Solar Now

ONTARIO, Calif. (KABC) — An Ontario woman claims she's out more than $80,000 after the solar company she hired to install panels on her rooftop, as well as a battery backup system, walked off the job.
"They dropped all the material where it was, and they left," Lorraine Hammer told Eyewitness News. "They've never been back."
Not only that, but she said dozens of roof tiles were removed and never replaced, leaving her without a functioning roof.
"Then we had the rains, and my roof was open," Hammer said. "The water was leaking into the light fixture in the bathroom all the way onto the floor."
Hammer said she signed the contract with My Smart House, LLC in September 2025 for $83,200.
She said the last email with the company she received was in December 2025, telling her they haven't forgotten about her project, and that every customer awaiting completion is being prioritized for scheduling in the incoming weeks.
According to the letter, the company is experiencing unexpected staffing changes.
"While we understand and share your frustration with these delays, we kindly ask that all communication with our remaining employees and customer support team remain professional and respectful," read the statement.
"These team members are working tirelessly to resolve the situation and get your system fully operational. Mistreatment or hostility will only slow communication and resolution."
Further adding to her frustration, Hammer claims that San Bernardino County told her that the company never filed for any building permits.
Hammer said while she didn't check references, she did check with the Contractors State License Board. While the company did have an active license, that license has since been suspended.
Eyewitness News has reached out to My Smart House, LLC for comment and we are awaiting a response. However, approximately two hours after we reached out for comment, Hammer claims that someone from a third-party solar company showed up at her home and told her he was going to try to finish the job.
Hammer said her message to others is a simple one.
"Beware. Check references. And don't sign anything the first time."

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By Canary Media

By Canary Media
Canary Media
31 March 2026
California lawmakers face a make-or-break choice about the state’s biggest and most successful virtual power plant program: Give it enough money to keep running this summer or scrap it altogether.
The administration of California Gov. Gavin Newsom (D) has proposed ending the four-year-old Demand Side Grid Support program, which pays homes and businesses to send rooftop solar power back to the grid or reduce their energy use during times of peak electricity demand. DSGS has more than 1 gigawatt of capacity, making it one of the biggest VPPs in the country.
The proposal has set off alarm bells for environmental advocates and clean energy companies, which say that eliminating the program would be a costly mistake. And some state lawmakers briefed on the plan have questioned the logic of ending a program that’s successfully delivering grid relief.
DSGS backers argue that the program saves money not only for those who participate but also for all Californians, who face some of the highest utility rates in the country.
A study conducted by consultancy The Brattle Group and commissioned by Sunrun and Tesla Energy, two companies with large numbers of solar-and-battery-equipped customers enrolled in the program, indicates that ​“DSGS is a significantly lower-cost alternative” to relying on costly fossil gas–fired power plants or other resources available during grid emergencies.
In February, the Newsom administration’s Department of Finance issued two budget proposals regarding DSGS. One proposes ending DSGS, which is administered by the California Energy Commission, and shifting its customers to another program administered by the California Public Utilities Commission — either a current program that has been far less successful to date or one that has yet to be created.
For the past two years, environmental and clean energy groups have been fighting to protect DSGS from a series of funding cuts ordered by the Newsom administration, and have so far been unsuccessful. ​“California has already invested years of effort and hundreds of millions of dollars to build out DSGS. It’s a model now for clean reliability,” said Laura Deehan, state director of Environment California, one of the dozens of environmental advocacy groups that have signed a letter protesting the plan. ​“We have to make sure we keep the lights on on the program and not abandon what’s already been built up.”
A coalition of industry groups that have enrolled customers in DSGS echoed that view in a March letter to state lawmakers. It warned that ​“dissolving an existing successful program and attempting to re-create the same type of program at a different agency causes delays, wastes public resources, and has no assurances that it will be as successful.”
Environmental and industry groups are throwing their weight behind the Newsom administration’s other budget proposal, which would instead increase DSGS funding. This alternative calls for shifting money from another, underfunded distributed energy program to DSGS, bringing its funding for the coming year to roughly $53 million, up from the $26.5 million now remaining in its budget.
This is still short of the $75 million that backers have been asking for, said Caleb Weis, clean energy campaign associate at Environment California. But it should be enough to ensure enrolled customers are ready to help the grid through what’s expected to be a much hotter summer and fall season than the state has seen over the past two years, he said.
“The DSGS program kicks on when the primary alternative would be importing expensive energy from out of state or firing up expensive peaker plants that are dirty and cost money just sitting there, not being used,” he added. Meanwhile, DSGS ​“has clean assets that are ready to protect the California system during times of extreme stress and high cost. It’s almost a no-brainer to use this.”
Supporters of the proposal to end DSGS have been less vocal. While the state has underscored that DSGS was always meant to be temporary, few other justifications have been offered for ending the program before its original 2030 sunset date — and no major stakeholders have come out in support of that plan.

The conversation around DSGS is heating up ahead of key budget decisions. California must pass its 2026–2027 budget by June 15, and that budget must be finalized before Aug. 31. Sometime between now and that deadline, state lawmakers will be forced to decide on the future of the program.
Lawmakers raised concerns about the proposal to scrap DSGS during a March 5 hearing of the Senate Budget Subcommittee on Resources, Environmental Protection, and Energy at the state capitol.
“DSGS has largely been a successful program,” said Sen. Eloise Gómez Reyes, a Democrat who chairs the subcommittee. ​“Why is the administration proposing to start over?”
David Evans, a staff finance budget analyst at the state’s Department of Finance, responded that the ​“original vision and intent of the program was not allowed for it to be an indefinite, ongoing program.” He highlighted the state’s ongoing budget shortfall, which the Newsom administration had cited as the rationale for cutting DSGS funding in 2024 and 2025.
But Gómez Reyes pushed back on that justification, noting that the administration’s alternative proposal — shifting funds from elsewhere — could allow DSGS to successfully operate this year without impacting the budget.
“If something is successful, and it appears that this is a successful program, why don’t we continue … even if we intended it to be something that was temporary?” she said.
Gómez Reyes also questioned the wisdom of shifting DSGS participants to the California Public Utilities Commission, given the agency’s comparative lack of success in managing VPP programs.
Under the CPUC’s oversight, California’s biggest utilities have largely failed to follow through on the state’s decade-old policy imperative to incorporate rooftop solar systems, backup batteries, smart thermostats, and other distributed energy resources into how they manage their grids. California remains well short of current targets on that front.
DSGS has been the most successful of a set of programs created in response to California’s grid emergencies in the years 2020 through 2022 designed to utilize individual customers’ devices to help the grid. Unlike those other programs, which are overseen by the CPUC and administered individually by the state’s three biggest utilities, DSGS is credited for its ease of enrollment, clear rules for participants, and availability to all state residents.
In particular, DSGS has been able to scale up and deliver grid relief much better than the Emergency Load Reduction Program, which the CPUC established in 2021.
Both programs enlist customers with batteries, EV chargers, smart thermostats, and other devices. But according to data provided by legislative staff for the March 5 hearing, while DSGS ended 2025 with an estimated 1,145 megawatts of peak load reduction enrolled — ​“enough to power the peak electricity demand for all of San Francisco” — ELRP has enrolled only about 190 megawatts. Its residential program was discontinued last year ​“due to very low cost-effectiveness.”
A recent test of both programs underscored once again the difference in scale. In July 2025, utilities measured how much solar-charged battery power capacity each program provided over the course of two consecutive hours.
The test delivered a total of 539 megawatts of capacity over that time. According to the Brattle Group’s analysis, roughly 476 megawatts of that capacity was provided by about 100,000 participants in the DSGS program — while only 64 megawatts came from ELRP participants.
Utility Pacific Gas & Electric lauded the test, noting that it ​“showed that home batteries can be counted on during peak demand.”
Sen. Catherine Blakespear, a Democrat, brought up the relatively poor performance of ELRP during the March 5 hearing. ​“It does seem like there are members of the legislature and stakeholders who really have a lot of confidence in DSGS and want it to continue, and that there’s a concern that ELRP is just not as effective,” she said. ​“We should focus back on the thing that’s already working and that might have a better chance of being successful.”
CPUC Executive Director Leuwam Tesfai noted at the hearing that ELRP isn’t the only alternative on the table. The budget proposal that would eliminate DSGS would also allow enrolled customers to join a new program administered by the CPUC. The agency has yet to create this new program but is actively exploring it as part of an ongoing proceeding scheduled to wrap up by the end of 2026, she said.
But Gómez Reyes replied that any work the CPUC might or might not undertake to create an alternative program to the ELRP wouldn’t be finished until ​“after we have completed this budget. And that becomes a problem for us as we make our decisions.”
It’s unclear how quickly state lawmakers and the Newsom administration will move to resolve these conflicts.
“It’s not out of the question that it goes through the end of August,” said Katelyn Roedner Sutter, California senior director at the Environmental Defense Fund, an environmental group that supports DSGS. ​“I hope it goes faster, because by the end of August is when we need to be drawing on some of these resources.”
Roedner Sutter also highlighted that the DSGS program is funded through taxpayer dollars. Most CPUC-administered programs, by contrast, are financed by authorizing utilities to pass on the costs of operating them to their customers.
“At a time when we’re trying to find ways to pay for these things outside of electricity bills, it makes less sense to move things over to the CPUC,” she said.
Sen. Josh Becker, a Democrat who authored a VPP bill that was vetoed by Newsom last year, told Canary Media that he would ​“strongly urge the administration to reconsider” ending the DSGS program and shifting its participants to a CPUC program. “[For] those in the legislature that have been focusing on this and care about this, it’s not a move any of us think is in the right direction.”
Becker highlighted that dozens of states are pursuing VPPs to make ​“better use of the clean energy resources that people already have in their homes to lower cost, to improve reliability, and to reduce pollution.” He has introduced another VPP bill in this legislative session that he said would instruct the CPUC to modify ​“rules that prevent these resources from participating fully in the market.”
Leah Rubin Shen, managing director at the trade group Advanced Energy United, said its member companies involved in DSGS support eventually shifting to a new program that might emerge from the kind of efforts that Becker and other lawmakers are proposing. But ​“you’ve got to make sure that everyone knows what the rules are, and that the rules aren’t going to change,” she said.
“DSGS has been a great program,” she said. ​“Keep it humming along for a few more years, until it’s supposed to be put to bed. And in the meantime, set up this market integration pathway that can funnel what we’ve learned from DSGS into something bigger and better.” 

Jeff St. John is chief reporter and policy specialist at Canary Media. He covers innovative grid technologies, rooftop solar and batteries, clean hydrogen, EV charging, and more.
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Lithuania’s solar power capacity reached 3,040 megawatts by the end of the previous year, according to a report from the International Energy Agency’s Photovoltaic Power Systems Programme. The nation added roughly 600 megawatts of solar capacity during that year.
Solar generation in Lithuania accounted for 14.2 percent of total national electricity consumption in the previous year, producing 1.79 terawatt-hours. The country ranks highly among European Union member states for solar generation per person.
Market growth has been largely driven by approximately 170,000 solar prosumers, who were responsible for about 70 percent of total solar electricity production. These participants have benefited from a net-metering scheme, though commercial entities have since moved to a net-billing system. Public investment support has also been available, typically covering a portion of installation costs for households and small-to-medium enterprises.
While technical permits have been granted for an additional 4 gigawatts of solar capacity, grid congestion is now a primary constraint. A research director contributing to the IEA-PVPS report indicated the market is nearing saturation due to limited grid capacity, noting that solar and wind already exceed national strategy targets for renewable electricity share.
Consequently, future solar market expansion is expected to be strongly linked to integration with storage technologies. Nearly 2 gigawatts of battery energy storage system facilities received technical permits in the previous year, and a recent tender procured significant storage capacity. Support schemes exist for hybrid solar-plus-storage farms and standalone storage systems. The integration of thermal energy storage is also increasing.
Lithuania’s National Energy Independence Strategy, adopted in 2024, targets 100 percent of electricity from renewable sources by 2030.
This report provides a comprehensive view of the solar cells and light-emitting diodes industry in Lithuania, 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 Lithuania.
The report combines market sizing with trade intelligence and price analytics for Lithuania. 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 Lithuania. 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 Lithuania.
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The project marks a step toward cheaper and faster solar energy.
An Australian electricity and gas provider has been using autonomous robots at its 250-megawatt solar farm, where nearly 500,000 panels are being installed ahead of schedule with the help of AI-powered machines.
The robots, designed to speed construction and improve safety, are being trialed at ENGIE’s photovoltaic Goorambat East Solar Farm. The site is located less than a mile south-east of the Goorambat township in Victoria.
“It will have a generating capacity of up to 250 MW, which is enough to power up to 105,000 average Victorian homes,” Justin Webb, ENGIE site representative said.
Developed by US-based Luminous Robotics, the LUMI S4 fleet uses AI-driven pick-and-place technology to autonomously lift and position solar modules onto racking structures.
Human crews then complete the securing process, cutting down on repetitive manual labor while increasing efficiency and reducing injury risks.
To develop the robots, Luminous Robotics secured USD 4.9 million in funding from the Australian Renewable Energy Agency (ARENA). The funds were part of its USD 10 million Solar ScaleUp Challenge.
The machines autonomously lifted and placed panels onto racking structures, at the Goorambat East site, located near the city of Benalla, approximately 130 miles northeast of Melbourne.
This division of labor reportedly reduces one of the most physically demanding aspects of solar farm construction. It also improves both safety and efficiency while allowing the human workforce to focus on skilled tasks.
The system, first used for pilings and now for panels, marks Luminous’ debut of its LUMI robots outside the US, showcasing the future of solar farm construction.
“The intended higher productivity of these autonomous systems will reduce the cost of renewable energy projects and enable projects to be built in less time – which will bring down energy costs for consumers and potentially allow more solar farms to be built,” Webb continued.
The solar farm will have a capacity of up to 250 MW once complete. It will supply electricity to more than 100,000 average homes. Commissioning has already begun, with first energization expected before the end of October 2025 and full operation targeted for mid-2026.
Webb revealed that the robots require skilled technicians to help upskill the renewable workforce and boost productivity. He noted they could also benefit solar projects in remote, harsh regions where conditions are unsafe for workers.
“In the longer term, with continued development, robots like these will also enable a reduction in health and safety related risks from construction projects, for example reducing the manual handling of heavy solar panels,” he explained.
Jay M. Wong, Luminous Robotics CEO and founder elaborated that the Australian deployment of the LUMI fleet provided vital data, performance insights and real-life results to support global adoption.
“Our LUMI robots exceeded our target production rate and fueled by support from the Australian Renewable Energy Agency (ARENA), we’re keen to accelerate our next phase where we fine tune the LUMI fleet’s capabilities,” Wong said.
Meanwhile, Luminous and ARENA are preparing to release and open source solar construction’s largest, most comprehensive robotics dataset in the coming months. “We believe this is the honest approach to truly democratize solar for humanity,” Wong concluded.
Based in Skopje, North Macedonia. Her work has appeared in Daily Mail, Mirror, Daily Star, Yahoo, NationalWorld, Newsweek, Press Gazette and others. She covers stories on batteries, wind energy, sustainable shipping and new discoveries. When she's not chasing the next big science story, she's traveling, exploring new cultures, or enjoying good food with even better wine.
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Scientific Reports volume 16, Article number: 8197 (2026) Cite this article
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Accurate forecasting of photovoltaic performance is essential for improving solar energy management, optimizing operational schedules, and supporting investment decisions. This study proposes a structured data-driven forecasting framework that integrates standalone learners with a hybrid boosting–aggregation strategy to predict two critical photovoltaic performance indicators: the optimal peak operating time (NOPT) and the power conversion efficiency (PCE). The methodology involves systematic data preprocessing, feature normalization, model training using both single and hybrid learners, and performance validation under identical experimental conditions. Multiple data-driven algorithms were examined using comprehensive statistical metrics, including R², RMSE, and U95. Among all models, the hybrid XGBA framework demonstrated superior predictive performance, achieving R² values of 0.9954 for NOPT and 0.9970 for PCE, and consistently low errors across all evaluation criteria. Model robustness and generalization were further assessed through uncertainty-based evaluation metrics. Sensitivity analyses highlight key influential parameters such as Emin Emax, and Ap, revealing their substantial contributions to model outputs. The proposed hybrid model provides a robust and highly accurate predictive tool that can reduce operational uncertainties, enhance energy yield, and support data-driven decision-making for photovoltaic plant operators and energy sector stakeholders.
The global transition to sustainable energy has highlighted photovoltaic (PV) technology as a pivotal solution for reducing greenhouse gas emissions and dependence on fossil fuels¹. Over the past decades, PV research has focused on enhancing power conversion efficiency (PCE), reducing production costs, and incorporating environmentally friendly materials, such as thin-film polymers and perovskite tandems². The integration of PV systems into diesel-based energy infrastructures, including microgrids, remote power stations, and hybrid vehicles, presents a hybrid solution that can improve fuel efficiency, reduce emissions, and extend engine lifespan^3,4,5. Such integration specifically aligns with several Sustainable Development Goals (SDGs), notably SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), by encouraging the use of renewable energy and decreasing the use of diesel^6,7,8.
One of the significant breakthroughs in PV technologies is organic photovoltaics (OPVs) that have been the focus of renewed interest in the last couple of years, a fact that can be related to the successful implementation of non-fullerene acceptors (NFAs) allowing single-junction devices to achieve power conversion efficiencies (PCEs) over 18%^9,10,11. Nevertheless, there will always be inefficiency-limiting processes in NFA-based systems, which are the main stumbling block for the understanding of these systems that, in turn, obstruct the rationality of human-computer-aided design of new donor–acceptor materials. It has been proven that the quadrupole moment of acceptors is the factor that most strongly influences the interfacial energetics, and high internal quantum efficiencies (IQEs) are generally observed when ionization energy offsets over 0.5 eV are used for exciton dissociation¹². Research on material modifications can be exemplified by the end-group engineering effected by fluorination and chlorination that, besides allowing charge transport, also makes recombination less likely, resulting in the ‘device’ overall performance ameliorating^13,14,15.
Machine learning (ML) methods are a significant factor in advancing research in renewable energy, especially in forecasting and optimizing photovoltaic (PV) systems^16,17,18,19. Keddouda et al.²⁰ developed artificial neural network (ANN) and regression models using meteorological data and operating temperature as inputs, achieving high predictive accuracy with R² values reaching 0.998. Kumari and Toshniwal²¹ conceived extreme gradient boosting with deep neural network (XGBF-DNN), which essentially integrates extreme gradient boosting forests and deep neural networks by the vehicle of ridge regression, thereby soaring not only the security but also the accuracy of a wide variety of climatic conditions. The use of such ensemble strategies underscores the viability of hybrid ML frameworks for addressing the unpredictability of PV system outputs in the real world.
Nonetheless, interpretability remains a major stumbling block, despite progress in predictive capabilities. Some XAI (Explainable AI) techniques, like SHAP²² and LIME²³, can provide an account of feature importance and develop local explanations; however, they are still largely untapped in PV research. Chen et al.²⁴ pointed out the difficulties related to terminology, cross-task evaluation, and the range of existing interpretability techniques; therefore, they suggested that more research should be carried out to enhance the transparency of the processes. Scott et al.²⁵ examined the use of benchmark machine learning algorithms to forecast photovoltaic power generation for building-scale renewable energy systems. Several models, including random forest, neural networks, support vector machines, and linear regression, were compared using operational data from a university campus to evaluate forecasting accuracy across different dataset sizes and prediction horizons. The results showed that random forest achieved the lowest average error, although no single algorithm consistently outperformed the others under all conditions. The study highlighted the importance of dataset characteristics and model usability when selecting forecasting approaches for integration into building management systems. Bhutta et al.²⁶ investigated the use of hybrid machine learning models to improve the prediction accuracy of solar power generation within smart grid systems. Hybrid deep learning architectures, including convolutional–recurrent, convolutional–LSTM, and convolutional–GRU networks, were applied to forecast key solar plant parameters such as power production, plane-of-array irradiance, and performance ratio. The results demonstrated that the hybrid convolutional–LSTM model achieved the highest predictive accuracy, yielding the lowest RMSE and MAE values across all evaluated variables. The findings indicated that hybrid machine learning approaches were effective in enhancing the efficiency and reliability of solar power generation forecasting in intelligent energy networks. Ridha et al.²⁷ proposed a hybrid photovoltaic power prediction framework integrating singular spectrum analysis, an adaptive beluga whale optimization algorithm, and an improved extreme learning machine. Singular spectrum analysis was applied to preprocess long-term PV time-series data, while the adaptive beluga whale optimization method was used to enhance exploration–exploitation balance and optimize model hyperparameters. The improved extreme learning machine further refined output weight estimation to enhance prediction accuracy. Comparative evaluations using benchmark functions and real-world PV data demonstrated that the proposed hybrid model outperformed existing optimization and hybrid learning approaches across multiple statistical performance metrics.
Although ML and hybrid models have achieved high accuracy in photovoltaic forecasting, existing studies mainly focus on single performance indicators and accuracy-driven evaluation. The simultaneous prediction of optimal operating time and efficiency, along with uncertainty-aware validation and robustness assessment, remains largely unexplored. Moreover, despite advances in hybrid learning, model interpretability and sensitivity-based physical insight are insufficiently integrated into PV forecasting frameworks. These limitations highlight the need for a unified, transparent, and decision-oriented modeling approach that balances accuracy, reliability, and practical applicability. In addition, the proposed XGBA model addresses the methodological gap in existing hybrid PV forecasting approaches by enabling simultaneous multi-target prediction, improving robustness and uncertainty-aware performance, and integrating sensitivity-based interpretability for enhanced operational insight.
The primary task in this research is to formulate a hybrid machine learning system capable of accurately predicting solar energy parameters, such as the number of optimal peak operating times (NOPT) and power conversion efficiency (PCE). The accurate forecasting of these targets can lead to better management of solar energy resources, higher-quality service, and easier financial planning. Standard single models often fail to capture the complex relationships between environmental variables and energy outputs. To address this problem, the paper presents the concept of cooperation models, the result of the successful interaction of multiple learning paradigms. Such a combination is based on integrating tree-based algorithmic predictability with metaheuristic optimization strategies, e.g., simulated annealing or genetic algorithms. The most important part of the proposed method is the role the Bat Algorithm (BAT) plays as a tuner. The BAT optimizer is a metaheuristic approach inspired by the echolocation behavior of bats. The advantages include effective exploitation and space searching, fast convergence, and high adaptability to the problem. Moreover, the possibility of balancing global search and regional refinement allows it to be used for adjusting the parameters of hybrid machine learning models. In effect, the performance of different targets, i.e., NOPT and PCE, is improved. In addition to this, this paper also relies on a few sensitivity analysis techniques systematically to explore the effects the input variables exert on the model outputs. The FAST sensitivity methodology gives a comprehensive interpretation of the variable importance. In contrast, the Accumulated Local Effects (ALE) method measures the effect of each input on the predicted outputs regardless of the underlying model. Besides that, post hoc statistical tests such as Dunn’s test are applied to support the significance and independence of model predictions. The joint use of multiple sensitivity tools enables the proposed models not only to be accurate but also interpretable, allowing the identification of critical factors affecting solar energy performance. Figure 1 shows the process of the study.
Process of the present study.
The RBF network, a member of the Artificial Neural Networks (ANNs) family, links input and output components without the use of mathematical formulae. Instead, it infers the model’s structure and unknown parameters only from the data²⁸. The RBF network consists of three layers: input, hidden, and linear output. As input vectors pass through the hidden layer, they undergo transformations that result in radial basis functions. These procedures use an activation mechanism based on the Gaussian distribution and have a solid basis in the properties of the Gaussian function. According to the literature, the Gaussian basis function ((:{mathcal{G}}_{j})) is defined by two essential parameters: width and center²⁹. The following is an expression for the function:
The width and center of the Gaussian basis function are denoted by (:{omega:}_{j}) and (:{gamma::}_{j}), respectively, while (:x) is the input pattern. The output neuron is commonly represented by:
Here, (:{U}_{j}) is the weight factor that connects the (:j)th hidden neuron to the output neuron, (:mathfrak{B}) is the bias coefficient, and (:n) is hidden neuron’s numbers. Figure 2 shows how the RBF model works using a flowchart.
Flowchart of the RBF.
XGBoost, a supervised learning technique, was used to train models for forecasting missing laboratory test data. Because of its effectiveness in model training, the extended distributed gradient boosting package XGBoost was chosen³⁰. This approach employs an adaptive binary splitting algorithm to iteratively select the optimal split at each stage, thereby producing an ideal model. Model selection procedures are enhanced by XGBoost’s resistance to overfitting and outliers due to its tree-based structure. Equation (3) defines the normalized goal of the XGBoost model during the (:s)th training phase. The loss function (:mathcal{L}mathcal{f}left({{x}^{left(sright)}}_{mathcal{p}},:{x}_{gt}right)) measures the difference between the predicted value (:{{x}^{left(sright)}}_{mathcal{p}}) and the corresponding ground truth (:{x}_{gt})
(:left| {omega :} right|^{2}) represents the (:mathcal{L}mathcal{f}2) norm of all leaf scores. The regularizer (:Omega :left( {f_{k} } right) = gamma :T + frac{1}{2}lambda :left| omega right|:^{2}) represents the complexity of the (:q)th tree. The parameters control the accuracy of the tree search, (:gamma:) and (:lambda:). Moreover, Fig. 3 shows the flowchart of the XGBR model.
Structure of the XGB model.
Averaging predictions from hundreds or even thousands of decision trees is how the random forest algorithm, an ensemble approach, creates multiple trees for regression. Each tree is derived from the Classification and Regression Tree (CART), which was first presented by Breiman et al.³¹. Data complexity shapes the learning process that each tree goes through. A decision tree is made up of decision and leaf nodes. According to Eq. (4), the input vector (:X={{x}_{1},{x}_{2},dots:,{x}_{m}}) maps to a scalar output (:Y) using a training set of (:n) observations ((:{R}_{n})).
By splitting the input data at each node until it reached a terminal leaf or satisfied stopping conditions, like a minimum sample size or maximum depth, the algorithm optimized split functions during the training phase. A prognostic function (:widehat{H}=(X,{R}_{n})) that can forecast results was created by this process. An ensemble of tree-structured base classifiers (:H=(X,{varTheta:}_{K})) was developed in Random Forest Regression³², where each (:{varTheta:}_{K}) denoted a random vector that identified a bootstrap sample of the training data or a subset of features. To ensure an equal selection probability, bootstrap sampling entailed drawing n observations from (:{R}_{n}) with replacement. This process was repeated across several bootstrap sets by the bagging procedure, producing a separate prediction tree for each set. The result was a set of (:q) trees (:widehat{h}left(X,:{S}_{n}^{{{Theta:}}_{1}}right),:…,:widehat{h}left(X,:{S}_{n}^{{{Theta:}}_{q}}right)). In contrast to a single decision tree, the outputs from all trees were averaged to produce the final predicted value, (:widehat{Y}), which improved accuracy and decreased variance³³.
The output of the (:l)th tree is denoted by (:{widehat{Y}}_{l}), where (:l) takes values between 1 and (:q).
By integrating bagging with ensembles of unpruned decision trees, Random Forest (RF) regression improves model robustness^32,33. RF is a computationally efficient method because it doesn’t require pruning, unlike other approaches. Only two parameters need to be adjusted for it to be simple: the number of trees ((:{n}_{tree})) and the number of randomly chosen predictors for every split ((:{m}_{try}))³⁴. In general, adding more trees increases accuracy and stability, but eventually, there comes a point at which more trees are no longer able to reduce error. Typically, a standard value of (:{n}_{tree})=500 is used. In addition to strengthening trees, increasing (:{m}_{try}) also makes trees more correlated with one another³⁵. Approximately two-thirds of the original dataset is included in each of the (:{n}_{tree}) bootstrap samples that are created during the RF process. To ensure diversity among trees, the optimal split is determined at each node using a random subset of predictors ((:{m}_{try})). While out-of-bag (OOB) samples, which are data not included in bootstrap sets, are used for validation to lower the risk of overfitting, predictions are aggregated through averaging for regression tasks. Figure 4 illustrates the application of the RF regression framework for prediction.
Flowchart of the RF model.
The echolocation method used by wild bats to find food served as the model for the BAT search algorithm. It was first presented by Yang^36,37,38,39 and is used to solve several optimization issues. Every virtual bat in the original population updates its position using echolocation in a homologous fashion. Bats use a perceptual mechanism called echolocation, which produces echoes by releasing a sequence of loud ultrasonic waves. Bats can identify a particular prey by using the delays and different sound levels that these waves return. A few guidelines are being researched to expand the BAT algorithm’s structure and take advantage of bats’ echolocation traits^40,41,42,43.
(a) Every bat uses echolocation features to differentiate between obstacles and prey; (b) Every bat flies at random with loudness (:{E}_{0}) and velocity (:{k}_{i}) at position (:{x}_{i}) with a fixed frequency (:{f}_{min}) varying wavelength (:lambda:) to find prey; it controls the frequency of its released pulse and modifies the rate of pulse release (:r) in the range of [0,1], depending on how close its aim is; (c) Every bat varies its frequency, loudness, and pulse release rate; (d) The loudness (:{E}_{m}^{iter}) shifts from a significant value (:{E}_{0}) to a minimum constant value (:{E}_{min}); Throughout the optimization process, each bat’s position (:{x}_{i}) and velocity (:{v}_{i}) should be specified and updated; the new solutions (:{x}_{i}^{t}) and velocities (:{k}_{i}^{t}) at time step (:t) are carried out by the following Eqs.^44,45:
Where (:phi:) is a random vector selected from a uniform distribution and falls between 0 and 1, after analyzing all of the positions among all (:n) bats, the current global best location is (:{x}^{*}). One may use either (:{f}_{i}) (or (:{lambda:}_{i})) to adjust the velocity change while setting the other component, as the velocity increment is the product of (:{lambda:}_{i}) and (:{f}_{i}). Each bat is given a frequency at random for implementation, which is uniformly selected from ((:{f}_{min}),(:{f}_{max})). Following the selection of one of the existing top solutions for the local search, a random walk is used to produce a new solution for every bat locally.
Where (:t) is the average loudness of all bats at this time step and (:epsilon:) is a random value that falls between 1 and 1. The volume may be set to any convenient number since, after a bat has located its prey, the loudness typically falls while the rate of pulse emission rises. Considering that (:{E}_{min}=0) indicates that a bat has just discovered its victim and has momentarily stopped making noise, one obtains:
Where (:gamma:) is a positive constant and (:beta:) is a constant in the interval [0,1]. The loudness tends to be zero as time approaches infinity, and (:{r}_{i}^{t}) equals (:{gamma:}_{i}^{0}).
This article provides details on various statistical metrics that account for the accuracy of predicting peak times (NOPT) and power conversion efficiency (PCE) in solar energy systems. One of these metrics is the coefficient of determination (R²), which is the measure of agreement between actual and predicted values, where a number close to one indicates a strong match. For instance, a solar module with an actual PCE of 18.5% and an expected value of 18.3% will exhibit a high R², indicating a perfect match between the two values. Besides this, the root mean square error (RMSE) gives the average size of differences between the values. If we consider an example where 49 NOPT is predicted instead of the actual 50, this will have a minimal impact on RMSE. The 95% confidence level uncertainty (U95) indicates prediction stability and helps to ensure that long-term forecasts are reliable. Correspondingly, MRAE and MDAPE are measures of error in percentage that are normalized and robust. At the same time, the prediction interval coverage probability (PICP) is a criterion that checks whether the actual NOPT or PCE values fall within the model’s predicted bounds. The mathematical formulations of the employed evaluation metrics are presented in Eqs. (11) to (16).
Where, (:{t}_{i}) is observed (actual) solar energy value at instance (:i), (:{p}_{i}) is predicted solar energy value at instance (:i), (:stackrel{-}{t}) and (:stackrel{-}{p}) are the mean of observed and predicted values, respectively. (:n) denotes the total number of observations. (:left[{low}_{i},:{up}_{i}right]) are lower and upper prediction interval bounds for the (:i)th prediction, and (:{k}_{i}) demonstrates the indicator variable, equal to 1 if the observed value lies within the prediction interval, and zero otherwise.
The hybridization strategy adopted in this study is designed to enhance nonlinear pattern learning by combining the complementary strengths of different learning paradigms rather than relying on a single-model structure. Single machine learning models, such as kernel-based learners or tree-based algorithms, are effective in capturing specific types of relationships; however, they are inherently limited in representing the full complexity of photovoltaic system behavior, which is governed by highly nonlinear, nonstationary, and interacting environmental and operational variables. In the proposed hybrid framework, gradient boosting models act as strong base learners capable of capturing high-order nonlinear interactions and abrupt regime changes, while the adaptive aggregation mechanism integrates multiple weak and strong predictors to reduce bias and variance simultaneously.
This fusion enables the model to learn both global trends and localized nonlinear responses, which are common in PV systems due to fluctuating irradiance, temperature-dependent efficiency, and extreme energy generation. Hybridization improves learning performance by mitigating the weaknesses of individual models. While single models may overfit local patterns or underperform in extrapolation regions, the fusion strategy stabilizes predictions through ensemble averaging and adaptive weighting, thereby improving generalization and robustness. This is particularly important for small-to-moderate datasets, where individual learners may exhibit high variance. Furthermore, the hybrid framework enhances error correction, as other models in the ensemble can compensate for mispredictions from a single model. This mechanism explains the observed reductions in RMSE and uncertainty bounds, as well as the consistent performance across the training, validation, and test datasets. Compared to single-model baselines, the hybrid approach demonstrates superior capability in learning complex nonlinear relationships while maintaining interpretability and stability, making it especially suitable for simultaneous forecasting of NOPT and PCE. As a result, the hybridization strategy directly addresses the limitations of standalone models and provides a more reliable and scalable solution for real-world photovoltaic system forecasting.
All data preprocessing procedures, hybrid machine learning model implementations (including the XGBA framework), training scripts, and evaluation workflows used in this study were custom-developed and implemented in Python. To ensure transparency and reproducibility, the complete source code, including model configurations, parameter settings, and execution instructions, is available from the corresponding author upon reasonable request. Requests for access can be directed to: asifmmd1in@gmail.com. The code is provided for academic and research purposes without restriction.
The dataset in this research includes 305 records with seven input variables, namely Ap, Amin, Amax, Ep, Emin, Emax and nyield, and the targets for prediction are the number of peak times (NOPT) and the power conversion efficiency (PCE), expressed as percentages. The dataset, obtained from⁴⁶, was partitioned into training (70%), validation (15%), and testing (15%) subsets. To ensure reproducibility and prevent data leakage, the dataset was explicitly partitioned into three mutually exclusive subsets: 70% (214 samples) for model training, 15% (46 samples) for validation, and 15% (45 samples) for independent testing. This splitting strategy was selected to provide sufficient samples for learning model parameters while reserving adequate data for unbiased hyperparameter tuning and final performance assessment. The validation subset was used exclusively for model selection and hyperparameter optimization, whereas the test subset remained completely unseen during the training and tuning phases. This strict separation ensures that reported test results reflect true generalization performance rather than memorization effects. Moreover, data splitting was performed in a deterministic, reproducible manner, and the same partitions were consistently applied across all single and hybrid models to ensure fair, transparent comparisons. This structured training–validation–testing workflow minimizes the risk of optimistic bias and aligns with best practices in machine learning–based photovoltaic performance modeling.
According to Table 1, the variables characterize various operational and environmental conditions related to solar energy systems. Specifically:
Ap expresses the peak absorption wavelength measured under standard test conditions (nm).
Amin and Amax represent the minimum and maximum absorption wavelength during the measurement period, reflecting daily and seasonal variations in sunlight exposure.
Ep denotes the peak emission wavelength (in nm) produced under the measured irradiance conditions.
Emin and Emax indicate the minimum and maximum emission wavelength across different operating regions, capturing fluctuations due to environmental and system variations.
is the absolute emission quantum yield, ranging from 0 to 100, calculated as the ratio of actual energy output to the available solar resource, reflecting system performance efficiency.
The target variables quantify predictive objectives:
NOPT, with a maximum value of 12.91%, indicates the number of optimal peak operating times for the PV system.
PCE, with a maximum of 4.36%, measures the efficiency with which solar irradiance is converted into electrical energy.
All measurements were collected using calibrated pyranometers for irradiance and standard energy meters for electrical output, ensuring accurate representation of environmental and operational conditions. Hence, this dataset not only signifies environmental variations but also captures system performance metrics, providing a solid foundation for building and validating predictive models. Before training and evaluating the predictive models, the raw dataset underwent a systematic preprocessing workflow to ensure data quality, consistency, and compatibility with machine learning algorithms. First, all input variables were normalized using min–max scaling to map values to 0–1, preventing features with larger numerical ranges from dominating the learning process. Second, missing values were handled using a two-step approach: records with minor missing entries (< 5% of the dataset) were imputed using linear interpolation based on neighboring temporal values, while records with substantial missing information were excluded to avoid introducing bias. This ensured that the final dataset retained meaningful variability without compromising integrity. Third, noise filtering was applied to smooth transient fluctuations in energy and irradiance measurements. A moving average filter with a window size of 3 was applied to the input features , , , , , and to reduce measurement noise while preserving significant trends relevant to model training.
Figure 5 shows a scatter matrix, which displays the distributions and pairwise relationships of the features in the dataset. On the diagonal, each panel represents distributions of individual variables, whereas off-diagonal plots show possible correlations and grouping between pairs of features. The nopt values are distributed mainly between 0 and 4, so most samples are within this range. Likewise, the PCE values are primarily concentrated between 0 and 4.4, consistent with their distribution in the dataset. By and large, the matrix delineates variable ranges, uncovers potential relationships (linear or nonlinear) and regions of concentration, thus giving a brief indication of feature behavior, which is handy for exploratory data analysis.
Scatter matrix plot for the distribution and relationships within the dataset across different feature subsets.
The computational complexity and training time of the proposed models were systematically analyzed to assess their practical feasibility and scalability. The runtime results clearly demonstrate the computational trade-off introduced by BA–based optimization across all models and both target variables (NOPT and PCE). In all cases, incorporating BA increased execution time by approximately 3–5 times compared with the corresponding base models, attributable to the iterative population-based search mechanism and the repeated fitness evaluations inherent to metaheuristic optimization techniques. Among the evaluated models, RBF consistently exhibited the lowest computational cost, both in its base configuration and when coupled with BA. For NOPT prediction, the RBF model required 9.47 s in the base form and 48.29 s with BA optimization, while for PCE prediction, the runtime remained similarly low (10.36 s in the base form and 52.87 s with BA). This behavior reflects the simpler mathematical structure and lower training complexity of kernel-based models, making RBF computationally efficient even under optimization. The XGBoost-based models showed moderate computational demand, with base runtimes of 16–18 s, increasing to 62–66 s after BA optimization. The additional overhead primarily stems from repeated tree construction, gradient boosting iterations, and hyperparameter evaluations during the optimization process.
In contrast, Random Forest exhibited the highest computational cost, particularly in its optimized form, with runtimes reaching 80–84 s, due to the large ensemble size, bootstrap sampling, and repeated evaluation of tree-based structures across BA iterations. From a scalability perspective, the observed computational trends indicate that training time grows approximately linearly with dataset size for RBF and near-linearly to moderately superlinearly for tree-based ensemble models. While BA-based hybridization introduces additional overhead, this cost is incurred offline during model development and optimization, whereas online inference remains computationally lightweight, enabling real-time deployment in photovoltaic monitoring systems. Regarding scalability to larger PV datasets and different climate zones, the proposed hybrid framework is inherently extensible. Larger datasets are expected to improve generalization while increasing training time proportionally, particularly for ensemble models. However, the modular design of the hybrid approach allows parallelization of BA fitness evaluations and tree construction, making it suitable for high-performance or cloud-based computing environments. Moreover, the data-driven nature of the models enables adaptation to diverse climatic conditions, provided that representative environmental and operational data from different regions are included during training.
3D wall plot illustrating the convergence behavior of the optimization process across iterations or parameters.
The random search procedure was conducted using predefined hyperparameter ranges that were selected based on model-specific constraints, prior literature, and preliminary sensitivity trials to ensure both computational feasibility and sufficient exploration of the solution space. For kernel-based hybrid models (RBBA), the length scale was sampled from a continuous logarithmic range of [10⁻³, 10¹], while the lower and upper bounds of the length scale were drawn from [10⁻⁵, 10⁻²] and [10³, 10⁶], respectively, allowing the model to capture both smooth and highly nonlinear functional relationships. For hybrid models (RFBA and XGBA), the number of estimators was randomly sampled from the interval [20, 1000], enabling evaluation of ensemble sizes from small to large. The maximum tree depth was explored within the range [5, 1000] to assess the trade-off between model expressiveness and overfitting risk, while the minimum number of samples required to split a node was sampled from [2, 150] to regulate tree granularity and stability. For boosting-based hybrids (XGBA), the learning rate was sampled from the continuous range [0.01, 0.9] to balance convergence speed and generalization performance. In addition, the column sampling rate per tree (colsample_bytree) was varied within [0.5, 1.0] to enhance feature diversity and reduce correlation among trees, and the number of leaves was sampled from [10, 100] to control the complexity of individual boosting trees.
From a computational standpoint, the random search was executed for a fixed budget of 200 independent hyperparameter evaluations per model–target pair, ensuring consistent and fair optimization across all frameworks. Each candidate configuration was trained on the training subset and evaluated exclusively on the validation subset using RMSE and R² as the primary selection criteria. Table 2 summarizes the hyperparameters optimized for the hybrid models used to predict solar energy targets, specifically NOPT and PCE. The hyperparameters are length scale, length scale bounds (lower and upper), number of estimators, maximum tree depth, minimum samples required to split a node, learning rate, colsample by tree for NOPT, and number of leaves for PCE. The model’s flexibility, complexity, and learning dynamics are controlled by these parameters, which in turn aim to achieve prediction accuracy as the ultimate goal. For example, the RBBA model has a length scale of 3.9516 for NOPT and 2.1531 for PCE, indicating the degree of smoothness of the underlying regression function. In tree-based hybrid models, the number of estimators for RFBA and XGBA are 321 and 246 for NOPT, and 846 and 21 for PCE, respectively, so that the differences in ensemble size and their effects on predictive performance are clear. All experiments were conducted under identical computational settings to ensure fair model comparison and reproducibility. The implementations were executed on a workstation equipped with an Intel^® Core™ i7 processor, 32 GB RAM, and a 64-bit Windows operating system. The models were implemented using Python (v3.9) with Scikit-learn, XGBoost, and NumPy libraries, which are widely adopted in ML research.
To address concerns regarding potential overfitting due to the small dataset size (305 samples), a 5-fold cross-validation procedure was implemented on three representative single models: RBF, RF, and XGB. Table 3 shows the 5-fold cross-validation results for the single models. The 5-fold results demonstrate consistent performance across folds, indicating robust generalization ability.
Table 4 summarizes the comprehensive performance of both single and hybrid models in forecasting the NOPT and PCE. The evaluation used a suite of statistical indicators, including R², RMSE, PICP, U95, MRAE, and MDAPE, to ensure a rigorous assessment of predictive accuracy and reliability. Among the single models, the XGB framework consistently outperformed RBF and RF, yielding the lowest error rates across RMSE, MRAE, and MDAPE, which highlights its superior ability to approximate the underlying solar energy dynamics. Nevertheless, the hybrid configurations markedly advanced the prediction quality beyond that of the standalone models. In particular, the XGBA model achieved exceptional results, with R² values of 0.9954 for NOPT and 0.9970 for PCE, thereby capturing nearly all variability observed in the actual system behavior. Furthermore, its minimal uncertainty values (U95 = 0.5346 for NOPT and 0.1526 for PCE) underscore the robustness and stability of its forecasts. These outcomes demonstrate that the XGBA model not only minimizes deviation from ground truth but also ensures reliable and consistent estimations, which are indispensable for effective scheduling, energy resource allocation, and risk reduction in solar energy management. Collectively, the results affirm the superiority of hybrid learning strategies, particularly XGBA, in providing both accuracy and resilience for practical decision-making in renewable energy systems.
Figure 7 shows a comparative graphical representation of the effectiveness of each model using the evaluation metrics, and it identifies the gap between single and hybrid models in terms of NOPT and PCE forecasting. With the value of the metric R² taken as an example, we can determine that the RF model is shown by having the lowest correlation; hence, it has a lower predictive ability; in short words, the respective model’s predictions deviate more from the actual peak operating times and PCE measured in real solar energy systems. As for RMSE, all the models yield lower errors for PCE than for NOPT, indicating that power conversion efficiency is a more accurate predictor than the number of peak times. The same direction can be drawn from the U95 values, which reveal that the predictions are more stable for PCE. On the other hand, MRAE and MDAPE scores are higher for PCE, which signifies that the values of relative and percentage errors are greater for peak time predictions. As for PICP, RBF, XGBA, and XGB are the models that allow the highest coverage for PCE, while XGB and RBBA are the best performers for NOPT, which indicates that these models offer the most reliable probabilistic forecasts in real-world solar energy applications.
Performance evaluation of the developed models using key statistical metrics. The best-performing models were selected based on their superior accuracy and reliability.
Figure 8 compares the scatter plots for NOPT and PCE predictions, which depict the degree to which the models’ predictions are accurate. The points representing the hybrid XGBA model are very close to the best-fit line. They are mostly located within the ± 15% deviation lines, showing that there is a good correlation between the predicted and actual values. In NOPT, this means that the model can accurately predict the optimal peak operating times. With PCE, the forecast is close to the actual power conversion efficiency of solar modules. From a financial perspective, such dependable projections enable solar farm managers and investors to schedule energy production more accurately, thereby making better use of resources and allowing for a higher level of confidence in revenue estimation. Correct predictions of peak times and efficiencies become the basis for making operational decisions that involve the organization of maintenance, energy trading, and capacity planning, all of which lead to a reduction in the economic risk and an increase in the overall profitability.
Scatter plot of predicted versus actual values on the test dataset, showing the performance of the selected models.
Table 5 provides an overview of the statistical comparisons of the best hybrid models for both NOPT and PCE targets in the testing phase. The values of NOPT that were measured vary from 0.1 (min) to 10.12 (max), with 4.3182, 3.8950, and 2.8322 being the mean, median, and standard deviation, respectively. The XGBA model is closest to these statistics, indicating that it not only captures the most frequent but also the extreme variations in the number of optimal peak operating times. The range of measured PCE values is from 0.15 to 4.1, with mean, median, and standard deviation being 1.7836, 1.7250, and 0.9239, respectively. All models offer minimum predictions that are in agreement. In contrast, the XGBA model achieves the maximum value (4.0875), which is closest to the measured maximum, indicating good model performance under peak efficiency conditions. The outcome of this study is that the hybrid models are helpful for solar energy systems as they not only depict the normal performance but also the peak outputs, and therefore, the operators can use the energy scheduling to achieve maximum revenue and minimize financial uncertainty by being able to predict the periods of high energy generation and efficiency.
Figures 9 and 10 show the significant prediction errors for each model and the NOPT and PCE targets. The XGBA model has the narrowest line through the origin, suggesting that almost all of its predictions are very close to the actual values. To be more specific, in Fig. 9, the error of the XGBA model is very close to zero, which is the range of -5 to + 5, while other models have errors in much wider ranges. This high accuracy enables the prediction of any number of peak operating times and power conversion efficiency with surprising accuracy. From an investor’s point of view, such accurate predictions are extremely valuable: they enable solar power investors and managers to estimate likely energy output and efficiency with a high degree of confidence, enabling them to better allocate resources, plan maintenance activities more effectively, and predict revenues more accurately. As a result, models such as XGBA can reduce financial risk, increase profit potential, and enhance decision-making in solar energy projects.
Histogram showing the distribution of prediction errors for the selected models.
Line plot of prediction errors for the selected models.
Table 6 presents the results of Dunn’s post hoc test for pairwise model comparisons alongside the Durbin–Watson (DW) statistics to assess the reliability and independence of model residuals. Dunn’s post hoc test is a non-parametric method used for multiple pairwise comparisons following a Kruskal–Wallis test and was selected because the performance metrics, such as RMSE and R², do not necessarily follow a normal distribution. This test evaluates whether the differences in model performance are statistically significant. The Durbin–Watson statistic measures autocorrelation in the residuals, with values ranging from 0 to 4; values close to 2 indicate no significant autocorrelation, values below 2 suggest positive autocorrelation, and values above 2 indicate negative autocorrelation. In this study, the XGBA model shows DW values of 1.9274 for both NOPT and PCE, which is very close to 2, confirming that the residuals are statistically independent. This independence implies that the model predictions are reliable and not biased by systematic correlation in the data. In contrast, several single or hybrid models exhibit DW values substantially below or above 2, indicating residual correlation and potentially less reliable predictions. Together, Dunn’s post hoc test and DW statistics provide a rigorous assessment of model validity: the former confirms that XGBA’s performance differences are statistically robust, while the latter demonstrates that the residuals are independent, supporting the model’s robustness and generalization capability.
Table 7 presents the confidence intervals (CIs) for RMSE and MDAPE across the training, validation, and testing phases for all single and hybrid models, for both target variables, NOPT and PCE. These intervals provide an explicit measure of prediction uncertainty and offer insight into the statistical stability and reliability of each modeling framework beyond pointwise performance metrics. During the training phase, all models exhibit relatively narrow confidence intervals, indicating stable learning and limited dispersion in prediction errors. For NOPT prediction, the hybrid models—particularly XGBA—show comparatively tighter RMSE and MDAPE intervals, suggesting more consistent error distributions than single-model counterparts. A similar trend is observed for PCE, where hybrid models demonstrate reduced uncertainty bounds, reflecting improved robustness during model fitting. During the validation phase, the confidence intervals slightly widen across all models, as expected, since predictions are evaluated on unseen data used for hyperparameter tuning.
Nevertheless, hybrid models maintain narrower CI ranges than single models for both RMSE and MDAPE. This behavior indicates enhanced generalization capability and reduced sensitivity to data variability, reinforcing the effectiveness of hybridization strategies in controlling prediction uncertainty. In the testing phase, confidence intervals widen further, reflecting realistic uncertainty under fully unseen data conditions. Despite this, the XGBA model consistently exhibits balanced, relatively compact CI ranges for both NOPT and PCE, demonstrating reliable performance and controlled error dispersion. The comparable CI widths across training, validation, and testing subsets indicate the absence of severe overfitting and confirm the statistical stability of the proposed hybrid framework.
Figure 11 shows the Taylor diagram for the difference between measured and predicted values. The RBF-based models outperform the other approaches for both nopt and PCE, achieving the highest correlation coefficients and standard deviations closest to the measured data, which results in the lowest overall error. Tree-based and ensemble models (RF, XGB, and their variants) capture general trends but show noticeable variance mismatch and reduced correlation, especially for PCE. Overall, the Taylor diagrams confirm the RBF model’s superior robustness and generalization, particularly in representing the system’s nonlinear behavior.
Taylor diagram for the difference between measured and predicted values.
Figures 12 and 13 present a combined sensitivity analysis assessing the impact of the input variables on the output variables NOPT and PCE, respectively. As per Fig. 12, the FAST sensitivity analysis identifies as the variable with the most significant influence on NOPT, exhibiting an 1 value of 1.45, while is the leading variable for PCE predictions with an 1 of 2.2. This indicates that the extreme values of generated electrical energy strongly govern both the optimal timing of peak operation and the PV system’s efficiency. In physical terms, reflects periods of minimal energy generation, which critically limit the identification of optimal peak times, whereas corresponds to the highest achievable energy output, directly affecting power conversion efficiency. Furthermore, the accumulated local effects (ALE) study portrayed in Fig. 13 reveals the possible influence of each variable on the model outputs, along with lower and upper confidence intervals for NOPT and PCE predictions. These analyses highlight that, in addition to and , variables such as also significantly contribute, reflecting the direct impact of solar irradiance on system performance. Physically, higher irradiance levels increase energy production and efficiency, while variations in the minimum and maximum energy values determine the system’s operational window and efficiency ceiling.
The different ranking of feature importance between FAST and ALE arises from their distinct perspectives: FAST captures global variance contributions, while ALE highlights local and conditional effects. For example, shows the greatest influence on NOPT in FAST because variations in minimal energy generation dominate overall prediction variance, whereas ALE indicates that has stronger local effects on PCE, reflecting its direct impact on peak conversion efficiency under high irradiance conditions. These findings not only unveil the most sensitive parameters but also provide actionable insights for PV system operators: by understanding which energy extremes and irradiance levels most strongly affect system performance, resource allocation, system design, and maintenance schedules can be optimized to maximize energy yield. This connection between model sensitivity and real-world PV behavior enhances the interpretability and practical relevance of the predictive framework.
FAST Sensitivity analysis depicting the effect of input variables on the model output.
Sensitivity analysis for the impact of input variables on the model’s output based on the ALE method.
Despite the high predictive accuracy and robustness of the proposed hybrid model, several limitations exist. First, the study primarily relies on historical PV systems and meteorological data, which may limit model performance under entirely new climatic scenarios or rapidly changing environmental conditions. Second, while the hybrid framework demonstrates strong accuracy and interpretability, exploring more advanced deep learning architectures, such as Transformers or Graph Neural Networks, was beyond the current scope. Third, uncertainty quantification was performed using standard evaluation metrics, but probabilistic forecasting and real-time adaptive prediction were not fully addressed.
Future work includes:
Integration of advanced reinforcement and deep learning-based hybrid models (e.g., Transformers, GNNs) to capture complex temporal and spatial dependencies in PV systems.
Development of probabilistic and real-time adaptive forecasting approaches to improve reliability under dynamic environmental conditions.
Expansion of the framework to include larger and more diverse PV datasets, enhancing generalization and practical applicability.
Further exploration of explainable AI techniques to deepen physical insight and improve transparency for operational decision-making.
These directions aim to enhance both the predictive performance and practical deployment of hybrid PV forecasting models in real-world energy systems.
In addition, the current dataset and modeling framework do not explicitly account for environmental disturbances, such as dust accumulation, humidity, partial shading, or soiling, which are known to influence photovoltaic system performance in real-world deployments. The absence of such factors may limit the generalizability of the predictions to field conditions where these disturbances occur. Nonetheless, the selected input variables, including , , , , , , and , indirectly reflect cumulative environmental effects on system performance. For example, variability in irradiance and energy output may partially capture the influence of transient shading or atmospheric conditions. To enhance applicability in operational settings, future studies should integrate additional environmental monitoring data, including humidity levels, dust deposition rates, soiling factors, and shading patterns. Incorporating these features into hybrid machine learning models can improve predictive robustness, reduce uncertainty under extreme or variable conditions, and increase the reliability of NOPT and PCE forecasts for real-world PV systems. This limitation does not diminish the current study’s contribution, as the framework provides a robust baseline for forecasting PV system performance under nominal environmental conditions and can readily be extended to include more complex environmental variables in subsequent research.
Beyond numerical accuracy, the proposed forecasting framework can be directly integrated into the operational workflow of real PV plants as a decision-support tool. In a practical deployment scenario, the trained model can be embedded within a plant energy management system to provide day-ahead or intra-day predictions of NOPT and PCE based on real-time or forecasted environmental inputs. Specifically, NOPT predictions enable operators to identify time windows during which the PV system operates at maximum effectiveness, supporting informed scheduling of load management, grid interaction, and energy storage charging or discharging. Accurate PCE forecasting allows continuous assessment of system health and performance degradation, facilitating early detection of faults, soiling, or suboptimal operating conditions. When predicted PCE deviates from expected values under similar irradiance and energy conditions, maintenance actions can be prioritized proactively.
Furthermore, the sensitivity analysis results provide actionable physical insight for system optimization. The dominance of variables such as and indicates that energy extremes critically influence both operational timing and efficiency, suggesting that operational strategies should focus on mitigating low-energy periods and maximizing utilization during high-energy intervals. This information can guide inverter control strategies, energy storage dispatch, and plant design adjustments, such as panel orientation or capacity planning. From an economic perspective, integrating the proposed model into PV plant operation can reduce uncertainty in energy yield forecasting, improve scheduling efficiency, and support more reliable participation in energy markets. The framework is scalable and adaptable to different plant sizes and climatic regions, making it suitable for both utility-scale PV plants and distributed solar installations. As a result, the proposed approach bridges the gap between high-accuracy data-driven modeling and practical, real-world PV system management.
Table 8 compares the proposed XGBA model with recent hybrid PV forecasting studies. Xu et al.⁴⁷ combined EEMD decomposition, XGBoost, LSTM, and Snake Optimization for PV power prediction, achieving reduced errors but focusing only on power series without addressing optimal operating points or efficiency. Renold et al.¹⁹ integrated TCN, LSTM, and GRU networks for short-term PV forecasting, improving accuracy and computational efficiency. Wang et al.⁴⁸ applied a stacking strategy of gradient-boosted and deep networks for solar irradiance and generation, achieving R ≈ 0.99. Tanyıldızı and Ağır⁴⁹ combined LSTM with SVM for very short-term PV forecasting, reporting R ≈ 0.9823 and RMSE ≈ 0.0300, demonstrating hybridization benefits over single models. The proposed XGBA model surpasses these approaches, achieving R² up to ~ 0.997 and very low RMSE for both NOPT and PCE. Unlike prior studies, it forecasts both optimal peak times and power conversion efficiency, offering broader applicability, robust generalization, and low prediction uncertainty.
This study introduced a framework to accurately predict solar energy parameters, including the number of optimal peak operating times (NOPT) and power conversion efficiency (PCE), using hybrid machine learning models optimized through the Bat Algorithm (BAT). Based on hyperparameter tuning, the performance of each model, including Radial Basis Function (RBF), eXtreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR), was improved by exploring the parameter space and achieving optimal predictive results with fewer iterations of the algorithm. The dataset consisted of 305 records with seven features, including solar irradiance (Ap, Amin, Amax), electrical energy output (Ep, Emin, Emax), and normalized energy yield (nyield), which collectively represented the environmental and operational conditions that influence solar energy systems. Numerical evaluation of the hybrid models highlighted the superiority of the XGBA model. Specifically, XGBA reduced the RMSE of the single XGB model in predicting NOPT by 40.155% and decreased the U95 value for PCE by 135.58%, demonstrating that this model was more accurate, stable, and robust across both targets. These enhancements suggested that the hybrid system was capable of predicting both average and extreme weather conditions, supporting effective management and scheduling of solar energy. In addition, three sensitivity analysis procedures were used to determine the effects of input variables on the models’ outputs. The FAST sensitivity analysis identified as the most crucial variable for NOPT, whereas for PCE, the highest first-order effect (1) corresponded to and the highest total effect (ST) to . These outcomes provided valuable insights regarding the drivers predominantly affecting solar energy performance and enabled informed decision-making. In general terms, the union of hybrid modeling, BAT optimization, and rigorous sensitivity analysis provided a stable, understandable, and highly performing system for predicting solar energy parameters and supporting strategic planning for solar energy projects.‎.
Data will be provided upon reasonable requests, and codes can be accessed in the GitHub repository (https://github.com/AsifMd-10/Accurate-Forecasting-of-Photovoltaic-Optimal-Points-and-Efficiency).
Photovoltaic
Organic photovoltaics
Power conversion efficiencies
Artificial neural network
Explainable AI
Local interpretable model agnostic explanations
Power conversion efficiency
Accumulated local effects
Radial basis function
Classification and regression tree
Coefficient of determination
95% Confidence level uncertainty
RBF + BAT
XGBR + BAT
Lowest Energy Generation
Normalized energy yield
Sustainable development goals
Non-fullerene acceptors
Internal quantum efficiencies
Machine learning
SHapley additive explanations
Number of optimal peak operating times
Bat algorithm
Fourier amplitude sensitivity testing
eXtreme gradient boosting regression
Random forest regression
Root mean square error
Prediction interval coverage probability
RFR + BAT
Efficient electrical energy produced
Highest energy generation
Direct solar irradiance
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Department of Electronics and Communication Engineering, GLA University, Mathura, 281406, India
Anjan Kumar
Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, India
Md Asif
College of Engineering, Applied Science University, Al Eker, Kingdom of Bahrain
Malak Naji
Department of Electrical and Electronics Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India
B. Spoorthi
Department of Electronics & Communication Engineering, Siksha ’O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India
Badri Narayan Sahu
Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
S. Radhika
College of Technical Engineering, the Islamic University, Najaf, Iraq
Marwea Al-hedrewy
College of Technical Engineering, the Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
Marwea Al-hedrewy
Department of General Science, Mamun University, Khiva, Uzbekistan
Egambergan Khudaynazarov
Faculty of Technology, Urgench State University, Urgench, Uzbekistan
Hayitov Abdulla Nurmatovich
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A.K. conceived the research idea, designed the methodology, and supervised the overall study. M.A. (corresponding author) managed the project administration, coordinated data collection, and led the manuscript preparation. M.N. performed data preprocessing, feature engineering, and contributed to model development. S.B. implemented the machine learning algorithms, carried out the computational experiments, and validated the results. B.N.S. conducted the statistical analyses, model evaluation, and contributed to the interpretation of findings. S.R. prepared the figures, visualizations, and supported the development of the sensitivity analysis. M.A.-h. contributed to literature review, background formulation, and technical editing of the manuscript. E.K. supported the experimental design, reviewed the technical content, and contributed to refining the methodology. H.A.N. contributed to result interpretation, proofreading, and preparation of the final draft. All authors reviewed and approved the final manuscript.
Correspondence to Anjan Kumar.
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Kumar, A., Asif, M., Naji, M. et al. Accurate forecasting of photovoltaic optimal points and efficiency using advanced hybrid machine learning models. Sci Rep 16, 8197 (2026). https://doi.org/10.1038/s41598-026-39031-3
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DOI: https://doi.org/10.1038/s41598-026-39031-3
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French independent power producer (IPP) Qair has secured PLN350 million (US$94 million) in funding from Bank Gospodarstwa Krajowego (BGK) to build renewable energy projects with a combined capacity of 203 MW in Poland. 
The funding will enable Qair to construct three renewable energy projects: an 80MW solar project in Wierzbica, a 105MW solar plant in Grudziądz, Kuyavian-Pomeranian Voivodeship, and an 18MW wind facility in Jenkowo, Lower Silesian Voivodeship.  
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Construction is scheduled to start in Q2 2026, with commissioning expected in 2027. The financing comes through Poland’s Energy Support Fund, a key pillar of the country’s National Recovery and Resilience Plan, backed by the EU’s Recovery and Resilience Facility. 
In other news, the French IPP has brought commisioned its 14.6 MW Beehive solar portfolio, comprising thirteen small-scale photovoltaic farms strategically distributed across the country.  
The portfolio combines multiple sites into an integrated system, optimising local energy production. The company said the project will strengthen the national grid while providing clean electricity to surrounding communities. 
As of early 2026, Qair has installed nearly 500MW of solar capacity. Across all markets, the firm has 1.7GW of renewables either in operation or under construction, with a 35GW development pipeline spanning 20 countries. 
Qair recently signed a power purchase agreement (PPA) with Brazilian LPG distributor Ultragaz for its 192MW Bom Jardim solar PV project in Ceará, northeastern Brazil. Construction on the plant began in 2024, and it reached commercial operations earlier this year. 

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There were 899 new grid-connected solar PVs installed in the fourth quarter of 2025, bringing the total number of installations in Singapore to 14,625.
Residential accounted for majority of the total installations (47.3% or 6,912 installations), followed by town councils & public housing common services (34.6% or 5,061 installations) and private (15.9% or 2,321 installations) sectors. Public service agencies constituted the remaining (2.3% or 331 installations) of total installations.
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As countries across the region accelerate efforts to balance food security with environmental sustainability, Oman is stepping up initiatives to modernise its agricultural sector while reducing its carbon footprint. In line with these goals, the Ministry of Agriculture, Fisheries, and Water Resources has introduced a new programme aimed at integrating renewable energy into farming practices.
The initiative, titled ‘Solar Energy – Sustainable Harvest’, focuses on deploying solar power systems across agricultural projects in Oman. It is designed to ease the financial burden of rising electricity costs for farmers and investors while supporting national efforts to cut carbon emissions.
Launched in collaboration with Nafath Renewable Energy and the Development Bank, the programme aims to accelerate the adoption of clean energy solutions, particularly for farms that rely heavily on electricity to operate irrigation systems, greenhouses, and other essential activities.
Officials said the initiative reflects the government’s broader strategy to promote sustainability, expand the use of renewable energy, and strengthen the agricultural sector’s contribution to national food security.
Under the scheme, eligible farms can receive financing of up to RO15,000 to install solar systems. The package offers zero interest for full-time farmers and a reduced rate of 3% for part-time farmers. It also includes a grace period of up to 1 year, with repayment terms of up to 7 years.
Priority will be given to farms involved in producing key fruits and vegetables under the national food security programme, particularly those using greenhouses, advanced irrigation systems, or projects that directly enhance domestic food production.
The ministry outlined several requirements for applicants, including officially registered agricultural land, either owned or under a valid usufruct agreement, and an agricultural electricity meter. Applicants must also provide ownership documents and a land survey map.
Additional conditions include holding a valid agricultural certificate, membership in a relevant agricultural association, and access to at least 100 square metres of open, unshaded land for the installation of solar panels.
Officials noted that the initiative is expected to reduce operating costs, lower dependence on conventional energy, and encourage wider adoption of renewable technologies, while supporting Oman’s long-term environmental and climate objectives.
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“Two and a half to three years ago, we committed to the shift from mono PERC to TOPCon because we believed mono PERC efficiency had reached its limit. At that time, many competitors said this was not the right path, but today all manufacturers are working with TOPCon,” Covarrubias noted.
“It was a bet — one we won —, and that is why we are now slightly ahead of the competition. TOPCon cell technology is here to stay,” he added.
The evolution of this technology has made it possible to increase module power output without modifying panel size, improving the efficiency of utility-scale solar projects. As a result, the company moved from its TigerNEO 1.0 version at 620 Wp to the 2.0 version at 630–635 Wp, reaching 670 Wp with the latest 3.0 version within the same footprint.
The module achieves cell efficiencies of between 26.7% and 27%, with module efficiencies ranging from 24.3% to 24.8%, bifaciality of 85%, and an annual linear degradation rate of just 0.35%, enabling longer system lifetimes and improved return on investment.
The product also demonstrates better performance under low-irradiance conditions, capable of generating up to 2.49% more energy during early morning and late afternoon hours.
Covarrubias uses an analogy to explain the impact of these technological improvements on solar projects: “I like to compare it to Formula 1 — we are gaining half a second per lap in a 50-lap race.”
The development of new modules is also fuelled by feedback from the solar ecosystem, particularly EPC contractors and developers involved in large-scale projects, allowing operational needs to be incorporated directly into design and manufacturing processes.
“We know not everything will be simple or feasible to implement, but feedback is essential. In many cases, it is local and highly customised by country, and that ultimately makes a difference throughout the entire process,” said the LATAM Sales Director at Jinko Solar.
At the same time, the company’s regional strategy is supported by a strong presence in Latin America, where it has chosen to differentiate itself through service. As a result, it currently holds around 30% market share in the region and close to 40% in Argentina.
Looking ahead, the company aims to maintain or increase its recent volumes and market share, while also seeking local players in Latin America capable of strengthening regional capabilities and retaining project development within the region.
“At Jinko Solar, we continuously compete internally with what we call ‘cross-regional’ models — companies executing projects in Latin America from other continents. Our wish is for Argentina to recognise its potential and seize opportunities across Latin America, optimising processes in Chile, Peru, Ecuador and Colombia,” Covarrubias stated.
“In other words, to use Argentina as a platform for service-driven growth and to further scale up the regional offering,” he concluded during his participation at the event.
by Strategic Energy Keep reading
The war in Iran and the closure of the Strait of Hormuz have triggered a sharp rise in international oil and gas prices, prompting Germany and the United Kingdom to make wind energy a central pillar of their immediate responses to the new energy crisis.
by Lucia Colaluce Keep reading
Expanding power transmission will be key to unlocking investment in renewables, storage and mining, according to the World Bank Group’s IFC.
by Strategic Energy Keep reading
The Chilean Renewable Energy and Storage Association presented a set of policy and technical recommendations aimed at strengthening grid integration, boosting energy storage and reducing the country’s reliance on imported fossil fuels, which still account for 63% of total energy consumption.
by Strategic Energy Keep reading
The war in Iran and the closure of the Strait of Hormuz have triggered a sharp rise in international oil and gas prices, prompting Germany and the United Kingdom to make wind energy a central pillar of their immediate responses to the new energy crisis.
by Lucia Colaluce Keep reading
Expanding power transmission will be key to unlocking investment in renewables, storage and mining, according to the World Bank Group’s IFC.
by Strategic Energy Keep reading
The Chilean Renewable Energy and Storage Association presented a set of policy and technical recommendations aimed at strengthening grid integration, boosting energy storage and reducing the country’s reliance on imported fossil fuels, which still account for 63% of total energy consumption.
A leading media group in digital marketing, strategic communication, and consultancy specialized in renewable energy and zero-emission mobility, with a presence in Latin America and Europe. We focus on helping companies position their brand in key markets, connecting with the main decision-makers in the energy transition.

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(Yicai) March 30 — Leading Chinese manufacturers of solar modules have raised the prices of their products, as China is lifting the 9 percent rebates on the export value-added tax for photovoltaic products.
Trina Solar has increased its official guidance prices for distributors three times this year. They have reached 89 to 93 Chinese cents per watt for 620W-650W medium-format and 715W-745W large-format modules, up between 8.1 percent and 8.5 percent from January.
China announced in January that it will eliminate the 9 percent VAT export rebates for solar products, including modules, cells, inverters, and components, effective April 1. Industry insiders believe that this, coupled with rising upstream raw material costs, will lead manufacturers to hike prices by about 6 to 7 Chinese cents per watt.
Jinko Solar raised prices of its scenario-specific special-process products, including its Tiger Neo 3.0 tunnel oxide passivated contact PV modules, which have an output of over 650W, at the beginning of this month. The average increase is around 30 percent to 40 percent from the lowest price.
The price hikes were mainly driven by surging silver prices, which put downstream manufacturers under collective pressure, a representative from a leading Chinese integrated module firm told Yicai, adding that their company adjusts capacity flexibly based on market conditions.
However, upward adjustments in corporate guidance prices have not fully translated into actual market price increases.
In centralized application scenarios, TOPCon solar modules’ actual delivery prices are 68 to 70 Chinese cents per watt, and those in the distribution market are 76 to 83 Chinese cents per watt, both significantly lower than leading companies’ guidance quotes, according to statistics from InfoLink Consulting.
Even though large manufacturers have raised prices, the market has not yet followed suit, InfoLink said. Given the current market situation, it will likely be difficult to have a full-scale implementation.
Previously accumulated low-price inventory held by distributors has been basically cleared, and actual ex-factory prices are rising, especially those of mainstream high-power modules, a Chinese PV module distributor told Yicai.
Distributors were barely receiving any orders for products quoted at 88 to 90 Chinese yuan per watt before, so they mostly delivered low-price inventory between December and January, the above distributor explained.
Solar product prices in overseas markets have been on the rise recently, particularly in the past two weeks, driven by surging demand from the Middle Eastern conflict’s impact on oil and natural gas prices, and the trend of buying at the start of a period of price growth rather than price decline, given the expectations from the VAT export rebate cancellation, according to InfoLink.
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Backed by the A-rated financial strength of Hanwha Group, the new division offers homebuilders a stable, complete solution that creates manufacturing jobs while addressing homebuyer affordability challenges
IRVINE, California, March 31st, 2026 – Qcells, a premier provider of complete energy solutions and leader in the U.S. solar manufacturing market, today announced the official launch of Qcells New Homes. This new division will work with homebuilders integrating onsite end-to-end clean energy solutions for new residential construction. Qcells New Homes’ offerings are designed to make it easier than ever for new homes to be powered by affordable, reliable clean energy solutions families can count on – especially as their concerns over energy demand and costs continue.

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• Offers Predictable Monthly Lease Payments: Lease rates are priced below average electric utility rates with no annual escalator, providing homeowners with immediate and long-term relief from the potential for rising utility costs.
• Enhances Resilience & Grid Value: Integrated battery storage allows the power produced by solar to be stored in the battery and can be sold to the grid to enhance reliability for the local community in states that support onsite clean energy programs, such as NEM 3.0 in California. Qcells New Homes also offers battery configurations that can provide reserved backup during outages, all while supporting broader grid stability needs throughout the local community.

“We are cutting out the intermediary to deliver solar panels assembled in America directly to new communities,” said Phil Narodick, President of Qcells New Homes. “Our team brings decades of experience supporting the homebuilding industry and provides builders with an integrated solar and storage solution designed to maximize system performance and homeowner value under evolving net metering programs, while delivering seamless energy solutions across the entire project lifecycle.”

For more information on Qcells New Homes, visit: https://us.qcells.com/new-homes.

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Published on: March 30, 2026 / Updated on: March 30, 2026 – Author: Konrad Wolfenstein
Oberndorf am Lech Agri-Photovoltaics: From Bavarian model project to billion-dollar market – electricity and wheat from the same field – Creative image: Xpert.Digital
Germany's energy transition faces a fundamental dilemma: We need enormous amounts of space for the expansion of solar energy, but agricultural land is a scarce and valuable resource. A record-breaking project in Swabia is now addressing precisely this conflict between food production and electricity generation. In Oberndorf am Lech, southern Germany's largest agri-photovoltaic plant has been connected to the grid. Wheat and sugar beets continue to grow beneath state-of-the-art, sun-tracking solar modules. What at first glance appears to be a futuristic solar park is, in reality, the blueprint for a new multi-billion-euro market. Whether it's farmers who benefit from lucrative additional income, investors seeking green returns, or industrial giants like Nestlé using it to decarbonize their production: Agri-PV is evolving from a niche topic to the sleeping giant of the energy transition. But can this technology truly put an end to the land dispute?
At the end of March 2026, southern Germany's largest agrivoltaic plant officially commenced operation in Oberndorf am Lech in the Donau-Ries district. What appears outwardly to be an ordinary solar park is, upon closer inspection, a pioneering technical and regulatory project with far-reaching economic implications. The Munich-based start-up Feldwerke Solar GmbH, founded in October 2023, built a plant on 28 hectares with an installed capacity of approximately 17 megawatts, which, theoretically, can supply around 5,000 to 6,000 households with electricity. The unique aspect: around 90 percent of the area remains actively usable for agriculture, allowing winter wheat or sugar beets to continue to be cultivated between the rows of modules.
The plant, named Triticum – Latin for wheat – was designed and built by MaxSolar, a company with experience in agri-PV technology and tracker systems. The investor is clearvise AG, which joined the project after successfully securing the feed-in tariff in March 2025. The investor saw the project as an opportunity to demonstrate the attractiveness of the agri-PV concept for farmers, institutional investors, and energy suppliers alike. Bavaria's Minister of Economic Affairs, Hubert Aiwanger (Free Voters), praised the plant as a flagship project, while Mayor Franz Moll described it as a model for the future of Germany's energy transition.
One of the most remarkable aspects of the Oberndorf project lies not in its sheer size, but in the speed of its realization. Only twelve months passed between securing the land and the project being ready for construction. The permitting process itself took just six months – a fraction of the two to three years typically required for conventional ground-mounted photovoltaic systems. This drastic time saving is no accident, but rather the direct result of a structural advantage that agri-PV projects have over conventional solar parks.
The decisive factor was the preservation of agricultural use. Conventional ground-mounted photovoltaic (PV) systems, which require rezoning, mandate compensatory areas and often extensive environmental impact assessments, significantly lengthening the permitting process. Since no additional compensatory areas for farmers were required for the agri-PV system in Oberndorf, the official procedure was considerably shortened. The project also enjoyed high acceptance among the local population, the municipality, and the authorities, further facilitating its smooth implementation.
That this speed of approval is unlikely to remain an isolated case is demonstrated by the revised Solar Package I, which came into force in May 2024. It extended simplified approval procedures and strengthened the overriding public interest in renewable energies – a political signal that further improves the framework for future agri-PV projects.
The technical foundation of the Oberndorf plant consists of single-axis tracking systems in an east-west orientation, so-called 2P tracker systems. This technology is the core of the economic promise of agri-PV. Unlike stationary, south-facing solar installations, the module rows follow the sun's path throughout the day. This not only enables a 20 to 30 percent higher electricity yield compared to conventional south-facing systems, but also offers an agronomic advantage: The tables can be raised to a fully vertical position when agricultural machinery needs to pass through for sowing, tillage, or harvesting.
Recent analyses by the Energy Economics Institute (EWI) demonstrate that tracker systems (modeled for 2024 in Brandenburg) achieve a market value up to 43 percent higher than fixed, south-facing systems – an advantage that becomes increasingly important during periods of midday electricity surpluses, as tracker systems produce more energy during the higher-volume morning and evening hours. The more consistent feed-in also reduces the load on the grid connection and lowers peak loads. Fraunhofer ISE confirms that intelligent tracker control allows for the targeted regulation of shading, light availability, and soil moisture – depending on the crop and weather conditions.
In addition to the solar panels, biodiversity strips up to two meters wide are being created beneath the modules, for example in the form of flowering strips for insects. This adds an ecological dimension to the system that goes beyond its purely energy and food benefits.
The economic appeal of agri-PV projects stems from several sources simultaneously. For farmers who make their land available for such projects, Feldwerke promises long-term additional income of up to €3,000 per hectare annually – without having to abandon agricultural use. The land retains its status as agricultural assets with all associated tax advantages; rezoning for commercial use is unnecessary. Following amendments to the Renewable Energy Sources Act (EEG) 2025, EU agricultural subsidies (CAP direct payments) for elevated agri-PV systems remain largely unchanged, as only the area actually lost to foundations and technical infrastructure is deducted.
For investors and project developers, the picture is more nuanced. The feed-in tariff for agri-PV electricity under the 2025 Renewable Energy Sources Act (EEG) ranges from 6.86 to 9.36 cents per kilowatt-hour for plants awarded contracts through the Federal Network Agency's auctions. Smaller, farm-adjacent plants up to 1 megawatt, which are considered privileged, will even receive a fixed maximum rate of 9.2 cents per kilowatt-hour for 20 years starting in 2026. This is significantly higher than the average for conventional ground-mounted PV plants, which achieved a volume-weighted award of only 4.84 cents per kilowatt-hour in the auction process for July 2025.
According to a survey by the project developer Metavolt, agri-PV systems achieve an average return of between eight and 22 percent with an equity investment of between five and 20 percent. The amortization period ranges from seven to 14 years, depending on the system type and available subsidies. For comparison: For a 1-megawatt system with preferential subsidies, the construction costs (CAPEX) amount to approximately €800,000, the annual loan payment with 90 percent financing is around €51,350, and the operating costs are approximately €17,650 per year.
An honest economic analysis cannot ignore the fact that agrivoltaic (Agri-PV) systems are significantly more expensive to install than conventional ground-mounted photovoltaic (PV) systems. A recent study by the Thünen Institute for Agricultural Technology, published in February 2026, quantifies the additional costs for agrivoltaic systems at between 4 and 148 percent compared to standard ground-mounted PV systems, with specialized applications such as apple orchards exhibiting the greatest cost differences. A comparison of the levelized cost of electricity (LCOE) shows that agrivoltaic systems with tracking cost around 5.66 cents per kilowatt-hour, while conventional ground-mounted PV systems cost approximately 5.03 cents – a cost difference of 0.63 cents per kilowatt-hour, which can, however, be more than offset by the higher feed-in tariff for agrivoltaic systems.
Critics, such as researchers at the Thünen Institute, argue that the costs of agrivoltaics far outweigh the agricultural benefits and call subsidies into question. An industry representative like Jochen Hauff of PV Magazine disagrees with this conclusion, pointing to the insufficient consideration of the market value benefits of tracker systems and the long-term climate resilience of agricultural land. This discourse is productive: it compels the industry to optimize its cost structures and place the economic promise of agrivoltaics on a more solid data foundation.
Another point of contention concerns the land lease market. Conventional solar parks without agricultural status can offer landowners lease payments of up to €3,000 to €4,000 per hectare – amounts that actively farming landowners simply cannot achieve on their leased land. Agri-PV mitigates this displacement effect, but does not eliminate it entirely. Farmers like Christoph Kern, a grain farmer in Rhineland-Palatinate, lose portions of their leased land to solar park investors who can pay more than twenty times the agricultural lease rate. Agri-PV concepts like Feldwerke's offer a middle ground by allowing farmers to continue cultivating their land and additionally sharing the solar revenues with them.
The Renewable Energy Sources Act (EEG) forms the regulatory backbone for every agri-PV project developer in Germany. Agri-PV is classified under the EEG as a special type of solar power plant and receives separate subsidies. Technical requirements include a minimum clearance height of 2.10 meters (Category 1) or 0.80 meters (Category 2 for vertical systems) above the module's lower edge, as well as compliance with DIN SPEC 91434, which stipulates that at least 85 percent of the area must be used primarily for agricultural purposes.
In 2025, the tender volume for special solar power plants was significantly increased from 300 to 800 megawatts per year. A new two-stage award procedure was also introduced, which gives preferential treatment to agri-PV plants in the first round, considerably improving their chances of winning a contract. The maximum bid in the tender process is 9.5 cents per kilowatt-hour, which is dynamically adjusted to the market price. This funding framework is deliberately designed to move agri-PV out of niche funding and into the mass market – a political signal that is currently driving rapid growth in the project pipeline in Germany.
Feldwerke alone states that, in addition to the 20 megawatts already operational, it has a further 350 megawatts under development. The company is currently planning an even larger plant in Oettingen, also in the Donau-Ries district, with approximately 20 megawatts on 30 hectares. This project is intended to be closely integrated into the regional economy and to scale up the Oberndorf model to a larger area.
 

New: Patent from the USA – Install solar parks up to 30% cheaper and 40% faster and easier – with explanatory videos! – Image: Xpert.Digital
The core of this technological advancement is the deliberate departure from conventional clamp mounting, which has been the standard for decades. The new, more time- and cost-effective mounting system addresses this with a fundamentally different, more intelligent concept. Instead of clamping the modules at specific points, they are inserted into a continuous, specially shaped support rail and held securely in place. This design ensures that all forces – whether static loads from snow or dynamic loads from wind – are distributed evenly across the entire length of the module frame.
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While projects like Oberndorf are primarily driven by specialized project developers and institutional investors, the Nestlé project in Biessenhofen in the Ostallgäu region demonstrates a second strategic logic: on-site industrial power generation through agri-photovoltaics. The Swiss food company is investing around three million euros in a 4.5-megawatt plant on 4.74 hectares, which is scheduled to go online in the second half of 2025. The plant, built by BayWa r.e., is expected to cover around a quarter of the total electricity consumption of the Nestlé plant in Biessenhofen, which produces, among other things, baby food, mayonnaise, and mustard.
What makes the Nestlé system special is its design as a so-called cow-PV system. The solar panels are mounted at different heights – two meters in the southern section for adult cows, and 1.80 meters in the northern section for calves. The distance between the rows is 3.30 meters, which allows the use of tractors and mowers for continued hay production. The cows directly benefit from the shade provided by the panels, which represents a real agronomic advantage given the increasingly hot summers in the Alpine foothills. Farmer Gerhard Metz is planning a new barn with automated milking technology for up to 65 cows and young stock in this context.
The Biessenhofen project complies with the new DIN SPEC 91434 standard and is a prime example of the industrial use of agri-PV for decarbonizing in-house production. Nestlé's approach demonstrates that agri-PV is not merely an investment opportunity for energy projects, but also a strategic tool for sustainability transformation for industrial companies seeking to reduce their Scope 2 emissions.
Beyond economic indicators, agri-PV offers a methodologically measurable agronomic benefit. The so-called Land Equivalent Ratio (LER) measures the efficiency of combined land use compared to separate management. An LER above 1.0 means that dual use on one area yields more than two separate areas for crops and electricity production combined. Initial field trials in Hohenheim showed an LER of around 1.5 for wheat grown in an agri-PV system with a tracking system – an increase in land-use efficiency of 50 percent. The Bioeconomy Council's background paper confirms that elevated agri-PV systems in Central Europe can typically increase the LER to between 1.6 and 1.8.
Another often underestimated aspect is the climate resilience of agricultural land under agri-PV conditions. Partial shading from solar modules protects plants from extreme sunlight and hail, reduces soil evaporation, and can contribute to stable crop yields even during extreme weather events. This is gaining practical importance in light of increasing climate change in southern Germany. At the same time, biodiversity strips under and between the modules create new ecological niches for insects and small animals – a benefit that does not exist in conventional intensive farming.
Compared to the frequently cited example of energy crops, agri-PV stands out particularly positively in terms of land use. Currently, around 14 percent of agricultural land in Germany is used for cultivating energy crops for biomass production. Even with the German government's ambitious PV expansion targets for 2030, a maximum of around 0.6 percent of arable land would be used for photovoltaic systems. The narrative of a systematic displacement of food production by solar energy thus proves to be significantly exaggerated upon closer examination.
The strategic dimension of agri-PV only becomes fully apparent when considering the national land potential. In a study published in early July 2025, the Fraunhofer Institute for Solar Energy Systems ISE analyzed all types of agricultural land in Germany for the first time – arable land, permanent grassland, and perennial crops such as fruit, wine, and berries. The result is remarkable: 500 gigawatts peak of agri-PV capacity could be installed on the most suitable areas – far exceeding Germany's official photovoltaic expansion target of 400 gigawatts by 2040.
In the technical scenario without soft restrictions, the researchers even identify a potential of 7,900 gigawatts peak, while in the more nature-friendly scenario, which takes flora and fauna conservation areas into account, the potential is still 5,600 gigawatts peak. These figures are not an academic exercise, but a concrete, mapped potential based on geographic information systems and real soil data. Study author Salome Hauger from the Fraunhofer ISE identifies the lack of grid connection points as the key limiting factor and calls for a prioritization of grid expansion.
In parallel, the Öko-Institut (Institute for Applied Ecology) identified approximately 4.3 million hectares of agricultural land as particularly suitable for agri-PV applications in its own analysis – corresponding to around 25 percent of Germany's total agricultural land. This figure underscores that the current stage of the market – a few pilot projects with a few megawatts of capacity – is still far from the widespread utilization of this potential.
The global market for agri-PV systems is experiencing exponential growth. Market size was estimated at approximately US$5.3 billion in 2023 and is projected to reach US$31.5 billion by 2032, according to market researchers, representing a compound annual growth rate (CAGR) of about 21.9 percent. Key growth drivers include government incentive programs, technological innovations in tracker systems and bifacial modules, and a growing awareness of the ecological and economic synergies of dual-use applications.
In Germany, the area used for ground-mounted photovoltaic (PV) installations rose to a total of approximately 45,200 hectares by the end of 2024. Of this, around 15,200 hectares (34 percent) are arable land and 12,200 hectares are so-called conversion areas such as former military sites or landfills. According to the German Federal Environment Agency, this growth has been steady in recent years and is projected to continue: By 2030, the area could increase to between 96,000 and 109,000 hectares, and by 2040 to between 150,000 and 195,000 hectares. With an increasing share of agrivoltaics within this new area, a significant portion of these areas would remain agriculturally productive.
The interest of institutional investors in agri-PV is growing rapidly. Project developers report a steadily increasing demand from the sustainable investment sector, because agri-PV can simultaneously address sustainability, economic viability, and the preservation of agriculture. The Triticum project in Oberndorf – with clearvise AG as the institutional investor and Feldwerke as the specialized project developer – is likely to serve as a blueprint for numerous other projects in southern and central Germany.
An honest economic analysis must also identify the structural barriers that are currently slowing the ramp-up of agri-PV. Besides the aforementioned higher investment costs compared to conventional ground-mounted PV, three factors in particular are limiting: the grid infrastructure, the feed-in tariff system, and the availability of reliable data on agronomic yields.
The grid infrastructure presents a significant barrier for many potentially suitable rural locations. The Fraunhofer Institute for Solar Energy Systems (ISE) identified a lack of grid connection points as a key limiting factor – a problem that necessitates structural investments in grid expansion, extending far beyond the decisions of individual project developers. While the German Renewable Energy Sources Act (EEG) provides for higher feed-in tariffs for specific solar installations, the revenue for agri-PV typically ranges between 6 and 9.5 cents per kilowatt-hour. Industry experts see a threshold of around 10 cents per kilowatt-hour as the threshold for genuine mass adoption – a figure that, under the current funding framework, is only nearly achieved for smaller, farm-adjacent installations up to 1 megawatt.
Data on actual agronomic yields under agri-PV conditions is still limited. Long-term, reliable field trial data spanning multiple harvest years and different crops are scarce. The Bavarian State Farm in Grub is currently conducting trials with three different system types to close this knowledge gap. While it is established practical knowledge among farmers that harvesting under modules is more laborious and time-consuming, the specific yield loss varies considerably depending on the system type, crop, and farm management.
Finally, the social dimension of competition for land should not be underestimated. Even though agri-PV significantly mitigates the displacement effect compared to conventional solar parks, new distribution questions arise: Who benefits from the lease payments and electricity generation – the landowner, the farmer, or the external investor? A transparent participation structure, such as the one Feldwerke is aiming for with revenue sharing for farmers, can foster acceptance, but it does not replace comprehensive societal regulation of this conflict of interest.
The project in Oberndorf am Lech marks a significant step forward for agri-PV in Germany. It demonstrates that large-scale projects using modern tracker technology can be implemented quickly, enjoy broad public acceptance, and are simultaneously economically viable. Commissioning coincides with a period in which the political framework has been significantly improved by the 2025 Renewable Energy Sources Act (EEG 2025) and the increased tender volume. The parallel development of the Nestlé project in Biessenhofen shows that the concept is attractive not only for profit-oriented energy projects but also for industrial self-sufficiency strategies.
The gap between today's pilot projects and a systemically relevant role for agri-PV in Germany's energy supply is still considerable. The Fraunhofer ISE's potential of 500 gigawatts peak on suitable land contrasts sharply with the actual deployment level, which is still in the double-digit megawatt range. The bottlenecks lie not in a lack of available land, but in grid infrastructure, capital availability, agronomic expertise, and the willingness of policymakers to adjust feed-in tariffs so that the market becomes self-sustaining. If this transformation succeeds, agri-PV would indeed be more than just a flagship project – it would be a central component of Germany's energy transition, structurally reconciling climate protection and food security.
 
 

Konrad Wolfenstein
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© March 2026 Xpert.Digital / Xpert.Plus – Konrad Wolfenstein – Business Development

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After state regulators rejected East Windsor’s argument that it’s carrying way too much of the burden of Connecticut’s solar energy industry, the town is preparing to appeal the approval of a highly unpopular solar array.
Municipal officials who were once advocates of DESRI Holdings’ enormous Gravel Pit solar project have changed positions, saying for more than a year that East Windsor no longer wants a 30-acre expansion of the facility.
They tried to persuade the Connecticut Siting Council that the town is now giving up too much acreage to solar panels, but at the beginning of March the council unanimously approved DESRI’s expansion.
The decision infuriated local opponents, who have applauded selectmen’s decision in mid-March to authorize legal action to try to reverse the vote. Residents are still circulating a petition against the Gravel Pit solar expansion and are up to a little over 2,400 signatures.
It is the kind of conflict that the Siting Council frequently encounters. Charged with advancing the needs of the entire state, the council periodically authorizes cell towers, trash plants, battery storage farms or solar facilities despite loud, angry — but very localized — opposition.
But East Windsor leaders insist this case stands out.
“Often times people think our constituents are bringing up NIMBY concerns,” state Rep Jamie Foster testified at the Capitol earlier this month. “What’s really happening is one community has all of the problems.”
In a letter to the council last year, East Windsor said it shouldn’t have to bear so much of the cost of solar energy.
“Achievement of the state’s renewable energy goals should be the responsibility of the entire state, not just a few rural towns,” the letter said. “The proposed expansion alone would be larger than Tobacco Valley Solar, which was the largest grid-scale solar project in Connecticut before Gravel Pit Solar.”
Foster, a Democrat whose district includes East Windsor, and First Selectman Jason Bowsza told state lawmakers that part of what’s wrong is the lack of enforcement when Siting Council-approved projects generate unwelcome side effects. Bowsza said homeowners nears large solar projects in East Windsor hear relentless high-pitched buzzing noises whenever the sun it out. The town has no authority to punish the owner, and no state agency has been willing or able to intercede.
“It’s intrusive on families and children, and there seems to be nobody willing to take ownership of addressing the underlying concerns of that,” Bowsza said. “It’s seemingly the purview of nobody to enforce.”
The Siting Council has maintained it needs independence from influence by town officials precisely because it must make decisions to benefit a whole region or the entire state, even when that displeases a specific community.
But Bowsza and Foster have been advancing an argument that after the Siting Council authorizes a project in a town, it does nothing to help ensure that the operators follow the rules and conditions they agreed to.
“They are adamantly not interested in addressing municipalities’ concerns,” Bowsza said.
East Windsor spent more than $60,000 on legal fees to take part in the months-long Siting Council proceedings on the Gravel Pit case, he said.
“We’ll spend more money on appeal. If we have to go beyond Superior Court into Appellate Court, we’ll have to spend more local dollars on that,” he said.
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KING WILLIAM — A solar facility in King William County is expected to open by the end of the year, more than two years later than originally planned, according to Dominion Energy.
The 77-megawatt Sweet Sue Solar facility was approved in 2020 and intended to open in October 2024. That date slipped to April 2026 and now won’t be done until the end of the year, the Board of Supervisors heard on March 23.
“The project was originally planned for October 1, 2024. We had some delays created by environmental discoveries,” John Behreandt, Dominion’s construction project manager for the project. “That caused us to have to delay, to take a step back and focus on what the environmental impacts really might be and what they could be if we continue.”
He said Dominion put “further enhancements in place” and involved more engineering firms in the solar facility.
Behreandt said work also stopped on the site when large storms swept through the area. Now, however, Dominion is working on the mechanical and electrical phases of the project and is revegetating the site, he said.
Dominion is rebuilding and stabilizing two dams near the solar farm to “improve stability and performance,” he said.
The solar farm is expected to be supplying electricity to the grid and local electricity users by the end of the year. About 150 workers are employed at the site.
Since Sweet Sue was approved in 2020, the county has taken a more skeptical approach to solar power. Last year, when a small solar project was rejected, Planning Commission Chair Darrell Kellum raised constituent concerns over water run-off issues at Sweet Sue.
David Macaulay, Davidmacaulayva@gmail.com
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SolarEdge Technologies (SEDG), a global leader in smart energy technology, hit a 52-week intraday high of $53.28 on Friday, March 20, before closing at $51.73. The party was short-lived, though. By noon on Monday, March 23, share prices had already dipped to $46.16.
Welcome to the volatile world of renewable energy stocks.
In this analysis, we’ll explain why renewable energy stocks are historically volatile, how AI is fueling demand for more clean and renewable energy, and how SolarEdge’s recent performance encapsulates the entire renewable energy sector.
Renewable energy stocks don’t typically fluctuate quite as much day-to-day as SolarEdge recently did. But they are historically prone to significant boom-and-bust cycles.
Maybe not to the degree of the oil industry, but there’s plenty of evidence that renewable energy stocks experience their shares of peaks and valleys.
Look at the Invesco WilderHill Clean Energy Fund (PBW) for example. This exchange-traded fund (“ETF”), according to Invesco, is composed of publicly traded companies that “are engaged in the business of advancement of cleaner energy and conservation.”
The fund saw major peaks from 2005 through 2007 before plummeting throughout 2008 and into 2009. Then came an uptick that lasted from mid-2009 until the spring of 2011. Following that, it experienced a steep drop that more or less lingered for around nine years.
Then, the clean and renewable energy markets exploded in the spring of 2020, partially driven by increases in wind and solar prices during the COVID-19 pandemic. Between March 2, 2020 and January 4, 2021, PBW shares skyrocketed by roughly 346%.
Just a year later, it saw a sharp decline of roughly 30%. And between January 3, 2022 and April 1, 2025, the ETF’s share price dropped another 78%.
Since that time, however, PBW shares have nearly doubled to around $32, where they sit today.
And it’s not just PBW. Another clean energy ETF, iShares Global Clean Energy Fund (ICLN), has followed a similar (although not as drastic) pattern.
Same thing for the Invesco Solar Fund (TAN).
Why such volatility for this sector? There are a few reasons.
These hurdles are enough to send the renewable energy sector into an occasional tailspin. But the industry may now be facing its most significant obstacle yet.
When President Donald Trump began his second term in 2025 after a four-year hiatus, the entire clean and renewable energy industry felt a chill up and down its collective spine.
Renewable energy was squarely in the Trump administration’s sights.
In the span of roughly 14 months since his inauguration, Trump and the federal government have:
Not surprisingly, the International Energy Agency projected the U.S. to add far less power from clean and renewable energy in the coming years than previously anticipated.
In its 2025 Renewables report, the agency stated that:
“The renewable energy growth forecast… for the United States is revised down by almost 50%. This reflects several policy changes, including the earlier phase out of federal tax credits, new import restrictions, the suspension of new offshore wind leasing and restricting the permitting of onshore wind and solar PV projects on federal land.”
The Trump administration’s policy shifts also have Deloitte predicting that annual solar, wind, and storage additions between 2026 and 2030 could fall to a range of 30 gigawatts (“GW”) to 66 GW. That’s down significantly from the projection of 54 GW to 85 GW prior to the signing of the One Big Beautiful Bill Act (“OBBBA”).
These are drastic downgrades. Despite the federal government’s recent push for fossil fuels over renewable energy, however, the industry has been thriving of late. The main reason isn’t surprising.
As we’ve covered here before, AI and its massive data centers require tons of power to operate.
Consider:
Despite the Trump administration’s mandates to roll back clean and renewable energy initiatives, the need for power boils down to one question: What type of energy is ready to deploy right now to satisfy the unquenchable power needs of data centers?
The answer is clean, renewable energy. Why?
For one, it’s all around us. Because renewable energy, like solar and wind, relies on natural resources, there’s no need to spend on imported fuel and other energy sources. That creates energy independence.
Renewable energy technologies and systems are also generally faster to build than fossil fuel plants. That can expedite access to new energy sources.
Plus, renewable energy can often bypass the traditional electric grid, which is facing an astounding 2,600 GW interconnection backlog — mostly composed of solar, wind, and storage capacity projects.
Solutions include on-site or “behind-the-meter” approaches, where power generation happens at the point of consumption. Imagine a solar farm or wind power source on or next to the site of a data center or factory, using power sources that avoid the bottlenecked public grid.
Perhaps most importantly, renewable energy is cheaper. Solar and wind, for example, were roughly 41% and 53% less expensive, respectively, than fossil fuels in 2024.
As of 2025, renewable energy (primarily wind, solar photovoltaic, and hydro) comprised 27% of the electricity used by data centers. By 2030, however, renewable energy could reach a 50% share on the back of global solar and wind growth.
That said, Operation Epic Fury in Iran may complicate matters for the renewable energy industry.
Typically, when war or trade tensions impact the oil and gas industry, alternative energy sources are the beneficiaries.
That may not be the case this time around as the war in Iran continues to rage.
Despite the closure of the Strait of Hormuz, which is choking off 20 million-plus barrels of oil and 290 million cubic meters of liquefied natural gas from passing through each day, renewable energy may not benefit from the fossil fuel supply disruption.
That’s because inflation concerns are rising as oil and gas prices have skyrocketed since the start of the war at the end of February.
And if higher inflation becomes reality, the cost of clean and renewable energy manufacturing will likely rise.
That’s what happened when Russia invaded Ukraine in 2022. The war drove prices for raw materials, labor, and logistics up, resulting in supply-chain issues for the industry. And that damaged the momentum renewable energy had built prior to the invasion.
The war in the Middle East has the potential to stagnate what has been a fast-growing industry. More investments in renewable energy helped clean power, such as wind, solar, and hydro, generate more electricity combined than coal in 2025.
And, surprising as this may be, clean energy stocks have recently outpaced AI stocks. In the past year, the ICLN fund rose roughly 55%. By comparison, Nvidia (NVDA) increased roughly 60%, while the Magnificent Seven tech stocks are projected to gain 26% in 2026.
In the first quarter of 2026, clean energy ETFs were still performing strongly, with positive year-to-date gains. This can be partially attributed to rate cuts by the Fed late last year, which lowered financing costs for renewable energy projects.
Despite some momentum in 2026, however, the renewable energy industry is still prone to volatility. SolarEdge is a perfect example.
Since the start of 2026, SolarEdge had been riding the wave of an impressive 65% rally that culminated in its 52-week high on March 20.
Then the trend reversed, and SolarEdge dropped nearly 10% on March 23.
Let’s examine why by using numbers from the company’s 2025 earnings call in February:
Lots of positives for SolarEdge, especially compared with its performance in 2024. This has led some analysts to upgrade their ratings for SEDG, including Jefferies, Bank of America Securities, and BWG Global.
Looking at the bigger picture, however, there are concerns.
Yes, SEDG’s stock price has roughly tripled in the past year. But over the past five years, SEDG is down around 83%. And the company hasn’t been profitable for about three years. Its negative 34.23% net margin and forecasted negative 4.54 earnings per share for 2026 tell some of the story.
For the rest of the story, just reread the bullet points above to see how much the company lost.
After its roughly 13% spike on March 20, the stock appeared overvalued based on its March 20 closing price of $51.73 versus its fair value estimate of $33.80. The reality for SEDG is that the market still sees the company as turning a corner rather than fully profitable. The 10% plunge in stock price was simply the market correcting itself.
Despite those losses, SolarEdge’s 2025 financials – especially when compared with 2024 – and its early 2026 performance suggest a turnaround for the company.
The overall renewable energy industry may soon get a push as well.
Per OBBBA, all wind and solar projects must commence construction by July 4 of this year to qualify for tax credits. Any projects starting after July 4 are required to be placed in service by the end of 2027 to receive credits.
So, in a way, Trump’s push to abolish clean energy actually gave the industry a short-term jolt by pushing companies to expedite their projects.
That includes SolarEdge, which has big plans for 2026 as it transitions from the “inventory-clearing” phase of the past couple years to a growth plan, particularly in the residential market.
Will these developments propel SolarEdge into profitability in 2026? And will it continue to mirror the rollercoaster the renewable energy sector continues to ride?
Time will tell.
Some macro-level concerns we covered (supply chain issues, rising interest rates, etc.) may inhibit renewable energy from achieving consistent growth. So, continued industry volatility wouldn’t come as a surprise.
However, if the renewable and clean energy industry can successfully adapt to the changing trade policies and supply chain disruptions, there’s plenty of promise.
If you’re looking to invest in renewable energy, SolarEdge remains an intriguing option based on its recent overall success. But this fact remains: Its shares are still extremely volatile, with 89 one-day moves greater than 5% over the past year.
A well-established clean energy ETF, on the other hand, offers more portfolio diversity and potentially more long-term growth. These ETFs may experience their share of volatility, but they’re typically a safer play.
Buckle up, investors. Renewable energy may take us all on a bumpy ride once again in 2026.
Regards,
David Engle
Editor’s note: It’s no secret that artificial intelligence is gobbling up energy at an unprecedented rate… straining America’s already vulnerable power grid.
All the big players are racing to find a new way to meet AI’s power-hungry daily demands, pouring in billions of dollars for alternative energy sources.
Regular folks can still get in on this tech, too – but time is running out.
Because Amazon (AMZN) may have cracked the code.
This breakthrough technology is being hailed as “the Holy Grail of Power,” and Amazon went all-in on it…
Get the details right here, including how to prepare and what to buy.

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US tracker manufacturer GameChange Solar has completed testing of its Genius Tracker system, which included exposing modules and trackers to “extreme earthquake conditions” and yielded results of “no structural damage” to components and no “meaningful power loss”.
The testing was completed at UC Berkeley’s Pacific Earthquake Engineering Research (PEER) Center in California, and saw four module and tracker configurations subjected to shaking designed to replicate the effects of earthquakes.
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Each configuration was subjected to two intensities of shaking based on the strength of shaking needed to ensure a “high” qualification level in the Institute of Electrical and Electronics Engineers (IEEE) 693 standard, which assesses the ability of electrical infrastructure to withstand seismic impacts. One level of shaking was set at ‘100% PL’, meaning it replicates all of the conditions needed to secure the IEEE 693 qualification, while the other was set at 50% of these conditions; a third level of testing, which made use of white noise, was first used to calibrate sensors used in the upcoming test.
A total of four configurations were tested: two using conventional purlins as mounting systems, and two using GameChange Solar’s ‘SpeedClamp’ module clamping system; each configuration was tested at an incline of zero degrees and 60 degrees. All four configurations reported “no damage” to components or a “loosening of the hardware” during the white noise and 50% IEEE 693 testing.
GameChange Solar reported that the configurations using purlins “passed all their runs for all categories” and that “no issues or noteworthy events” were reported. Similarly, the configuration testing the SpeedClamp system at zero degrees also reported no issues, and was additionally tested at 25% of the IEEE 693 standard “to build confidence” among the testers.
However, the company noted that the SpeedClamp configuration at 60 degrees saw one of the modules separate from the structure eight seconds into the 100% IEEE 693 test, leading to the test being abandoned for safety reasons. There was no further damage to the module that came loose or the components and structures left behind.
“The tests used only two posts which caused the tracker to be a simply supported beam instead of the typical multi post continuous beam as deployed in the field,” wrote Scott Van Pelt and Jacob Wynne, GameChange Solar chief engineer and solar project engineering, in their report on the testing, ‘Seismic Shake Table Testing of Single-Axis Solar Trackers’.
“We suspect that the increased bending contributed to the PV module jumping out of the SpeedClamp during the 100% High PL test run. In other words, it may be that this test represented a far more conservative test condition than would be seen in the field.”
The report’s conclusion adds that the company’s trackers spend “a relatively small amount of time at high tilt angles such as 52 or 60 degrees,” suggesting that this failed experiment may not reflect on the performance of the company’s products in the field. Indeed, perhaps encouraged by the strong performances of the SpeedClamp configuration at zero degrees, Van Pelt was optimistic in his overall conclusions.
“We now have conclusive evidence that a solar tracking system, such as GameChange’s Genius Tracker, can be designed to withstand the forces associated with a meaningful earthquake.”
The news follows GameChange Solar’s signing of a 258MW tracker deal with Hassan Allan Constructions for a project in Egypt earlier this month.

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Australia is a global leader in rooftop solar adoption, with more than 4.3 million households and small businesses installing photovoltaic (PV) systems as of Feb 2026. Rooftop battery installations in Australia have also experienced significant growth in recent years, particularly following the introduction of financial incentives like the Cheaper Home Batteries Program in July 2025 which saw solar batteries become eligible for small-scale technology certificates under the Small-scale Renewable Energy Scheme. With a discount of around 30 per cent on the upfront cost for any systems from 5 kWh to 100 kWh, more than 236,000 batteries have been installed since the launch of the program.
However, due to the 12-month reporting lag associated with certificate creation in data published by the Clean Energy Regulator, this figure likely understates the true number of installations and should be treated as an estimate. The Federal Government has indicated that installations have continued to grow, with more than 250,000 batteries installed as of March 2026.
Despite this rapid uptake, an important question still remains: Who actually benefits from these subsidies?
While battery rebate programs are designed to accelerate adoption, they may disproportionately benefit households with greater financial capacity to afford upfront costs, even after subsidies. This raises concerns about equity and the distributional impacts of energy transition policies.
This article will draw on postcode-level data using the Socio-Economic Indexes for Areas (SEIFA – based on census data) with scale from Advantage to Disadvantage to access the socioeconomic distribution of battery uptake since the program launched. It is worth noting that SEIFA measures measure the average characteristics of everyone living in the area rather than individual characteristics. Given the next 2026 Census data will not be published until 2028, the 2021 Census data is the most currently available for this analysis. Though some postcodes may have changed significantly in the past five years ago, particularly fast-growing outer suburban areas, 2021 still provides a great overview for assessing the geographic equity of the battery program uptake.
Figure 1: Battery Installations by SEIFA Decile across the nation since Cheaper Home Batteries Program launched (from July 2025 to Feb 2026)

Source: Australian Energy Council’s analysis based on CER data as of 16 March 2026 & ABS
Decile 1 = most disadvantaged 10 per cent of areas nationally, decile 10 = most advantaged 10 per cent. If battery installations are concentrated in decile 10, that says they’re installed in the top 10 per cent most advantaged postcodes in Australia.
When ABS calculates SEIFA deciles, it ranks all postcodes nationally, including remote and regional areas with very high disadvantage.
 
Other than Tasmania, a clear pattern can be observed across every state with installations increasingly concentrated in higher deciles (see table 1). This shows a clear imbalance in adoption. Households in the most advantaged areas (deciles 8-10) account for 41.0 per cent of all installations, significantly higher than any other groups. In comparison, nationally, disadvantaged areas (deciles 1-3) represent only 17.8 per cent, while below-average (deciles 4-5) and above-average (deciles 6-7) groups account for 18.8 per cent and 22.4 per cent, respectively.
Table 1: Proportion of total battery installations by SEIFA Deciles

Source: Australian Energy Council’s analysis based on CER data as of 16 March 2026 & ABS
The equity gap can also be seen in system size (figure 2). Households in the lowest socioeconomic areas (deciles 1 – 3) install batteries averaging 22.3 to 22.9 kWh, compared to 24.8 to 25.6 kWh in the most advantaged areas (deciles 8 -10) with a difference of around 2-3 kWh per system. Wealthier households are not only more likely to install batteries, but also install larger battery systems, capturing greater storage capacity and higher bill savings. This points to a secondary equity gap alongside unequal access.
 
Figure 2: Average system size by SEIFA Deciles
 
Source: Australian Energy Council’s analysis based on CER data as of 16 March 2026 & ABS
 
Across most states, Figure 3 shows that higher socioeconomic areas (deciles 8-10) consistently install larger battery systems. New South Wales and Queensland show the greatest difference, with decile 8-10 systems averaging 27 to 28 kWh compared to 24 to 25 kWh in lower socioeconomic areas. Victoria and South Australia display a similar but flatter trend. Western Australia has the largest relative gap nationally, with systems in lower socioeconomic areas averaging just 17.6 kWh versus roughly 20 kWh in higher socioeconomic areas. Tasmania shows smaller system sizes across all deciles.
Figure 3: Battery Installations and average system size by SEIFA Decile and by states (sort from highest to lowest installations)


Source: Australian Energy Council’s analysis based on CER data as of 16 March 2026 & ABS
 
New South Wales, Victoria, and Western Australia together account for 65 per cent of national battery installations, yet all three show a pronounced socioeconomic skew. In each state, a disproportionate share of installations occurred in the top 30 per cent of advantaged areas (deciles 8-10) – 46.1 per cent in NSW, 45.1 per cent in WA, and 41.5 per cent in VIC.
Queensland presents a more balanced distribution, though uptake still skews toward wealthier areas. Notably, it has the highest share of installations among disadvantaged households (22.9%) of any large state, alongside a comparatively lower share in advantaged areas (32.9%), suggesting rebates have reached a broader cross-section of the community.
Queensland and South Australia have high shares of detached housing stock, 74.8 and 78 per cent respectively, making battery installation more accessible across all socioeconomic groups. Both states also have widespread rooftop solar uptake, and since the program subsidises battery add-ons to existing solar systems, this contributes to a more even distribution of installations.
South Australia shows the most even spread of any mainland state, with installations distributed across disadvantaged (28.9 per cent), below average (17.8 per cent), above average (25.9 per cent), and advantaged (27.4 per cent) areas. Two factors help explain this. First, high retail electricity prices create a strong financial incentive for households across all income levels to adopt batteries for bill savings. Second, South Australia’s virtual power plant rebates are stackable with the federal rebate, further reducing payback periods – particularly lowering the barrier to entry for lower-income households.
Tasmania is the only state where disadvantaged postcodes account for the largest share of installations, at 42.6 per cent, and the lowest share in advantaged areas, at just 19.7 per cent (deciles 8–10). This likely reflects several factors, including relatively high home ownership rates even in lower-income and regional areas – 68 per cent in deciles 1–3, compared to 77 per cent in deciles 8–10 – alongside lower property prices, which make battery investment more accessible. Strong financial incentives from the rebate, particularly for households facing high electricity costs, also appear to be driving uptake.
The ACT’s concentration of installations in upper deciles does not necessarily indicate inequitable distribution within the territory. As a high-income jurisdiction, driven largely by its concentration of federal public servants, defence personnel, and other professionals, no ACT postcodes fall below decile 8, meaning the upper-decile skew reflects the territory’s demographic composition rather than unequal access.
The Northern Territory shows a skew toward advantaged areas (51.3 per cent) with a relatively low disadvantaged share (10.5 per cent). However, given the territory’s small total number of installations, these figures should be interpreted with caution.
Combining battery installation data with socioeconomic indexes allows for an assessment of whether current policy settings are equitably supporting household access to clean energy technologies. While socioeconomic advantage is a key driver of battery adoption nationally, state-level factors, including policy settings, retail electricity prices, and housing characteristics ,play an important role in shaping uptake patterns. Tasmania, South Australia, and Queensland are the states where the program is achieving the most equitable outcomes, while NSW, Victoria, and Western Australia exhibit the largest equity gaps and represent the greatest need for targeted policy attention.
 
As South Australia heads to the polls this Saturday, energy is shaping up as one of the key issues. The debate is being driven by two very different visions for the state’s future. Based on current polling, Peter Malinauskas’s Labor Government is expected to be re-elected, likely with an increased majority. However, it remains unclear who will form the opposition, with recent polling suggesting Pauline Hanson’s One Nation is currently favoured to take that role. This sets up the election as a clear comparison between two very different approaches to energy. Read more.
The energy system is complex and decarbonising the grid adds further complexity. It requires significant new investment to ensure coal plants can exit without having an impact on the reliability of the grid. It comes with unavoidable costs and will take time to get right. It is increasingly important given this context that the energy transition is well understood. Selective framing of data to apportion blame works against a broad understanding and has the potential to undermine customer confidence and support for the transition. Read more.
A new year has brought major developments across Australia’s energy markets, with new regulatory interventions alongside record-breaking renewable generation. The Federal Government’s Solar Sharer Offer marks a significant shift in retail market design, while the wholesale market delivered historic renewable output and much lower prices, driven largely by strong wind and growing battery capacity. We take a look at what these changes mean for customers, retailers and the reliability of the power system, and where old challenges continue to resurface.
Send an email with your question or comment, and include your name and a short message and we’ll get back to you shortly.

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The decline in photovoltaic stocks will be significantly exacerbated by the cancellation of photovoltaic tax rebates, which will substantially increase module costs.  富途牛牛
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The Iran War Is Revealing the Messy Middle of Our Renewable Energy Transition  The New York Times
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Mostly cloudy and windy. Periods of rain this morning. High 82F. Winds SSW at 25 to 35 mph. Chance of rain 70%. Higher wind gusts possible..
Variably cloudy with scattered thunderstorms. Low near 65F. Winds S at 10 to 15 mph. Chance of rain 50%.
Updated: March 31, 2026 @ 8:50 am
Lawrence County Presiding Commissioner Bob Senninger speaks to the crowd at a meeting with Geronimo Power representatives about a solar farm and battery storage site being built in Jasper and Lawrence counties around the north side of La Russell to provide power for what’s being called a hyperscale data center. Globe | John Hacker

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Lawrence County Presiding Commissioner Bob Senninger speaks to the crowd at a meeting with Geronimo Power representatives about a solar farm and battery storage site being built in Jasper and Lawrence counties around the north side of La Russell to provide power for what’s being called a hyperscale data center. Globe | John Hacker
MOUNT VERNON, Mo. — Nearly 40 residents of Jasper and Lawrence counties listened Monday at the Lawrence County Health Department as representatives of Geronimo Power presented plans for a 640-acre solar farm in western Lawrence County.
The Lawrence County Commission heard from Geronimo project manager Mark Jones, community engagement specialist Samantha Meadows, attorney Mark Brady and permit specialist Alia Mohammad on the scope and scale of the solar farm in Lawrence County tied to a data center site nearby in Jasper County.
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SARCOXIE, Mo. — A Minnesota technology firm is planning a data center and solar energy park on land south of Missouri Highway 96 and west of L…
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March 31, 2026 — 5:03pm
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ClearVue Technologies has taken a crucial step towards the commercial rollout of its new building-integrated photovoltaic (BIPV) solar roofing and carpark products after locking-in a key certification from the International Electrotechnical Commission (IEC).
The smart building materials company has teamed with manufacturing partner Helios Power to receive the globally recognised international certification for a new range of photovoltaic solar panels.
Unlike conventional rooftop photo-voltaic (PV) modules, the ClearVue-Helios products use metal backplates to create a weatherproof barrier above existing roof materials. The company says this feature can significantly extend roof life and reduce thermal transmission into buildings.
ClearVue says the design supports fire safety classification under IEC 61730/UL 790 Class A, while also improving wind load and typhoon performance.
The steel-backed panels can be made trafficable for maintenance access or produced with an aluminium back sheet for lighter-weight installations where roof structural limits apply.
With the all-important IEC certification now in hand, ClearVue will begin seeking Australia’s Clean Energy Council (CEC) certification pathway required for the local market.
ClearVue Technologies chief executive officer and managing director Douglas (Doug) Hunt said: “ClearVue and Helios cooperation has been based on a shared view of the future in terms of efficient and reliable new building materials. These certifications will be the first of many achieved as several additional cooperative products are under development.”
EC breakout: ‘With each certification milestone we strengthen our competitive position in the BIPV market.’ ClearVue Technologies chief executive officer and managing director Douglas (Doug) Hunt.
Hunt added that gaining national and international certifications are a “critical priority”, as the company moves towards full commercial deployment of the products.
The company said it is also continuing joint R&D with Helios aimed at improving installation efficiency, lifting product performance and electrical safety, and reducing overall installation costs.
ClearVue said the collaboration will also help accelerate the company’s offshore product deployment and support further certification of additional products for international markets.
Is your ASX-listed company doing something interesting? Contact: mattbirney@bullsnbears.com.au
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In Alabama, a years-long battle over one of the nation’s highest backup fees for residential solar customers may have finally come to an end.
A federal judge ruled last week that Alabama Power can continue charging its small solar customers one of the highest standby charges in the nation, dismissing a lawsuit that argued the fee was illegal under the Public Utility Regulatory Policies Act.
“I am frustrated that Alabama Power solar customers like me have to pay an extra monthly fee in order to reduce our power bills,” Mark Johnston, one of the plaintiffs, said in a news release after the ruling.
Solar advocates in Alabama say the fee, which charges customers with an average residential solar array around $39 per month, significantly stifles the residential solar market in the state by nearly doubling the payback time for a solar installation. 
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Alabama ranks 51st in residential solar capacity among U.S. states plus Puerto Rico and the District of Columbia, trailing only North Dakota, according to the Solar Energy Industries Association, a solar industry trade group. Per capita, Alabama ranks last. 
Alabama Power, which provides power to roughly two thirds of the state, charges its customers that generate their own electricity a monthly fee of $5.41 per kilowatt of capacity installed. 
The average size of a U.S. residential solar array in 2024 was 7.2 kilowatts, according to the Lawrence Berkeley National Laboratory. The fee would add $38.95 each month to the customer’s bill regardless of how much electricity the customer consumes or puts back on the grid. 
Alabama Power says the fee is needed to cover costs of maintaining the grid when the solar panels aren’t producing, at night or in cloudy weather. 
“Customers who rely on the grid must help pay for the grid,” the company said in an emailed statement. “We are pleased the court agreed with the Public Service Commission’s determination that customers who choose to use Alabama Power for backup service should pay their share of costs to maintain the grid.”
Johnston, an Episcopal priest and retired executive director of Camp McDowell, pays about $32 per month for his 6 kW system. 
“This charge discourages additional residential solar systems in the state, a source of clean, renewable power that decreases the use of fossil fuels,” Johnston said. “I want lower electricity bills and a better environment for my children and grandchildren.”
The Southern Environmental Law Center and Ragsdale LLC filed the lawsuit on behalf of customers paying the charge and environmental groups that argued the fee was unlawfully stifling the small-scale solar industry in Alabama. 
The Alabama Public Service Commission and Alabama Power filed a motion to dismiss the challenge, granted Wednesday by Judge Annemarie Carney Axon, in the U.S. District Court for the Middle District of Alabama.
The SELC said it is examining the decision and its clients’ legal options. 
“This is a disappointing day for Alabama Power customers who want to use solar energy to get relief from some of the highest electricity bills in the nation,” said Christina Tidwell, a senior attorney in SELC’s Alabama office, in a news release. “Not only are we missing out on the bill savings that could be realized through installing rooftop solar, but we’re also missing out on opportunities for job creation and economic development.”
Alabama Power has come under increased scrutiny for its high power bills in recent months. 
An Inside Climate News investigation found that Alabama Power had the highest total residential power bills in the country in 2024, and the highest electricity rates in the Southeast. 
Environmental advocates have continuously challenged Alabama Power’s capacity reservation charge since it was approved by the Public Service Commission in 2013. The decision was appealed to the Alabama PSC and then to the U.S. Federal Energy Regulatory Commission. 
Though FERC did not agree to initiate an enforcement action regarding the fee when it examined the case in 2021, Chairman Richard Glick and Commissioner Allison Clements issued a concurrence to express “concern” that the fee may be in violation of federal utility law, and said the petitioners had “presented a strong case that the Alabama Commission failed to adhere to the regulations set forth in FERC Order No. 69.”
The commissioners were concerned about the way Alabama Power calculated the costs for back-up power, saying company had not demonstrated that a solar customer’s profiles were different enough from a non-solar customer to justify the charge, and the company’s methods had “combined apples and oranges” by relying on actual data and projections to determine the cost difference between solar and non-solar customers. 
The District Court judge ruled otherwise, dismissing the plaintiffs’ suit, saying “the plaintiffs have not presented any evidence from which a factfinder could conclude that Alabama Power violated [PURPA].”
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The fee is not the only policy in Alabama that advocates say is holding back solar in the state. Alabama does not offer net metering, where solar customers are credited the same amount for electricity they put on the grid as the electricity they use. 
Instead, customers who feed excess energy back onto the grid are only credited the amount of money it would cost Alabama Power to generate the same amount of electricity at one of its power plants, an amount much lower than retail rates. 
“Alabama communities are dealing with harmful impacts of our state’s reliance on fossil fuels; meanwhile, Alabama Power and the PSC are chilling clean, bill-reducing solar power,” Jilisa Milton, executive director of the Greater-Birmingham Alliance to Stop Pollution (GASP), said in a news release. “Solar energy offers a unique opportunity for residents of Alabama to take control of their energy costs, reduce their carbon footprints, and contribute to a cleaner environment.”
Alabama Power’s solar fee has long stood out as one of, if not the, highest in the country for small-scale solar users. 
Some utility regulators have rejected fees outright, while others have allowed such fees in much lower amounts or have limited fees to systems larger than a certain size. 
Georgia Power, also owned by Alabama Power’s parent Southern Company, proposed a fee similar to Alabama’s in 2013. Georgia Power withdrew its proposed fee as opposition mounted in the Georgia Public Service Commission. Alabama’s Public Service Commission approved the fee. 
In Virginia, solar customers only pay a standby charge if their array is larger than 15 kilowatts, and that limit is likely to increase soon.
Earlier this month, the Virginia General Assembly passed a bill to increase the threshold for projects that require customers to pay the standby charge to 20 kilowatts, meaning larger projects would be eligible for the standby charge exemptions. The bill is awaiting a signature from Democratic Gov. Abigail Spanberger.
That average standby charge for residential customers amounts to between $25 to $75 a month, but sometimes can be more than $100 a month, according to the Virginia League of Conservation Voters.
“Overall—this model creates a disincentive for Virginians to invest in larger systems that meet their full energy needs, which is how this bill can help,” said Lee Francis, Chief Program and Communications Officer of the Virginia League of Conservation Voters.
Alabama Power said its fee is intended to prevent other customers from bearing costs of infrastructure required to serve solar customers when the panels are not producing. 
“Alabama Power supports customers who want to install solar or other onsite generation, and we do not charge customers for using rooftop solar,” the company said. “However, if those customers want to stay connected to Alabama Power’s grid to meet their electricity needs when their system cannot, they must pay their share of grid costs so other customers are not unfairly burdened.”
Inside Climate News Virginia reporter Charles Paullin contributed to this report. 
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State regulators approved incentives for three projects totaling 355 megawatts, hoping storage can come online faster than other energy sources.
By Rambo Talabong
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When Evelyne Nongkseh’s newborn daughter developed jaundice in August 2023, her family of six was in panic. Nongkseh, then 30, had delivered her third child just two weeks earlier. She rushed to Gnanamma Healthcare Centre in her village Jaidoh in the West Khasi Hills, in India’s northeastern state of Meghalaya, hoping to get immediate treatment.
But when she arrived, there was no electricity at the centre and phototherapy unit that treats neonatal jaundice by breaking down excess bilirubin in an infant’s blood, was also not functioning. “I was worried sick,” Nongkseh recalls. “There was no electricity, and it was raining heavily. I had to go back home that evening.”
The next morning, the health centre’s administrator, Sahay Lily, suggested keeping the infant in the morning sunlight until a vehicle could be arranged to take them to a hospital in the nearest town, Nongstoin. The town, despite being located only 10 kilometres away took nearly two hours to reach because the heavy rains had damaged the roads. At Nongstoin’s hospital, Nongkseh’s daughter received the necessary treatment and recovered.
“The electricity problem is common in our village. Not just me, many parents have faced similar problems,” Nongkseh says. Her experience was once an everyday occurrence for healthcare workers, with frequent power cuts disrupting healthcare services in the region.
In this hilly, rain-swept region of Meghalaya, frequent power cuts are usually driven by severe weather-related damage to infrastructure, the state’s difficult terrain, and the impact of heavy rainfall on its largely hydroelectric power system. This in turn has impacted the functioning of healthcare facilities.
Power cuts have forced health workers to deliver babies in candlelight, sterilise instruments in boiling water heated using charcoal, and even turn away patients when services could not be safely delivered. In many cases, vaccines would spoil when ice-lined refrigerators (ILR) shut down. Sometimes emergency procedures had to be delayed or dropped altogether.
For patients that would come in the night, the medical team would examine them using mobile phone flashlights and candles laments Lily, a nurse with over three decades of experience. Baby deliveries, she says, have been conducted in complete darkness.
The challenge was not limited to a few remote outposts. Sub-centres, primary health centres, and even larger facilities across the state struggled with unreliable power.
But things changed for the better during the COVID-19 pandemic.
When the entire country and the world was struggling to cope with the pandemic, Meghalaya too found itself confronting a public health emergency with limited infrastructure, shortages of skilled personnel, and an already fragile healthcare system.
As patient footfall surged in this period, the government of Meghalaya, along with the National Health Mission (NHM), planned for reliable backup electricity supply solutions, to make up for the irregular grid power.
“It was truly a matter of life and death,” says Ibamanlang Nongri, the state programme manager for the NHM in Meghalaya. “For healthcare delivery, uninterrupted electricity is crucial, because we are dealing with human lives.”
During that time, solar-powered electricity emerged as a practical solution amid a larger push to adopt renewable energy across sectors, especially in healthcare facilities, to reduce dependence on coal-based grid power. According to a Union Health Ministry’s report, 87% of India’s hospitals rely primarily on grid electricity.
Soon after, under the NHM and the Meghalaya Chief Minister Solar Mission (CMSM), the state began installing solar power in health facilities in partnership with SELCO Foundation, a non-profit that advances sustainable development by linking social innovation with decentralised renewable energy (DRE) systems.
 
The CM’s Solar Mission, launched in 2023 is a ₹500-crore initiative spread over five years and offers up to 50-70% subsidy for solar hybrid systems in hospitals, health centres, and other commercial establishments to address the state’s power deficit, ensure round-the-clock electricity, enable uninterrupted healthcare services, and reduce reliance on conventional grid power.
“In the first phase, around 100 health facilities were solarised,” Nongri says, starting with Ri-Bhoi and East Garo Hills districts.
Since then, the programme has expanded to more than 530 healthcare centres in the state, which include almost all sub-centres and Primary Health Centres (PHCs) in Meghalaya. Now the plan is to bring solar power to newly constructed sub-centres in the region as well.
The Gnanamma Healthcare Centre, where Nongskeh was once turned away due to no electricity, now operates round the clock with solar power that was installed here in February 2025 supported by the Meghalaya government’s solar transition by Christian Health Association of India (CHAI) and World Resources Institute (WRI).
Almost immediately, the daily operations at the centre, which serves eight to ten villages with a total population of roughly 4,000 to 5,000 people, changed.
“Night-time deliveries are now conducted under bright lights,” Lily says. Vaccines are stored safely in ice-lined refrigerators (ILRs) that run without interruption. Nebulisation, blood tests, phototherapy, and laboratory work can now be carried out at any hour, she notes.
“The centre is brightly lit even when the village is dark,” she says. Additionally vaccination and medical camps that were often conducted in small halls with poor lighting and irregular electricity have been shifted to the Gnanamma centre.
Similar changes have unfolded at Mawlyngngad’s sub-health centre in the East Khasi Hills district. Onika Wankar, a mid-level health provider and community health officer, has worked there since 2020.
When she joined, there was no solar power. Electricity that came from the power station at Umiam dam was frequently cut. “Whenever there was less water, the electricity supply would be shut down,” she says, adding that deliveries would become extremely difficult to carry out. “We had to use candles and mobile phone flashlights, and even burn charcoal to keep a baby warm and boil water to sterilise instruments.”
But when solar panels were installed on the centre’s small tin roof in 2023, operations eased out. “Outpatient Department (OPD) attendance has increased from 10–20 patients per day to 40–50 now. We now have 24×7 electricity, so night-time work has become safe and manageable,” she says. “People have developed trust in the sub-centre.”
Equipment that once sat idle, like oxygen concentrators, sterilisers, radiant warmers, and vaccine refrigerators, now runs reliably, bringing life and light back into the clinic. At Gnanamma, a six-kilowatt solar plant paired with 22.6 kilowatt-hours of battery storage provides up to three days of backup on cloudy days.
“The idea was to see how this facility could move towards net-zero electricity,” says Rishikesh Mishra, Programme Manager at WRI India, which is working to build evidence for scaling solar energy in challenging terrains like Meghalaya by providing technical support, data, and policy guidance.
Today, solar power meets nearly all of the centre’s energy needs from lighting and cooling to powering critical medical equipment. “In the last three months, grid electricity usage has dropped to zero,” Mishra adds.
However, he adds, “Solar should complement, not replace, existing systems.” He points out that demand-based system design, predictable financing, and strong monitoring are what make these systems sustainable in the long term.
 
Banner image: Solar panels installed on the roof of a healthcare facility in Meghalaya. Image by Pratik Chakraborty.
If the Green Revolution rode on the strength of chemicals derived mainly from fossil fuels, now there is a shift in the thinking on how agriculture is being done in India, with a thrust on growing indigenous crop varieties and following natural farming practices. In the industrial sector, with initiatives such as ‘Make in India’, […]
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SDA
31.3.2026 – 08:44
The Madrisa Solar photovoltaic plant produced more electricity than forecast in its first winter. According to the energy company Repower, the Graubünden plant is the first alpine solar power plant in Switzerland. The largest construction phase of the project will begin at the start of May.
Keystone-SDA
31.03.2026, 08:44
SDA
The plant generated around 1.5 gigawatt hours of electricity in the winter half-year from October to March, the energy company Repower announced on Tuesday. Around 3,600 solar modules were in operation during this period, which corresponds to around 20 percent of the planned total plant.
The aim is to connect around 70 percent of the plant to the grid by November. Full commissioning is planned by the end of 2027.
The plant is located at around 2000 meters above sea level and is designed for high winter power generation. After the first snowfall, the output increased by around 15 percent thanks to the reflected light. This was made possible by so-called bifacial solar modules, which produce electricity on both sides.
The electricity produced is used by Elektrizitätswerke des Kantons Zürich (EKZ) and Bergbahnen Klosters-Madrisa. Together with their own systems, the mountain railroads would have generated more energy than they needed to operate during the winter season, Repower added.
The plant was built by Madrisa Solar AG. Repower, EKZ and the municipality of Klosters each hold a one-third stake in the company. The total investment amounts to CHF 70 million.
Two other large Alpine solar plants are currently being built in Graubünden as part of the federal government's Solar Express: NalpSolar by Axpo and SedrunSolar by Energia Alpina. Both are located in the municipality of Tujetsch in the Surselva. Madrisa Solar was the first of these plants to be connected to the grid as part of the Solarexpress last September.

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Source: © Andy Carter/Ikon Images
By James Mitchell Crow2025-09-16T09:28:00+01:00
With rock-solid stability and rapidly improving efficiency, could a mineral-derived photovoltaic material called kesterite finally push thin film solar into the mainstream? James Mitchell Crow talks to the experts
Kesterite’s solar potential: Kesterite, a mineral-derived photovoltaic material, is gaining attention for its low cost, earth-abundant, non-toxic composition and excellent stability – making it a promising alternative to existing thin-film solar technologies like CIGS and cadmium telluride.
Efficiency breakthroughs: After a decade-long plateau at 12.6% power conversion efficiency (PCE), recent innovations have pushed it to nearly 17%, with expectations to reach 20% within five years – crossing the threshold for commercial viability.
Chemical and crystallisation advances: Progress has come through chemical solution processing, solvent innovations (especially using 2‑methoxyethanol) and precise control of crystallisation and defect suppression during fabrication, including alloying and hydrogen treatments.
Tandem cell promise: Kesterite’s compatibility with silicon makes it a strong candidate for tandem solar cells, especially in wide-bandgap configurations, potentially enabling higher energy conversion at low cost and with minimal toxicity.
This summary was generated by AI and checked by a human editor
There’s a familiar ring to the origin story of kesterite, the latest would-be solar super-material.
Kesterite was discovered when geologists working a remote Russian mountain range came upon an unusual mineral with a distinct metallic lustre, and took a sample back to the lab for analysis. Decades later, researchers in Japan demonstrated that semiconducting thin films of kesterite had photovoltaic (PV) properties, and concluded that it had potential as a very low cost solar cell.
So far, so perovskite: another promising thin-film PV pioneered in Japan, derived from a mineral unearthed from a Russian mountainside. But from the point in each story where researchers first demonstrated a light-induced trickle of electricity from each material, the two tales could hardly be more different.
PV perovskite proved so amenable to researchers’ enhancements, its solar power conversion efficiency (PCE) rose with unprecedented speed, exceeding 20% within 10 years and continuing to climb. Kesterite’s solar efficiency, in contrast, climbed slowly to 12.6% then stalled completely, not budging for a decade.
In 2022, researchers finally broke through kesterite’s PCE ceiling to reach 13% – and the kesterite community’s luck finally changed. In the past three years, a string of record-breaking kesterites have pushed its PCE upward with perovskite-like speed. The latest materials almost touch 17%, and further gains are imminently expected, says Qingbo Meng, who directs the Center for Clean Energy at the Chinese Academy of Sciences in Beijing and whose team holds the current kesterite PCE record.
‘We entered the kesterite solar cell field when the efficiency was only around 5%,’ Meng says. ‘Today, certified efficiencies have exceeded 16%, with an average annual increase of nearly 1 percentage point.’ Within the next five years, he says, kesterite solar cell efficiency should reach or exceed 20%, crossing the threshold to commercial viability.
During kesterite’s dark decade, many in the field conceded defeat with the recalcitrant material and went to work on perovskites instead. For the few researchers who never quit kesterite research, the material’s shining potential upsides as a solar technology stopped them from stepping away.
Kesterite PV combines copper, zinc and tin with the chalcogen ions sulfur, selenium or a blend of both, in the formula Cu₂ ZnSn(S,Se)₄ – gaining it the nickname CZTSSe. Each element in kesterite is inexpensive, earth-abundant and non-toxic – characteristics it shares with silicon but few other solar materials.

Source: American Mineralogist Crystal Structure Database / Royal Society of Chemistry
Kesterite has copper, zinc (orange), tin (grey) and sulfur or selenium (yellow) atoms in its structure
These attributes give kesterite an advantage over two already-commercialised thin film solar technologies, says kesterite researcher Alice Sheppard, who recently completed her PhD with David Fermin at the University of Bristol, UK. Copper-indium-gallium-selenide (CIGS) solar cells contains two scare elements, indium and gallium. ‘As for cadmium telluride solar, tellurium is scarce and cadmium is toxic,’ she says. Both technologies have remained relatively niche products in the global PV market.
Kesterite’s makeup also gives it theoretical advantages over photovoltaic perovskites, and not just because the best ones contain toxic lead. The fourth key property that kesterite solar shares with silicon is excellent stability. Kesterite PV is a thin film of virtually the same stuff dug from the Russian mountainside, just with zinc in place of the original mineral’s iron. ‘Kesterite is inherently stable at room temperature,’ Fermin says.
Photovoltaic perovskite, in contrast, is a hybrid combination of organic and inorganic ions such as methylammonium, lead and iodide, which shares only its crystal structure with the parent mineral. The ions in these hybrid materials are prone to drift out of position in the crystal lattice, leaving performance-sapping defects.
‘The thermodynamically stable phase of perovskite is one in which lead iodide goes one way and the methyl ammonium goes the other way,’ Fermin says. This structural instability can be accelerated by heat, light and moisture, which can rapidly degrade perovskite solar performance. In accelerated degradation tests, perovskite PCE can decline rapidly. ‘Kesterites start lower, but keep on going,’ Fermin says.
If kesterite’s starting efficiency could be pushed to a higher number, its broad attributes suggest a bright future. The key factor still limiting kesterite PCE – which despite recent gains, remains far below its 32% theoretical maximum – is not defects that develop during its operation, but those formed during its fabrication. ‘The issue since the beginning has been how to ensure that each element goes into the right place in the crystal lattice,’ says Fermin.
Among thin film semiconductors, kesterite fabrication poses a particular challenge, says David Mitzi, a photovoltaic materials researcher at Duke University in North Carolina, US. ‘With kesterite, the metal ions all have roughly the same ionic size, and they have the same preferred coordination, so they readily switch sites in the crystal lattice,’ Mitzi says. ‘This creates disorder that can introduce electronic defects that in turn can reduce photovoltaic performance.’
Adding to the challenge, there is a characterisation technology gap for studying structural defects and their effects in a material as complex as kesterite. ‘Even for simpler materials, it’s not easy – but when you’ve got four, five or more elements, understanding what is driving performance degradation is very difficult,’ Mitzi says.
The dearth of characterisation methods for kesterite defect analysis is the crux of kesterite solar’s slow development, Fermin says. ‘We know, for instance, that copper and zinc can swap places, and that tin can go into the wrong place – but we don’t know how much these things happen or how bad they are,’ he says.
What techniques we do have to detect defects in kesterite can’t quantify them, so can’t guide efforts to improve performance. ‘There can be absolutely no difference in the Raman spectrum, electron microscopy and X-ray diffraction analysis of a kesterite that gives you 2% efficiency and one that gives you 15%,’ Fermin says.
Computational analysis has also been of limited use, Meng adds. ‘Although theoretical models predict several defect types, there is significant inconsistency with experimental results, primarily due to discrepancies between theoretical crystallization models and actual CZTSSe growth processes,’ he says. ‘We therefore focus on experimentally establishing direct correlations between defect types and device performance.’ Gains in kesterite performance have come at the expensive of extensive experimental effort, and a lot of trial and error.
Before joining Duke, Mitzi worked at IBM, where he led the team that pushed kesterite performance above 9.6% in 2010, and then to 12.6% in 2013 – setting the performance benchmark that would last for a decade. Among the team’s innovations, the key step was developing a benchmark solution processing method for growing the kesterite thin film. ‘That process, based on hydrazine as a solvent, provided a facile pathway to clean films with very nice grain structure that were very amenable to getting to high performance,’ Mitzi says.
The team also optimised the material’s ratio of sulfur to selenium to tune its bandgap for maximum energy capture from sunlight, and borrowed methods from CIGS manufacture to passivate surface defects. ‘Beyond these things, it was just a lot of work, making thousands of devices to get to the point where you can reproducibly make them with high performance,’ Mitzi says. Shortly after completing the project, Mitzi stepped away from kesterite research after leaving IBM for academia (see box Size matters below).
Shortly after setting the 2013 kesterite solar performance record that would stand for almost a decade, thin film PV researcher David Mitzi left his position at IBM to join Duke University. At the same time, Mitzi refocused his research on alternate thin film PV materials – and not just for a clean break from his former industry role, he says. ‘What I like to work on is discovering new materials,’ he says.
The recent history of PV research and development highlights the importance of continuing to explore new materials, Mitzi says. ‘Success in a solar technology depends on a lot more than just demonstrating a high performing device,’ he says – noting PV perovskite’s ongoing instability issues.
One direction we’ve worked in is replacing the zinc with barium
To successfully commercialise a PV product, manufacturing factors including reproducibility, yield, cost, materials availability and the compatibility of a new fabrication process with existing manufacturing facilities are all important, Mitzi says. ‘An example of that is CIGS, which can get to high performance, but hasn’t been able to compete with cadmium telluride and other solar technologies because it is a more complex material, harder to manufacture reproducibly using a high throughput process.’
To expand the available options, Mitzi has explored materials close to kesterite but that should be less inherently prone to defects during their fabrication. ‘In kesterite, the similar ionic size and chemical behaviour of copper, zinc and tin facilitate their swapping of sites, creating defects,’ Mitzi says. ‘One direction we’ve worked in is replacing the zinc with barium, a much larger atom that generally has different preferred coordination than zinc.’
As kesterite’s most important electronic conduction bands derive mainly from the copper, tin and the chalcogens in the material, switching out the zinc should reduce certain defects while maintaining a similar electronic band structure enabling PV performance, Mitzi rationalised. So far, his team has got these materials up to around 6% solar efficiency. ‘That needs to go higher – but I think the work furthers the understanding of how crystal structure can be used to control defects toward higher performing materials,’ he says.
Among thin film PV materials, making high quality kesterite PV is particularly reliant on getting the chemistry right.
As a broad family, these materials can be made by physical means, via vacuum deposition of precursors onto a substrate. Alternatively, they can be made chemically, coating the substrate with a solvent-based ‘ink’ of the precursors, then allowing the PV film to crystalise as the ink dries during a heat-based annealing step.
‘In all other thin film PV technologies, the best performing devices are made by vacuum-based physical vapor deposition,’ Fermin says. But for kesterite PV, the record-breaking materials have all been made by chemical solution processing.
It’s an observation that points to the key role that chemical complexes pre-formed in the ink can play in minimising structural defects as the kesterite crystalises, Fermin says. ‘We and others have shown that just by changing something like a zinc acetate to a zinc chloride in the precursor solution, you can change the performance of the device by several percentage points – it’s absolutely remarkable.’
The choice of solvent also seems to shape the precursor complexes that form in the ink, influencing the quality of the resulting kesterite film and its PV performance, Fermin and Sheppard have found. In one study, they showed they could control the distribution of tin in a kesterite film – and boost device PCE almost two percentage points – by adjusting the ratio of a cosolvent mixture of isopropanol and dimethylformamide.
The lack of resources severely hindered technical progress in this area – but work in the field never ceased
Kesterite’s strong solvent dependency posed a major problem for researchers looking to push kesterite PV performance past Mitzi’s 12.6% record, set using hydrazine. As a carbon-free solvent, hydrazine was in many ways ideal for making clean, high quality, residue-free kesterite films. But it is also explosive and highly toxic. ‘Hydrazine-based solvents are extremely hazardous, making it difficult for many teams to conduct similar research due to safety concerns,’ Meng says.
Having to rework kesterite production to use more environmentally friendly – but less fabrication-friendly – solvents proved to be a major setback in kesterite PV performance. Confounding the field, innovative approaches to supress defects and boost kesterite PCE failed to show the anticipated benefits.
The longer that kesterite PV remained in the doldrums, the more researchers and funders jumped ship – often into perovskite research. ‘The lack of human and financial resources severely hindered technical progress in this area,’ Meng says. ‘Nevertheless, exploratory work in the field never ceased.’
Searching for an alternative solvent to hydrazine, Meng and his collaborators focused on dimethyl sulfoxide (DMSO) and 2‑methoxyethanol (MOE). ‘Developing a new baseline process with these less-easy solvents, that allow testing one parameter at a time to start progressing step by step to higher performance, was crucial to our record finally being broken,’ says Mitzi.
Kesterite PCEs began slowly climbing again, and in 2022 Meng and his collaborators reported a device with 13% PCE. ‘After eight years of relentless effort, solar cells fabricated with DMSO-based eco-friendly solutions broke the 12.6% efficiency record,’ Meng says.
Increasingly, the field has settled on MOE as the best solvent, and all kesterite PCE records subsequently set have all used MOE. There seems to be something special about MOE, Sheppard says. ‘We think there’s complexation in there which just doesn’t happens in other solvents,’ she says. Understanding MOE’s magic is a current focus of Sheppard and Fermin’s research.
Beyond the precursor ink, controlling the chemistry as the kesterite crystallises during thermal annealing is essential for growing high quality thin films, Meng says. ‘Since these materials crystallise via solid-state reactions, we believe precise control of the crystallisation process is essential for effective defect suppression,’ he says.
Kesterite PV annealing typically involves heating the film in selenium or sulfur vapour, to incorporate the correct ratio of chalcogenide into the final material. Mastering selenisation to minimise selenium vacancies in the material is one focus of Meng’s work. Selenium vacancies are one of the main factors limiting kesterite efficiency, he says.
‘Selenium vacancies mainly originate from selenium loss during the later stages of selenisation,’ Meng says. ‘We have introduced surface compound coatings to effectively inhibit selenium evaporation and reduce defect levels.’ Fully controlling the selenisation step remains the team’s core research direction, he adds.
The team suspected that incomplete cation exchange during crystallisation, leaving metal ions in the wrong spot in the final material, was another likely source of defects. They showed that multicomponent elemental alloying – adding small amounts of other metals into the precursor ink – could weaken metal–chalcogenide bonds and accelerate atomic exchange. The strategy boosted kesterite PCE to a new record of 14.6%, the team reported.
Most recently, the team used alloying to deliberately introduce benign vacancies in the kesterite surface, freeing up copper and zinc ions to swap into the correct position during annealing. The technique improved copper–zinc order by enabling the two ions to swap positions in stepwise fashion via a vacant site rather than in a single concerted step, and
Source: © Ao Wang et al/Springer Nature Limited 2025
Getting the CZTS layer right is key to improving the solar cell’s performance
In kesterite, oxygen isn’t a big problem. But the sodium ions that the oxygen is usually found stuck to can be. ‘Kesterite can contain a lot of sodium, which diffuses into the material from the sodalime glass used as a kesterite solar cell substrate,’ Hao says. ‘If you have a lot of sodium, that can cause a problem.’
Sodium’s association with oxygen offered a chance to sweep it aside, Hao realised. ‘We know that oxygen can be driven out by hydrogen – so we thought, let’s see whether the hydrogen can move this sodium via the oxygen,’ Hao says. ‘And we found it works.’ Hydrogen treatment drove the sodium from the crystal bulk to the surface – where its presence can benefit the material’s surface conductivity and enhance the efficiency of the resulting kesterite solar cell.
Hao demonstrated the effect in wide-bandgap kesterite – where PCE had been stuck at 11% since 2018. The team initially pushed wide bandgap kesterite PCE to 11.4%, but has already gone higher. ‘Hydrogen annealing is like a baseline for us now because we know it works very well,’ she says. The team’s latest certified materials have jumped to 13.2%.
The hydrogen treatment step also works on CIGS solar cells, the team has shown – so it should certainly be applicable to other types of kesterite, used in combination with recent ink formulation and crystal engineering advances, Hao says. ‘Low bandgap kesterite also has the sodium problem, so I think the hydrogen should work for them as well,’ she says.
Recent progress has reenergised the field to keep pushing kesterite research forward, Hao says. ‘Everyone is really excited about it, and the discussion is very active.’
‘This rapid progress gives me great confidence for the future,’ Meng says. ‘As the advantages of kesterite technology become more widely recognized, I believe funding and talent will return to the field, driving further breakthroughs.’
The energy now flowing back into kesterite research is a marked turnaround from the situation five years ago, when nothing seemed to be working, Hao says. With reliable new baseline methods for high quality kesterite fabrication now in hand, many of these ideas would be worth revisiting.
‘It’s like a bucket with lots of holes in – patching the smaller holes barely has any effect when you have a lot of water leaking out through the big ones,’ Hao says. With several big holes in kesterite performance now fixed, patching the smaller ones should now have a much more noticeable impact.
Given kesterite’s fundamental strengths as a low cost, non-toxic, earth abundant, stable material, if its efficiency can be pushed just a little higher, its PV future could be very bright, Hao says. ‘If we can get it to 20% efficiency then I think it will really take off, since it meets all the criteria for the type of material we want to be using.’
Despite its humble mineral origins, perhaps kesterite will be the thin film solar story with the trope-defying happy ending.
James Mitchell Crow is a science writer based in Melbourne, Australia
 



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The public like them, and price spikes make the logic obvious. It's time Labour went all in
If ever there were a time to double down on renewable energy, it is now.
Liquified natural gas (LNG) prices spiked after Iranian attacks on Qatar’s LNG facility at Ras Laffan earlier this month. Indeed, prices have nearly doubled since the start of the war. With gas providing the vast majority of heating in the UK – and gas-fired power stations still used to generate nearly 30 per cent of our electricity – a rise like this will put a terrible extra strain on household finances, at a time when we can least afford it.
Experts are warning that when the Government’s quarterly price cap comes in at the end of June, bills are expected to jump by £332 to an average of £1,972 a year.
But for those households which can afford them, or are eligible for grants, solar panels can significantly lessen the blow. Up to 5 million low-income homes can get grants for solar panels as part of the Government’s £15bn Warm Homes Plan, announced in January.
A typical home with rooftop solar panels could save around £500 a year on its energy bills, according to Government figures. With an average installation cost of around £7,000, that means the investment is typically paid off in eight to 15 years, depending on the efficiency of the panels – and how much sun they see.
And with the prospect of soaring bills those savings are likely to be considerably higher still in the coming years.
So after hitting record levels last year, household solar panel installations look set to soar again this year. Octopus Energy, the UK’s largest energy supplier, has seen a 54 per cent jump in solar panel sales since the start of the Iran conflict as households look to protect themselves from the scourge of soaring gas prices.
And this week, the Government said that most new homes in the UK will be required to have solar panels from later this year.
But the biggest change in solar policy is news that “plug-in” solar panels could be available to buy in the UK for the first time as soon as the summer, through retailers such as Amazon and Lidl.
At around £400, these panels are much cheaper than traditional rooftop solar and can be put on balconies, in outdoor spaces or fixed to an outside wall that catches the sun. Unlike traditional solar panels, which can be complex to install, these panels are plugged directly into a mains socket. They are particularly suited to people living in flats or rented homes and have the added advantage of being portable, so you can take them to your next home.
The Government estimates that a typical UK home could save £70 to £110 a year on their energy bills from plug-in solar, meaning a family could make their money back in around four years.
Energy Secretary Ed Miliband has linked the war with a need to swap to solar and wind power to reduce bills and improve energy security. “The Iran war has once again shown our drive for clean power is essential for our energy security so we can escape the grip of fossil fuel markets we don’t control,” he said last week.
Nothing new there then. But I have a sense that he sees in the Iran war an unimpeachable argument against the net zero naysayers, a watertight case for renewable energy that will resonate with voters like nothing else – namely through the benefit to their pockets.
Previously, the financial benefits of clean energy have been hard to discern thanks to the upfront costs of electricity grid updates and new solar and wind power plants plus the fact that many of the cost savings won’t be felt until further down the line.
It was never quite that simple – not least because huge investment would still have been needed to update our creaking electricity system even if we’d stuck to fossil fuels.
However, with gas prices soaring once again – before they even had a chance to recover from the spikes generated by the Ukraine war – it is becoming blindingly obvious that solar and wind power are the way to go.
Not everyone has got the memo, though. Kemi Badenoch and other figures on the right have shouted about exploiting North Sea gas instead. It may look like a good idea. But in reality it wouldn’t even work as a short-term fix.
Production has been falling for a long time – last year, production was about 20 per cent of what it was in 2000, near its peak. The Government’s own numbers say that 93 per cent of the oil and gas which could be extracted from the North Sea fields has been extracted. What is left is difficult and expensive to get at. It would take time to step up activity too, so would hardly help with the immediate bills facing householders. And even if that weren’t the case, the gas price is set internationally, so we would still be at the mercy of wars and other global events.
More to the point, the uptick in solar panel sales and huge interest in the plug-in panels coming to supermarkets soon suggest that many ordinary Britons have already made their minds up. Rebecca Dibb-Simkin, chief product officer at Octopus Energy, said last week that “we are seeing a fundamental shift in the national psyche when it comes to energy”.
The Government has a chance to capitalise on this and needs to hammer the point home – paving the way both for a huge increase in solar household installations and much larger wind and solar farms.
There are signs it is starting to do this, and that Miliband is on the case. But the Government more generally still needs to go much further, and educate the public about the benefits of solar power at every opportunity.
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Syracuse solar program saves on bills, no home install  WSYR
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(Alliance News) – Espe Spa announced Tuesday the signing of three new photovoltaic contracts in Italy, representing a total capacity of approximately 18.1 MWp and a combined value of roughly EUR11.1 million, with completion scheduled by 2026.

Two of the orders involve the construction of ground-mounted solar plants. The first, located in the province of Bologna, consists of an 8.7 MWp facility utilizing tracker technology for an independent operator, valued at approximately EUR5.2 million.

The second covers three plants in the province of Belluno with a total capacity of 5.5 MWp and a value of approximately EUR3.9 million, commissioned by a new client active in the granular materials sector.

The third contract entails revamping and repowering works on an existing plant in the province of Rovigo, totaling 3.9 MWp for a value of approximately EUR2.0 million, underscoring the company’s expertise in optimizing brownfield assets.

These new mandates strengthen Espe’s positioning in the utility-scale segment and bolster its order backlog, which currently stands at approximately EUR92.2 million. This provides visibility through the first half of 2027, with a significant concentration in the photovoltaic sector.

By Giuseppe Fabio Ciccomascolo, Alliance News senior reporter

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German researchers at the Technical University of Munich (TUM) have identified and developed a solution to prevent weather-induced deterioration of perovskite solar cells.
In partnership with the Karlsruhe Institute of Technology (KIT), DESY (Deutsches Elektronen-Synchrotron), and the KTH Royal Institute of Technology in Stockholm, the team uncovered the microscopic mechanisms underlying the material’s deterioration during temperature swings.
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The discovery—published in a study in Nature Communications—revealed a degradation occurring during an initial “burn-in” phase, during which cells can lose up to 60% of their relative performance. The research highlights the importance of thermal cycling and how it impacts the degradation of perovskite solar cells early on.
“If we want these cells on every roof, we have to ensure they don’t just perform in the lab, but endure the stress of the seasons,” said Prof. Peter Müller-Buschbaum, chair of functional materials at TUM school of natural sciences.
Peter Müller-Buschbaum’s research team identified the microscopic causes of this instability and developed new design strategies to make the top layer of tandem solar cells more robust and withstand real-world conditions.
“We revealed that a microscopic tug-of-war triggers this loss,” explained Dr. Kun Sun, lead author of the study. “Tensions arise inside the material and its structure changes—this costs power.”
The researchers’ approach focuses on stabilising the fragile crystal structure with specially designed molecular “anchors”. The solution was published in a second paper—published in ACS Energy Letters—where the researchers used special organic molecules that act as spacers, holding the structure together—like a molecular scaffold.
The results showed that the bulkier organic molecule, 1,4-phenylenedimethylammonium (PDMA), acted as a superior anchor and resulted in a more robust solar cell that remains stable under the mechanical stress of rapid heating and cooling.
“By understanding these microscopic mechanics, we are paving the way for a new generation of solar modules that are both highly efficient and durable enough for decades of outdoor use,” said Müller-Buschbaum.
The issue of stability has long been a challenge in the commercialisation of perovskite technology, as evidenced by several research papers published over the past few years, including one from the University of Sydney last October.
Perovskite has also been at the centre of the Q4 edition of PV Tech Power in 2024, which is accessible to our Premium subscribers. The article covered the realities and expectations of the technology as it aims to become the next dominant generation for solar PV.

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Research from the University of Turku has identified a specific household cleaning agent that can impair solar module performance. The investigation assessed the suitability of common cleaning products for maintaining photovoltaic panels.
The study found that most household cleaners, including glass cleaner and isopropanol, are appropriate for the task and do not negatively affect the light transmittance of module glass. Dishwashing liquid, however, was a notable exception. This product was shown to alter the optical properties of anti-reflective coated solar panel glass.
Scientists observed that while the dishwashing liquid likely does not cause permanent damage to the coating, the transmittance of glass cleaned with it did not recover to its original level, even after the glass was rinsed. They noted that a visually clean surface does not guarantee optimal light transmission.
The experiments involved immersing glass fragments from an unused silicon solar panel in various cleaning solutions for an extended period. In tests on unsoiled glass, all other chemicals improved transmittance, while dishwashing detergent reduced the peak measurement by approximately one percent. When cleaning glass soiled with cultivated algae, the detergent left the transmittance peak nearly four percent lower than samples cleaned with more suitable agents.
Analysis confirmed the antireflective coating itself remained intact after cleaning. The researchers concluded that cleaning impacts the surface evenly and that power output decreases in proportion to the loss in optical transmittance. On average, the transmittance for soiled glass cleaned with dishwashing detergent was about three percent lower compared to glass cleaned with the best-performing agents, suggesting a similar impact on power generation.
The research group has now begun investigating soiling caused by snow accumulation in Nordic conditions, where significant solar radiation coincides with high electricity demand in late spring. This recent work follows other international findings that some widely used photovoltaic cleaning agents can damage anti-reflective coatings, underscoring the importance of product selection to prevent long-term performance loss.
This report provides a comprehensive view of the detergents and washing preparation industry in Finland, 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 detergents and washing preparation landscape in Finland.
The report combines market sizing with trade intelligence and price analytics for Finland. 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 Finland. 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 detergents and washing preparation 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 Finland.
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 detergents and washing preparation dynamics in Finland.
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.
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From comparing surveys and system design to warranties and aftercare, discover the main questions to ask when getting a quote
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For many homeowners, an important part of the decision to get solar panels is working out who to trust with the installation.
This is partly because many quotes look similar at first glance. Two installers might offer a system with the same number of panels and a battery of a similar size, yet the real differences can be buried in the details. Key factors to consider are how well the system has been designed for your home and whether savings estimates are realistic. It’s also worthing thinking about what warranties are actually included, and what support you will get if something goes wrong years later.
That’s why choosing a solar panel installer requires more than just comparing prices. You also need to work out whether the company in question understands your home, can justify its recommendations, and will still be useful once the scaffolding has come down.
Use our comparison tool to get free quotes from leading solar panel installers.
The first checks are the simplest, but they matter. Before anything else, make sure the installer is accredited through a recognised scheme such as MCS or Flexi-Orb. If an installer is not properly certified, that should be a major red flag. Ed Janvrin, director of franchise at OVO, explains: “If the installer isn’t MCS certified, the customer won’t be eligible to sign up for the Smart Export Guarantee (SEG).”
Accreditation alone is not enough, though. Alfie Ireland, head of operations at Sunsave, says it is also worth looking closely at customer reviews and the company’s broader track record. Buyers should ask whether the solar installer has strong credibility, including a high Trustpilot score, and whether it is likely to still be around in 10 years’ time if support is needed.
A solar panel system should last you decades, so if an installer disappears after a couple of years, any promise of support or workmanship cover may disappear with it.
A good installer should not give you a meaningful quote without first understanding the property.
That does not always mean an in-person visit at the start. Many installers now begin with a remote assessment using satellite imagery, digital design tools and photos supplied by the customer. Janvrin says OVO’s process starts with a consultation of around 10 to 30 minutes, followed by a virtual survey using software to assess the roof, loft, electricity meter and scaffolding access. Ireland also says installers increasingly assess homes remotely before providing a tailored quote and savings estimate.
What matters is not whether the first survey is virtual or physical, but whether it is thorough. A proper assessment should examine factors such as roof condition, usable roof space, pitch, orientation, shading, tile type, scaffolding complexity and the likely route for wiring.
Most importantly, a good installer should be prepared to tell you when your home is not suitable for solar. Janvrin says that is one of the key functions of the survey process: confirming whether the property will genuinely benefit. “Not every home will benefit from it, and we’d rather inform a customer up front,” he says. That may not be what a customer wants to hear, but it is a far better outcome than being sold a poor-fit system.
If the installer proceeds to the next stage, there may also be a more detailed technical survey. Janvrin says OVO carries out a free in-person survey after quote acceptance to check for safety hazards and confirm the practical realities on site. Ireland adds that customers may also be asked to send photos of areas such as the loft, electricity meter and preferred battery location before an installation goes ahead.
A good quote should show that the solar panel system has been designed around the property and your energy use.
That starts with the number of panels. Janvrin says OVO sizes systems by looking at annual energy consumption, site-specific solar yield and roof safety requirements, while aiming to cover at least 50 per cent of the household’s electricity needs. Meanwhile, Ireland adds that a good design makes full use of all suitable roof space within safety guidelines, arguing that if a home can only fit around six panels, it is rarely worth going ahead.
The inverter choice should also be justified. Ireland says most installations perform well with a standard string inverter, and that microinverters are usually only necessary where there is significant unavoidable shading.
The same applies to batteries. Janvrin says the right battery size depends on the number of panels and the household’s energy use. A useful question for buyers to ask directly is: why has an installer chosen a specific size, and how does it relate to usage pattern? A battery recommendation should be rooted in your likely surplus generation and when you actually use electricity, not in a generic upsell.
Solar is a mature technology, but that does not mean every quote is as straightforward as it looks.
According to Ireland, one of the most common ways people get oversold is being told they need microinverters or optimisers. These can be helpful in specific cases, particularly where shading is a serious issue, but they significantly increase the total cost of a system and are often unnecessary. He argues that many shading-related problems can instead be addressed through better system design, such as placing panels on different strings.
Janvrin points to another common oversell: battery sizing without a clear rationale. If an installer cannot explain why a battery of a certain capacity suits your home, that should raise questions. Bigger is not always better if the system is not properly matched to your generation and usage.
Savings projections also deserve scrutiny. Buyers should ask how the installer has calculated the expected savings, what assumptions sit behind those figures, and whether those assumptions are realistic for the way the household actually uses electricity.
The goal here is not to assume every installer is exaggerating, but to simply make sure your quote is based on design logic rather than sales logic.
Underselling can be just as much of a problem as overselling. Ireland says one of the most common omissions is bird protection. This relatively modest add-on helps stop pigeons nesting under solar panels, which can otherwise lead to mess, noise, reduced output and even safety issues. Leaving it out may make the quote look cheaper, but adding it later is far more expensive because scaffolding may need to go back up.
Warranties are another area where important details can get glossed over. Janvrin warns that some installers highlight cover for panels and inverters but say little about wiring, fixings and fittings. Buyers should make sure they understand what is covered across the entire system, not just the components that are easiest to market.
It’s also worth checking whether the installer is handling the paperwork that sits behind the installation. Ireland says that if an inverter larger than 3.68kW is being installed, the home will usually need permission from the Distribution Network Operator through a G99 application before it can connect to the grid. Any competent installer should manage that process for the customer.
Solar quotes often feature impressive-looking warranty language, but it’s important to understand the difference between product cover and meaningful support.
With solar panel systems, there are several layers of warranty. There may be product warranties on panels, inverters and batteries; performance warranties on panels and batteries; and a workmanship warranty from the installer. In theory, that sounds reassuring.
In practice, the picture is more mixed. Ireland notes that workmanship warranties are often only two to five years, which is short compared with the 30- to 40-year lifespan of a typical solar panel system. He also points out that batteries and inverters may need replacing well before the panels do, and that many homeowners will struggle to judge for themselves whether a product is underperforming against its warranty terms.
This is why aftercare matters so much. Ireland argues that long-term maintenance support is at least as important as the warranties themselves, because otherwise the homeowner may be left to navigate faults, replacement parts and warranty claims alone.
Janvrin puts more emphasis on the value of a stronger workmanship promise, noting that OVO offers a 10-year workmanship warranty alongside standard 25-year panel performance warranties. Whether or not an installer offers cover on that scale, a key question for buyers is who will actually help if something fails years later?
It’s easy to focus on survey day and install day, but a good installer should also be able to explain what happens afterwards.
Janvrin says that once installation is complete, OVO’s installer helps the customer set up a monitoring app so they can track generation in real time, and leaves a handover pack including the MCS certificate so they are ready to sign up for the Smart Export Guarantee. Ireland says most homes will also receive access to an app from either the installer or inverter manufacturer that shows what the system is generating, storing and exporting.
That monitoring is useful, but it is not the same as ongoing support. Ireland argues that many homeowners will not know whether a subtle drop in output is normal or a sign that something is wrong. For that reason, it says installer monitoring and long-term maintenance support can be one of the most valuable parts of the package.
This is one of the clearest ways to distinguish between a company that is focused on getting the install done and one that is prepared to support the system over time.
By the time you are choosing between quotes for solar panels, a short list of questions can tell you a lot.
A strong installer should be able to answer all of those questions clearly and without becoming defensive.
The main thing to remember when buying solar panels is that choosing solely on price can be a mistake.
A low quote may reflect thinner support, weaker warranties, missing extras, poor system design or lower-quality equipment. That doesn’t mean the most expensive option is automatically the best either. But if one installer is dramatically cheaper than the rest, it is worth finding out exactly what has been left out, simplified or assumed away.
With solar, the goal is not just to get panels on the roof. It is to end up with a system that performs well, is properly supported, and continues to deliver value over the long term.
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Researchers in Singapore have developed fully vacuum-processed ultrathin perovskite solar cells with absorber layers as thin as 10 nm, achieving high transparency and stable efficiencies up to 12%. These cells balance optical transparency and electrical performance, offering scalable, design-flexible photovoltaics suitable for seamless integration into buildings.
Image: Nanyang Technological University (NTU)
Researchers from Nanyang Technological University (NTU) in Singapore have developed ultrathin perovskite solar cells with absorber layers as thin as just tens of nanometers.
The research work tackles a key challenge in the development of transparent photovoltaics: balancing optical transparency with electrical performance without sacrificing scalability or manufacturability while maintaining minimal instrument safety.
“We push perovskite solar cells to the ultimate thickness limit, demonstrating fully vacuum-processed devices with absorbers down to around 10 nm compared to the conventional 500–700 nm range, making them both efficient and aesthetically beautiful and see-through,” NTU researcher Annalisa Bruno told pv magazine. “This represents a step toward scalable, design-flexible photovoltaic systems suitable for seamless integration into buildings.”
For their experiments, the scientists used planar methylammonium lead iodide (MAPbI3) perovskite films grown on a Spiro-TTB hold transport layer (HTL) and and a self-assembled monolayer (SAM). Film thickness was varied from 10 to 700 nm, with optical studies showing bandgap widening at ultrathin scales due to quantum confinement.
X-ray diffraction (XRD) showed that the film’s composition and crystal orientation change with thickness, improving charge flow in p.i.n. devices. Field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) confirmed that the films are smooth, uniform, and stable even at just 10 nm thick.
The perovksite cell design consisted of a substrate made of glass and indium tin oxide (ITO), the Spiro-TTB HTL, the perovskite absorber, a buckminsterfullerene (C₆₀) electron transport layer (ETL), a bathocuproine (BCP) buffer layer, and a silver (Ag) metal contact.
Tested under standard illumination conditions, the cells built with 10 nm, 30 nm, and 60 nm absorbers achieved power conversion efficiencies of 7%, 11%, and 12%, respectively, and they maintained their performances also in the low-illumination regime.
Moreover, the 30 nm and 60 nm devices showed the highest reported light-utilisation efficiency (LUE) for ultrathin devices, indicating a favourable balance between transparency and performance. The 10 nm cell, by contrast, showed reduced open-circuit voltage and some hysteresis, suggesting processing optimisation is needed.
“The 60 nm-thick cell achieved an average visible transparency of about 41% with a power conversion efficiency close to 8%, with a LUE of 3.13. These values of LUE, with further optical engineering, have the potential to reach LUE values above 5%,” said the research lead author Luke White. “All devices exhibited near colour-neutral transparency, with a colour rendering index of 79.7, suggesting compatibility with architectural requirements.”
The cell design was presented in “Ultrathin Fully Vacuum-Processed Perovskite Solar Cells with Absorbers Down to 10 nm,” published in ACS Energy Letters.
“Our findings are particularly relevant for the built environment, which represents a significant share of global energy demand,” said Bruno. “Technologies that enable buildings to generate electricity without altering their appearance are expected to play a central role in the expansion of distributed renewables. Perovskite materials are especially promising in this context, thanks to their tunable optical properties, compatibility with low-temperature processing, and potential for large-area manufacturing.”
From pv magazine Global
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RIESEL, Texas (KWTX) – A nonprofit is partnering with solar farms across the nation to set up bee hives on small plots of land, providing therapy for veterans and first responders while supporting pollinator populations.
At the Roseland Solar Farm in Riesel veterans and first responders are paired with experienced beekeepers to care for the bee hives on the site and harvest their honey.
“Our mission at Hives for Heroes is to save bees and save lives,” said Steve Jimenez, president and founder of Hives for Heroes.
Jimenez, a veteran himself, explains that he wanted to find a way to help others who may be struggling.
“We have an extremely high suicide rate, you know 4x the normal rate of society,” he shared.
At Hives for Heroes, participants are given a new purpose… saving the declining bee population.
Jesse Puckett with the renewable energy company Enel said when construction started on the Roseland Solar Farm, they began looking for a beekeeping partner.
“We were looking for ways to increase biodiversity on our site. You know through our sustainability initiatives a lot of our work is supporting pollinator friendly habitats,” Puckett said.
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That’s how they found Hives for Heroes, approaching them in 2024.
“One of the things that we were looking for is a partner that has really great community influence,” Puckett shared, “Hives for Heroes really checks a lot of those boxes for us so it’s a really great partner for us to have.”
The site now has a fully functioning hive that makes honey and pollinates the surrounding area.
“We have about 4×4 square foot, which is all we need! Which is wonderful cause then the bees take the rest of the land,” Jimenez said.
Jimenez says for many veterans and first responders the hands-on work has become a passion, adding that they’re now helping educate others.
“Other veterans and first responders, even schools, have come out to see the bees,” he shared, “we put them in the gear and they go through like inspections for example, and they’re educating the community and that’s a huge component of the outreach.”.
Hives for Heroes currently has bee hives at two Enel solar farms in Texas, one here in Central Texas and another near Bryan-College Station.
Both Jimenez and Puckett said they hope to grow that number over the next few years.
“We would love to see how our projects can continue to provide that land access, which is a barrier for many that are looking to get into beekeeping,” Puckett said.
Those interested can sign up here. No experience is needed, and the organization provides all training and equipment for free.
Copyright 2026 KWTX. All rights reserved.
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Power Roll’s perovskite solar cell adopts an innovative structure that does not require indium tin oxide (ITO), the material used in transparent conductive oxide (TCO) substrates that typically account for 40–60% of materials cost in conventional perovskite solar cells.

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Scientists achieve ‘impossible’ solar efficiency in renewables breakthrough  Yahoo
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U.S. Solar Panels Market Analysis
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JA Solar’s Grid-Connected C&I Microgrid – Empowering Reliable, Sustainable Commercial and Industrial Energy  SolarQuarter
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© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.
“There was so much innovation.”
Photo Credit: iStock
Water heaters may not be the most flashy products that come to mind when thinking about innovative technological breakthroughs. But few contraptions impact everyday lives more.
That’s because heating water can account for between about 12% and 18% of a home’s energy use, according to Energy Star and the U.S. Department of Energy, respectively.
CleanTechnica’s Joe Wachunas got a firsthand look at the latest tech that’s built to lower those percentages at the 2026 Air-Conditioning, Heating, and Refrigeration Expo in Las Vegas in February.
“There was so much innovation … Every boiler manufacturer seems to be coming out with a heat pump, which is fantastic news for the climate,” Wachunas wrote. 
Efficient, next-generation heat pump water heaters — some of which are already commercially available — can save homeowners hundreds of dollars a year or more. Energy Star’s experts have said that certified models can deliver around $550 annually for a family of four, or $5,610 in energy bill savings over the unit’s lifetime, compared to a standard electric model.
Efficient units reduce reliance on oil, coal, and gas for power generation, thereby limiting harmful air pollution. Cala’s model is a unique, intuitive version that can sync with solar panels. It’s an air-source product that uses electricity to operate a heat pump, which transfers warmth from ambient air into water. It operates at up to 200% to 500% efficiency, Cala’s experts have said, whereas traditional water heaters run at around 60% to 93%. 
The system resembles typical cylindrical water heaters that are staples in many basements. But there’s a lot more happening inside this forward-thinking appliance. 
Cala predicts hot water use based on the home’s demand history and heats water on demand, providing for even greater efficiency. Its performance improves over time, and it can maximize its operation by running when electricity rates are lowest. Home solar removes utility rate concerns altogether by generating homegrown power, according to Cala’s team. 
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Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers in your area.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Solar panels can save you more than $50k over their 25-year lifespan, and EnergySage can help you save as much as $10k on installation. Which begs the question — isn’t that worth an email or two?
The unit can be managed by an app. It works fully without Wi-Fi, but an internet connection unlocks the system’s full potential. 
Cala’s team said its innovation represents the transformation of a household function that has been consistent for well over a century. 
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“For 130 years, water heaters have been designed to heat when the water in the tank is cold. Cala is different. It optimizes water heating based on your home’s unique hot water needs,” according to company experts. 
Water heating costs can vary depending on region, electricity rates, and other factors. Even the temperature of the water entering the home makes a difference. Meanwhile, Cala’s team has said its unit can save you between $179 to $767 a year, depending on your current appliances, fuel source, and other factors, such as family size.
The tech comes with a 10-year parts warranty and a three-year labor promise when customers use a company-vetted installer. It’s part of an exciting time for technology that manages a core household function. 
Heat pumps, for reference, are already popular HVAC upgrades as well. 
“As heat pumps continue to make inroads on space and water heating in the U.S., manufacturers are responding by building lots of great products to meet our country’s need for clean, affordable heat,” Wachunas wrote.
Get TCD’s free newsletters for easy tips to save more, waste less, and make smarter choices — and earn up to $5,000 toward clean upgrades in TCD’s exclusive Rewards Club.
© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.

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120W Foldable ECO-WORTHY 10W Solar Panel – 18V Battery Charger For Camping, Off-Grid Lighting Manual Pdf  ruhrkanal.news
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Ingka Investments, as a part of its INR 10 billion (EUR 97.5 million) renewable energy commitment to India, launches a 210 MWp solar installation in Bikaner, Rajasthan, making it the company’s first renewable investment in the country.
Ingka Investments, the investment arm of Ingka Group, the largest IKEA retailer, has made a 100% stake investment in a subsidy-free 210 MWp solar project located in Rajasthan, India. The solar project has reached ready-to-build status, and construction will start shortly. Start of operations is scheduled in December 2026. The total expected production is 380 GWh per year.
Frederik de Jong, Head of Renewable Energy at Ingka Investments, says: “This is a milestone acquisition for us – it marks the first renewable energy investment for Ingka Investments in India – a country of utmost importance both for IKEA retail and the IKEA supply chain. The new solar project in India will produce 380 GWh of renewable energy annually – more than enough to power our growing retail, shopping centre, and distribution operations. It’s a big step in making our retail business in India more sustainable, efficient, and future-ready.”
The investment is part of the EUR 7.5 billion the company has committed to supporting 100 percent renewable energy consumption across the value chain and beyond by 2030.  Ingka Investments has so far invested and committed EUR 4.2 billion into renewable energy projects in wind and solar energy worldwide.
In India, the company is working with ib vogt, an integrated large-scale solar PV developer headquartered in Germany with a strong presence in India. ib vogt Solar India will also be the partner for construction, and the first three years of operations. The construction and operations of the solar project will provide significant local employment, estimated to be around 450 people during construction and 10 to 15 during operations.
Patrik Antoni, CEO, IKEA India, shared, “At IKEA, sustainability is at the heart of everything we do. Over the past eight years, we’ve invested in making our retail journey more sustainable. Designed with energy efficiency at the core, two of our large-format stores in Bangalore and Navi Mumbai are LEED Gold certified, and we are working towards Platinum certification in Gurugram and Noida. As a founding member of RE100, we are on track to power our operations with 100% renewable energy by 2025. We’re also proud of our 100% zero-emission EV deliveries in key cities and are committed to expanding this across all future markets. With EV charging stations in our stores and energy-saving solutions for our customers, and now also an investment in a solar project, we’re inspiring positive change and contributing to a cleaner, more sustainable future for India.”
As a global business operating in 31 countries, Ingka Group is committed to the Paris Agreement and to contribute to limiting the global temperature rise to 1.5°C. In November 2023, the company strengthened its climate targets in alignment with the Science Based Targets initiative (SBTi) Corporate Net-Zero Standard.
The targets were approved by SBTi in April 2024 and include a commitment to reduce absolute greenhouse gas emissions from the value chain by at least 50% by FY30 (compared to FY16 baseline) and reach net zero emissions by 2050, without relying on carbon offsets to meet these absolute reduction targets.
 
About Ingka Investments
Ingka Investments is the investment arm of Ingka Group, the largest IKEA retailer. They invest in assets, manage companies, and operate strategic businesses to preserve and create value for Ingka Group and IKEA – now, and for generations to come. Taking a long-term approach, they responsibly invest across six strategic areas: forestland, renewable energy, real estate, circular, financial markets, and business acquisitions and venture investments. Ingka Investments has committed to invest EUR 7.5 billion by 2030 into utility-scale wind and solar projects to increase production of renewable energy as well as investing in technologies to support the energy transition, from storage solutions to charging stations.
Further information on Ingka Investments: https://www.ingka.com/what-we-do/ingka-investments/
About Ingka Group 
With IKEA retail operations in 31 markets, Ingka Group is the largest IKEA retailer and represents 87% of IKEA retail sales. It is a strategic partner to develop and innovate the IKEA business and help define common IKEA strategies. Ingka Group owns and operates IKEA sales channels under franchise agreements with Inter IKEA Systems B.V. It has three business areas: IKEA Retail, Ingka Investments and Ingka Centres. Read more on Ingka.com. 
For further information, journalists and media professionals can contact us at [email protected] or by calling +46 70 993 6376. 
IKEA Solar Farm
Frederik de Jong, Head of Renewable Energy at Ingka Investments
Patrik Antoni, CEO, IKEA India
Meeting with the Union Minister of State for Environment, Mr Kirti Vardhan Singh
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