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Scientific Reports volume 15, Article number: 36618 (2025) Cite this article
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This study investigates the influence of magnesium (Mg) doping on ZnO thin films prepared through spin coating to enhance their efficiency and stability in perovskite solar cells (PSCs). The incorporation of Mg significantly enhanced charge transport, reduced recombination losses, and enhanced overall device stability. The structural, optical, morphological, and electrical properties were investigated using UV-Vis spectroscopy, field-emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), and Hall Effect measurement. SCAPS-1D simulation software was utilized to study the performance of the solar cell under different Mg doping concentrations in order to determine the optimum conditions for the maximum power conversion efficiency (PCE). Simulation findings exhibit adequate utility regarding power conversion efficiency (PCE) gain of around 21.89% with optimized Mg doping, which is equivalent to undoped ZnO film performance. While the PEC performance of the doped ZnO is comparable to that of its undoped counterpart, this study reveals notable improvements in optical tunability, charge transport properties, and, in simulations, reduced defect-related trap densities that suggest more favorable band alignment. Simulation results show enhanced PCE of 21.89% under optimal Mg doping compared to undoped ZnO, along with substantial band alignment adjustments, optical tunability, and charge transport. While efficiency enhancements are marginal, these developments signify future possibilities toward enhanced device longevity. Results displayed are SCAPS-1D simulation-based, and experimental validation is required to determine device-level stability and performance. It should be noted that any additional device fabrication or physical characterization are not possible at this time; hence, conclusions are limited to the simulation results and film-level characterizations given in this document.
Perovskite solar cells (PSCs) have emerged as a promising alternative to traditional silicon-based photovoltaics, achieving notable efficiency improvements in a relatively short period^1,2,3,4,5. Their solution-processable nature, high absorption coefficients, and adjustable bandgap offer significant advantages in manufacturing flexibility and potential cost reduction^6,7,8,9. However, the long-term stability of PSCs remains a critical challenge that must be addressed before they can be widely adopted commercially^10,11,12. To overcome this hurdle, researchers are actively exploring various material and structural modifications to enhance both performance and durability. A key focus in improving PSC performance is optimizing electron transport layers (ETLs), with ZnO thin films being a preferred choice due to their excellent electrical conductivity and high optical transparency^13,14,15,16. Nonetheless, pure ZnO is characterized by high surface defects, which can lead to increased charge recombination and reduced overall efficiency. To address this limitation, researchers have investigated doping of ZnO with various elements, such as magnesium (Mg)^17,18, and gallium (Ga)¹⁹,  as well as other metallic elements^20,21,22. This approach has shown potential in enhancing the electronic properties of ZnO, modifying its band structure, and improving stability. Magnesium (Mg²⁺) is a particularly promising dopant because its ionic radius (0.72 Å) is very similar to Zn²⁺ (0.74 Å) and would likely substitute into the ZnO lattice with minimal distortion. The incorporation of Mg decreases the ZnO bandgap, passivates oxygen vacancy defects, and enhances UV stability²³. Several reports indicate that Mg-doped ZnO can tune the bandgap from ~ 3.25 eV (undoped) to > 3.4 eV depending on concentration, which also improves transparency and reduces non-radiative recombination^24,25. Even more exciting, pioneering work^26,27 has shown that dopant engineering in ZnO has a major impact on perovskite solar cell efficiency and stability, providing additional motivation for this work. By fine-tuning the composition and properties of the ETL through doping, scientists aim to develop more efficient and durable PSCs, thereby facilitating their integration into next-generation solar cell technologies.
While ZnO-based electron transport layers (ETLs) offer several advantages, their widespread use in perovskite solar cells (PSCs) is hindered by challenges such as limited chemical stability and suboptimal charge extraction. Magnesium (Mg) doping can effectively address these issues by increasing the bandgap, enhancing charge carrier mobility, and reducing recombination losses^28,29,30. Although Mg-doped ZnO thin films have been explored to improve PSC performance, the specific combination of spin coating as a fabrication method, with an emphasis on both efficiency and stability, is less frequently highlighted in this field. Recent studies have focused on the structural and optical properties of Mg-doped ZnO thin films, which are crucial for PSC applications. For example, research on Mg₀.₂₉Zn₀.₇₁O/ZnO bilayers has shown that annealing induces Mg diffusion across the interface, significantly altering the photoluminescence spectra³¹. At higher annealing temperatures, this diffusion leads to a transition from discrete near-band-edge UV luminescence peaks to a single peak, indicating a fully intermixed layer³². Mg incorporation reduces surface roughness and modifies nonlinear optical properties, such as second and third harmonic generation, as demonstrated by another study on spray pyrolyzed Mg: ZnO thin films, suggesting potential for optoelectronic applications. Investigations into Zn₁₋ₓMgₓO nanocrystals synthesized using low-temperature techniques have also revealed that increasing the Mg content results in a bandgap increase and lattice compaction up to 4 eV for x = 0.17, along with notable changes in photoluminescence behavior^33,34,35,36. These studies highlight the importance of Mg doping in modifying the optical and structural properties of ZnO thin films, which can be leveraged to enhance the stability and performance of PSCs.
This study explores the ideal concentration of magnesium (Mg) doping to improve the performance of zinc oxide (ZnO) in perovskite solar cell (PSC) applications. The research uses the spin-coating method to fabricate and characterize the physical and opto-electronic properties of magnesium-doped zinc oxide (MZO) thin films. Following this, the photovoltaic performance of perovskite solar cells is assessed through SCAPS-1D simulations. SCAPS-1D has been utilized widely for simulating perovskite solar cells by solving the Poisson and continuity equations in order to predict device performance parameters, including J–V characteristics, band alignment, and interfacial recombination effects³⁷.
The development of ZnO-based thin films has significantly influenced the performance of perovskite solar cells. ZnO, with its high electron mobility and favorable energy band alignment, is widely utilized as an electron transport layer (ETL) in PSCs. However, inherent defects in undoped ZnO, such as oxygen vacancies and surface states, contribute to charge recombination and instability^38,39. Therefore, modifying ZnO through doping techniques has been extensively studied to optimize its electrical and optical properties.
The electron transport layer (ETL) plays a crucial role in the functionality of perovskite solar cells (PSCs) by facilitating efficient extraction and transport of charge carriers while minimizing energy losses^40,41. Among the commonly used ETL materials such as ZnO, SnO₂, and TiO₂, zinc oxide (ZnO) has garnered significant attention owing to its ease of fabrication and superior electrical properties. These attributes make ZnO a promising candidate for the large-scale production and commercialization of PSCs^42,43. However, the use of pure ZnO as an ETL is not without challenges, as it often exhibits photochemical instability when exposed to ultraviolet (UV) radiation, which can lead to degradation of the sensitive perovskite layer and ultimately compromise the overall performance and longevity of the solar cell^{44,45,46,47,48}. To address these limitations and enhance the stability of ZnO-based ETLs, various doping strategies have been explored. Magnesium (Mg) doping, in particular, has shown promising results in mitigating photochemical instability issues associated with pure ZnO. By incorporating Mg into the ZnO lattice, the resistance of the material to UV-induced degradation is significantly enhanced. This improved stability, combined with the inherent advantages of ZnO, has led to notable improvements in PSC efficiency⁴⁹. The Mg-doped ZnO ETL not only retains the favourable electrical properties of ZnO, but also provides a more robust interface with the perovskite layer, leading to improved charge extraction and reduced recombination losses^50,51,52. These advancements in ETL engineering have contributed to ongoing efforts to develop high-performance, stable, and commercially viable perovskite solar cells.
Mg doping in ZnO alters its electronic structure and significantly enhances its optoelectronic properties, making it an excellent candidate for electron transport layers (ETLs) in perovskite solar cells (PSCs). Incorporating Mg into the ZnO lattice widened the bandgap, thereby improving the transparency of the material in the visible spectrum. This increased transparency allows for better light transmission to the active layer of the solar cell, potentially boosting overall device efficiency⁵³. Moreover, Mg doping suppresses intrinsic defect states within the ZnO structure, particularly reducing the density of deep-level trap states that can act as recombination centres for the charge carriers. The reduction in trap states and improved electronic structure of Mg-doped ZnO lead to enhanced electron conductivity and charge carrier mobility^54,55,56. This improvement in the charge transport properties is crucial for the performance of ETLs in PSCs, as it enables more efficient extraction of electrons from the perovskite layer and their transport to the electrode. The combination of higher transparency and improved charge transport results in reduced recombination losses within the solar-cell structure. Consequently, Mg: ZnO ETLs can contribute to higher open-circuit voltages, improved short-circuit currents, and enhanced power conversion efficiencies in PSCs. These advantages make Mg-doped ZnO a promising material for improving the performance and stability of perovskite-based photovoltaic devices.
The structural properties of Mg: ZnO (ZMO) thin films have been studied to optimize their performance in PSCs. Mg incorporation into the ZNO structure leads to a change in the lattice constants compared to the pure ZNO, which is due to the substitution of Zn²⁺ ions with Mg²⁺ ions, which have a smaller ionic radius. The crystallite size and orientation of ZMO films can be controlled by adjusting the Mg concentration and deposition parameters, allowing for the tailoring of film properties to suit specific device requirements⁵⁷. Figure 1 presents a glimpse of the comparison between ZNO and ZMO thin film structures.
Reprinted with permission from ref⁵⁸. Copyright 2012, American Chemical Society.
Comparison between the chemical structure and microstructure of pure ZnO and Mg-doped ZnO.
On the other hand, ZMO films are expected to exhibit comparatively high transparency in the visible region, with a blue shift in the absorption edge depending on the Mg content. This optical tuning is highly advantageous for PSCs, as it allows for better light transmission to the perovskite absorber layer while maintaining effective charge separation⁵⁷. Furthermore, photoluminescence studies have shown that Mg doping can reduce defect-related emissions in ZnO, potentially leading to decreased charge recombination at the ETL/perovskite interface. The combination of these structural and optical enhancements makes Mg: ZnO a promising candidate for high-performance ETLs in next-generation PSCs, offering the potential for improved device efficiency and stability^59,60.
The fabrication of Mg-doped ZnO thin films involves a multi-step process aimed at achieving uniform film deposition with optimal crystallinity. The precursor solution consists of zinc acetate tetrahydrate and magnesium nitrate dissolved in ethanol, followed by continuous stirring to ensure homogeneity. The ZnO precursor solution was prepared by dissolving 2195 mg of zinc acetate tetrahydrate and 625 mg of ethanolamine in 100 mL of ethanol to create a 0.1 M solution. The ethanolamine acts as a stabilizing agent, ensuring a clear and homogeneous solution. The mixture was stirred continuously until the zinc acetate seemed to be completely dissolved. The resulting solution was then filtered using a 0.22 μm PTFE filter to remove the undissolved/agglomerated particles to ensure a clear solution for film development using a spin-coating system, as shown in Fig. 2.
Fabrication process of ZMO thin films; precursor solution (Sol-gel) to film development (spin coating process).
The spin-coating technique is then employed at 4000 rpm to deposit the solution onto FTO substrates, forming a smooth and defect-free thin film. The spin-coating process involves three steps: initially, the substrate was spun at 1000 rpm with an acceleration of 30 rpm/s² for 10 s. The speed was then increased to 4000 rpm with an acceleration of 30 rpm/s² and maintained for 40 s to ensure even spreading of the solution onto the substrate. Finally, the speed was reduced back to 1000 rpm with an acceleration of 30 rpm/s² for 10 s to finalize the coating process. Following deposition, the films undergo annealing to convert the precursor films into the desired ZnO form. The high-temperature annealing process further enhances the film’s crystallinity by removing residual organic compounds. Several samples were subjected to annealing at 300 °C on a hot plate for different durations ranging from 5 to 25 min. These annealing time variations have been observed in the film properties and performance.
To analyze the fabricated films, multiple characterization techniques were employed. XRD was used to confirm the crystal structure and phase composition of Mg: ZnO films. UV-Vis spectroscopy provided insights into their optical transmittance and bandgap variation. FESEM and EDX analysis was conducted to examine the surface morphology and elemental composition. Finally, Hall Effect measurements were performed to evaluate carrier concentration, mobility, and resistivity, ensuring that Mg doping effectively enhanced electrical properties.
A simulated PSC structure of CuO/Cs₂AgBiBr₆/ZnO: Mg/FTO was used (as shown in Fig. 3) to assess the impact of Mg doping on ZnO thin films. The ZnO: Mg layer’s doping concentration and thickness were varied systematically to determine the optimal parameters for efficiency enhancement.
Schematic of the PSC device structure.
UV-Vis spectroscopy confirmed increased transparency for Mg-doped ZnO, with an optimal bandgap of 3.1 eV. During the UV-Vis characterization process, all samples underwent exposure to light as part of assessing their performance in solar cells. This characterization involved subjecting the samples to varying wavelengths of light to evaluate their absorption capabilities across the solar spectrum. The resulting absorbance data (presented in Fig. 4), collected at different wavelengths, offered valuable insights into the efficiency of light absorption by the samples across the spectrum, contributing to a comprehensive understanding of their solar cell performance.
UV-Vis absorbance spectra of pure ZnO (a), and Mg-doped ZnO (b) thin films and the bandgap shift (c) in terms of doping content.
FESEM images indicated better grain distribution, leading to smoother film surfaces. The FESEM characterization was performed on two samples: pure ZnO at a 15-minute annealing time and ZnO: Mg with a 2% doping concentration. The images of the tested samples are shown in Fig. 5. FESEM pictures of ZnO thin films show considerable changes in surface morphology between undoped ZnO (a) and magnesium-doped ZnO (b) samples. Images were acquired using FESEM at the magnifications and scale bars reported in the figure. Because additional FESEM imaging is not possible at present, the comparisons of surface morphology included in this manuscript are qualitative in nature. Quantitative grain-size or pore analysis will be performed in future work when raw images and full imaging metadata are available.
Surface morphology of (a) undoped ZnO and (b) 2 at% Mg-doped ZnO thin films. The images illustrate enhanced grain uniformity and reduced clustering due to Mg incorporation.
The undoped ZnO has a surface covered in small, distinct dots, most likely ZnO particles. These dots suggest a granular appearance, with ZnO particles appearing loosely packed rather than equally dispersed across the surface. This non-uniformity in particle distribution is supposed to cause differences in surface roughness and affect the film’s optical and electrical properties. The granular structure of ZnO shows that particles tend to agglomerate in clusters rather than create a continuous, smooth film. From figure (b), the magnesium-doped ZnO, on the other hand, has a significantly different surface morphology, with particles arranged more uniformly and evenly. The particles in ZnO: Mg appear to be more evenly distributed throughout the surface, indicating a more consistent coating. This even distribution is due to the effects of magnesium doping, which improves the homogeneity and consistency of the ZnO coating. The presence of magnesium ions may allow for a more regulated development process, resulting in a finer and more consistent surface texture. The introduction of Mg²⁺ produces a smoother and more uniform film morphology, presumably because Mg²⁺ can act as a substitute for Zn²⁺ with minimal lattice strain, thus allowing for homogeneous nucleation and growth. In RSC electrodeposited films, Mg doping created “more compact, flat, and smoother films” with grains that were uniformly distributed⁶¹. Likewise, sol–gel–derived ZnO: Mg films transitioned from wrinkle-like to smoother surface features with higher Mg contents⁵⁴. This increased uniformity in ZnO: Mg may improve the optical transparency and electrical conductivity, making it more appropriate for applications like transparent conductive electrodes and thin-film transistors.
The XRD patterns of Mg-doped ZnO thin films with varying doping concentrations (0–3 at%) exhibit obvious differences in peak intensity and shape, as indicated in Fig. 6. All the samples exhibit characteristic ZnO peaks, confirming the wurtzite hexagonal phase⁵² and agree with JCPDS card No. 36-1451]. The appearance of prominent peaks indicates that the films are polycrystalline in nature. No secondary phases or impurity peaks appear, suggesting that Mg is well incorporated into the ZnO lattice without altering the crystal structure. It was discovered that the relative strength of the principal XRD peaks ((1 0 0), (0 0 2), (1 0 1)) varies with Mg doping concentration. The variation of relative peak intensities may be related to the replacement of Zn²⁺ ions by Mg²⁺ ions. This can be attributed to the fact that the ionic radius of Mg²⁺ (0.57 Å) ions is smaller than that of Zn²⁺ (0.60 Å)⁶². However, this dissimilarity between the ionic radii could lead to the micro-strain and dislocation densities.
X-ray diffraction pattern of undoped ZnO, Mg-doped ZnO thin films.
A notable observation is that a minor shift in peak position is observed with rising Mg concentration, which may be attributed to lattice distortion due to the replacement of Zn²⁺ (0.74 Å) by the slightly smaller Mg²⁺ ions (0.72 Å). This change confirms the development of a solid solution and reflects lattice strain due to Mg incorporation that can be seen in Table 1. While peak intensity is sometimes used as a proxy for crystallinity, in our data, the Mg-doped films exhibit lower peak intensities than the undoped film (e.g., the (101) reflection drops from ~ 1000 counts to ~ 500 counts at 2–3 at% Mg). Because intensity is also affected by film thickness, preferred orientation, and measurement conditions⁶³, we do not use intensity alone to infer crystallinity here. Instead, we note that the persistence of well-defined wurtzite reflections across all samples indicates phase retention, and we discuss charge-transport improvements based on morphology/optical trends and simulated band alignment rather than absolute peak intensity. The improved crystallinity and lack of secondary phases in the Mg-doped ZnO indicate that low doping levels can have an equally great influence on structural order, and correspond with our expectations based on previous studies on doping.
The crystallite size (D) of the films was estimated using a well-known formula. Furthermore, to realize the atomic displacement, dislocation densities and microstrains were evaluated using the equation given previously⁶⁴.
where D is the average crystallite size, β is the FWHM of the reflection peak with the same maximum intensity in the diffraction pattern, λ is the X-ray wavelength (0.15406 nm), θ is the Bragg diffraction angle, and n is a factor that is nearly equal to unity for the lowest dislocation density. The estimated results are shown in Table 1.
It was found that peak height along the plane (101) was decreased with the increase of Mg. These phenomena indicate that the crystallographic structure of ZnO thin films is deteriorating due to ionic replacement. As the Zn²⁺ ions are replaced by Mg²⁺ ions, as we mentioned earlier, the micro-strains are becoming stronger. The increase of dislocation density and micro-strain, with the increase of Mg doping, could also be realized from the change of mean crystallite sizes. Also, the Mg²⁺ ions migrating to the ZnO lattice may lead to point defects and/or interstitial defects, which may influence the dislocation of atoms in the film.
Energy Dispersive X-ray Spectroscopy (EDX) performed in conjunction with Field Emission Scanning Electron Microscopy (FESEM) provides detailed information about the elemental composition of the sample. The EDS result (shown in Fig. 7) for undoped ZnO (a) shows a high concentration of zinc (Zn) and oxygen (O), as expected for ZnO. The quantitative examination reveals that Zn and O are the primary ingredients, with oxygen having a weight% (Wt%) of 18.35% and an atomic percentage (at%) of 59.36%, and zinc having a Wt% of 7.89% and an at% of 6.24%. The spectrum also shows peaks for tin (Sn) and silicon (Si), with tin present in large amounts at 72.18% Wt% and 31.47% at%. The elevated tin content is due to the use of fluorine-doped tin oxide (FTO) glass as a substrate throughout the experiment. The low presence of silicon could be attributed to trace contaminants or the equipment environment. Figure 7 (b) reveals that the integration of Mg into the ZnO matrix, with a Wt% of 3.74% and at% of 5.98%. This confirms the successful doping of magnesium into the ZnO structure. The oxygen and zinc contents are consistent with those in pure ZnO, with oxygen at 25.28% Wt% and 61.37% at%, and zinc at 7.94% Wt% and 4.72% at%.
Elemental Composition and EDX spectrum graph of ZnO (a) and ZMO (b).
The Hall effect measurements of pure ZnO thin films revealed significant differences in the electrical properties based on the annealing duration (in Table 2). The sample annealed for 15 min exhibited the highest carrier concentration of 4.121E + 20 cm³/Vs, while the sample annealed for 10 min showed the lowest carrier concentration of 1.308E + 15 cm³/Vs. The highest mobility was observed in the sample annealed for 5 min, whereas the lowest mobility was found in the 10-minute annealed sample. These variations can be attributed to differences in crystallinity and defect density introduced during the annealing process. Shorter annealing times might not fully activate the dopants or heal defects, while optimal annealing enhances crystallinity and reduces defect density, improving electrical properties. Excessive annealing, however, may introduce new defects, reducing carrier concentration and mobility. For the ZMO thin films, the sample with 2% Mg doping exhibited the lowest carrier concentration of 9.471E + 16 cm³/Vs, while the undoped sample had the highest carrier concentration of 6.032E + 20 cm³/Vs (obtained results are summarized in Table 3). The highest mobility was recorded for the 2% magnesium-doped sample, whereas the 0.5% doped sample had the lowest mobility.
These results suggest that magnesium doping at an optimal concentration can significantly enhance mobility by reducing grain boundary scattering and defect density, despite reducing carrier concentration due to compensation effects or formation of deep-level traps. Conversely, excessive doping might introduce additional scattering centers, reducing overall carrier concentration but still enhancing mobility due to improved crystalline quality. Hall Effect measurements showed enhanced electron mobility and reduced resistivity, indicating improved charge carrier dynamics. These findings suggest that low-level Mg doping introduces beneficial structural modifications that compensate for the reduced carrier concentration by enhancing mobility, a known trade-off in optimized ZnO-based systems.
The simulation performance (conducted by using SCAPS-1D simulation tools) of the PSCs configured with ZnO films and ZMO (prepared with different doping concentrations) films was analyzed to determine the optimal doping concentration that effectively improves the PSCs’ performance. To model device performance for the structure: FTO/ZnO: Mg (ETL)/Cs₂AgBiBr₆ (absorber)/CuO (HTL) (as in Fig. 3), SCAPS-1D was adopted. As additional experimental device fabrication and advanced characterization (i.e., accurate FWHM measurement from XRD, calibration of instrumental broadening, XPS, TRPL) are not achievable at this time, the simulation input parameters were selected from reported representative values in the literature and adjusted, where permissible, for consistency in comparison with the film-level measurements provided in Sect. 4. Where uncertainties exist, sensitivity sweeps were performed to ensure reported trends are robust to reasonable parameter variation. The principal parameter sets used for the simulations are summarized below in Table 4; the ranges indicate that the parameters were modified according to the sensitivity sweep windows to test the robustness of the trends^65,66,67,68.
Tables 5 and 6 summarize the PCEs of the studied structures. The optimal Mg concentration of 10% yielded the highest PCE of 22.18%, confirming the effectiveness of Mg doping in improving charge transport and reducing recombination losses. The effect of absorber layer thickness and temperature variations was also studied, showing that an optimal thickness of 0.8 μm and operation at 32 °C provided maximum efficiency.
Based on Table 5, the highest efficiency obtained was for 25 min of annealing. The variances of the efficiency may be due to changes in the material’s properties, including defect density, crystallinity, and grain size, which are affected by the annealing process. The results suggest that the ideal annealing period is 25 min, which correlates to the maximum PCE, showing that the annealing procedure substantially increased the ZnO film’s quality without introducing any negative impacts. The efficiency may also be affected by the thin film’s preparation, which led to some errors and imperfections in the film’s uniformity and quality. While the highest efficiency was seen at 1% magnesium doping concentration (Table 6), this could be due to a variety of factors that improve the overall performance of the ZnO thin film. Magnesium doping can improve ZnO’s electrical and optical characteristics by reducing defect density while increasing carrier concentration and mobility⁶⁹. By doping with magnesium, these enhancements are predicted to result in more efficient charge transfer and carrier collection, resulting in higher power conversion efficiency (PCE). Mg atoms, when doped into the ZnO lattice, replace zinc atoms and introduce more free carriers, increasing the carrier concentration. This increased carrier concentration enhances the film’s conductivity, allowing for more effective charge separation and lowering recombination losses.
From Fig. 8(a), it can be seen that the I-V curves are almost identical for the samples annealed for different annealing times. This might suggest that the electrical behaviour of the pure ZnO is not quite affected by the variations of the annealing times. For C-V and C-f curves (Fig. 8b and c), the graphs are quite different compared to the other, where the capacitance value for the C-V curve is a bit higher and the C-f curve is a bit lower. These results may be slightly different due to the thin film preparation or some error during the characterization test, where the equipment measurements have some errors, which can lead to differences in the results. However, Mg doping resulted in a more uniform and defect-free crystalline structure, reducing trap states that would normally capture and recombine charge carriers. As a result, overall charge transport efficiency improves, resulting in better short-circuit current density and fill factor, both of which are necessary for high PCE (Fig. 8d, f). Figure 8e indicates that the optimal doping level stabilizes the capacitance, suggesting reduced trap states and increased carrier concentrations. Finally, Mg doping significantly improves the performance of the ZnO thin film. The maximum efficiency among the measured concentrations is 1% magnesium-doped ZnO, showing that this doping level optimizes the film’s electrical and structural properties. The enhanced carrier concentration, together with improved charge transport and lower recombination losses, results in higher power conversion efficiency, proving the favourable effect of magnesium doping on ZnO thin films for solar cell applications. The trend of recombination losses decreasing upon incorporation of Mg can be explained by energy-level tuning and decreased defect activity. Incorporating Mg²⁺ doping raises the conduction band of ZnO, improving band alignment with the Cs₂AgBiBr₆ absorber, thereby leading to reduced interfacial recombination (e.g., “Mg doping modifies the band alignment which leads to reducing the interface recombination and increasing the Voc”⁷⁰. In addition, Mg also introduces shallow donor states, which increase electronic conductivity and reduce recombination paths for trap states⁷¹. Together, these effects explain the lower simulated recombination rates observed in SCAPS and are consistent with previous reports of Mg: ZnO electron transport layers in perovskite and dye-sensitized solar cells⁷².
I-V, C-V, and C-f characteristics of ZnO (a–c) and Mg-doped ZnO (d–f) thin films, showing enhanced performance metrics and reduced capacitance fluctuations with optimized doping. Note: The SCAPS-1D results presented here are predictive and not directly compared with experimental device J–V curves, as device fabrication and full photovoltaic characterization were not part of the present study. Nevertheless, the simulated trends are consistent with known material properties and with reported Mg: ZnO ETL behavior in perovskite solar cells, e.g. Refs^50,73.
While device-level experimental verification was not part of this study, the SCAPS-1D simulation showed useful results that are in Line with known material properties and trends observed in similar systems. These simulations provide some predictive value for future experimental work. The maximum experimentally studied Mg doping concentration in this work was at 3 at%, whereas the simulations explored doping up to 10 at% to evaluate a theoretical bound. This helps to illustrate the potential limits of enhancement factors beyond what is currently practical in fabrication.
This study demonstrates that incorporating magnesium into ZnO thin films can enhance perovskite solar cell performance by improving optical and electrical characteristics. The magnesium dopant has successfully increased the efficiency of PSC by significantly enhancing various electrical and optical properties. The addition of magnesium dopants, in particular, has resulted in significant improvements in critical parameters such as short-circuit current density (J_sc), open-circuit voltage (V_oc), and Fill Factor (FF). The optical properties of the perovskite layer have been positively influenced by magnesium doping, leading to enhanced light absorption and improved utilization of incident sunlight, thereby contributing to the overall efficiency enhancement of the PSC.
Future work should aim to validate these findings experimentally and explore long-term operational stability, scalable processing techniques, and alternative dopants for further optimization. Additionally, optimizing perovskite compositions and exploring scalable fabrication methods, such as slot-die coating, will be pursued. Environmental impact assessments and lifecycle analyses will ensure sustainability, while integration with real-world photovoltaic systems will validate practical applications. Advanced characterization techniques like time-resolved photoluminescence and computational modeling will provide deeper insights into charge dynamics and device performance. Investigating alternative doping elements and comparing their effects with magnesium doping will also be explored to identify potential improvements.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
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This work was supported by the Malaysian Ministry of Higher Education through FRGS grant FRGS/1/2020/TK0/UM/02/33, and HICoE grant no. JPT. S (BPKI)2000/016/018/015JId.4 (21)/2022003HICOE. The authors express their gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project (Grant No. PNURSP2025R12), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
The authors express their gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project (Grant No. PNURSP2025R12), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Institute of Sustainable Energy, Universiti Tenaga Nasional (UNITEN), Jalan Ikram- Uniten, Kajang, 43000, Selangor, Malaysia
Mohammad Nur-E-Alam, Boon Kar Yap & Tiong Seih Kiong
Department of Electrical and Electronic Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, 43000, Selangor, Malaysia
Boon Kar Yap & Tiong Seih Kiong
Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Jalan Universiti, Kuala Lumpur, 50603, Malaysia
Mohammad Aminul Islam
Miyan Research Institute, International University of Business Agriculture and Technology (IUBAT), Dhaka, 1230, Bangladesh
Mohammad Aminul Islam
Centre of Printable Electronics, Institute for Advanced Studies, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
Mohammad Aminul Islam
Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Jalan Universiti, Kuala Lumpur, 50603, Malaysia
Tan Chou Yong
Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya, 63100, Selangor, Malaysia
Kah-Yoong Chan
Centre for Advanced Devices and Systems, Centre of Excellence for Robotics and Sensing Technologies, Multimedia University, Persiaran Multimedia, Cyberjaya, 63100, Selangor, Malaysia
Kah-Yoong Chan & Gregory Soon How Thien
Institute of Electronics, Atomic Energy Research Establishment, Bangladesh Atomic Energy Commission, Dhaka, 1349, Bangladesh
Mohammad Khairul Basher
Centre for Promotion of Research, Graphic Era (Deemed to be University), Clement Town, Dehradun, India
Mohammad Khairul Basher
Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O Box 84428, Riyadh, 11671, Saudi Arabia
Nissren Tamam
Faculty of Graduate Studies, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka, 1216, Bangladesh
Mayeen Uddin Khandaker
Applied Physics and Radiation Technologies Group, CCDCU, Faculty of Engineering and Technology, Sunway University, 47500 Bandar Sunway, Selangor, Malaysia
Mayeen Uddin Khandaker
Department of Physics, College of Science, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
Mayeen Uddin Khandaker
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M.N-E-A.: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. B.K.Y: Visualization, Validation, Supervision, Formal analysis, Data curation. M.K.B.: Writing – review & editing, Visualization, Methodology, Formal analysis. G.S.H.T.: Visualization, Validation, Formal analysis. K.-Y.C.: Visualization, Validation, Formal analysis. T.C.Y.: Visualization, Validation, Formal analysis. T.S.K.: Visualization, Supervision, Formal analysis. M.A.I.: Writing – review & editing, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. N.T. and M.U.K.: Writing – review & editing, Formal analysis, Data curation, Funding. All the authors have read and agreed to the published version of the manuscript.
Correspondence to Mayeen Uddin Khandaker.
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For at least a decade, developing countries across Asia and Africa have worried about growing dependent on China. They’re concerned about debt traps, coercive policies and hidden costs that might push their economies toward crisis.
Crisis has come and that logic has been turned on its head. After six weeks of the U.S. and Israel’s war on Iran and its ensuing counterattacks, it is the countries that bet on Chinese supply chains that are faring better than the ones that trusted Pax Americana.

Consider Pakistan. By now it should have been in the middle of yet another economic and social implosion. It has always been vulnerable to energy price shocks, given that it imports almost all of its energy, much of it through the Strait of Hormuz. The country has $130 billion in external debt and a persistent current account deficit, and so the slightest nudge should have tipped it over into a familiar spiral: Emergency requests to the International Monetary Fund, 18-hour power blackouts, unrest on the streets.
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The 166 MWp solar and 80 MWh battery storage project in Visayas aims to boost grid stability and support the Philippines’ renewable energy targets.
April 11, 2026. By News Bureau
Levanta Renewables, a renewable energy platform backed by Actis — has announced the award and signing of an Engineering, Procurement and Construction (EPC) contract with China Energy Engineering Group (CEEC) for the Barotac Solar Power and Battery Energy Storage System (BESS) project in the Visayas, Philippines.
The contract covers the full EPC scope for a 166 MWp solar photovoltaic (PV) facility integrated with an 80 MWh BESS, designed to enhance grid stability and support reliable power supply in the region.
Pramod Singh, CEO of Levanta Renewables, said, “This award marks an important step in advancing our greenfield portfolio in the Philippines, reflecting Levanta’s end-to-end capabilities across development, financing, construction and operations. By combining Levanta’s development expertise with CEEC’s EPC capabilities, we are well positioned to deliver the project to high standards of execution, reliability and safety.”
Guo Jizhong, Chairman of CEEC Anhui Electric Power Design Institute, Stated, “We are honoured to partner with Levanta Renewables on this important project. Leveraging our global EPC experience and commitment to quality and safety, we will ensure the successful delivery of this project to the highest standards.”
Kou Bin, General Manager of CEEC Northeast No. 2 Electric Power Construction, added, “We are greatly honoured to join hands with Levanta Renewables on this new energy project. We will fully leverage our professional strengths in overseas EPC services, uphold the highest standards of safety and quality, and work closely with all partners to ensure the project is delivered to the highest level of excellence.”
The project will support the Philippines’ target of achieving a 35 p renewable energy share by 2030, while contributing to the expansion of clean, reliable power capacity in the Visayas region.

Solar to BESS: Reliability Begins with Advanced Sealants, Explains Manish Gupta, Fasto Adhesive

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Multi‐functional Phase‐Changeable Salt for Inverted Perovskite Solar Cells  Wiley
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3-Way DC Miniature Circuit Breaker – 50A, 1000V, IP65 Waterproof, For Solar PV, RVs, Off-Grid Systems  ruhrkanal.news
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Exclusive: India delays coal flexibility plan as solar power curbs rise, document shows  Reuters
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Home » General » Business » Buyers criticise ‘poor quality’ China’s clean energy for creating costly ‘dependencies’
Posted By: Gopi April 11, 2026

New Delhi, April 11 (SocialNews.XYZ) China’s push to become a global supplier of renewable energy technology is drawing sharp criticism from Africa, South Asia and Latin America, alleging Chinese solar panels, wind turbines and batteries to be low‑quality and poorly suited to local conditions, a new report has said.
The report from Nepal Aaja also argued that these projects are also tied to financing that creates long‑term dependencies.
 
“China’s financing model typically ties loans to technology imports, meaning countries that accept Chinese funding are compelled to purchase Chinese equipment. Once locked into these supply chains, recipient nations find themselves dependent not only on Chinese hardware but also on Chinese spare parts, technicians, and after-sales services,” the Nepal-based media house said.
China’s RE exports gain strength from state-subsidized overcapacity, designed to absorb domestic surplus but they fail to meet the long-term needs of recipient nations. “The result is a wave of underscale technology—cheap upfront, but expensive in maintenance and replacement,” the report said.
Recipient nations of Chinese equipments in Africa and Southeast Asia have complained of frequent breakdowns, short lifespans with panels failing to withstand local climatic conditions, undermining electrification projects.
“Grid integration has proven inefficient, leading to costly repairs and delays in electrification projects. In Latin America, wind turbines imported from China have been criticized for their short lifespan compared to European alternatives,” the report noted.
China tries to position itself as the indispensable supplier of renewable technology, but the real aim is to lock the Global South into its orbit, creating asymmetric dependencies, with almost nil technology transfer.
“This dependency undermines their ability to hedge strategically in a multipolar world. Nations that might otherwise balance ties between China, the West, and regional powers are constrained by their reliance on Beijing’s energy ecosystem,” it said.
Leaders in Africa and Latin America called the relationship with China as a form of neo‑mercantilist exploitation in which energy ties are used to secure broader political concessions. China used the leverage to its advantage in UN voting alignment, Belt and Road expansion, and bilateral trade negotiations.
Source: IANS

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SECI Seeks ₹660 Crore Loan For 200 MW Solar Project In Madhya Pradesh  SolarQuarter
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Uttarakhand Electricity Regulatory Commission (UERC), the Indian state power regulator, has issued a draft FY 2026-27 review order dated April 07, 2026, and has invited stakeholder comments by 04.05.2026. For PV projects to be commissioned on or after 01.04.2026, UERC has proposed benchmark capital cost of INR 285.32 lakh/MW (~$313,852/MW) and generic tariff of INR 3.96/kWh (~$0.044/kWh). The proposal has been based on module price of $0.081 /Watt, annual degradation of 0.50%, applicable GST, custom duty, and CPI and WPI-linked escalation. The PV cost breakup has included INR 92.17 lakh/MW (~$101,387/MW) for PV modules, INR 40 lakh/MW (~$44,000/MW) for land, and INR 153.15 lakh/MW (~$168,465/MW) for civil works and related components. The draft has also proposed Canal Bank PV at INR 300.00 lakh/MW (~$330,000/MW) and INR 4.09/kWh (~$0.045/kWh), Canal Top PV at INR 320 Lakh/MW (~$352,000/MW) and INR 4.26/kWh (~$0.047/kWh), Solar Thermal at INR 1200 Lakh/MW (~$1,320,000/MW) and INR 11.95/kWh (~$0.131/kWh), GRPV/GSPV at INR 2/kWh (~$0.022/kWh), and BESS at INR 160 Lakh/MW (~$176,000/MW) with INR 2,54,583/MW/Month (~$2,800/MW/Month).

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Renewable surge fuels multi-year boom to India’s power equipment industry: Report  ANI News
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Jupiter International and AMPIN Energy Transition commissioned an integrated solar cell and module manufacturing facility in Bhubaneswar, Odisha, through their joint venture, AMPIN Solar One.
1.3 GW Capacity to Boost Domestic Solar Production
The facility features an annual production capacity of 1.3 GW, positioning it as a significant addition to India’s solar manufacturing landscape. Moreover, the facility has been established under the Government of India’s Production-Linked Incentive (PLI) scheme, which aims to strengthen domestic manufacturing capabilities and reduce import dependence. The solar modules produced at the facility will not only support AMPIN’s in-house renewable energy projects but will also be supplied to third-party developers, thereby enhancing supply chain reliability across the sector.
Strengthening India’s Energy Transition Goals
The joint venture underscores a strategic move toward building a self-reliant and resilient solar manufacturing ecosystem in India. By combining Jupiter International’s manufacturing expertise with AMPIN’s project development capabilities, the partnership is expected to accelerate the deployment of high-quality solar solutions nationwide.
Leadership Perspective: Focus on Scale and Quality
Alok Garodia, Chairman and Managing Director of Jupiter International Ltd, emphasized, “The inauguration of the manufacturing facility of AMPIN Solar One Private Limited is a significant step toward building a stronger domestic manufacturing backbone for India’s energy transition. This platform brings together scale, manufacturing depth, and quality-focused execution to enable the reliable supply of high-performance cells and modules from within the country. We are proud to partner with AMPIN and the Government of Odisha in advancing clean energy ambitions.”
A Strategic Step Toward Clean Energy Leadership
As reported by pv-magazine-india.com, the commissioning of this facility reinforces India’s commitment to energy transition and local manufacturing. As demand for solar power continues to grow, such integrated facilities will play a crucial role in ensuring sustainable, scalable, and cost-effective renewable energy solutions across the country.

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Today, Anker Solix unveiled its brand-new Solarbank. The Anker Solix Solarbank Max AC is now available to pre-order directly from the manufacturer and comes with an early-bird discount off the recommended retail price (RRP) of €2,299. It is currently only available to pre-order in selected countries, such as Germany, the Netherlands and France. Pre-order customers can also save on the BP7000 expansion battery (MSRP €1,799) and benefit from additional incentives.
The official pre-sale begins on 26 May 2026. Unlike previous models, such as the current Solarbank 3 Pro, the new Solarbank Max AC from Anker Solix has no PV inputs or MPPTs. Instead, this new release is a plug-and-play home storage solution designed to retrofit existing solar systems. It enables the straightforward storage of surplus energy, which is becoming less lucrative due to falling feed-in tariffs. With the Solarbank Max AC, surplus electricity can be stored for later use rather than being fed into the grid for a pittance.
Disclaimer: Notebookcheck is not responsible for price changes carried out by retailers. The discounted price or deal mentioned in this item was available at the time of writing and may be subject to time restrictions and/or limited unit availability.
Boasting a capacity of 7 kWh, the Anker Solix Solarbank Max AC is the largest Solarbank to date. The storage capacity can optionally be expanded up to 42 kWh using BP7000 expansion batteries, with each battery adding an extra 7 kWh. The bidirectional inverter supports up to 3,500 W for rapid charging and discharging. Up to 800 W can be fed into the home network simply by plugging the Solarbank Max AC into a standard wall outlet. With professional installation via a Wieland socket, however, the full 3,500 W capacity can be utilised. A 3,680 W off-grid socket is also available for backup power during outages.
Power output to the home can be adjusted to match real-time consumption in seconds via a smart meter. It also supports dynamic electricity tariffs to further reduce energy costs. "Anker Intelligence", including the "Anker" voice assistant, is designed to intelligently plan usage. According to Anker Solix, integration with Home Assistant and other systems is possible thanks to standardised Open API and Modbus protocols.
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Zambia has launched a Carbon Feed-In Premium (CFIP) call for solar PV projects with storage totaling up to 300 MW. The programme is backed by Norway under Article 6 cooperation and supported by the NACA Fund.Eligible projects must range between 30 MW and 100 MW AC and include at least 30 minutes of battery storage.Projects must connect to the national grid, with ZESCO acting as primary offtaker.Applicants must demonstrate financing gaps with IRR below a 12.5 % benchmark.Submissions are due by 31 May 2026, with project selection expected by end-June 2026.Selected projects will receive carbon premium payments over at least 10 years.

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LONGi, a Xi’an, China-based solar technology company, has won the iF DESIGN AWARD 2026 for its eHome BIPV solar roof tiles. The award was presented in the Product Design/Building Technology category, according to the company’s statement. LONGi said the residential PV roofing product was developed to combine electricity generation with roof integration for homeowners. The system features modular lines, dark matte finishes, and near-seamless joining craftsmanship, allowing it to function as part of the roof structure. According to LONGi, the product meets the highest Class A fire safety requirements and carries a 30-year product and performance warranty. The company also added that the system supports full-coverage deployment to improve energy density per unit area, including on smaller or structurally constrained villa roofs, while also enabling real-time monitoring of generation, storage, and consumption through its home energy management system.

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Saatvik wins Rs 108.75 cr solar module order  Manufacturing Today India
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The University of Hawaiʻi is investing $14 million in a solar and battery storage project at its University of Hawaiʻi–West Oʻahu campus, aiming to significantly expand its renewable energy footprint and move closer to systemwide net-zero targets.
The project will install solar panel canopies over existing parking lots, creating dual-use infrastructure that generates clean energy while providing shaded parking. Planning and design are underway, with construction expected to begin in August 2026.
Once completed, the photovoltaic system is projected to generate approximately 2.38 million kilowatt-hours annually, enough to power about 270 homes. The installation is expected to supply roughly 50% of the net-zero energy required for the West Oʻahu campus and play a key role in reducing reliance on imported fossil fuels.
The initiative also includes an industrial-scale battery storage system designed to enhance resilience. In the event of a power outage, the system will support critical campus operations, a crucial capability given Hawaiʻi’s isolated island grid.
The solar canopy project is part of a broader sustainability strategy across the UH system. A subsequent phase will focus on upgrading campus chillers with high-efficiency units and advanced control systems, scheduled for fiscal year 2027, as the university continues its push toward full net-zero energy.
All buildings at the West Oʻahu campus are LEED-certified and incorporate energy-efficient systems, including existing solar installations of approximately 100 kilowatts per building. The campus also utilizes rainwater catchment for irrigation and benefits from access to public transportation, including on-campus bus and rail services.
Funding for the project is being sourced through a combination of campus funds, state capital improvement program allocations, and federal tax incentives. Project management is being led by the UH Office of Project Delivery and the UH West Oʻahu Office of Planning and Design, with support from local partners Elite Pacific Construction and RevoluSun.
KEY QUOTE:
“The new PV system is designed to offset 100% of the campus cooling load, significantly reducing our dependence on imported fossil fuels while lowering greenhouse gas emissions. Producing clean energy while providing shade just makes sense, it’s the right thing to do, and it also strengthens our resilience as a community.”
Miles Topping, Director Of Energy Management, University Of Hawaiʻi System

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Varanasi, a district in Indian State Uttar Pradesh, has recorded 3,122 solar panel installations in March under the PM Surya Ghar: Muft Bijli Yojana. By the 2026 first quarter, the district’s cumulative solar panel installations had reached 35,069, placing Varanasi second in Uttar Pradesh. The figures were shared at a pre-event awareness programme for the Uttar Pradesh Energy Expo (UPEX) 2026, held at Vikas Bhawan in Varanasi recently. The programme was organised by the UP State Chapter of the PHD Chamber of Commerce and First View in collaboration with UP NEDA and SEVA, with support from KEI Industries Ltd. During the event, officials discussed solar sector opportunities, the existing policy framework, and scheme-led adoption by households, farmers, and industries under PM Surya Ghar and PM-KUSUM. UPEX 2026 is scheduled to be held in Lucknow from May 7 to 9, with more than 150 exhibitors and over 60 speakers expected.

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Clean the Sky – Clean Energy Joint Ventures  trendhunter.com
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Silicon Ranch defends Stockton solar project at Bay Minette meeting amid community concerns  Gulf Coast Media
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A major solar farm approved by the government on Wednesday, 8 April, is set to power thousands of homes, but concerns remain over its impact on agricultural land in Lincolnshire.
The Springwell Solar Farm is expected to become the UK’s largest power-generating solar development, with the capacity to supply electricity to more than 180,000 homes annually – about half of all households in Lincolnshire.
The project forms part of a wider expansion of renewable energy, marking the 25th nationally significant clean energy scheme approved since July 2024.
See also: Record 157 solar farms approved amid food security fears
While the government has highlighted solar power as one of the cheapest forms of energy and central to reducing reliance on volatile fossil fuel markets, the scheme has prompted concern over the loss and use of high-quality farmland.
North Kesteven District Council objected to the development, citing its impact on “best and most versatile” agricultural land, with close to half of the site falling into this classification.
The council also raised issues relating to landscape, biodiversity, and the long-term use of rural land.
Council leader Richard Wright said the authority supported renewable energy in principle, but maintained that developments must be appropriately located.
He said: “Those objections we did raise were mainly in respect of how the scheme impacted on best and most versatile agricultural land.”
He added that mitigation measures should be prioritised. “We would still ask that, through careful location of the panels and on-site infrastructure, this is kept to a minimum,  and also to consider battery technologies that have lower environmental impact and are demonstrably the safest,” he said.
The government has defended the project as part of a broader effort to strengthen domestic energy supply amid global instability, including conflicts affecting international fuel markets.
Energy minister Michael Shanks said: “Solar is one of the cheapest forms of power available and is how we get off the roller-coaster of international fossil fuel markets and secure our own energy independence.”
Opposition remains from some political figures, including Reform UK MP Richard Tice, who called the decision “disgraceful” and “appalling”.
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BAY MINETTE, Ala. (WALA) – The company behind a controversial solar farm in north Baldwin County says it’s committed to the project and moving forward.
Silicon Ranch executives spent nearly four hours with north Baldwin residents Wednesday night. Thursday, they traveled and reflected on what they’d heard. They said they hope residents heard them too.
“We’re buying the land. We’re under contract. We’ve issued notice to close. We have a contract with Alabama Power to deliver 260 megawatts by the end of 2028. The project was approved by the Public Service Commission and we have obligations that we have to honor,” said Matt Beasley, chief commercial officer at Silicon Ranch.
Public meeting draws large crowd
More than 180 people turned out for a protest and the public meeting that followed Wednesday. Dozens had questions ranging from land management during the construction process to buffer zones and wildlife corridors. Silicon Ranch president and co-founder Reagan Farr stayed to answer all of them.
One resident asked about the project’s purpose. Farr said the company sells power and renewable energy certificates to Alabama Power, which can use the renewable energy credits as it chooses.
Another resident asked about local jobs. Farr said once the array is built, it will function as an agricultural operation with shepherds and agrivoltaic technicians onsite, but in smaller numbers. Agrivoltaic technicians manage the land and vegetation around solar panels.
By the end of the meeting, one thing was clear. Silicon Ranch is committed to this project. Company officials said their timeline will keep them on track to avoid any injunctions or moratoriums.
“What we’re focused on right now is finalizing a design that applies the learnings and takeaways from our studies and reviews so that we know where we’re building and importantly, we know where we’re not and yes, we’re going to file the application when we are ready to be able to continue to stay on course,” Beasley said.
Not everyone left the meeting feeling reassured. One resident said they felt worse after the meeting. Another said they had understood the land purchase was not final, but learned it was a done deal.
The Baldwin County Commission passed a resolution Tuesday, approving a referendum to establish zoning in Planning District 3. That’s created a tighter timeline for Silicon Ranch to turn in applications for building permits. They’ll need to do that within the 90-day period the probate office has to hold the special election. Otherwise, a no vote could put the development on hold.
Copyright 2026 WALA. All rights reserved.

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Lemoore is investing $24 million in an infrastructure project to become a “smart city,” according to a press release. The project will be completed by Energy Systems Group, an engineering services company. The ribbon cutting is expected by July 2027, and some upgrades have already begun.
The project will increase the sustainability and resilience of multiple city facilities and systems while upgrading key equipment, saving the city an estimated $14.8 million. The city received $876,000 in grants and tax credits to offset the cost of the project.
A total of 12 total EV charging stations will be spread across three locations: the Recreation Center, Kings Lions Park, and Heritage Park, with hopes that this will bring more people into Lemoore to charge their cars.
Solar-paneled carports are coming to the Recreation Center and 40 G St., which will provide shade and electricity. More energy sources mean that these solar panels will reduce the city’s overall energy expenditures and dependence on utilities, according to the City. 
“If we modernize the infrastructure, it’s a hedge against rising energy costs, which are going up in California,” said Amelia Cottrell, senior business development manager at Energy Systems Group. California electricity rates were 80% higher than the national average in 2024, according to the Public Policy Institute of California.
By upgrading HVAC systems and installing LED lighting in multiple city buildings, the city will use less electricity, which also reduces costs.
“We’ve had many different crazy heatwaves, and having new HVAC really combats a really hot summer day when it might break,” said Cottrell, adding that the City is upgrading its systems to make them more resilient to climate change.
Energy Systems Group is also installing 7,401 Advanced Metering Infrastructure water meters, which increase efficiency by automatically reading how much water is being used across the city. Meters used to be manually read by city staff, but in the future, AMI meter readings will be uploaded into an online database for residents to track their water usage. This digitized water system provides the City with real-time data, which will help identify leaks.
“if there’s any problems, you know right away because every resident can log on to a dashboard and say, ‘How much water am I using? OK, do I have any leaks?’,” said Cottrell.
The wastewater system is also getting digitized into a control system that pulls information from plant equipment into one digital platform. That centralized monitoring helps the city make better, data-driven decisions. The wastewater system is also getting much-needed equipment upgrades to its variable frequency drives, aerators, and more, which in the long run will lead to less maintenance.
“These were at the end of their useful life, so they needed to be replaced. And we replaced them with more efficient measures,” said Cottrell. “We’re upgrading systems that might fail.”
The city’s IT system and wastewater treatment facility are getting backup generators.
“If the power goes out for any which reason, we make sure that critical services are being offered to the city residents,” said Cottrell. If, for example, there is a fire nearby and the power needs to be shut off, now with backup generators, key systems can continue operating.
Cottrell says that by digitizing city infrastructure, the city becomes more connected, giving city staff better visibility over key systems and helping them make better decisions.
“if something breaks, they can see it on the screen and react to it faster,” she said.
These digital systems also self-monitor and alert staff before problems occur. By upgrading equipment that might break and replacing it with more energy-efficient models, the City saves money.
“The City is thinking proactively and doing this in a smart way,” said Cottrell.
“There’s community benefits that may be not obvious in the beginning, but once this project is up and running, I think residents will start to see really an improvement of the quality of life,” said Cottrell.
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A ‘BUTTERFLY solar farm’ that was set to be introduced to Wrexham will no longer be going ahead.
RWE Renewables UK has confirmed that it has pulled out of plans which would have seen a 99.9MW Butterfly solar and battery storage project built adjacent to the A483, and between Johnstown to the West and Bangor on Dee in the East.
The project would have generated enough low cost, green electricity to power the equivalent of over 34,775 typical Welsh homes.
The designs for the Butterfly Solar Farm (also known as Glöyn Byw Solar Farm), had been developed by RWE Project Manager Robin Johnson – a wildlife conservationist and trained ecologist.
The design consisted of around 260 acres of panelled area, to be planted with a diverse grass mix and maintained by sheep grazing allowing the site ‘to considerably improve local biodiversity and opportunities for wildlife’.
But, in a letter shown to The Leader this week, Mr Johnson has now confirmed that the project has been scrapped.
The decision comes following a consultation period that was held last year.
In the letter, Mr Johnson says: “Following our informal and formal consultations held last year for the proposed Butterlfy/Glyn Byw solar farm on land between Johnstown to the west and Bangor-on-Dee to the east, RWE has taken the decision not to progress further with the proposed scheme.
Read more
Plan to turn Wrexham University fitness suite into office space
Plans to turn part of Wrexham care home into houses approved
Volunteers ‘heartbroken’ after reservoir drained following toad rescue effort
Lane closures planned on Wrexham road near A483 for two weeks
“Following detailed review of grid connection availability and overall project viability, it has been concluded that the site cannot be advanced at this time.”
He added: “While this outcome is regrettable, RWE would like to thank all stakeholders who have engaged constructively during the project’s early stages.
“Solar and co-located battery projects remain central to RWE’s UK renewables strategy, and we continue to invest in projects that can be delivered efficiently and make a meaningful contribution to national and local energy goals.”
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Global Renewable Energy Hits 49% Capacity In 2025 As Solar Leads Record Growth – RE Statistics 2026  SolarQuarter
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FS India Solar Ventures, a subsidiary of First Solar, has won Solar Energy Corporation of India’s auction to supply 260 MWp of domestically manufactured DCR solar PV modules for a project in Madhya Pradesh.
November 10, 2025. By Mrinmoy Dey

Solar to BESS: Reliability Begins with Advanced Sealants, Explains Manish Gupta, Fasto Adhesive

Anand Jain of Aerem Solutions on Scaling Solar, Storage, and Finance for Sustainable India

JIRE CEO Amit Kumar Mittal Explains Rising Role of Energy Storage and Green Hydrogen in India

Icon Solar Modules Are Engineered for India’s Harsh Conditions, Says Rajat Shrivastava

Mobile Charging and Energy Storage Will Drive India’s EV Adoption: Mobec’s Harry Bajaj

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Australian solar cell innovator SunDrive has been given another helping hand by the Australian Renewable Energy Agency (ARENA) to support the firm bringing its home-grown tech to market.
Provided under ARENA’s Advancing Renewables Program, SunDrive has been issued a $25.3 million grant to scale and commercialise its copper metallisation solar cell technology to take its research and development facility in South Sydney to a 300 MW commercial scale production capability.
The funding will support local development, deployment and refinement of production tools in addition to undertaking cost modelling to support commercialisation. Modules will be produced at SunDrive’s Kurnell facility for in-field testing and early market acceptance.
Calling it a major milestone for SunDrive and Australian solar innovation, the firm said:
“The first commercial demonstrator tool has been built, and customer demos are now underway, showing how Australian research can translate into real-world manufacturing capability.
This latest grant adds to previous funding from ARENA of $14 million to demonstrate SunDrive’s copper metallisation technology.
SunDrive uses a copper-based process to replace the silver used in solar cells, aiming to cut costs and improve efficiency while supporting ARENA’s Ultra Low-Cost Solar goal.
“Not only is copper more abundant and cheaper than silver, but SunDrive’s unique manufacturing process also results in higher cell and module efficiencies which could have huge benefits for global decarbonisation efforts,” said  ARENA CEO Darren Miller.
A  2023 University of New South Wales (UNSW) study suggested if silver continues to play such an important role solar manufacturing, it will use up to 98% of the world’s current silver reserves by 2050; particularly given the rise of ‘passivated contact’ solar technology that requires 2 to 3 times more silver says SunDrive.
According to ARENA, the solar manufacturing industry is currently using a third of global industrial silver.
In April 2021, SunDrive achieved 24.48% cell efficiency with its copper tech, making it the most efficient commercial-size solar cell ever created at the time. By September 2022, cell efficiency had increased to 26.41%. I wasn’t able to determine if there have been efficiency improvements since.
The ARENA-funded project will be in collaboration with China’s Suzhou Maxwell Technologies Co Ltd and Jiangsu Vistar Equipment Technology Co Ltd, two long established solar cell manufacturers. In May this year, SunDrive inked a Joint Development Agreement (JDA) with the pair to co-develop and distribute commercial-scale direct-copper plating tools to advance development and production of high-efficiency heterojunction (HJT) solar cells.
“By partnering with world-leading solar cell equipment manufacturers, Maxwell and Vistar, we’re now showing how our record-breaking tech, combined with SunDrive’s engineering innovation, can scale to industrial production, making solar more efficient with a more abundant and affordable material by replacing silver with copper,” said SunDrive.
It’s not the first time the firm has teamed up with Chinese solar heavyweights. In October last year, SunDrive announced it was leading an application for funding under the Albanese Government’s Solar Sunshot program in a joint venture with Trina Solar. SunDrive says the proposed facility in Sydney will create more than 300 skilled jobs and have an annual production capacity of 1.2 gigawatts.
There’s been no further news on this that I’m aware of, but ARENA is yet to earmark all funding from that round.
When we’ll see SunDrive solar panels finally available here in Australia still isn’t clear.
If you’re looking for Australian-made solar panels for your home’s rooftop right now, then the only show in town is Tindo Solar — and Tindo make good modules. The firm manufactures its panels in Adelaide with local and imported components — among the imported elements are the solar cells. But expect to pay a premium well above the prices of good-quality budget Chinese brands such as Trina.
If country of manufacture doesn’t play a major role in your purchasing decision, discover and what you need to know to pick a good solar panel brand.
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Michael caught the solar power bug after purchasing components to cobble together a small off-grid PV system in 2008. He’s been reporting on Australian and international solar energy news ever since.
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Plans to build a 26 GW solar, wind, and green hydrogen project in Western Australia’s Pilbara are set to accelerate with the federal government providing a $21 million funding injection to further advance the project.
Image: ICE
Intercontinental Energy (ICE) has secured a $21 million (USD 14.73 million) grant from the federal government to support the development of its Australian Renewable Energy Hub (AREH) project, that aims to combine 26 GW of renewables to produce 1.6 million tonnes of green hydrogen per year.
Perth-headquartered ICE said the Australian Renewable Energy Agency (ARENA) funds will support the next detailed phase of technical, economic and regulatory progress on the AREH, being developed in Western Australia’s Pilbara region.
The studies will focus on green hydrogen production at Boodarie near Port Hedland, and integration with industrial partners, as well as environmental, water and social licence considerations for the project which would underpin green iron efforts in the Pilbara by producing large volume and low-cost green hydrogen.
AREH Chief Executive Officer Neil Parker said the new funding, that comes after the project was granted Major Project Status by the federal government in 2024, would accelerate the hub’s development.
“This funding allows us to advance the rigorous engineering, design and commercial analysis needed to progress AREH and its ability to supply new industrial clusters in the Pilbara,” he said.
“It brings us closer to delivering large‑scale, low‑cost green hydrogen, supporting a Future Made in Australia agenda and positioning the Pilbara as a leading global centre for green iron manufacturing.”
The 26 GW project, previously known as the Asian Renewable Energy Hub, is proposed for a 6,500-square kilometre site about 250 km northeast of Port Hedland, and would service industries throughout the Pilbara such as mines and mineral processing.
At full capacity, AREH could produce up to 1.6 million tonnes of green hydrogen per annum. Hydrogen produced by the mega project, now wholly owned by ICE after oil and gas major BP pulled out of the project last year, is planned to be used for ammonia and green iron production.
Isaac Hinton, head of ICE’s Australian operations, said the project can help transform the Pilbara, enabling new industries and positioning the region as a global centre for green iron.
“For the Pilbara to remain a major source of jobs and growth for Australia in the decades ahead, it must evolve from exporting iron ore to producing green iron,” he said.
“As global customers increasingly demand affordable green iron, there is an incredible strategic opportunity for the Pilbara to combine its world-class renewable energy and iron ore resources to capture more of the value chain.”
ICE has previously indicated a final investment decision was expected in 2028 with first power anticipated in 2030.
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© Fraunhofer ISE / Foto: Dirk Mahler | Performing light-induced copper deposition on an inline electroplating system for the metallization of c-Si solar cells with a layer stack of nickel, copper, and silver.
Scientists at the Fraunhofer Institute for Solar Energy Systems ISE have succeeded in reducing the silver consumption of TOPCon solar cells to 1.1 milligrams per watt peak.
Currently, TOPCon solar cells require an average of 10 to 12 milligrams of silver per watt peak. To the reduction, they tested an electroplating-based inline metallization process on pilot systems developed by RENA Technologies GmbH. By combining ultrashort UV laser structuring with the electrochemical deposition of nickel, copper, and silver, the research team produced M10-sized TOPCon solar cells with an efficiency of 24 percent. Compared to PERC solar cells, TOPCon solar cells have higher silver consumption, hence solar cell manufacturers are under particular cost pressure to reduce it.
While silicon heterojunction and IBC solar cells are already successfully metallized with printed silver-copper or pure copper contacts, printed copper metallization for TOPCon solar cells is still in the testing phase. At the same time, this is currently the most widely produced cell type and the one with particularly high silver consumption. Electroplated copper contacts have the potential to almost completely replace the silver requirements of TOPCon solar cells. Nickel serves as a diffusion barrier against copper migration into the cell, copper handles the electrical conduction, and a minimal amount of silver remains as oxidation protection.
“So-called nickel/copper electroplating could be firmly established in the photovoltaic market within two to three years,” says Dr. Sven Kluska, group leader for electrochemical processes at Fraunhofer ISE. “It would offer many advantages for solar cell manufacturers, even if they have to integrate electroplating equipment into their production process as an initial investment.”
Working in a consortium with the equipment manufacturer RENA Technologies GmbH, the scientists demonstrated in the research projects “EURO” and “SHINE PV” that electroplating metallization is technically feasible and can be implemented on an industrial scale. They metallized several batches of M10 TOPCon solar cells on an inline electroplating system, achieving efficiencies of 24 percent. This corresponds to the efficiency of the reference solar cells, whose silver contacts were applied using the conventional screen-printing process. To verify compliance with low contact resistance and high fill factors, they demonstrated a fill factor of 82.1 ± 0.3 percent for a batch of 186 TOPCon solar cells. The solar modules manufactured with the solar cells demonstrated very good stability in degradation tests according to IEC61215.
“Metallization via electroplating could also lead to significantly less dependence on China than is currently the case with silver pastes for the screen-printing metallization commonly used today,” said Dr. Florian Clement, Head of the Metallization and Structuring Technologies Department at Fraunhofer ISE. “Equipment and chemicals for copper electroplating come from European and American manufacturers; there is a global market for raw copper, without a concentration on Chinese suppliers. At the same time, we at Fraunhofer ISE are working intensively to establish European, resilient supply chains for copper-based screen-printing metallization.”
In the screen-printing process as well, there is the option of replacing silver pastes with hybrid silver-copper or pure copper pastes. However, implementation on TOPCon solar cells is considerably more difficult compared to silicon heterojunction solar cells with a TCO layer (transparent conductive oxide layer), which acts as a copper diffusion barrier; this is why researchers worldwide are also further developing electroplating metallization for TOPCon solar cells.
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A research team in the United States has fabricated a type of solar cell using screen-printed copper contacts on its rear side, according to a report from pv magazine. The cells, which feature a silver front contact, underwent a laser-enhanced contact optimization process to reduce electrical resistance at the contact points.
The team utilized a specialized copper paste designed to inhibit copper diffusion. This paste can be screen-printed and fired in air and is compatible with commercial silver pastes. The cells were built on standard n-type wafers, with a boron-diffused emitter on the front and a full-area TOPCon stack on the rear. The silver front grid was fired at a high temperature, while the copper rear contact was fired at a lower temperature to prevent copper migration.
The laser optimization treatment was applied with varying electrical bias to improve performance. Analysis showed the treatment drastically lowered contact resistivity at the rear. Microstructural examination revealed that copper was confined to the intended poly-silicon layer, improving contact without degrading key electrical parameters.
Through systematic optimization of printing and firing conditions, the team found that a specific firing temperature range provided stable voltage and fill factor, with performance degrading at higher temperatures due to copper diffusion. Electroluminescence imaging confirmed improved contact quality at the optimal temperature.
When compared to reference cells using full silver contacts, the copper-contacted cells demonstrated comparable open-circuit voltage and pseudo fill factor, though with marginally lower short-circuit current and fill factor. The optimized copper cells achieved an efficiency of 24.3%, which was slightly below that of the silver-reference cells. Stability testing under thermal stress showed negligible performance change over an extended period.
The researchers concluded that the high-efficiency copper-contacted cells present a viable opportunity to replace more expensive silver contacts in production, as the required tools and processes are already industry-standard. The work was detailed in the journal Solar Energy Materials and Solar Cells by a team including academics from a U.S. Department of Energy national laboratory, the Georgia Institute of Technology, and a copper paste specialist firm.
Interactive table based on the Store Companies dataset for this report.
This report provides a comprehensive view of the solar cells and light-emitting diodes industry in the United States, tracking demand, supply, and trade flows across the national value chain. It explains how demand across key channels and end-use segments shapes consumption patterns, while also mapping the role of input availability, production efficiency, and regulatory standards on supply.
Beyond headline metrics, the study benchmarks prices, margins, and trade routes so you can see where value is created and how it moves between domestic suppliers and international partners. The analysis is designed to support strategic planning, market entry, portfolio prioritization, and risk management in the solar cells and light-emitting diodes landscape in the United States.
The report combines market sizing with trade intelligence and price analytics for the United States. It covers both historical performance and the forward outlook to 2035, allowing you to compare cycles, structural shifts, and policy impacts.
This report provides a consistent view of market size, trade balance, prices, and per-capita indicators for the United States. The profile highlights demand structure and trade position, enabling benchmarking against regional and global peers.
The analysis is built on a multi-source framework that combines official statistics, trade records, company disclosures, and expert validation. Data are standardized, reconciled, and cross-checked to ensure consistency across time series.
All data are normalized to a common product definition and mapped to a consistent set of codes. This ensures that comparisons across time are aligned and actionable.
The forecast horizon extends to 2035 and is based on a structured model that links solar cells and light-emitting diodes demand and supply to macroeconomic indicators, trade patterns, and sector-specific drivers. The model captures both cyclical and structural factors and reflects known policy and technology shifts in the United States.
Each projection is built from national historical patterns and the broader regional context, allowing the report to show where growth is concentrated and where risks are elevated.
Prices are analyzed in detail, including export and import unit values, regional spreads, and changes in trade costs. The report highlights how seasonality, freight rates, exchange rates, and supply disruptions influence pricing and margins.
Key producers, exporters, and distributors are profiled with a focus on their operational scale, geographic footprint, product mix, and market positioning. This helps identify competitive pressure points, partnership opportunities, and routes to differentiation.
This report is designed for manufacturers, distributors, importers, wholesalers, investors, and advisors who need a clear, data-driven picture of solar cells and light-emitting diodes dynamics in the United States.
The market size aggregates consumption and trade data, presented in both value and volume terms.
The projections combine historical trends with macroeconomic indicators, trade dynamics, and sector-specific drivers.
Yes, it includes export and import unit values, regional spreads, and a pricing outlook to 2035.
The report benchmarks market size, trade balance, prices, and per-capita indicators for the United States.
Yes, it highlights demand hotspots, trade routes, pricing trends, and competitive context.
Report Scope and Analytical Framing
Concise View of Market Direction
Market Size, Growth and Scenario Framing
Commercial and Technical Scope
How the Market Splits Into Decision-Relevant Buckets
Where Demand Comes From and How It Behaves
Supply Footprint and Value Capture
Trade Flows and External Dependence
Price Formation and Revenue Logic
Who Wins and Why
How the Domestic Market Works
Commercial Entry and Scaling Priorities
Where the Best Expansion Logic Sits
Leading Players and Strategic Archetypes
How the Report Was Built
Major US solar manufacturer
Residential & commercial solar
Former Cree LED business
Spin-off from SunPower
Specialty & high-power LEDs
LED technology & solutions
Advanced photonics
Residential solar panels
CIGS solar technology
US & Canadian manufacturing
North American manufacturing
US-made solar panels
US operations of Korean parent
3D architecture LEDs
High-quality lighting
High-brightness microdisplays
Disinfection & purification
US crystalline silicon solar
Next-generation tandem cells
Tandem cell technology
Manufacturing equipment
Turnkey production lines
Distributor & assembler
Residential & commercial
Former Philips business
Specialty & horticultural
Military & commercial
Aluminum nitride substrates
Materials for UV LEDs
US division of Kyocera
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The Canadian province of Ontario has concluded its largest-ever public power procurement initiative, according to a report from pv magazine. The province’s Independent Electricity System Operator has granted approval to 12 solar projects with a total capacity of 915.1 megawatts. It has also agreed to two wind projects with a combined 400 megawatts of capacity.
This marks the first significant competitive procurement for new wind and solar resources in Ontario in more than ten years. The 14 approved projects together represent over 1.3 gigawatts of new generating capacity. They are projected to contribute more than 3 terawatt-hours of new annual electricity to the provincial grid, an amount sufficient to power more than 350,000 homes.
Each selected project features a minimum of 50 percent Indigenous equity ownership. The approved solar projects vary in size from 9 megawatts to 200 megawatts, with four exceeding 100 megawatts. The largest solar projects include the 200-megawatt Dunns Valley Solar site, the 167.2-megawatt CarbonFree Fort Frances project, the 154-megawatt CarbonFree Kynoch project, and the 141.25-megawatt Massey Solar project.
The procurement process, known as Long-Term 2 Energy Window 1, was initially announced in 2024. The chosen projects will receive 20-year agreements and are anticipated to achieve commercial operation by the beginning of May 2030. The system operator is currently finalizing contracts with the selected proponents and will disclose the associated pricing once contracts are fully executed, which is expected by May of this year.
The President and CEO of the Canadian Renewable Energy Association stated that the procurement outcome demonstrates renewable energy is prepared to address demands for rapid growth. The association previously projected that Canada’s total solar capacity could rise significantly by the middle of the next decade, largely due to upcoming utility-scale procurements across nine provinces.
Interactive table based on the Store Companies dataset for this report.
This report provides a comprehensive view of the global solar cells and light-emitting diodes industry, tracking demand, supply, and trade flows across the worldwide value chain. It explains how demand across key channels and end-use segments shapes consumption patterns, while also mapping the role of input availability, production efficiency, and regulatory standards on supply.
Beyond headline metrics, the study benchmarks prices, margins, and trade routes so you can see where value is created and how it moves between exporters and importers worldwide. The analysis is designed to support strategic planning, market entry, portfolio prioritization, and risk management in the global solar cells and light-emitting diodes landscape.
The report combines market sizing with trade intelligence and price analytics. It covers both historical performance and the forward outlook to 2035, allowing you to compare cycles, structural shifts, and policy impacts across countries and regions.
For the global report, country profiles provide a consistent view of market size, trade balance, prices, and per-capita indicators. The profiles highlight the largest consuming and producing markets and allow direct benchmarking across peers.
The analysis is built on a multi-source framework that combines official statistics, trade records, company disclosures, and expert validation. Data are standardized, reconciled, and cross-checked to ensure consistency across time series.
All data are normalized to a common product definition and mapped to a consistent set of codes. This ensures that comparisons across time are aligned and actionable.
The forecast horizon extends to 2035 and is based on a structured model that links solar cells and light-emitting diodes demand and supply to macroeconomic indicators, trade patterns, and sector-specific drivers. The model captures both cyclical and structural factors and reflects known policy and technology shifts.
Each country projection is built from its own historical pattern and the regional context, allowing the report to show where growth is concentrated and where risks are elevated.
Prices are analyzed in detail, including export and import unit values, regional spreads, and changes in trade costs. The report highlights how seasonality, freight rates, exchange rates, and supply disruptions influence pricing and margins.
Key producers, exporters, and distributors are profiled with a focus on their operational scale, geographic footprint, product mix, and market positioning. This helps identify competitive pressure points, partnership opportunities, and routes to differentiation.
This report is designed for manufacturers, distributors, importers, wholesalers, investors, and advisors who need a clear, data-driven picture of global solar cells and light-emitting diodes dynamics.
The market size aggregates consumption and trade data at country and regional levels, presented in both value and volume terms.
The projections combine historical trends with macroeconomic indicators, trade dynamics, and sector-specific drivers.
Yes, it includes export and import unit values, regional spreads, and a pricing outlook to 2035.
The report provides profiles for the largest consuming and producing countries, enabling benchmarking across peers.
Yes, it highlights demand hotspots, trade routes, pricing trends, and competitive context.
Report Scope and Analytical Framing
Concise View of Market Direction
Market Size, Growth and Scenario Framing
Commercial and Technical Scope
How the Market Splits Into Decision-Relevant Buckets
Where Demand Comes From and How It Behaves
Supply Footprint, Trade and Value Capture
Trade Flows and External Dependence
Price Formation and Revenue Logic
Who Wins and Why
Where Growth and Supply Concentrate
Commercial Entry and Scaling Priorities
Where the Best Expansion Logic Sits
Leading Players and Strategic Archetypes
Detailed View of the Most Important National Markets
How the Report Was Built
Largest solar manufacturer globally
Leading monocrystalline silicon producer
Major module and cell producer
High-efficiency cell and module maker
Global manufacturer and project developer
Major player in US and EU markets
Integrated PV product manufacturer
Leading thin-film CdTe manufacturer
World's largest solar cell producer
ABC cell technology leader
Major LED component and display maker
Pioneer and major supplier of LED chips
Historically leading innovator in LED technology
Leading European optoelectronics supplier
High-power LED and automotive lighting
One of world's largest LED chip producers
Major LED packaging and component supplier
Leading Taiwanese LED chip manufacturer
Innovator in WICOP and SunLike technologies
LED components for automotive and IT
IBC cell technology leader
Solar project developer and manufacturer
Integrated PV manufacturer
Historically significant in both fields
Rapidly growing cell and module producer
Solar manufacturing arm of Chint Group
Module manufacturer with US focus
Leading Indian solar manufacturer
LED packaging and lighting solutions
Major LED packaging company
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IndexBox, Inc.
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Claymont, DE 19703, USA
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On a hot summer afternoon in Chicago’s South Side, electricity bills spike just as the air turns unbearable. For many families, the choice is stark: run the air conditioner and risk falling behind on bills, or sweat it out and hope the heat breaks. This is the reality for millions of Americans living in rental housing, multifamily buildings, or neighborhoods where installing rooftop solar is simply not an option.
For years, solar energy has been framed as a climate solution. Too often, it’s only an option for people who can afford a house, a roof, and thousands of dollars upfront. Community solar is quietly changing that equation.
Instead of panels on individual rooftops, community solar projects place solar arrays in shared locations — such as vacant lots, brownfields, former landfills, parking canopies, or agricultural land — allowing multiple customers to subscribe to and benefit from the energy produced. The benefits of the solar output flow to individuals, businesses, nonprofits, and other community members who receive credits on their utility bills without installing anything themselves.
Increasingly, these programs are being designed with equity at the center.

Community solar isn’t new, but recent “Solar for All” initiatives are pushing the model further by prioritizing low- and moderate-income households located in communities that face disproportionate energy costs and climate pollution.
For instance, the Illinois Solar for All program was created to expand access to clean energy for households earning 80% or less of the area’s median income. Participants can subscribe to community solar projects with guaranteed savings, often without credit checks or upfront fees. For families already spending a disproportionate share of income on utilities, even modest monthly savings can be meaningful.
New York has taken a similar approach. The state now leads the nation in installed community solar capacity, supported by policies that encourage projects to serve renters, affordable housing developments, and environmental justice communities. In recent years, hundreds of New Yorkers have subscribed to community solar, many of whom would never have been able to afford installing rooftop solar panels. On average, subscribers save 5-10% on their electricity bills, which can translate to $100-$200 per year depending on usage.
For some households, that may sound modest. But for a senior on a fixed income, that could mean covering a month of prescription medication . For a parent, it could mean groceries that stretch to the end of the month, a child’s school supplies, or simply the ability to keep the air conditioning running during a heat wave without fear of falling behind. Community solar doesn’t just lower bills — it creates breathing room.
Washington D.C.’s Oxon Run Solar Project shows what’s possible when local governments think creatively. Built on a former landfill in Southeast D.C., the project supplies solar power to nearby households, delivering direct bill credits to residents who have historically faced high energy costs and underinvestment.
These programs don’t just reduce emissions. They reduce inequality.

Energy burden — the percentage of household income spent on energy bills — is one of the clearest indicators of energy inequity. Low-income households in the United States often spend up to three times more of their income on electricity and energy costs than higher-income households. Black, Latino, and Indigenous communities face disproportionately high energy burdens due to historic redlining, chronic underinvestment, and closer proximity to polluting infrastructure.
According to the U.S. Department of Energy, community solar plays a key role in expanding clean energy access by lowering barriers related to homeownership, credit requirements, and upfront costs – obstacles that have historically excluded many households from the renewable transition.
Just as important, community solar projects can be built faster and at scale. A single solar array can serve hundreds or even thousands of households, accelerating emissions reductions while spreading financial savings more evenly.
The benefits don’t just stop at the meter.
Community solar projects create local jobs in construction, maintenance, and administration. Nationwide, the solar industry already employs hundreds of thousands of workers, and community-scale projects are among the fastest-growing segments of the market. These are jobs that cannot be outsourced because they involve on-site construction, electrical work, operations, and long-term maintenance that must be performed locally.
There’s also a resilience advantage. Distributed solar generation, including community solar, reduces reliance on large, centralized fossil fuel power plants. Those facilities are especially vulnerable to extreme weather — including heat waves, hurricanes, and winter storms — which can overwhelm aging electric grids and trigger widespread outages.
In 2021, during Winter Storm Uri in Texas, millions of residents lost power for days as centralized power plants and natural gas infrastructure failed in freezing temperatures. More than 200 people died, and families were left without heat, light, or safe drinking water. The crisis exposed how fragile heavily centralized systems can be in the face of extreme weather.
By generating electricity closer to where it is used locally, sited solar can help stabilize electric systems during periods of high demand. When paired with energy storage — such as batteries that store excess power for use during outages or peak house — community solar can continue delivering electricity even when the broader grid is disrupted. Modern grid management tools further enhance this resilience by balancing supply and demand in real time, reducing the scale and duration of outages during extreme weather events.
PULLQUOTE: “Energy justice means the goal of achieving equity in both the social and economic participation in the energy system, while also remediating social, economic, and health burdens on those historically harmed by the energy system.” – U.S. The Department of Energy, Office of Energy Justice

Where community solar thrives, it is almost always because strong policy made it possible.
States like Illinois, New York, and Colorado have passed legislation requiring utility companies to support community solar development and ensure a portion of their capacity is reserved for low-income subscribers. In 2022, the Inflation Reduction Act authorized major federal investment through new grants and tax incentives aimed at underserved communities, but ongoing political challenges now threaten how fully those commitments are realized — if they are realized at all.
But access is still unequal. Many states — including Alabama, Mississippi, and South Carolina — lack enabling legislation to create or manage community solar programs, leaving millions without the option to participate. In others, program caps and restrictive enrollment limits in states such as Massachusetts, Minnesota, and New York have periodically constrained how much community solar capacity can come online, slowing progress just as demand slows. .
This patchwork approach highlights a broader truth: the clean energy transition isn’t just about technology — it’s about political will.
The United States is at a crossroads. Electricity demand is rising as extreme heat intensifies, vehicles electrify, and aging infrastructure strains under growing pressure. At the same time, millions of households remain locked out of clean energy simply because they rent, lack upfront capital, or live in states without enabling policies.
Community solar offers something rare in today’s political climate: a solution that lowers bills, reduces emissions, strengthens grid resilience, and expands economic opportunity all at once. It turns clean energy from a private upgrade into shared infrastructure.
Solar power doesn’t just belong on rooftops. It belongs in neighborhoods.
Earth Day 2026 is centered on one powerful idea: Community action. Because real climate solutions don’t start in boardrooms — they start where people live.
Community solar is a perfect example of what happens when neighbors, local leaders, and grassroots organizers work together to reshape energy systems so they serve everyone, not just a privileged few. These projects succeed because communities demand them, design them, and benefit from them collectively.
Here’s how you can turn community action into real impact:
You can help expand access to clean energy by supporting community-led solar initiatives and pushing for policies that make them widespread and equitable. Add your name to the Renewable Energy Petition asking world leaders to triple renewable energy generation by 2030. If you’re in the U.S., send a message to your local lawmakers asking them to stop rollbacks and promote renewable energy so community solar can grow in every neighborhood.
Climate change is global, but solutions are built block by block. When communities organize, share power, and act together, clean energy becomes more than a technology. It becomes a tool for justice.
by Samantha Burchard earthday.org

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Updated on: 11 April,2026 10:03 AM IST &nbsp|&nbsp London
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The United Kingdom has approved the 800MW Springwell Solar Farm, set to power around 180,000 homes. The move marks a major step in the country’s renewable energy push, alongside initiatives like rooftop solar and space-based solar exploration

The solar farm will be located between Lincoln and Sleaford. REPRESENTATION PIC/ISTOCK

The UK government has approved the country’s largest power-producing solar farm. 
The 800MW Springwell Solar Farm, located between Lincoln and Sleaford, is expected to generate enough electricity to power around 180,000 homes. 
In February, the UK took steps toward space-based solar power after a study found it could supply clean electricity. 
Recent measures include rolling out plug-in solar systems in retail stores and mandating solar installations as standard for all new homes in England.
This story has been sourced from a third party syndicated feed, agencies. Mid-day accepts no responsibility or liability for its dependability, trustworthiness, reliability and data of the text. Mid-day management/mid-day.com reserves the sole right to alter, delete or remove (without notice) the content in its absolute discretion for any reason whatsoever

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India is at the cusp of a solar manufacturing revolution, and the race to become a hub for solar panel manufacturing in India is well and truly underway. With the country targeting up to 500 GW of non-fossil fuel capacity by 2030, domestic production of solar modules and components has become of strategic importance. The drive is not merely to add more solar power installations, but to build an indigenous manufacturing ecosystem that encompasses the best solar panel manufacturing company in India, and a globally competitive supply chain.
Between 2014 and 2025, India’s solar module manufacturing capacity listed under the Approved List of Models & Manufacturers (ALMM) has grown dramatically — from just 2.3 GW to over 100 GW. In this context, the list of top 10 solar panel manufacturers in India, including leading companies that manufacture solar panels, modules, cells, and related components, matters more than ever. These firms are not just suppliers of equipment — they are central players in India’s attempt to shift from being a solar project importer to becoming a solar manufacturing hub.
India’s solar manufacturing story is moving at a pace:
What this means for investors and industry watchers:
The business of manufacturing solar panels in India is shifting from small-scale and opportunistic to large-scale, strategic, and globally competitive. As we turn to the top solar panel manufacturers in India, the ones with manufacturing scale, technology prowess, and supply-chain control will stand out.
Waaree Energies, founded in 1990 and headquartered in Mumbai, is the largest solar panel manufacturer in India. The company currently operates an annual module manufacturing capacity of 15 GW, alongside a 5.4 GW solar-cell plant in Chikhli, Gujarat — the state leading India’s renewable manufacturing.
In August 2025, Waaree expanded with a new 1.8 GW AI-enabled line, raising its total to 16.8 GW. It aims to reach 25 GW by FY 2027 through backward integration into cells, ingots, and wafers. Waaree Energies has also become one of the most trusted solar panel manufacturers in Gujarat, exporting high-efficiency modules to the US and Europe.
Products: Solar PV modules: monocrystalline, polycrystalline, N-type, HJT, bifacial, flexible & BIPV modules.
Headquartered in Kolkata with production units in West Bengal and Tamil Nadu, Vikram Solar is a top-tier solar panel manufacturing company in India recognized for technological excellence. As of 2025, it has scaled its capacity to 4.5 GW, up from 3.5 GW a year earlier, and plans continuous upgrades to support TOPCon and HJT technologies.
Vikram Solar ranks among the top solar panel manufacturers in India for both domestic and export markets, having secured large contracts including a 1 GW supply to JSW Neo Energy. It serves 39 countries and has been rated a “Top Performer” by PVEL for seven consecutive years — a rare distinction among Indian manufacturers.
Products: PV modules using mono-PERC, bifacial, TOPCon, and HJT technologies. (Also provides EPC services.)
A part of the Adani Group, Adani Solar is India’s most vertically integrated solar panel manufacturer, covering the entire value chain from ingots and wafers to cells and modules. Headquartered at Mundra SEZ in Gujarat, it operates 4 GW of cell and module capacity with plans to scale to 10 GW by mid-2026.
Adani Solar has shipped over 15,000 MW of modules worldwide and is positioned as a key player among the top solar panel manufacturers in Gujarat and India. Its vertically integrated operations make it a strategic asset for India’s goal of becoming a solar export hub.
Products: Solar PV modules (Mono PERC, TOPCon, bifacial) • Solar ingots & wafers (upstream manufacturing)
Based in Hyderabad, Premier Energies is a well-established solar panel manufacturing company in India, producing both cells and modules. It operates 3.2 GW of cell capacity and 6 GW of module capacity as of 2025, with plans to expand to 10–11 GW by FY 2027.
The company has secured orders worth over INR 2,700 crore for 2,059 MW of solar modules and cells, cementing its status among India’s top solar panel manufacturers. Premier Energies focuses on high-efficiency cells, multi-busbar modules, and R&D to serve India’s growing solar exports market.
Products: Solar cells • PV modules (mono-PERC, multi-busbar, high-efficiency configurations)
Founded in 2011 and based in Surat, Goldi Solar has emerged as one of the fastest-growing solar panel manufacturers in Gujarat. The company expanded its capacity from 3 GW to 14.7 GW within a year, making it a leading solar panel manufacturer in India’s renewable sector. Goldi is building an AI-powered module plant and plans to launch 4 GW of cell manufacturing by 2026, advancing India’s self-reliance agenda. Its export-grade modules are highly demanded in Europe and Africa.
Products: High-efficiency PV modules (mono-PERC, bifacial, advanced automation)
Emmvee Solar, headquartered in Bengaluru, is among the fastest-growing solar panel manufacturers in India. The company operates across two major verticals — solar PV modules and solar water-heating systems — giving it a unique position in the renewable energy ecosystem.
The company’s module manufacturing capacity is estimated at ~6.6 GW and cell capacity around ~2.5 GW as of 2025. The company plans to expand these to 16.3 GW (modules) and 8.94 GW (cells) by mid-2028, positioning itself among the top solar panel manufacturers in India. Emmvee’s production facilities follow international quality standards and have obtained TÜV, UL, and IEC certifications. Its diverse product mix allows it to cater to residential, industrial, and utility-scale projects both domestically and in exports to Europe and Africa.
Products: Solar PV modules; solar water-heating systems
Founded in 2015 and based in Haryana, Saatvik Green Energy is an emerging solar panel manufacturer with a strong regional presence among solar panel manufacturers in Delhi NCR. The company has expanded its module manufacturing capacity to 3.8 GW as of 2025, and is setting up a greenfield facility in Odisha with 4.8 GW cell and 4 GW module capacity.
In FY 2024-25, the company reported revenue of INR 2,192.5 crore and net profit of INR 213.9 crore. Its product range includes mono-PERC and N-TOPCon modules, including half-cut and multi-busbar designs, supplying utility, commercial, and residential projects.
Products: Solar PV modules (mono-PERC, N-TOPCon), solar pumps & related solutions
Rayzon Solar, headquartered in Surat, has rapidly positioned itself as one of the leading solar panel manufacturers in Gujarat. The company currently operates with an installed capacity of approximately 6 GW, with ambitious plans to reach 12 GW by late 2025. Its expansion is supported by automation, advanced manufacturing, and a public issue filing of INR 1,500 crore aimed at scaling production lines and entering upstream cell manufacturing.
Rayzon’s focus on advanced technologies such as TOPCon and bifacial modules aligns with India’s push for high-efficiency solar adoption. The company has already executed several large-scale EPC projects, solidifying its reputation as a high-quality solar panel manufacturer.
Products: Solar PV modules (mono-PERC, N-type TOPCon, bifacial) and upcoming cell manufacturing
RenewSys India (part of the ENPEE Group) is distinctive for producing both solar modules and key upstream components (encapsulants, backsheets). As of 2025, RenewSys operates ~3 GW of module capacity, with encapsulant capacity of 69 million sqm and backsheet capacity of 24 million sqm.
Its manufacturing units in Bengaluru and Hyderabad serve both domestic and international clients, making it one of India’s few fully backward-integrated solar panel manufacturers. RenewSys’s supply partnerships across Asia, the Middle East, and Africa enhance India’s global competitiveness in solar manufacturing.
Products: Solar PV modules, encapsulants, backsheets
ReNew Power, primarily known as a renewable-energy developer, has expanded its footprint to become one of the largest solar panel manufacturers in India. Headquartered in Gurugram, the company operates solar-module manufacturing plants in Jaipur and Dholera (Gujarat), with a combined capacity of 6.4 GW (modules) and 2.5 GW (cells).
The company’s strategic advantage lies in leveraging its project portfolio of over 14 GW operational capacity, ensuring strong in-house demand for its modules. ReNew Power’s entry underscores a growing trend of renewable-energy developers integrating backward into manufacturing to ensure supply stability and cost competitiveness.
Products: Solar PV modules; solar cells
Also Read: Top Green Hydrogen Stocks in India: Opportunities and Leading Players
Setting up a solar panel manufacturing plant in India has become increasingly cost-efficient thanks to economies of scale and policy incentives. On average, establishing a solar panel manufacturing company in India costs between INR 4 crore and INR 6 crore per MW of module capacity, depending on automation level, imported machinery, and chosen technology (polycrystalline, mono-PERC, or TOPCon).
A fully integrated 1 GW facility covering ingots, wafers, cells, and modules requires INR 4,000 – INR 5,500 crore in capital. The government’s Production-Linked Incentive (PLI) scheme has allocated more than INR 24,000 crore to subsidize around 48 GW of integrated manufacturing lines. Alongside this, customs duties of 25 % on cells and 40 % on modules, concessional green-energy tariffs, and accelerated-depreciation benefits have improved industry profitability.
These incentives are drawing new entrants like Rayzon Solar and Emmvee Solar, both expanding capacity aggressively. Analysts expect average production costs to fall below USD 0.20/Wp by FY 2027, making Indian solar panel manufacturers competitive with peers in Southeast Asia.
Gujarat has emerged as the industrial backbone for solar panel manufacturers in India, housing some of the largest names among the Top 10 Solar Panel Manufacturers in India — including Waaree Energies, Adani Solar, Goldi Solar, and Rayzon Solar. More than 45 % of India’s installed module capacity lies here. Gujarat’s port access, renewable-power availability, and dedicated zones such as Dholera SEZ and Surat Solar Park make it the primary hub for solar panel manufacturers in Gujarat.
Delhi NCR, by contrast, functions as a downstream hub—home to EPC contractors, distributors, and component assemblers. Firms such as Saatvik Green Energy (Ambala) and RenewSys maintain sales and service bases here. NCR’s logistical proximity to large industrial and commercial consumers also supports rapid adoption of rooftop and captive solar systems.
Meanwhile, Delhi NCR serves as a commercial and distribution center, home to EPC contractors, distributors, and mid-tier solar panel manufacturers in Delhi and Delhi NCR, such as Saatvik Green Energy and RenewSys India. The region’s strong logistics network supports the rapid deployment of rooftop and industrial solar systems across northern India.
Together, these hubs illustrate how the nation’s solar manufacturing landscape is geographically diversified — with Gujarat focusing on large-scale industrial production and Delhi NCR driving end-market connectivity and service integration.
India’s solar manufacturing ecosystem has evolved from a fragmented base into a globally competitive sector. Driven by strong government policy, rising domestic demand, and a surge in exports, solar panel manufacturers in India are achieving economies of scale once exclusive to East Asian producers.
Companies such as Waaree Energies, Vikram Solar, and Adani Solar now rival China’s second-tier players in output and efficiency, while firms like Premier Energies and RenewSys India strengthen upstream integration. With over 100 GW of module capacity and 25 GW of cells, India is on course to become a net exporter of solar equipment before the decade’s end.
Mahesh Yadav is a prolific writer with over a decade of professional experience. A person of wide interests, he found his true calling in the field of investing and specifically the stock market. He has an amazing skill of presenting the most complex financial concepts (and there is no shortage of complexity in investing) in simple language and terms.
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A faceted roof encircles a central courtyard at Écosystème D, an energy hub and engineering workspace in Dunkirk designed by architecture studios Snøhetta and Santer Vanhoof.
According to Snøhetta and Santer Vanhoof, the 1,200 square metres of photovoltaic panels that top the angular roof produce more energy than the building consumes. The surplus energy is distributed to the surrounding port development, which is currently being transformed into an innovation hub.
Made from a wooden structure clad in metal panels, Écosystème D contains a technology hall, an incubator, a training centre, a showroom and workspaces.
It was designed to facilitate research and training in renewable energy through collaborations with industrial and institutional partners, including engineering schools and companies committed to decarbonisation.
“Rooted in the port landscape of Dunkirk, Écosystème D is a positive energy building designed to serve as a catalyst for energy transition,” said Snøhetta.
“The entire structure is part of a low-impact, bioclimatic architecture that embodies innovation.”
Designed to minimise energy needs, the building was arranged around a central planted courtyard that lets light and natural ventilation in.
A wide staircase with stepped seats rises around the courtyard, connecting the training centre, workspaces, labs, and administrative spaces.
The shape of Écosystème D was designed around the prevailing winds of the port location, with an angular roof that rises and falls between nine and 18 metres tall depending on the needs of the interior space below.
Overhanging roofs help shade the interior, and the building’s envelope was designed to have a high thermal performance, including its triple-glazed windows, insulation and joinery.
Other projects recently completed by Snøhetta include a theatre in Australia cloaked in an undulated glass facade and a theatre in France that was renovated to have a tilted glass hall.
The photography is by Nicolas Fussler.
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A daily newsletter containing the latest stories from Dezeen.
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Top 10 Solar Panel Manufacturers in Hyderabad  Renewable Affairs
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Topic:Storms
Three days of storms and giant hail have wreaked havoc on parts of south-east Queensland before the summer storm season has even arrived.
Hailstones of up to 8 and 9 centimetres hit parts of Queensland over the weekend. What caused them to be so large?
The rapid change in weather left many homes and vehicles damaged, and has seen more than 2,500 insurance claims lodged.
Esk, north-west of Brisbane, was one of the hardest-hit locations, with giant hail measuring up to 9 centimetres.
Clifton and Pratten in the Darling Downs were also battered by the storms.
The early November event was far from record-setting in terms of destruction and hail size, but still caused significant damage to rooftop solar panels.
The Potter family saw giant hail at their home in Pratten, west of Warwick. (Supplied: Glen and Jo Potter)
Experts warn that if left unchecked by qualified professionals, solar panel damage can have deadly consequences. Here's what you need to know
If you suspect there may be some damage to your system, there are a few steps you can take to protect your home and family.
Matthew Duncan from Master Electricians Australia — a peak industry body — said the number one thing to avoid was the temptation of climbing on your roof.
Rooftop solar in Australia reached a record height of 4 million installations as of 2024, according to the federal energy department.  (ABC News: John Gunn)
"There can be quite a bit of damage to a solar system impacted by hail, or extreme weather events, like our recent cyclones," he said.
"Once that damage has been done to the glass face, the electrical wiring underneath is no longer protected.
"[Damage can] make electricity flow where it shouldn't, sometimes it's to the roof, if it's metallic that could become alive and possibly start a fire."
If you're concerned that your panels or system may have been impacted, Mr Duncan said best practice was to look for a "shutdown" procedure.
Mr Duncan says he thinks the dangers of electricity are well understood by the general public. (Supplied: Matthew Duncan)
"Normally, this is attached on the inverter. If you can't find that, generally, the first step is to look at your main switchboard," he said.
"Look for the 'main switch' inverter and turn that off.
"Next, find your inverter. There will be a switch called the PV array DC isolator. If you shut that off, that's going to secure all the cabling and make it safe."
But if in doubt, the best thing you can do is call in an expert, like a local electrical contractor, Mr Duncan said.
As storm season kicks into gear, he said it was a good idea for homeowners to ensure they're maintaining their solar system, with annual inspections by a qualified professional.
Mr Duncan says contacting your local solar electrician is the best way to avoid further damage.  (ABC: Glyn Jones)
Most likely, yes — but it's worth checking in with your individual insurer to make sure.
Looking at hail specifically, a spokesperson for the Insurance Council of Australia (ICA) said damage would typically be covered.
"The ICA encourages policyholders to check their Product Disclosure Statement (PDS) and speak directly with their insurer with any questions or to make a claim." 
Insurers recommend shutting down the solar system by switching off its power during a storm.  (Reuters: Tim Wimborne, file photo)
Consumer group Choice said solar panels were considered part of your house, and were therefore typically covered by home insurance, assuming your policy covered particular weather events like hailstorms.
"You should make sure the insurer amount is enough to cover the cost of the solar panel system," a Choice spokesperson said.
"The TL;DR (too long didn't read) is don't touch anything unless it's to make safe or prevent further damage, take photos of damage and let the insurer's assessors handle things."
The number one thing to avoid is climbing onto your roof if you suspect damage, Mr Duncan says. (ABC News: Monish Nand)
Meanwhile, insurer RACQ said it covered "insurable events" like hailstorms under its home insurance policy.
"During a storm, it is advised that you shut down your solar system using the safe isolation procedure as a safety measure."
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Scientific Reports volume 16, Article number: 11730 (2026) Cite this article
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Accurate probabilistic solar photovoltaic (PV) power forecasting is essential for the reliable integration of solar energy into modern power grids. This study evaluates four uncertainty quantification methods for short-term PV forecasting: Adaptive Conformal Inference (ACI), Deep Quantile Regression (DQR), Bayesian Long Short-Term Memory (BLSTM), and CatBoost quantile regression. ACI is applied as a post-processing technique that adaptively adjusts prediction intervals based on recent forecast errors. We propose a novel modification to ACI in which the miscoverage parameter is reset at the start of each day to prevent the accumulation of calibration errors during nighttime periods when PV output is zero. This reset addresses the interval inflation commonly observed in standard ACI under strong diurnal variability, leading to more stable and reliable prediction intervals. Using a five-year dataset from Wroclaw University of Science and Technology, the modified ACI achieves the highest coverage (90.96%) with a mean interval width of 12.8% of peak power. BLSTM performs comparably with 83.32% coverage and 13.74% width. CatBoost yields the sharpest intervals (11.2%) but lower coverage (81.07%), while DQR provides the lowest coverage (79.48%) and the highest Winkler score. Although tested on a single site, the data-driven, model-agnostic nature of ACI supports generalization, and its independence from weather forecasts ensures robustness.
He Tracking Clean Energy Progress report of the International Energy Agency (IEA) for the year 2023 has noted that in the year 2022 compared to all other renewable energy sources including wind energy, solar PV generation witnessed an increase of 270 TWh reaching a high of about 1300 TWh¹. This growth has in fact changed the tracking status of solar PV to “on track” from “more effort needed”. It is also mentioned that in order to reach the Net Zero Scenario by 2050 yearly additions to capacity should be about 3 times what was added in 2022 indicating further significant investments in this energy technology. Such accelerated adoption of solar PV is not without consequences, it would lead to several issues such as increased variability and uncertainty in power supply, challenges in grid stability, the need for advanced energy management strategies, voltage surges, producers participation in the electricity markets and problems related to management of balancing reserves^2,3. A solution to the challenges mentioned above is to deploy a solar PV power forecasting system that would give information concerning the future power outputs of the concerned system which would aid decision making regarding the management of critical energy infrastructure and services^4,5. Solar power forecasting methods can be classified based on numerous characteristics. Depending upon the forecast horizon they can be classified as ultrashort term methods that include forecasts ranging from mere seconds to minutes⁶, short-term forecasts that have a range from minutes to 48 h to 72 h ahead. This includes both the intra-day and day ahead forecasts⁷. Medium-term forecasts from few days to a week⁸ and Long-term forecasts from a week to as long as a few years ahead⁹. The methods can also be classified based on the type of algorithm used for producing the forecasts, it can be a statistical method such as an AutoRegressive Integrated Moving Average (ARIMA) model¹⁰ or a machine learning model such as the Convolutional Neural Network (CNN)¹¹ model or a hybrid model combining the two. Based on whether the forecasting model produces a single point value of power as the forecast output or gives intervals of power as a forecast with a certain confidence level it can be classified into deterministic or probabilistic forecasting models^12,13. Within the probabilistic class of forecasting models there is a differentiation between parametric and non-parametric models where parametric models assume a specific form for the underlying probability distribution of the data. These models typically require the estimation of a certain number of parameters from the data, which define the structure and behavior of the model¹⁴. This approach is advantageous when the data adheres well to the assumed distribution, as it allows for more straightforward interpretation and computation¹⁵. In contrast, non-parametric models do not assume any specific form for the distribution. Instead, they often rely on the data itself to model relationships and trends¹⁶. These models are particularly useful when there is limited knowledge about the underlying distribution of the data or when the data does not fit well into any known parametric form, providing flexibility and adaptability¹⁷. The above-mentioned information is also presented in Fig. 1. Recent research works on the topic of probabilistic short term solar power forecasting are as follows. A sophisticated Bayesian Optimization – Long Short-Term Memory (BO-LSTM) model integrated with time-frequency correlation mapping is employed to predict ultra-short-term solar power outputs in¹⁸. This model is rigorously evaluated against a variety of benchmark models, including Adam-LSTM, Sgd-LSTM, Rmsprop-LSTM, and Adadelta-LSTM, as well as Multi-Layer Perceptron (MLP) variants like Adam-mlp and Adagrad-mlp. Utilizing data collected over a year from a commercial solar PV station in North China, this method utilizes periodicities and variability intrinsic to solar power generation. The probabilistic forecasts are evaluated using the Power Interval Normalized Average Width (PINAW) error metric, where the BO-LSTM model demonstrates superior performance, reducing error margins compared to the benchmarks.
Classification of solar power forecasting methods.
The study¹⁹ employs a approach by integrating a Transformer-LUBE (Lower Upper Bound Estimation) model with advanced data imputation techniques, including XGBoost, Predictive Mean Matching (PMM), and bootstrapping, to enhance solar PV power forecasting. Using data from ten solar farms in Taiwan and enriched with Numerical Weather Predictions from the Taiwan Central Weather Bureau, this model quantifies uncertainty through Lower Upper Bound Estimation, enabling robust prediction intervals. When compared to other Artificial Intelligence (AI) models like Artificial Neural Networks (ANN), LSTM networks, Gated Recurrent Unit (GRU) networks, and XGBoost, this hybrid model demonstrates superior accuracy and reliability.
In²⁰ is introduced Ensemble Conformalized Quantile Regression (EnCQR), a probabilistic forecasting method that combines Quantile Regression (QR) with Conformal Prediction (CP) to generate adaptive and valid prediction intervals (PIs). EnCQR enhances the robustness and sharpness of PIs by integrating ensemble learning into QR models, which allows it to adaptively adjust PI widths based on local data variability. The method was tested on five real-world datasets, including wind power generation, solar energy production, and electricity consumption. For the forecasting models, they employed LightGBM, Neural Networks (NN), and Gradient Boosted Decision Trees (GBDT) to implement quantile regression, and compared the results with standard CP, QR, and standalone ensemble methods. The evaluation results show that EnCQR consistently outperforms standard QR and CP approaches in terms of key metrics such as Prediction Interval Coverage Probability (PICP) and Prediction Interval Width (PIW). Specifically, EnCQR achieved more accurate and tighter PIs while maintaining valid coverage. The data sets used for evaluation were sourced from publicly available repositories on energy production and consumption, such as wind power and solar generation from regional energy grids. In terms of performance, EnCQR achieved a superior balance between reliability (PICP) and efficiency (PIW) across all tested datasets, highlighting its effectiveness for nonstationary and heteroscedastic time series.
A forecasting model using a hybrid neural network that integrates sub-models for parameter estimation with a Meta-Learner to optimize these estimates is described in²¹. This model specifically employs bounded Kumaraswamy distributions to handle the intrinsic variability and limits of renewable energy outputs. Tested against both standard parametric models and advanced non-parametric techniques, such as Quantile Regression and Mixture Density Networks, the proposed approach uses data from both wind and solar sources across diverse geographical settings, including a year’s worth of data from commercial wind farms and solar plants.
The paper²² investigates PV power prediction for a 2.680 kWp photovoltaic system installed in a mountainous region, using experimentally measured data collected at 10-second intervals between February 23 and March 28, 2023. The dataset includes irradiance, temperature, voltage, current, and power, captured with sensors integrated into an outdoor experimental setup. Using this data, the authors develop and compare 14 different predictive models, including deep learning models (LSTM, Modular Neural Network, Radial Basis Function Neural Network), classical machine learning models (Support Vector Regression, Decision Tree, Random Forest, Ridge Regression, Kernel Ridge Regression), and multiple variants of linear and quantile regression. Each model is evaluated across five forecasting intervals (10 s, 1 min, 30 min, 1 h, and 1 day). The results show that the Radial Basis Function Neural Network (RBFNN) achieves the best performance for most horizons particularly at 10-second, 1-minute, 30-minute, and 1-day intervals while the Kernel Ridge Regressor provides the lowest RMSE for the 1-hour horizon. Overall, the study demonstrates that nonlinear ANN-based models, especially RBFNN, offer superior predictive accuracy for PV systems operating under the highly variable outdoor conditions of mountainous regions.
In²³ extensive regional solar PV output data, which includes numerical weather prediction (NWP) and historical output data, is leveraged to validate the proposed forecasting method. This method employs granule-based clustering (GC) to effectively segment and utilize this data, enhancing prediction intervals (PIs) for very short-term PV outputs. The direct optimization programming (DOP) further refines this approach by optimizing the overall performance cost function of the PIs. The results of this study have been compared with several benchmark forecasting models to demonstrate the effectiveness of the proposed methods. These benchmarks include traditional parametric models as well as various nonparametric approaches. The latter category includes extreme learning machine (ELM), quantile regression, and machine learning-based linear programming approaches.
According to the previously mentioned classifications for solar power forecasting methods this article presents a probabilistic forecasting method for short term solar PV power forecasting that is non-parametric and is based on Adaptive Conformal Inference. While uncertainty quantification is achieved through ACI, the underlying forecasting model is a deep learning-based LSTM stacked model. The contributions of the paper are as follows:
Development of a novel ACI procedure suitable for short term probabilistic PV power forecasts.
ACI as a tool for quantifying uncertainty in deep learning models such as the LSTM stacked model.
Comparison of the ACI performance against other state-of-the-art uncertainty quantification approaches such as the Deep Quantile Regression, Bayesian LSTM and a gradient boosting based CatBoost ensemble.
While ACI has been proposed as a general post-hoc uncertainty quantification method, existing formulations implicitly assume a continuous and smoothly evolving time series. Solar PV data, however, exhibits a strong diurnal discontinuity with long nighttime zero-generation periods, causing the adaptive miscoverage parameter αₜ to drift upward and inflate interval widths in standard ACI implementations. To address this limitation, we introduce a novel daily miscoverage-reset mechanism that reinitializes αₜ at the start of each day. This modification prevents error accumulation across diurnal boundaries and enables ACI to recalibrate adaptively to each day’s irradiance conditions. As a result, the modified ACI remains sensitive to intraday variability without carrying over irrelevant calibration artifacts from the preceding night. Empirically, this enhancement allows ACI to achieve the highest coverage (90.92%) while maintaining competitive interval sharpness, demonstrating that the proposed adjustment is essential for deploying ACI reliably in solar PV forecasting environments. The rest of the manuscript is divided into the following sections. Section II presents the LSTM stacked model and the uncertainty quantification tools investigated, which include the ACI, the DQR, the Bayesian LSTM and the CatBoost ensemble. Section III presents the data description and data preprocessing methods used. Section IV presents the evaluation metrics used to assess the performance of the forecasting models, Section V presents the results which is then followed by the conclusions and references.
LSTM models are quite popular in the literature for solar PV power forecasting due to their ability to capture temporal dependencies in time-series data, which is important given the variable nature of solar energy production²⁴. These models possess memory cells that maintain information across long sequences, allowing for better modeling of the dynamic changes in solar power output influenced by environmental factors like cloud cover and temperature^25,26. The structure of an LSTM cell is widely available in existing literature²⁷ and will not be repeated in this manuscript. The architecture of the forecasting model used is the stacked LSTM version which is shown in Fig. 2 for the ACI uncertainty quantification method.
Stacked LSTM architecture for ACI uncertainty quantification.
The architecture of the stacked model for the DQR uncertainty method is similar to that shown in Fig. 2. The difference in architecture arises due to the fact that for ACI the dense layer has only 1 neuron because it is a form of post processing uncertainty quantification method. For the DQR the dense layer of the architecture has 3 neurons since it directly predicts the quantiles [5th, 50th and 95th ]. Apart from differences in the dense layer the rest of the architecture for both uncertainty quantification approaches are identical. It includes an input layer which prepares the input data in a 3-D format of total number of samples, number of time steps and input variables or features. The input layer is followed by two layers of LSTM cells. This is followed by the respective dense layers and the output layer which simply presents the output based on the dense layer’s configuration.
To define clearly the mathematical model. The following variables are used. The historical dataset is denoted as:
and the goal is to forecast the subsequent (:left({T}_{1}right)) observations for:
The ACI procedure begins with a dataset comprising (:{T}_{0}) initial observations (:left({x}_{1},{y}_{1}right)),…,(:left({x}_{{T}_{0}},{y}_{{T}_{0}}right)) in (:{mathbb{R}}^{d}times:mathbb{:}mathbb{R}). The goal is to forecast and construct prediction intervals for the subsequent (:{T}_{1}) observations²⁸. For each forecast step (:t) within the range (:left[{T}_{0}+:1,:{T}_{0}+:{T}_{1}right]), the previously known values (:{y}_{t:-:{T}_{0}}:),….,(:{y}_{t-1}) are used to establish the forecast intervals. To form an interval (:{C}_{a}) at a specific miscoverage rate (:alpha::in :left[text{0,1}right]), condition (1) must hold defining (:{C}_{a}) as a valid interval.
To build the interval, the following steps must be followed. The data up to (:{T}_{0}) is split into random training ((:{T}_{rt}:)) and calibration sets ((:Ca{l}_{t})). Then a predictive model (µ’) such as the stacked LSTM model is trained on (:{T}_{rt}) and its performance is evaluated on (:Ca{l}_{t}). The performance is measured by a metric called the conformity score ((:{S}_{cal})) defined by (4) where (:i) represents values in the calibration set, (:mu^{prime}left({x}_{i}right)) is the predicted value and (:{y}_{i}) is the actual value.
The (1 – (:{alpha:}_{t})) quantile of the conformity scores (:{{Q}_{1-:alpha:}}_{t}left({S}_{ca{l}_{t}}right)) is computed to determine the interval size centered around the prediction for each step²⁸. Since this procedure is adaptive, in the above-mentioned process the miscoverage rate (:{alpha:}_{t}) is dynamically updated at each step using a learning rate (:gamma:) which influences the interval size according to (5) and (6).
Flowchart representation of applying the modified ACI procedure.
Figure 3 Flowchart representation of applying the modified ACI procedure.
If the actual value (:{y}_{t}) falls outside the predicted interval (:{C}_{{alpha:}_{t}}) then (:{alpha:}_{t+1}) is adjusted to be less than or equal to (:{alpha:}_{t}), increasing the interval size for subsequent forecasts, and vice versa. Given the non-continuous nature of solar PV power data, we propose a modification where the miscoverage rate (:alpha:) is reset at the end of each day to a predetermined level. This adjustment addresses the impractical expansion of prediction intervals that occurs when (:alpha:) exceeds 1 this is described by (7). This scenario often occurs overnight when solar PV panels are inactive, and the correlation between the output power at the end of one day and the beginning of the next is weak. (:{mathcal{T}}_{EOD}) represents the end of each day.
The above implementation is further explained visually through a flowchart in Fig. 3. It should also be noted that the need for this modification is not dataset specific but arises from a fundamental characteristic of solar PV generation: the globally consistent diurnal cycle, during which nighttime output drops to zero and temporal continuity between days is inherently weak. Such structural discontinuities occur in PV installations worldwide, meaning that the proposed daily miscoverage-reset mechanism is broadly applicable to any solar forecasting context employing ACI.
Unlike ACI, which is a post-processing uncertainty quantification method applied after the primary model generates point predictions, the DQR method integrates uncertainty quantification directly into the forecasting model²⁹. In DQR, the model is trained to predict specific quantiles of the target distribution as part of the forecasting process. This direct approach allows DQR to simultaneously produce forecasts and quantify uncertainty by generating prediction intervals, between quantiles such as the 5th, 50th, and 95th quantile³⁰. This integration enables the model to capture the inherent variability and uncertainty of the data more naturally during the forecasting process, rather than relying on a separate post-hoc adjustment as with ACI.
The DQR model is described in (8) where (:widehat{{y}_{q}}) is the predicted quantile at level (:q) and as mentioned above take the values corresponding to the 5th, 50th, and 95th quantiles. (:x) is the input data and (:{theta:}_{q}) represents the model parameters specific to (:q). These are the weights and biases values that are determined during model training.
The DQR model is trained by minimizing a quantile loss function, also known as the pinball loss, which is designed to focus on the accuracy of the quantile predictions³¹. The pinball loss for a given quantile is calculated according to (9). Where (:y) is the actual observed value. This loss function is non-symmetric and penalizes underestimation and overestimation differently, depending on the quantile being predicted. For example, for the 5th quantile, the loss function penalizes underestimation more heavily because the model should be conservative, ensuring that only 5% of the actual values fall below the predicted value. Conversely, for the 95th quantile, overestimation is penalized more, as the model should capture the higher end of the distribution, ensuring that 95% of the actual values are below the predicted value.
The total loss function for the model, when predicting multiple quantiles, is the sum of the pinball losses across all quantiles calculated according to (10).
In order to improve the coverage provided by the DQR algorithm a continuous adaptive quantile adjustment procedure is used in this study. This approach dynamically adjusts the predicted quantiles based on their performance relative to the actual values in the previous time step. Specifically, after each forecast, the predicted lower and upper quantiles are scaled using adjustment factors, which either widen or tighten the prediction interval based on how well the current interval captures the actual value. The adjusted lower and upper quantiles are computed by multiplying the predicted quantiles by their respective adjustment factors α and β, as shown in the Eqs. (11) and (12).
The Bayesian LSTM model inherently incorporates uncertainty estimation into the model during the training and inference stages. By placing probabilistic priors over the weights and biases of the network, Bayesian LSTM enables the direct estimation of predictive uncertainty alongside the primary forecasts³². The approach presented in this paper leverages Monte Carlo sampling with dropout during inference to approximate the posterior distributions of the model’s parameter³². As a result, it provides prediction intervals that naturally capture the variability and uncertainty present in the data, eliminating the need for external adjustment methods. This integrated approach ensures that the model learns to account for uncertainty throughout its operation, yielding probabilistic forecasts that are both robust and computationally efficient³³.
The architecture of the model in this study is built on the one shown in Fig. 2 where Monte Carlo (MC) dropout layers are introduced during both the training and inference phases. These layers enable uncertainty quantification by approximating Bayesian inference through stochastic forward passes³⁴. The model comprises two LSTM layers, followed by a dense output layer. Dropout layers are positioned after each LSTM layer, with a dropout rate p of 0.2, introducing stochasticity to the weight connections. The Bayesian aspect is achieved by enabling the dropout mechanism during inference, thus simulating multiple realizations of the model and generating a distribution of predictions. Mathematically, the output (:{h}_{t}) of the LSTM layer at time step (:t) is computed as in (13):
Where, (:{x}_{t})is the input at tine (:t), (:{h}_{t-1}) is the hidden state from the previous time step, (:{W}_{h}) and (:{U}_{h}) are weight matrices, (:{b}_{h}) is the bias vector and (:sigma:) is the activation function. The inclusion of dropout applies a masking vector m sampled from a Bernoulli distribution defined by (14):
where (:p) is the dropout rate. During forward passes, the modified output becomes as shown in (15):
where (:odot:) represents element-wise multiplication. To estimate uncertainty, MC sampling is employed during inference by performing N stochastic forward passes with dropout activated. This generates a distribution of predictions from which the mean prediction and predictive intervals are derived. The predictive intervals are computed as the 5th and 95th percentiles of the distribution according to (16):
This approach ensures that the model captures both the central tendency and the variability of the target distribution, making it well-suited for probabilistic forecasting³⁵.
CatBoost or Categorical Boosting is a gradient boosting algorithm designed to handle both numerical and categorical features efficiently. Unlike traditional gradient boosting algorithms, CatBoost incorporates ordered boosting and target-based statistics (TBS) to mitigate prediction shifts and reduce overfitting³⁶. In this paper separate CatBoost models are being trained for lower and upper quantiles, as well as the median prediction. Each model was trained using the same input features but with distinct quantile loss functions. The algorithm minimizes the quantile loss function as shown in (17)³⁷.
where, (:{y}_{i}) is the actual value, (:widehat{{y}_{i}}) is the predicted value, (:alpha:) is the quantile level and (:+) in the suffix denotes the positive part of the expression. CatBoost builds oblivious decision trees, which split the data based on the same condition across all branches at a given depth³⁷. The function at each iteration t is represented as shown in (15). Where, (:{F}_{t}left(xright)) is the model prediction at iteration (:t), (:eta:) is the learning rate and (:{h}_{t}left(xright)) is the weaker learner that approximates the residuals.
The weak learner (:{h}_{t}left(xright)) is chosen to minimize the residual error between the true target and the current prediction according to (19).
A key feature of CatBoost is the use of ordered boosting to prevent target leakage. In ordered boosting, the training examples are randomly permuted, and each example’s prediction is computed using a model trained on the preceding data points only. This approach ensures that no information from the current or future samples influences the prediction. It also replaces categorical features with their target-based statistics (TBS)³⁷ as shown below
where, (:mu:) is the prior mean of the target, (:beta:) controls the regularization strength and (:{1}_{{x}_{j}={x}_{i}}) is an indicator function.
The dataset utilized in this study was gathered from rooftop solar PV panels mounted on top of the building belonging to the Department of Electrical Engineering and Electrotechnology Fundamentals, Wroclaw University of Science and Technology. The setup comprises three distinct types of solar panels, each with a peak power output of 5 kW: monocrystalline, polycrystalline, and CIGS (Copper Indium Gallium Selenide). While the focus of this study is on data from the monocrystalline panels, the machine learning model developed using historical weather data and power output can be generalized and applied to the other panel types as well. The key parameters measured include solar irradiation (W/m²), module surface temperature (°C), ambient air temperature (°C), wind speed (m/s), and power output (kW).
While the preparation of the input data for model training varies for both the ACI and DQR procedures, in this study the features that are used apart from time related features are the historical output power data and irradiation.
Solar PV panel output power visualization.
Figure 4 illustrates the trends in the power output data over a period of five years, from January 2014 to January 2019. The dataset is split into a training and validation set, with 80% of the data used for training the model and the remaining 20% reserved for validation. The validation set, completely unseen by the model during training, is important for assessing the model’s generalization ability and forecast accuracy.
This study has two types of input features. The first one as already described is the historical power output data from the solar PV panels. The second one is time related features. They are important because they capture the natural cyclical patterns that affect solar power generation. Time-related features such as the hour of the day, day of the week, and month of the year are crucial because solar energy production is inherently tied to the Earth’s rotation and orbit³⁸. For example, the hour of the day reflects the daily solar cycle, with power generation typically peaking around midday and dropping to zero during the night. The month of the year captures seasonal variations, as longer daylight hours and higher sun angles during summer months lead to increased solar output, while shorter days and lower angles during winter reduce it. Failing to account for its periodic nature can mislead machine learning models³⁹.
The cyclical features are modelled into two components using the sine and cosine functions⁴⁰. This method ensures that the values are mapped onto a continuous circular space. For a feature (:Y), the cyclic encoding formula is given by (21) and (22) where, (:Y) is the cyclical time feature such as hour of the day or number of the month, max_value is the maximum possible value which is 24 for hours and 12 for months and (:{Y}_{sin}:) and (:{Y}_{cos}:) together represent the cyclical nature of (:Y).
The input features considered in this research vary in terms of scale, distribution, and measurement units. To prevent the LSTM models from becoming too sensitive to these differences, and to prevent issues such as large weight values during training, it is essential to apply normalization techniques. Normalizing the input data helps to bring all features to a consistent range, improving the stability and performance of the model. For this reason, min-max normalization, as expressed in Eq. (23), is applied to rescale the variables. Where, (:{x}_{i}) represents every point value of the features considered, (:minleft(xright)) represents the smallest value of the feature and (:maxleft(xright)) the largest value.
Rolling window quantiles involves calculating quantiles over a moving subset of data points (known as a window) within a time series. As the window slides over the data, quantiles are recalculated based on the values in that window, capturing local variability within the dataset. This approach provides information as to how quantile values change dynamically over time and is a crucial step involved in implementing DQR. In this study the window size chosen is 5 considering the past 5 h of values. The algorithm to implement the same is shown below:
Rolling Window Quantile Algorithm.
Since ACI is a post-processing uncertainty quantification method the data preprocessing steps are the same as that for a point forecasting LSTM model. In this case it is necessary to reshape the input data in a three-dimensional format of number of time samples, number of timesteps and the number of features. This format is also produced by means of a rolling window that slides over the entire input data reshaping it. Algorithm 2 describes this process.
Rolling Window Algorithm.
We evaluate the prediction intervals using four complementary metrics. Coverage assesses reliability by measuring how often true values fall within the predicted intervals. Mean Interval Width (MIW) reflects sharpness, with narrower intervals indicating greater precision. The Winkler Score combines both reliability and sharpness, penalizing intervals that are either too wide or fail to cover the true value. Finally, CRPS offers a holistic view of the probabilistic forecast quality by comparing the full predictive distribution to the observed outcomes. Validity is calculated as the percentage of time steps where the actual values fall within the predicted lower and upper quantiles. The formula for the same is shown in (24) where 1 is an indicator which equals 1 if the actual value lies between the predicted intervals and 0 otherwise, (:N) is the total number of predictions, (:actua{l}_{i}) is the actual value at time step (:i), (:{text{Q}}_{lower_i}) and (:{text{Q}}_{upper_i}) represent the lower and upper interval predictions at time step (:i).
MIW as described by (25) indicates the breadth of forecast prediction intervals. Narrow intervals suggest greater precision but risk missing the true values. Conversely, wider intervals are more likely to capture actual outcomes but reduce the forecast’s precision.
MIW reflects how wide or narrow the forecast prediction intervals are. Narrower intervals indicate higher precision, but if the intervals are too narrow, they might fail to capture the actual values. Wider intervals, on the other hand, may ensure coverage of the actual values but lower the precision.
The third evaluation metric is the Continuous Ranked Probability Score (CRPS) which is a scoring rule that measures the accuracy of probabilistic forecasts by evaluating the difference between the cumulative distribution function (CDF) of the forecast and the actual observed value. Unlike deterministic metrics such as Mean Absolute Error (MAE), CRPS generalizes these to probabilistic settings by comparing the entire predicted distribution with the actual observation. This metric not only assesses the closeness of the forecast to the actual value but also incorporates the sharpness of the predictive distribution. It is described by (26) where, (:Fleft(zright)) is the predicted cumulative distribution of the forecast, (:y) is the actual observation and (:{1}_{{zge:y}}) is the Heaviside function which equals 1 if (:zge:y) and 0 otherwise. A lower CRPS value indicates better probabilistic forecasts as it signifies that the predicted distribution aligns more closely with the observed data⁴¹.
The final metric used in this study is the Winkler score, which evaluates the quality of prediction intervals by jointly accounting for their width and their ability to capture the true observation. Unlike metrics that consider only interval sharpness or only coverage, the Winkler score penalizes intervals that are too wide as well as those that fail to contain the actual value⁴². For a significance level α, the score is defined as in (27), where ‘width’ denotes the difference between the upper and lower bounds, and (:{y}_{text{actual}}) is the observed value. A lower Winkler score indicates a more efficient interval one that is both narrow and reliably covers the true outcome making it a widely used measure for comparing probabilistic forecasting models.
The results for this study are obtained over a validation set that is one year long and entirely unseen by the forecasting algorithms during training. Table 1 summarizes the comparative performance of the four probabilistic forecasting methods. In terms of coverage probability, ACI achieves the highest at 90.96%, significantly outperforming the other methods. Bayesian LSTM follows with 83.32%, while CatBoost and DQR trail with 81.07% and 79.48%, respectively. The superior coverage of ACI underscores its ability to dynamically adapt prediction intervals to varying forecast uncertainty, ensuring that actual values are consistently captured.
Bayesian LSTM, though not adaptive in the same way as ACI, benefits from Monte Carlo dropout, allowing it to generate probabilistic forecasts through multiple stochastic forward passes. This mechanism supports the estimation of uncertainty by capturing a spread of plausible outcomes. However, its performance in terms of coverage (83.32%) remains lower than ACI’s 90.96%, indicating that some true values fall outside the prediction intervals. DQR and CatBoost, which are based on fixed quantile regression, offer less flexibility in adjusting intervals dynamically, resulting in even lower coverage levels of 79.48% and 81.07%, respectively. These figures suggest that static quantile estimators may struggle to accommodate changing data uncertainty over time.
With respect to interval sharpness, as measured by the MIW, DQR produces the widest intervals at 0.700 kW, followed closely by Bayesian LSTM at 0.687 kW. ACI yields narrower intervals at 0.620 kW despite achieving the best coverage, indicating its ability to maintain efficiency while being reliable. CatBoost achieves the sharpest intervals at 0.560 kW, demonstrating its strength in concentrating predictions more tightly around expected values, albeit with a trade-off in coverage performance.
In terms of CRPS, which jointly assesses the calibration and sharpness of predictive distributions, DQR shows the weakest performance with a score of 0.775, indicating poorly calibrated intervals that are not only wide but also misaligned with the actual values. ACI, while excelling in coverage, has a higher CRPS of 0.539, reflecting its conservative nature in interval estimation. Bayesian LSTM fares better with a CRPS of 0.463, balancing uncertainty estimation with predictive sharpness. CatBoost attains the lowest CRPS of 0.360, suggesting that among the models, it provides the most accurate and sharp probabilistic forecasts in terms of distributional calibration.
Winkler scores provide an additional measure by penalizing both coverage failures and excessive interval width. CatBoost again outperforms other methods with the lowest Winkler score of 1.017, indicating the most efficient uncertainty quantification. ACI and Bayesian LSTM follow with Winkler scores of 1.705 and 1.750, respectively. Despite their higher coverage, these scores reveal a slight inefficiency in their interval construction, especially for ACI, which favors conservative bounds. DQR performs the worst with a Winkler score of 2.008, further reinforcing its inadequacy in balancing coverage and sharpness.
Overall, the comparative evaluation demonstrates clear performance differences between the four uncertainty quantification methods in terms of both reliability and interval sharpness. The modified ACI method consistently provides the most balanced behavior, achieving the highest empirical coverage while maintaining relatively narrow intervals, which confirms the effectiveness of the daily miscoverage reset in stabilizing interval widths under strong diurnal variability. Bayesian LSTM also delivers competitive coverage but produces wider intervals, reflecting the inherent variability introduced by Monte Carlo sampling. CatBoost achieves the sharpest intervals across most horizons, but at the cost of noticeably lower coverage, indicating a tendency to underestimate predictive uncertainty. DQR shows the weakest performance, with both low coverage and high Winkler scores, suggesting insufficient flexibility to capture distributional asymmetry in rapidly changing PV conditions. Taken together, these results highlight that ACI provides the most robust trade-off between reliability and efficiency, making it a promising and computationally practical solution for operational PV forecasting scenarios where consistent uncertainty calibration is essential.
In addition to forecast accuracy, computational efficiency is an important consideration for operational deployment. Table 1 summarizes the total training and evaluation times of the four approaches. Bayesian LSTM is the most computationally demanding at 7 min 11 s, primarily due to the repeated stochastic forward passes required for Monte Carlo dropout sampling during inference. CatBoost is the fastest method, completing training in 1 min 15 s because of its optimized tree-based boosting architecture and lightweight quantile objective. ACI and DQR exhibit comparable runtimes of 4 min 26 s and 4 min 40 s, respectively, with ACI adding only negligible overhead beyond the base LSTM model. All experiments were conducted on a Lenovo laptop equipped with an AMD Ryzen 7 5700U CPU (16 logical cores, 1.8 GHz), 16 GB RAM, and integrated Radeon Graphics, running Windows 11 Home (64-bit).
Figure 5 presents four days randomly selected from the validation set of how the Adaptive Conformal Inference (ACI) method constructs prediction intervals for different days across the test set. These days were selected to illustrate the behavior of ACI under varying solar conditions, given that ACI achieved the highest coverage among all methods while also having moderate sharpness. The results show the trade-off between coverage and interval width across different solar generation patterns.
Forecast interval performance.
On February 24th, 2018, a day characterized by low solar generation, ACI produces relatively narrow intervals throughout most of the day, particularly during the early morning and evening hours when the generation is close to zero. However, during the peak generation period, the intervals widen significantly, with a noticeable overestimation of the peak value. Despite this, the actual values remain within the constructed intervals except during hours 8:00 and 10:00. For June 15th, 2018, a day with consistent solar availability, the intervals remain relatively well-calibrated around the actual values. The intervals are slightly wider around the midday peak, accommodating the increased variability in solar power generation. Importantly, ACI successfully covers the actual values throughout the entire day. On November 5th, 2018, which exhibits a more variable solar profile, ACI adapts by constructing intervals that capture most of the fluctuations in generation. While the coverage remains high, some intervals appear slightly wider than necessary, reflecting ACI’s trade-off between capturing variability and maintaining sharpness. Lastly, July 1st, 2018, represents a more complex scenario where solar generation fluctuates more erratically. The constructed intervals widen at various points in the day to accommodate uncertainty, but there are still instances of slight under-coverage which can be noticed at hours 11:00 and 14:00. This suggests that while ACI maintains high coverage on average, it may struggle to construct consistently sharp intervals when the underlying data exhibits irregular fluctuations.
This study assessed the performance of four uncertainty quantification methods Adaptive Conformal Inference (ACI), Deep Quantile Regression (DQR), Bayesian LSTM, and CatBoost for short-term probabilistic solar PV forecasting. Using a five-year dataset, we evaluated reliability, sharpness, and practical applicability, with particular emphasis on the proposed modification to ACI involving a daily miscoverage reset. The results reveal distinct strengths and limitations across the methods and highlight the importance of adaptive uncertainty calibration for PV systems characterized by strong diurnal variability.
The main outcomes can be summarized as follows:
ACI achieved the highest coverage (90.96%) while maintaining reasonably narrow intervals, demonstrating the effectiveness of the daily miscoverage-reset mechanism.
Bayesian LSTM showed balanced performance, providing moderate coverage and interval width, benefiting from Monte Carlo dropout–based uncertainty estimation.
CatBoost produced the sharpest intervals, but at the cost of lower coverage, indicating a tendency to underestimate predictive uncertainty.
DQR exhibited the weakest results, with low coverage and high Winkler scores, suggesting insufficient flexibility for rapidly changing PV conditions.
The modified ACI method provided the best trade-off between reliability and efficiency for operational short-term PV forecasting.
Overall, the modified ACI framework emerges as the most robust and adaptable approach for practical PV forecasting applications, offering consistently reliable uncertainty quantification under real-world diurnal variability conditions. Future research may extend the present study in several directions. First, the adaptive nature of ACI can be enhanced by exploring dynamic or data driven strategies for tuning the learning rate (:gamma:)and the baseline miscoverage level (:{alpha:}_{0}), potentially improving interval sharpness without sacrificing coverage. Second, integrating ACI with ensemble learning or Bayesian frameworks may yield more robust uncertainty estimates, particularly under highly variable weather conditions. Third, evaluating the modified ACI procedure on diverse PV installations across different climatic regions would help further validate its generalizability. Finally, investigating hybrid architectures that combine conformal methods with advanced deep learning models such as transformer-based or state-space forecasting networks may offer additional gains in reliability and computational efficiency for next-generation probabilistic PV forecasting systems.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Vishnu Suresh discloses support for the research of this work from Funder National Science Centre (NCN), Poland. Grant Number: 2022/06/X/ST8/00393.
Open access funding provided by Vellore Institute of Technology.
Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wroclaw, 50-370, Poland
Vishnu Suresh
School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, India
B. Sri Revathi
School of Aeronautics and Astronautics, Zhejiang university, Hangzhou, 310000, Zhejiang, China
Josep M. Guerrero
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**Vishnu Suresh: ** Conceptualization, Data curation, formal analysis, funding acquisition, investigation, methodology, project administration, software, visualization, writing – original draft and writing – review & editing. **B. Sri Revathi: ** Software, supervision and validation. **Josep M. Guerrero: ** Funding acquisition and supervision.
Correspondence to B. Sri Revathi.
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Suresh, V., Revathi, B.S. & Guerrero, J.M. A non-parametric adaptive conformal inference based probabilistic hour-ahead solar PV power forecasting method. Sci Rep 16, 11730 (2026). https://doi.org/10.1038/s41598-026-40911-x
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India achieved the milestone of 50 per cent of its cumulative electric power installed capacity from non-fossil fuel sources in June 2025, five years ahead of the 2030 target set under its Nationally Determined Contribution (NDC) to the Paris Agreement. India is marching towards achieving 500 GW of installed non-fossil fuel electricity capacity by 2030. As of 31 March 2026, India’s total installed non-fossil fuel capacity stood at 283.46 GW, of which solar energy has emerged as a significant component of this growth.
The solar sector has expanded at an unprecedented pace over the past decade, increasing 53.28 times since 2014. India saw solar energy installed capacity increasing from 2.82 GW in March 2014 to 150.26 GW in March 2026, i.e., an increase of 147.44 GW. The 150.26 GW includes 110.43 GW of utility scale, 25.73 GW of rooftop and 14.10 GW of KUSUM & off-grid projects. The Distributed Renewable Energy (DRE) from rooftop solar panels emerged as a major contributor. India’s solar module manufacturing capacity has also increased from 2.3 GW in 2014 to about 172 GW, as of 31 March 2026.
According to the IRENA Renewable Energy Statistics 2026, India now ranks 3rd globally in renewable energy installed capacity. India now ranks behind only China and the United States in total renewable capacity. As of 2026, India is the third largest solar energy producer in the world.

While India has significantly boosted self-reliance with domestic solar module manufacturing capacity, jumping from 2.3 GW in 2014 to about 172 GW in 2026. India remains heavily reliant on importing polysilicon, ingots, and wafers for solar panels from China. India still depends on China for over 50 per cent of its solar PV cells and solar modules. However, owing to rapid capacity expansion in the country, India is increasingly sourcing its solar modules and solar PV cells domestically. Let’s look at the breakdown of how India gets its solar panels from. 
Polysilicon, ingots, and wafers are primarily imported from China. Though, India’s domestic production capacity is around 2 GW for ingots and wafers. 
Solar PV cells are also imported from China and other Southeast Asian nations. The government is pushing domestic manufacturing through the Approved List of Models and Manufacturers (ALMM) framework.
Solar PV modules are largely assembled in India using imported cells and wafers. With a robust domestic capacity of about 172 GW of solar modules capacity, major solar panel manufacturers in india include Waaree Energies, Tata Power Solar, and Indosol Solar.
Currently, China controls about 75 per cent or more of global manufacturing capacity in solar PV supply chain. Dominating the global solar photovoltaic (PV) supply chain, China produces approximately 91 per cent of the world’s polysilicon, over 97 per cent of ingots and wafers, and 80 to 92 per cent of global solar cell and 83 to 86 per cent of module production capacity.
To reduce reliance on China, India’s Renewable Energy Ministry in March 2026 proposed a mandate to use only locally made solar ingots and wafers from June 2028. Meanwhile, from 1 June 2026, all the government-backed or private companies solar projects are mandated to use domestically manufactures solar cells.
With this, India aims to use domestically made components across the entire solar panel manufacturing chain. This applies to government-backed and utility-scale projects, industrial and commercial projects, as well as schemes like PM Surya Ghar Muft Bijli Yojana.
India is undergoing a solar manufacturing renaissance, rapidly transitioning from import dependence to bolstering domestic PV supply chain. This expansion has been driven by supportive government policies such as production-linked incentives (PLIs) to incentivise local solar manufacturing, high import tariffs on finished solar panels, and approved list of models and manufacturers (ALMM) creating a robust domestic demand for solar installations.
India is rapidly expanding its solar manufacturing capacity. With initiatives like ‘Make in India’ and the Approved List of Models and Manufacturers (ALMM), India’s domestic solar panel manufacturing is picking pace.
Some of the key implementations by the Ministry of New & Renewable Energy (MNRE) in FY 2025-26 include the reduction in the GST rate on renewable energy devices & parts for their manufacture from 12% to 5% (September 2025), thus reducing the landed cost of solar equipment for domestic buyers: project developers, DISCOMs, rooftop solar installers, and captive users.
The Renewable Ministry (MNRE) also issued the modified “Solar Systems, Devices, and Components Goods Order, 2025” on 27 January 2025, which incorporates the latest versions of Indian standards for solar PV modules, storage batteries, and SPV inverters. The order also provides standards for the determination of efficiency of SPV modules.
India is the founding memeber of the International Solar Alliance (ISA). India hosts some of the world’s largest solar parks, including the Bhadla Solar Park in Rajasthan which is India’s largest and the world’s 11th largest as of 2026, with a capacity of 2,245 MW. India has also established nearly 70 solar parks, including the Gujarat Hybrid Renewable Energy Park being built near Khavda in the Rann of Kutch will generate 30 GW.
Also read: Which country ranks first in solar energy production?

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Australian solar panel manufacturer Tindo Solar is putting its money where its mouth is, offering a generous payment to eligible owners of its panels for valid warranty claims that result in module replacement.
In addition to a 25-year product warranty on all Tindo solar panels covering components, transport, removal and reinstallation, the company will pay $1,000 to a customer for a valid warranty claim requiring replacement of a Tindo N-Type solar panel.
The premium warranty currently applies to the Walara range, Australia’s first N-Type module manufactured locally. The older Tindo Karra series used P-type Mono-PERC cells. The additional warranty feature applies to qualifying panels purchased after 1 November 2025.
Tindo panel owners need to register their system for the premium warranty offer within 60 days of installation; providing the original purchase invoice, installation date and address, and proof of installation by a “CEC-approved installer”. I assume that means an *SAA-accredited* installer as the CEC (Clean Energy Council) exited this role when the accreditation scheme transitioned to Solar Accreditation Australia in May last year.
In the *very* unlikely event of, say, ten panels failing in a system, that doesn’t mean a payment of $10,000. The premium warranty will provide a single payment of $1,000 per installation site over the lifetime of the warranty.
Still, Tindo obviously has a lot of confidence in its gear. The company states its panels:
“… only fail 1 in every 200,000, making our panels 200 times more reliable as the global average.”
According to the firm, each solar panel is put through a rigorous quality control process before leaving its manufacturing facility in Mawson Lakes, Adelaide.
“Our panels are world-class and we are matching that with revamped aftermarket servicing,” said Tindo CEO Richard Petterson. “The owner of a Tindo panel doesn’t just have worry-free equipment – they also have worry-free aftermarket care.”
Further details on the Premium Warranty Offer can be found here.
Tindo Solar was founded in Adelaide in 2011. It opened a new factory close to the original facility in Mawson Lakes in 2021 that currently has around 60 employees. In August this year, the company was awarded $34.5 million in new Solar Sunshot funding to build on its range of products and to support scaling up of its production from 20 MW to 180 MW annually.
The firm also has its sights set on a future ‘gigafactory‘ capable of producing up to 1 GW of  modules per annum. Tindo says the project would lead to 230 direct jobs, 900 jobs in supply chain and economic activity of around $300 million annually.
If you’re looking for Australian-made solar panels, Tindo is the only show in town. But being manufactured locally and positioning itself as a premium brand, Tindo’s modules cost significantly more compared to products from Chinese competitors.
The SolarQuotes solar panel comparison table has the 440-Watt Walara as having an estimated recommended retail price of $260.
—-
UPDATE 12 November, 2025: We’ve just received updated pricing from Tindo on the 440-Watt Walara and it has dropped dramatically; working out to around $191 each RRP. The comparison table is being updated.
—-
The same wattage panels from good quality budget brands such as Canadian Solar, Trina, Jinko, JA and Aiko range from ~$120 – $140 each.
Tindo manufactures panels using solar cells and various other components imported from overseas, but the company has committed to and has been making some progress on increasing local content. For example, last year the firm signed an agreement with Australia’s largest aluminium extruder for the supply of module frame materials.
The company is currently listed as a SolarQuotes recommended panel brand and Tindo Solar panel reviews on SolarQuotes from Australian customers have been generally positive, averaging 4.7 stars based on 131 ratings overall.
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Michael caught the solar power bug after purchasing components to cobble together a small off-grid PV system in 2008. He’s been reporting on Australian and international solar energy news ever since.
honestly, I’d just prefer they had replacement panels of the same size / type available to be fitted for the warranty period, so you didn’t have to replace the rest of a string if a panel failed.
So by Australian made they mean screwed together in Australia from imported parts? And they may get around to making the frame locally. All at higher cost and subsidised at huge cost. Doubt the Chinese are worried.
I hope you are only considering a solar panel from a vertically integrated manufacturer in that case? As many of the mainstream manufacturers do exactly this, just in other countries.
Thats only if you really care which country they are assembled in, rather than the quality, cost and the warranty. Tindo seem good panels but cant see how their panels only fail 1 in 200 000 times There is a reviewer from Tas who claims 4 of hers failed, so they would have to have made 800 000 of them at least. She also claims they wouldn’t honour the warranty on the microinverters or panels made before 2019 when the company changed hands.
I installed 26 Tindo panels 4 years ago and have not had a problem since.
I export 10-15 KW daily as I don’t have suitable location for a battery.
Aussie Aussie Aussie
I think most people dont have an issue with their panels. There’s not much to go wrong, there are no moving parts. The standard warranty should be enough. If there is a one in a thousand chance of something going wrong, the $1000 offer is worth $1. Even at one in a hundred its only worth $10.
I think it is just one of those cunning marketing ploys that will cost the country next to nothing but perhaps encourage more sales – most importantly by getting people to consider an expensive locally made panel that they might otherwise have rejected.
Retailers in particular may use it as a good marketing tool for less price sensitive customers. Most panels these days are really reliable and long lasting particularly the cheaper reputable brands. Problems usually relate to inverters or poor installation and sometimes storm or other damage.
Anyway, in just 10 years time what will $1,000 be worth in spending power? Typically, less than half what it is now. And 20 years? Probably about $300. And new panels would probably be about half or less than their current price. Would that convince me to buy a considerably more expensive panel than my current Trina ones? No way!
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Beyond the rather low efficiency of today’s solar panels in converting the power of the sun into electricity, the transformational potential of solar energy is presently held back by battery storage technology.
A new, molecular-scale breakthrough could unlock a new path to long-term solar energy storage for heating homes and providing hot water – without a conventional battery in the equation.
How in the world would that work? To answer that, we need to take a quick dive into the world of electrochemistry. So grab your coffee and settle in.
Batteries store power as chemical potential energy. The energy stored in a chemical battery exists as a sort of tension and imbalance in how atoms and electrons are arranged between two materials. When a battery charges, external energy forces electrons and ions into higher-energy configurations where they wouldn’t naturally want to stay, creating potential energy. It’s the chemical equivalent of lifting a weight onto a high shelf or compressing a spring.
That potential energy remains stored as tension until the circuit closes, and the electrons can flow through that circuit from the anode back to the cathode toward a lower-energy state. In energetic terms, they’re simply moving downhill, releasing that stored potential energy, which we harness as electrical current flowing through the circuit.
It’s a system that works remarkably well, which is why batteries have become the backbone of modern electronics. But, like everything else in life, they also have limits. Over time, batteries begin to degrade and release a chalky white residue, or else begin to swell up and release heat – familiar warnings of failure. They also rely on complex materials, and aren’t always ideal for storing energy over long periods.
For solar power in particular, batteries introduce extra steps. First, sunlight must be converted through photovoltaic panels into electricity, which is then stored in a battery. When that energy is needed, it has to be pulled back out, routed through a circuit, and converted again into something usable, whether that’s light, heat, or motion.
But researchers at UC Santa Barbara say they’ve managed to vastly simplify the overall system. In a groundbreaking study recently published in Science, the team claims to have developed an organic molecule capable of absorbing sunlight and storing it directly within its own chemical bonds. And this molecule beats the energy density by weight of all but the most experimental (and dangerous) lithium batteries.
The molecule, called Pyrimidone, is derived from structures related to the building blocks of DNA. Here, the team has modified it into a compact system designed specifically to capture solar energy. Scientists refer to technologies like this as Molecular Solar Thermal Storage, or MOST.
“In MOST systems, energy is stored in chemical bonds rather than as heat or electrical charge,” said Han Nguyen in an email to New Altas. “Chemical bonds are generally stable, which allows energy to be stored for long periods without significant loss. In our pyrimidone-based system, the energy is stored in a strained form called the Dewar isomer. Once the molecule is in this form, it remains there until we deliberately trigger its release of energy.”
What she’s describing happens within a single molecule. Instead of moving electrons between materials, this system works internally. When sunlight hits the structure, it shifts into a strained configuration that locks potential energy into its chemical bonds.
In some ways, the molecule behaves like a tiny molecular mousetrap. Sunlight sets the trap, pushing the structure into a tense, high-energy position. Chemists refer to this kind of structural switch as photoisomerization, a process in which light changes a molecule’s geometry without breaking it apart.
In this system, that reversible shape change acts as the storage cycle itself. To release the energy, an acid catalyst is applied. What makes it especially interesting to the modern energy storage mix is that the energy is released as heat, not electricity – “enough heat to boil water,” according to the study.
Most renewable energy systems today are designed to store electricity, when in fact what you often want to come out the other end is actually heat. Hot water, many industrial processes, and building heating all rely on thermal energy, so energy stored in traditional batteries needs to go through another conversion step. The MOST system is designed to cut out the middle man and meet that need directly.
“We see it as a complementary technology, not a replacement for what already exists,” said Han Nguyen. “The energy landscape increasingly relies on photovoltaic panels paired with lithium-ion batteries, and those systems are excellent for electricity. But roughly half of global energy demand is for heat — warming homes, cooking, providing hot water — and for that application, a system that stores and delivers heat directly is a more natural fit.”
In terms of efficiency, this is a genuinely remarkable energy storage solution. It holds 1.6 megajoules of energy per kilogram of material. That equates to around 444 Wh/kg – nearly twice what you’d typically see in the lithium-ion packs running today’s EVs, and not far off what CATL has achieved with its frankly scary 500 Wh/kg “condensed battery.”
But the technology is still in its early stages, and researchers are currently working to improve efficiency, durability, and scalability before the system can move beyond the lab.
“The most immediate challenge is improving how efficiently the molecules charge under sunlight,” said Nguyen. “At present, our pyrimidone absorbs primarily in the ultraviolet range, which represents only a small fraction of the solar spectrum. We need to shift absorption toward visible wavelengths to make better use of the energy available outdoors.”
Researchers are also exploring structural tweaks to the molecule that could expand its absorption range into the visible light spectrum while maintaining its energy density and stability.
Beyond improving how the molecules absorb sunlight, the team is also focused on making the system practical to use.
“On the device side, we are working to replace the homogeneous acid catalyst used in our proof-of-concept experiments with heterogeneous catalysts, i.e., solid catalysts that can be embedded in a flow channel and reused indefinitely,” said Nguyen.
That means swapping out a one-time-use liquid component for a solid material that can be built into a reusable system. It’s a shift that would allow the technology to cycle repeatedly, capturing and releasing heat without needing to be reset each time.
With those pieces beginning to fall into place, even at this early stage, the team’s work is already reshaping how we think about energy storage. For more than a century, storing energy has largely meant relying on batteries. Here, that shift takes a different form, with sunlight captured and held not in metals and moving electrons, but in the shape of molecules themselves.
This study was published in the journal Science.

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One of four livestock guardian dogs leads and gathers the flock during a recent demonstration at the Silicon Ranch Solar site in Logan County.

One of four livestock guardian dogs leads and gathers the flock during a recent demonstration at the Silicon Ranch Solar site in Logan County.
Beneath thousands of high-tech solar panels in Logan County, a flock of 800 sheep is performing a job traditionally reserved for diesel-powered lawnmowers.
The operation is led by Brad Carothers, owner of New Slate Land Management, and his business partner and wife, Katie. The couple recently relocated from Ohio to south-central Kentucky to grow their business, which merges age-old agricultural techniques with modern renewable energy infrastructure.
Currently, 800 ewes graze the site, a number Carothers expects will grow to 2,000 when the facility is fully stocked. The flock consists primarily of Katahdin and Katahdin-Dorper hair sheep, breeds chosen specifically for the local climate.
“We chose this breed for its excellent maternal abilities and parasite resistance to the hot, humid weather,” Carothers said.
While most solar farms utilize mechanical mowing, Carothers argues that sheep provide a “regenerative” benefit that machines cannot match. Using livestock keeps the land in agricultural production rather than turning it into a strictly industrial site.
“When sheep are used for vegetation management, the land remains in agriculture versus strictly mechanical mowing,” Carothers said. “Grazing sheep promotes nutrient cycling and feeds soil microbes that make up a healthy grassland ecosystem. Their animal impact and waste contribute to the gradual buildup of organic matter and topsoil.”
The partnership also reduces physical risks to the multimillion-dollar solar infrastructure. Unlike tractors, which can accidentally throw rocks into panels or bump into equipment, sheep move safely around the arrays.
In turn, the solar panels provide a unique benefit to the flock. The arrays offer shelter from sun, wind, rain, and snow. During the peak of summer, the panels limit sun exposure to the ground, preventing the soil from drying out and creating a more favorable environment for grass growth.
Managing a flock in a specialized environment requires unique logistics. Water is provided via a hay wagon carrying large totes filled from an on-site source. Carothers also provides minerals, salt, and standard veterinary care similar to a traditional farm setting.
To protect the herd from local predators like coyotes, the site is patrolled by four livestock guardian dogs, including Maremma, Akbash, and Anatolian Shepherds.
By implementing sheep grazing, Carothers estimated that mechanical mowing is typically reduced by 50% to 75%, significantly lowering diesel fuel and chemical herbicide use on the property.
The partnership began when Silicon Ranch, the owner of the solar site, purchased breeding stock from the Carothers’ Ohio farm for a project in Georgia. When the Logan County site opened, it provided the couple with an opportunity to expand their operations and move to Kentucky permanently.
For Carothers, who grew up in the suburbs of Columbus, Ohio, before starting a seedstock flock, the model offers a stability that traditional livestock markets often lack.
“We are vegetation management contractors and shepherds,” Carothers said. “The stable income from the vegetation management contract is important to growing our business.”
He believes this model could be a solution for other Kentucky farmers looking to diversify their land use, especially for first-generation farmers who struggle to find affordable access to land.
Carothers noted that the biggest misconception about solar grazing is that the animals are brought in only for “photo ops” and then removed.
“The sheep live on the site year-round,” Carothers said. “When people drive by, often they won’t see sheep from the road. This is because they are rotationally grazed throughout the site and may only be in one particular area for three to four weeks per year.”
Looking ahead, the “New Slate” legacy aims to be one of community integration. Carothers has hired local employees, purchased supplies from local stores, and plans to work with local schools to educate the next generation about the intersection of agriculture and energy.
“The sheep and solar industries working together are an excellent example of merging agriculture and energy production for the future,” Carothers said.
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Article supplied by SOFAR Solar
SOFAR Solar has once again been recognised as the top photovoltaic (PV) brand in Australia for 2025 by esteemed EUPD Research.
This prestigious recognition is based on installer feedback, market performance, and the strong trust the brand has built in the Australian solar industry.
Key insights:
“SOFAR’s continued success in Australia reflects a brand that has built a strong presence and a loyal following,” said Daniel Fuchs, Chief Customer Officer at EUPD Research.
“Based on hundreds of interviews with installation companies, it’s clear that SOFAR has a very high level of awareness, recognition, and satisfaction, making it a well-deserved winner of this award.”
SOFAR’s commitment to Australia
Having worked diligently in the Australian market for over a decade, SOFAR continues to invest in local operations with local technology teams in New South Walkes, Victoria, Queensland, Western Australia, South Australia, and warehouse in five states (for rapid delivery). This local presence ensures faster response times, timely technical support, and a deeper understanding of the Australian energy landscape and policies.
What the Award means for SOFAR
This recognition highlights our ongoing commitment to innovation and sustainability in the solar industry. SOFAR remains focused on strengthening their position in the Australian market and continuing to support the nation’s transition to renewable energy.
SOFAR thanks its partners, installers and customers who have made this achievement possible.
For more information, visit the SOFAR Solar Residential PV-ESS Solutions webpage.

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Northern Vietnam Power Utility Accelerates Grid Upgrades And Rooftop Solar Push To Meet Rising Demand  SolarQuarter
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