommended were maintaining airflow around panels, checking for weeds or tree growth that might cast shade, and keeping battery and inverter spaces well-ventilated. He also said extra system capacity can provide a cushion against summertime efficiency losses during periods of heavy demand.
Adding battery storage to a solar setup is one of the best ways to protect your home during outages, save money on energy, and go off grid. It can also help households store daytime solar generation for use later, which is especially useful when hot weather reduces panel efficiency. You can explore EnergySage for information about home battery storage options, including competitive installation estimates.
EnergySage’s free tools can also make the shopping process easier. With EnergySage’s help, most people can save up to $10,000 on solar purchases and installations. EnergySage’s solar map shows the average cost of a home solar panel system on a state-by-state basis, along with details on solar panel incentives in each state. Together, these resources can help you get the best price for rooftop solar panels and access available incentives.
💡Go deep on the latest news and trends shaping the residential solar landscape
As the OP put it, “don’t overlook summer heat. It’s not just an inconvenience, it’s a real performance factor.”
These stories add more context to the summer solar picture. They also touch on the incentives and financing options that can shape what people install and how much they end up paying over time.
• In Pennsylvania, a homeowner logged 144 kWh in one day, output fellow solar owners praised.
• Homeowners doing the math found solar panel leases can trim upfront costs without guaranteeing savings.
• In California, rooftop solar incentives plunged, and sales slumped in a once-booming market.
The broader point is that solar performance depends on more than one factor. Panel output in extreme heat, battery backup, incentives, and financing can all affect how well a system delivers when power demand climbs.
Get TCD’s free newsletters for easy tips, smart advice, and a chance to earn $5,000 toward home upgrades. To see more stories like this one, change your Google preferences here.
© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.

source

Global Solar PV O&M Market Reaches 348 GWdc As Vendor Consolidation Accelerates—Wood Mackenzie Report  solarquarter.com
source

Humans have been finding creative ways to harness the Sun’s heat and light for thousands of years. But the practice of converting the Sun’s energy into electricity — what we now call solar power — is less than 200 years old. Yet in that short time, solar power has revealed the Sun’s limitless potential to power an increasingly technological society. Since the 1950s, NASA has harnessed the energy of the Sun to power spacecraft and drive scientific discovery across our solar system. Today, NASA continues to advance solar panel technology and test new innovations.

Video credit: NASA’s Goddard Space Flight Center/Lacey Young
In 1839, French scientist Alexandre Edmond Becquerel (who was 19 at the time) was working in his father’s laboratory, experimenting with two metal sheets placed in an electricity-conducting liquid. As he shined light on the device, he detected a weak electric current — what we now know to be a flow of electrons through the material. This phenomenon was the first demonstration that light could generate electricity, known today as the photovoltaic effect.
By 1884 selenium had been incorporated in the world’s first solar array, which was installed on a New York City rooftop. Scientists continued to develop and experiment with selenium and other photovoltaic materials for the next 70 years, but practical applications were limited by their low efficiency – only about 1% of light energy could be converted to electricity.
That’s when scientists at Bell Labs used an abundant material called silicon to create the first solar cell that achieved 6% efficiency. Solar panels today use this same basic design, with adjustments that have allowed industrial and commercial solar panels to achieve between 15% and 23% efficiency.
Silicon is an abundant material used in many technological applications because it is a very good “semiconductor,” or material whose ability to carry electric current can be easily manipulated by adding energy. In typical solar cells, silicon is layered in three thin sheets. A middle layer is made of pure silicon. The outer two silicon layers are injected with other elements (typically phosphorous on one side, and boron on the other) that differ in their capacity to “donate” or “accept” electrons. As light strikes the pure silicon layer, it energizes the silicon’s electrons, which then begin to move within the material. Those electrons are attracted to the silicon layer designed to “accept” electrons, leading to a buildup of negative and positive charges in the outer layers. These two sides are then connected with wires to form a circuit that facilitates the flow of electrons from one side to the other, generating usable power.
Silicon-based solar cells power many of NASA’s spacecraft, including the James Webb Space Telescope. Learn more about why this abundant material is used in solar panels in this excerpt from NASA’s Elements of Webb video series.
The Soviet Union kicked off the space race with the launch of Sputnik on Oct. 4, 1957, quickly followed by the United States’ Explorer 1 on Jan. 31, 1958. But as both satellites ran exclusively on battery power, they were dead within a few weeks. In March 1958, the United States launched the first solar-powered spacecraft, Vanguard 1 (pictured at right), which transmitted data for the next six years.
Some missions, such as NASA’s Parker Solar Probe, require specialized solar panels that can operate in extreme environments. Flying on an elliptical orbit into the Sun’s hot outer atmosphere, Parker Solar Probe uses solar panels angled away and partially shaded from the Sun. It also uses a special cooling system to ensure the system isn’t overwhelmed by heat and was designed to be extra robust to deal with the intense ultraviolet rays it receives when close to the Sun, which can degrade materials rapidly. The spacecraft’s elliptical orbit also takes it far from the Sun, even beyond Venus. Engineers designed the solar array to compensate for how the light changes at different distances to the Sun, which alters the color and intensity of the sunlight it receives.
At Jupiter, which receives 25 times less light than Earth, the Juno spacecraft (pictured at right) needs three 30-foot-long panels to generate 500 watts of energy — about how much a typical refrigerator uses. Its orbit around Jupiter also helps keep the solar panels almost constantly exposed to sunlight to maximize power generation.
Solar power becomes less viable for missions that venture even farther, where there’s not even enough light to charge a battery. Deep space missions like NASA’s Voyager 1 and 2 rely instead on energy from the radioactive decay of plutonium-238 to keep them running well into interstellar space.
NASA scientists and other researchers around the world are working to improve the efficiency and durability of solar panels. In addition to using silicon, scientists have discovered that adding a layer of minerals known as perovskites can dramatically improve panel efficiency. Perovskites help capture bluer visible wavelengths, complimenting silicon’s redder wavelength coverage and allowing a solar cell to capture more light. In 2023, several independent research teams created small perovskite-silicon solar cells that exceeded 30% efficiency, and the best experimental cells today are approaching 50% efficiency.
NASA is also developing technology for flexible and rollable solar panels that can improve their use in constrained spaces. Using different materials for the base layer of a solar panel can make a panel lighter and more flexible — essential attributes for space missions that need to be packed into a small space in a rocket. The first two sets of solar arrays used by NASA’s Hubble Space Telescope in the 1990s and 2000s were designed with solar cells mounted to a flexible blanket-like material so they could be rolled up and stowed to fit inside the space shuttle cargo bay for launch.

In 2009, NASA and its partners started working on the next iteration of flexible solar panels called roll-out solar arrays (ROSAs). These arrays, which unfurl like a roll of paper towels, are even lighter and more affordable than previous arrays. They have been used on NASA’s DART (Double Asteroid Redirection Test) mission, on commercial geostationary satellites, and on the International Space Station to augment its traditional solar array. NASA plans to include ROSAs on Gateway, an orbiting outpost crucial to NASA’s Artemis campaign.

NASA is also involved with envisioning the next generation of solar power usage in space. To advance the Artemis campaign, NASA tasked three companies with developing and building prototypes of vertical deployable solar array systems to power human and robotic exploration of the Moon. Most space solar array structures are designed to be used horizontally, but on the Moon, vertically oriented structures atop tall masts will be needed to maximize sunlight collection at the lunar poles, where the Sun stays close to the horizon. Scientists are also investigating the feasibility of space-based solar power, which would collect sunlight from space and beam the energy back to Earth, potentially serving remote locations across the planet to supplement power transmission infrastructure on the ground.
Along with working to improve the efficiency of solar panels, NASA is also looking beyond photovoltaics to an old technology: sails. Humans have crossed open waters by sail for thousands of years. And now, NASA is working on a system to traverse space using solar sails. Unlike photovoltaics, which work by capturing the energy of light, solar sails use the pressure of light. When a photon, or individual particle of light, bounces off a reflective solar sail, it imparts a small push. With enough photons, these tiny nudges can move an entire spacecraft, much like how traditional sails harness the multitude of tiny air molecules that make up the wind. In the future, solar sails could replace heavy propulsion systems and enable longer-duration and lower-cost missions.
In 2024, the Advanced Composite Solar Sail System, a microwave-sized spacecraft, launched to test a new composite boom — a sail’s framework — made from materials that are stiffer and lighter than previous boom designs. The spacecraft has a solar sail measuring about 860 square feet — about the size of six parking spots. The seven-meter-long boom that holds out the solar sail can collapse into a bundle that would fit in your hand, which allowed it to fit compactly inside the spacecraft. The mission demonstrated the boom’s deployment and is now testing the sail’s performance using a series of maneuvers to adjust the spacecraft orbit using the sail angle. The technology could eventually allow for future sails up to half the size of a soccer field, enabling travel to the Moon, Mars, and beyond.
Explore NASA's Sun-related stories and download high-resolution images of the solar system, agency missions, and more.
Just as a sailboat is powered by wind in a sail, solar sails employ the pressure of sunlight for propulsion, eliminating the need for conventional rocket propellant.
On Oct. 2, 2024, the Moon will pass in front of the Sun, casting its shadow across parts of Earth. 
Our closest star is so much more than meets the eye.
NASA explores the unknown in air and space, innovates for the benefit of humanity, and inspires the world through discovery.

source

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 Reports volume 14, Article number: 25351 (2024) Cite this article
12k Accesses
13 Citations
Metrics details
Climate change, as a critical global concern, has fueled our efforts to address it through different strategies. In response to the critical worldwide issue of climate change, we suggested a Photovoltaic (PV) system at the National University of Sciences and Technology (NUST) in Islamabad, Pakistan (latitude: 33.724530 N, longitude: 73.046869, terrain elevation: 552 m). Islamabad is located in a region blessed with enormous solar resources, boasting a daily horizontal solar irradiance of 1503.45 kWh/m² and an average daily solar irradiance of 5.89 kWh/m², with an exceptional solar fraction of 98.99%. The ambient air temperature, averaging 23.21 °C, reaches its maximum in June and its minimum in December. Our research thoroughly evaluates the system’s performance, accounting for various losses and utilizing modern PVsyst software. Over the course of 18 years, our PV system is expected to save 75,478.60 tons of CO₂, the equivalent of planting 348,754 teak trees. Furthermore, the cost of energy generation is an affordable 0.0141 US $/kWh, much lower than traditional rates, including the Sherif cost of 0.028$/kWh. Along with the performance research, we conducted a detailed cost analysis, projecting the starting cost and cash flow, and discovered that the plant would be in surplus within 12 years of installation. Our system is positioned to generate 11,270,771 kWh/year with a respectable performance ratio (PR) of 76.2% and a Capacity Utilization Factor (CUF) of 16%. Our findings not only highlight the potential of renewable energy but also provide important insights for future sustainable energy programs.
Energy is a major driver of modern economies, essential for education, healthcare, agriculture, and employment, and serves as a pillar of a country’s economic viability. Pakistan, a growing country covering 803,950 km² and ranking sixth in the world in terms of population, faces a severe energy crisis¹. Only 62% of the population has grid access, resulting in a per capita electricity supply of only 520 kWh². Notably, around 58% of the country’s people live in distant rural areas that are largely disconnected from the grid. This energy shortage, reaching 9–15 GW, has a significant impact on the nation’s businesses and causes severe blackouts and load shedding, with an annual economic cost of $3.8 billion^3,4. Pakistan has a varied spectrum of renewable energy resources, including hydro, solar, wind, and biomass, to address these difficulties⁵. Importantly, Pakistan is rated as one of the top 20 most appealing countries in the world for renewable investment but in recent years, Pakistan has seen an increasing imbalance between electricity demand and supply, which is most noticeable during the hot summer months. This imbalance has resulted in widespread power outages, with cities seeing up to 10–12 h of blackout and rural areas facing even longer outages of 16–18 h⁶. Pakistan relies substantially on thermal power, accounting for 68% of its total electricity generation capacity of 183,540 Gigawatt hours (GWh)⁷. Notably, the country lacks proven oil, coal, and natural gas deposits, as well as domestic power production capacity. This critical energy imbalance is highlighted by an average daily electricity gap of 4.7 million kWh, which can reach 7.5 million kWh on hot summer days⁸. In response, the Pakistan government is actively advocating for a transition to sustainable and renewable energy sources, such as wind and solar power, in order to address acute electricity shortages. Currently, renewable energy accounts for a small portion of Pakistan’s overall energy mix, with the country relying heavily on fossil fuels to cover its energy needs. This reliance on fossil fuels not only puts a tremendous strain on the national economy, but also creates a number of environmental issues, such as the greenhouse effect, CO₂ emissions, global warming, and unpredictable weather patterns. Furthermore, the continued use of fossil fuels depletes natural resources at an alarming rate. As a result, there is an urgent need to develop a new energy economy in which renewable sources such as wind, solar, and biomass take center stage. This transformation not only offers to minimize the import expense linked with fossil fuels, but it also promises to address the current climate-related concerns. Pakistan’s energy problem is mostly caused by the country’s dependency on thermal resources such as coal, oil, and natural gas, all of which are not only expensive but also in limited supply⁹. Furthermore, hydroelectricity, which was formerly a considerable source, has recently declined in importance. Renewable energy sources, on the other hand, currently account for only 0.3% of the country’s energy needs, a fraction too small to relieve the situation¹⁰. Solar energy emerges as a powerful solution to these difficulties among the diversity of renewable energy options¹¹. Globally, it has positioned itself as the most cost-effective technology, with the ability to transform Pakistan’s energy landscape. Large-scale solar photovoltaic and wind turbine projects have assumed precedence in Pakistan’s Sustainable Action Plan 2009¹², which was amended in 2013, owing to falling technology costs. These semiconductors incorporated within solar modules provide a dependable, maintenance-free, and environmentally beneficial source of energy that is adaptable to changing climatic circumstances. In the following parts, we will go deeper into the literature, investigating the history and possibilities of solar energy in Pakistan, and charting a course toward a more sustainable and energy-rich future. Figure 1 depicts a combined line graph illustrating key solar parameters for various locations in Pakistan (Direct Normal Irradiation—DNI, Specific Photovoltaic Power Output—PVOUT, Global Horizontal Irradiation—GHI, Diffuse Horizontal Irradiation—DIF, and Global Tilted Irradiation at Optimum Angle—GTI opta). The data was sorted in ascending order based on DNI values, allowing for a clear comparison of solar resource potential across different locations. Kharan has the greatest GTI opta rating among these places, while simultaneously having the best DNI, GHI, and PVOUT value, indicating ideal conditions for solar energy generation. Nawabshah has the greatest DIF. This graph depicts how these factors vary among places, highlighting the specific regions in Pakistan with the greatest solar resource potential. Figure 2(a)) depicts an aerial map of Islamabad’s National University of Sciences and Technology (NUST). Meanwhile, Fig. 2(b) depicts the solar PV potential map for this specific area. These maps demonstrate Islamabad’s enormous solar energy potential, making it a desirable place for electricity production via solar PV installations.
Solar resource variability in different regions of Pakistan.
(a) Aerial map of NUST. (b) Solar PV power potential. (Source: Google Maps, 2024. Used under principles of fair use for educational and non-commercial purposes. Available from: https://www.google.com/maps).
Pakistan’s electricity generation is mostly based on oil, gas, hydropower, and nuclear energy, which contribute 35.3%, 29.1%, 30%, and 5.5%, respectively, to total power production¹³. Despite considerable domestic coal deposits, the usage of coal is modest, accounting for barely 0.1% of the energy mix but since Pakistan has a large potential for solar energy, getting an annual average of 1900–2200 kWh/m² of global solar radiation¹⁴. Pakistan’s steady solar radiation dispersion favors solar energy uses¹⁵. Various photovoltaic modules and system schemes have been investigated, making renewable energy system planning easier¹⁶. Techno-economic feasibility studies have been critical in examining the performance of low-cost renewable energy systems in Pakistan, particularly off-grid solutions for small loads¹⁷. Pakistan has an estimated solar energy reserve of up to 100,000 MW due to its ample sunshine⁷. Recognizing the potential of solar energy, the government prioritized the Quaid-e-Azam Solar Park project in Bahawalpur, Punjab. Pakistan updated its grid code and standard project documentation in March 2013 to encourage the development of solar power plants, offering incentives such as tax breaks and an 18.5% return on investment to entice investors¹⁸. These activities are in line with the country’s aim of reducing carbon emissions and managing electricity supply issues. Pakistan adopted legislation favoring hydropower, wind power, and photovoltaic energy to hasten the shift to renewable energy sources¹⁹. Collaboration with the US government and the World Bank has resulted in geographical solar energy and wind resource mapping studies that highlight Pakistan’s tremendous solar power potential, estimated at around 1600 GW, with certain places rivalling the world’s highest MENA region²⁰. The need of well-maintained weather stations of accuracy was underlined in these investigations. Furthermore, talks on grid-connected PV power systems in rural communities, as well as the necessity for a realistic energy strategy, underscore Pakistan’s commitment to a sustainable energy future. Despite an increase in private thermal-based independent power plants, Pakistan’s power generation capacity was challenged by a financial crisis, resulting in a peak power shortfall of 4743 MW during the summer season^2,21. This scene emphasizes the importance of continuing efforts to expand renewable energy and construct robust power infrastructure for long-term growth. According to the International Energy Agency (IEA), global solar PV capacity reached 402 gigatonnes (GW) at the end of 2017, with projections showing an additional development of nearly 580 GW, cementing its position as a frontrunner in renewable power capacity growth²². Akram et al.²³ investigated the complexities of institutional and technical impediments to solar energy adoption in a separate study. Over the last two decades, the European Commission has invested heavily in solar energy research in order to reduce pollution, reduce carbon emissions, improve energy security, and diversify European nations’ energy portfolios. The difference-in-differences (DID) methodology was used by²⁴ to examine the impact of emission trading systems (ETS) on economic growth and carbon emissions. Their research advocated carbon trading policies based on renewable energy projects. Akhtar et al.²⁵ did a cost-saving comparison before and after the deployment of cost-saving certified emission reduction (CCER) systems. Over the last two decades, the European Commission has invested heavily in solar energy research in order to reduce pollution, reduce carbon emissions, improve energy security, and diversify European nations’ energy portfolios. Queseda et al.²⁶ used the Sigma Plot optimization tool to estimate parameters in the logistic model, which performed better than linear models. Khatri et al.²⁷ demonstrated that logistic modeling is a reliable tool for estimating and projecting data over long periods of time and has been applied in various domains. References^28,29,30,31 used machine learning algorithms to measure global solar radiation and forecast diffuse solar radiation for the humid-subtropical climatic zone. Ur Rehman et al.³² designed an off-grid PV system for household users, demonstrating the diverse application of solar energy modeling and research. Raza et al.³³ used Pakistan’s Logarithmic Mean Division Index to conduct a comprehensive analysis of CO₂ emissions from electricity generation from 1978 to 2017. According to projections, the electricity industry alone will produce around 277.9 million metric tons of CO₂ emissions by 2035.Grainger et al.³⁴ calculated the baseline emission factor for the power production sector by considering characteristics such as annual fossil fuel consumption net efficiencies, annual energy outputs, and carbon emissions. Their findings provided a weighted average baseline emissions factor of 0.606 tons of CO₂/MWh for wind and solar power projects in Pakistan, underlining the potential for carbon emissions reductions through renewable-based power projects. A 5 MW SPV power plant was designed using PVsyst software for 50 Iranian cities, with Bushier having the highest capacity factor and Anzali having the lowest, for a mean capacity factor of 22.27%³⁵. In Serbia³⁶, studies were conducted to estimate the potential for producing electricity using 1 MW solar power plants employing the various types of solar PV modules available, and it was discovered that CdTe solar modules produce more electricity. Elhodeiby et al.³⁷ provided a performance analysis of a 3.6 kW rooftop grid-connected solar photovoltaic system in Egypt. All the electricity generated by the system was routed into the 220 V, 50 Hz low voltage grid and monitored for a year. Veerendra Kumar et al.³⁸ in 2018 and 2019, the United States (USA) produced 10.6 GW and 13.3 GW of solar photovoltaic (PV) panels, respectively. The cumulative operating photovoltaic capacity in the United States topped 76 GWDC by the end of 2019, up from just 1 GW at the end of 2009. Under the framework of research and development, the International Energy Agency (IEA) Photovoltaic Power Systems Programme (PVPS) has specified a set of 13 aims for the expansion of operation, performance, and monitoring of solar photovoltaic plants. Recently, large-scale photovoltaic power plant studies have been done³⁹. References^40,41 did a study on solar power plants (1523 kW and multi-MW) located in the Canaries (Spain), they discovered that the measured specific yields were within 3% of the simulated findings, these solar power plants were meticulously managed and located in the best possible places. Researchers utilized PVsyst to examine the potential of 44 Saudi Arabian locations for grid-connected solar power plants with a 10 MW installed capacity. The tool assisted them in forecasting energy output, greenhouse gas GHG) emissions, and financial aspects of the proposed solar power plants⁴². According to Satsangi et al.⁴³ Indian 40 kWp GIPV system had photovoltaic array efficiency of 9.36%, inverter efficiency of 90.9%, and overall system efficiency of 8.51%. In another study⁴⁴, Antonanzas et al. assessed a 12-kW solar power plant using the International Solar Project Model. They discovered that the best-case scenario for this plant was to meet power demand using 27% solar energy and 73% grid electricity. They were able to achieve a minimum reduction in greenhouse gas emissions of 23 t of CO₂ per year by using this strategy. A structured methodology was utilized to examine the integration of solar plants into weak distribution systems in grid connection studies⁴⁵. The performance ratios, yield energies, reference energies, capacity utilization factors, and energy efficiency of various solar photovoltaic systems were also examined⁴⁶. Various research projects throughout the world have conducted detailed studies of solar photovoltaic (PV) systems, revealing light on their performance in a variety of climates and situations for instance in Pakistan, operating in a similar semi-arid environment, studies found that TF-Si technology outperformed OPV, CIGS, and TF-Si systems inside a 100 MW installation, particularly in humid conditions⁴⁷. In a 13 MW configuration using bifacial and CIGS technologies, energy generation in South Korea’s semi-arid region was slightly lower than expected⁴⁸. In Iran’s arid desert, a 10 MW installation using Poly-Si, A-Si, and OPV technologies revealed that the highest maximum power output occurred in July, with OPV having a significant advantage⁴⁹. In Sweden, the study investigated hybrid systems integrating CIGS, Mono-Si, and Poly-Si technologies, finding promise for improvement but recognizing the problem of competing with traditional PV modules and flat plate collectors⁵⁰. Meanwhile, in Spain’s sun-drenched expanse, the inquiry spans six power facilities outfitted with a variety of PV technologies and tracking systems, including fixed tilt, single-axis, and dual-axis setups⁵¹. Singapore, known for its tropical climate, used modeling findings to determine the best placements and inclinations for solar panels, focusing on east-facing façades and panel slopes of 30 and 40 degrees⁵². Maritime Germany’s exploration of power plants, on the other hand, found that panels with 95% bifaciality outperformed those with 50%, boasting an amazing 7% gain in yield⁵³. Utility-scale comparison studies of photovoltaic (PV) systems have been done to discover the most efficient module technology⁵⁴. In Kuwait, for example, an 11.15 MW solar PV plant was examined, with two PV technologies pitted against each other: a 5.5 MW thin-film installation and a 5.6 MW polycrystalline silicon installation. This comparison investigation revealed no major differences between the two methods⁵⁵. Individual modules were also subjected to rigorous testing, with the module performance ratio (PR) for 23 different solar module technologies assessed across four different sites over the course of a year⁵⁶. Burssens et al.⁵⁷ conducted a comprehensive assessment of eight PV technologies in Belgium under difficult climate circumstances. Their findings revealed that monocrystalline silicon (mono-Si), polycrystalline silicon (poly-Si), and heterojunction (HIT) modules performed best in high irradiance environments, while CIGS modules performed well in low irradiance areas. Building-integrated photovoltaics, bifacial modules with different orientations, microgrids, car park shelters, and rooftop systems were also investigated, these studies contribute to a better knowledge of PV technology performance in various circumstances, assisting in the development of more efficient solar energy systems^58,59.
Climate change is an obvious truth, and Pakistan is one of the countries hardest hit by its impacts. The country has the formidable task of combating climate change, with climate-related calamities taking countless lives and causing significant economic losses each year⁶⁰. Pakistan’s energy and power industries have long been substantial contributors to Greenhouse Gas emissions, owing to the country’s strong reliance on fossil fuels for electricity generation. The answer to reducing these emissions is to switch to cleaner and renewable energy sources⁶¹. It should be noted that the economic rewards from such activities frequently do not fully pay the expenditures of emission reduction. Climate finance instruments, such as the Clean Development Mechanism, can help to promote the adoption of renewable energy technologies (RET) that reduce emissions. The overuse of fossil fuels is a major driver of this transformation. Carbon-based fossil fuel combustion not only generates CO₂ but also a variety of pollutants, including particulate matter, worsening climate change⁶². Notably, the energy sector bears a disproportionate share of the burden, accounting for nearly three-quarters of carbon dioxide emissions, one-fifth of methane emissions, and a significant amount of nitrous oxide. According to World Bank data⁶³, Pakistan’s carbon emissions have increased dramatically during the 1980s, with an annual growth rate of roughly 6.4%. While the pace of growth slowed after 2004, it remained above the global average, with emissions totaling 432.50 million tons (Mt) of CO₂ equivalent in 2019 and 679.34 million tons (Mt) in 2022. Photovoltaic power plants, an important component of renewable energy, have enormous potential for lowering carbon emissions and improving the environment. Their impact on the environment is fairly minor. This study adopts the United Nations Framework Convention on Climate Change (UNFCCC)⁶⁴ technique for computing marginal carbon emissions to analyze the carbon emission reductions resulting from investments in photovoltaic power. Given Pakistan’s high electricity demand, cumulative electricity generation from prioritized photovoltaic power projects over their operational lives might reach a mind-boggling 50.15 billion kWh, enough to power over 250,000 local houses⁶⁵. When the power generation data for each solar power project is combined with the marginal carbon emission factors, the average yearly carbon emission reduction ascribed to these priority projects is predicted to be 28.23 million tons (Mt) of CO₂-equivalent. This overall carbon emission decrease totals an astonishing 27.45 million tons (Mt) of CO₂-equivalent throughout the 25-year service span⁶⁶. This illustrates the significant environmental advantages and carbon emission reductions feasible through investments in photovoltaic power installations, underlining their critical role in Pakistan’s fight against climate change.
The International Energy Agency developed the performance measures to assess the efficiency of grid-connected solar PV installations^67,68. These characteristics include energy output, solar resource usage, and total system losses, allowing for a full assessment of the system’s performance. The performance ratio, final PV system yield, and reference yield are among the essential criteria examined. These indicators add up to a full assessment of the solar PV system’s ability to harness solar energy and its overall efficiency in turning sunshine into electricity. Within the scope of our analysis, the most relevant parameters include energy output, array yield, final yield, reference yield, module efficiency, inverter efficiency, system efficiency, energy loss (comprising array capture loss and system loss), performance ratio, and capacity factor. These standardized indicators, recognized as essential benchmarks, form the basis for evaluating the performance of our large-scale photovoltaic power plant allowing for an effective and comparative assessment of its efficiency and productivity. The “Array Yield” is the time it takes the PV plant to produce array DC energy (E_a) at its nominal solar generator power (P_o). The values are kilowatt-hours per day per kilowatt peak (kWp). This statistic is used to assess the efficiency and productivity of a PV plant, taking into account variations in solar resource availability and system performance (1), (2)⁶⁹,
The “Reference Yield” is determined by dividing the total in-plane irradiance by the reference irradiance. It quantifies the peak sun hours or insolation in units of kWh/m² and characterizes the solar radiation resource accessible to the system (3), (4)⁶⁹. It represents the obtainable energy under ideal conditions. The reference yield describes the solar radiation resource for the PV. system and is affected by the PV array’s position, orientation, and month-to-month and year-to-year weather fluctuation. The reference yield is influenced by factors such as the geographical location and orientation of the photovoltaic array, as well as the variability in weather conditions both on a monthly and annual basis^70,71,
where, H_t = Total Horizontal irradiance on array plane (W h/m₂) and G_o = Global irradiance at STC (W/m²).
The term ‘Final Yield’ (YF) refers to the total alternating current (AC) energy produced by the PV system over a specified time period. It represents the number of hours the PV array would have to run at maximum capacity to generate the same quantity of electricity. This parameter is crucial in determining the efficiency and performance of a solar PV system (5)⁷²,
The capacity factor is an important parameter in assessing the overall performance and productivity of a power generation system, showing its ability to produce energy consistently through time. The inverter efficiency, also known as conversion efficiency, is determined by the ratio of AC power generated by the inverter to DC power produced by the PV array system, and the system efficiency is calculated as PV module efficiency multiplied by inverter efficiency, (6)–(9)⁷²,
Natural energy losses occur in different components of a grid-connected Solar Photovoltaic (SPV) power plant operating under real-world conditions. The monitored data generated from the system’s performance is used to analyze these inherent losses. Energy losses in solar photovoltaic (SPV) power plants are unavoidable due to a variety of variables. Understanding and correcting these inefficiencies is critical for improving plant efficiency and energy generation. Monitoring effectively delivers vital insights for improved performance (10)⁷²,
These losses are attributed to cell temperatures surpassing 25 °C, leading to thermal inefficiencies. The thermal capture loss (LCT) is quantified as the difference between the reference field and the corrected reference field (11), (12)⁷²,
Losses generated by energy conduction within the photovoltaic modules contribute to miscellaneous capture loss (LCM). Addressing these diverse factors plays a crucial role in minimizing losses and optimizing the SPV power plant’s overall performance.
To estimate the carbon footprint reduction achieved by the solar power plant, we used PVsyst software to simulate the system’s performance and calculate the avoided CO₂ emissions. The software uses the following equations to compute the CO₂ reduction (13)-(15)^73,74,
Where AEP represents the annual energy production in kWh/year, the installed capacity is in kWp, and the specific yield is in kWh/kWp/year. The CO₂ emission reduction is measured in tons per year, the emission factor is in kg CO₂ per kWh, the total CO₂ savings are in tons, and the plant lifetime is measured in years.
The carbon footprint reduction per installed kWp is measured in tons of CO₂ per kWp, and the annual CO₂ reduction per kWp is measured in tons/kWp/year (16) and (17)^73,74,
We started with 13 possibilities in our search for the best PV panels for our solar plant (Longi Solar, Mitsubishi, Panasonic, Samsung, Solar Frontier, Solimpeks, Kyocera, KDG Energy, JP Solar, JinkoSolar, Hulk Energy Technology (HET), Helios USA, Ferrania Solis). These options were thoroughly evaluated based on the following major criteria (efficiency, cost, warranty, compatibility, monitoring, cost-effectiveness, safety, and environmental impact) mentioned in Fig. 3a as C1, C2, C3…., C8. Our data-driven methodology gave each criterion an equal weight of 8 points. Longi Solar emerged as the best option after this evaluation. While Solimpeks and Kyocera were less expensive, they had restrictions. Mitsubishi Electric excelled in capacity but had to make sacrifices. Solar Frontier outperformed in terms of environmental performance but suffered efficiency and compatibility issues. This thorough methodology offers a data-backed selection customized to your long-term energy objectives.
Decision matrix for the selection of (a) solar PV, (b) inverter.
We started with 17 possibilities in our search for the best inverter for our solar plant. These inverters, which included respectable names like Huawei Technologies, Azzurro ZCS, BYD, Cefem Solar System, Delta Energy, GE Solar, Hyundai, Mitsubishi Electric, Satcon, Sepsa, SofarSolar, Tabuchi Electric, and Turbo Energy, were meticulously scrutinized across a matrix of critical criteria. These criteria included Inverter Efficiency, Capacity and Sizing, Reliability and Warranty, Compatibility, Monitoring and Communication, Cost and Budget, Safety and Grid Compliance, and Environmental Impact mentioned in the Fig. 3 (b) as C1, C2, C3…., C8. Using an equal weight of 8 points for each criterion, our data-driven review highlighted the advantages and disadvantages of each inverter option. While Huawei Technologies emerged as the clear winner, with superior performance in all areas, it’s important to note that alternatives such as Sepsa, SofarSolar, Tabuchi Electric, and Turbo Energy held their own, demonstrating unique capabilities that may correspond with specific project requirements. This rigorous procedure ensures that your selection aligns with your solar energy requirements and environmental goals.
We conducted a thorough analysis utilizing a decision matrix that included different crucial factors required for solar plant installation to pick the optimal site. The evaluation procedure entailed examining three separate sites, namely S1, S2, and S3, based on important characteristics that greatly determine each location’s potential. Solar Irradiance, Space Availability, Proximity to Electrical Infrastructure, Environmental Impact, Cost of Installation, Ground Security, Community Impact, and Maintenance Accessibility were among the criteria considered. The combined line graph (Fig. 4) provide a clear and instructive visual picture of how each site performed in relation to these critical criteria. Site S2 consistently outperformed, receiving the top marks across the bulk of the tested categories. Notably, it outperformed in Solar Irradiance, Space Availability, Proximity to Electrical Infrastructure, and Environmental Impact. Because of this aggregate strength, Site S2 is a particularly advantageous location for solar plant development, providing excellent conditions for efficient energy generation. Site S1, while not outperforming S2, performed admirably in numerous areas, particularly Solar Irradiance, Proximity to Electrical Infrastructure, and Community Impact. Site S3, on the other hand, while showing potential in terms of Solar Irradiance and Community Impact, confronts problems in terms of Space Availability, Installation Cost, and Environmental Impact. In conclusion, this comprehensive analysis and the resulting graphical representation enable decision-makers to make well-informed choices when selecting the most suitable site for solar plant installation, Site S2 emerges as the preferred choice, presenting a harmonious balance of favorable criteria that aligns with the project’s objectives and environmental considerations.
Comparison of criteria for given 3 sites for the SPV at NUST.
The schematic diagram in Fig. 5 outlines the layout of an advanced 8.79 MW solar power facility at the National Sciences and Technology Park (NSTP) within the National University of Sciences and Technology (NUST) in Islamabad, Pakistan. PV modules efficiently gather solar energy and convert it from direct current (DC) to alternating current (AC) power utilizing modern inverters and transformers. High-tension switchgear guarantees that energy is transferred seamlessly to the 33 kV input/output (I/O) switchyard, where it is metered before being integrated into the utility grid. The lower half focuses on the generator-grid link, which is regulated by a control system that includes power conditioning for quality assurance. This design maximizes energy generating efficiency while avoiding the need for substantial storage. The geographical location of small, isolated power systems has a considerable impact on performance, with local climate influencing module temperature and energy generation. Precise modeling, including regression analysis, allows for production forecasting, grid integration, and system health prediction, all of which improve overall sustainability.
Layout of 8.79 MW gid-connected solar power plant at NUST.
The National University of Sciences and Technology (NUST) in Islamabad, which is located at 33.647279° latitude and 72.99987° longitude, provides a strategic advantage due to its copious year-round sun radiation. Islamabad has consistently high insolation levels, with approximately 2945 h of annual sunshine, which equates to over 6400 trillion kWh of solar energy potential. The detailed yearly climate data is illustrated in Table 1. Furthermore, the region’s high temperatures, which can reach 45.5 °C, contribute to its aptitude for solar power generation. For solar panels in Pakistan, the ideal direction is generally south facing, which corresponds to an azimuth angle of approximately 180°. There is no land rent since the site location is the property and falls under the premises of the National University of Sciences and Technology (NUST). Global horizontal radiation, horizontal diffuse irradiation and global horizontal irradiation, clear sky is shown in Fig. 6(a), while the sun path and solar azimuths in Fig. 6(b). The proposed plant is located at the Northeast of the National Sciences and Technology Park (NSTP) (Fig. 7) and the map data of the selected site is shown in Table 2.
(a) Global horizontal, and horizontal diffuse irradiation. (b) Sun path and solar angles.
Aerial view of the proposed site location for the SPV (Source: Google Maps, 2024. Used under principles of fair use for educational and non-commercial purposes. Available from: https://www.google.com/maps).
The 11.5 MW solar power facility at NUST, Islamabad, covers 9.36 acres of land and is divided into six strategic blocks, which are further subdivided into twelve sub-blocks totaling 8.79 MW capacity. The system efficiently integrates AC power into the 33 kV grid via a double fed primary winding transformer with 96 × 8 strings in Block A and 40 × 9 strings in Block B, which are coupled to inverters via string combiner boxes. This plant is extremely adaptable, collecting power from the grid during low solar radiation periods to ensure uninterrupted power supply. Its substantial battery storage improves energy stability, cementing its position as a dependable and versatile energy source.
The tilt angle of the PV array is kept equal to the latitude of the corresponding location to get maximum solar radiation⁷⁵. Figure 8 provides an interesting analysis of tilt angle selection and its impact on solar irradiance levels throughout the year in the context of our study on solar energy optimization. This image is made up of numerous crucial aspects, each of which helps us grasp this critical aspect of solar energy generation. The blue line depicts the baseline irradiance levels without any tilt angle modifications. This line graphically depicts the natural change in solar exposure that occurs throughout the year. The green line then shows the impact of using a fixed tilt angle of 29.5°. This consistent tilt angle modifies the solar irradiance pattern, resulting in a consistent but altered level of solar energy throughout the year. Finally, the orange line emphasizes the system’s adaptability through varied tilt changes, it shows how the sun irradiance levels adapt to shifting angles, providing important insights into the system’s adaptability. This chart shows the variable tilt angles in degrees, and it serves as a visual reference for the modifications we performed in our analysis. The combined depiction in Fig. 8 is a valuable tool for determining the sort of tilt system to be utilized, whether it is fixed or seasonal tilt, and the best tilt angle to optimize solar energy output across all seasons. Furthermore, our research found that using seasonal tilt angles resulted in a 36.31% increase in solar irradiation when compared to no tilt scenarios. Furthermore, the average percentage change in irradiance between seasonal tilt and fixed tilt was discovered to be 16.59%, highlighting the advantages of an adaptive tilt strategy for increased solar energy efficiency.
Comparative analysis of solar irradiance levels and tilt angle adjustments over months.
The monthly electrical consumption data for the National Sciences and Technology Park (NSTP) at the National University of Sciences and Technology (NUST) solar power plant in Islamabad, Pakistan, shows significant seasonal changes. These swings in consumption are directly related to seasonal and environmental change (Fig. 9). Lower demand is seen during the winter months, gradually increasing when spring arrives, and peaking during the hot summer months. As the year passes and temperatures fall, so does the power demand, which reaches its lowest point in the fall and early winter. These fluctuations in monthly electrical consumption are critical in optimizing the performance of the solar power plant and providing a continuous energy supply in response to seasonal variations.
Monthly electricity consumption at the National Sciences and Technology Park (NSTP), NUST in MWh.
The solar panels used in this system are Longi_LR5-72_HTH_600M by Longi Solar 14,664 modules, boasting 600 Wp power output detailed specifications in (Table 3) measured at the standard testing conditions which are irradiance of 1000/m², air mass 1.5 g, and cell temperature of 25 °C. These monocrystalline silicon panels were chosen because they are more efficient than other cell types, such as polycrystalline and amorphous silicon. Aluminum frame, Tempered AR glass structures with glass foil, Jbox IP 68, MC4 EVO2 or mateable HPBC Half-Cut connections. With a 3.5-meter structure-to-structure and leg center-to-center distance, as well as small 35-mm gaps between neighboring panels, the work stresses precision. The ground and the bottom module edge are separated by 450 mm. Regular bi-monthly cleaning and high-density encapsulation technologies improve performance by 0.019–0.230%. Figure 10 depicts the critical model parameters for our investigation. In (a), (b), and (c), we detail the shunt resistance (Rsh) at 280 ohms, the series resistance model (Rs) at 0.231 ohms (with a maximum of 0.235 ohms), the apparent dV/dI at 0.37 ohms, the diode saturation current (IoRef) at 0.019 nA, and the diode quality factor (Gamma) at 1.003. In Fig. 11, (a) shows the exponential behavior of Rsh as a function of incident irradiance, with Rshunt set to 1400 ohms and an exponential value of 5.5. (b) shows the temperature coefficients muPMax and muVoc, emphasizing their gamma dependence and serving as critical output parameters for the classic one-diode model, and (c) shows the incidence angle modifier’s behavior on the effective profile. Similarly, Fig. 12 shows the current vs. voltage behavior of (a) Incident irradiation (b) cell temperature, and (c) series resistance and P_mpp. Figure 13 shows power vs. voltage behavior of (a) incident irradiation (b) cell temperature, and (c) series resistance and P_mpp. Figure 14 illustrates the efficiency at power behavior of (a) incident irradiation and cell temperature (b) cell temperature and incident global irradiation, and (c) shunt resistance and incident global irradiation. These factors are essential for comprehending our system’s behavior. This all-encompassing approach provides maximum energy generation and system efficiency.
(a) I/V curve. (b) P/V curve, and (c) relative efficiency.
(a) Rshunt exponential as irradiance. (b) Voltage and power with module temperature, and (c) Incidence angle modifier.
Current vs. voltage behavior of (a) Incident irradiation. (b) Cell temperature, and (c) Series resistance and Pmpp.
Power vs. voltage behavior of (a) Incident irradiation. (b) Cell temperature, and (c) Series resistance and Pmpp.
Efficiency at power behavior of (a) incident irradiation and cell temperature, (b) cell temperature and incident global irradiation, and (c). shunt resistance and incident global irradiation.
In this grid-tied solar photovoltaic (PV) system, the inverters play a crucial role in converting DC power into AC power. The Huawei Technologies SUN2000-50KTL-M3-380 V, H inverter was chosen. It has a 380 V operating voltage and a dual stage, detailed specifications in Table 4 measured at the standard testing conditions which are irradiance of 1000/m², air mass of 1.5 g, and cell temperature of 25 °C. LED indicators, integrated WLAN compatibility, and the FusionSolar APP for monitoring are among the control features. This inverter works with SUN2000 1100/1300 W optimizers. The system in Fig. 15 operates at a medium voltage of 611 V and has an amazing inverter efficiency of 98.26%. The configuration is intended for an average temperature of 23.1 °C. It has a high-power factor (cos) of 1.00 and tan values that ensure optimal performance. At about 35 °C, the inverter handles an apparent power of KVA and generates a nominal AC power of 50 KVA, with a maximum AC power output of 55 KVA. For safety reasons, the power limiter is set at 45.5 kWac at 50 °C and 38.2 kWac at 60 °C. Four MPPT inputs, 5.5 W night consumption, I_sol monitoring, a DC switch, and adjustable ENS and AC disconnect options are also included. This all-encompassing approach provides maximum energy generation and system efficiency for this solar plant.
Power in DC comparison with (a) Top left—efficiency power in (b) top right—efficiency power out (c) bottom left—power out in AC, and (d) bottom right—maximum power with temperature.
This 8.78 MW solar power plant’s transformer is rated at 1.5 MVA and has the Vector group designation DY5Y5 four-winding transformer (double story transformer). It has a 390 V primary voltage and is directly connected to the 33 kV switchyard on the secondary side. The primary current rating is 2.24 A, and the secondary current rating is 980 A, proving its capacity to handle power needs efficiently. The transformer has an excellent efficiency of around 96.45%, which ensures low energy losses during conversion. It works at a nominal power of 9.65 MVA under standard test conditions (STC), with an iron loss of 1158 kVA, or around 12% of the nominal power. The copper loss is 113.33 kVA, which is equal to 100% of the nominal power, and the coils have an equivalent resistance of 3 × 0.27 m s.
A weather monitoring station situated adjacent to the power plant in the (Southwestern Edge) records data on wind speed, ambient temperature, and solar radiation (Fig. 9). The control room of the solar power plant is outfitted with cutting-edge equipment for real-time monitoring and optimization. A SCADA system provides precise control, while data analytics software analyzes sensor data for trend analysis and maintenance forecasting. Data transfer is ensured by communication infrastructure, and the facility is protected by security mechanisms. An Energy Management System (EMS) manages energy distribution, while predictive maintenance technologies maintain component health and an easy-to-use Human-Machine Interface (HMI) assists operators. Backup power systems ensure continuous operation, allowing for efficient and dependable energy generation.
The amount of solar energy received per unit area, known as solar irradiance, has a considerable impact on the peak power production of solar modules. Higher irradiance levels result in enhanced power generation, while lower levels result in decreased output. The link between temperature and power output, on the other hand, is more complicated. Even with enough solar exposure, rising temperatures cause a decrease in power output above a particular threshold. This temperature-related effect must be taken into account for improving solar system performance. Furthermore, even with constant solar irradiation, power generation drops significantly as the ambient temperature rises due to thermal characteristics. Understanding the relationship between sun irradiance, temperature, and power production is critical for increasing energy efficiency in the design and operation of solar power systems.
The annual global horizontal irradiation is 802.04 kWh/m², and the global incident energy incident on the collector plane annually is 1580 kWh/m². The total energy obtained from the output of the PV array is 12,343,298 kWh, the effective grid value comes out to be 11,539,692 kWh, the effective solar value is 559,187 kWh and the effective energy to the grid is 6,715 kWh (Table 5). The annual efficiency (E_out) of the PV array concerning the rough area is 21.42%. Figure 16 depicts a thorough overview of major solar PV system performance parameters. The particular E_out array for the rough region is estimated at 0.1815, stressing its potential for energy generation, whereas the specific E_out array for the cell area is observed at 0.0001. The inverter efficiency, which averages 0.9786, demonstrates the system’s efficiency in converting generated energy. The worldwide incident on the collector plane, which averaged 1681 kWh/m², demonstrates the abundance of solar radiation accessible for energy production. Throughout the year, the sky diffuse incident in the collection plane averaged 459.7, emphasizing diffuse radiation contributions. The Incident Angle Modifier (IAM) value for global radiation is 0.9810, indicating that the system is adaptable to different sun angles, but the IAM factor for circumsolar radiation is 1, indicating that optimal collection from direct sunlight is achieved. With a soiling factor of 0.9000, the system’s cleanliness is critical to its functioning. During operation, the average module temperature is 35.57 degrees Celsius, which has an impact on total efficiency. Finally, the array runs for an average of 3910 h every year, evenly spreading energy output across the months. The dynamic behavior of the average module temperature during system operation is depicted in (f analysis takes a multidimensional approach, taking into account many elements and trends. The impact of an effective global correction factor on module temperature is shown in Figure (a), emphasizing the relevance of this parameter in system performance. We investigate how the number of occurrences affects temperature patterns in subfigure (b), revealing light on the temporal aspects of system operation. (c) and (d) shed light on the relationship between useful system energy and global incident energy, as well as the proportion of useful system energy in the overall context. These visualizations, when combined, provide a full view of module temperature dynamics and lead to a better understanding of system behavior during operation. These insights provide a comprehensive picture of the system’s performance and efficiency. Figure 17 shows the behavior of average module temperature during operation with (a) effective global correction factor, (b) number of occurrences, (c) useful system energy vs. global incident energy, and (d) percentage of useful system energy.
Behavior of major performance parameters of solar power plant.
Behavior of average module temperature during running with (a) Top left—effective global correction factor (b) top right—number of occurrences (c) bottom left—Useful system energy vs. global incident energy, and (d) bottom right—percentage of useful system energy.
The information shown here indicates the monthly performance coefficients for a solar power system over a range of factors. When the trends are examined (Fig. 18), it is clear that the solar irradiance, denoted by Yr (kWh/m²/day), normally rises from February to June, indicating greater solar energy availability during the spring and early summer months. In contrast, it falls from July to December, with the lowest values recorded in December, which corresponds to the winter season. The Lc (kWh/kWp/day) numbers, which measure energy yield per unit installed capacity, follow a similar pattern, with the highest values occurring in May and June and the lowest in December. Similarly, the Ya (kWh/kWp/day) numbers, which indicate the actual energy production efficiency of the solar panels, show a seasonal tendency. Ls (kWh/kWp/day), on the other hand, represents the losses caused by shade or other causes. With minor deviations, these values remain largely stable throughout the year. Yf (kWh/kWp/day) represents energy delivered to the grid and has a similar trend to Ya, with the highest values in May and June. The variables Lcr (ratio), Lsr (ratio), and PR (ratio) represent various system performance ratios. Throughout the year, they remain rather steady. Overall, the data indicates that the solar power system is most efficient throughout the spring and early summer months, with efficiency decreasing over the winter season due to reduced solar irradiation and shorter daylight hours. Daily irradiance (Yr) averages 4.88 kWh/m², with an energy yield (Lc) of 1.01 kWh/kWp. The average energy available for capture (Ya) is 3.77 kWh/kWp, and shading losses (Ls) are 0.10 kWh/kWp. The total energy production (Yf) is 3.88 kWh/kWp. Loss ratios (Lcr and Lsr) remain low at 0.211 and 0.019, assuring dependability. The average performance ratio (‘PR’) is 0.767 or 76.7%, indicating efficient energy conversion.
Trends of important performance coefficients for our solar power plant at NUST.
The LC value (Loss of Collector) for the PV array is 1.1 kWh/kWp/day, whereas the LS value (Loss of System) for the entire system, including inverters and other components, is 0.08 kWh/kWp/day. In addition to these data, the YF value (Yield Factor), which includes inverter output, is 3.85 kWh/kWp/day. These data are important in evaluating the overall performance and efficiency of the solar PV system, as shown in (Fig. 19). It’s worth mentioning that the system’s average reference incident energy is 5.033 kWh/m²/day, which serves as a baseline for assessing its energy generation capabilities and system losses. These insights aid in optimizing the system’s performance and energy production.
Comprehensive assessment of solar PV system performance, losses, and efficiency metrics.
The global horizontal irradiance measures 1503 kW h/m², while the effective irradiation on the collector plane is 1484 kW h/m², as a result, there is an 11.8% loss in energy. The solar energy incident on the solar panels is converted into electrical energy. After the PV conversion process, the nominal array energy amounts to 13,367,557 kWh. The PV array achieves an efficiency of 21.42% under standard test conditions (STC). The array’s virtual energy obtained is 11,561,564 kWh with 4.85% module degradation loss, 0.78% PV loss due to irradiance level, 4.86% PV loss due to temperature, 0.75% module quality loss, 3.57% mismatch loss including the degradation dispersion, and 0.89% ohmic wiring loss. Factoring in inverter losses, the final available energy at the inverter output is 11,312,423 kWh and 10,756,458 kWh ready for the grid (Fig. 20).
Detailed loss diagram for complete system.
Some of the power generated by the solar plant is used for internal purposes. The power output varies daily depending on radiation levels, but the consumption for the plant’s load remains constant, day or night. The goal is to have a large gap between energy export and consumption. The solar plant utilizes 140 kWh during the day and 230 kWh at night. The notion of net metering is used to calculate the electricity utilized by internal utilities as well as the power exported to the grid. Furthermore, power usage is controlled by solar radiation, and the facility records daily and nocturnal use. Power generation may be halted due to poor weather circumstances such as heavy rainfall or low radiation.
The “Performance Ratio” is calculated by dividing the final yield by the reference yield. It serves as a comparison metric, indicating the plant’s actual output relative to its potential output, considering various factors such as irradiation, panel temperature, grid availability, aperture area size, nominal power output, and temperature correction values. The performance ratio provides valuable insights into the efficiency and effectiveness of the solar PV plant, reflecting how well it utilizes available solar resources and performs under real-world conditions⁷⁶. The annual average value of the Performance Ratio (PR) is approximately 76.18%. The highest PR value recorded was 79.10% in December, while the lowest PR value was 74.6% in June. By analyzing the PR values, system malfunctions can be identified and deducted. The data displays a solar power system’s monthly performance ratio (PR) and capacity utilization factors (CUF) over the course of a year. In terms of PR, the system’s performance is reasonably consistent, with PR values ranging from 74.6% in June to 79.0% in November, with an average PR of around 76.6%. This demonstrates that solar irradiance is converted into electricity with consistent efficiency throughout the year. CUF readings, on the other hand, show a seasonal tendency, with the maximum CUF of 21.78% in July and the lowest of 8.90% in December, reflecting fluctuations in energy generation caused by weather and solar angles. The CUF is roughly 15.09% on an annual basis, illustrating the system’s ability to make effective use of its installed capacity, with higher usage during peak solar months (Fig. 21). These PR and CUF insights aid in evaluating the overall performance and energy production efficiency of the solar power system.
Average performance ratio (PR) and capacity utilization factor (CUF).
In a comprehensive global study, solar PV systems were tested across varied climate conditions, with Pakistan’s semi-arid climate standing out as a good choice (Table 6). The 11.5 MW solar power plant in Pakistan has an excellent Performance Ratio (PR) of 76.18% and a Capacity Factor (CF) of 15.09%. This exceptional combination produces a Reference Yield of around 2,155,442 kWh, proving Pakistan’s proficiency in solar energy usage. Pakistan’s success has been mirrored in other countries, including as Kuwait, Saudi Arabia, and Chile, each of which has its unique climate and PV technology. Kuwait’s subtropical climate benefits from Poly-Si technology, resulting in a Reference Yield of 3,256,122 kWh. Saudi Arabia obtains an 88.29% PR using Mono-Si technology in its arid desert circumstances, giving in a Reference Yield of 2,648,800 kWh. Meanwhile, Mono-Si technology thrives in Chile’s arid climate, producing an incredible Reference Yield of 2,107,040 kWh. These findings demonstrate PV technologies’ adaptability to a variety of climates, as well as the global significance of solar energy as a sustainable power source. While each area has its own set of challenges and opportunities, these studies demonstrate solar energy’s ability to meet the world’s energy needs while also supporting environmental sustainability. Pakistan’s outstanding success serves as an example for other countries seeking to fully exploit the potential of solar energy.
The completion of an 8.78 MW solar power plant at the National University of Sciences and Technology (NUST) is not only a big step toward clean energy, but it is also a significant contributor to reducing carbon footprint. The carbon balancing is predicted to save 75,478.60 tons of CO₂ emissions over the course of 18 years, based on calculations performed using PVsyst software, which estimates the environmental impact of solar power installations. This equates to an astounding 4,193.259 tons of CO₂ reduction each year, underlining its environmental sustainability even further. Furthermore, the carbon footprint reduction rate per installed kWp is 8.579 tons of CO₂ and the annual reduction rate is 0.477 tons of CO₂ per kWp.
Our 18-year cost analysis of the NUST power plant project provides useful insights into the project’s financial dynamics. Maintenance expenditures have risen steadily throughout the years, as shown in Fig. 22. According to statistical data, maintenance expenses climb at an annual pace of around 12.2% on average. This graph represents the usual operational costs associated with the operation of a large-scale solar power plant. A cash flow and payback period can be calculated using the known cost of the entire solar system retrofit and is illustrated that within 12 years of the installation, the cash flow will be positive in net profit (Fig. 23). The following assumptions were considered:
No Financial Loans: It is important to highlight that no financial loans were taken to cover the initial investment in the solar system retrofit. As a result, no connected interest rates affect the financial analysis.
Exclusion of Depreciation: For the sake of this analysis, the depreciation of the investment has been ignored, focusing simply on the project’s initial and ongoing financial considerations.
Considerations for Inflation: A yearly inflation rate of 8.26% has been considered in the calculations. This compensates for anticipated buying power depreciation over time, resulting in a more realistic projection of future cash flows.
We stressed financial resiliency considering unplanned maintenance expenditures, system downtime, and changing energy pricing or regulations.
Environmental and social impact: We evaluated environmental and social benefits, such as lower carbon emissions and community impact, in our analysis.
Scalability: We investigated the possibility of future growth for higher energy generation and revenue.
Alternative Investments: We assessed the risk-return profiles of our project and compared them to traditional investments.
Power Price Escalation: To account for the changing energy landscape, the estimates include a 1.6% yearly increase in power prices.
Maintenance Expenses: To sustain operating efficiency, the system’s maintenance expenses have been projected to be 12.2% of the entire initial CAPEX (Capital Expenditure) cost.
Power Tariff Benchmark: The home power tariff in Pakistan is $0.09 per kWh as of 2023.
Sensitivity Analysis: We looked at how changes in variables, such as electricity prices or maintenance expenses, affected project outcomes.
Long-Term Sustainability: We looked beyond repayment to assess the project’s long-term viability.Conclusion.
Financial analysis of solar PV system over 18 years.
Cash flow analysis for solar PV system over 18 years.
An 8.75 MW grid-connected Photovoltaic (PV) system has been proposed for The National University of Sciences and Technology (NUST) in Islamabad, Pakistan, in response to the important worldwide issue of climate change. This strategic location, rich in solar resources, makes a compelling case for harnessing clean and sustainable energy. The following are the important themes and findings from our extensive research:
Abundant Solar Resources: Islamabad has a daily solar irradiation of 5.89 kWh/m² and a solar percentage of 98.99%. This makes it an excellent position for capturing solar energy.
Environmental Impact: Our PV system is expected to avoid 20,000 tons of CO₂ emissions over a five-year period, which is equivalent to planting 348,754 teak trees. This emphasizes the system’s enormous environmental advantages.
Electricity Generation at a Low Cost: Our PV system generates electricity at a low cost of 0.0141 US $/kWh, making it a financially feasible and sustainable alternative to traditional energy sources.
Inverter Efficiency: For the project, the Huawei Technologies SUN2000-50KTL-M3-380 V, H inverter with an efficiency of 98.26% was chosen, ensuring optimal energy conversion.
Adaptive Tilt Strategy: Our research found that using seasonal tilt angles increased solar irradiation by 36.31%, improving overall energy efficiency.
High-Efficiency Panels: For their efficiency and longevity, monocrystalline silicon Longi_LR5-72_HTH_600M panels with a power output of 600 Wp were chosen.
Performance Indicators: PR values have regularly ranged from 74.6 to 79.0%, with an average of around 76.6%. The annual average of Capacity Utilization Factor (CUF) values is 15.09%.
Net Metering: A net metering system has been deployed to calculate electricity utilization for internal utilities and power export to the grid, ensuring optimal energy management.
Our findings not only illustrate the enormous potential of grid-connected renewable energy, but also provide useful insights for future sustainable energy programs.
In conclusion, our PV system at NUST, Islamabad, is a promising solution for combating climate change and promoting sustainability. It serves as a paradigm for clean energy adoption by using copious solar resources, innovative technology, and cost-efficiency. This project lays the groundwork for a more environmentally friendly and sustainable future.
The datasets generated and/or analyzed during the current study are not publicly available due to concerns about potential cloning or misuse but are available from the corresponding author on reasonable request.
Kamran, M. Current status and future success of renewable energy in Pakistan. Renew. Sustain. Energy Rev. 82, 609–617. https://doi.org/10.1016/j.rser.2017.09.049 (2018).
Article  Google Scholar 
Aized, T., Shahid, M., Bhatti, A. A., Saleem, M. & Anandarajah, G. Energy security and renewable energy policy analysis of Pakistan. Renew. Sustain. Energy Rev. 84, 155–169. https://doi.org/10.1016/J.RSER.2017.05.254 (2018).
Article  Google Scholar 
Ishaque, H. Is it wise to compromise renewable energy future for the sake of expediency? An analysis of Pakistan’s long-term electricity generation pathways. Energy Strateg. Rev. 17, 6–18. https://doi.org/10.1016/j.esr.2017.05.002 (2017).
Article  Google Scholar 
Anwar, J. Analysis of energy security, environmental emission and fuel import costs under energy import reduction targets: a case of Pakistan. Renew. Sustain. Energy Rev. 65, 1065–1078. https://doi.org/10.1016/j.rser.2016.07.037 (2016).
Article  Google Scholar 
Ali, Z., Liaquat, R., Husain Khoja, A. & Safdar, U. A comparison of energy policies of Pakistan and their impact on bioenergy development. Sustain. Energy Technol. Assess. 46, 246. https://doi.org/10.1016/j.seta.2021.101246 (2021).
Rafique, M. M. & Rehman, S. National energy scenario of Pakistan—Current status, future alternatives, and institutional infrastructure: an overview. Renew. Sustain. Energy Rev. 69, 156–167. https://doi.org/10.1016/j.rser.2016.11.057 (2017).
Article  Google Scholar 
Khatri, S. A. et al. Solar photovoltaic potential and diffusion assessment for Pakistan. Energy Sci. Eng. 10(7), 2452–2474. https://doi.org/10.1002/ESE3.1149 (2022).
Article  Google Scholar 
Razmjoo, A., Ghazanfari, A., Østergaard, P. A. & Abedi, S. Design and analysis of grid-connected solar photovoltaic systems for sustainable development of remote areas. Energies 16, 3181. https://doi.org/10.3390/EN16073181 (2023).
Qazi, U., Jahanzaib, M., Ahmad, W. & Hussain, S. An institutional framework for the development of sustainable and competitive power market in Pakistan. Renew. Sustain. Energy Rev. 70, 83–95. https://doi.org/10.1016/J.RSER.2016.11.152 (2017).
Article  Google Scholar 
Tao, J. et al. Pakistan’s electrical energy crises, a way forward towards 50% of sustain clean and green electricity generation. Energy Strateg. Rev. 40, 100813. https://doi.org/10.1016/J.ESR.2022.100813 (2022).
Article  Google Scholar 
Das Valasai, G. et al. Overcoming electricity crisis in Pakistan: a review of sustainable electricity options. Renew. Sustain. Energy Rev. 72, 734–745. https://doi.org/10.1016/J.RSER.2017.01.097 (2017).
Article  Google Scholar 
Aziz, R. & Ahmad, M. Pakistan’s Power Crisis. https://www.jstor.org/stable/pdf/resrep12401.pdf (2023).
Rafique, M. M. & Ahmad, G. Targeting sustainable development in Pakistan through planning of integrated energy resources for electricity generation. Electr. J. 31(7), 14–19. https://doi.org/10.1016/j.tej.2018.08.001 (2018).
Article  Google Scholar 
Mirjat, N. H. et al. Long-term electricity demand forecast and supply side scenarios for Pakistan (2015–2050): a LEAP model application for policy analysis. Energy 165, 512–526. https://doi.org/10.1016/J.ENERGY.2018.10.012 (2018).
Article  Google Scholar 
Mirjat, N. H. et al. A review of energy and power planning and policies of Pakistan. Renew. Sustain. Energy Rev. 79, 110–127. https://doi.org/10.1016/j.rser.2017.05.040 (2017).
Article  Google Scholar 
Jan, I., Ullah, W. & Ashfaq, M. Social acceptability of solar photovoltaic system in Pakistan: key determinants and policy implications. J. Clean Prod. 274, 123140. https://doi.org/10.1016/J.JCLEPRO.2020.123140 (2020).
Article  Google Scholar 
Abdullah et al. Acceptance and willingness to pay for solar home system: Survey evidence from northern area of Pakistan. Energy Rep. 3, 54–60. https://doi.org/10.1016/J.EGYR.2017.03.002 (2017).
Article  Google Scholar 
Khosa, A. A. et al. Performance analysis based on probabilistic modelling of Quaid-e-Azam Solar Park (QASP) Pakistan. Energy Strateg. Rev. 29, 100479. https://doi.org/10.1016/J.ESR.2020.100479 (2020).
Article  Google Scholar 
Zafar, U., Ur Rashid, T., Khosa, A. A., Khalil, M. S. & Rahid, M. An overview of implemented renewable energy policy of Pakistan. Renew. Sustain. Energy Rev. 82, 654–665. https://doi.org/10.1016/J.RSER.2017.09.034 (2018).
Article  Google Scholar 
Han, M. et al. Unveiling China’s overseas photovoltaic power stations in Pakistan under low-carbon transition. Land 11, 1719. https://doi.org/10.3390/LAND11101719 (2022).
Tahir, Z. R. & Asim, M. Surface measured solar radiation data and solar energy resource assessment of Pakistan: a review. Renew. Sustain. Energy Rev. 81, 2839–2861. https://doi.org/10.1016/J.RSER.2017.06.090 (2018).
Article  Google Scholar 
Memon, Q. A., Rahimoon, A. Q., Ali, K., Shaikh, M. F. & Shaikh, S. A. Determining optimum tilt angle for 1 MW photovoltaic system at Sukkur, Pakistan, Int. J. Photoenergy 167. https://doi.org/10.1155/2021/5552637 (2021).
Akram, I. Design and analysis of 300 MW solar configuration and its comparison with Quaid-e-Azam solar park. In International Conference on Engineering and Emerging Technologies, ICEET 2018, vol. 2018, 1–6. https://doi.org/10.1109/ICEET1.2018.8338638 (2018).
Fredriksson, A. & de Oliveira, G. M. Impact evaluation using difference-in-differences. RAUSP Manag. J. 54(4), 519–532. https://doi.org/10.1108/RAUSP-05-2019-0112/FULL/PDF (2019).
Article  Google Scholar 
Akhtar, S., Hashmi, M. K., Ahmad, I. & Raza, R. Advances and significance of solar reflectors in solar energy technology in Pakistan. Energy Environ. 29(4), 435–455. https://doi.org/10.1177/0958305X18758487 (2018).
Article  Google Scholar 
Quesada, B., Sánchez, C., Cañada, J., Royo, R. & Payá, J. Experimental results and simulation with TRNSYS of a 7.2kWp grid-connected photovoltaic system. Appl. Energy 88(5), 1772–1783. https://doi.org/10.1016/J.APENERGY.2010.12.011 (2011).
Article  ADS  Google Scholar 
Khatri, S. A. et al. A logistic modelling analysis for wind energy potential assessment and forecasting its diffusion in Pakistan. Front. Energy Res. 10, 860092. https://doi.org/10.3389/FENRG.2022.860092/BIBTEX (2022).
Article  Google Scholar 
Park, S. et al. Article prediction of solar irradiance and photovoltaic solar energy product based on cloud coverage estimation using machine learning methods. Atmosphere (Basel) 12(3), 395. https://doi.org/10.3390/ATMOS12030395 (2021).
Article  ADS  Google Scholar 
Vennila, C. et al. Forecasting solar energy production using machine learning, Int. J. of Photoenergy 167. https://doi.org/10.1155/2022/7797488 (2022).
Narvaez, G., Giraldo, L. F., Bressan, M. & Pantoja, A. Machine learning for site-adaptation and solar radiation forecasting. Renew. Energy 167, 333–342. https://doi.org/10.1016/J.RENENE.2020.11.089 (2021).
Article  Google Scholar 
Jebli, I., Belouadha, F. Z., Kabbaj, M. I. & Tilioua, A. Prediction of solar energy guided by pearson correlation using machine learning. Energy 224, 109. https://doi.org/10.1016/J.ENERGY.2021.120109 (2021).
Ur Rehman, A. & Iqbal, M. T. and Design and control of an off-grid solar system for a rural house in Pakistan. In 11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020 786–790. https://doi.org/10.1109/IEMCON51383.2020.9284867 (2020).
Raza, M. Y. & Lin, B. Decoupling and mitigation potential analysis of CO2 emissions from Pakistan’s transport sector. Sci. Total Environ. 730, 139000. https://doi.org/10.1016/J.SCITOTENV.2020.139000 (2020).
Article  CAS  PubMed  Google Scholar 
Grainger, C. A. & Zhang, F. Electricity shortages and manufacturing productivity in Pakistan. Energy Policy 132, 1000–1008. https://doi.org/10.1016/J.ENPOL.2019.05.040 (2019).
Article  Google Scholar 
Besarati, S. M., Padilla, R. V., Goswami, D. Y. & Stefanakos, E. The potential of harnessing solar radiation in Iran: Generating solar maps and viability study of PV power plants. Renew. Energy 53, 193–199. https://doi.org/10.1016/J.RENENE.2012.11.012 (2013).
Article  Google Scholar 
Bajc, T. & Kostadinović, D. Potential of usage of the floating photovoltaic systems on natural and artificial lakes in the Republic of Serbia. J. Clean Prod. 422, 598. https://doi.org/10.1016/j.jclepro.2023.138598 (2023).
Elhodeiby, A., Metwally, H. & Farahat, M. H. Performance Analysis of 3.6 Kw Rooftop Grid Connected Photovoltaic System in Egypt (2011).
Veerendra Kumar, D. J. et al. Performance evaluation of 1.1 MW grid-connected solar photovoltaic power plant in Louisiana. Energies (Basel) 15(9), 420. https://doi.org/10.3390/EN15093420 (2022).
Article  Google Scholar 
Sundaram, S. & Babu, J. S. C. Performance evaluation and validation of 5 MWp grid connected solar photovoltaic plant in South India. Energy Convers. Manag. 100, 429–439. https://doi.org/10.1016/J.ENCONMAN.2015.04.069 (2015).
Article  ADS  Google Scholar 
Purohit, I. & Purohit, P. Performance assessment of grid-interactive solar photovoltaic projects under India’s national solar mission. Appl. Energy 222, 25–41. https://doi.org/10.1016/J.APENERGY.2018.03.135 (2018).
Article  ADS  Google Scholar 
Yadav, A. K., Sharma, V., Malik, H. & Chandel, S. S. Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based radial basis function neural network. Renew. Sustain. Energy Rev. 81, 2115–2127. https://doi.org/10.1016/J.RSER.2017.06.023 (2018).
Article  Google Scholar 
Susanto, J., Shahnia, F. & Ludwig, D. A framework to technically evaluate integration of utility-scale photovoltaic plants to weak power distribution systems. Appl. Energy 231, 207–221. https://doi.org/10.1016/J.APENERGY.2018.09.130 (2018).
Article  ADS  Google Scholar 
Satsangi, P. Performance Analysis of Grid Interactive Solar Photovoltaic Plant in India. https://www.sciencedirect.com/science/article/pii/S097308261831038X (2018).
Antonanzas, J., Urraca, R., Martinez-De-Pison, F. J. & Antonanzas-Torres, F. Solar irradiation mapping with exogenous data from support vector regression machines estimations. Energy Convers. Manag. 100, 380–390. https://doi.org/10.1016/j.enconman.2015.05.028 (2015).
Article  ADS  Google Scholar 
Rezazadeh, S. et al. A novel prime numbers-based PV array reconfiguration solution to produce maximum energy under partial shade conditions. Sustain. Energy Technol. Assess. 47, 498. https://doi.org/10.1016/j.seta.2021.101498 (2021).
Article  Google Scholar 
Satpathy, P. R., Aljafari, B., Thanikanti, S. B. & Madeti, S. R. K. Electrical fault tolerance of photovoltaic array configurations: experimental investigation, performance analysis, monitoring and detection. Renew. Energy 206, 960–981. https://doi.org/10.1016/j.renene.2023.02.103 (2023).
Article  Google Scholar 
Jamil, I. et al. Performance evaluation of solar power plants for excess energy based on energy production. Energy Rep. 9, 1501–1534. https://doi.org/10.1016/J.EGYR.2022.12.081 (2023).
Article  Google Scholar 
Zhang, T., Nakagawa, K. & Matsumoto, K. Evaluating solar photovoltaic power efficiency based on economic dimensions for 26 countries using a three-stage data envelopment analysis. Appl. Energy 335, 120714. https://doi.org/10.1016/J.APENERGY.2023.120714 (2023).
Article  Google Scholar 
Makkiabadi, M. et al. Performance evaluation of solar power plants: A review and a case study. Processes 9, 2253. https://doi.org/10.3390/PR9122253 (2021),
Yang, Y., Campana, P. E., Stridh, B. & Yan, J. Potential analysis of roof-mounted solar photovoltaics in Sweden. Appl. Energy 279, 115786. https://doi.org/10.1016/J.APENERGY.2020.115786 (2020).
Article  Google Scholar 
Martín-Martínez, S., Cañas-Carretón, M., Honrubia-Escribano, A. & Gómez-Lázaro, E. Performance evaluation of large solar photovoltaic power plants in Spain. Energy Convers. Manag. 183, 515–528. https://doi.org/10.1016/J.ENCONMAN.2018.12.116 (2019).
Article  ADS  Google Scholar 
Arnaout, M. A. & Yun, G. Performance evaluation of a building integrated PV (BIPV) at Heriot–Watt University Malaysia. IOP Conf. Ser. Earth Environ. Sci. 268(1), 012055. https://doi.org/10.1088/1755-1315/268/1/012055 (2019).
Article  Google Scholar 
Reker, S., Schneider, J. & Gerhards, C. Integration of vertical solar power plants into a future German energy system. Smart Energy 7, 100083. https://doi.org/10.1016/J.SEGY.2022.100083 (2022).
Article  Google Scholar 
Singh, T. S. D., Shimray, B. A., Singh, A. B. & Meitei, S. N. Performance measurement of 5 kWp rooftop grid-connected SPV system in moderate climatic region of Imphal, Manipur, India. Energy Sustain. Dev. 73, 292–302. https://doi.org/10.1016/j.esd.2023.02.009 (2023).
Article  Google Scholar 
AL-Rasheedi, M. et al. Performance evaluation of a utility-scale dual-technology photovoltaic power plant at the Shagaya Renewable Energy Park in Kuwait. Renew. Sustain. Energy Rev. 133. https://doi.org/10.1016/j.rser.2020.110139 (2020).
Sharma, R. & Goel, S. Performance analysis of a 11.2 kWp roof top grid-connected PV system in Eastern India. Energy Rep. 3, 76–84. https://doi.org/10.1016/J.EGYR.2017.05.001 (2017).
Article  Google Scholar 
Burssens, S. et al. Electricity Production Plan for Belgium post-2025. Transdiscipl. Insights 1(1), 1–8. https://doi.org/10.11116/TDI2017.1.1 (2018).
Article  Google Scholar 
Awuku, S. A., Muhammad-Sukki, F. & Sellami, N. Building integrated photovoltaics—The journey so far and future. Energies15, 1802. https://doi.org/10.3390/EN15051802 (2022).
Li, Y., Li, L., Deng, W., Zhu, D. & Hong, L. Building integrated photovoltaic (BIPV) development knowledge map: A review of visual analysis using CiteSpace. Buildings 13, 389. https://doi.org/10.3390/BUILDINGS13020389 (2023).
Jamil, M. H., Ullah, K., Saleem, N., Abbas, F. & Khalid, H. A. Did the restructuring of the electricity generation sector increase social welfare in Pakistan? Renew. Sustain. Energy Rev. 157, 112017. https://doi.org/10.1016/J.RSER.2021.112017 (2022).
Article  Google Scholar 
Mir, K. A., Park, C., Purohit, P. & Kim, S. Comparative analysis of greenhouse gas emission inventory for Pakistan: part I energy and industrial processes and product use. Adv. Clim. Change Res. 11(1), 40–51. https://doi.org/10.1016/J.ACCRE.2020.05.002 (2020).
Article  Google Scholar 
Xiuhui, J. & Raza, M. Y. Delving into Pakistan’s industrial economy and carbon mitigation: an effort toward sustainable development goals. Energy Strateg. Rev. 41, 100839. https://doi.org/10.1016/J.ESR.2022.100839 (2022).
Article  Google Scholar 
Younas, U. et al. Pakistan geothermal renewable energy potential for electric power generation: a survey. Renew. Sustain. Energy Rev. 63, 398–413. https://doi.org/10.1016/J.RSER.2016.04.038 (2016).
Article  Google Scholar 
Somani, R. Global warming in Pakistan and its impact on public health as viewed through a health equity lens. Int. J. Soc. Determ. Health Health Serv. 53(2), 154–157. https://doi.org/10.1177/27551938231154467 (2023).
Article  Google Scholar 
Xu, L., Wang, Y., Solangi, Y. A., Zameer, H. & Shah, S. A. A. Off-Grid Solar Pv Power Generation System In Sindh, Pakistan: A techno-economic feasibility analysis. Processes7, 308. https://doi.org/10.3390/PR7050308 (2019).
Shah, S. A. A. et al. Techno-economic analysis of solar PV electricity supply to rural areas of Balochistan, Pakistan, Energies 11, 1777. https://doi.org/10.3390/EN11071777 (2018).
Ayompe, L. M., Duffy, A., McCormack, S. J. & Conlon, M. Measured performance of a 1.72 kW rooftop grid connected photovoltaic system in Ireland. Energy Convers. Manag. 52(2), 816–825. https://doi.org/10.1016/J.ENCONMAN.2010.08.007 (2011).
Article  ADS  Google Scholar 
Kazem, H. A., Khatib, T., Sopian, K. & Elmenreich, W. Performance and feasibility assessment of a 1.4 kW roof top grid-connected photovoltaic power system under desertic weather conditions. Energy Build. 82, 123–129. https://doi.org/10.1016/J.ENBUILD.2014.06.048 (2014).
Article  Google Scholar 
Kumar, N. M., Kumar, M. R., Rejoice, P. R. & Mathew, M. Performance analysis of 100 kWp grid connected Si-poly photovoltaic system using PVsyst simulation tool. Energy Procedia 117, 180–189. https://doi.org/10.1016/j.egypro.2017.05.121 (2017).
Article  Google Scholar 
Tahri, F., Tahri, A. & Oozeki, T. Performance evaluation of grid-connected photovoltaic systems based on two photovoltaic module technologies under tropical climate conditions. Energy Convers. Manag. 165, 244–252. https://doi.org/10.1016/J.ENCONMAN.2018.03.065 (2018).
Article  ADS  CAS  Google Scholar 
Marion, B. et al. Performance parameters for grid-connected PV systems. In Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference 1601–1606. https://doi.org/10.1109/PVSC.2005.1488451 (2005).
Navothna, B. & Thotakura, S. Analysis on large-scale solar PV plant energy performance–loss–degradation in coastal climates of India. Front. Energy Res. 10, 948. https://doi.org/10.3389/fenrg.2022.857948 (2022).
Li, K., Fan, H. & Yao, P. Estimating carbon emissions from thermal power plants based on thermal characteristics. Int. J. Appl. Earth Observ. Geoinf. 128, 103768. https://doi.org/10.1016/j.jag.2024.103768 (2024).
Article  Google Scholar 
Deng, Y. et al. Life cycle-based carbon emission reduction benefit assessment of centralized photovoltaic power plants in China. Sustainability 15(23), 16323. https://doi.org/10.3390/su152316323 (2023).
Article  CAS  Google Scholar 
Das, M. & Mandal, R. Effect of solar radiation and operating factor of the PV module on the loss of load probability of a PV-battery system. Mater. Today Proc. 56, 2767–2773. https://doi.org/10.1016/J.MATPR.2021.10.010 (2022).
Padmavathi, K. & Daniel, S. A. Performance analysis of a 3 MWp grid connected solar photovoltaic power plant in India. Energy Sustain. Dev. 17, 615–625. https://doi.org/10.1016/J.ESD.2013.09.002 (2013).
Article  Google Scholar 
Mulaudzi, S. K., Bull, S. & Makhado, R. A. Potential of households’ solar PV consumption in South Africa. Afr. J. Sci. Technol. 14(3), 730–739. https://doi.org/10.1080/20421338.2021.1894728 (2022).
Article  Google Scholar 
Zhao, P. & Tian, W. Research on prediction of solar power considering the methods of statistical and machine learning—based on the data of Australian solar power market. IOP Conf. Ser. Earth Environ. Sci. 1046(1), 012006. https://doi.org/10.1088/1755-1315/1046/1/012006 (2022).
Article  Google Scholar 
Bellos, E. & Tzivanidis, C. Solar concentrating systems and applications in Greece—A critical review. J. Clean. Prod. 272, 122855. https://doi.org/10.1016/J.JCLEPRO.2020.122855 (2020).
Article  Google Scholar 
Leggieri, A. & Semeraro, T. A new perspective of solar renewable energy for South Italy using the floating photovoltaic system. IOP Conf. Ser. Mater. Sci. Eng. 960, 19. https://doi.org/10.1088/1757-899X/960/2/022019 (2020).
Abdullah, G. & Nishimura, H. Techno-economic performance analysis of a 40.1 kWp grid-connected photovoltaic (GCPV) system after eight years of energy generation: A case study for Tochigi, Japan. Sustainability 13, 7680. https://doi.org/10.3390/SU13147680 (2021).
Zhang, X., Dong, X. & Li, X. Study of China’s optimal concentrated solar power development path to 2050. Front. Energy Res. 9, 724021. https://doi.org/10.3389/FENRG.2021.724021/BIBTEX (2021).
Article  Google Scholar 
Odetoye, O. A. et al. Large-scale solar power in Nigeria: The case for floating photovoltaics. In Proc. 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development, NIGERCON (2022). https://doi.org/10.1109/NIGERCON54645.2022.9803156 (2022).
Patlitzianas, K. D. Solar energy in Egypt: significant business opportunities. Renew. Energy 36(9), 2305–2311. https://doi.org/10.1016/J.RENENE.2011.03.006 (2011).
Article  Google Scholar 
Bragagnolo, J. A., Taretto, K. & Navntoft, C. Solar energy in Argentina. Solar 2, 120–140. https://doi.org/10.3390/SOLAR2020008 (2022).
Hassaan, M. A., Hassan, A. & Al-Dashti, H. GIS-based suitability analysis for siting solar power plants in Kuwait. Egypt. J. Remote Sens. Space Sci. 24(3), 453–461. https://doi.org/10.1016/J.EJRS.2020.11.004 (2021).
Article  Google Scholar 
Del Sol, F. & Sauma, E. Economic impacts of installing solar power plants in northern Chile. Renew. Sustain. Energy Rev. 19, 489–498. https://doi.org/10.1016/J.RSER.2012.11.038 (2013).
Article  Google Scholar 
Parkins, J. R., Rollins, C., Anders, S. & Comeau, L. Predicting intention to adopt solar technology in Canada: the role of knowledge, public engagement, and visibility. Energy Policy 114, 114–122. https://doi.org/10.1016/J.ENPOL.2017.11.050 (2018).
Article  Google Scholar 
Veras, R. B. S. et al. Assessing economic complementarity in wind–solar hybrid power plants connected to the Brazilian grid. Sustainability 15, 8862. https://doi.org/10.3390/SU15118862 (2023).
Alraddadi, M. & Conejo, A. J. Operation of an all-solar power system in Saudi Arabia. Int. J. Electr. Power Energy Syst. 125. https://doi.org/10.1016/J.IJEPES.2020.106466 (2021).
Jed, M. E. H. et al. Analysis of the performance of the photovoltaic power plant of Sourdun (France). Int. J. Sustain. Eng. 14(6), 1756–1768. https://doi.org/10.1080/19397038.2021.1971321 (2021).
Article  Google Scholar 
Salimi, M. & Hosseinpour, M. & Borhani, T. N. Analysis of solar energy development strategies for a successful energy transition in the UAE. Processes 10, 1338. https://doi.org/10.3390/PR10071338 (2022).
Silva, J. A. & Andrade, M. A. Solar energy analysis in use and implementation in Mexico: a review. Int. J. Energy Sect. Manag. 14, 1333–1349. https://doi.org/10.1108/IJESM-01-2020-0010/FULL/PDF (2020).
Article  Google Scholar 
Rausser, G., Chebotareva, G., Smutka, L., Strielkowski, W. & Shiryaeva, J. Future development of renewable energy in Russia: a case of solar power. Front. Energy Res. 10, 201. https://doi.org/10.3389/FENRG.2022.862201 (2022).
Article  Google Scholar 
Jed, M. E. H. et al. Performance analysis of 954,809 kWp PV array of Sheikh Zayed solar power plant (Nouakchott, Mauritania). Renew. Energy Focus 32, 45–54. https://doi.org/10.1016/J.REF.2019.11.002 (2020).
Article  Google Scholar 
Download references
The project was carried out in collaboration with the Net Zero Industry Innovation Centre at Teesside University and the U.S.-PAKISTAN Center For Advanced Studies in Energy (USPCASE) at NUST, Islamabad. The authors would like to extend their sincere gratitude to Dr. Philipp Späth from the Institute of Environmental Social Sciences and Geography, Albert-Ludwigs-Universität Freiburg, Germany, for his invaluable assistance and insightful discussions during the project. Additionally, special thanks go to Mr. Hasnain Zia from the Department of Mechatronics Engineering, NUST, for his valuable contributions to clerical tasks.
Open access funding provided by University of Gävle.
Department of Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
Shabahat Hasnain Qamar & Khalid Zia Khan
Net Zero Industry Innovation Centre, Teesside University, Ferrous Road, Middlesbrough, TS2 1DJ, UK
Dawid Piotr Hanak
U.S.-PAKISTAN Center for Advanced Studies in Energy (USPCASE), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Majid Ali
University of Gävle, Gävle, Sweden
Joao Gomes
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
S.Q: Formal analysis, Data Curation, Writing—Original Draft. D.H: Resources, Writing—Review & Editing. M.A: Conceptualization, Supervision. J.G: Validation, Funding acquisition. K.K: Visualization.
Correspondence to Shabahat Hasnain Qamar or Joao Gomes.
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
Qamar, S.H., Hanak, D.P., Ali, M. et al. Design, modeling and cost analysis of 8.79 MW solar photovoltaic power plant at National University of Sciences and Technology (NUST), Islamabad, Pakistan. Sci Rep 14, 25351 (2024). https://doi.org/10.1038/s41598-024-74187-w
Download citation
Received: 23 July 2024
Accepted: 24 September 2024
Published: 25 October 2024
Version of record: 25 October 2024
DOI: https://doi.org/10.1038/s41598-024-74187-w
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.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Scientific Reports (Sci Rep)
ISSN 2045-2322 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source

Almost 4000 hectares of New Zealand farmland have been leased or sold to foreign-owned solar companies over the past four years without first being offered to New Zealand interests, under exemptions granted by the Overseas Investment Office.
OIO records reveal multiple solar projects over the past four years have included largely leasehold deals with the companies, extending from the Far North to Otago. The total area acquired is 3917ha.
Normally, under overseas investment regulations when overseas interests wish to acquire farmland that is classed as sensitive under the Overseas Investment Act, it must be offered for acquisition on the open market to New Zealanders first. 
The prescriptive rules require at least 30 working days both online and in print.
The scale of the exempted projects leased to overseas interests varies between a 53ha project near Auckland, to a 460ha project this year in Taranaki, leased to Stratford Solar, a joint project between Contact and Lightsource, a global solar energy developer. The  approval of 1536ha to NZ Clean Energy Ltd  does not appear in the latest OIO exemption updates (see accompanying article).
Acquisition of land for solar projects was particularly intense in 2023 when 1750ha was obtained for leasing. 
Solar projects dominate the OIO’s exemptions list, and the office is required to state the reasons for exempting companies from seeking local interest. 
Typically, on most of the exemptions the office states that publicising the property increases risks around alerting the foreign company’s competitors to the location of the project, increasing likelihood of those competitors acquiring it.
In the case of leasehold land it usually also notes the use is temporary, some of it will also still be used for grazing, and it is not a permanent loss of New Zealanders’ opportunity to buy the land given the leasehold arrangement. 
Farmland leases for solar projects  typically run for a lengthy 35 years.
The creep of unadvertised, exempted solar leases across farmland has rung alarm bells with Federated Farmers.
Feds energy spokesperson Greg Anderson said the stealthy nature of the solar conversions will be of huge concern to a lot of farmers and Federated Farmers is watching closely.
“There are a lot of similarities with the carbon farming situation, where there could be a lot of unintended consequences further down the line. When there’s no public consultation, you don’t know these solar farms are going in until they’re already up – but by then it’s too late.”
He called for greater transparency around the projects to ensure communities are aware of what is going on, and to give greater opportunity for scrutiny.
“Land Information NZ’s own guidance says a solar farm may stop being a farm once it’s built. That means a later sale to an overseas buyer may not face the farmland rules that a normal farm sale would.”
Receive news highlights and a preview of the upcoming paper in your inbox.
8 Weld Street,
P.O Box 529,
Feilding, New Zealand

0800 85 25 80

source

Reader support is at the core of our nonprofit news model. Together, we can keep the environment in the headlines.
By Stephen Porder / Professor
September 4, 2026
Share

The seemingly endless debate in Rhode Island about clearing trees to put up solar panels reared its head again in a recent column in ecoRI News by Frank Carini, who wrote that “Bulldozing trees to build utility-scale renewable energy facilities — 4 megawatts, in this case — essentially eliminates any of the project’s climate benefits.” The article goes on to discuss very real concerns associated with clearing trees: loss of habitat, fragmentation of continuous forest, and water quality impacts.
I’ve studied forests all over the world for the past 20 years, and I share these concerns. I completely agree with Mr. Carini that “Rhode Island needs to get serious about protecting the environment.” But let’s get the math right before we start the harder discussion about the tradeoffs that we have to make to address the climate crisis.

Let’s start with the benefits a forest provides from a carbon perspective (these are not the only benefits — I’m just starting there). According to the U.S. Department of Agriculture’s Forests of Rhode Island, Rhode Island forests cover about 400,000 acres, and contain about 26 million “short” tons of wood above ground.
(Carbon math is complicated by a number of unit conversions. The United States uses “short tons,” which is 2,000 pounds. The rest of the world, and scientists, use “metric tons,” which is 1,000 kilograms or 2,220 pounds. Also, some studies report the “biomass” of wood, which is literally how much it weighs, while others report the mass of carbon in that wood, which is about 50% of the total biomass. Either way, if wood is burned the carbon stored in it is converted to carbon dioxide. Carbon dioxide is a molecule that has one carbon atom and two oxygen atoms, and thus has a mass that is 3.67 times greater than carbon alone. Thus, a forest that has 60 short tons of biomass stores 60 x 0.91 = 55 metric tons of biomass, and 55 divided by 2 = 27.5 metric tons of carbon. If this forest is burned and converted to CO2, it releases 27.5 x 3.67, about 100 tons of carbon dioxide. For this article, when I say “tons” I mean “short tons.”)
Of course, trees have roots in the soil, and roots typically add about 20%-25% to the mass, so let’s call it 32 million tons of wood. Spread over roughly 400,000 acres, that’s 80 tons of wood per acre, or 40 tons of carbon stored per acre, which if burned, would release about 150 tons of carbon dioxide. The numbers will be higher in older, more intact forests, and lower in younger, degraded forests.
So, does clearing that forest for solar panels “essentially eliminate the project’s climate benefit” by turning all that carbon stored in trees into carbon dioxide in the air?
To answer this question, we have to ask how long it would it take for solar panels, producing clean electricity and thus avoiding fossil fuel combustion at power plants, to make up for the carbon dioxide released during the initial tree clearing? Here’s the math: an acre of ground-mounted solar panels in Rhode Island produces about 260 megawatt-hours of electricity a year (more recent production data suggests this is a conservative estimate). Our utility grid relies heavily on burning gas to produce electricity, and thus releases about 0.27 tons of carbon dioxide for each megawatt-hour produced.
Since solar displaces that gas, each megawatt-hour of solar-produced electricity reduces greenhouse gas emissions. In this example, multiply the 0.27 tons of carbon dioxide per megawatt-hour by 260 megawatt-hours per acre, and we see each acre of solar panels keeps about 70 tons of carbon dioxide out of the air each year (I’m rounding off my numbers because both the emissions from the grid and the amount of electricity the panels produce varies from year to year and with technology). Thus, in about two years, an acre of solar panels will make up for the release of carbon dioxide associated with land clearing.
But wait, you might say, a forest isn’t static. It’s actively taking carbon dioxide out of the air. We also need to think about the carbon dioxide they would take up if we hadn’t cleared them. That’s true, but the number is small. An acre of average forest in Rhode Island takes a few tons of carbon dioxide out of the air each year. That’s far, far less than the 70 tons of carbon dioxide that solar panels keep out of the air every year. And solar panels last a long time, at least 20 years and typically more. Over that time period, an acre of solar panels will have kept 70 tons x 20 years = 1,400 tons of greenhouse gas pollution out of the air — the forest those panels replaced would have stored about 200. (Assuming the grid remains as dirty as it is today, which hopefully it won’t because we’ll agree to deploy massive wind and solar resources.)
Thus, from a simple carbon perspective, cutting down trees to put up solar panels makes sense, at least right now, when our electricity production depends so heavily on fossil fuel combustion. But before we all sharpen up our chainsaws, let’s think more carefully about the context of climate change and the need for our energy transition off fossil fuels and onto renewables.
For the first time since the Industrial Revolution, Rhode Island has the chance to generate its own clean, renewable, affordable power. We can free ourselves from fossil dependence and price spikes driven by geopolitical events by building our supply of affordable power, all while doing our part to fight the global challenge of climate change. Climate change is THE biggest threat to the environment in Rhode Island and across the world — full stop. And human energy use from burning fossil fuels is responsible for the lion’s share of the threat. While Rhode Island’s energy transition will not stop climate change, neither will climate change be stopped unless everyone participates.
In my opinion, these facts make it clear we should devote a non-trivial amount of land for the deployment of renewables — there is no free lunch. If we are going to make the transition off fossil fuels, we will need tens of thousands of acres of solar panels across the state (plus hundreds of windmills offshore). There is no way to phase out fossil fuels without this scale of renewable deployments. Where should it go?
A report prepared for The Office of Energy Resources suggested covering most of our parking lots, commercial and industrial sites, and brownfields could produce a substantial fraction of Rhode Island’s current electricity demand. Putting solar on these sites are is typically be more expensive than on undeveloped land, so for a given dollar spent we will get fewer renewables. In addition, putting solar on these sites will require assessments by thousands of property owners, review by the utility of each interconnection, and myriad other hurdles that will bring deployment to a crawl. Meanwhile our emissions continue unabated. And of course, if we are successful in reducing our emissions, our current electricity demand is much lower than it will have to be in the future as we electrify our cars, home heating, and industry. These sites alone will not suffice, even if we covered every one of them.
Where else could we look? Rhode Island has about 60,000 acres of farmland, which has already been cleared (and thus the carbon from trees already lost). But opposition to putting solar on local farms appears even stronger than opposition to clearing trees. We also have over 5,000 acres of golf courses which only benefit the few who play golf, but I’m guessing covering them with solar wouldn’t go over well either.
Of course, we also have 400,000 acres of forests. Is cutting 1% of them for solar panels acceptable? One-tenth of one percent? Ten percent? And who gets to decide which forest is too valuable to cut, and what is OK to sacrifice? Value is a human concept, and humans disagree about what is valuable.
There is no scientific answer to these values-based questions. What is beyond debate is that our rapidly warming climate, caused by burning fossil fuels, is an existential threat for every ecosystem and habitat on the planet, and that only the widespread deployment of renewables can stop climate change. In Rhode Island it will take tens of thousands of acres of panels to kick our fossil fuel addiction. Where to put them is a debate we need to have immediately.
There is no perfect solution; we are faced with a series of “less bad” choices. We are going to need to compromise with each other, and understand that one person’s forest sanctuary, or golf course, or business-critical parking lot, is another person’s ideal solar site. We’re going to need to weigh the speed of renewable deployment against the benefit of putting them in the “ideal” site.
But let’s get the facts straight. Clearing trees for solar panels does not “eliminate the project’s climate benefits.”
Stephen Porder is the Acacia Professor of Ecology, Evolutionary and Organismal Biology and Environment and Society at Brown University. He is also the co-founder of Possibly, a sustainability science podcast heard weekly on Ocean State Media, and the author of Elemental: Five elements that changed Earth’s past and will shape our future. All views expressed in this editorial are his alone, and do not represent those of Brown University.”  

Your email address will not be published. Required fields are marked *
Name*
Email*
Save my name and email in this browser for the next time I comment


Δ
Reader support is at the core of our nonprofit news model. Together, we can keep the environment in the headlines.
10 Davol Sq., Suite 100
Providence, RI 02903

Stay in the know by subscribing to our weekly enewsletter.
Copyright © 2026. All Rights
Reserved.
Website designed by Gravity Switch
Powered by Roy Web Design
English
Powered by OneTap
Default

Default

source

Deliveries will begin in 2027 for three utility-scale projects in North Carolina and Texas, US.
Inox Solar Americas has signed an agreement to supply 767MW of photovoltaic (PV) modules to a US renewable energy developer and independent power producer (IPP).
The customer, which was not named, develops, finances, owns and operates utility-scale solar and energy storage projects.
The gold standard of business intelligence.
Find out more
Combine business intelligence and editorial excellence to reach engaged professionals across 36 leading media platforms.
Find out more
The modules will be used for three utility-scale solar developments in North Carolina and Texas. The individual capacities are approximately 71MW, 102MW and 594MW, with deliveries due to start in 2027.
Inox Solar Americas, which manufactures solar PV modules and cells in the US, said the projects would use its Vega Series bifacial modules. These are offered in single-glass and dual-glass configurations and use Galaxion N-Type PV cells.
The company said the modules are designed for utility-scale use and support high levels of US domestic content.
Inox Solar Americas president and CEO Ashok Nair said: “Our customers are looking beyond module performance to domestic content, supply-chain transparency, regulatory compliance, product reliability and long-term bankability.
“This agreement demonstrates our ability to meet these priorities with reliable, high-performance PV modules manufactured in the US.”
Inox Solar’s US manufacturing and sourcing plans are being built to comply with Prohibited Foreign Entity and Foreign Entity of Concern rules, along with domestic content, supply chain traceability and applicable US trade and energy policy requirements.
Measures cited by the manufacturer include tighter supplier qualification, more domestic sourcing, and greater visibility over components and their origin. It said the aim is to help projects meet compliance obligations and to lower supply-chain risk.
Inox Solar Americas stated that the award reflected the technical, commercial, supply chain and risk factors that developers, IPPs, investors and lenders weigh when choosing module suppliers for long-term solar assets.
The contract will serve large project pipelines through domestic manufacturing and long-term customer support.
The gold standard of business intelligence.
Find out more
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
Find out more
Give your business an edge with our leading industry insights.
Give your business an edge with our leading industry insights.



Visit our Privacy Policy for more information about our services, how we may use, process and share your personal data, including information on your rights in respect of your personal data and how you can unsubscribe from future marketing communications. Our services are intended for corporate subscribers and you warrant that the email address submitted is your corporate email address.
View all newsletters from across the GlobalData Media network.
Power industry news, data and in-depth articles on the global trends driving power generation, renewables and innovation
Powered by
© GlobalData Plc 2026

source

Pick the engineering stories that matter and get them in your inbox.
Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience.
All Rights Reserved, IE Media, Inc.
Follow Us On
Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience.
All Rights Reserved, IE Media, Inc.
Clean energy can finally afford to be fully clean.
Clean energy can finally afford to be fully clean. Scientists have created a tin-based perovskite material equipped with a built-in defense against air and moisture, functioning much like a molecular raincoat for solar cells.
The advance is the result of a joint effort led by the University of Wisconsin–Madison, the National Laboratory of the Rockies, and partner institutions. 
Interestingly, this tin perovskites could become a promising eco-friendly alternative to hazardous lead-based cells. However, the previous iterations of tin-based materials typically degrade rapidly when exposed to air and water. This new “raincoat” solves this issue.
The collaborative team revealed a molecular redesign that gives tin-based solar cells built-in protection against these elements. It tackles the single largest obstacle preventing non-toxic perovskite photovoltaics from hitting the commercial market: durability.
“We wanted to find a way to protect these materials while preserving the properties that make them attractive for solar cells,” said Song Jin, a UW–Madison professor of Chemistry. 
With solar panels covering millions of acres, the risk of toxic leaks from damaged or discarded panels remains the biggest roadblock keeping lead-based cells off the market.
Tin perovskites possess superior light-absorbing and electronic properties, but degrade in a short time when exposed to air and moisture. 
Rather than adding a bulky physical layer over the solar cells, the team designed protection straight into the material’s microscopic structure. It all came down to atomic fine-tuning.
Song Jin and his team experimented with different halogen atoms (fluorine, chlorine, and bromine) to alter the material’s organic components. This study engineered a mixed-dimensional (2D/3D) heterostructure.
Interestingly, when the chlorinated version was introduced, something remarkable happened: the perovskite crystals packed together far more tightly than before.
That tight atomic packing acts as a molecular shield. Water and oxygen can’t squeeze inside, creating a protective barrier that keeps them out of the cells.
This structural shift improved both performance and durability. Compared to conventional materials, the new chlorinated tin perovskite maintained its integrity for months in open air. It even survived days fully submerged in water without dissolving.
Further, theoretical models confirmed the mechanism: the tight molecular structure physically blocks oxygen and water from seeping into the vulnerable tin core.
To test its practical viability, the team built working solar cells in partnership with researchers Lei Chen and Kai Zhu at the National Laboratory of the Rockies.
The devices achieved a 16.2 percent power-conversion efficiency, which makes them among the most efficient tin-based cells ever created.
After 1,600 hours sitting in dry air, the cells retained over 95 percent of their initial output. Under severe operational stress with continuous simulated sunlight at a blistering 55°C (131°F), the cells held onto 80 percent of their power after 1,000 hours.
Mostly, solar design meant choosing efficiency or durability. This material shows that companies don’t have to compromise.
“What is exciting is that a relatively small change in the material’s design produces such a large improvement in stability,” said Christopher T. Triggs, who recently received his doctorate in materials chemistry at UW–Madison. 
“It shows how designing the organic components and controlling the way perovskite structures pack together can provide powerful protection of the resulting perovskite materials from the surrounding environment,” the first author added. 
Recognizing the commercial potential of a lead-free, long-lasting solar cell, the Wisconsin Alumni Research Foundation and the National Laboratory of the Rockies have jointly filed a patent for the technology. 
The study was published in the journal Nature Materials. 
Mrigakshi is a science journalist who enjoys writing about space exploration, biology, and technological innovations. Her work has been featured in well-known publications including Nature India, Supercluster, The Weather Channel and Astronomy magazine. If you have pitches in mind, please do not hesitate to email her.
Premium
Follow

source

India plans 2-hour battery storage mandate for new solar, wind projects  Firstpost
source

MTerra Solar Phase 1 Begins Commercial Operations With 600 MW Solar-Storage Capacity In Philippines  solarquarter.com
source

JSW Energy crosses 15 GW installed capacity, adds 1,572 MW in FY27
Singapore delays SAF levy for cargo flights by one year
WeWork India to build 10 MWp solar plant in Karnataka, eyes 100% renewable power
Green hydrogen projects boost VOC Port’s potential as India-Europe green shipping corridor node
E15 on track to become Iowa’s top-selling fuel in 2026
Philippines considers raising ethanol blend to 15% as it explores new feedstock
ISO cuts India’s fuel ethanol output forecast, sees lower sugar diversion
Goa’s E20 network expands to 90 stations; ethanol-free petrol available at two outlets
E20 Fuel Row: Supreme Court dismisses plea for mandatory ethanol labelling on petrol pumps
Suzuki, Maruti Suzuki to add 11 biogas plants using cow dung, farm waste
Elan Energy wins 4 BPCL consultancy assignment CBG projects
Thailand expands biodiesel, biogas push to cut transport emissions and oil dependence
Government says revised CBG pricing to have limited impact on gas consumers
Galia Green Power secures €89.3 million financing for biogas and biomethane expansion in Poland
Higher biodiesel blends cut emissions from 7,000 UK buses
Indonesia steps up preparations for B60 biodiesel mandate in 2027
US EPA plans to restore biofuel demand lost to small refinery exemptions
Indonesia’s B50 biodiesel rollout reaches 75-80% nationwide, full coverage targeted by October
Belgium approves Sunoil Biodiesel for sustainable fuel supply
UK to end subsidy support for small-scale biomass plants
J&K should tap biomass potential to reduce fossil fuel dependence: Omar Abdullah
Serbian pellet prices hit record €350 amid supply shortage
Biomass: AI model identifies optimal conditions to boost biogas from palm waste
Cyberabad civic body plans green waste-to-bio-briquette plant
Green hydrogen projects boost VOC Port’s potential as India-Europe green shipping corridor node
Wales & West Utilities launches green hydrogen vehicle trial in Swansea
Algeria, Germany deepen energy cooperation with focus on hydrogen and low-carbon technologies
India’s installed power capacity projected to surpass 2,000 gw by 2047, says ENCIS Outlook Report at Bharat Electricity 2026
Kenya: SBM Bank commits $17m to Safer Power for clean-energy equipment manufacturing
Singapore delays SAF levy for cargo flights by one year
Axens to provide technology for Canada’s biomass-based SAF projects
China’s SAF exports surge 70% in July, with Europe taking all shipments
Europe’s degraded-land crops could meet just 4% of 2050 bio-SAF demand: study
Niutech to supply plastic pyrolysis line for SAF production
WeWork India Management Limited announced plans to develop a 10 MWp (DC) ground-mounted captive solar power plant in Karnataka, a move the company said marks a significant milestone in its goal to transition to 100 per cent renewable electricity by March 2028, according to a media release issued by the company on September 3, 2026. The Board of Directors of WeWork India accorded in-principle approval for the project at a meeting held the same day, the company said in a regulatory filing under Regulation 30 of the SEBI (Listing Obligations and Disclosure Requirements) Regulations, 2015.
Targeted for commissioning in FY27, the plant is expected to generate approximately 15 to 16 million units of clean electricity annually, the company said. Once operational, it is expected to raise the share of renewable electricity across WeWork India’s portfolio from close to 40 per cent currently to approximately 50 per cent. Nearly 80 per cent of the plant’s output will support the company’s operations, including 10 centres in Bengaluru, while the remaining 20 per cent will be reserved for future growth, according to the release.
The company said Karnataka was chosen as a strategic location for the investment, with Bengaluru representing its largest market at 30 operational centres and the state accounting for approximately 30 per cent of its total electricity consumption across its portfolio. Karan Virwani, Managing Director and CEO of WeWork India, said Bengaluru’s scale made it the natural starting point for an investment of this size, adding that building owned renewable generation capacity would help reduce the carbon footprint of operations while providing greater certainty over energy costs over the next 25 years. He said the company wanted sustainability to make strong business sense rather than function as an initiative separate from its core operations.
With a design life of 25 years, the solar plant is expected to offer greater long-term visibility over electricity costs for a significant part of WeWork India’s Bengaluru operations, helping insulate the portfolio from fluctuations in grid tariffs, the company said. By combining owned renewable generation, captive consumption and open access, WeWork India said it was building a more diversified energy sourcing model aimed at delivering both lower-carbon operations and greater cost certainty.
Implementation of the project remains subject to satisfactory completion of due diligence and receipt of requisite consents and approvals, the company said in its filing. Expected to be commissioned by the first quarter of 2027, the solar plant marks WeWork India’s shift from renewable electricity procurement toward direct investment in renewable generation capacity, a step the company said was intended to ensure that its growth is accompanied by a more resilient, efficient and lower-carbon energy model as it scales its portfolio.
JOIN OUR MAIL LIST
I agree to get email updates
Please Select “I agree to get email updates” options.
Email field is required to subscribe.

You Have Successfully Subscribed to the Newsletter
JOIN OUR MAIL LIST
I agree to get email updates
Please Select “I agree to get email updates” options.
Email field is required to subscribe.

You Have Successfully Subscribed to the Newsletter

BioEnergy Times brings the latest News & Information from India & around the world related to the BioFuels and BioEnergy.
Contact us: news@bioenergytimes.com

© Copyright 2026 BioEnergy Times

source

Join The Investor WhatsApp Channel

[gtranslate]
Developed by Lumira Moz Energy, a Chinese company investing in Mozambique, the project is designed to establish a vertically integrated industrial base for the production of photovoltaic cells, precision cutting of silicon wafers and manufacturing of high-efficiency solar modules.
The facility will occupy approximately 1.6 hectares within Beluluane Industrial Park and will be developed in two phases. Investment in the first phase alone exceeds USD 10 million.
Rather than importing only finished solar equipment, the project creates the potential for more of the manufacturing process to take place in Mozambique, supporting industrial diversification, specialised skills development and the growth of a domestic clean-energy supply chain.
Speaking at the groundbreaking ceremony, Minister of Economy Basílio Muhate highlighted: “The Lumira Moz Energy project is completely aligned with the Government’s vision for economic diversification and for strengthening Mozambique’s position as an industrial hub for energy products and technologies. Lumira has also chosen the best location in the country for this investment: MozParks Beluluane Industrial Park, with the infrastructure and regulatory framework needed to support industrial development.”
Photo: MozParks
For Maputo Province, the investment adds a new technology-driven industry to an already established manufacturing base.
Governor of Maputo Province Manuel Tule said: “Beluluane Industrial Park is very important for Maputo Province. It has created an industrial base that brings investment, jobs and economic activity to the province. Even in a period of significant market changes, including those affecting Mozal and the aluminium sector, Beluluane continues to grow, attract new industries and create new opportunities. The arrival of Lumira is another example of that continued development.”
Operated by MozParks, Beluluane Industrial Park has developed over more than 26 years into Mozambique’s largest industrial hub. Today, the 700-hectare park hosts more than 70 companies and supports over 10,000 direct jobs across manufacturing, agro-processing, technology, logistics and industrial services.
Its location gives companies access to the N4 regional transport corridor, the ports of Maputo and Matola and neighbouring South African and Eswatini markets, while industrial infrastructure and reliable power have supported the park’s expansion into increasingly diverse sectors. The arrival of Lumira MozEnergy represents another step in that diversification.
Onório Manuel, CEO of MozParks, said the investment is particularly important because of the industrial capability it brings to Mozambique.
“Lumira brings a new manufacturing activity to Beluluane and adds solar technology to the Park’s industrial base. Our role is to make it easier for investors to establish and operate in Mozambique. In this case, through close coordination with the Municipality of Matola-Rio, the necessary administrative procedures and documentation for the start of the project were completed within 48 hours. This is the kind of practical support MozParks provides to its partners so that investments can move from decision to implementation as efficiently as possible.”
Photo: MozParks
Deng Junfeng, General Director of Lumira Moz Energy, highlighted the company’s experience working with MozParks during the establishment of the project.
“We are very impressed by the way MozParks works and by the support we have received throughout this process. This is exactly the kind of efficient and practical environment Chinese investors are looking for when considering opportunities in Africa. Based on our experience, we would confidently recommend MozParks to other Chinese companies interested in investing and establishing operations in Mozambique.”
About MozParks
MozParks is a developer and operator of Sustainable Economic Zones, established as a public-private partnership between the Mozambican Government Agency for Investment and Export Promotion (APIEX) and the African Sustainable Economic Zones Alliance (ASEZA). MozParks manages parks in Maputo and Nampula provinces, and is expanding into Cabo Delgado province. To date, MozParks has attracted over 70 companies from 18 countries, contributing to income generation for more than 120,000 people in Mozambique and securing over USD 4 billion in investments.
For more information about MozParks and its initiatives, please visit MozParks Website or contact us at [email protected].
Source: MozParks / Press Release
©Copyright- 2025

source

Worcester, Massachusetts–(Newsfile Corp. – September 4, 2026) – Mass Megawatts (OTCID: MMMW) announces innovative salt rejection with the goal toward reducing the cost of the Solar Desalination to the same cost as tap water. The salt rejection innovation uses a method to energized boundary layers based upon the knowledge of laminar flow in wind energy and aerodynamic principles. The global Desalination market, currently valued at $26 billion a year, is expected to grow to more than $40 billion before 2033.
Several devices have been introduced to efficiently desalinate salt water and attempt to eliminate the clogging of the desalination process with the accumulation of salt on parts of the system during the process using a substantial amount of electricity and expensive materials. Recent advances avoiding the cost of electricity and expensive material hold significant promise for low-cost seawater desalination. However, salt accumulation is key obstacle for reliable adoption.
Our new technology demonstrates a more efficient method of salt transport enabled with localized solar concentration and salt rejection. It also offers a strategy for high performance solar evaporation.
The primary goal is removing the "salt foul" caused by the salt accumulation which is largely caused by slow moving water with an increasing salt density due to the ongoing evaporation in the solar desalination process. At the same time, the new technology uses low-cost materials to reduce the capital cost of the solar desalination units. With the objective of reducing each square meter of the solar desalination unit to a cost of less than four dollars, desalinated water can be delivered at a cost less than tap water.
This particular innovation uses wind energy related aerodynamic principles for creating an optimal shape for the microchannel diffusers improved with the use of rotating tubes toward enhancing aquadynamic (water related aerodynamic behavior). The new technology reduces turbulence for more salt efficient salt rejection. It is an important step toward avoiding salt accumulation on the solar desalination process.
Specifically, using an aerodynamically optimal shaped microchannels and moving salt rejection tubes , the salt can be pulled rather than pushed through the microchannels like a wind diffuser pulling air through a small tunnel area since the pressure is lower with less salt particles on the cold side of the barrier with microchannels. The aerodynamic enhancing shape of both the input area (upper hot area) and the diffuser (lower cold bulk water area) allows a swift and steady salt rejection without the turbulence of previous methods that would slow down the salt rejection process and cause salt accumulation.
Using the Mass Megawatts solar tracker shown on our company's web site www.massmegawatts.com, there can be further improvement of the solar desalination performance for a small additional cost. The company's Solar Tracking System (STS) is a new patent pending product that significantly reduces the payback period for solar power investments. It is designed to automatically adjust the position of solar panels to directly face the sun as it travels from East to West throughout the day. Unlike other solar tracking technologies, the Mass Megawatts Solar Tracker utilizes a low-cost framework that adds stability to the overall system, while improving energy production levels.
This press release contains forward-looking statements that could be affected by risks and uncertainties. Among the factors that could cause actual events to differ materially from those indicated herein are: the failure of Mass Megawatts Wind Power (MMMW), also known as Mass Megawatts Windpower, to achieve or maintain necessary zoning approvals with respect to the location of its power developments; the ability to remain competitive; to finance the marketing and sales of its electricity; general economic conditions; and other risk factors detailed in periodic reports filed by Mass Megawatts Wind Power (MMMW).
Contact:
info@massmegawatts.com
www.massmegawatts.com
Select market data provided by ICE Data Services. Select reference data provided by FactSet. Copyright © 2026 FactSet Research Systems Inc.Copyright © 2026, American Bankers Association. CUSIP Database provided by FactSet Research Systems Inc. All rights reserved. SEC filings and other documents provided by Quartr.© 2026 TradingView, Inc.

source

The Australian Energy Market Operator (AEMO) said rooftop solar met 99.9% of South Australia’s total electricity demand at 1.30pm on 31 August, reducing electricity demand from grid-scale generation to just 2 MW – a new winter record.
The market operator said the high rooftop solar generation and negative wholesale prices also saw batteries charging at above-average rates, absorbing excess renewable electricity for use during the evening peak period.
The South Australia result coincided with a new winter minimum operational demand record across the broader National Electricity Market (NEM).
AEMO said mild temperatures and clear skies across the southeast of the country reduced minimum operational demand to a record 11,992 MW at 1.30pm on Monday, surpassing the previous winter low of 12,144 MW set in August 2024.
At the time of the NEM minimum demand low, rooftop solar was contributing close to 54% of underlying demand while renewables, including rooftop solar, and grid-scale solar and wind, accounted for 71% of generation.
Battery charging accounted for about10.5% of total generation, while just over 2% was being used for hydro pumping.
“These records demonstrate how rapidly the electricity system is changing, with high renewable generation increasingly being balanced in real time by batteries, hydro and gas, supported by the transmission network,” AEMO said, adding that the conditions “reinforce the need to plan for a power system that is increasingly dynamic – with the flexibility, storage, transmission and operational capability needed to maintain a secure and reliable supply as the energy transition accelerates.”
Australia leads the world in rooftop PV penetration, with 41% of the nation’s residential, commercial and industrial premises hosting systems that deliver a combined 28.3 GW generation capacity.
In South Australia, 57% of the state’s rooftops are home to solar arrays, equivalent to 2.92 GW of generation capacity.

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: [email protected].
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: [email protected].
Comments
Please login to comment
Martedì, 22 Settembre 2026
11:00 – 12:00 CEST, Roma
Monday, October 26, 2026
10:30 am – 11:30 am CEST, Berlin, Paris, Madrid
Thursday, September 10, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Tuesday, September 15, 2026
5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid
Our special edition for Intersolar South America 2026 is here!
Discover the latest insights into the Brazilian solar market – in Portuguese.
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy.
pv magazine USA hosts its multi-day virtual event on U.S. solar and energy storage, covering domestic manufacturing, distributed energy and the growing role of solar-plus-storage in meeting AI-driven power demand.

You have no items in your basket.

source

Lockton Re
Our Story
Culture & Careers
News & Insights
Offices
Contact Us
ARTICLES / NOVEMBER 10, 2024
The use of photovoltaic (PV) systems to generate clean sustainable energy is well established within the built environment, with installations becoming more of a ‘norm’, rather than an exception. However, the installation of PV systems to a building can introduce new hazards which may increase the likelihood or severity of a loss. Data obtained by The Independent in 2023 indicates a rise in the number of fires (opens a new window) involving solar panels, with six times the number in 2022 compared with 10 years earlier.  
Such losses are not restricted to residential buildings and can also impact commercial properties.  An example being a fire in a 45,000 m (opens a new window) in Peterborough, UK, in Feb 2024, requiring over fifty firefighters to bring the blaze under control.  
It is important, therefore, to ensure robust property protection principles are adequately considered throughout the full project lifecycle of a PV system to ensure any hazards are identified, assessed, and controlled. 
PV Systems 
PV systems consist of semiconductor materials encapsulated by glass and a polymer/glass backing, which generate direct current (DC) when exposed to sunlight. An inverter is used to convert this power to alternating current (AC) current for practical use by the facility or transferred to the grid when power exceeds use. Systems are typically ground mounted (generally preferred by property insurers) or roof mounted secured with mechanical fixings or ballast.  
Common property hazards to be assessed when considering the installation of roof mounted PV systems include: 
Fire 
PV systems introduce new electrical components such as wiring, invertors, control equipment as well as the PV panels themselves. These components can be subject to failure, damage, or heating, increasing the risk of fire. Systems can also be damaged from external fire exposure. 
Windstorm 
PV systems can be damaged from wind or other debris if not adequately designed/installed. 
Hail 
PV systems can be damaged from hail exposure if sufficient resistance is not provided. 
Snow / rain 
Roof damage can result from excessive load of snow/rainwater combined with the weight of the PV system. 
Earthquake 
PV systems can move in the event of seismic activity resulting in damage and the potential for fire. 
 The installation of a PV system can introduce new components which may increase the likelihood or severity of a loss. Examples influencing the likelihood include: 
Electrical wiring faults (loose, damaged, or inappropriate connectors). Connectors are reported to be a common source of PV system fires. A 2017 study (opens a new window) identified PV DC connectors, inverter and DC isolators accounted for 84% of PV system related fires.  
Overheating or failure of PV modules or inverters 
Examples influencing the severity include: 
The PV system altering the fire behaviour of the roof, increasing the spread of fire and/or restricting firefighting efforts. 
A PV system fire damaging the roof cover resulting in firefighting water entering the building damaging equipment and stock below. 
PV system design approach 
A range of property protection guidance for the design, installation and management of roof mounted PV systems is readily available. Such guidance may sometimes exceed requirements of local building regulations. However, the implementation of robust property protection principles reduces the likelihood of a large loss, improves business resilience, and should be considered more favourably by property insurers.  
Example guidance includes: 
NFPA Codes including NFPA 70, National Electrical Code, NFPA 70B, Standard for Electrical Equipment Maintenance, NFPA 1, Fire Code (opens a new window)
FM Property Loss Prevention Data Sheet 1-15: Roof-Mounted Solar Photovoltaic Panels (opens a new window)
RISCAuthority. RC 62:  Recommendations for fire safety with PV panel installations (opens a new window)
Zurich Resilience Solutions — Photovoltaic (PV) systems on buildings (opens a new window)
Consult your broker and insurer at an early stage to agree on an acceptable solution for all stakeholders. 
A key consideration is the planned location of any roof mounted PV system. Carefully consider existing or proposed roof construction materials, penetrations, and equipment, avoiding installation on combustible roof systems.  Thermal barriers can help mitigate the influence of an existing combustible roof structure in some circumstances. Other considerations include the age and condition of the existing roof (it is harder to repair or recover a roof once a PV system has been installed) and avoiding installation of PV systems on roof areas over high value or business critical operations which may be susceptible to water damage. 
The development of a corporate policy on the design, installation and management of PV systems helps guide implementation of best practice throughout the lifecycle of the installation, including: 
Concept:  Identify and agree best practice principles to guide the suitable location of PV systems (including roof upgrades where needed), suitable structural analysis of roof areas, identification and mitigation of hazards (both fire and natural hazards), review and suitability of electrical infrastructure and selection of suitable contractors.  For example, those accredited to a national trade body and working to suitable standards. In the UK this might include the MCSCertification Scheme Requirements or UK BS 7671 IET Wiring Regulations 18^th edition or later. 
Design:  Apply local building, electrical and fire codes in addition to the property protection principles agreed with your broker/insurer. Select suitable PV components and protection systems such as fire detection, surge protection, plus fire control/suppression and lightning protection where needed. 
Installation:  Ensure installation work is conducted in accordance with suitable requirements such as the MCS and IET PV Code of Practice in the UK. Ensure the installation is conducted in accordance with the design, utilising an appropriate quality assurance process and representative.  
Operation:  Ensure inspection, testing and maintenance of the PV system is conducted in accordance with local requirements and manufacturers guidelines. Ensure any changes are controlled through a management of change process. 
Planning for emergencies:  Update the site emergency plan to include events involving the PV system and associated components. Ensure drawings are updated with key information including location of PV systems, isolation points, access, and fire water supplies. Collaborate with the local fire brigade, sharing information and developing pre-fire plans.  
The documents referenced above provide additional guidance.  
With the ongoing focus on clean energy, the installation of PV systems will continue to grow. There are positive aspects to such systems, but the potential risks need to be considered and managed. For further information, please visit the Lockton Risk Control page (opens a new window), or contact your broker or insurer.  
by  Mark Middleton
Risk Management Executive
+44 207 933 1632 (opens a new window)
mark.middleton@lockton.com (opens a new window)
For more info
Orlando Gonzalez
Risk Engineering Practice Leader
+1 786 761 6078 (opens a new window)
orlando.gonzalez@lockton.com (opens a new window)
Patrick Lo
Loss Control Consultant
patrick.lo@lockton.com (opens a new window)
Lucas Pfannenstiel
Property Engineering Leader
+1 816 960 9251 (opens a new window)
lpfannestiel@lockton.com (opens a new window)
Lynnda Segura
Vice President, Risk Engineering
+52 7100 2709 (opens a new window)
lynnda.segura@lockton.com (opens a new window)
Alert
© 2026 Lockton Companies. All rights reserved.

source

Construction of a new integrated solar manufacturing facility officially began on 4 September 2026 at MozParks Beluluane Industrial Park near Maputo.
Developed by Lumira Moz Energy, a Chinese company investing in Mozambique, the project is designed to establish a vertically integrated industrial base for the production of photovoltaic cells, precision cutting of silicon wafers and manufacturing of high efficiency solar modules. The production capacity in each case is not stated. The facility will occupy approximately 1.6 hectares within Beluluane Industrial Park and will be developed in two phases, with investment in the first phase alone exceeding US$ 10 million.
Rather than importing only finished solar equipment, the project creates the potential for more of the manufacturing process to take place in Mozambique, supporting industrial diversification, specialised skills development and the growth of a domestic clean energy supply chain. It is unknown what brand the products will be marketed under.

Image credit: MozParks
Speaking at the groundbreaking ceremony, Minister of Economy Basílio Muhate said the Lumira Moz Energy project aligns with the government’s vision for economic diversification and for strengthening Mozambique’s position as an industrial hub for energy products and technologies. He added that Lumira had chosen what he described as the best location in the country for this investment, citing MozParks Beluluane Industrial Park’s infrastructure and regulatory framework to support industrial development.
Author: Bryan Groenendaal






Δ

August 10, 2026
August 20, 2026
August 17, 2026
August 13, 2026
August 24, 2026
September 3, 2026
Disclaimer | Privacy Policy | Terms & Conditions | Returns Policy | Intellectual Property | Cookie Policy
© 2019 – 2026 GBA Digital Media Group. All Rights Reserved
Website Design | Website Maintenance
Copyright Green Building Africa 2026.

Subscribe to our weekly Top 5 Stories
"*" indicates required fields
Δ

source

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 Reports volume 15, Article number: 2023 (2025) Cite this article
3590 Accesses
8 Citations
Metrics details
A Publisher Correction to this article was published on 18 March 2025
This article has been updated
The integration of photovoltaic (PV) technologies is vital for achieving sustainable energy solutions in isolated systems. However, A critical challenge that remains is maintaining the sustainability of these systems under the fluctuating conditions of solar irradiance, which is key for isolated energy systems. This study hypothesizes that the sustainability of PV systems can be accurately assessed through a new metric that incorporates performance consistency, variability, and resilience, using real-time energy production data alongside GIS-based solar radiation models. By analyzing fixed PV, concentrated PV (CPV), and dual axis tracking PV (DATPV) systems over a three-year period (2017–2019), The analysis indicates that DATPV systems achieved the highest energy output, with energy ratios exceeding 300% in 2019, though this was accompanied by substantial variability in performance. Fixed PV systems demonstrated the most stable performance, with a consistency term reaching 0.93 and a sustainability score of 0.87 in 2019. CPV systems performed moderately, with a sustainability score of 0.66 in 2017. These results highlight the trade-off between energy capture and operational stability, which is critical for sustainable energy management in isolated systems.
The concept of sustainability has evolved significantly, particularly in the context of energy systems¹. According to², sustainable development is defined as meeting the needs of the present without compromising the ability of future generations to meet theirs. However, over time, the definition of sustainable energy has often been oversimplified, focusing primarily on environmental protection using renewable sources such as solar and wind, while ignoring critical social and economic dimensions, especially for low-income communities¹. Traditional sustainability frameworks frequently fail to address energy affordability and accessibility for vulnerable populations, leading to an imbalance between environmental goals and immediate energy survivability for these communities^3,4.
Recent studies highlight the limitations of this global perspective, calling for a more localized approach to sustainability. As^3,5 argue, energy availability plays a crucial role in poverty eradication and improving living standards, particularly in regions with limited access to modern energy services. This indicates a need for sustainability assessments that better consider the immediate energy needs of low-income communities, rather than solely focusing on future environmental benefits.
Recent advancements in PV systems have underscored the need for enhanced sustainability metrics, particularly in diverse environmental and operational contexts^6,7. For instance, Chandel et al. (2024) analyzed the integration of PV-powered thermoelectric cooling systems for transitioning towards net-zero energy buildings under variable solar loading conditions, emphasizing the interplay between solar irradiance variability and system reliability⁸. Similarly, Tajjour et al. (2023) demonstrated how energy management systems could optimize the performance of PV microgrids, offering solutions for maximizing power generation under varying conditions⁹. These findings highlight the critical role of dynamic solar conditions and energy management in improving the sustainability of PV systems.
Studies have also explored geographical challenges, such as Tajjour et al. (2023), who investigated power generation enhancements in a grid-connected PV system in hilly terrains, where irradiance variability posed unique challenges¹⁰. Furthermore, the optimization of hybrid PV systems combining solar and wind resources in remote locations, as reviewed by Rawat and Chandel (2013), shows the importance of adaptability and efficient energy management¹¹. These studies illustrate the need for sustainability metrics that account for irradiance variability for different PV systems.
Numerous studies have investigated the viability of solar photovoltaic (PV) systems, concentrating on sustainability dimensions such as technical, economic, and environmental aspects¹². For instance¹³, explored the feasibility of using PV and Concentrating Solar Power (CSP) in copper mining in Chile, employing financial tools like benefit-to-cost ratio and internal rate of return (IRR). Similarly, studies on rooftop PV systems in industrial and commercial sectors in India showed strong economic feasibility, with payback periods as short as 3.72 years¹⁴. Additionally, research in the Middle East, such as Gaza and Qatar, has explored solar energy’s potential in addressing electricity shortages and integrating hybrid PV-SOFC systems in industrial applications^15,16. In South Africa and Turkey, analyses demonstrated the economic and environmental benefits of large-scale PV systems, despite challenges like high initial costs^17,18.
In Egypt¹⁹, highlighted the significant potential of solar PV, particularly in the educational sector, while studies in Saudi Arabia and China underscored the financial and energy-saving advantages of PV systems in industrial processes, particularly when integrated with dual-axis tracking systems^20,21. Similarly, research in Pakistan revealed that grid-connected PV systems offered greater energy sustainability and economic benefits than off-grid systems, reducing energy demand by up to 35%²². In Morocco’s Souss-Massa Basin, PV systems were identified as a critical solution for water-energy-food (WEF) nexus challenges, especially in transitioning from fossil-fuel-based groundwater pumping²³.
Although economic feasibility and environmental impacts of PV systems are well-documented, current sustainability metrics remain focused on these dimensions, often neglecting operational challenges associated with isolated PV systems. Most studies primarily assess grid-connected systems, which are inherently more reliable and efficient. However, the operational complexity of isolated systems—such as energy storage, reliability, and real-time energy management—remains underexplored, particularly for regions where grid connectivity is limited^24,25. The deployment of isolated PV systems in such areas presents unique challenges that require new sustainability metrics, particularly those that assess the social impact, energy resilience, and operational adaptability of these systems. The variability in solar irradiance due to climate change further complicates system performance, yet this factor is often inadequately considered in traditional sustainability assessments²⁶.
On the other hand, the Photovoltaic Geographical Information System (PVGIS) is a sophisticated, GIS-based tool that provides precise solar radiation estimates and PV energy production simulations. PVGIS calculates key irradiance values such as Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), and diffuse radiation, which are essential for a wide range of solar energy applications, including fixed PV, Concentrated Photovoltaic (CPV), and Dual-Axis Tracking PV (DATPV) systems. By integrating satellite-derived data with advanced meteorological models, PVGIS accounts for atmospheric variables like cloud cover, aerosol content, and elevation, ensuring highly accurate solar resource assessments for both fixed-axis and dual-axis tracking systems.
Several studies have utilized PVGIS models to improve the reliability of PV power generation²⁷, short-term PV power forecasts²⁸, and enhance PV system designs²⁹. The versatility of PVGIS makes it a critical tool for researchers, policymakers, and system designers, enabling data-driven decision-making for renewable energy planning and system optimization. In this research, PVGIS is employed to generate daily solar irradiation data for each month under clear and cloudy sky conditions, offering a critical understanding of the solar energy potential and performance for PV, CPV, and DATPV systems³⁰.
This study introduces a novel sustainability metric that quantifies the impact of climate change by comparing Geographic Information System (GIS)-based solar radiation models with real-world solar radiation data across three distinct years. This comparison helps capture how well the system can adapt to climate variability over time.
Solar irradiance variability, driven by changing weather patterns and long-term climate change, can influence the performance and reliability of PV systems. By analyzing discrepancies between modeled and actual solar radiation, this metric aims to capture the system’s ability to adapt to climate variability and ensure consistent energy output, as shown in Fig. 1.
The conceptual framework of the proposed metric.
The main contributions can be summarized as follows:
This study introduces a new sustainability metric that combines performance consistency, and variability into a single comprehensive score.
Unlike previous approaches that analyze these factors separately, the proposed metric offers an integrated assessment of PV system sustainability.
The metric focuses on the comparison between real energy production and GIS-modeled energy production, providing a more accurate understanding of system performance.
This approach introduces a new dimension to sustainability evaluations by assessing how closely real-world performance aligns with modeled expectations over time.
The proposed metric enhances the ability to make informed decisions regarding the planning and management of isolated renewable energy systems under varying solar conditions.
This paper is structured as follows: Sect. 2 introduces the methodology used for developing the new sustainability metric, including the integration of GIS models with historical solar irradiance data. Section 3 presents a detailed case study of PV, CPV, and DATPV systems in Cairo, Egypt, across three years, highlighting the performance variability under different solar conditions. Section 4 discusses the results of the sustainability analysis, comparing the consistency, variability, and overall sustainability metrics of the three PV systems. Finally, Sect. 5 concludes the paper by summarizing the key contributions and outlining potential future work aimed at improving the sustainability of isolated PV systems under varying climatic conditions.
A climate change sustainability criterion for isolated systems should prioritize two key factors: consistency, and resilience. Consistency refers to the ability of the system to maintain energy production close to the expected daily energy output. Resilience involves the system’s capacity to handle variability beyond the modeled energy during the year, particularly in response to unpredictable climatic shifts. The proposed sustainability metric can be formulated as a weighted combination of the system’s long-term performance consistency, the deviation from expected performance based on GIS-based models, and a resilience factor that accounts for energy output variability. This metric offers a comprehensive assessment of a system’s sustainability by integrating both modeled expectations and real-world performance under varying conditions.
The Sustainability Metric (SM) is a weighted score that combines the long-term consistency of system performance, the deviation from GIS-modeled energy outputs, and a variability factor that captures daily fluctuations in energy production. The formula is expressed as
Where: N: Total number of days in the dataset (365 for a full year), R_i: Ratio of real energy production to GIS-modeled energy production on day i, expressed as a percentage, and V: Variability factor of daily ratios R_iover the year.
The performance consistency term is designed to evaluate how closely the system’s actual energy production aligns with the ideal performance, which is represented as 100% of the expected output based on GIS models. This term is calculated by averaging the absolute deviations between the real energy production and the modeled value over a period of N days. The smaller the deviation from the ideal 100%, the higher the performance consistency. A lower deviation reflects a system that consistently produces energy in line with the modeled expectations, which is crucial for ensuring consistent performance in isolated PV systems.
The variability factor (V) quantifies the degree of fluctuation in daily energy production by calculating the ratio of the standard deviation (σ) to the mean (µ) of the daily production ratios. This factor captures the day-to-day variability in the system’s performance, with higher variability indicating greater instability in energy output. A system with high variability will exhibit significant deviations from its average energy production, which is undesirable in isolated PV systems that require stable, predictable energy generation.
As such, higher variability results in a lower sustainability score, emphasizing the importance of minimizing fluctuations for long-term system resilience. The closer the SM value is to 1, the more sustainable the system is, meaning it consistently produces energy close to the expected amount, with minimal variability. Systems with high fluctuations (high ) and large deviations from expected performance will have a lower SM.
Cairo, Egypt (30.0444°N, 31.2357°E), is examined. Cairo is characterized by high solar irradiance levels, making it an ideal location for solar energy harvesting. This location’s semi-arid climate provides a mixture of clear skies and intermittent cloud cover, allowing for a comprehensive analysis of solar radiation variability throughout the year. Historical data for different solar irradiances spanning 12 months was compiled for the selected study locations for three different years, i.e. 2017–2019, as referenced³¹. Figure 2 shows the studied historical daily energy behaviours for different PV systems.
Measured daily energy : a PV, b DATPV, c CPV.
Historical data show large variations from year to year, driven by unpredictable climate events or anomalies, which complicates accurate forecasting of future performance. Moreover, the increasing effects of climate change make it harder to predict future irradiance patterns based solely on past trends. therefore, relying only on historical data can be enormously confusing. Figure 3 illustrates the average energy (kWh/m²) output for the PV, CPV, and DATPV systems on a monthly basis across the year.
GIS based average daily energy for different PV systems.
DATPV systems show the highest energy output throughout the year, with especially strong performance in the summer months (June to September). This aligns with Fig. 2 showing a higher daily real energy relative to other systems. The system’s tracking mechanism clearly maximizes solar energy capture, particularly when solar irradiance is at its peak during the summer. CPV systems are lower than DATPV but consistently outperform the fixed PV system across the year. This is expected, given the concentrated photovoltaic system’s ability to focus sunlight onto a smaller area, increasing its energy production. While fixed PV systems show the lowest monthly energy output, reflecting the absence of any tracking or concentrating technology. The system performs more consistently across the year but lacks the ability to significantly increase energy production during peak solar months.
On the other hand, while radiation models like GIS-based solar radiation models and average daily energy models provide valuable estimates, they often fail to capture the full variability of solar irradiance due to factors such as weather patterns, seasonal changes, and localized environmental conditions. This limitation can lead to significant discrepancies between modeled performance and actual system behavior. As a result, relying solely on these models can be equally misleading, especially when comparing the sustainability of different PV systems, as they may not fully reflect the operational challenges and real-world performance of these systems over time.
These challenges highlight the need for a new criterion to assess the sustainability of PV systems. A more consistent metric would integrate both historical data and radiation models, analyzing irradiance patterns to ensure a realistic and sustainable evaluation. Given that solar irradiance is the primary driver of PV power generation, such a criterion would better account for radiation variability and offer a clearer picture of the long-term sustainability of PV systems, especially for isolated applications where consistent energy output is critical.
A comprehensive analysis is presented of the ratios between actual daily solar energy production and the modeled values R_i, which serve as key indicators of the systems’ performance under varying solar irradiance conditions. These ratios, illustrated in Fig. 4, provide insights into the efficiency and variability of different PV technologies, such as fixed PV, CPV, and DATPV, over multiple years. By examining these ratios, the section highlights how each system responds to real-world fluctuations in solar irradiance and evaluates their sustainability, reliability, and suitability for isolated energy systems.
Daily R_i ratios : a PV, b DATPV, c CPV.
Figure 4 demonstrates that the DATPV system in 2019 exhibited the highest variability in daily energy ratios, surpassing 300%, compared to lower variability in 2017 and 2018. This indicates that DATPV’s ability to track the sun throughout the day significantly enhanced energy capture, particularly in 2019. In contrast, the energy ratios for both fixed PV and CPV systems were lower, with CPV showing fluctuations between 0% and 180%, while fixed PV remained more stable, generally close to 100%.
Throughout the three years, DATPV consistently produced more energy compared to CPV and fixed PV systems. However, its higher variability, particularly in 2019, underscores a trade-off between maximizing energy capture and maintaining consistent output, especially critical for isolated systems. In contrast, the more stable performance of PV and CPV systems offers better predictability for energy storage and battery management, crucial for ensuring reliability in isolated PV systems.
The higher energy output DATPV systems, as shown in Fig. 4, results from their ability to dynamically adjust to the sun’s position across both azimuth and elevation angles. This feature allows DATPV systems to maximize energy capture, especially in regions like Egypt, which are characterized by high solar irradiance and minimal cloud cover. However, the sustainability of DATPV systems is not uniform across all geographical areas. In regions with frequent cloud cover, lower solar angles, or significant seasonal variation, the variability in daily energy production may outweigh the benefits of increased energy capture. For example, the Egyptian case demonstrates a clear advantage for DATPV systems in energy output but also highlights increased variability, with sustainability metrics for DATPV scoring lower than those of fixed PV systems due to higher fluctuations. These findings underscore the importance of region-specific analysis when determining the most sustainable PV technology, as local climatic and operational factors significantly influence system performance and reliability.
The results provide a detailed comparison of the performance of PV, DATPV, and CPV systems across three years (2017, 2018, and 2019), using three key metrics: consistency term, variability term, and the overall sustainability metric, as shown in Table 1.
The consistency term highlights how closely each system’s real energy production aligns with the modeled energy output. Fixed PV demonstrates the highest consistency across all years, particularly in 2019 (0.93), indicating a stable and predictable performance. DATPV shows lower consistency, reflecting greater fluctuations due to its dependence on dynamic solar tracking, with 2019 scoring slightly higher (0.75) than previous years. CPV remains moderately consistent across all years, with values around 0.84.
The variability term measures day-to-day fluctuations in energy output. Here, fixed PV once again outperforms DATPV and CPV, particularly in 2019 (0.94), underscoring its stability. DATPV exhibits the highest variability, particularly in 2018 and 2019, which can be attributed to its more sensitive tracking mechanisms. CPV’s variability remains between that of fixed PV and DATPV, indicating it has better control over fluctuations than DATPV but is less stable than fixed PV.
The overall sustainability metric combines these factors, and the results show that fixed PV systems consistently rank the highest in sustainability, with 2019 reaching 0.87, reflecting its stable performance and low variability. DATPV, while capable of higher energy capture, struggles with variability and consistency, resulting in the lowest sustainability scores, particularly in 2018 (0.46). CPV systems, with moderate consistency and variability, score mid-range in sustainability, peaking at 0.66 in 2017.
In conclusion, while DATPV offers potential for higher energy output, its variability reduces its overall sustainability, especially in isolated systems. Fixed PV demonstrates the best balance between performance stability and reliability, making it the most sustainable option based on these metrics. CPV provides a middle ground, offering a mix of energy capture potential and moderate stability.
This study presents a comprehensive analysis of the sustainability of different PV technologies for isolated energy systems, using a newly developed metric that integrates performance consistency and variability. The findings show that while DATPV systems capture the highest energy output, their sustainability is compromised by significant fluctuations, with variability terms as low as 0.63 in 2018. In contrast, fixed PV systems offer the most reliable performance, achieving a consistency term of 0.93 and a sustainability score of 0.87 in 2019, making them the most suitable for isolated systems requiring steady energy output. CPV systems demonstrate moderate performance, with a balance between energy production and stability, peaking with a sustainability score of 0.66 in 2017. These results underline the importance of evaluating not just energy output but also the consistency and adaptability of PV systems to variable solar conditions. The proposed sustainability metric provides a more comprehensive understanding of system performance, allowing for more informed decision-making in the planning and management of isolated renewable energy systems. Future work will explore the integration of battery sustainability and advanced energy management systems to further enhance the sustainability of isolated PV systems, ensuring optimized energy storage and distribution under variable solar conditions.
Future research should focus on refining the proposed sustainability metric to incorporate the impact of energy storage technologies. Investigating the integration of hybrid renewable energy systems, such as PV-wind or PV-biomass combinations, could provide a more holistic perspective on sustainability, particularly for isolated systems. Moreover, applying the metric to a broader range of geographical locations and climatic conditions would validate its adaptability and robustness.
The datasets used and generated during the current study are available from the corresponding author upon reasonable request.
A Correction to this paper has been published: https://doi.org/10.1038/s41598-025-92483-x
Halawa, E. Sustainable energy: Concept and definition in the context of energy transition—A critical review. Sustainability 16, 1523. https://doi.org/10.3390/su16041523 (2024).
Article  MATH  Google Scholar 
Brundtland Report. The World Commission on Environment and Development—Our Common Future. Oxford University Press (1987).
Vera, I. L. & Langlois. Energy indicators for sustainable development. Energy 32, 875–882 (2007).
Article  MATH  Google Scholar 
Wolde, Z., Wu, W., Ketema, H., Karikari, B. & Liu, X. Quantifying sustainable land–water–energy–food Nexus: The case of sustainable livelihoods in an East African Rift Valley. Atmosphere 13(638), 00–1212 (2022).
Google Scholar 
Fan, J. et al. Energy policies for sustainable livelihoods and sustainable development of poor areas in China. Energy Policy 39, 12 (2011).
Article  CAS  Google Scholar 
Rawat, R. & Chandel, S. S. Review of maximum-power-point tracking techniques for solar-photovoltaic systems. Energy Technol. 1, 438–448. https://doi.org/10.1002/ente.201300053 (2013).
Article  MATH  Google Scholar 
Malik, P. & Chandel, S. S. Performance enhancement of multi-crystalline silicon photovoltaic modules using mirror reflectors under Western Himalayan climatic conditions. Renew. Energy 154, 966–975. https://doi.org/10.1016/j.renene.2020.03.048 (2020).
Article  Google Scholar 
Chandel, R. et al. Experimental analysis and modelling of a photovoltaic powered thermoelectric solid-state cooling system for transition towards net zero energy buildings under different solar loading conditions. J. Clean. Prod. 442, 141099. https://doi.org/10.1016/j.jclepro.2024.141099 (2024).
Article  CAS  MATH  Google Scholar 
Tajjour, S. & Chandel, S. S. Experimental investigation of a novel smart energy management system for performance enhancement of conventional solar photovoltaic microgrids. Discov. Energy 3, 8. https://doi.org/10.1007/s43937-023-00021-5 (2023).
Article  ADS  MATH  Google Scholar 
Tajjour, S. et al. Power generation enhancement analysis of a 400 kWp grid-connected rooftop photovoltaic power plant in a hilly terrain of India. Energy Sustain. Dev. 77, 101333. https://doi.org/10.1016/j.esd.2023.101333 (2023).
Article  MATH  Google Scholar 
Rawat, R. & Chandel, S. Simulation and optimization of solar photovoltaic-wind stand alone hybrid system in Hilly Terrain of India. Int. J. Renew. Energy Res. 3, 595–604 (2013).
MATH  Google Scholar 
Mostafa, N. A. & Aboelezz, A. Feasibility-sustainability study of power generation using solar energy at an industrial site: A case study from Egypt. Energ Sustain Soc 14, 36. https://doi.org/10.1186/s13705-024-00460-5 (2024).
Article  MATH  Google Scholar 
Behar, O., Sbarbaro, D. & Moran, L. Which is the most competitive solar power technology for integration into the existing copper mining plants: Photovoltaic (PV), concentrating solar power (CSP), or hybrid PV-CSP?. J. Clean. Prod. 287, 125455. https://doi.org/10.1016/j.jclepro.2020.125455 (2020).
Article  Google Scholar 
Kumar, H. S. Rooftop Solar for C&I Consumers in Tamil Nadu. https://settn.energy/events/rooftop-solar-for-ci-consumers-in-tamil-nadu. Accessed 20 Nov 2021 (2021).
Nassar, Y. F. & Alsadi, S. Y. Assessment of solar energy potential in Gaza Strip-Palestine. Sustain. Energy Technol. 31, 318–328. https://doi.org/10.1016/j.seta.2018.12.010 (2019).
Article  MATH  Google Scholar 
Al-Khori, K., Bicer, Y. & Koç, M. Comparative techno-economic assessment of integrated PV-SOFC and PV-battery hybrid system for natural gas processing plants. Energy 222, 119923. https://doi.org/10.1016/j.energy.2021.119923 (2021).
Article  MATH  Google Scholar 
Sewchurran, S. & Davidson, I. E. Technical and financial analysis of large-scale solar-PV in eThekwini Municipality: Residential, business and bulk customers. Energy Rep. 7, 4961–4976. https://doi.org/10.1016/j.egyr.2021.07.134 (2021).
Article  MATH  Google Scholar 
Catalbas, M. C., Kocak, B. & Yenipınar, B. Analysis of photovoltaic-green roofs in OSTIM industrial zone. Int J Hydrogen Energy 46(27), 14844–14856. https://doi.org/10.1016/j.ijhydene.2021.01.205 (2021).
Article  ADS  CAS  MATH  Google Scholar 
Abd Elsadek, E. M., Kotb, H., Abdel-Khalik, A. S., Aboelmagd, Y. & Abdelbaky Elbatran, A. H. Experimental and techno-economic analysis of solar PV system for sustainable building and greenhouse gas emission mitigation in harsh climate: A case study of Aswan educational building. Sustainability 16, 5315. https://doi.org/10.3390/su16135315 (2024).
Article  Google Scholar 
Azzam, Y. A. & Ibrahim, N. A feasibility study of the use of solar PV energy in Saudi Arabia: a case study. J. Sol. Energy-T ASME 140(4), 140–172. https://doi.org/10.1115/1.4039551 (2018).
Article  MATH  Google Scholar 
Li, X. et al. Optimal design and techno-economic analysis of renewable-based multicarrier energy systems for industries: A case study of a food factory in China. Energy 244, 123174. https://doi.org/10.1016/j.energy.2022.123174 (2022).
Article  CAS  Google Scholar 
Mehmood, A., Ren, J. & Zhang, L. Achieving energy sustainability by using solar PV: System modelling and comprehensive techno-economic-environmental analysis. Energy Strategy Rev. 49, 101126. https://doi.org/10.1016/j.esr.2023.101126 (2023).
Article  MATH  Google Scholar 
Almulla, Y., Ramirez, C., Joyce, B., Huber-Lee, A. & Fuso-Nerini, F. From participatory process to robust decision-making: An agriculture-water-energy nexus analysis for the Souss-Massa basin in Morocco. Energy Sustain. Dev. 70, 314–338. https://doi.org/10.1016/j.esd.2022.08.009 (2022).
Article  Google Scholar 
Elazab, R. et al. Reviewing the frontier: Modeling and energy management strategies for sustainable 100% renewable microgrids. Discov. Appl. Sci. 6, 168. https://doi.org/10.1007/s42452-024-05820-6 (2024).
Article  Google Scholar 
Dahab, A. A., Adma, M. A., Hassan, H. & Elazab, R. Global challenges and economic feasibility in achieving 100% renewable energy. Recent Adv. Electr. Electron. Eng. 17, 868. https://doi.org/10.2174/0123520965313603240529110810 (2024).
Article  Google Scholar 
Okay, N. C., Sencer, A. & Taskin, N. Quantitative indicators for environmental and social sustainability performance assessment of the supply chain. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-024-05210-3 (2024).
Article  MATH  Google Scholar 
Psiloglou, B. E., Kambezidis, H. D., Kaskaoutis, D. G., Karagiannis, D. & Polo, J. M. Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece. Renew. Energy 146, 1372–1391. https://doi.org/10.1016/j.renene.2019.07.064 (2020).
Article  Google Scholar 
Elazab, R., Eid, J. & Amin, A. Reliable planning of isolated building integrated photovoltaic systems. Clean Energy 5(1), 32–43. https://doi.org/10.1093/ce/zkaa028 (2021).
Article  MATH  Google Scholar 
Elazab, R., Saif, O., Amin, A. M. A. & Metwally, M. D. Adaptive cloudiness index for enhanced photovoltaic energy prediction and management in low-income smart homes using geographic information system. Discov. Appl. Sci. 6, 127. https://doi.org/10.1007/s42452-024-05793-6 (2024).
Article  MATH  Google Scholar 
Huld, T., Müller, R. & Gambardella, A. A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy 86, 1803–1815 (2012).
Article  ADS  MATH  Google Scholar 
National Renewable Energy Laboratory (NREL) home page. (n.d.). NREL. https://www.nrel.gov/. Accessed 30 Sept 2024.
Download references
Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Faculty of Engineering, Helwan University, Cairo, Egypt
Rasha Elazab & Mohamed Daowd
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
R.E. and M.D. share all activities equally in this research.
Correspondence to Rasha Elazab or Mohamed Daowd.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original online version of this Article was revised: The original version of this Article contained an error in Figure 1 where the text was replaced by symbols.
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
Elazab, R., Daowd, M. New geographic information system based sustainability metric for isolated photovoltaic systems. Sci Rep 15, 2023 (2025). https://doi.org/10.1038/s41598-025-85222-9
Download citation
Received: 03 October 2024
Accepted: 01 January 2025
Published: 15 January 2025
Version of record: 15 January 2025
DOI: https://doi.org/10.1038/s41598-025-85222-9
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.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Scientific Reports (Sci Rep)
ISSN 2045-2322 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source

GREW Solar secures INR 430 crore repeat order from leading IPP for high efficiency G12R TOPCon modules across India.
September 04, 2026. By EI News Network
GREW Solar has secured a repeat order worth INR 430 crore from a leading independent power producer (IPP) for the supply of high efficiency G12R solar photovoltaic modules for utility scale projects across multiple locations in India.
The order strengthens the existing relationship between GREW Solar and the IPP, with the company citing continued confidence in its module technology, product quality and execution capabilities.
GREW Solar will supply G12R modules based on N type TOPCon cell technology. The modules are designed for utility scale applications, with a focus on higher efficiency, energy generation and long term performance.
Vinay Thadani, CEO and Director, GREW Solar, said that the repeat order reflects the customer’s confidence in the company’s product quality, technology and ability to deliver consistently. He added that GREW Solar plans to continue investing in advanced technology, manufacturing capacity and quality systems as India’s renewable energy sector expands.
The company said that growing power demand and India’s transition towards a cleaner energy mix are supporting demand for high efficiency solar PV modules. Developers are increasingly seeking modules that can improve energy generation and project economics.
GREW Solar, a venture of the Chiripal Group, operates a 6.5 GW solar PV module manufacturing facility in Dudu, Rajasthan, and plans to expand its module manufacturing capacity to 11 GW. It is also establishing an 8 GW solar PV cell manufacturing facility in Narmadapuram, Madhya Pradesh.
The company manufactures N type TOPCon G12R solar PV modules for utility scale and other solar applications and is expanding its presence in domestic and international markets.

Naveen Gupta, Trev Mobility, Says Premium EV Mobility Must Compete on Reliability and Service

Advanced Coated Steel Can Improve Solar Asset Life, Reduce Maintenance, Says Ranjan Dhar

India’s CNG, CBG and Hydrogen Push Makes it Strategic Market for KonveGas: Alexander Enulescu

GreenLine Mobility CEO Madhur Taneja Explains the Shift Towards Integrated Green Freight

Bondada Group Targets Major BESS Expansion Amid Rising Demand for LDES: Dr. Raghavendra Rao

source

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 Reports volume 15, Article number: 2124 (2025) Cite this article
5190 Accesses
32 Citations
Metrics details
Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach’s accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France & PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 & STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.
To ensure the proper functioning of a photovoltaic (PV) system, precise implementation of its model is the primary requirement. To design, simulate and evaluate the performance of a solar PV system, some essential parameters should be identified precisely. The required parameters for the complete modelling of the solar PV cells are the photon-generated current, the diode ideality factors, the shunt resistance, the series resistance and the diode saturation current¹. Generally, for a given solar PV cell/module, these intrinsic parameters can be identified either by referring to the datasheet available with the manufacturers or by utilizing the experimental I-V data. However, both of them may not be available. Several procedures for extracting these parameters have been proposed previously. Analytical, numerical and meta-heuristic techniques are the three popular techniques substantially attempted recently². Initially, an appropriate model is selected for identification. However, the precision of the parameters obtained from identification is an important aspect for the overall modelling, adequate sizing and performance assessment of solar PV systems. Apart from that, the primary data from the manufacturers are restricted very often. The data relating to V_oc (open-circuit voltage), I_sc (short circuit current), I_mpp and V_mpp (the maximum power point current and voltage respectively) considering standard test conditions, etc., are mainly provided. The exact parameters are not readily available, so the issue is prominent in developing appropriate modelling. In addition to this, in reality, the PV model parameters do get affected owing to variations in environmental conditions. So, the present model parameters may not replicate the real ones. To model the PV cells and modules accurately, it is still an open forum and urges for further research. This motivates the present study to formulate an adequate approach that can provide an optimal solution to the parameter identification problem applied in PV systems.
In the formulation of the identification of system parameters for the PV model, the analytical technique comprises solving a set of associated transcendental equations at important points of the I-V characteristics³. The main advantages in this case are a simpler approach, calculation time reduction, relatively accurate results, faster computation, etc. A fast and accurate analytical technique was suggested in⁴. It utilized the information from datasheet available from the manufacturers. Later, the authors in⁵ suggested an approach based on Lambert’s W-function, which is an improvised and exact analytical technique. Also, another analytical method utilizing Co-content function was suggested in⁶. Lately, analytical equations were solved by a method that was graphical⁷. It is found that, under normal weather conditions, the analytical methods perform reasonably better. However, the accuracy and consistency of the results are affected by the variations in the weather conditions. Apart from that, they are computationally less effective, and complexity increases as larger number of system parameters are required to be identified when applied to larger and more complex solar PV systems.
The numerical techniques in comparison to analytical methods, are more accurate in optimally computing the parameters. This is due to the consideration of all the points in the characteristic curve in the analysis. Initially, the numerical techniques based on Newton’s method and non-linear least squares was suggested in⁸ for estimating the five parameters of a PV cell. Subsequently a resistive-companion strategy⁹ was recommended and it yielded comparatively better results than the analytical ones. Later on, Newton-Raphson-based improvisation in identifying parameters was suggested for solar PV modules^10,11. The major limitation of all the numerical-based techniques is the large computational time. In addition to the above, it is found that these techniques may lead to reduced accuracy in results, particularly in identifying a large number of solar PV parameters and presenting mere close approximations of the initial conditions. These techniques also depend on the prior system’s conditions and there is a higher probability that the solution may get caught at local minima.
In subsequent developments, evolutionary and stochastic-based techniques are extensively applied to handle the major cons in identifying the solar PV model parameters by applying numerical and analytical techniques. The following reasons made these techniques more popular: (1) they do not involve differentiation; (2) they do not have strict requirements of continuity; (3) they present simplicity of approach; (4) they do not involve the complex mathematical formulation of system objectives; (5) the flexibility to enhance the searching ability either by control factor variation or by a hybrid approach. This inspires to do further research to formulate a right approach based on evolutionary optimization techniques in comparison to the numerical and analytical methods.
In current scenario, parameter estimation techniques based on different meta-heuristics like Genetic algorithm (GA)¹², Artificial bee swarm optimization(ABSO)¹³, Pattern search (PS)¹⁴, Particle swarm optimization (PSO)¹⁵, Bird mating optimizer (BMO)¹⁶, Mutative-scale parallel chaos optimization algorithm (MPCOA)¹⁷, Orthogonally-adapted gradient-based optimization (OLGBO)¹⁸, Genetic algorithm based on non-uniform mutation (GAMNU)¹⁹, Symmetric chaotic gradient-based optimizer (SC-GBO)²⁰, Guaranteed convergence particle swarm optimization (GCPSO)²¹, Enhanced vibrating particles system (EVPS)²², Improved Grey wolf optimizer (IGWO)²³, were developed and implemented for identifying the PV parameters. A General algebraic modelling system (GAMS) tool was suggested for PV parameter extraction in²⁴. Subsequently, other improved techniques such as Chaotic-driven Tuna swarm optimizer (CTSO)²⁵, Supply-demand-based optimization algorithm (SDOA)²⁶, Tree growth algorithm (TGA)²⁷, Supply-demand-based optimization (SDO)²⁸, Hybrid successive discretisation algorithm (HSDA)²⁹, Improved adaptive differential evolution (IADE)³⁰, Marine-predator algorithm (MPA)³¹, Slime mould algorithm integrated Nelder-Mead simplex strategy and chaotic map (CNMSMA)³², New Hybrid³³, Chaotic improved artificial bee colony (CIABC)³⁴, Improved cuckoo search algorithm (IMCSA)³⁵ etc., were also suggested. Moreover, Rao-2 and Rao-3³⁶ algorithms, Stochastic slime mould algorithm(SMA)³⁷, Electric eel foraging optimizer (EEFO)³⁸, Enhanced prairie dog optimizer(En-PDO)³⁹, mountain gazelle optimizer (MGO)⁴⁰, Arithmetic optimization algorithm (AOA)⁴¹ and Forensic-based investigation algorithm (FBIA)⁴² with improved performances were proposed for parameter identification. However, the major inferences that can be made are: Firstly, all the approaches are not free from the algorithm control factors, secondly, it is difficult to indicate one of them as the Universal best approach for all problems/applications^43,44.
Hybrid techniques based on meta-heuristic techniques are very effective due to coordinated searching and reducing the limitations of one approach by the other⁴⁵. Few hybrid approaches are successfully applied for solar PV parameters’ identification, such as Hybrid Firefly and Pattern Search Algorithms (HFAPS)⁴⁶, Hybrid bee pollinator Flower pollination algorithm (BPFPA)⁴⁷, Collaborative intelligence of different swarms⁴⁸, Hybrid gazelle-Nelder–Mead (GOANM) algorithm⁴⁹ and Hybrid mountain gazelle pattern search(MGPS) optimizer⁵⁰. These techniques are recommended to deal with the inherent demerits of individual techniques, permit different combinations of individual techniques, and then appear as a competent technique to the identify the parameters of solar PV models. Nevertheless, these techniques’ performances are problem-dependent. Secondly, their accuracy, robustness, and convergence speed are regulated by the judicious selection of algorithm’s control factors. Out of these two possibilities, an enhanced meta-heuristic technique with adaptively adjusting control factors is chosen in this study.
Even though extensive research has been done recently, formulating a better PV parameter identification is still a difficult problem to resolve. Finding an enhanced process with better accuracy and robustness in the computation of parameter estimation of various PV models is indispensable. A meta-heuristic method based on Differential evolution(DE) optimization is adopted due to its many pros as follows: (1) it is a derivative-free algorithm; (2) flexibility in enhancing the performance by adjusting algorithm control factors; (3) capable of handling a variety of objective functions having different forms of convexity and continuity; (4) not many factors need to be adaptively varied for enhancing the performance at different stages of searching; (5) comparatively less probability of facing confinement to the local minima; (6) the approach is simple, and easy to implement, program, modify and hybridize with other methods without much complexity in design and formulation; (7) the final result and search strategy is independent of initial random solutions chosen indicating the consistency in arriving at the optimal result. However, the performance of DE can be enhanced by adaptively varying its control factors and this can also overcome its shortcomings like getting caught at local minima due to misleading effects of certain problem landscapes, less ability to drag the population over long-distances for searching in case the crowded population is contained within a smaller region of the search space, increased computational time when applied to complex multi-objective and higher dimensional problems and oscillation around optimum result due to wrong adjustment of step size etc⁵¹. These research gaps motivate the present study to formulate a better technique for solar PV parameter identification and testing under a wide variety of cases most desirable for real-time application.
In recent times it has been seen that by adaptively varying the control factors not only in case of DE but also in the case of other stochastic approaches, the efficacy of searching during the exploration and exploitation stages is substantially improved. Looking at the DE algorithm, it is evident that two primary factors mutation and crossover and two secondary factors initialization and selection along with the population size affect the performance mainly. In this study, EDE is suggested by considering a stage-specific mutation procedure of the conventional DE algorithm. To augment the overall performance and circumvent the confinement issues at local minima, the EDE is proposed with stage-based control factors (mutation factor, cross-over rate) to attain improved precision with quicker convergence, resulting in optimum parameter values. Secondly, this approach is applied extensively for solar PV system parameter identification considering different PV model configurations.
The key contributions made in this work are the following:
A modified technique of DE mentioned as EDE is articulated for identifying the parameters of various PV cells and modules.
Adaptive adjustment of the mutation factors and crossover rates is suggested for augmenting the explore and exploit abilities of the EDE during the search.
Several cases of simulation, statistical results are showcased and discussed to assess the performance of the proposed EDE technique.
Based on the values of root mean square error (RMSE), extensive results are presented to compare and validate that the proposed EDE technique has better accuracy and robust searching capability in comparison to other reported techniques.
The overall structure of the article is as follows: Section “Introduction” presents the background, motivation, and research gap and also highlights the study’s contribution. Following the brief introduction, Section “Description of different PV models and objective function formulation” presents a detailed description of the modelling of solar PV and the single objective formulation of the problem considering all the system parameters and related constraints. The conventional DE algorithm, the proposed EDE technique are briefly described, highlighting all the major modifications suggested along with the statistical performance in Section “Differential evolution technique for parameter identification”. The results of identification and simulation are illustrated and analysed in Section “Results and analysis”, followed by discussion in Section “Discussion” and concluding remarks in Section “Conclusion”.
The electrical equivalent circuits along with their parameters are referred to as the PV cell and modules to be modelled. For accurate design, simulation and analysis, several reduced and complex models must be proposed. This section presents a brief description of different PV models with related parameters and the objective function formulation for the models.
The single diode-based electrical equivalent circuit of the PV cell model is comprised of a photon-generated current source, an antiparallel diode, a series and a shunt resistor. Figure 1 demonstrates the above model^10,21.
Electrical equivalent circuit of the PVCMSD.
The following equation is derived by application of Kirchoff’s current law(KCL) to Fig. 1:
where I_cpg, I_cd, and I_cp. denote photon-generated current, the current flowing in the diode, and the current flowing in the shunt resistance R_p respectively. At the same time, I, V and R_se signify the PV cell current, the PV cell voltage, and the series resistance respectively.
The current through R_p is expressed as:
where I_s, α, and V_t denote the saturation current of the diode, the diode ideality factor and the thermal voltage respectively. The existing relation can be expressed as follows:
where, T, q and k_bm symbolize the cell/module temperature in K, the electron charge (1.60217 × 10⁻¹⁹) in C, and Boltzmann constant (1.38065 × 10⁻²³) in J/K respectively.
Using Eqs. (1), (2) and (3) the PV cell current is calculated using Eq. (5) as:
The electric equivalent circuit of a PV cell model based on double diode comprises a photon-generated source of current, two diodes antiparallel to the current source, a series and a shunt resistor^14,21. Figure 2 demonstrates the PVCMDD.
Electrical Equivalent circuit of the PVCMDD.
Application of KCL to the Fig. 2 results in the following equation:
where I_cpg denotes photon generated current, I_cd1 denotes the current flowing in diode D₁, I_cd2 denotes the current flowing in diode D₂, I_cp. denotes the current through shunt resistance R_p and I denotes the cell output current. Here, the current in diode D₁ can be expressed as shown next in Eq. (7):
where, I_s1 signifies the saturation current of diode D₁, α₁ signifies the ideality factor of diode D₁. In the same manner, the current in diode D₂ can be expressed as shown next in Eq. (8):
where I_s2 signifies the saturation current of diode D₂ and α₂ signifies the ideality factor of diode D₂. Therefore, the PV cell output current is given as:
The equivalent electric circuit of the PV cell model based on triple diode is comprised of a photon-generated current source, three antiparallel type diodes, a series and a shunt resistor^23,26,27 as demonstrated in Fig. 3.
Electrical Equivalent circuit of the PVCMTD.
Application of KCL to Fig. 3, gives the following equation:
where I_cpg denotes photon generated current, I_cd1 denotes the current flowing in diode D₁, I_cd2 denotes the current flowing in diode D₂, I_cd3 denotes the current flowing in diode D₃, I_cp. denotes the current through shunt resistance R_p and I denote the cell output current. The expressions for currents I_cd1 and I_cd2 are given by Eqs. (7) and (8) respectively in the Sect. 2.2.
In the same manner, the current in diode D₃ can be expressed as shown below in Eq. (11):
where, I_s3 signifies the saturation current of diode D₃ and α₃ signifies the ideality factor of diode D₃ in Fig. 3.
The final PV cell output current is expressed by Eq. (12) as follows:
Solar cells are connected in series or series-parallel combinations to form a PV module. The PV modules in this study are comprised of series combinations of N_s number of solar cells. If the PV module comprises single diode-based PV cells, then the output current of the module is given by Eq. (13) as:
where, V signifies voltage across the PV module. The I_cpm implies the equivalent photon-generated current of the module while I_sm signifies the equivalent saturation current of the diodes in the module, R_sem signifies the equivalent series resistance of the module and R_pm signifies the equivalent shunt resistance of the module. In addition, α_m implies the ideality factor of the module and V_tm implies the thermal voltage of the module. The existing relation can be related between these factors as follows.
The module output current can be finally expressed as
The PV model parameter identification task is transformed into a single-objective optimization problem. This work’s single objective function (OF) is taken as the root mean square error (RMSE) between experimental and calculated (estimated) currents of the PV models under consideration. Therefore, the optimization problem can be formulated as follows:
Minimize
where, M denotes the number of experimental current-voltage data points of the PV model under inspection, k denotes the index of experimental data, ϑ denotes the parameter set of the PV model to be identified and (:{h}_{k}left(vartheta:,V,Iright)) signifies the current error function of the PV model. If the RMSE value is very small, it means that a better set of PV model parameters(ϑ) is identified. The aim is to minimize RMSE by using EDE in order to attain optimal parameters.
From each PV model, the current error function (:{h}_{k}left(vartheta:,V,Iright)) is obtained as follows:
For PVCMSD, ϑ = { I_cpg, I_s, α, R_p, R_se }.
For the PVCMDD, ϑ ={I_cpg, I_s1, I_s2, α₁, α₂, R_p, R_se}.
For PVCMTD, ϑ ={I_cpg, I_s1, I_s2, I_s3, α₁, α₂, α_3,R_p ,R_se},
For PVMM, ϑ = {I_cpm, I_sm, α_m, R_pm, R_sem},
To obtain the estimated (calculated) current I for a PV model, the respective current error function (:{h}_{k}left(vartheta:,V,Iright)=0) is solved using Newton-Raphson’s method. In this method, the new value of the estimated current for a PV model is evaluated iteratively from its older estimate and its derivative, as shown in Eq. (21), until the condition |(:{h}_{k}left(vartheta:,V,Iright))| <10⁻¹⁰ is fulfilled.
Here, Fig. 4 summarises the entire parameter identification process as adopted in the study.
The block-diagram of the parameter identification procedure using EDE.
Here, R.T.C. France silicon solar cells⁸ operating at 1000 W/m² and 33° C and PVM-752-GaAs thin film cell²⁸ operating at 1000 W/m² and 39° C are taken into consideration for the parameter identification. For module parameter identification, polycrystalline modules of Sharp ND-R250A5²¹ and STP6 120/36⁵² and monocrystalline module of STM6 40/36⁵² are considered. The I-V data points of the respective cells and modules retrieved from the experiment are considered for their parameter identification.
In comparison to many other evolutionary-based approaches, the DE algorithm has proven its performance to arrive at optimal results in a wide variety of engineering applications^53,54,55,56. The major factors that attract the DE for its extensive application are it can be applied successfully for multi-modal functions and single/multiple objectives-based problems preventing being trapped at a local minimum.
The overview of the elementary operations of the DE algorithm is segregated into four phases and presented as follows.
The initial population is randomly generated with dimension P X D. Each element x_{i, j} in the initial population is randomly generated and initially subjected to the restrictions within the respective variable lower and upper limits i.e., (x_{j}^{{lo}} leqslant {x_{ij leqslant }}x_{j}^{{up}}). The population matrix with the predefined dimension is generated as follows:
where, i = 1,2,3,…., P referring to the population size and j = 1,2,3,…, D referring to the total number of variables to be optimized. After the initialization phase as discussed, the other three preliminary phases are followed sequentially as mutation, crossover and selection for the DE algorithm.
During each generation ‘g’ and in this phase, a mutant vector (V_{{ij}}^{g}) is generated as presented in Eq. (23) by applying a mutation strategy to the current parent population (X_{{ij}}^{g}).
where (X_{{best}}^{g}) refers to the best vector solution selected according to the fitness value and objective during the generation ‘g’. The indices n₁ and n₂ are mutually exclusive integers in nature. These are selected from the set {1,2,3,4,…., P} subjected to the condition that n₁ ≠ i, n₂ ≠ i. (F_{i}^{g}) refers to the mutation factor that regulates the mutation process. It is selected within interval [0,1]. In the basic DE algorithm, (F_{i}^{g}=F) and it is considered to be a constant value.
The trial vectors (U_{{ij}}^{g}) are generated during the crossover phase considering the mutant vector (V_{{ij}}^{g}) generated in the mutation phase as presented by Eq. (24).
where rand [0,1) refers to any number randomly chosen within the interval [0,1]. It is uniformly generated every time for i and j. The j_rand refers to the randomly chosen integer within the boundary [1, D] for each i. The (CR_{i}^{g}) refers to the crossover rate. It regulates the crossover operation by doing an average fraction of vector components. In this stage according to the crossover factor value, proportionally the mutant vectors are inherited further. (CR_{i}^{g}) is generally chosen from the interval [0,1]. In the basic DE algorithm, (CR_{i}^{g}) = CR and it is taken to be a
constant value.
The selection process is conducted to select the better solution vectors according to the respective fitness value comparison between the (:{X}_{i,j}^{g}) and (:{U}_{i,j}^{g}) vectors. The selection is done according to the comparative fitness values computed from the objective function (:OF) stated in Eq. (16). This operation can be presented as follows in Eq. (25).
where, (:{X}_{i}^{g+1}:)refers to the recently generated population vector and these vectors will be carried for the next generation following similar steps of the process from mutation to crossover to selection phase. Here, (:OFleft({U}_{i,j}^{g}right)) and (:OFleft({X}_{i,j}^{g}right)) refer to the target vector’s and the trial vectors’ objective function values respectively.
But these processes stop when the generation no ‘g’=gen_max. Herein, gen_max implies maximum generation count.
The improper setting of the dominant algorithm factors those having a critical role in the searching performance, may make the DE sensitive to the loss of diversity. This may lead to poor exploration and exploitation abilities⁵⁷. In addition to this inappropriate mutation and crossover strategy may cause saturation in optimal searching due to either over exploration or may cause premature convergence due to overexploitation. Not regulating the control factors according to the existing position, may lead to oscillations near the optimal solution. In this, the various stages are determined and segregated accordingly. The stage-specific strategies of the mutation operation along with the adaptively changing/varying mutation factor and crossover rate are used to fetch the best output from the exploration and exploitation stages. Multiple mutation and crossover strategies can fetch better exploration and exploitation abilities⁵⁸. The overall movement towards the optimum and best position in the generation ‘g’ and is given by
The value of (:{M}_{g}) can be normalized according to the maximum (:{M}_{gleft(maxright)}) and minimum (:{M}_{gleft(minright)}) values in the last five generations with respect to the present generation ‘g’ as follows:
The search stages are determined according to the following strategy:
The reason for the above strategy formulation is due to the large initial solution space and step size, which initially (:stackrel{-}{{M}_{g}}) will be larger than the average value of (:{M}_{g}) in the last five consecutive generations. After a few generations pass, due to short step size and narrow solution space, (:stackrel{-}{{M}_{g}}) there will have lesser value or it will keep constant due to saturation. Secondly, it is justified to segregate different stages of solution space to formulate the search strategy. As the solution is nearer to the optimal position, there will be no transfer possibility at that stage. This avoids the possibility of oscillation near the optimal value and leads to rapid convergence^58,59. The mutant individual (:{V}_{i,j}^{g}) is computed by adopting different strategies for both the exploration and exploitation stages separately as follows:
The reason behind such a formulation is to adopt a larger step size to bring more diversity in the search that leads to a better exploration.
The mutation factor (:{F}_{i}^{g}) and the crossover rate (:{CR}_{i}^{g}) are adaptively computed during various stages of search in generation ‘g’ according to the strategy presented in^59,60.
The idea behind such a formulation is that the mutation factor (:{F}_{i}^{g}) values are high initially as larger step size is required during the exploration stage of search while lower values of crossover rate (:{CR}_{i}^{g}) are needed initially to avoid confinement at local best values. But a smaller step size is required during the exploitation stage of the search, hence lesser values of (:{F}_{i}^{g}) are needed, while higher crossover rates (:{CR}_{i}^{g}) are needed to speed-up the convergence process. The flow-chart of suggested EDE technique is illustrated next in Fig. 5.
The flow-chart of EDE.
Here, gen_max = 1000, P = 10*D. The phases in basic DE also take place sequentially in EDE considering the stage-based modifications as stated in Eqs. (29)–(31). The termination criterion is the same as in basic DE. The ranges for the identification of PV cell and module parameters are presented in Tables 1 and 2 respectively.
In order to justify the effectiveness of the proposed EDE, some benchmark test functions are considered for testing it and comparing its performance with the EEFO, MGO, AOA, FBIA and DE techniques. Here, Table 3 presents the description of the Benchmark test functions taken for the analysis.
The statistical performance analysis of the EDE technique from benchmark function tests over 30 independent runs is compared to those with EEFO, MGO, AOA, FBIA and DE techniques for a population size P = 60 and 100 generations. The minimum, maximum, mean and standard deviations for each function and each algorithm are provided in Table 4. From these values it is clear that EDE presents better performance.
From the results in Table 4, it is quite obvious that the proposed EDE performs much better in the Benchmark tests as compared to the recently proposed EEFO, MGO, AOA, FBIA and the original DE algorithms.
Also, the non-parametric method of Wilcoxon signed rank test is applied to (EDE, EEFO), (EDE, MGO), (EDE, AOA), (EDE, FBIA) and (EDE, DE) pairs for comparison and resulting p-values and h-values are determined. The comparative results from this test are presented in Table 5.
In Table 5, it is observed from the p-values and h-values obtained by pairwise comparisons of the algorithms in the Wilcoxon signed rank test that there is a significant difference in the performance of the EDE when compared to other techniques, including EEFO, MGO, AOA, FBIA, and original DE. As, the p-values are less than 0.05, it reflects that EDE is clearly the winner amongst the six.
Moreover, the updated convergence graphs of EDE, EEFO, MGO, FBIA and DE for the benchmark functions F1, F2, F3 and F4 are illustrated in the Fig. 6(a), (b), (c) and (d) respectively. It can be observed from Fig. 6 that EDE converges to optimal values at a faster rate as compared to EEFO, MGO, AOA, FBIA and original DE.
The convergence graphs of EDE, EEFO, MGO, FBIA and DE for different benchmark functions.
The identified parameters and RMSE computed by the EDE proposed technique are compared with the respective values obtained by other techniques to justify the efficiency of the proposed EDE approach. All the simulation studies are carried out in MATLAB R2018b software.
This section presents the parameter identification results for RTC France PVCMSD, PVCMDD and PVCMTD from its experimental I-V data⁸.
Here, the five parameters of the PVCMSD are identified. Around 60 independent runs are conducted and the optimal parameters for the solar PV model under consideration are identified. The parameters obtained by the EDE approach are compared to other techniques. The comparative results are tabulated in Table 6. It can be concluded that the parameters identified by the EDE technique result in an RMSE of 7.730062e-4, which is much lesser than the RMSE observed in other techniques such as ABSO, BMO, MPCOA and OLGBO.
The obtained I-V characteristic of the PVCMSD using the identified parameters is shown in Fig. 7, along with the I-V characteristic monitored by using the experimental data. It is observed that both characteristics match each other closely, reflecting the proposed technique’s accuracy.
The experimental I-V characteristics and the I-V characteristics with identified parameters for PVCMSD.
In Fig. 8, the comparison between the graph of Individual absolute errors (IAE) in current using EDE technique and other techniques is reflected for each experimental data. The computed IAE values with EDE technique are lesser than the IAE values with other techniques at all indices of experimental data. Hence, the result reveals that the proposed technique’s accuracy is better than other techniques.
Comparison of IAE result with proposed and other techniques for PVCMSD.
The section enumerates all the seven identified parameters of the PVCMDD. The optimal set of identified parameters is chosen from the results obtained by 60 independent runs. The parameters identified by the EDE technique and other techniques considered for comparison are summarised in Table 7.
The obtained RMSE in the identification of parameters through the application of the EDE approach is 7.419648e-4. It is lesser than the RMSE values in comparison to the obtained values by the application of ABSO, BMO, OLGBO and MPCOA. The I-V curves with identified parameters match exactly with the I-V curves obtained experimentally to each other very closely, as demonstrated in Fig. 9. This indicates that the EDE proposed procedure is capable of identifying the optimal parameters for the solar PV cell accurately. In addition to this, it can reproduce exactly the real I-V characteristics of the solar PV cell similar to experimental results, as depicted in Fig. 9.
The experimental I-V characteristics and the I-V characteristics with identified parameters for PVCMDD.
The IAE values acquired with the proposed technique are evaluated in comparison with the IAE values obtained by applying the other techniques based on the experimental data obtained. These are illustrated in Fig. 10. The IAE values at the 2nd, 3rd, 5th, 6th, 8th, 9th, 10th and 15th experimental data indices with EDE technique are slightly higher than the IAE with other techniques based on the 26 experiment data points undertaken in this study. On the other hand, the IAE values with the EDE technique are considerably lesser for the remaining data indices in comparison to other techniques considered in this study. The overall performance is enhanced in the case of the proposed EDE approach.
Comparison of IAE result with proposed and other techniques for PVCMDD.
The nine parameters pertaining to the PVCMTD are identified in the present case. The optimal set of final identified parameters is attained from 60 independent runs. Table 8 enumerates the parameters identified by the EDE proposed technique and other techniques to present a comparative view.
It is noticed that the RMSE involved in the EDE technique is 7.33228e-4. It is lesser than the RMSE value observed in IGWO, OLGBO, SC-GBO and CTSO. Figure 11 illustrates the experimental I-V and I-V curves using identified parameters that almost match closely. From Fig. 11, it is noticeable that the EDE proposed algorithm can precisely identify the parameters of the solar PV cell accurately and replicate the real I-V characteristics.
The experimental I-V characteristics and the I-V characteristics with identified parameters for PVCMTD.
In Fig. 12, the IAE values computed using the proposed technique are evaluated by comparison with the IAE values found from the other techniques for each experimental data. The proposed technique results in slightly higher IAE values in the 1st, 14th and 19th experimental data compared to the IAE values with the other techniques. However, the IAE values with the proposed technique at the remaining data points are significantly lesser in comparison to those with other techniques considered in this study. From this, it can be inferred that the identification performance is enhanced substantially by applying the proposed EDE technique.
Comparison of IAE result with proposed and other techniques for PVCMTD.
This section presents the results of identifying parameters for PVM-752-GaAs thin film PVCMSD, PVCMDD and PVCMTD from its experimental I-V data²⁸.
Here, the five parameters of the PVCMSD are identified. The optimal parameters for the solar PV model under consideration are chosen from conducting the 60 independent runs. Table 9 illustrates the comparison of the parameters identified by the EDE optimization technique and other techniques. It is detected that the parameters identified by the EDE technique result in an RMSE of 1.59256e-4. It is the least compared to the RMSE, resulting from the other identification techniques such as SDOA, TGA, SDO and HSDA.
The I-V characteristic of the PVCMSD using the identified parameters is showcased in Fig. 13, in addition to the I-V characteristic based on the experimental data. It is noticed that both characteristics match each other very closely. It shows the accuracy of the EDE technique.
The experimental I-V characteristics and the I-V characteristics with identified parameters for PVCMSD.
The IAE values acquired with the EDE technique are analysed in comparison to the IAE values using other techniques based on each experimental current data in Fig. 14. The IAE in current values at all the experimental data indices with the EDE technique are much lower than those IAE values with other techniques.
Comparison of IAE result with proposed and other techniques for PVCMSD.
The seven parameters of the PVCMDD are identified here. The optimal set of identified parameters is selected from 60 independent runs. The PVCMDD parameters identified using the EDE technique and other techniques are presented in Table 10. The RMSE in identifying parameters by the EDE optimization technique is 1.408989e-4. This value is relatively lesser than the RMSE values obtained in SDOA, TGA, SDO and HSDA.
The I-V curve from the experimental data and the I-V curve with identified parameters match each other closely, as shown in Fig. 15. The proposed EDE can identify the optimal parameters of the solar PV cell accurately. Moreover, the real I-V characteristics can also be reproduced by the EDE optimization technique.
The experimental I-V characteristics and the I-V characteristics with identified parameters for PVCMDD.
In Fig. 16, the computed IAE values by the proposed EDE technique are evaluated by comparing them with the obtained IAE values from other techniques considered in each experimental data point. The IAE values in the case of all the experimental data indices in comparison to the proposed EDE technique are much lesser than those IAE with other techniques. Hence, the proposed EDE technique can enhance solar PV parameter identification performance.
Comparison of IAE result with proposed and other techniques for PVCMDD.
The nine parameters pertaining to the PVCMTD are identified by utilizing the I-V experimental data of the PVM-752-GaAs PV cell. The optimal set of identified parameters is selected from the 60 independent runs. Table 11 summarises the parameters identified by the proposed technique and other techniques considered for comparison.
The RMSE in the estimation of parameters by the EDE approach is 1.30181e-4. It is smaller than the RMSE value estimated by the CNSMA, SDOA, TGA and MPA. The experiment and identification-based I-V curves obtained are very similar and substantially close. This is evident from Fig. 17. It can be concluded that the EDE algorithm can identify the optimal system parameters for the solar PV cell precisely. This method can be used to accurately reproduce the real-time I-V characteristics.
The I-V characteristics based on experimental data and the I-V characteristics with identified parameters for PVCMTD.
In Fig. 18, the comparison between the IAE values based on the EDE technique with the IAE values computed using other techniques considered in this study are illustrated for each experimental data. The IAE values with the EDE approach are observed to be substantially lesser than the IAE values computed using the other techniques. Hence, the proposed technique showcases improved accuracy and overall performance.
Comparison of IAE result with proposed and other techniques for PVCMTD.
The five parameters of the above polycrystalline PVMM are identified in the present section using its experimental I-V data²¹. Here, 60 independent runs are conducted to identify the optimal set of parameters for the module considered in this study. The comparison of the parameters extracted by the EDE technique and other techniques is made in Table 12. It is noticed that, the parameters identified by the EDE technique result in an RMSE of 7.697716e-3. It is the least compared to the RMSE resulting from other identification techniques such as EDE, GAMS, HSDA and GCPSO.
The experimental I-V and I-V curves with identified parameters are similar and close enough, as shown in Fig. 19. This indicates that the EDE algorithm can accurately identify the optimal PV cell parameters of the ND-R250A5 PVMM. This technique may be used to reproduce the real-time I-V characteristics for other applications and modelling purposes.
The experimental I-V and I-V characteristics with identified parameters for polycrystalline PVMM.
In Fig. 20, the IAE values acquired using the EDE technique are evaluated by comparing them to the IAE values computed using other recent techniques for each experimental data. The IAE values at the 20th and 21st indices with the EDE technique are a little higher than the IAE values with the other techniques out of the 36 experimental data indices. Nevertheless, at the remaining indices of the experimental data points, the IAE values resulting from the EDE approach are significantly lesser in comparison to those with other techniques. So, EDE can improve PV parameter identification performance with accuracy and robustness.
Comparison of IAE result with proposed and other techniques for polycrystalline PVMM.
This section identifies five parameters of the above monocrystalline PVMM from its experimental I-V data⁵². The optimal parameters set for the above module are selected from 60 independent runs. The comparison of the PV cell parameters identified by the EDE optimization approach and other techniques is tabulated in Table 13. The related system parameters identified using the EDE technique result in an RMSE of 1.772095e-3. It is the least as compared to the RMSE resulting from other identification techniques such as IMCSA, New Hybrid, Graphical and the technique implemented by Tong and Pora⁵².
The I-V characteristic of the STM6 40/36 PVMM using the identified parameters and the experimental I-V characteristic is shown in Fig. 21. It is noticed that both characteristics match each other very closely. It shows the accuracy of the proposed EDE technique.
The experimental I-V characteristics and the I-V characteristics with identified parameters for polycrystalline PVMM.
The IAE values attained with the EDE technique are evaluated by comparing the IAE values with the other techniques for each experimental current data and is illustrated in Fig. 22. It is found that the IAE values with the proposed EDE technique at the 3rd ,4th ,5th ,13th and 14th experimental data indices are slightly higher than those with the other techniques. However, for the remaining experimental data indices, the IAE values with the EDE technique are much lesser than the IAE with the other techniques. So, the proposed technique presents a better accuracy than the other mentioned techniques.
Comparison of IAE result with proposed and other techniques for monocrystalline PVMM.
The five parameters of the above polycrystalline PVMM are identified in the present section using its experimental I-V data⁵². The optimal parameters set for the above module are selected out of 60 independent runs. The comparison has been done between the parameters extracted by the EDE technique and other techniques considered and is tabulated in Table 14. It is noticed that the parameters identified by the proposed technique result in an RMSE of 1.224258e-2. This value is the lowest compared to the RMSE, resulting from other identification techniques such as IMCSA, CIABC, New Hybrid and the technique developed by Tong and Pora⁵².
The experimental I-V curve and I-V curve with identified parameters substantially close and almost overlap each other, as revealed by Fig. 23. Therefore, the EDE algorithm can find the optimal internal parameters of the STP6 120/36 PVMM accurately. Also, it may be used to regenerate the real-time I-V characteristics for other applications and modelling.
The I-V characteristic from the experiment and the I-V characteristic with identified parameters for polycrystalline PVMM.
In Fig. 24, the IAE values computed using the EDE technique are compared with the IAE values acquired from other techniques for each experimental data. With the proposed EDE technique, the IAE values at the 1st and 22nd experimental data indices are slightly higher than those IAE values with other techniques. Nevertheless, at the remaining data indices, the IAE values with EDE technique are substantially lesser in comparison to those in the other mentioned techniques. Hence, the overall performance is improved by the EDE technique.
Comparison of IAE result with proposed and other techniques for polycrystalline PVMM.
The following critical analysis can be presented from the research work carried out in this article:
PV parameter identification has been done extensively by various methods in recent times. However, still it is an open forum for research due to the environmental factor impacts, system parameter variation due to internal operational factors and the degradation of parameter due to long-time operation/ageing. This makes PV parameter estimation more complex to solve for accurate results.
Hybrid techniques have recently been applied for PV parameter identification due to their mutually supporting ability and nullifying individual limitation features.
In this study, a single objective formulation is done. The PV parameter identification can be also formulated as a multi-objective optimization problem by considering various errors such as squared power errors at open circuit, short -circuit and at MPP etc.
With adaptively varying control factors or applying new strategy to compute the control factors of an optimization technique generally improves the searching ability both during exploration and exploitation stages. This research is one of the attempts in this direction to propose a novel algorithm by adaptively varying the mutation factor and crossover rates of basic DE technique. These modifications, improve the quality of the optimal solution, which can be inferred from the least RMSE values obtained in parameter identification; thereby presenting higher accuracy as compared to other well-established techniques.
The article proposes an EDE algorithm for the parameter identification of the equivalent circuits of the PVCMSD, PVCMDD, and PVCMTD of RTC France and PVM-752-Ga-As, two different types of solar cells. Also, the parameters for the equivalent circuits of PVMM for three different modules are identified, i.e., ND-R250A5, STM6 40/36 and STP6 120/36, by applying the proposed EDE technique. The critical inferences that can be pointed out from the obtained results are (1) The proposed approach EDE results in better searching during exploration and exploitation stages. The major reason is due to dynamically varying mutation and crossover factors that regulate the searching strategy in a better way; (2) The IAE graphs and RMSE values validate the proposed technique comparatively concerning other techniques. The results reflect that the proposed approach outperforms many other techniques in terms of accuracy and efficiency when applied to the cells and modules; (3) Considering the identified parameters, the simulated I-V characteristics closely match the experimental I-V characteristics of tested PV cells and modules. Graphical demonstrations and tabular results justify the proposed technique’s superior performance. The proposed technique can be accepted as a promising method for the identification of different PV cells and module parameters efficiently and accurately.
In real-time establishment and operation, many environmental factors particularly temperature and irradiance variations, dust, snowfall and shading act adversely on the power produced by the PV arrays. These factors need to be focussed in the study as they affect the performance of the identification algorithms. Future research could be directed towards developing an improved and hybrid technique for parameter identification with better exploration ability, lesser complexity and faster convergence. Finally, the objective functions’ nature and formulation significantly impact the technique’s performance to efficiently and accurately identify the parameters. The formulated objective function may be designed as a function of the difference between the experimental power and the power with identified parameters, i.e., root means square error in power. Therefore, this issue needs further research for the optimal solution.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Jordehi, A. R. Parameter estimation of solar photovoltaic (PV) cells: a review, renew. Sustain. Energy Rev. 61, 354–371 (2016).
Article  MATH  Google Scholar 
Abbassi, R., Abbassi, A., Jemli, M. & Chebbi, S. Identification of unknown parameters of solar cell models: a comprehensive overview of available approaches, renew. Sustain. Energy Rev. 90, 453–474 (2018).
Article  MATH  Google Scholar 
Toledo, F. J. & Blanes, J. M. Analytical and quasi-explicit four arbitrary point method for extraction of solar cell single-diode model parameters. Renew. Energy. 92, 346–356 (2016).
Article  MATH  Google Scholar 
Cubas, J., Pindado, S. & Victoria, M. On the analytical approach for modeling photovoltaic systems behavior. J. Power Sources. 247, 467–474 (2014).
Article  MATH  CAS  Google Scholar 
Jain, A., Sharma, S. & Kapoor, A. Solar cell array parameters using Lambert W-function. Sol Energy Mater. Sol Cells. 90(1), 25–31 (2006).
Article  MATH  CAS  Google Scholar 
Ortiz-Conde, A., Sánchez, F. J. G. & Muci, J. New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I–V characteristics. Sol Energy Mater. Sol Cells 90(3), 352–361 (2006).
Article  MATH  CAS  Google Scholar 
Bencherif, M. & Brahmi, N. Solar cell parameter identification using the three main points of the current–voltage characteristic. Int. J. Ambient Energy. 43(1), 3064–3084 (2022).
Article  MATH  CAS  Google Scholar 
Easwarakhanthan, T., Bottin, J., Bouhouch, I. & Boutrit, C. Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int. J. Sol Energy. 4(1), 1–12 (1986).
Article  CAS  Google Scholar 
Liu, S. & Dougal, R. A. Dynamic multiphysics model for solar array. IEEE Trans. Energy Convers. 17(2), 285–294 (2002).
Article  ADS  MATH  Google Scholar 
Villalva, M. G., Gazoli, J. R. & Filho, E. R. Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24(5), 1198–1208 (2009).
Article  ADS  MATH  Google Scholar 
Abdulrazzaq, A. K., Bognár, G. & Plesz, B. Accurate method for PV solar cells and modules parameters extraction using I–V curves. J. King Saud University-Engineering Sci. 34(1), 46–56 (2022).
Article  Google Scholar 
Zagrouba, M., Sellami, A., Bouaïcha, M. & Ksouri, M. Identification of PV solar cells and modules parameters using the genetic algorithms: application to maximum power extraction. Sol Energy 84(5), 860–866 (2010).
Article  ADS  CAS  Google Scholar 
Askarzadeh, A. & Rezazadeh, A. Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Appl. Energy. 102, 943–949 (2013).
Article  ADS  MATH  Google Scholar 
AlHajri, M. F., El-Naggar, K. M., AlRashidi, M. R. & Al-Othman, A. K. Optimal extraction of Solar Cell parameters using pattern search, renew. Energy 44, 238–245 (2012).
Google Scholar 
Soon, J. J. & Low, K. S. Photovoltaic model identification using particle swarm optimization with inverse barrier constraint. IEEE Trans. Power Electron. 27(9), 3975–3983 (2012).
Article  ADS  MATH  Google Scholar 
Askarzadeh, A. & Rezazadeh, A. Extraction of maximum power point in solar cells using bird mating optimizer-based parameters identification approach. Sol. Energy 90, 123–133 (2013).
Article  ADS  MATH  Google Scholar 
Yuan, X., Xiang, Y. & He, Y. Parameter extraction of solar cell models using mutative-scale parallel chaos optimization algorithm. Sol Energy. 108, 238–251 (2014).
Article  ADS  MATH  Google Scholar 
Yu, S. et al. Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization. Optik 252, 168513 (2022).
Article  Google Scholar 
Saadaoui, D., Elyaqouti, M., Assalaou, K. & Lidaighbi, S. Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation. Energy Convers. Manag. 12, 100129 (2021).
MATH  CAS  Google Scholar 
Khelifa, M. A., Lekouaghet, B. & Boukabou, A. Symmetric chaotic gradient-based optimizer algorithm for efficient estimation of PV parameters. Optik 259, 168873 (2022).
Article  MATH  Google Scholar 
Nunes, H. G. G., Pombo, J. A. N., Mariano, S. J. P. S., Calado, M. R. A. & De Souza, J. F. A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization. Appl. Energy. 211, 774–791 (2018).
Article  ADS  Google Scholar 
Gnetchejo, P. J. et al. Enhanced vibrating particles system algorithm for parameters estimation of photovoltaic system. J. Power Energy Eng. 7(8), 1 (2019).
Article  Google Scholar 
Ramadan, A. E., Kamel, S., Khurshaid, T., Oh, S. R. & Rhee, S. B. Parameter extraction of three diode solar photovoltaic model using improved grey wolf optimizer. Sustainability 13(12), p6963 (2021).
Article  Google Scholar 
Gnetchejo, P. J. et al. Important notes on parameter estimation of solar photovoltaic cell. Energy Convers. Manag. 197, 111870 (2019).
Article  MATH  Google Scholar 
Kumar, C. & Mary, D. M. A novel chaotic-driven tuna swarm optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules. Optik 264, 169379 (2022).
Article  MATH  Google Scholar 
Shaheen, A. M., El-Seheimy, R. A., Xiong, G., Elattar, E. & Ginidi, A. R. Parameter identification of solar photovoltaic cell and module models via supply demand optimizer. Ain Shams Eng. J. 13(4), 101705 (2022).
Article  Google Scholar 
Diab, A. A. Z. et al. Tree growth based optimization algorithm for parameter extraction of different models of photovoltaic cells and modules. IEEE Access. 8, 119668–119687 (2020).
Article  MATH  Google Scholar 
Xiong, G., Zhang, J., Shi, D. & Yuan, X. Application of supply-demand-based optimization for parameter extraction of solar photovoltaic models. Complexity 2019, 1–22 (2019).
Article  MATH  Google Scholar 
Cotfas, D. T., Deaconu, A. M. & Cotfas, P. A. Hybrid successive discretisation algorithm used to calculate parameters of the photovoltaic cells and panels for existing datasets. IET Renew. Power Gener. 15(15), 3661–3687 (2021).
Article  MATH  Google Scholar 
Jiang, L. L., Maskell, D. L. & Patra, J. C. Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm. Appl. Energy. 112, 185–193 (2013).
Article  ADS  MATH  Google Scholar 
Rezk, H. & Abdelkareem, M. A. Optimal parameter identification of triple diode model for solar photovoltaic panel and cells. Energy Rep. 8, 1179–1188 (2022).
Article  MATH  Google Scholar 
Liu, Y. et al. Boosting slime mould algorithm for parameter identification of photovoltaic models. Energy 234, 121164 (2021).
Article  CAS  Google Scholar 
Lidaighbi, S. et al. A new hybrid method to estimate the single-diode model parameters of solar photovoltaic panel. Energy Convers. Manag : X. 15, 100234 (2022).
MATH  Google Scholar 
Oliva, D. et al. A chaotic improved artificial bee colony for parameter estimation of photovoltaic cells. Energies 10(7), 865 (2017).
Article  MATH  Google Scholar 
Kang, T., Yao, J., Jin, M., Yang, S. & Duong, T. A novel improved cuckoo search algorithm for parameter estimation of photovoltaic (PV) models. Energies 11(5), 1060 (2018).
Article  MATH  Google Scholar 
Premkumar, M., Babu, T. S., Umashankar, S. & Sowmya, R. A new metaphor-less algorithms for the photovoltaic cell parameter estimation. Optik 208, 164559 (2020).
Article  CAS  Google Scholar 
Kumar, C., Raj, T. D., Premkumar, M. & Raj, T. D. A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik 223, 165277 (2020).
Article  MATH  CAS  Google Scholar 
Izci, D., Ekinci, S., Abualigah, L., Salman, M. & Rashdan, M. Parameter extraction of photovoltaic cell models using electric eel foraging optimizer. Front. Energy Res. 12, 1407125 (2024).
Article  Google Scholar 
Izci, D., Ekinci, S. & Hussien, A. G. Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm. Sci. Rep. 14, 7945 (2024).
Article  ADS  PubMed  PubMed Central  MATH  CAS  Google Scholar 
Abbassi, R. et al. An accurate metaheuristic mountain gazelle optimizer for parameter estimation of single-and double-diode photovoltaic cell models. Mathematics 11(22), 4565 (2023).
Article  MATH  Google Scholar 
Abbassi, A. et al. Improved arithmetic optimization algorithm for parameters extraction of photovoltaic solar cell single-diode model. Arab. J. Sci. Eng. 47(8), 10435–10451 (2022).
Article  MATH  CAS  Google Scholar 
Shaheen, A. M., Ginidi, A. R., El-Sehiemy, R. A. & Ghoneim, S. S. A forensic-based investigation algorithm for parameter extraction of solar cell models. IEEE Access. 9, 1–20 (2020).
Article  MATH  Google Scholar 
Abd, E. et al. Optimal parameters extracting fuel cell based gorilla troops optimizer. Fuel 332 : 126162. (2023).
Manoharan, P. et al. Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm. Sci. Rep. 14(1), 20979 (2024).
Article  MathSciNet  PubMed  PubMed Central  MATH  CAS  Google Scholar 
Premkumar, M. et al. A reliable optimization framework for parameter identification of single-diode solar photovoltaic model using weighted velocity‐guided grey wolf optimization algorithm and Lambert‐W function. IET Renew. Power Gener. 17(11), 2711–2732 (2023).
Article  MATH  Google Scholar 
Beigi, A. M. & Maroosi, A. Parameter identification for solar cells and module using a hybrid Firefly and Pattern Search algorithms. Sol Energy 171, 435–446 (2018).
Article  ADS  MATH  Google Scholar 
Ram, J. P., Babu, T. S., Dragicevic, T. & Rajasekar, N. A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation. Energy Convers. Manag. 135, 463–476 (2017).
Article  ADS  MATH  Google Scholar 
Nunes, H. G. G., Pombo, J. A. N., Bento, P. M. R., Mariano, S. J. P. S. & Calado, M. R. A. Collaborative swarm intelligence to estimate PV parameters. Energy Convers. Manag. 185, 866–890 (2019).
Article  ADS  Google Scholar 
Ekinci, S., Izci, D. & Hussien, A. G. Comparative analysis of the hybrid gazelle-nelder–mead algorithm for parameter extraction and optimization of solar photovoltaic systems. IET Renew. Power Gener. 18(6), 959–978 (2024).
Article  Google Scholar 
Izci, D. et al. A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models. Electr. Eng. 1–21 (2024).
Langdon, W. B. & Poli, R. Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans. Evol. Comput. 11(5), 561–578 (2007).
Article  MATH  Google Scholar 
Tong, N. T. & Pora, W. A parameter extraction technique exploiting intrinsic properties of solar cells. Appl. Energy 176, 104–115 (2016).
Article  ADS  MATH  Google Scholar 
Fakhouri, H. N. et al. Hybrid four Vector Intelligent Metaheuristic with Differential Evolution for Structural single-objective. Eng. Optim. Algorithms 17(9), 417 (2024).
MATH  Google Scholar 
Zhang, X., Zhong, C. & Abualigah, L. Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms. Soft. Comput. 27(7), 3921–3939 (2023).
Article  PubMed  MATH  Google Scholar 
Chakraborty, S. et al. Differential evolution and its applications in image processing problems: a comprehensive review. Arch. Comput. Methods Eng. 30(2), 985–1040 (2023).
Article  PubMed  MATH  Google Scholar 
Chauhan, S., Govind, V., Kumar, A. & Laith, A. Conglomeration of reptile search algorithm and differential evolution algorithm for optimal designing of FIR filter. Circuits Syst. Signal Process. 42(5), 2986–3007 (2023).
Price, K. V. Differential evolution, in (eds Zelinka, I., Snášel, V. & Abraham, A.) Handbook of Optimization: from Classical to Modern Approach 187–214 (Springer, 2013).
Chapter  MATH  Google Scholar 
Wang, L., Zhou, X., Xie, T., Liu, J. & Zhang, G. Adaptive differential evolution with information entropy-based mutation strategy. IEEE Access. 9, 146783–146796 (2021).
Article  MATH  Google Scholar 
Yu, W. J. et al. Differential evolution with two-level parameter adaptation. IEEE Trans. Cybern. 44(7), 1080–1099 (2013).
Li, Y., Wang, S. & Yang, B. An improved differential evolution algorithm with dual mutation strategies collaboration. Expert Syst. Appl. 153, 113451 (2020).
Article  MATH  Google Scholar 
Download references
This article has been produced with the financial support of the European Union under the REFRESH – Research Excellence For Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition and paper was supported by the following project TN02000025 National Centre for Energy II. The authors would like to express their sincere gratitude to Stanislav Misak for his exceptional supervision, project administration, and overall guidance throughout the course of this project. His expertise and support were instrumental to its success.
Department of Electrical Engineering, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India
Shubhranshu Mohan Parida, Vivekananda Pattanaik, Subhasis Panda & Binod Kumar Sahu
Department of Electrical Engineering, Synergy Institute of Technology, Bhubaneswar, India
Vivekananda Pattanaik
Department of Electrical Engineering, Srinix College of Engineering, Balasore, India
Subhasis Panda
Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India
Pravat Kumar Rout
Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India
Mohit Bajaj
Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
Mohit Bajaj
College of Engineering, University of Business and Technology, Jeddah, 21448, Saudi Arabia
Mohit Bajaj
ENET Centre, VSB—Technical University of Ostrava, Ostrava, 708 00, Czech Republic
Vojtech Blazek & Lukas Prokop
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Shubhranshu Mohan Parida, Vivekananda Pattanaik, Subhasis Panda: Conceptualization, Methodology, Software, Visualization, Investigation, Writing- Original draft preparation. Pravat Kumar Rout, Binod Kumar Sahu: Data curation, Validation, Supervision, Resources, Writing – Review & Editing. Mohit Bajaj, Lukas Prokop, Vojtech Blazek: Project administration, Supervision, Resources, Writing – Review & Editing.
Correspondence to Mohit Bajaj.
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-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
Parida, S.M., Pattanaik, V., Panda, S. et al. Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique. Sci Rep 15, 2124 (2025). https://doi.org/10.1038/s41598-025-85115-x
Download citation
Received: 29 May 2024
Accepted: 01 January 2025
Published: 16 January 2025
Version of record: 16 January 2025
DOI: https://doi.org/10.1038/s41598-025-85115-x
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.