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Research and insight to improve policy and governance at local, national, and global levels.
Author
Henry Lee
Author
Windy Dewi
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Much of the North American Arctic remains dependent on fossil fuels, both for heating and electricity generation. Such dependence creates greater economic and energy insecurity, and increased health impacts for those relying on older, less efficient generators. In remote areas where the sun stays below the horizon for months in the winter, the idea of investing in solar energy that is intermittent and reliant on natural sources may seem counterintuitive. However, the findings in this paper indicate that the case for solar electricity in the Far North may be stronger than previously thought.
The populations in the Arctic regions of Canada, Alaska, Greenland, and parts of Russia are stable or declining. Over the last decade, electricity demand growth has been flat overall in these regions. There are two local exceptions: 1) where electricity is being used to supply a load formerly provided by direct fuel use, such as heating, and 2) where an additional load has emerged, such as a new mineral extraction project. Neither exception is common, although the latter will expand if demand for strategic minerals, such as rare earths, leads to new mining projects.
The marginal cost of power in most Arctic villages is the cost of diesel fuel. Each kilowatt hour (kWh, or kilowatt-hour of electricity) produced by a new solar facility is a kWh-e of power not generated by a diesel facility. If the annualized capital cost of the renewable generator, measured in cents per kWh, is less than the cost of diesel fuel that would otherwise be burned, then the solar system may be a good investment. The capital costs of an existing diesel facility are sunk—that is, consumers will still have to pay these costs whether the plant is used or not. For example, if a village invests in a solar system, the remaining capital costs of the diesel generator and its maintenance expenses will still have to be paid by consumers. Power from the diesel generator will be needed when it is not sunny, which comprises a significant part of the year in Alaska. The only cost savings from such an investment stem from the cost of the diesel oil that is not burned when the solar-generated electricity is consumed. If the total cost of the solar-generated electricity is greater than the cost of the diesel oil not burned, then communities should forego investing in solar generation. If the cost of the diesel oil is greater than the investment in solar, the investment in a solar photovoltaic (PV) system could be worth seriously exploring.
Several factors in the North American Arctic affect this calculation. Most villages in this region are not interconnected by either electric transmission systems or roads. Each village acts as a mini grid without access to economies of scale enjoyed by larger cities and towns. The absence of roads means that equipment and fuel must be delivered by sea or air. Delivering diesel fuel or sections of a renewable generator by barge can be expensive. In some instances, villages can only be accessed by airplanes, and therefore, the costs of transporting either diesel fuel or renewable systems can be extremely high. In remote villages, the cost of transportation can double the effective price of energy.
Unless villages can be reached by large ships, diesel supplies arrive once or twice per year, locking in the cost of the fuel for six months or more. For example, if oil is delivered in November at a certain price, the cost of diesel fuel in that community will not change until the next cargo arrives, which could be in May or June. When global oil prices are rapidly increasing, locking in the price can be beneficial, but when prices are falling, the opposite is true. Fluctuating fossil-fuel prices cause problems everywhere, but those are compounded for most Arctic villages by multi-month lock-in of periodic price peaks for what is generally their only option for electricity generation. 
This paper looks at the potential for solar power in the North American Arctic, using northwest Alaska as a case study. Admittedly, the villages in this region vary considerably. Some are in boroughs that contain mining or oil and gas developments; these villages have sources of revenue that are not available to villages in other boroughs. Some villages border the coast and thus are vulnerable to sea level rise and land erosion, while others are inland with limited transportation access. Some towns are financially self-sufficient, while others must rely on financial assistance. There is no typical “village.” Villages in the Canadian Arctic, in Greenland, and in western Siberia differ in some ways from those in Alaska but share their isolation and dependence on diesel-fired generators.
The metric for calculating the cost of a solar PV system is the levelized cost of energy (sLCOE). This metric divides the total cost of the energy system over its lifetime by the total energy produced over the same period.¹ It combines capital costs (i.e., estimates of debt and equity), operation and maintenance (O&M), performance, and fuel costs. It does not include financing elements such as graduated discount rates, depreciation costs, and battery costs. (See the Department of Energy National Renewable Energy Laboratory² for more information on sLCOE calculations.)
We have selected six villages for our analysis: Kivalina, Kotzebue, Deering, Selawik, Ambler, and Kobuk. They were selected because of:
As North Slope oil production dwindles, Alaska’s state treasury faces decreasing revenue, forcing the state to cut its budget. These cuts affect university research and governmental data collection. Therefore, obtaining information on electricity costs, investments, and other energy-related data at the city or village level is challenging. Available data often vary with observations on the ground.  During the 2021-2023 period, several locations observed prices as high as $9 per gallon,⁴ and in one case as high as $16 per gallon.⁵ Yet the official prices often were much lower. Finally, costs in one village may not be consistent with those collected in another. However, from discussions with people on the ground, we are confident that the numbers used in our calculations are reasonable.
The retail market for power in rural Alaska is influenced by a variety of subsidies. For instance, the Power Cost Equalization (PCE) program provides rural communities with relief from high electricity rates due to high diesel fuel costs.⁶ Administered by the Alaska Energy Authority, the program seeks to equalize electricity costs in rural Alaska with those in urban areas by providing a per kWh subsidy on electricity rates.

In Nome, the PCE program subsidizes the local electricity rate, reducing the cost per kWh. This allows Nome residents to pay electricity costs more comparable to those in the relatively urban parts of the state. The PCE program has a substantial impact: 2020 estimates show that in some communities it lowers residential prices by nearly 50%. As a result, consumers in rural Alaska do not pay the actual cost of power. 
Alaska has also implemented a subsidy program to stimulate the deployment of renewable energy. The Alaska Renewable Energy Fund, established in 2008, encourages investment in renewable generation, but most of the $317 million in state expenditures from this fund has been used to develop hydropower and wind generation. It was not until 2023 that the fund was committed in perpetuity—as a permanent component of Alaska’s energy infrastructure policy.⁷ As a result, it does not provide a reliable representation of solar generation costs, nor can it provide year-on-year data trends. Consequently, we have created hypothetical solar cost profiles, using data from the National Renewable Energy Laboratory (NREL) plus rough estimates of transport and operation costs.
As mentioned earlier, the capital costs of existing diesel generators are sunk and must be paid regardless of how often the asset is used. Any new solar capacity reduces the amount of diesel fuel consumed. An analysis of the economic viability of solar investment compares the cost of power from a renewable facility to the cost of the diesel oil not consumed. For instance, if a solar farm was built to serve a village but only produced power for an average of six hours per day for seven months, the comparison would be the total cost of generating the solar power compared with the cost of the diesel oil not consumed in that seven-month period. 
For example, if 100% of the electricity in a hypothetical community was supplied by diesel generators, the consumers would pay for the capital costs of the generators as well as the costs of fueling and maintaining them. If the community chooses to invest in solar energy, the existing electric generator will not disappear. It would still supply much of the community’s electricity. The consumers would still have to pay for the capital costs of the electric generator because no configuration of solar systems—even with batteries—can meet more than a portion of the region’s electricity demand due to the number of hours without sunlight. Solar energy can only save a portion of the annual consumption of diesel oil. The question is whether the amount saved would justify the solar facility.
What if a village had no electricity and was starting from scratch, but intended to meet a portion of its demand with solar energy? Would this scenario significantly change the economics? No, the village would still have to invest in diesel generators to back up the solar (or wind) system. Even with battery storage, solar-generated electricity would not be sufficient to meet demand 24/7. Solar investments do not eliminate the need for diesel generators; they only reduce the amount of electricity needed from those generators and thus the amount of diesel oil that the generator uses. In summary, for solar-powered electricity to be economic in northwest Alaska, it must be cheaper than the diesel oil consumption foregone.
Table 1 presents the average diesel prices for six communities in northwest Alaska from 2018 to late 2023, as reported by the Northwest Arctic Borough. 
What is striking about these numbers is that they differ substantially from one community to another and from one year to another.
Unlike using a pipeline or an electric transmission line, where the cost of transport is predictable and regulated, the cost of transporting oil products in Alaska is market-driven and often set by negotiations with the barge or plane owner. As mentioned earlier, these costs can differ from week to week, depending on availability and weather. In 2022 and 2023, the cost of generating electricity from diesel increased, driven by the global increase in petroleum prices. Although these elevated prices may prove to be transitory, they demonstrate the volatility of fossil fuels. 
Due to these and other factors, electricity generated by diesel generators at unsubsidized wholesale prices is estimated to cost more than double the 2022 U.S. national average wholesale price of $0.11/kWh.⁸ In locations such as Kobuk and Ambler, the difference can be five to six times higher. It is important to remember that the prices that consumers see are the subsidized prices, not the actual prices. Further, smaller generation systems can be more expensive to run than larger systems on a cost per kilowatt basis because they are often much less efficient.
The cost of the diesel fuel burned per kWh-e generated is calculated by dividing the cost of diesel fuel ($/gallon) by the fuel efficiency of the generator, measured in kWh-e per gallon:
Fuel cost ($/kWh-e) = diesel cost ($/gal) / generator fuel efficiency (kWh-e/gal) 
The diesel costs in our calculations are from Table 1. The generator fuel efficiencies, acquired from the Northwest Arctic Borough Community Profiles, are shown in Table 3, below. Table 3 also shows the generator efficiencies expressed as a percentage, calculated by dividing the fuel efficiency in kWh-e by the thermal energy content of diesel fuel. Distillate Oil No. 1 contains 139,000 Btu/gal, while Distillate Oil Diesel contains 137,381 Btu/gal.⁹ Given that the difference is about 1%, we use 139,000 Btu/gal in our calculations. This yields a thermal energy equivalent of 139,000 Btu/gal / 3412 Btu/kWh-t = 40.74 kWh-t/gal, where kWh-t describes thermal kilowatt-hours. 
The fuel efficiencies in Table 2 may be overestimates, since many village diesel generators are getting older and may no longer be operating at optimal efficiencies as recorded for the Northwest Arctic Borough. The magnitude of this effect is impossible to estimate with available data. Notwithstanding, the equation above demonstrates that fuel costs per kWh are very sensitive to changes in generation efficiency.
Table 3 shows the fuel costs for electricity generation from diesel in our selected villages, calculated as described. These are conservative estimates, and it is probable that actual generator efficiencies are lower due to degradation over time.
Solar energy development in the Arctic faces unique challenges due to extreme seasonal variations in daylight hours, limited solar insolation, and harsh weather conditions. Alaska’s high latitude results in extreme variations in the number of daylight hours throughout the year. During the winter months, the region experiences limited sunlight, with some areas experiencing complete darkness for several weeks. Conversely, in summer months, there are extended periods of daylight, which provide abundant solar energy. Thus, solar systems can produce substantial electricity in the summer months and negligible amounts during mid- winter.
The harsh weather conditions in Alaska, including extreme cold, snow, and ice, also pose challenges for solar panel installation, operation, and maintenance. Snow and ice accumulation on panels can reduce their efficiency while extreme cold can impact their performance. The remote and isolated nature of many communities complicates the transportation and installation of solar panels and associated equipment; the absence of existing renewable energy infrastructure amplifies these difficulties. The expected monthly solar output and the seasonal load profiles of communities can differ substantially across the state. As mentioned earlier, our analysis presumes that a kilowatt-hour produced from renewable sources displaces a kilowatt-hour that would otherwise be generated by stove oil.
The LCOE calculation for the cost of solar generation included:
Solar irradiance: This is the fundamental driver of a solar panel’s energy output, determined by geographical location, time of year, and time of day. PVWatts is a calculator developed by NREL for estimating the energy production and cost savings of grid-connected solar PV systems. By providing information about a specific location, system size, and other parameters, users can analyze the expected performance of a solar PV system. See Appendix 1 for additional information about PVWatts.
PVWatts uses solar radiation and weather data, along with user inputs, to model the energy output of a PV system over time. It considers factors such as solar panel efficiency, system losses, and local weather conditions to provide an estimate of the system’s energy production. As one would expect, the amount of power produced during the four months from November through February is negligible. Interestingly, the highest insolation rates in northwest Alaska are in April (over six hours per day), as June and July are traditionally cloudier. 
Inputting the system size and using the default assumptions offered by PVWatts, for each month of the year for each village, we calculate the annual energy production from a 1.35 MW solar farm in each village based on location-specific solar irradiation and geographic parameters. This value determines the power generating ability of a system over the course of its lifetime to determine the sLCOE for solar.
In our analysis, summarized in Table 3, we use three estimates for solar PV costs: 1) a typical or base estimate, 2) a low-cost estimate, and 3) a high-cost estimate. We compare these estimates to diesel prices for 2018-2023. This forms our ‘base case’. The low case represents a cost that is 20% lower than the base case, representing steep price declines in costs for purchasing and installing solar PV systems. The high case is represented by a 20% increase from the base case, illustrating more challenging situations such as an increase in the costs of transporting, installing and maintaining solar PV systems.
Through the years, the cost of generating electricity from diesel has risen, primarily driven by increases in the cost of fuel, which in some villages reached levels over $10 per gallon in late 2023. Given historical fluctuations in global oil markets, diesel prices could rise and fall substantially from one year to the next. As discussed earlier, solar generation is more expensive in rural Alaska than in other parts of the United States due to higher transport, installation, and O&M costs. Notwithstanding, the capital costs of new solar systems continue to fall.
In Table 5, our analysis shows that in 2023 solar was cost-effective in four out of the six villages studied—Kotzebue, Selawik, Ambler, and Kobuk—even under base or high-cost scenarios. However, in Kivalina and Deering, solar generation remains more expensive than diesel, especially in the base and high-cost estimates. In these two communities, diesel fuel costs are lower due to better access or efficiency, making diesel the more economical option under current assumptions.
Our estimates may be biased against solar due to the potentially high efficiency numbers for diesel generators we employ. Furthermore, we ignore the health externalities from exposure to air pollution, which can be particularly acute in some Alaskan villages. Crude oil price fluctuations would further impact this price comparison. For example, if oil prices drop to 2021 levels, generating electricity with diesel would be less expensive in Kotzebue, Deering, and Kivalina. On the other hand, if they remain at 2023 levels, solar generation would be cost effective in five of the six communities. The cost of diesel oil will remain volatile and, thus, the cost of oil generated electricity will fluctuate significantly from one year to the next. On the other hand, the cost of electricity from a solar system will be a function of its initial capital costs and will not change over time. If one believes that world oil prices will decrease into the range of $50 per barrel and stay there for the next decade, then solar is a much less attractive option for northwest Alaska. If one believes that that oil prices will fluctuate from $50 to around $100 per barrel then investing in solar energy is an option worth considering.
As outlined in the rationale above, we use a discount rate of 5.8% and a project lifetime of 25 years. If one assumes that discount rates will be lower, the relative advantage of substituting solar power for diesel-fueled generation improves. The opposite is the case if discount rates increase. The private discount rate will reflect the cost of equity, which in Alaska will be greater than 10%. If solar systems have no subsidies and must be funded only by private equity (i.e., no debt available), then the cost of solar becomes less competitive. This outcome would be true for almost any fossil-fueled generation.
As shown in Figure 1, in places like Ambler and Kobuk where diesel costs are high, solar can be a viable alternative by a significant margin. The high cost of diesel in these places can be partially attributed to high non-fuel expenses (~35% higher than in Kotzebue) as a result of distance from ports and small populations. Smaller populations tend to either have to transport smaller quantities of fuel at one time, making each trip more expensive, or buy in bulk and bear additional storage costs and price fluctuations at the time of purchase. Kivalina also has very high non-fuel expenses (56% higher than Kotzebue), but its proximity to ports allows for relatively cheap diesel prices (in recent years, cheaper than Kotzebue). As a result, in Kivalina, diesel generation remains more economical than solar generation.
There are four factors that might alter these numbers, each of which favors renewable energy. 
Carbon pricing initiatives are currently in force in 55 national jurisdictions across the globe, with an additional 44 in subnational jurisdictions.¹⁷ Hafstead and Picciano at Resources for the Future calculate that a $40 per ton tax would increase diesel prices by approximately $0.40 per gallon.¹⁸ By itself, a $40 per ton carbon tax would significantly improve the cost competitiveness of solar energy. That said, as of 2025, the likelihood of a carbon tax or a cap-and-trade program being enacted by Congress in the next few years remains slim.
A technological breakthrough that would have a positive impact for small Arctic villages unconnected to transmission networks would be cost-competitive electricity storage and, specifically, mid- and long-term battery storage. Battery systems that would allow residences to store two to three days of power at a competitive price would dramatically expand the number of kilowatts that could be harvested from a solar system, particularly in the shoulder months of May and August. They would also allow villages to build larger solar systems, thereby taking advantage of economies of scale and reducing the cost per kWh of the power produced. Accessibility remains a key question. In 2021, NREL projected shorter term (4-5 hours) battery storage prices to reach $208 per kWh by 2030 and $156 by 2050.¹⁹ Recent projections in 2025²⁰ show that battery prices have already slipped below the $200 per kWh goal. While shorter-term storage has advantages, the overall benefits are limited. 
Storage for periods of time measured in days as opposed to hours would provide significant benefits, but such technologies are not yet available at commercially competitive prices and are unlikely to be so prior to 2030.²¹ While longer and more cost-effective storage would increase the kilowatt-hours of available solar-generated electricity while reducing the number of hours that diesel generators would have to operate, there are several caveats. First, batteries transported to northwest Alaska will be much more expensive than in the lower 48 states. The more batteries that are installed, the more hours that solar can be used, but also the more expensive the storage costs. Under these conditions, the cost advantage of solar generation is so narrow that batteries might shift our cost comparison to favor diesel facilities. That said, as battery technologies and costs improve, this situation may change. Secondly, even if today’s batteries were cost-effective, diesel generation will be needed to provide electricity for 20-30% of the day. As we have said repeatedly, solar power by itself does not replace the need for some diesel generation; it only reduces the overall quantity of diesel oil that must be burned.  
Diesel oil contains more than 40 toxic air contaminants and increases the risk of respiratory illnesses, heart and lung disease, and cancer.²² Residents of northwest Alaska are known to be more vulnerable to respiratory diseases, such as asthma, and such diseases exhibit higher rates among Native children.^23,24 Alaskan communities have some of the highest rates of respiratory morbidity documented for any Native population – susceptibility to which is heightened by exposure to diesel exhaust – is five times higher in Alaska Native children than the general U.S. population. 
The combustion of diesel oil in generators that cannot meet federal air standards is a major contributor to these health problems. In 2020, Congress exempted Alaska from EPA Tier 4 restrictions on particulate emissions from diesel generators, locking in emission levels that exceed national standards, to allow villages to avoid purchasing more expensive equipment and paying higher maintenance costs.

While the actual costs of air pollutants for Alaska’s villages require additional analysis beyond this paper’s scope, exposure to these pollutants raises the local cost of burning diesel oil. That is, the village population pays for both the diesel fuel and the health costs of breathing the exhaust from burning that fuel. The result is that investment in renewable generators that emit no particulates or other toxic air contaminants provides even greater benefits than a simple kWh comparison would indicate.
As discussed earlier, the largest subsidy in Alaska has been the PCE program, which reduces the actual price of electricity generated from diesel-fueled facilities. This subsidy is an implicit disincentive to invest in renewable power. It is unlikely to be removed, since it enables poorer Alaskans to access affordable electricity, but perhaps it might be amended to work in tandem with subsidies to help accelerate the deployment of renewables and, eventually, storage.
In 2021 and 2022, Congress passed additional subsidy programs to accelerate the deployment of clean energy. These programs (including the Renewable Energy Production Tax Credit [PTC], the Investment Tax Credit [ITC], and the Clean Renewable Energy Bonds [CREB]) have the potential to reduce the capital costs of both wind and solar generators. Tax credits in the Inflation Reduction Act (IRA) would have had a large impact on the relative economics of solar energy across the United States, including the Arctic regions of Alaska.
However, the Trump administration and the U.S. Congress oppose the continued funding of many subsidies and tax credits under the IRA. With the passage of recent legislation, most of the renewable electricity tax credits will be phased out over the next two years.²⁵ Future administrations may reverse these actions and pass new subsidies that would enhance the relative economics of solar energy advantage, but over the next three years, renewable energy alternatives are unlikely to receive measurable federal subsidies.  
Diesel fuel prices have historically been volatile, fluctuating from one year to another. More importantly for Alaska, the expense of transporting fuel, particularly for Arctic villages that are not directly on the coast, adds a very high-cost premium. To an extent, dependence on diesel oil creates an inherent financial security problem for both the state and villages. Neither has any control over oil price fluctuations, which can affect both state and local budgets. An advantage of renewables is that most of their costs are upfront; thus, there is no cost uncertainty once the system is operational, and the impact on budgets becomes predictable. There is a clear security advantage for renewable investments.  
As shown in our analysis, solar-powered systems are currently economical options in many parts of rural Alaska, with some regions enjoying greater benefits than others. Alaska subsidizes diesel fuel, and the total cost of these subsidies in 2019 was over $29.6 million. We attempted to filter out these subsidies, but we may not have succeeded in every case. Further, we did not account for the carbon and air pollution externalities that should be added to these prices. If storage options continue to advance and costs come down, the option of renewables plus storage could be a boon to Arctic communities.
The notion of building solar-powered systems in the Arctic regions of Alaska seems counter-intuitive given the long dark winters and the extreme cold. However, energy markets are changing, the costs of these systems are declining, and the total costs of relying on diesel generators are becoming better understood and remain high. Further, the public is becoming more aware of the impacts of burning diesel fuel on public health. There is a clear trend towards greater use of solar energy, and, if coupled with less expensive storage, this trend is likely to accelerate over the next ten years, even in the coldest and most remote areas of Alaska.
PVWatts is a web application developed by the National Renewable Energy Laboratory (NREL) that estimates the electricity production of a grid-connected, roof- or ground-mounted photovoltaic system based on a few simple inputs. To use the calculator, information about the system’s location and basic design parameters is inputted manually by the user. PVWatts then calculates estimates of the system’s annual and monthly electricity production. PVWatts uses a set of assumptions that are appropriate for flat-plate photovoltaic systems with crystalline silicon or thin film modules. PVWatts results are not appropriate for systems using concentrating collectors, or for modules using novel cell technologies or module designs.
The kilowatt-hour per year (kWh/year) value that appears at the top of the “Results” page (as seen in Figure 1) and the monthly values in the table are sums of the hourly output values over those periods. PVWatts calculates these values using long-term typical year solar resource data. These results do not represent the quantity of electricity that a system generates in a particular year. Instead, they represent the typical electric production expected over a given period. In general, you can expect the system’s total electrical output for a given month of a particular year to vary by as much as ±30% from the long-term typical value. Similarly, the total annual output for a particular year may vary from the long-term typical value by as much as ±10%. For the purpose of this analysis, we assume that this value represents the typical system annual output.
To access PVWatts, visit: https://pvwatts.nrel.gov/index.php.
We would like to thank the Arctic Initiative for its support of this project. We would especially like to thank Yanchao Li, Rachel Mural, and Liz Hanlon. Several individuals provided substantial guidance, information, and support in the development of this paper, including Jenny Kwon, Ingemar Mathiasson, Fran Ulmer, and John Holdren. While we benefitted tremendously from their suggestions, the authors take full responsibility for the findings and conclusions of the research.
Lee, Henry and Windy Dewi. “Solar Energy in the Arctic: A Case Study of Northwest Alaska.” Belfer Center for Science and International Affairs, September 14, 2025
Solar Energy in the Arctic: A Case Study of Northwest Alaska
 
 
Lee, Henry and Windy Dewi. “Solar Energy in the Arctic: A Case Study of Northwest Alaska.” Belfer Center for Science and International Affairs, September 14, 2025
AI, Data Centers, and the U.S. Electric Grid: A Watershed Moment
Decarbonizing the U.S. Electricity Grid: Policy and Regulatory Frameworks and Challenges
From Arctic Initiative
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Discussions between Xcel Energy and St. Croix County on a joint development agreement concerning construction of a new solar farm in western Wisconsin have broken down, with Xcel attorneys formally leaving the negotiating table last month.
The Ten Mile Creek project calls for 300 megawatts to be generated on solar panels spread across 2,980 acres of leased land in St. Croix County. The project also includes building and routing a new transmission line to the existing grid connections at Xcel’s Allen S. King Power Plant in Oak Park Heights.
St. Croix County Administrator Ken Witt announced Xcel’s intentions at the County Board of Supervisors meeting on June 2.
“Their response is, ‘No JDA, we plan to file at the end of the year with the (state Public Service Commission) and maybe we’ll talk again after that.’ So that’s the status,” Witt said of the letter sent on behalf of Xcel Energy. “It takes two to negotiate, so there’s not really any action that you can take to force them to.”
On the part of Xcel Energy, company officials told the Pioneer Press that county officials had stopped communicating with Xcel’s team for months, only to submit a substantially different set of conditions.
“While the county has suggested that Xcel Energy chose to end negotiations, the reality is that discussions had ended seven months prior after the county stopped meeting or communicating with us,” Xcel spokesperson Chris Ouellette said. “When a revised draft was eventually shared, it differed materially from prior versions and included significant new provisions that had not been discussed. At that point, we determined it was not productive to continue discussions based on that version of the document.”
Talks between the county and Xcel regarding the project have been ongoing since at least January 2025, when Xcel staff presented to the St. Croix County Board of Supervisors.
The project has been controversial for many landowners, who have voiced concerns about the potential property value impacts of a large-scale solar array close to their homes, as well as possible impacts to local wildlife and road infrastructure during construction, among other concerns. The panels would exist on parcels of private property, where Xcel officials would enter 35-year leasing contracts.
Theoretically, a joint development agreement would provide contractual protections between the county and a developer on a proposed project.
From the county’s perspective, the draft agreement needed to address potential local impacts such as emergency response planning, road use and repair, drainage, decommissioning of the solar farm, financial assurances, setbacks, buffering, lighting, wildlife protections and agricultural considerations.
Attorneys representing Xcel — Lisa Agrimonti and Haley Waller Pitts of the Minneapolis firm Fredrikson & Byron — said in a letter to St. Croix County legal representatives dated May 20 that the county’s most recent proposal went “well beyond the original intent and scope of those discussions,” and would impose additional burdens on Xcel Energy, as well as the county.
They continued that the terms could “conflict with Public Service Commission of Wisconsin and engineering requirements.”
“For example, although we have discussed this issue in detail already, the proposed draft would require Xcel Energy — a fully regulated public utility — to post financial assurance with the county related to decommissioning,” the attorneys wrote. “This new cost is in excess of PSCW requirements and outside of the county’s authority.”
They wrote that the county also added more than 40 new paragraphs plus revisions to other sections “without any discussion with Xcel Energy and their legal team.”
The Xcel legal representatives ended the letter by writing that it would be “more appropriate to recommence discussions after a Certificate of Public Convenience and Necessity application is filed with the PSCW, to the extent the parties see value in doing so at that time.”
On Wednesday, Ouellette reiterated that after the application is filed, the project details will be fully defined, and the county will have had the chance to review the contents.
Xcel would be open to future discussions at that time, she said.
The Ten Mile Creek project has drawn intense local interest. Back at the January 2025 meeting, residents filled the St. Croix County board room and hallways, bringing up concerns regarding the loss of agricultural land, possible impacts to property values, wildlife and local infrastructure, among other issues.
A month later, attorney Rebecca Roeker of Milwaukee-based Attolles Law presented to the county board regarding the merits of pursuing a joint development agreement. The county reviewed such a proposal for months, and in November hired Attolles Law to negotiate those terms. Soon after, Roeker and Xcel met for about four hours discussing potential terms of the joint development agreement.
Also in November, Xcel Energy announced plans to pare down the proposal to its current size. Originally, the Ten Mile Creek proposal included up to 650 megawatts of solar panel production on 5,000 acres spread across some 60 square miles of St. Croix County.
In February, the St. Croix County Community Development Committee reviewed the updated joint development agreement completed after the negotiations between Roeker and Xcel’s legal team, but the committee declined to recommend approval. Based on requests from the public, the committee said additional changes needed to be made to the agreement, and negotiations with Xcel should continue.
In April, Roeker incorporated those additional changes in the joint development agreement, and sent an updated proposal to Xcel Energy. But in May, Xcel’s legal team responded that they would be walking away from negotiations.
On June 11, the Community Development Committee recommended creating a resolution that acknowledges that Xcel terminated negotiations, while also stating that St. Croix County remains willing to restart talks if Xcel decides to join them. The county board is scheduled to consider such a resolution in July.
Xcel Energy representatives have said that they expect to file an application with the Public Service Commission of Wisconsin this year.
“At this time, we do not anticipate any impact to the overall project timeline,” Ouellette said.
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MANSFIELD, Ohio — A large piece of land that’s sat vacant for decades will soon generate energy for a portion of north central Ohio.
The city of Mansfield has reached an agreement to build a solar farm that will be installed on top of an abandoned landfill. 
Since 1990, the landfill has not been used, over 150 acres of land that will eventually turn into renewable energy for surrounding businesses. This new development will produce up to 35 megawatts of energy. 
“New green power generated will ideally be used here in the industrial park,” said Barrett Thomas, an economic developer in Mansfield. “They are solar panels on concrete ballast. It’s a ballast system literally sitting on top of the grass. No poles, no punctures into the land that is the cap holding the landfill in place.”
The city is partnering with CEP Renewables to bring the project to life. Once the solar farm is complete, CEP plans to take on some responsibilities of the land, lightening the load on some expenses.
“Once that solar installation goes in, the solar company is now responsible for maintaining the land,” Thomas said. “It takes a lot of expenses away from the Solid Waste Authority. It generates new power, so there will be a fee that goes to the county. Richland County would benefit from that new fee, and the city owns the land, so the city would obtain the lease payment for the land.”
This project is expected to bring economic growth and opportunity in Mansfield.
“As energy becomes more and more scarce, we have to look at other options,” said Mansfield Mayor Jodie Perry. “Our industrial park has lots of businesses in it, and we are trying to recruit more in it as well.”
The installation of the farm will allow more businesses to move in and use the source of energy. 
“The city is able to put back into use land that we have been taking care of but not receiving any benefits from, as well as providing more power to industrial users locally,” Perry said.
While construction on the farm is still underway and no completion date has been set, the city is already welcoming industrial businesses into their new facilities. Eventually, these businesses will be able to benefit from the renewable energy that will be produced from the solar farm. 

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Scientific Reports volume 16, Article number: 17022 (2026) Cite this article
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Saudi Arabia’s Vision 2030 includes a “Green Hajj” initiative to reduce the environmental impact of the annual pilgrimage. This study identifies optimal locations for large-scale solar PV plants near Meena, Muzdalifah, and Arafat in Makkah using GIS-based Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP). Key criteria assessed were solar irradiance, PV output, terrain, infrastructure proximity, and land use. The analysis categorized land suitability into five levels: 10.38% of the area (mainly northeast) is most suitable for PV with an estimated output of 1830 kWh/kWp/year; highly suitable zones comprise 10.87%, moderately suitable 23.58%, low suitability 26.02%, and 29.15% is unsuitable due to challenging terrain or proximity to protected areas. The most- and highly suitable categories could produce 6.75 GW of electricity; just 10% (675 MW) would meet Hajj’s peak demand of 500–600 MW. This approach offers a robust method for sustainable energy planning in sensitive, high-demand regions.
Heavy dependence on fossil fuels has driven up greenhouse gas emissions and heightened climate concerns. Fuels like petroleum, coal, and natural gas are limited and release large amounts of CO₂ when burned^24,37,45. In 2018, energy-related CO₂ emissions hit a record 33.1 gigatons, highlighting the need for clean energy alternatives. Renewable sources—solar, wind, water, and geothermal—are sustainable, widely available, and help cut emissions while supporting technologies such as green hydrogen. Many countries are investing in large-scale solar PV farms to address climate change and improve energy security. Research highlights solar energy’s key role in reducing emissions and increasing energy independence, with cost-benefit analyses showing clear environmental and economic benefits for solar and wind projects^3,17. In line with the United Nations Sustainable Development Goal 7 (Affordable and Clean Energy), many nations – including traditionally fossil-fuel-dependent ones – are accelerating policies to shift to deploy clean energy infrastructure (United Nations, 2021).
Saudi Arabia exemplifies this shift. Historically, the country’s electricity has been generated almost entirely from oil and natural gas²³. In 2018 only about 0.05% of its 383.8 TWh electricity output came from renewable sources¹¹. Solar energy is plentiful, but its adoption has been limited by cheap fossil fuels and technical issues such as heat and dust reducing efficiency. In Mina, Muzdalifah, and Arafat, installing solar panels is challenged by land allocated for pilgrims, safety requirements, and dense tent configurations that conflict with PV systems without significant redesign. Advances in cooling and dust-resistant PV technology, rising electricity demand, and the need to reduce CO2 emissions are driving interest in solar power⁴⁴. Over the past twenty years, Saudi Arabia’s electricity generation capacity has increased from 133.354 TWh/year to 449.426 TWh/year. The Saudi government launched an ambitious renewable energy program as part of Vision 2030⁵⁰. The Kingdom aims to install 9.5 GW of renewable capacity by 2030 – a target in which solar power is expected to play a leading role. Long-term strategies even envisage around 40 GW of solar capacity in later decades. This policy shift is driven by soaring domestic energy demand (which nearly tripled from 2000 to 2017) and a desire to reduce reliance on fossil fuels. Saudi Arabia’s geography and climate are highly advantageous for solar development: it receives some of the world’s highest solar irradiance levels and has vast expanses of undeveloped land suitable for solar farms. Leveraging these natural resources could transform the energy landscape and aligns with national goals for diversified, sustainable growth³⁵.
Within this context, the Holy Sites region of Makkah – specifically the pilgrimage areas of Meena, Muzdalifah, and Arafat – presents a compelling opportunity for large-scale solar PV implementation. Each year, these sites host millions of pilgrims during Hajj, leading to substantial increases in electricity demand for applications such as cooling, lighting, water desalination, and transportation. Peak power loads in the area reach 500–600 MW during Hajj, supplied mostly by distant oil-fired plants. This creates both logistical inefficiencies and environmental burdens^{1,2,4,16,21,26,28,42}. Building local solar capacity reduces dependence on distant power sources, lowers costs, and boosts resilience. The holy sites have ideal conditions for PV generation—arid climate, year-round sun, flat land, and little vegetation. Solar farms here would advance the “Green Hajj” initiative by lowering emissions and promoting renewables, while also supporting Saudi Arabia’s climate and energy goals.
However, optimally siting solar PV plants in this sensitive region is a complex spatial decision problem. It requires balancing various technical, environmental, and logistical factors (solar resource availability, terrain suitability), environmental considerations (land use, protected areas), and economic/logistical constraints (proximity to existing infrastructure and demand centers)^{36,43,46,47,53}. Combining Multi-Criteria Decision Analysis (MCDA) with Geographic Information Systems (GIS) offers an efficient approach for evaluating solar farm sites. Studies show suitability depends on factors such as solar irradiation, topography, climate, and access to grids and roads. GIS merges spatial data, while MCDA—particularly the Analytical Hierarchy Process (AHP)—helps prioritise each criterion. AHP assigns weights based on expert input and compares spatial parameters for decision-making purposes.
Numerous case studies have used GIS-MCDA for renewable energy site selection in regions such as North Africa, Asia, and the Middle East, demonstrating its use in identifying suitable locations^8,38,29; (Ouchani et al., 2021). While newer methods such as fuzzy logic, TOPSIS, and hybrid models address uncertainty and alternative comparison, AHP is still popular for its straightforward and reliable approach to weighting criteria^22,38,43. Akkas et al., applied AHP, ELECTRE, TOPSIS, and VIKOR methods to evaluate potential sites for solar PV farms in the Anatolian Region of Turkey⁵. AHP was applied in Tunisia to select wind-solar sites^46,47. Research indicates that the central and southern regions of the country are highly suitable for solar installations, with the potential to generate approximately 781.83 TWh of energy annually from solar sources. In the Karapinar region of Konya, Turkey, AHP analysis determined that 13.92% (840.07 km²) of the study area exhibits high suitability for solar farm development⁵². In Oman AHP method demonstrated that the central region has high suitability for solar energy farm establishments (Al-Awadhi, Al Ramimi, Al Jabri, & Abulibdeh, 2025). Monte Carlo and Fuzzy AHP methods, combined with GIS, were used in Cameroon to rank hybrid solar-wind sites for electricity and hydrogen production³⁹. AHP has been also applied to solar PV site selection in Bangladesh, Morocco, Kuwait, the United States, and Egypt^{15,30,32,36,41}.
Several studies in Saudi Arabia have utilised AHP for analysing site suitability for solar PV plants. Al Garni et al., for example, assessed potential PV plant locations and reported that 16% (300,000 km²) of the country’s area is considered suitable for developing utility-scale PV power plants, with the most appropriate regions located in the north and northwest. The identified suitable lands generally correspond to areas near main roads, transmission lines, and urban centres⁸. A study in Riyadh region, of Saudi Arabia, using the AHP method found that the north and northwest parts of the region—covering 16,748 km²—are the most suitable sites, with an 80% suitability rating¹⁰.
AHP-TOPSIS ranked solar, wind, biomass, geothermal, and their hybrids as energy potential options in the country. Solar was identified as the top alternative, with hybrid solar-wind next¹⁴. In the Western region of Saudi Arabia, the Analytic Hierarchy Process (AHP) was employed to assess the potential for Concentrated Solar Power (CSP). The evaluation indicated that approximately 70% of the province’s land is suitable for CSP development, with Makkah, Taif, Al-Khumra, and Turbah identified as the most advantageous locations³¹. A recent study assessing solar energy potential across various regions in the country found that, for PV technology, the Abha region ranked first with a performance score of 91%, indicating its exceptional suitability. Jeddah followed, achieving a performance score of 83%⁹. Whereas a study evaluated the solar PV potential of 17 selected cities in Saudia Arabia⁷, found that Tabuk city, located in northern Saudi Arabia, has the highest potential at 87%, while six other cities scored at or above 80%. Other studies have utilised AHP across the entire Mekkah Province^23,34, these studies have not necessarily focused on the Holy Sites. Additional studies have examined various locations throughout Saudi Arabia^27,40. To the best of our knowledge, this is the first study to specifically target the Muslims’ Holiest Sites in Saudi Arabia. This study offers new insights by applying AHP to a very specific context. It focuses on the key locations of the Hajj pilgrimage—Mina, Muzdalifah, and Arafat—which are operationally important and culturally sensitive, yet have not previously been the focus of detailed suitability assessments. The study also determines criteria weights based on the unique logistical and security needs of accommodating millions of pilgrims, unlike broader national or regional analyses. Additionally, it carefully considers land-use constraints due to sacred sites and temporary pilgrimage infrastructure, turning the “Green Hajj” policy into a practical spatial framework. As a result, this research goes beyond general regional recommendations to provide concrete, location-specific guidance for integrating sustainable energy into one of the world’s most complex and significant settings.
This paper presents a GIS-based AHP framework to assess solar PV suitability in the Holy Sites region of Makkah. The specific objectives are: (1) to establish a set of evaluation criteria (environmental, climatic, topographic, and infrastructural) relevant to solar farm siting in the study area; (2) to determine criterion weights through AHP pairwise comparisons, based on expert assessments of their relative importance; (3) to generate a spatially explicit suitability map (30 m resolution) by integrating the weighted criteria in GIS; and (4) to analyze the geographic distribution of high and low suitability zones, including estimates of solar energy output in optimal areas, and discuss implications for planning. The study aims to inform Saudi Arabia’s renewable energy initiatives within the context of the Hajj sites, illustrating how energy solutions may be integrated with cultural and environmental considerations. The manuscript is structured as follows: Sect.  2 outlines the study area, Sect.  3 details the methods, Sect.  4 covers results and discussion, and Sect.  5 summarizes the conclusions.
The study focuses on the environs of Meena, Muzdalifah, and Arafat Fig. 1, which are key sites in the Hajj pilgrimage, located in the Makkah region of western Saudi Arabia. Geographically, this area lies in the Hejaz mountains at the edge of the Saudi Arabian Shield. Meena is a narrow valley situated about 8 km east of the Grand Mosque (Masjid al-Haram) in Makkah, roughly at 21.42° N, 39.89° E. It is often referred to as the “City of Tents” due to the tens of thousands of temporary tents that accommodate pilgrims during Hajj. Muzdalifah is an open plain about 9 km southeast of Meena (approx. 21.39° N, 39.89° E), located along the route between Meena and Arafat. Pilgrims gather and rest overnight at Muzdalifah after the Day of Arafat. Arafat is a broader area centered around Jabal Arafat (Mount Arafat), about 20 km southeast of central Makkah (around 21.35° N, 39.99° E). The Plain of Arafat is the site of the peak Hajj ritual (the Wuquf on the Day of Arafat), where pilgrims stand in prayer from noon until sunset on the 9th day of the Islamic month Dhu al-Hijjah.
Study area location
The region features an arid climate with very low annual rainfall (often less than 100 mm/year) and high summer temperatures (up to 45 °C). It receives some of the highest global horizontal irradiance worldwide, averaging 2000–2200 kWh/m² per year. The abundant solar resource, undeveloped land, and existing infrastructure make the area suitable for solar PV development, though careful site selection is needed to avoid conflicts with pilgrimage activities and environmental, topographic constraints, permanent structures, and any environmental or topographic constraints (e.g., mountainous slopes, flash flood pathways, etc.). In this study, we defined a contiguous study area encompassing the valleys and plains of Meena, Muzdalifah, and Arafat and their immediate surroundings (approximately a few hundred square kilometers in total area). The boundary was set to include areas suitable for evaluating potential solar farm sites to support holy site energy needs. Urban zones in Mekkah, Mina, Muzdalifah, and Arafat—with dense development and permanent pilgrim facilities—were excluded and marked as “Holy sites limit” in Fig. 1.
This method tackles energy issues at holy sites and offers a model for renewable energy planning in sensitive areas, balancing technical needs with preservation. Our findings provide actionable guidance for policymakers and developers supporting Saudi Arabia’s renewable energy goals and the requirements of its holiest Islamic locations.
Prior to outlining the methodology, we wish to confirm that conducting this research in Saudi Arabia did not require any institutional, national, or international guidelines or permissions.
Geographic Information Systems (GIS) are highly proficient in spatial analysis, enabling the geographic modeling of problems to yield results through advanced computer processing. This methodology is particularly effective for assessing site suitability, forecasting outcomes, interpreting spatial changes, and identifying patterns.
This study employed GIS-based Multi-Criteria Decision Analysis (MCDA) to assess several criteria and identify optimal locations for solar photovoltaic (PV) farms in the Holy Sites region. Figure 2 outlines the workflow, which included: (1) selecting criteria and gathering relevant data, (2) utilizing the Analytic Hierarchy Process (AHP) for MCDA, and (3) creating suitability maps through spatial overlay methods. Spatial analysis was performed with ArcGIS Pro, and criterion weights for the GIS evaluation were calculated using MS Excel.
Workflow diagram for PV solar plant site selection procedures
For solar farm site suitability, we identified twelve criteria from literature and local context, grouped into Climatic, Topographic, Environmental, and Economic/Infrastructural categories. These cover solar potential, terrain, land use, and infrastructure access. Table 1 lists each criterion, its data, and sources.
All spatial datasets were projected to WGS 84 / UTM zone 39 N and resampled to a 30 × 30 m grid for consistent cell-based analysis. While this high resolution helped identify suitable parcels, it reduced detail in coarser datasets like climate surfaces. Each criterion was represented as a GIS layer and standardized on a suitability scale from 1 (very low suitability) to 5 (very high suitability). Normalization is required to integrate criteria with varying units and ranges in a consistent manner. A combination of benefit functions (where higher criterion values are preferable) and cost functions (where lower values are preferable) was used for reclassification. For instance, Global Horizontal Irradiance (GHI) scores increase with higher irradiance (benefit), while slope and distance from grid decrease with steeper terrain or greater distances (cost). Class thresholds were determined based on literature sources and expert advice. As an example, areas with slopes exceeding 15° received low scores due to construction and panel orientation challenges, whereas areas with slopes below 3° received the highest scores. Land cover was reclassified into five classes (1 to 5) representing ranks of importance using GIS: open undeveloped land was rated suitable (score 5), while built-up or restricted areas, such as urban zones, existing site facilities, and major roads, were considered unsuitable (score 1). For distance-based criteria (roads, grid, settlements), Euclidean distance rasters were calculated, and locations within optimal ranges of infrastructure (e.g., 0.5–5 km from a road) were assigned higher suitability. Areas less than 100 m from infrastructure were categorized as “restricted” due to right-of-way considerations, and those beyond 10 km were considered less favorable. All criterion layers were converted into five-class rasters reflecting relative suitability for solar PV site selection.
The Analytic Hierarchy Process (AHP) is designed to explicitly integrate multiple conflicting criteria in decision-making⁴⁹. AHP has been developed by Saaty⁴⁸. It has been widely employed in research to evaluate the suitability of solar PV sites, reflecting its growing adoption as a robust decision-making tool^{8,18,19,20,29,33,43,51}. AHP uses expert knowledge-based pairwise comparison procedure to judge the relative significance/hierarchy of the criteria^46,47. These judgments form a Pairwise Comparison Matrix (PCM) that shows the overall relative importance and preferences of the experts. The AHP tool relies on fundamental four steps: defining the hierarchical structure using PCM (step 1); relative weighting of criteria (step 2); consistency assessment of the PCM by calculating Consistency Ratio (CR) (step 3); and finally weighted-overlay of criteria layers with their weights in GIS (step 4).
Pairwise comparison involves evaluating two criteria at a time to determine their relative importance toward achieving a specific goal, using expert-knowledge and insights and a consistent scale. Typically, the standard 1–9 scale introduced by Saaty⁴⁸ is applied: a score of 1 indicates both criteria are equally influential, while a score of 9 signals that one criterion has a much greater effect on the mapped variable than the other. In this study, AHP was organized with a single hierarchical level comprising 12 criteria beneath the overarching goal of “optimal solar PV site selection”. Energy planning experts familiar with Saudi Arabia and the Hajj sites were consulted in conducting pairwise comparisons for solar site selection. Three domain experts were selected based on their extensive experience in solar energy planning, GIS-based environmental analysis, and familiarity with the Saudi Arabian context, particularly the Holy Sites region. The experts were engaged independently through individual structured interviews. Each expert was provided with a detailed description of the twelve selected criteria and the objective of the study. They were then asked to independently complete pairwise comparison matrices using Saaty’s 1–9 scale (Table 2), assessing the relative importance of each criterion pair for solar PV site selection in the study area.
To aggregate the individual judgments and derive a single representative comparison matrix, the Geometric Mean Method was employed. This method is recommended in AHP for aggregating group decisions as it preserves the reciprocal property of the pairwise comparison matrices. Specifically, for each entry a_iⱼ in the aggregated matrix, the geometric mean of the corresponding entries from the three experts’ matrices was calculated. This aggregated matrix was then used to compute the final criterion weights (Table 6) and the Consistency Ratio (CR = 0.024), ensuring the collective judgment was both consistent and representative of the expert panel’s consensus. This structured approach enhances the transparency and replicability of the weight derivation process.
If a criterion “i” is assigned a non-zero value (ranging from 1 to 9) in comparison to a factor “j”, then the reciprocal value is attributed to “j” when compared to “i” (ranging from 1 to 1/9). This reciprocal relationship reflects the dynamic interaction between the two factors and constitutes a fundamental component of the AHP’s comprehensive analytical methodology. Through pairwise comparisons, qualitative judgments is translated into quantitative weights. Accordingly, a PCM is formulated according to Saaty’s scores previously derived. The diagonal scores are set at 1, as each factor was being compared to itself. In our case a twelve by twelve PCM was formulated, as shown in Table 4 below.
The PCM were normalized using the eigenvector approach (Saaty, 2003). The eigenvector approach is the standard AHP way to convert a PCM into a normalized priority (weight) vector. The normalized PCM elements were calculated by dividing the element values, in Table 4, by their respective total column values, shown in Table 5. Then the eigenvector values were subsequently calculated for each criterion by dividing each sum of row values in the normalized PCM by the total criteria count (12 criteria in our case). The eigenvector and the criteria weights are shown in the last two columns in Table 5.
The pairwise criteria judgment is based on subjective comparisons and can lead to potential inconsistencies in the PCM that can lead to fraud weightings of the criteria. Consistency ratio (CR) measure was introduced by Saaty to quantitatively assess the consistency of the criteria weighting^48,49. CR helps assess the logical coherence and internal consistency of the relative judgments provided by experts, as it quantifies the extent of such inconsistencies. A CR value below 0.1 (< 10%) indicates an acceptable level of inconsistency, meaning the derived weights are sufficiently consistent and reliable for use in the subsequent analysis. If the CR exceeds 0.1, it signals significant contradictions, and a revision of the expert comparisons is recommended to improve coherence.
The CR is calculated by dividing the Consistency Index (CI) by the Random Consistency Index (RI) value, as shown in Eq. (1).
where, CI is the consistency index, and RI is the random consistency index. The CI is calculated using Eq. (2):
Where: λ mαx: a total value of Weighted SUM Values divided by Criteria Weights. n: the number of the applied criteria in the study.
Following the derivation of the criterion weight vector from the PCM in Table 5, the principal eigenvalue (λ max) was calculated. This involved computing the weighted sum vector (by multiplying the original PCM by the derived weight vector) and then dividing each element of this resulting vector by its corresponding criterion weight. The principal eigenvalue (λ max) is the arithmetic mean of these resultant values. For this study, λ max was calculated to be 12.4. This value represents the principal eigenvalue (λ max). Substituting this value into Eq. (2) yields:
({text{CI }}={text{ }}({text{12}}.{text{4}}, – ,{text{12}}){text{ }}/{text{ }}({text{12}}, – ,{text{1}}),=,0.{text{4 }}/{text{ 11}}, approx ,0.0{text{36}})
This very low CI value indicates a minor deviation from perfect consistency and confirms the reliability of the established criterion weights.
The principal eigenvalue measures how much a matrix deviates from consistency and equals the sum of the eigenvalues for the considered factors. For a PCM to be nearly consistent, its principal eigenvalue should be at least equal to the number of factors (n), which is 12 in this case. In this study, the PCM’s principal eigenvalue was 12.4, exceeding 12. The calculated CI value was 0.036, confirming that the criteria weights were established correctly.
RI value, in Eq. (1) above, is obtained from the Random Index scale⁴⁸, was 1.54 due to the number of criteria in the study (12 Criteria), Table 3 below.
Following the assessment of criteria-weight consistency as outlined in Step 2, each criterion within the GIS thematic layer was classified into five distinct groups using the Natural Breaks (Jenks) method in ArcGIS Pro. This technique organizes data into categories based on inherent groupings, ensuring maximum similarity within each group and pronounced differences between separate groups (ESRI, ²⁵). Groups in each thematic layer are then assigned values from 1 to 5 through reclassification, considering both benefits (where higher values indicate greater preference) and costs (where lower values are more desirable). This process standardizes each criterion within a thematic layer into a five-level ordinal suitability score, with 1 representing the lowest suitability and 5 indicating the highest. This transformation enabled the integration of diverse data types into a unified analytical framework while ensuring that each criterion’s classes reflect meaningful and practical distinctions in suitability.
After the weights of criteria, and the five suitability scores within each criterion thematic-layer are determined, all criteria layers were integrated in ArcGIS through a weighted overlay to calculate a suitability score for each 30 m cell. Each criterion raster was multiplied by its respective weight (Wₖ), and the results were added together to generate a composite suitability score (E_iⱼ) for each cell. Wₖ denotes the weight of criterion k, and S_iⱼₖ is the standardized suitability score (1–5) of cell ij for criterion k, as indicated by Eq. 3 below:
This formula is essentially a weighted overlay operation, and it was executed using the Weighted Overlay in ArcGIS Pro for efficiency. The result is a continuous suitability value for each 30 m cell.
The weighting of evaluation criteria was carried out using AHP, a robust multi-criteria decision-making method for converting qualitative expert judgments into quantitative priorities⁴⁸. A total of twelve criteria relevant to solar photovoltaic (PV) site selection were evaluated: solar radiation, wind speed, precipitation, slope, aspect, elevation, settlement proximity, road network, power line proximity, relative humidity, temperature, and land use. Each pair of criteria (i, j) was compared based on its relative influence on solar PV site suitability using Saaty’s fundamental 1–9 scale, where a score of 1 denotes equal importance, and values of 3, 5, 7, and 9 represent increasing degrees of importance of one factor over the other. Reciprocals (e.g., 1/3, 1/5) are used to reflect inverse importance relationships between criteria^8,48. If criterion i is judged to be x times more important than criterion j (so ({a_{ij}}=x)), then criterion j must be (1/x) as important as criterion i (so ({a_{ji}}=1/x)). This reciprocal property (({a_{ij}}=1/{a_{ji}})) ensures logical symmetry in the pairwise comparison matrix and supports consistent derivation of criteria weights. The judgments were provided by domain experts in solar energy systems, GIS-based planning, and environmental assessment, with specific knowledge of Saudi Arabia’s climatic and infrastructural context. For example, solar radiation was ranked five times more important than precipitation (value = 5), underscoring the primacy of insolation in determining PV performance. Slope was ranked three times more important than settlement proximity, due to its direct impact on installation feasibility and structural stability. Road network proximity was judged significantly more important than power line proximity (value = 6), reflecting the practical need for access during installation and maintenance. In contrast, land use was treated as moderately more important than humidity, acknowledging that undeveloped or non-restricted land is essential, while atmospheric moisture poses a lesser constraint in arid climates. All 66 pairwise comparisons formed a complete 12 × 12 PCM matrix in Table 4, from which the normalized PCM, eigenvector, and criteria priority weights were derived in Table 5. The normalized PCM elements were calculated by dividing the element values, in Table 4, by their respective total column values, and the eigenvector values were calculated for each criterion by dividing each sum of row values in the normalized PCM by the total criteria count 12. The eigenvalues are multiplied by 100 to produce the final weights in percentage used in the weighted overlay GIS model. The Consistency Ratio (CR) was computed to assess the internal coherence of the judgments. With a CR of 2.4%, which is well below the acceptable threshold of 10%, the matrix exhibits strong consistency, confirming the reliability of the expert inputs. In summary, this weighting process allowed each criterion’s influence on solar PV suitability to be explicitly quantified based on informed expert assessments. These weights form the basis of the spatial decision-making model used to generate the final suitability map.
The AHP weighting results reveal the relative significance of the evaluated criteria in determining suitable solar PV sites (Table 6).
Prior to applying the weights from Table 5 in the GIS overlay analysis, the spatial criteria were prepared through three interconnected stages illustrated in Figs. 3, 4 and 5. Figure 3 presents the spatial distribution of the twelve input criteria in their original form, highlighting the geographic variability of each influencing factor. The Natural Breaks (Jenks) classification method is used to group values in each criterion into 5 groups. Subsequently, these groups were assigned values from 1 to 5, i.e. standardized into five ordinal suitability classes (1 = lowest suitability, 5 = most suitable) as shown in Fig. 4. Finally, Fig. 5 presents the core output of the study: the composite suitability map generated by applying the AHP-derived weights (Table 6) to the reclassified layers using the weighted overlay tool in GIS. This map categorizes the study area into five final suitability classes for solar PV development.
Spatial distribution of the twelve input criteria used in the solar PV site suitability analysis. The criteria include climatic factors (wind speed, humidity, precipitation, temperature, global horizontal irradiance), topographic factors (slope, aspect, elevation), and infrastructural/environmental factors (built-up area, proximity to power lines, road network, and land use). Green areas indicate more favorable conditions for solar PV development, while red zones represent less suitable regions
These classes were then standardized to a scale of 1 to 5 for suitability, with 5 being most suitable and 1 least suitable, as illustrated in Fig. 4. This method grouped similar values and maximized variance between classes, setting clear thresholds in the data (e.g., steep versus flat slopes, high versus low irradiance). For example, wind speeds over 6.4 m/s, GHI above 2180 kWh/m²/year, and locations within 2 km of roads received top suitability scores (class 5). Each reclassified layer was weighted and combined to create the final suitability map.
Reclassification of each criterion into five suitability classes using the Natural Breaks (Jenks) classification method, which identifies natural groupings and gaps in the data. Class 1 representing the lowest level of suitability and Class 5 denoting the highest
AHP produced relative weights for the twelve criteria (Table 5), showing their impact on solar PV site selection in Makkah based on expert judgment and regional features.
Climatology data indicate that solar radiation was the most significant factor (21.5%), as solar energy output depends on solar insolation. Figure 3 displays elevated solar irradiance in the northern and northeastern regions of the study area, which corresponds to higher significance in Fig. 4 and results in greater suitability scores in those areas shown in Fig. 5. This finding aligns with prior studies in arid environments, which consistently identify solar resource availability as the single most critical driver of PV siting^{6,9,12,13,14,38}.
Road network proximity ranked as the second most important factor (17.5%), highlighting the key role of accessibility in construction, operation, and maintenance. The high weight indicates experts’ focus on logistical feasibility, since even areas with ample land may face challenges if site access is poor. Roads around holy sites and in the north-central region, shown in Figs. 4 and 5, lead to higher suitability in nearby zones in the final results.
Two topographic factors- aspect (14.4%) and slope (12.2%) followed- were identified as influential, as both criteria affect irradiance exposure and constructability. South-facing slopes, Fig. 3, received higher scores due to their orientation with solar angles in the Northern Hemisphere. Flat or gently sloping terrain (also shown in Fig. 3) minimizes self-shading and allows for efficient PV layout. These considerations contributed to the designation of favorable zones in the north and northeast.
Wind speed (6.4%) and land use (6.8%) were identified as moderately significant factors in the analysis. While moderate wind contributes to passive cooling of photovoltaic modules, spatial variability within the study area remained limited. Land use map served to distinguish barren zones, which are deemed suitable, from built-up areas or regions with vegetation, considered less suitable. As shown in Fig. 3, the majority of the territory is classified as non-urban, offering enhanced flexibility for development.
Precipitation (5.6%), settlement proximity (4.4%), humidity (4.3%), and elevation (2.6%) were less influential but included for a thorough assessment. Low rainfall and humidity, typical of the arid region, had minor effects on PV soiling or cooling due to limited spatial variation (Fig. 3). Elevation also showed little variation and only modestly influenced temperature.
The lowest-weighted criteria were power line proximity (2.5%) and temperature (1.9%). This is notable: while power lines are necessary for interconnection, the experts indicated that new lines could be extended if the site is otherwise optimal. Likewise, while high ambient temperatures can reduce PV efficiency, the uniform heat across Makkah reduced the discriminatory power of this factor. Both criteria had limited influence on the final suitability output, see Sect.  4.3 Suitability Map and Spatial Distribution.
The findings show that climatic factors—Solar radiation (21.5%), Wind speed (6.4%), Precipitation (5.6%), Humidity (4.3%), and Temperature—with solar radiation having the highest proportion, made up nearly 40% of the overall decision weight. Topographic constraints were next at 29.2%, highlighting the significance of land form for physical feasibility and performance. Economic/infrastructure factors contributed 24.4%, indicating a notable but lesser role compared to environmental criteria. Land use, classified under the environmental category, accounted for 6.8% of the total, which is relatively small but relevant for considerations such as avoiding urban or restricted areas. The AHP matrix’s consistency ratio was 0.024, suggesting reliable and coherent assessments.
During the final stage of analysis, the twelve standardized criteria were integrated according to their AHP-derived weights using the Weighted Overlay tool in ArcGIS Pro. This methodology resulted in a composite suitability surface for solar PV farm development, classified into five categories: Unsuitable, Low, Moderate, High, and Most Suitable, as depicted in Fig. 5. The ‘Holy Sites Limit’ region, shown in grey, is excluded from the analysis. The region is depicted in the figures to provide demonstration and context. The resulting map displays a heterogeneous spatial distribution determined primarily by climatological, topographical, economic, and infrastructure accessibility factors, with environmental constraints exerting a comparatively lesser influence.
Photovoltaic Solar Farm Suitability Map for the Vicinity of the Holy Regions
The most promising zones- classified as “High” and “Most Suitable” (green colored classes) -are predominantly concentrated in the northeast sectors of the study area. These zones correspond to relatively flat open lands that receive excellent solar radiation (GHI) and are unencumbered by development with minimal land use conflict, as shown in Figs. 3, 5 and 6. These regions benefit from being far enough from the densely built pilgrimage camps to have available land, yet close enough to infrastructure like main roads and a power substation near Meena and Muzdalifa to facilitate grid connection. They also have near-optimal orientation and minimal slope, broad valley floors or gently undulating terrain. Based on our analysis, these regions account for 21.25% of the total land area. Approximately 10.38% of the study area is classified as “Most Suitable,” and 10.87% falls under the “High” suitability category, as shown in Fig. 6. These zones have PV output potentials of 1830 and 1810 kWh/kWp/year, respectively, according to Solargis PVOUT data for standard 1-axis tracking PV systems. This is a significant proportion, given the solar resource, indicating a large potential for solar development. Such output is comparable to some of the highest-yield solar farm sites globally, reflecting the superb insolation in Makkah’s climate.
Areas of suitability classes, percentage of total land, and potential PV
Moderately suitable areas (yellow color), which comprise 23.58% of the study region, are primarily found in the central and southeastern portions. These locations have adequate solar irradiance but may present certain limitations, including steeper slopes, increased distances from infrastructure, or more complex land cover such as gravel plains or elevated terrain. Some internal sections between high-suitability zones are also classified within this category. These regions generally exhibit one or two limiting factors that result in mid-range scores. For instance, the northern section near Makkah city benefits from strong solar irradiance but features steeper terrain and greater proximity to urban areas, necessitating careful planning to prevent conflicts with existing land uses. Similarly, certain interior locations may have marginally higher elevations or increased distances from roads, reducing their accessibility. Although these areas remain suitable for development, they may require additional investment in site grading or infrastructure connections.
Areas classified as Unsuitable (29.15%) and Low Suitability (26.02%), which together constitute 55.17% of the total area, are primarily located in the south, southwest, and the northwestern hills. These regions also include zones surrounding the Holy Sites region, which are designated as restricted areas for practical and cultural reasons. Factors such as steep terrain, elevated altitude, proximity to sacred sites (including Meena and Arafat), and distance from roads and electrical infrastructure contribute to these classifications. Land adjacent to pilgrimage camps and developed zones received low suitability scores to maintain buffers around holy sites. These locations are shown in orange and red in Fig. 5 and have lower photovoltaic output potential, averaging 1650–1740 kWh/kWp/year, as illustrated in Fig. 6.
The results indicate that climatic and topographic factors were predominant, collectively representing more than 68% of the decision weight, as illustrated in Fig. 7. In contrast, infrastructure and land use, while essential, contributed less to the overall determination. This outcome aligns with the region’s ample availability of undeveloped land and the strategic emphasis on optimizing energy output. .
Relative influence of grouped criteria on solar PV site selection derived from AHP weights. Climatic factors (39.7%) were the most influential, followed by topographic (29.2%), economic/infrastructure (24.4%), and environmental (6.8%) criteria, highlighting the dominant role of solar resource and terrain in determining optimal sites
A valid consideration arising from the spatial distribution of results (Fig. 6) is the relative distance of the identified “Most Suitable” zones from the primary electricity demand centers in Makkah and the Holy Sites. While proximity to load centers is undeniably a key economic and technical factor in power plant siting, the criterion of ‘distance to Makkah/the Holy Sites’ was not explicitly integrated into the AHP model. This deliberate omission is justified by the unique geographical and infrastructural constraints of the study area. The immediate peripheries of Makkah, Mina, Muzdalifah, and Arafat are characterized by intensive urban development, permanent pilgrim facilities, and the extensive seasonal tent cities, leaving virtually no contiguous parcels of vacant land suitable for utility-scale PV development. Furthermore, the limited undeveloped areas within a close radius are predominantly situated within the rugged terrain of the Hijaz foothills. These zones exhibit steep slopes (> 15°), complex topography, and significant shadowing effects, rendering them economically and technically infeasible for large-scale solar farms-a fact already captured and penalized by the low suitability scores from the slope and aspect criteria in our analysis. Consequently, incorporating a distance-to-demand criterion would not have shifted the high-suitability classification closer to the city. Instead, it would have systematically assigned low scores to the nearby areas due to the dual constraints of land unavailability and topographical incompatibility. Therefore, the model’s prioritization of solar irradiance, terrain slope, land-use compatibility, and access to roads and grid infrastructure logically identified the northeastern sectors as optimal. These areas, though farther from the demand epicenter, represent the only feasible compromise, offering the essential combination of high solar yield, constructable flat terrain, and minimal land-use conflict, which outweighs the increased transmission distance in this specific context.
The spatial analysis herein demonstrates that integrating GIS and AHP is an effective approach for renewable energy site selection in the challenging context of the Hajj holy sites. By quantitatively balancing environmental, technical, and economic criteria, we can pinpoint zones where solar investments will yield maximum benefit with minimal obstacles. For Saudi authorities and stakeholders, a key implication is that there is no shortage of good sites around Makkah for solar PV: concerns about land availability or resource variability should not hinder solar projects in this area. Instead, planning can focus on how to implement projects in the high-suitability zones – for example, securing the land (which likely belongs to public or religious trusts), and scheduling construction in phases that do not interfere with Hajj operations.
From a planning perspective, the availability of over one-fifth of the study area as highly viable for solar farm development is promising. These zones offer minimal land-use conflict, excellent insolation, and flat topography—ideal conditions for cost-effective implementation. Potential clusters near major transportation corridors and grid access points (e.g., northeast of the Holy Sites) can facilitate smooth integration into local infrastructure, such as Hajj support facilities. This suitability criteria and configuration align with findings from other GIS-AHP solar siting studies in arid environments. For instance, studies in Saudi Arabia found that climatic factors (solar radiation, temperature, .etc) significantly influence solar sites selection^8,9, consistent with our highest criterion weight of 21.5% (Table 6). Nevertheless, notable distinctions were observed. In many urban or agricultural contexts, land use considerations typically carry significant weight. In contrast, our study in Makkah found that land use (environment) was of lesser concern due to the extensive availability of open desert. Unlike analyses that necessitate the exclusion of protected or vegetated areas, our research encountered minimal environmental constraints, which diminished the need to prioritize land use in site selection. One recommendation is to pursue a pilot solar farm in the identified high-suitability zones (northeastern of the study area, which is relatively remote from pilgrim activities). A medium-scale PV plant (e.g., 50–100 MW) could be built there and tied into the local grid that supplies the Hajj facilities. This would immediately cut diesel or oil-fired generation needs during Hajj, reducing emissions and serving as a visible testament to the Green Hajj initiative. Over time, additional farms could be added in other high-suitability pockets, potentially creating a network of solar parks encircling the holy sites. These could be connected via a ring transmission line to provide redundancy and ensure reliable supply even if one site is shaded or down for maintenance.
This study advances the field of renewable energy planning by applying a spatially explicit decision-support framework to a uniquely challenging and policy-relevant context. The contribution is multi-layered. Geographically, it fills a critical gap by providing the first high-resolution suitability analysis dedicated exclusively to the immediate surroundings of Islam’s Holiest Sites, whereas prior work in Saudi Arabia and the Makkah region has operated at provincial or national scales^8,31,34. Methodologically, the AHP weights reflect expert judgment tailored to the Hajj’s specific logistical realities (e.g., prioritizing road access for maintenance during brief non-Hajj periods), offering a nuanced model not replicable by generic suitability studies.
A major methodological strength relevant to the primary goal of reducing logistical burdens associated with distant generation is the explicit integration of both infrastructure and proximity constraints. Rather than consolidating these factors into a single criterion, they are addressed through a combination of high-weight and restrictive criteria. The strong emphasis on Road Network Proximity (17.5%, Rank 2) ensures that selected sites are logistically appropriate for construction and ongoing operations. Additionally, the Land Use and Settlement Proximity criteria collectively establish an effective buffer, limiting development near holy sites and pilgrimage infrastructure to prevent potential conflicts. The consideration of Power Line Proximity further ensures that grid connection costs are incorporated into site evaluations. As a result, optimal locations identified in the northeast (Fig. 6, now relabeled as Fig. 5) are not only technically advantageous in terms of solar yield and terrain suitability but are also situated at practical distances from demand centers—facilitating cost-effective grid connections while preserving the sanctity and operational integrity of the pilgrimage zones. This integrated comprehensive approach directly addresses the challenges of “logistical inefficiencies and environmental burdens” linked to remote oil-fired power plants.
Practically, the 30 m-resolution output (Fig. 6) transitions from theoretical regional potential to identifiable project parcels, directly serving the planning needs of entities managing the Holy Sites. Politically, it operationalizes the “Green Hajj” initiative of Saudi Vision 2030, providing a science-based pathway to reduce the pilgrimage’s carbon footprint. Thus, the novelty lies not in the core GIS-AHP technique, but in its targeted application to generate previously unavailable, actionable intelligence for sustainable development at the intersection of high energy demand, extreme logistical complexity, and profound cultural significance.
An important consideration is whether appropriate land can supply the peak Hajj demand of 500–600 MW. Photovoltaic output potentials for the “Most Suitable” and “High” suitability categories were estimated at 1830 and 1810 kWh/kWp/year, placing them among the top globally (see Fig. 7). Together, these regions span roughly 225 km². The “Most Suitable” and “High Suitability” zones offer excellent PV output potentials—1830 and 1810 kWh/kWp/year, respectively (Fig. 6), ranking among the world’s highest yields. These areas total about 225 km² (110 km² and 115 km²). For utility-scale PV in arid regions, a conservative power density is 30 MW/km².
“Most Suitable” category (110 km², 1830 kWh/kWp/year):
({mathbf{Installed}}{text{ }}{mathbf{capacity}}:{text{11}}0 times {text{3}}0,=,{text{33}}00{text{ MWp}},=,{text{3}}.{text{3}}0{text{ GWp}})
({mathbf{Annual}}{text{ }}{mathbf{energy}}:{text{3}}.{text{3}}0{text{ GWp}} times {text{1}}.{text{83TWh}}/{text{GWp}}/{text{yr}},=,{text{6}}.0{text{4 TWh}}/{text{yr}})
({mathbf{Average}}{text{ }}{mathbf{power}}{text{ }}{mathbf{equivalent}}:{text{6}}.0{text{4}}/{text{876}}0,=,0.{text{69 GWavg }}( approx ,{text{69}}0{text{ MWavg}}))
“High Suitability” category (115 km², 1810 kWh/kWp/year):
({mathbf{Installed}}{text{ }}{mathbf{capacity}}:{text{115}} times {text{3}}0,=,{text{345}}0{text{ MWp}},=,{text{3}}.{text{45GWp}})
({mathbf{Annual}}{text{ }}{mathbf{energy}}:{text{3}}.{text{45 GWp}} times {text{1}}.{text{81 TWh}}/{text{GWp}}/{text{yr}},=,{text{6}}.{text{24TWh}}/{text{yr}})
({mathbf{Average}}{text{ }}{mathbf{power}}{text{ }}{mathbf{equivalent}}:{text{6}}.{text{24}}/{text{876}}0,=,0.{text{71 GW}}_{text{avg}}( approx ,{text{713 MWavg}}))
Combined (both categories).
({mathbf{Total}}{text{ }}{mathbf{capacity}}:{text{6}}.{text{75 GWp}})
({mathbf{Total}}{text{ }}{mathbf{annual}}{text{ }}{mathbf{energy}}:{text{12}}.{text{28 TWh}}/{text{yr}})
({mathbf{Total}}{text{ }}{mathbf{average}}{text{ }}{mathbf{power}}:{text{1}}.{text{4}}0{text{ GWavg}})
Based on the preceding calculations, the total potential installed capacity is approximately 6.75 GW, with an estimated annual electricity generation of roughly 12.3 TWh. This substantial resource suggests that developing only 10% of the identified high-quality land (around 22.5 km²) could yield approximately 675 MW, sufficient to meet the Hajj peak demand. The analysis demonstrates that the AHP-GIS land allocation method offers spatially and environmentally suitable options, while also providing technical adequacy for energy security objectives. Consequently, the “Green Hajj” initiative emerges as an achievable and realistic goal.
Several limitations should be acknowledged in this study. Firstly, the criteria weights, validated by consistency ratio, are derived from expert opinion at a specific point in time; these weights could be subject to change with input from different experts or evolving policy directives, although solar resource dominance is likely to remain. Secondly, this static analysis does not capture seasonal variability; because Hajj occurs during a shifting lunar month, associated conditions may differ should the event take place in winter as opposed to summer. Nevertheless, the use of multi-year climate data is intended to reflect typical environmental conditions. Third, this study utilized high-resolution (30 m) data and applied strict criteria thresholds. Data sources include some uncertainty, such as interpolated climate surfaces from NASA, and effects of local terrain may not be fully captured. Future research could address uncertainty analysis or incorporate Fuzzy AHP for more refined suitability classifications. Although there are certain limitations, the study serves as a proof-of-concept for renewable energy planning at sites including the Holy Sites. It shows how data-driven methods can be integrated with broader initiatives, such as Green Hajj, to offer practical recommendations. By identifying areas most suitable for solar development, the resulting suitability map acts as a decision support tool. Authorities can consult this map together with land ownership details, proximity to substations, and other pertinent factors when evaluating project locations.
The deployment of solar farms near the Holy Sites is projected to reduce carbon emissions and improve air quality for pilgrims, in line with Islam’s principles of environmental stewardship. The GIS-AHP analysis indicates high-potential solar PV zones near Meena, Muzdalifah, and Arafat, supporting Saudi Arabia’s renewable energy objectives and the Green Hajj initiative. This method can be adjusted for solar site selection in other regions by modifying criteria and weights, which may support wider adoption of renewable energy globally.
While this study offers an initial suitability analysis, there are several ways to make it more robust and applicable. Additional criteria such as dust and dew point temperature could be included, as these factors influence particle accumulation on solar panels and affect their productivity. Performing a comprehensive sensitivity or uncertainty analysis—like a Monte Carlo simulation—on the AHP weights would help measure how stable the suitability rankings remain when expert opinions vary. The model’s results could be validated by directly involving stakeholders such as energy planners, local authorities, and community representatives, which would enhance the practical value of the findings. Broadening the scope to consider different technological scenarios, such as bifacial panels or hybrid solar-wind systems, and analyzing their performance under future climate projections may also yield important insights for adaptive long-term planning.
This study presents a GIS-based AHP framework to identify optimal sites for large solar PV projects in the complex environment of the Holy Sites in Makkah. Its main contribution is applying this model to a specific micro-region, bridging Saudi Arabia’s renewable energy goals with the challenges of the “Green Hajj” initiative. By factoring in local constraints and expert input, our suitability map provides planners with actionable guidance, setting it apart from previous regional studies. By integrating climatic, topographic, environmental, and infrastructural variables, a comprehensive suitability map was produced to identify optimal locations for solar development surrounding Meena, Muzdalifah, and Arafat. Results demonstrate that approximately 21.25% of the analysed area is categorised as High or Most Suitable for PV installation, primarily situated in the northeast, where solar irradiance levels reach up to 1830 kWh/kWp/year. These areas are further distinguished by relatively flat terrain, minimal land use conflict, and favourable access to road and grid infrastructure. An additional 23.58% of the territory is classified as Moderately Suitable, indicating that nearly 45% of the region has potential for solar farm deployment with appropriate planning and investment. Conversely, the remaining 55.17% is designated Low or Unsuitable due to challenging topography, elevated terrain, or cultural constraints associated with the Holy Sites. The analysis estimates a total potential capacity of about 6.75 GW and annual generation of 12.3 TWh, from the combined “Most Suitable” and “Highly Suitable” classes. Developing just 10% of this top-quality land (22.5 km²) could provide 675 MW, enough for Hajj peak demand.
The AHP weight analysis indicated that climatic factors are the principal determinants in the process of PV plant siting for the Holy Sites, contributing 39.7% to the overall influence. Within this category, solar radiation (21.5%) and wind speed (6.4%) emerged as the most significant variables. Topographic considerations represented 29.2%, underscoring the importance of slope and aspect. Meanwhile, economic and infrastructural factors—including proximity to roads and power lines—accounted for 24.4%, with land use comprising 6.8%. These results align with findings from comparable studies conducted in arid regions of Saudi Arabia, which similarly highlight the critical roles of solar resources and terrain characteristics in evaluating site suitability. The identified zones with high suitability offer considerable potential for establishing clean energy infrastructure to serve the Hajj and its surrounding facilities. The deployment of solar photovoltaic (PV) systems in these regions can reduce reliance on diesel generators, lower emissions, and improve power reliability during peak pilgrimage periods. This aligns with Saudi Arabia’s Vision 2030 and supports the objectives of the “Green Hajj” initiative. Given the significant anticipated energy output, investments in infrastructure- such as the expansion of transmission lines- are well justified. Notably, regions northeast of the study area and of Meena are optimal choices for utility-scale solar installations, including dual-use applications like photovoltaic parking canopies. Beyond the immediate context of Makkah, this study highlights the value of GIS-AHP methodologies for solar site selection in areas with environmental and cultural sensitivities. The approach is transparent, flexible, and scalable, facilitating informed planning that both honors heritage sites and maximizes renewable energy generation. Further research could expand this framework by integrating variable energy demand profiles, storage solutions, or hybrid microgrid systems.
In summary, the landscapes surrounding Islam’s holiest sites can be utilised for both spiritual events and sustainable energy initiatives. Implementing solar planning may assist Saudi Arabia in lowering the carbon emissions associated with the Hajj, demonstrate advancements in clean energy, and contribute to environmentally conscious development within these areas.
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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Applied Geography and GIS Program, Department of Humanities, College of Arts and Sciences, Qatar University, Doha, Qatar
Sarra Ouerghi
Department of Geography and GIS, Faculty of Arts & Humanities, King Abdulaziz University, Jeddah, Saudi Arabia
Nouf Al Jadaani
Qatar Environment and Energy Research Institute (QEERI) Program, Department of Humanities, College of Arts and Sciences, Qatar University, Doha, Qatar
Yasir Mohieldeen
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S. O. : Conceptualization, Methodology, Formal analysis, Writing – review & editing, Writing – original draft, Visualization, Software, Investigation, Data curation, Supervision, Resources, Project administration,.N. A.: Conceptualization, Writing – review & editing, Formal analysis, Data curation.Y. M.: Writing – review & editing, Validation, Resources, Conceptualization, Methodology, Formal analysis, Software.
Correspondence to Yasir Mohieldeen.
The authors declare no competing interests.
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Ouerghi, S., Al Jadaani, N. & Mohieldeen, Y. ​​GIS-based AHP multi-criteria mapping of potential solar PV power plant development: a case study in the vicinity of Holy Sites, Saudi Arabia. Sci Rep 16, 17022 (2026). https://doi.org/10.1038/s41598-026-46353-9
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German body sees major impact of solar energy on country’s economy  Yahoo Finance
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How Iberdrola is Expanding Portugal’s Solar Energy Sites  Sustainability Magazine
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Giving concessions for renewable projects to foreign corporations will not help solve the country’s energy crisis.
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The Russia-Ukraine conflict and the United States-Israel war on Iran have exposed how fragile energy systems built on dependency and external markets truly are.
This cycle of fuel crises and price shocks should be encouraging countries dependent on energy imports to address energy deficits and mitigate the impoverishment they cause among citizens. And yet few are undertaking the bold actions needed to improve energy independence.

Tunisia is certainly not one of them. The country’s energy deficit currently stands at roughly $3.8bn – nearly 51 percent of its total trade deficit – and has grown every year since 2000, driven by rising domestic consumption and a structural failure to build genuine energy sovereignty. The Tunisian authorities, however, are pursuing the wrong policies to address the problem.
They have hedged their bets on the privatisation of the energy sector, as reflected in the recent approval of five renewable energy concessions. The projects allow foreign multinationals to extract profits from renewable energy production and dump costs on the Tunisian people. This approach will not solve Tunisia’s energy crisis; instead, it will deepen its energy dependency while transferring public wealth into private hands.
On January 29, five new concession contracts for electricity production from renewables were submitted to the Tunisian parliament for approval.
The five solar plants – Khobna and Mezzouna in Sidi Bouzid in central Tunisia, El Ksour and Sagdoud in Gafsa in the west, and Menzel Habib in Gabes on the coast – would have a combined capacity of roughly 598 megawatts, with a total investment estimated at $560m. They would be granted to foreign multinationals.
In the following months, concern about the proposed projects grew. On April 21, the Electricity and Gas Federation, a trade union organisation, held an urgent news conference laying out the concrete mechanics of what the parliament was being asked to approve. The concessions, they argued, would reduce STEG, Tunisia’s national public utility, to a mere grid operator, while electricity production would be handed to foreign companies. Infrastructure costs would be paid by the public, while profits would leave with the corporations.
This is a standard model, exported wholesale from the structural adjustment playbook of the 1990s, now repackaged in the language of green transition.
Furthermore, according to the Tunisian Economic Observatory, the five concessions would benefit from extensive tax exemptions and stabilisation clauses that could undermine Tunisia’s fiscal sovereignty. There would be no meaningful technology transfer, weak local integration, and limited employment opportunities, which raised serious concerns about the developmental value of these projects.
The observatory also reported that under these contracts, carbon credits generated through emissions reductions on Tunisian territory could be transferred to the multinationals rather than remaining a public asset.
Concerns over this practice had already sparked opposition before these five concessions reached parliament. Last year, the Electricity and Gas Federation organised a strike denouncing the transfer of carbon credits to private developers. Notwithstanding the opposition, the five concessions came to entrench and expand this mechanism, allowing project developers to claim credits and use them to access international subsidy programmes. Incentives that were intended to support a national energy transition would thus be captured by private actors to boost their profits.
The public awareness raised by the federation and independent media mobilised public opinion against the concessions. Workers and activists staged a protest outside the parliament. Nevertheless, the five concessions were voted through, and the contracts were approved. The energy minister and a senior Ministry of Industry official were dismissed to placate public anger and distance the ruling elite from the controversial projects.
The concessions were pushed through with the justification that the country needs them to reduce its energy deficit, to cut its dependence on Algerian gas, which currently supplies about 60 percent of the country’s natural gas needs, and to meet its commitment to reach 35 percent renewables in the energy mix by 2030.
At first sight, this may sound reasonable, but it rests on a selective reading of the numbers and a deliberate narrowing of what counts as a solution.
The most glaring omission concerns the nature of the deficit itself. About 73 percent of Tunisia’s energy comes from petroleum products (gasoline and diesel), consumed overwhelmingly by a transport sector built around private transportation.
Addressing it requires a fundamentally different set of choices: Investment in public transport, restrictions on car imports, progressive taxation on high-consumption vehicles, etc. It also means thinking regionally. Reducing petroleum imports requires strengthening domestic refining capacity, and specifically investing in and upgrading the Tunisian Company of Petroleum Industries (STIR). This demands revisiting the kind of regional cooperation that was once within reach.
In 2012, for example, Tunisia and Libya discussed a joint refinery project at the coastal town of Skhira that could have significantly advanced energy sovereignty for both countries. The $2bn project was suspended due to the conflict in Libya, which made a steady crude supply impossible to guarantee. Eventually, it was quietly abandoned not because it lacked merit, but because this kind of sovereign regional cooperation threatened the interests of European hegemonic powers that profit from exporting refined petroleum products to the region.
Libya exports crude oil but imports refined products; Tunisia, with far fewer resources, is caught in the same extractivist logic, also exporting primary commodities (raw materials and agricultural produce) as well as a limited number of semi-industrial or manufactured products while remaining dependent on imports for high-value industrial and technology products. A shared refinery would have broken that cycle in the energy sector, to some extent.
Countries that continue to be subordinated to foreign powers are rarely allowed to industrialise, move up the value chain, or build the kind of productive sovereignty that would reduce their dependence on external markets and empower them to challenge imperialist domination. The buried refinery project is a case study in how that domination operates – not merely through direct prohibition, but also through the slow, structural foreclosure of alternatives.
The five solar concessions are another iteration of the same logic. They do not address the real structural issues of Tunisia’s energy deficit. They do not build domestic industrial capacity. They do not transfer technology. What they do is open a new frontier for international capital accumulation dressed, as the trend dictates, in the language of transition, sustainability, and development.
Few would dispute the urgency of transitioning towards renewable energy. The question that matters is how, by whom, and in whose interest.
Tunisia’s energy crisis is real. But its solution is not the further privatisation of public resources under foreign management and neocolonial schemes. What is required is a fundamentally different set of choices: Public control over energy production and distribution, genuine technology transfer, investment in domestic industrial capacity, a shift in the consumption paradigm through energy efficiency and public transport, and regional cooperation that builds sovereignty rather than deepening dependency.
The neoliberal corporate-led model has demonstrated its limits in financial crises, in pandemics, and in the geopolitical shocks now reshaping the global economy. Each new crisis should serve as an alarm. Instead, they are consistently used as a pretext for doubling down on the same failing logic.
We must transition. But we must insist on transitioning on our own terms with public control, democratic oversight, and genuine inclusive development defined by the needs of the many, not the profit margins of the few.
The views expressed in this article are the authors’ own and do not necessarily reflect Al Jazeera’s editorial stance.

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This Solar EV Claims Up to 1,000 Miles of Range and Adds 40 Miles a Day  Autoblog
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For the first time, researchers have used more detailed criteria—like water depth and temperature—to get a more accurate idea of how many floating solar panels some U.S. reservoirs could hold. Even in their most conservative estimates, the country’s reservoirs offer huge potential for future development and could host projects with capacities of up to 77,000 megawatts. Photo from Getty Images
Federal reservoirs could help meet the country’s solar energy needs, according to a new study published in Solar Energy.
For the study, Evan Rosenlieb and Marie Rivers, geospatial scientists at the U.S. Department of Energy National Renewable Energy Laboratory (NREL), as well as Aaron Levine, a senior legal and regulatory analyst at NREL, quantified for the first time exactly how much energy could be generated from floating solar panel projects installed on federally owned or regulated reservoirs. (Developers can find specific details for each reservoir on the website AquaPV.)
And the potential is surprisingly large: Reservoirs could host enough floating solar panels to generate up to 1,476 terawatt hours, or enough energy to power approximately 100 million homes a year.
“That’s a technical potential,” Rosenlieb said, meaning the maximum amount of energy that could be generated if each reservoir held as many floating solar panels as possible. “We know we’re not going to be able to develop all of this. But even if you could develop 10% of what we identified, that would go a long way.”
Levine and Rosenlieb have yet to consider how human and wildlife activities might impact floating solar energy development on specific reservoirs. But they plan to address this limitation in future work.
This study provides far more accurate data on floating solar power’s potential in the United States. And that accuracy could help developers more easily plan projects on U.S. reservoirs and help researchers better assess how these technologies fit into the country’s broader energy goals.
Floating solar panels, also known as floating PV, come with many benefits: Not only do these buoyed power plants generate electricity, but they do so without competing for limited land. They also shade and cool bodies of water, which helps prevent evaporation and conserves valuable water supplies.
“But we haven’t seen any large-scale installations, like at a large reservoir,” Levine said. “In the United States, we don’t have a single project over 10 megawatts.”
Previous studies have tried to quantify how much energy the country could generate from floating solar panels. But Levine and Rosenlieb are the first to consider which water sources have the right conditions to support these kinds of power plants.
In some reservoirs, for example, shipping traffic causes wakes that could damage the mooring lines or impact the float infrastructure. Others get too cold, are too shallow, or have sloping bottoms that are too steep to secure solar panels in place.
And yet, some hydropower reservoirs could be ideal locations for floating solar power plants. A hybrid energy system that relies on both solar energy and hydropower could provide more reliable and resilient energy to the power grid. If, for example, a drought depletes a hydropower facility’s reservoir, solar panels could generate energy while the facility pauses to allow the water to replenish.
And, to build new pumped storage hydropower projects—which pump water from one reservoir to another at a higher elevation to store and generate energy as needed—some developers create entirely new bodies of water. These new reservoirs are disconnected from naturally flowing rivers, and no human or animal depends on them for recreation, habitat, or food (at least not yet).
In the future, the researchers plan to review which locations are close to transmission lines or electricity demand, how much development might cost at specific sites, whether a site should be avoided to protect the local environment, and how developers can navigate state and federal regulations. The team would also like to evaluate even more potential locations, including other, smaller reservoirs, estuaries, and even ocean sites.
The research was funded by the Solar Energy Technologies Office and the Water Power Technologies Office in DOE’s Office of Energy Efficiency and Renewable Energy (EERE).
Access the study to learn more about the immense potential for floating solar plants in the United States, or visit AquaPV to dig into the data on specific reservoirs.
NREL is the U.S. Department of Energy’s primary national laboratory for renewable energy and energy efficiency research and development. NREL is operated for DOE by the Alliance for Sustainable Energy LLC.
Last Updated April 28, 2026
The National Laboratory of the Rockies is a national laboratory of the U.S. Department of Energy, Office of Critical Minerals and Energy Innovation, operated under Contract No. DE-AC36-08GO28308.

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Chinese company plans to install 2.1 million solar panels in Alentejo  Portugal Resident
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From pv magazine Australia
Stellar PV’s plan to establish a 2 GW silicon ingot pulling and wafer manufacturing plant near Queensland, Australia, has been awarded “major project status” by the Australian government, putting it on the fast track for regulatory approvals.
Sydney-based Stellar PV is looking to build a polysilicon ingot pulling and wafering facility close to the city of Townsville. The low-emissions plant would process polysilicon to produce silicon ingots and then convert the ingots to silicon wafers.
The company said the project will support high-value solar manufacturing that turns Australian expertise and resources into globally competitive capability and provide an alternative supply chain for both domestic and global solar markets.
“This unlocks a significant opportunity for Australia to move beyond exporting raw critical minerals and into high-value processing and manufacturing, creating a trusted, high-quality alternative supply chain for photovoltaic wafers,” said Stellar Chief Executive Officer Louise Hurll.
Stellar PV said being awarded major project status recognizes the national significance of the estimated AUD 400 million ($281 million) project and the strategic importance of establishing Australia as a global hub for solar manufacturing.
The designation ensures the project will receive direct support from the federal government’s Major Projects Facilitation Agency, including help navigating regulatory approvals related to areas such as environment, biosecurity and foreign investment.
The announcement follows the recent release of an interim report that confirmed the feasibility of the planned manufacturing facility.
The pre-feasibility review indicates the facility is technically and commercially achievable and there are no environmental, regulatory or site-related barriers that would limit progression to the next stage of the feasibility study.
The study, supported by the Australian Renewable Energy Agency (ARENA) as part of the AUD 1 billion federal Solar Sunshot program, says preliminary findings “indicate that establishing Australia’s first large-scale ingot and wafer facility is technically feasible and commercially promising.”
“Early assessments of the process design, equipment options, site requirements and ESG commitments provide a sound basis for progressing to detailed engineering studies,” it said, noting that market case is strong, with European and United States “actively looking” for wafer supplies as their cell and module capacity expands.
“Australia’s low-cost renewable energy, trusted trade position and supportive policy settings give us a clear advantage in meeting this demand,” the report said. “With production credits and CAPEX support, our early findings indicate the project can achieve globally competitive pricing, deliver high-value regional jobs and sovereign capability, and relieve one of the most significant chokepoints in the global clean-energy supply chain.”
Stellar PV said the report provides a solid foundation to proceed to the next stage of the project, which is to deliver feasibility study, front-end engineering design, and preparation and submission of development applications for the facility. The company is aiming for production in late 2028, pending regulatory approvals.
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Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
April 01 – August 31, 2026
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
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From pv magazine Australia
Renewables developer Edify Energy has awarded Gamuda company DT Infrastructure the EPC contracts for its Smoky Creek and Guthrie’s Gap and Ganymirra and Majors Creek solar and battery projects in regional Queensland.
The adjacent Smoky Creek and Guthrie’s Gap power stations, near Biloela in central Queensland, will together feature 600 MW of solar and 600 MW/2,400 MWh of battery storage.
The co-located Ganymirra and Majors Creek power stations, being developed near Townsville in the state’s north, include a combined 300 MW of solar and 300 MW/1,200 MWh of battery energy storage.
Sydney-based Edify Energy, now owned by Canadian investment group La Caisse, said both projects will use DC-coupled hybrid configurations and utilize grid-forming inverter technologies designed to enhance the stability and resilience of the power network.
Edify Energy Chief Executive Ben Warne expects the developments to make a major contribution to the National Electricity Market with the adoption of the best and latest in solar, battery and inverter technology to bring stable and dispatchable solar energy to the network in the most efficient way possible.
“We are proud of the significant role these major generators will play in the transition towards an affordable, reliable and sustainable energy future,” he said, adding the projects will also provide meaningful injections into the local regional economies. “These projects will create significant jobs during construction, support local communities and industry and assist in delivering the infrastructure needs of Queensland’s energy system consistent with the Queensland Energy Roadmap.”
While construction proper is expected to start in the coming months, Edify Energy said early pre-construction and design works have already commenced on both projects. The developer is targeting delivery and operations in 2028.
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The June issue of pv magazine Global is out now!
Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
April 01 – August 31, 2026
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy.
Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way!
We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team.
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Scientific Reports volume 16, Article number: 12915 (2026) Cite this article
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With the rapid penetration of photovoltaic (PV) generation into modern power grids, accurate and robust ultra-short-term PV power forecasting is increasingly important for real-time dispatch and frequency regulation. However, PV power series are volatile, nonlinear, and uncertain at short time scales, challenging conventional methods. This paper proposes a hybrid ultra-short-term forecasting framework that integrates secondary decomposition with advanced learning models. First, key features are screened and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes PV power into intrinsic mode functions (IMFs). Sample entropy quantifies IMF complexity, and K-means clusters IMFs into high- and low-frequency components. High-frequency components are further decomposed by Black-winged Kite Algorithm (BKA)-Variational Mode Decomposition (VMD) to enhance stationarity and reduce manual parameter tuning. The resulting high-frequency sub-signals are predicted using Online Kernel Extreme Learning Machine (OKELM), while low-frequency components are modeled by a Convolutional Neural Network (CNN)-Echo State Network (ESN) to capture spatiotemporal patterns. Final ultra-short-term forecasts are obtained via additive reconstruction. Experiments on datasets from the Ningxia PV station (China) and the Desert Knowledge Australia (DKA) Solar Energy Centre achieve (R^2) values of 99.6987% and 99.0635% in comparative and validation experiments, respectively, demonstrating high accuracy across different geographic locations and seasons. Improved PV power forecasting reduces uncertainty, supports grid stability, enables more efficient dispatch and reserve scheduling, and lowers operating costs and curtailment.
Photovoltaic (PV) power generation systems are direct conversion mechanisms that transform sunlight into electricity without relying on any mechanical or mobile device. They harness solar energy, an inexhaustible resource, making them sustainable and environmentally friendly energy solutions. One of the key advantages of PV systems is their long service life, coupled with minimal maintenance requirements, ensuring consistent and reliable energy production over extended periods¹. However, PV power generation is significantly influenced by meteorological factors. The complexity and variability of these factors result in strong intermittency and volatility in PV power output. These characteristics pose challenges in system stability, power generation forecasting and planning, and power management when PV is operated on a large-scale grid-connected basis. Accurate forecasting for power enables the power dispatch department to formulate scheduling plans, ensuring the safe and stable operation of the power system. Therefore, enhancing research on precise ultra-short-term PV power forecasting is crucial for maintaining grid stability and real-time power balancing. Improved ultra-short-term PV power forecasting reduces uncertainty, enhances grid stability, enables more efficient dispatch and reserve scheduling, and lowers operating costs and curtailment, delivering better economic benefits.
In existing research, models for PV power forecasting generally include physical, statistical and artificial intelligence methods². Physical models use numerical weather predictions as inputs for physical equations, but though interpretable, often fail to capture the nonlinear nature of PV power³. In contrast, statistical models such as Autoregressive Integrated Moving Average (ARIMA)⁴ and improved Markov Chain approaches⁵ offer solid mathematical foundations and improved stability, yet struggle with long-term dependencies and complex nonlinearities, limiting their effectiveness for PV power generation.
Deep learning has emerged as a promising alternative for PV power forecasting because it can automatically learn nonlinear representations and temporal dependencies from data. Sun et al.⁶ used Convolutional Neural Networks (CNN) to correlate PV output with contemporaneous sky images, successfully demonstrating the feasibility of image-based ”now-casting” for solar systems. Zhang et al.⁷ developed Gated Recurrent Unit (GRU) to construct prediction intervals under different weather conditions, improving forecasting reliability under variability. Sun et al.⁸ proposed Long Short-Term Memory (LSTM) model that explicitly exploits spatial and temporal correlations among neighboring PV sites, leading to higher prediction accuracy. Joo et al.⁹ focused on Echo State Network (ESN) due to its efficiency and fast training speed, showing that it could significantly outperform tuned LSTM models in terms of accuracy. Li et al.¹⁰ applied a Temporal Convolutional Network (TCN) for day-ahead PV power forecasting and reported a 20%–30% reduction in Root Mean Square Error (RMSE) over baseline methods. Khalil et al.¹¹ used a Transformer model, achieving a forecasting Mean Absolute Error (MAE) of 0.9377 and enabling proactive fault mitigation. Singh and Alam¹² tried N-BEATS model pre-trained on temperature data, having a notable reduction of 30–40% in Mean Absolute Percentage Error (MAPE), indicating the effectiveness of leveraging pre-trained models. Suresh¹³ introduced Patch Time Series Transformer (PatchTST), which delivered superior accuracy over both classical persistence methods and other Transformer baselines. Liang et al.¹⁴ attempted Inverted Transformer (iTransformer) model into the distributed PV power prediction problem, which enables the model to capture correlations among limited meteorological features to fully exploit the data potential. Moreover, Online Kernel Extreme Learning Machine (OKELM) provides an alternative lightweight predictor with online updating ability, and is often used in streaming forecasting settings¹⁵.
However, individual machine learning models have limitations, prompting research into hybrid models that combine data decomposition and machine learning to address data non-stationarity and boost accuracy^16,17. Commonly used decomposition methods include Empirical Mode Decomposition (EMD)¹⁸, Ensemble EMD (EEMD)¹⁹, and Complementary EEMD with Adaptive Noise (CEEMDAN)²⁰, though mode mixing issues remain. Moreover, a single decomposition stage is often insufficient for highly non-stationary PV power series. After an initial decomposition, the high-frequency components may still exhibit strong volatility and residual mode mixing, which degrades the downstream forecasting performance. To further enhance component stationarity and separability, recent studies have adopted secondary decomposition frameworks. For example, Zhang et al.²¹ employed EMD method to denoise the signal, and the residual signal was further decomposed using Variational Mode Decomposition (VMD) to minimize mode aliasing and improve accuracy. In another study, Zhang et al.²² proposed VMD combined with CEEMDAN secondary decomposition method for the original signal decomposition, to reduce the signal volatility and reduce the complexity of feature mapping the PV data. Furthermore, Liu et al.²³ selected two variables with the highest correlation and decomposed them using VMD, CEEMD, and Singular Spectrum Analysis (SSA) to extract more diverse and informative features. In this context, VMD constrains the bandwidth of each mode and can refine the sub-series obtained from the first-stage decomposition; however, its performance depends on key parameters that are typically manually tuned. Therefore, metaheuristic algorithms such as Northern Goshawk Optimization (NGO) and Grey Wolf Optimizer (GWO) have been used for parameter optimization, and this study adopts the Black-Winged Kite Algorithm (BKA) to optimize key VMD parameters, alleviating over- and under-decomposition and further reducing mode mixing and endpoint effects^{24,25,26,27,28}.
Despite these advances, challenges persist: most models use only a single decomposition method, leading to unresolved mode mixing and frequency overlap, and dual-decomposition models often lack parameter optimization. Moreover, many decomposition-based forecasting frameworks apply a single predictor uniformly to all decomposed components, rather than designing component-specific models to match heterogeneous characteristics, which limits the effective exploitation of decomposition. PV power data remain highly nonlinear, non-stationary, and coupled, with noise and sparsity making feature extraction difficult. Thus, single data processing methods struggle to achieve ideal forecasting results.
To improve the accuracy of ultra-short-term PV power forecasting and enhance the stability of the power grid, this paper proposes a novel hybrid forecasting model, termed CEEMDAN-BKA-VMD-OKELM-CNN-ESN, which integrates multistage signal decomposition and advanced deep learning techniques. The primary contributions of this study are as follows:
(1) The VMD algorithm in which k and (alpha) have a great influence on the decomposition results. To avoid large errors caused by manual determination of these parameters, this study uses BKA to optimize the VMD hyperparameters. This enhancement reduces the complexity of modeling and improves the overall efficiency of the process.
(2) The secondary decomposition strategy combines two advanced decomposition algorithms, CEEMDAN and BKA-VMD, to process the PV power generation sequences. Based on the high-frequency subsequence reconstructed by CEEMDAN decomposition, the secondary decomposition is carried out by BKA-VMD. The method effectively reduces the non-stationarity of the data and comprehensively extracts the intrinsic features of the data.
(3) To overcome the limitations of a single model in capturing the historical data characteristics of PV power, this study introduces a dual-branch modeling structure. The high-frequency components are predicted using an OKELM, while the low-frequency components are processed using a CNN-ESN model, where the CNN extracts spatial features and the ESN captures temporal dynamics. This design maximizes the representation of different data characteristics.
(4) A hybrid ultra-short-term PV power forecasting method combining a secondary decomposition strategy and deep learning integration is proposed, which improves forecasting accuracy and robustness, and its effectiveness and generalization are validated across different geographic locations and seasons.
The structure of this paper is as follows: Section 2 describes the theory and structure of the CEEMDAN-BKA-VMD-OKELM-CNN-ESN forecasting method. Section 3 introduces the experimental dataset and model’s indicators. Section 4 presents the experimental process and the results of data analysis in detail and compares them with other models. Section 5 provides the conclusion.
EMD decomposes nonlinear, non-smooth signals into IMFs, but often suffers from mode aliasing. CEEMDAN addresses this by adding adaptive noise, reducing reconstruction error. The main step is:
(1)White noise is added to the original signal I times, generating noisy sequences (omega ^1, omega ^2,…, omega ^I). Each sequence is decomposed by EMD to obtain (IMF_1^1, IMF_1^2,…, IMF_1^I). The first IMF is then calculated as their average:
Update the corresponding residual value to
(2)To update the residual signal (r_1), add white noise again and complete the EMD decomposition to find the new (IMF_k), and update the residuals. The process is iterated until all modes are fully extracted.
(3)After completing all the layers of decomposition, the final raw signal x is decomposed into multiple (widetilde{IMF}_k) and a sum of residual signals, as follows:
where K is the number of levels of decomposition.
(4)During the exploration phase, fishermen initially focus on independent searches, using group encirclement as a supplementary search method. As the search progresses, the environmental advantage gradually shifts to the fishermen, and fishermen rely primarily on group encirclement while using individual advantages as a supplement. In this model, we use (alpha) to represent the capture rate parameter.
Where EFs is the current number of evaluations, and MaxEFs is the maximum number of evaluations.
The BKA is a simple and effective meta-heuristic optimization algorithm, which is divided into migration and attack phases. The process is as follows:
(1)Initialization phase: A set of random solutions is created, and the position of each black-winged kite (BK) is represented as a matrix.
(2)Attacking behavior: A mathematical model for the attack behavior of BK is shown:
where (y_{t+1}^{i,j}) and (y_t^{i,j}) represent the position of the (i^{th}) BK in the (j^{th}) dimension in the t and ((t+1)^{th}) iteration steps, respectively. r is a random number that ranges from 0 to 1, and p is a constant value of 0.9. And T is the total number of iterations, and t is the number of iterations that have been completed so far.
(3) Migration behavior: A mathematical model for the migration behavior of BK is expressed in
where (L_t^{i,j}) represents the leading scorer of the Black-winged kites in the (j^{th}) dimension of the (i^{th}) iteration so far. (F_i) represents the current position in the (j^{th}) dimension obtained by any BK in the t iteration. (F_{ri}) represents the fitness value of the random position in the (j^{th}) dimension obtained from any BK in the t iteration. And C(0, 1) represents the Cauchy mutation.
VMD is an adaptive, non-recursive method for decomposing signals into smooth, multi-scale components. It overcomes endpoint effects and mode aliasing seen in EMD, offering a stronger mathematical foundation. By solving a variational problem, VMD effectively handles complex, non-stationary signals.
Firstly, the variational problem is constructed by assuming that the original signal f is decomposed into k components. Each component should be a modal component with finite bandwidth centered around a specific frequency. Additionally, the sum of the estimated bandwidths of each modality should be minimized. The constraint is that the sum of all modes must equal to the original signal. Consequently, the VMD constrained variational model is as follows:
where (u_k = {u_1, u_2, ldots , u_K}) is the function of each mode, and (omega _k = {omega _1, omega _2, ldots , omega _K}) is the center frequency of each mode.
To solve the constrained optimization problem, it is necessary to transform the constrained variational problem into an unconstrained variational problem. By utilizing the quadratic penalty term and the Lagrange operator, the above equation is transformed into:
where (alpha) is the penalty parameter, and (lambda) is the Lagrange multiplier.
For all (omega ge 0), update the generalized letter (hat{u}_k):
Updating the generalized letter (omega _k):
For all (omega ge 0), a double boost
where (gamma) denotes the noise tolerance limit.
Repeat the above steps until the iterative constraints are satisfied:
The construction of this constrained variational model allows VMD to effectively deal with complex non-smooth signaling.
Typically, the selection of VMD parameters relies on practical experience, and the choices of decomposition layer k and penalty factor (alpha) significantly affect the decomposition results²⁹. The value of k directly determines the number of decomposed modal components. An inappropriate selection of k can lead to under-decomposition; a large k value results in false modes, while a small k value fails to extract the hidden features of the time series effectively. The value of (alpha) affects the bandwidth of the modal components. A small (alpha) leads to mode mixing, hindering feature extraction, while a large (alpha) causes loss of local information. Since these parameter selections heavily rely on subjective judgment, it is crucial to optimize the parameters of VMD. The mechanism of the proposed BKA-VMD is shown in Fig. 1.
The mechanism of the proposed BKA-VMD.
The parameters of VMD are optimized using the BKA algorithm. For nonlinear and complex signals, multiscale permutation entropy (MPE) is used as the fitness function due to its superior stability and noise resistance. The expression is as follows:
where the value of (H_p(M)) indicates the extent of unpredictability and intricacy in the time series and m is the embedding dimension.
Traditional neural network models are typically trained using predefined training samples. However, as new samples are continuously added, the forecasting error tends to increase with conventional models. To address the issue of model updates during the process of sample accumulation and enhance forecasting accuracy, the OKELM algorithm is employed for predicting high-frequency signals¹⁵. The steps involved in the OKELM modeling process are as follows:
(1)Calculate the initial one using (t_{N+1}’) samples ({(x_i, t_j)}^{N+l-1}) at the current moment,
Here, (W_N) is a ((l-1) times (l-1))-dimensional square matrix, and (q_N) is a constant.
(2)Using (X_{N+1}) as the input, the predicted value (t_{N+1}’) for the corresponding output is computed based on Equation (23).
(3)Once the true value of (t_{N+1}) is obtained, the model’s forecasting error (e = |tN – tN’|) for this sample is calculated. If Equation (23) is satisfied, the pair is updated according to Equation (24) to obtain (D_{N+1}); otherwise, (D_{N+1} = DN).
(4)Using the updated (D_{N+1}), (lambda _{N+1}) is computed based on Equation (24). The old samples ((x_N, t_N)) are removed, and (W_{N+1}^{-1}) is calculated from (D_{N+1}) according to Equation (26). (varepsilon _{N+1}) is then computed from (D_{N+1}), and (lambda _{N+1}) is recalculated according to Equation (21).
(5)Let (N = N+1), return to step (2).
Structure of CNN.
CNN is a deep learning neural network model that excels at processing large amounts of data information with automatic hierarchical feature extraction³⁰. By utilizing a convolutional kernel, the model can effectively reduce the number of parameters, parameters, thereby mitigating the risk of overfitting and enhancing computational efficiencies are widely employed for classification and regression tasks and typically comprise several key layers: a convolutional layer, a pooling layer, a fully connected layer, an input layer, and an output layer, which is shown in Fig. 2.
ESN is a type of recurrent neural network (RNN) proposed by Jaeger, which features a large, fixed, and sparsely connected reservoir with dynamic memory capability, which is shown in Fig. 3. Unlike traditional RNNs, ESNs avoid the complexity of backpropagation through time (BPTT) by only training the layer weights, which significantly reduces the training time while retaining the network’s temporal modeling ability³¹.
Structure of ESN.
Given an input sequence u(t), the reservoir state (x(t) in mathbb {R}^{N_r}) is updated according to:
where (W_{in}) is the input weight matrix, W is the reservoir (internal) weight matrix, and (W_{fb}) is the optional feedback weight matrix.
The output y(t) is then computed as:
where (W_{out}) is the learned output weight matrix, and [u(t), x(t)] denotes the concatenation of input and reservoir state vectors.
The CNN-ESN hybrid model combines the powerful feature extraction capabilities of CNN with the dynamic temporal modeling strength of ESN. In this structure, CNN is employed as a spatial encoder to capture localized patterns or short-term dependencies in the input data, while ESN is used to learn the temporal dependencies from the extracted features.
Given a time-series input (X = [x_1, x_2, ldots , x_T]), a one-dimensional convolutional layer with multiple kernels is first applied to extract high-level local features from the input sequence. Let (F_t) denote the feature maps at time t, where C is the number of channels (filters) and L is the length of the feature vector after convolution. These features are then fed into the reservoir of the ESN, which evolves over time.
This hybrid architecture leverages CNN’s ability to extract translation-invariant spatial representations and ESN’s efficiency in temporal sequence modeling, resulting in improved accuracy and generalization in time-series forecasting tasks³².
PV power generation exhibits strong nonlinearity and variability due to fluctuating environmental conditions, making it challenging for traditional single methods to achieve high-precision ultra-short-term forecasting. To address this issue, we propose a CEEMDAN-BKA-VMD-OKELM-CNN-ESN forecasting method for ultra-short-term PV power that systematically decomposes and reconstructs the PV power sequence, enhancing its predictability. The model consists of four key stages. First, we extract PV power generation data from a PV power plant located in Ningxia during the spring of 2017, providing a real-world dataset for analysis. Second, the PV power sequence is divided into training and test sets and subsequently decomposed using the CEEMDAN algorithm. Then, sample entropy and K-means clustering are used to divide the IMFs into high-frequency and low-frequency components. The high-frequency components are further decomposed using the BKA-VMD method, while the low-frequency components are retained as trend signals. Third, OKELM is applied to predict high-frequency signals, and CNN-ESN is employed to capture and predict the temporal trends from the low-frequency part. For high-frequency modes, the signal is highly non-stationary with rapid oscillations and possible distribution drift. Kernel-based ELM in OKELM provides strong nonlinear approximation with fast training, while its online updating mechanism can promptly adapt the model to newly arriving samples. This makes OKELM more suitable for tracking local, fast-varying patterns in high-frequency components than batch-trained models. We adopt OKELM to model the high-frequency components in PV power forecasting. Low-frequency modes mainly describe the underlying trend and long-range temporal structure. We therefore employ CNN-ESN for these components: the CNN extracts trend-related multi-scale patterns from sliding windows, while the ESN reservoir provides dynamic memory to integrate these features over longer horizons. As only the readout layer is trained, CNN-ESN can learn smooth trend evolution efficiently and stably³³. Finally, the forecasting results from both branches are additively integrated to obtain the final PV power forecast, which is compared with other methods to validate the model’s effectiveness. By leveraging advanced decomposition and hybrid modeling techniques, the proposed approach significantly improves adaptability and predicts accuracy of ultra-short-term PV power forecasting. The structure of the CEEMDAN-BKA-VMD-OKELM-CNN-ESN PV power forecasting method proposed in this paper is shown in Fig. 4, which is mainly divided into four stages.
(1) Extract PV power generation data from a PV power plant located in Ningxia during the spring of 2017.
(2) After dividing the PV power sequence into training and test sets, apply CEEMDAN decomposition to obtain IMFs.
(3) Compute the sample entropy of each IMF and use K-means clustering to divide them into high-frequency and low-frequency components. The high-frequency IMFs are further decomposed using the BKA-VMD method and predicted using OKELM. The low-frequency IMFs are directly modeled using the CNN-ESN network.
(4) Integrate the forecasting results of high- and low-frequency components to generate the final ultra-short-term PV power forecast. The performance of the proposed method is evaluated by comparing it with other benchmark models, highlighting its improved adaptability and forecasting accuracy.
The overall flow of the photovoltaic power forecasting.
To verify the effectiveness and generalization ability of the proposed CEEMDAN-BKA-VMD-OKELM-CNN-ESN method, two PV power datasets are investigated in this study. Both datasets are collected from grid-connected PV plants and are employed for feature correlation analysis, feature selection, and forecasting performance evaluation.
The first dataset is obtained from a PV power plant located in Ningxia, China, during the spring of 2017. The installed capacity of the plant is 150 kW. The sampling interval is 15 min, and the dataset features contain: component temperature, ambient temperature, air pressure, humidity, total radiation (horizontal), direct radiation, diffuse radiation, and PV power.
The second dataset is collected from a PV power plant from the Australian Desert Knowledge Edge Solar Centre (DKASC, https://dkasolarcentre.com.au/downloadlocation=alice-springs) from January 1, 2022, to December 31,2022. The PV plant system is based on Trina Solar, and its array power rating is 23.4 kW. The sampling interval is 5 min. The dataset features contain: time index, PV active power, relative humidity, weather temperature, global horizontal irradiance, diffuse horizontal irradiance, wind direction, daily rainfall, wind speed, tilted-plane diffuse irradiance, and tilted-plane global irradiance.
Factors such as instantaneous failure of PV modules and manual recording bias can easily lead to missing recording data or deviation from the actual value. The direct use of abnormal data for forecasting will affect the convergence degree of the PV power forecasting. Therefore, the raw data are first examined to identify erroneous records, and missing values are imputed using mean filling. Additionally, to avoid the adverse effects caused by difference in magnitude and outlier sample data, the data are processed using a normalization method. This maps each feature’s data to range [0, 1]. The normalization and inverse normalization formulas are as follows, respectively.
where (x_i) is the raw data, (x_i’) is the normalized data, (x_{max }) is the maximum value of the variable, and (x_{min }) is the minimum value of the variable.
The PV power features of this PV plant contain three types of solar radiation features: solar scattered radiation, solar direct radiation, and total horizontal solar radiation. In PV power forecasting, the high correlation between the features causes the model to suffer from multicollinearity, which subsequently leads to inaccurate estimation of the model regression coefficients and the explanatory and diminishes both the explanatory and predictive performance of the model. To address the issue of multicollinearity, linear and nonlinear correlations between features are initially analyzed using Pearson, Spearman and Kendall correlation coefficients³⁴. The Pearson, Spearman and Kendall correlation coefficients are follows:
where (u_i) and (v_i) are the (i_{th}) value of the two variables respectively; (overline{u}) and (overline{v}) are the means of the two variables respectively; (d_i) is the rank difference of the (i_{th}) value of the two variables, i.e., the difference between the positions of the two variables in the numerical order; N is the number of data points; C is the number of pairs of samples with consistent order; D is the number of pairs of samples with inconsistent order.
The values of the three correlation coefficient methods are all range between ([-1,1]), where a positive value is positive correlation, a negative value is negative correlation. The larger the absolute value, the stronger the correlation. The results of correlation analysis between each feature factor and PV power are presented in Table 1.
The specific hyperparameter settings employed in our proposed hybrid framework are detailed in Table 2. These parameters were carefully selected to optimize the performance of four core modules. For the initial decomposition via CEEMDAN, a noise ratio ((epsilon)) of 0.2 and an ensemble size of 500 were used to ensure stable mode extraction. In the secondary decomposition stage, BKA-VMD was configured with a population size of 10 and 30 iterations to efficiently search for the optimal k within [1, 10] and (alpha) within [1000, 8000]. Regarding the prediction models, the CNN-ESN, tasked with low-frequency component modeling, utilized 32 filters and a spectral radius of 0.8 to capture temporal dependencies, while the OKELM for high-frequency components adopted an RBF kernel function with specific penalty and kernel parameters, penalty coefficient C of (2^{10}) and kernel parameter (mu) of (2^{-4}), to enhance generalization capability.
The experiments were conducted on a Windows 11 system equipped with an AMD Ryzen 7 5800H processor, 16 GB of RAM, and an NVIDIA RTX 3060 GPU. The proposed model was implemented using the Keras framework in a Python 3.7 environment. Under this hardware configuration, the average runtime for a single prediction process was approximately 143 seconds.
To comprehensively and reliably evaluate the forecasting performance of the proposed forecasting model, mean square error (MSE), RMSE, MAE, MAPE and coefficient of determination ((R^2)) are used as evaluation indexes. The computational equations for these metrics are as follows:
where (y_i) denotes the original PV power data, (overline{y}_i) denotes the average value of the PV power data, and (hat{y}_i) denotes the predicted value of the model.
MSE, RMSE, MAE, and MAPE are metrics where smaller values indicate lower forecasting errors, smaller deviations from true values, and thus higher forecasting accuracy. The (R^2) is a metric used to evaluate the goodness of fit of a model, where values closer to 1 indicate stronger explanatory power and higher forecasting accuracy.A comprehensive analysis of these metrics provides a thorough evaluation of the model’s predictive capability and performance.
Before the decomposition of the original data, the maximum and minimum normalization is used to normalize the photovoltaic power series data. Since CEEMDAN can adaptively obtain the number of eigenmode components according to the series data. The original data is decomposed into a few subsequences, with the results shown in Fig. 5.
CEEMDAN results of photovoltaic power.
The first line of the figure represents the original sequence data, and the second to the thirteenth lines represent the IMF subsequences obtained after CEEMDAN decomposition. As can be seen in Fig. 5, the complexity and randomness of the decomposed sequences are reduced compared with the original sequences, and the fluctuation degree of the sequences is also reduced sequentially, with the sequence gradually becoming more stable. To analyze the complexity of the decomposed sequence more intuitively, this paper uses the sample entropy to calculate the sequence complexity.
From the entropy values of the decomposed subsequence samples, it can be observed that the complexity of the subsequence after CEEMDAN decomposition is gradually reduced. The degree of frequency fluctuation and randomness is also progressively diminished, and the complexity of different sequences exhibit a certain level of similarity.
The decomposed subsequences were reconstructed by comparing the sample entropy values of different subsequences using K-means clustering. They were then reconstructed into a high-frequency sequence Co-IMF1 and low-frequency sequences Co-IMF2 and Co-IMF3, which are shown in Fig. 6.
K-means Clustering Results for Multiple IMF Components.
The reconstructed high-frequency sequence was subjected to secondary decomposition to extract finer features. We first applied standard VMD with fixed parameters and subsequently employed the BKA-VMD method, where key parameters were adaptively optimized by the BKA algorithm. The comparative decomposition results are illustrated in Fig. 7 where BKA-VMD exhibits significant advantages over standard VMD in several aspects.
Regarding boundary effects, the components extracted by BKA-VMD show smoother fluctuations at the edges, effectively mitigating the end effects seen in VMD. Meanwhile BKA-VMD achieves higher modal purity. Unlike standard VMD, which often suffers from under-decomposition or over-decomposition due to improper parameter selection, the proposed method ensures a more concentrated frequency distribution and minimizes spectral overlap. Finally, BKA-VMD demonstrates superior capability in separating high-frequency noise from low-frequency trends, allowing for a more accurate capture of intrinsic signal changes. These improvements directly contribute to the enhanced forecasting accuracy of the overall model.
Decomposition results. (a) VMD; (b) BKA-VMD.
By comparing the predicted PV power of each comparison experiment with the predicted and actual values of the proposed model on the same graph, the final comparison graph between the predicted and actual values is obtained as shown in Fig. 8. And the group with the best training effect for each model is recorded, and the results are listed in Table 3 and Fig. 9.
Forecasting results of model.
Radar chart comparison of evaluation metrics for different forecasting models.
The results reveal a clear performance ladder across the competing models, with the proposed CEEMDAN-BKA-VMD- OKELM-CNN-ESN consistently ranking first on all metrics, indicating the most faithful reconstruction of PV power dynamics. Among deep baselines, iTransformer is the strongest, yet the proposed model further tightens the fit by markedly lowering the typical error level while nearly halving the relative deviation, and the much larger drop with 8.2896% in MSE suggests that it is particularly effective at suppressing occasional large misses such as ramps or sharp fluctuations rather than only improving average cases. In contrast, Among the baseline methods, the LSTM and GRU models exhibit the lowest accuracy, with RMSE values of 6.3152 and 6.3685, and MAPE values of 33.9420% and 34.7936%, respectively, highlighting their limited robustness to the nonstationary, multi-scale characteristics of the series; convolution- and attention-based models progressively improve but still leave a notable gap to the proposed framework. Overall, the across-the-board reductions in RMSE/MAE/MAPE together with the highest (R^2) support the conclusion that the decomposition-plus-specialized-learning strategy delivers more stable and accurate forecasts than single-architecture baselines.
The comprehensive comparison against a spectrum of competitive baselines reveals that, while these baseline models represent the forefront of time-series modeling, they share a fundamental limitation: they all attempt to model the raw, highly non-stationary PV generation series within a single, monolithic latent space.
In comparison with classical LSTM and GRU, the limitations of simple sequence modeling are evident. These models yield the highest errors, with RMSEs of 6.3152 and 6.3685, and MAPEs hovering around 34%. This poor performance stems from their struggle to handle the high-frequency volatility inherent in solar power. In contrast, our proposed method reduces the RMSE by approximately 69% compared to these baselines. This massive reduction confirms that relying solely on memory gates is insufficient for non-stationary data, whereas our decomposition-based approach effectively simplifies the input complexity.
Regarding the intermediate deep learning models such as TCN, standard Transformer, and N-BEATS, we observe a noticeable improvement over LSTM and GRU baselines but a continued gap with our method. For instance, while N-BEATS achieves a respectable RMSE of 4.2976, it still lags significantly behind our model’s 1.9289. Similarly, the standard Transformer achieves an (R^2) of 98.12%, which, while high, is overshadowed by our near-perfect 99.69%. These advanced architectures outperform LSTM and GRU by utilizing convolution or self-attention to capture longer dependencies, yet they still process the raw, noisy signal directly. Our results demonstrate that explicitly separating noise via BKA-VMD serves as a superior feature engineering step compared to the internal feature extraction of these intermediate models.
Most critically, the comparison against the forecasting models, PatchTST and iTransformer, highlights the unique value of our hybrid strategy. PatchTST and iTransformer are currently considered benchmarks in time-series forecasting due to their patching and inverted attention mechanisms, achieving RMSEs of 3.9976 and 3.4656, respectively. However, our proposed framework still outperforms the best baseline (iTransformer) by reducing the MSE from 12.0104 to 3.7208−a remarkable 69% reduction in variance. This proves that even the most advanced end-to-end deep learning architectures cannot match the precision of a divide-and-conquer system that assigns specialized learners (OKELM and CNN-ESN) to specific frequency components.
To verify the statistical significance of the above prediction results, we conducted a Diebold–Mariano(DM) test on them. During the DM test, the proposed model was designated as the first model, while the other models were respectively designated as the second models. The results of the DM test are reported in Table 4.
Based on the DM test results of the three loss functions (MSE, MAE and MAPE), the null hypothesis was rejected at the 5% significance level, and the DM test statistic values were all negative. This fully proves that the proposed model has superior predictive capabilities in the photovoltaic power prediction task compared to other models.
Forecasting performance of different model variants in the ablation study.
Radar chart comparison of evaluation metrics for different forecasting models in ablation study.
To thoroughly assess the contribution of each component in the proposed CEEMDAN-BKA-VMD-OKELM-CNN-ESN framework, we conducted an ablation study. The purpose of this study is to isolate the effect of each module−CEEMDAN-based decomposition, BKA-driven parameter optimization, VMD-based secondary decomposition, and the dual learners OKELM and CNN-ESN−on the overall forecasting performance. This design enables us to verify the necessity and effectiveness of individual components in improving predictive accuracy and robustness. Specifically, we implement the ablation study by systematically removing or replacing one module at a time from the full framework and then evaluating the resulting variants on the same test set using the metrics MSE, RMSE, MAE, MAPE, and (R^2). The quantitative results are reported in the Table 5, Fig. 10 and Fig. 11.
The limitations of standalone predictors are evident. The single OKELM and CNN-ESN models exhibit the highest errors, with RMSEs of 6.3902 and 6.2386, respectively. Their relatively low (R^2) values indicate that without signal preprocessing, these models struggle to learn the mapping between historical inputs and future outputs due to the superposition of noise and trends in the raw PV data.
The introduction of CEEMDAN marks the first tier of improvement. By decomposing the non-stationary series into IMFs, CEEMDAN reduces the complexity of the input features. Consequently, the CEEMDAN-OKELM model lowers the RMSE to 5.7010, and CEEMDAN-CNN-ESN further reduces it to 5.1090. While this represents a tangible gain, the improvement is capped because standard CEEMDAN may still leave residual high-frequency components that contain mode mixing, hindering precise prediction.
To address this, the secondary decomposition via VMD proves critical. As shown in the table, the CEEMDAN-VMD-OKELM model achieves a significant drop in MSE to 22.6865 (from 32.5076 in the CEEMDAN-only version). This validates that further decomposing the volatile IMFs into band-limited sub-modes helps disentangle the complex, chaotic signals that a single decomposition stage cannot handle.
Furthermore, the optimization via BKA is shown to be indispensable. By adaptively tuning the VMD parameters, the CEEMDAN-BKA-VMD-OKELM model reduces the MSE even further to 14.9851. This substantial improvement over the standard VMD variant confirms that BKA effectively prevents the loss of useful information caused by improper parameter selection (k and (alpha)) in VMD, ensuring that the decomposed sub-modes are physically meaningful and easier to forecast.
Finally, the comprehensive framework achieves the state-of-the-art performance. By integrating the strengths of all modules−using OKELM for high-frequency fluctuations and CNN-ESN for low-frequency trends−the proposed model achieves a dramatic reduction in error metrics, with the MSE plummeting to 3.7208 and the MAPE reaching a minimal 5.9927%. This final leap demonstrates that the divide-and-conquer strategy, combined with adaptive optimization, is far superior to any single-stage or unoptimized hybrid approach.
Seasonal forecasting performance on the DKA dataset: (a) Spring, (b) Summer, (c) Autumn, and (d) Winter.
To further verify the generalization ability of the proposed model under different geographical locations and seasonal variations, we conducted a robustness experiment using the Australia dataset (year 2022). The test samples were grouped by season, namely Spring, Summer, Autumn, and Winter. The forecasting performance was evaluated using MSE, RMSE, MAE, MAPE, and (R^2).
As reported in Table 6 and Fig. 12 the proposed model achieves consistently strong performance across all seasons, with (R^2) remaining above 99.06%. Specifically, Autumn yields the best overall accuracy, where MSE is 0.1074, RMSE is 0.3278, MAE is 0.1708, MAPE is 0.0997%, and (R^2) reaches 99.5511%. Spring also shows competitive results with (R^2) of 99.4119%, while Summer and Winter maintain stable errors and high goodness-of-fit, demonstrating that the proposed model is robust to seasonal pattern shifts and can generalize well under varying environmental conditions.
The forecasting of ultra-short-term PV power is crucial for improving PV dispatch strategies and ensuring the safe and stable operation of power-system equipment. This paper proposes a hybrid ultra-short-term PV power forecasting framework, CEEMDAN-BKA-VMD-OKELM-CNN-ESN. The original PV power series is orderly decomposed by CEEMDAN and BKA-VMD into multi-frequency subsequences, which effectively reduces nonstationarity and facilitates the extraction of informative patterns. Specifically, the decomposed components are categorized by frequency: the stable low-frequency trends are predicted by the CNN-ESN model to capture long-term dependencies, while the fluctuating high-frequency components are handled by OKELM to efficiently track rapid changes. This targeted approach ensures that the distinct characteristics of each sub-signal are modeled by the most suitable predictor.
Experimental comparative results comprehensively demonstrate that the proposed hybrid methodology significantly enhances ultra-short-term PV power forecasting precision, providing a critical reference for the further optimization of power grid dispatch strategies. This study establishes a robust framework for ultra-short-term generation prediction, effectively overcoming the stochastic nature of solar irradiance to achieve superior accuracy. In terms of quantitative performance, the proposed model exhibits exceptional metrics on the test dataset, recording an MSE of 3.7208, an RMSE of 1.9289, and a remarkably high goodness-of-fit with (R^2) of 99.6987%. The model successfully captures the complex, non-linear dynamic characteristics of solar energy generation with minimal deviation. When benchmarked against competitive baselines, the advantage of our approach is substantial. In comparison with the iTransformer, a strongest baseline model, as well as other mainstream algorithms, our method demonstrates a decisive performance lead, reducing the MSE and RMSE by approximately 69.0% and 44.3%, respectively. This significant error reduction proves that the proposed multi-stage decomposition and ensemble strategy offers a much more effective solution for error mitigation than single-structure deep learning models. Furthermore, the seasonal validation on the DKA dataset confirms the model’s distinct robustness against environmental variations. The model maintains high stability across all seasons, with the most precise forecasting results observed during the Autumn season, yielding a minimal MSE of 0.1074 and RMSE of 0.3278. Even under varying meteorological conditions, the model consistently aligns with actual power outputs.
In summary, by consistently outperforming existing optimization algorithms and mainstream forecasting models across all evaluated metrics, this study establishes a robust framework for ultra-short-term PV power prediction. The proposed hybrid approach effectively overcomes the stochastic nature of solar irradiance to achieve superior accuracy, proving its validity and effectiveness. Consequently, this method offers significant practical value for enhancing the operational stability of ultra-short-term PV power systems and provides a critical reference for the further optimization of power grid dispatch strategies.
However, this study is limited by the available samples and focuses on a single PV power time series. Future work will focus on: (1) integrating multi-modal data (e.g., sky images, satellite observations, and IoT sensor measurements) to enable nowcasting with high spatiotemporal resolution; (2) enhancing model interpretability via explainable AI and physics-informed/physics-guided learning, so that predictions are trustworthy and actionable for operational decision-making; (3)optimizing the model’s computational efficiency (e.g., exploring simplified or alternative signal decomposition methods to reduce preprocessing overhead) exploring lightweight architectures to facilitate its deployment on edge-computing platforms for real-time forecasting in practical PV plant operations; and (4) adopting advanced probabilistic forecasting frameworks to strengthen uncertainty quantification under extreme weather events, thereby improving grid operational resilience.
The measurement data from a PV power plant located in Ningxia during the spring of 2017 for feature factor correlation analysis and feature selection. The installed capacity of this PV power plant is 150kW, and the data sampling interval is 15mincontaining 8 features. The datasets generated and/or analyzed during this study are publicly available on the Figshare platform at https://figshare.com/s/7a26e8c40a049cb305a0 and https://dkasolarcentre.com.au/download?location=alice-springs. The repository is distributed under the permissive MIT open-source license and is permanently archived through Figshare’s preservation partnership with the Software Heritage Foundation, ensuring long-term accessibility. Users are free to download, reuse, and adapt the data, provided that appropriate credit is given to the original authors and all license terms are observed.
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College of Mathematics and System Science, Xinjiang University, Urumqi, 830017, China
Shuyi Xue
Shanghai Electric Vehicle Public Data Collecting, Monitoring and Research Center, Shanghai, 100081, China
Lei Li
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Shuyi Xue wrote the the manuscript. Shuyi Xue and Lei Li conducted the experiment(s). Shuyi Xue and Lei Li analysed the results. All authors reviewed the manuscript.
Correspondence to Shuyi Xue.
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ATWATER — While some lament the loss of farmland to solar farms, it is solar farms that are making it possible for Mark Schleski to realize a longtime goal of being an agricultural producer.
He grew up in the Twin Cities metropolitan area, but developed a passion for agriculture when spending time as a youth on his grandparents’ farm in Renville County.
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“Ten years ago I had the idea of grazing sheep on solar sites, but I didn’t know how to get into it,” said Schleski.
“It’s given an opportunity for small guys like me to get into farming,” he said.
It provides him the grazing land he needs but is unable to afford. The high cost of land is the biggest obstacle for those wanting to get into farming, he pointed out.
He spoke while he and his wife, Valerie, and their sons and daughter hosted a field day at one of the six solar farms on which they graze their sheep. The Schleskis, the Minnesota Lamb and Wool Producers, and Minnesota Native Landscapes offered a look at this version of “agrivoltaics” at the Aurora Solar Project site on the east side of Atwater on June 6.
Schleski called it “one of my favorite sites.” Along with the rows of solar panels and their inverters, the 35-acre site includes a couple of cattail-ringed wetlands set among the diverse mix of pollinator-friendly vegetation, full of flowers and grasses, that fill the site like a carpet. “Peaceful,” said Schleski. “Ducks flying out here, birds chirping.”
Solar panels on the 4-megawatt capacity site are turning sunlight into electricity as 152 ewes graze. The sheep spend most of their time feeding in the shade of the panels.
Sheep provide “supergood management” of the vegetation that solar power companies plant under their panels, according to Jodee Nohner. A sheep producer herself, she is also the grazing manager for Minnesota Native Landscapes. It helps solar power companies across Minnesota manage the vegetation on their solar sites.
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Nohner works with solar producers at sites all across Minnesota, as well as in neighboring states, on behalf of Minnesota Native Landscapes. She manages 1,100 sheep at one large solar site.
She works with a number of sheep producers like Schleski. They need land for grazing sheep and are willing to transport their animals to various sites.
There are currently 546 utility-sized solar power sites scattered around Minnesota, according to Cleanview, which monitors solar development.
The Schleski family lives on a 6-acre farmplace in Renville County, which is the base for their lambing operation. They rely on access to solar sites located from Lake Lillian to Montevideo to provide the grazing land they need for their ewes. Mark and Valerie both have off-farm jobs. The sites they use are all within 35 miles of their rural Renville home.
For solar sites, sheep nearly do it all. They keep the vegetation trimmed so it doesn’t shade the solar panels. Sheep help keep the weeds in check, reducing the amount of herbicide needed for site management. They also fertilize the vegetation, keeping it healthy and productive for its role as pollinator habitat.
Grazing is better than mowing too in that it produces no thatch, which mowing creates. Dry thatch represents a fire risk for solar sites, said Nohner.
Even when sites are grazed, her team with Minnesota Native Landscapes will still do some mowing. They also do herbicide spraying as needed to suppress noxious weeds.
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The Atwater site is one of 16 that are part of Enel Green Energy’s Aurora Solar projects in Minnesota. It has a similar but larger site near Paynesville.
The Atwater site was recognized a few years ago with a habitat award for its pollinator vegetation, according to Nohner. While the sheep look at flowers as “candy” and munch on them first of all, the pollinator value of the site is not lost, she said.
Minnesota Native Landscapes manages each site by balancing the needs of solar production, grazing and pollinator vegetation. The Schleski sheep will graze at this site for four to five weeks before the family will move them elsewhere. After the sheep leave, the flowering plants will bloom with vigor thanks to the nutrients they have left behind.
For sheep producers, there is a learning curve and challenges that come with solar sites.
“They didn’t really think about sheep when they were designing these things,” said Nohner of the solar sites. Grazers need space for trailers and trucks.
While the sites are entirely fenced, the fencing was designed with the intent to keep wildlife out, and not necessarily to keep livestock in. The Schleskis had one ewe that learned to crawl under the fence at the Atwater site for greener pastures. One time, it got her a ride in the back of a squad car, Mark Schleski said with a laugh.
He keeps his phone number posted on the fence outside the solar site. Neighbors called him when the errant ewe escaped.
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The family hauls water to a portable tank on site for the sheep. A trail camera connected to a cell phone allows them to keep track of when it will need a refill.
No different than grazing sheep in a “regular” pasture, the Schleskis have to keep watch over their flock and the fencing. They lost six ewes to coyotes last year, and are now training guard dogs to help them with protecting the animals.
Nohner said not only do sheep provide the best vegetation management, they also do not pose some of the problems other livestock do. Goats are more apt to damage equipment than sheep, for one thing, she said.
Cattle are generally too large for the typical solar farm, but solar farms can be designed with the large bovines in mind. The University of Minnesota Morris and the West Central Research and Outreach Center in Morris operate a dairy agrivoltaics system. The solar panels are on elevated towers and provide shade for the cows on pasture.
Overall, taking advantage of solar sites for grazing sheep works well for the Schleski family. Most important, it helps make the economics work. Instead of having to buy costly feed for their ewes, the farm is paid for grazing the sheep on these sites, Schleski explained.
Nohner said solar power companies are especially attentive to keeping good relationships with their neighbors. Sheep do a great job of keeping their pollinator-friendly sites free of weeds, and people appreciate seeing that the land remains in some form of agricultural production.
“In the end, we’re in the eyes of the public, so we have to make sure we look good here too,” said Schleski.
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Scientific Reports volume 16, Article number: 17940 (2026) Cite this article
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In the realm of photovoltaic (PV) solar systems, optimizing the extraction of maximum power from solar panels is paramount for enhancing efficiency and overall performance. This paper introduces a novel MPPT algorithm based on the Fuzzy Fishier Mantis Optimizer (FFMO) method tailored specifically for PV solar systems. The new algorithm combines the effective searching and using abilities of the FFMO method with the special features of PV panels to continuously find the maximum power point (MPP) as environmental conditions change. For the solar PV battery system, we provide a fuzzy logic-based maximum power tracking and an optimized proportional integral-based voltage controller using the Fishier-Mantis optimizer. We utilize an algorithm that employs fuzzy logic to maximize PV panel output, irrespective of environmental circumstances. We use the Fishier Mantis optimizer technique to adjust the proportional integral controller’s gain, which keeps the voltage steady across the load and helps the fuzzy logic MPPT get the most power from the PV panel. MATLAB Simulink has been used to model and test the whole system. The Fishier Mantis Optimizer is a particle swarm optimization and a genetic algorithm competitor. The whole system has been tested for constant irradiation, variable irradiance, and varied load circumstances. The PV battery system with proportional integral control shows better results in all situations based on the test results of the fuzzy-based MPPT and the Fishier-Mantis optimizer algorithm.
Photovoltaic (PV) systems are increasingly vital for renewable energy generation, providing a sustainable means to address rising global electricity demand^1,2. However, their performance is frequently constrained by environmental factors such as partial shading and fluctuating solar irradiance, which lead to complex, nonlinear power–voltage (P–V) characteristics with multiple local maxima³. Under such conditions, Maximum Power Point Tracking (MPPT) algorithms are essential to ensure optimal energy extraction⁴. Traditional MPPT methods, including Perturb and Observe (P&O) and Incremental Conductance (IncCond), often experience slow convergence and persistent oscillations around the maximum power point (MPP), particularly in dynamically changing or partially shaded environments⁵. Metaheuristic approaches, such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Dragonfly Optimization (DFO), improve convergence but still face challenges such as entrapment in local minima, high computational complexity, and instability due to random parameter settings⁶. To overcome these limitations, this study proposes a Fuzzy Fishier Mantis Optimizer (FFMO) algorithm, a hybrid MPPT approach that integrates fuzzy logic with the Fishier Mantis Optimizer (FMO). Inspired by the predatory behavior of mantis shrimp, FFMO combines FMO’s balanced exploration–exploitation capability with fuzzy logic’s adaptive, rule-based control to dynamically adjust the duty cycle of a DC–DC converter. This enables rapid convergence, minimized oscillations, and robust operation under partial shading and varying environmental conditions. Unlike standalone FMO or other fuzzy–metaheuristic hybrids, FFMO simultaneously optimizes fuzzy membership functions and proportional–integral (PI) controller gains in real time, achieving high efficiency (up to 98.78%) and fast settling times (as low as 0.0735 s). MATLAB–Simulink 2022a simulations confirm FFMO’s superiority over state-of-the-art algorithms, including Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and Adoptive Cuckoo Optimization Algorithm (ACOA).
The main contribution of this study is to:
Maximize PV power extraction under partial shading by minimizing losses in the system.
Design and implement the DC–DC converter and select appropriate loads for the PV system.
Apply the FFMO algorithm for both normal and partially shaded conditions.
Optimize PI-based voltage control parameters using FFMO for improved tracking performance.
Validate the proposed approach through comprehensive simulations across different operating scenarios.
The originality and key contributions of this study include:
Presenting a novel, nature-inspired metaheuristic algorithm to accurately optimize control parameters.
Extending and evaluating the FFMO approach against prior MPPT methods.
Implementing an adaptive fuzzy–FMO framework that reduces oscillations, improves convergence, and handles complex partial shading effectively.
Providing practical implementation insights through MATLAB–Simulink and atmospheric field studies.
Contributing to more efficient, cost-effective, and robust PV systems suitable for industrial applications.
FFMO effectively addresses the primary challenges faced by conventional and metaheuristic MPPT methods, including slow convergence, local minima entrapment, oscillations, and high computational requirements. By combining the global search capabilities of FMO with fuzzy logic’s adaptive control, FFMO provides fast, reliable, and high-efficiency power tracking, demonstrating significant potential for real-world PV applications, including grid-connected and storage-integrated systems.
Recent technological improvements in solar cells have slightly enhanced panel voltage, current, and efficiency. Reference⁷ proposes a fuzzy neural controller for active power control in hybrid AC/DC microgrids with decentralized PV sources, ensuring efficient power sharing and reliable MPPT under partial shading conditions. The modified version of the Bat algorithm was employed in⁸ to determine the maximal power point of a photovoltaic system with partial shading capability and an incremental conductance algorithm. A modified Bat algorithm with partial shading capability is employed in⁹ to determine the optimum power point of a PV solar system.
The different methods based on the Deep Learning and artificial neural network¹⁰, metaheuristic based on the partial shading capability¹¹, fuzzy based¹², partial shading mitigation based on metaheuristic¹³, metaheuristic based^14,15 are used to find the MPPT algorithm.
Recent advancements in hybrid MPPT methods have explored combinations of fuzzy logic and metaheuristic algorithms to address partial shading challenges^7,16. For instance, The Improved Manta Ray Foraging Optimization (IMRFO) algorithm enhances the performance of MPPT in PV systems under partial shading conditions. It does so by achieving faster convergence, higher tracking accuracy, and a more stable power output. However, these benefits come at the cost of increased computational complexity¹⁷. The proposed hybrid INC–IBSC MPPT controller enhances PV system performance by combining the MPP-seeking capability of the incremental conductance algorithm with a Lyapunov-based integral backstepping controller, achieving faster tracking, higher efficiency (99.94%), and improved stability under dynamic conditions. While validated under uniform irradiance, it outperforms conventional INC and other recent MPPT methods, suggesting potential for extension to partially shaded PV systems¹⁸. The hybrid Ant-Fuzzy Optimization (AFO) algorithm enhances MPPT in PV systems under partial shading by combining ACO and FL for high-accuracy, fast, and robust maximum power point tracking, despite increased complexity¹⁴. Although previous studies have combined fuzzy logic with swarm intelligence algorithms such as Particle Swarm Optimization) PSO) and Cuckoo Search (CS) for maximum power point tracking (MPPT)^14,16, the FFMO algorithm distinguishes itself by the integration of Fruit Fly Mantis Optimization (FMO), inspired by the unique predatory behavior of mantis shrimp. Unlike PSO or CS, which often exhibit oscillations and slow convergence under partial shading (as discussed in Sect. 1), FFMO employs a balanced exploration–exploitation strategy, further enhanced by fuzzy logic’s adaptive tuning of membership functions. This synergy enables FFMO to converge more quickly (e.g., 0.0406 s in Case 2) and achieve higher efficiency (such as 95.73% in Case 2) compared to other hybrid approaches. For instance, fuzzy-PSO techniques ¹⁴ typically achieve efficiencies below 90% under similar conditions, whereas FFMO consistently exceeds 95% efficiency across all tested cases (Table 5). This superior performance arises from FFMO’s dynamic adjustment of fuzzy rules via FMO optimization, which reduces oscillations and ensures robust MPPT even in complex shading environments. A photovoltaic cell is a semiconductor device that converts sunlight into electrical energy. PV systems face problems like high cost, limited reliability, and efficiency issues. Hence, Simulation and modeling are important for enhancing performance and designing PV applications¹⁹.
Many solar cells are connected in series or parallel to create a module, as a single solar cell produces minimal power. The modules are subsequently connected to produce a PV array with the desired voltage and current²⁰. The nonlinear characteristics of photovoltaic cells are contingent upon the levels of radiation and temperature. The efficiency of a PV array is diminished when a portion of it is shaded, resulting in a more intricate power curve with multiple peaks^21,22.
Shading can be caused by nearby buildings, trees, chimneys, or dust on the panel surface²³. MPPT algorithms are used to optimize efficiency under changing conditions of load. MPPT is applied by a controller that interfaces with the PV module’s power converter.
Figure 1 illustrates this operating point on the current–voltage and power-voltage curves. Figure 1a Current–voltage (I-V) and (b) Power-voltage (P–V) characteristic curves of a solar PV array under uniform irradiance. The x-axis represents voltage (V) ranging from 0 to 36.8 V (V_oc), the y-axis in (a) represents current (A) from 0 to 8.08 A (I_sc), and the y-axis in (b) represents power (W) from 0 to 220.5 W (P_mp). The maximum power point (MPP) is marked at V_mp = 30 V and Imp = 7.35 A.
(a) Voltage–current, (b) Power–voltage characteristic contours of solar PV arrays Solar photovoltaic array characteristic curves²⁴.
The solar PV Characteristic is nonlinear and various with temperature and irradiation. generally, there is two various parameters have to be introduced:
The short circuit current (ISc) is the current flowing through a PV cell when the voltage is negative. The Open Circuit Voltage (VOC) is the voltage of a photovoltaic cell when the current flowing through it is negative.
The photovoltaic array operates at its highest efficiency at a unique point on the I-V parabola, known as the maximum power point (MPP).
But under partial shading conditions, these characteristic curves become more complex and more than one peak appear, as seen in Fig. 2. Figure 2. (a) Current–voltage (I-V) and (b) Power-voltage (P–V) characteristic curves of a solar PV array under partial shading conditions, showing multiple peaks. The x-axis represents voltage (V) ranging from 0 to 36.8 V per module, scaled by the number of series-connected modules. The y-axis in (a) represents current (A) from 0 to 8.08 A, and the y-axis in (b) represents power (W) with multiple local maxima, where the global MPP is marked.
Current–voltage characteristic curves during partial shading conditions and Power-voltage characteristic curves during partial shading conditions²⁵.
Various MPPT schemes have been proposed and studied to improve tracking performance²⁶. However, some of these methods suffer from oscillations near the MPP and slow response, making them less effective under rapidly changing weather conditions²⁷. To address this issue, Kota and Bhukya²⁸ proposes a new MPP tracker with control scheme based on ANN for detecting the global maximum power point under partial shading conditions. In photovoltaic applications, DC-DC converters are commonly employed to establish the connection between the PV module and the load. In order to extract the maximum power, the load must be dynamically adjusted to match the current and voltage of a PV panel.
Initially, real-time voltage and current data from the PV module are sensed and fed into a fuzzy logic controller, which interprets the input using a predefined rule base to generate adaptive control signals for rapid convergence towards the MPP. The Fishier Mantis Optimization (FMO) algorithm, inspired by the foraging and attack behavior of mantis shrimp²⁹, is embedded within the fuzzy logic structure to dynamically adjust membership functions and optimize decision-making parameters, thereby reducing oscillations around the MPP. The combined FFMO strategy ensures robust tracking by exploiting global search capabilities of FMO while maintaining the fast response and rule-based adaptability of fuzzy logic. The optimized duty cycle generated by the FFMO is applied to the DC-DC converter to continuously extract maximum power from the PV system under varying irradiance and temperature levels. The overall method is illustrated in Fig. 3.
Diagram of proposed method.
A DC–DC boost converter was employed to interface the PV array with the load, ensuring proper impedance matching for maximum power extraction. The converter was designed for an input voltage range of 30–36.8 V, corresponding to the PV module’s V_mp and V_oc, and an output voltage of 48 V, suitable for typical battery charging application. A switching frequency of 20 kHz was selected to balance conversion efficiency with component size. The inductor value was determined using:
where ({V}_{in}=30, {V}_{out} =48,) ({f}_{s}=20 kHz,) and (Delta {rm I}_{L}) is the inductor current ripple (20% of the maximum current, 7.35 A). This yielded an inductance of approximately 1.2 mH.
Similarly, the output capacitor was sized to minimize voltage ripple using:
where ({rm I}_{out})≈4.59A, duty cycle D = 0.3852 (series) or 0.21088 (parallel), and allowable ripple (Delta {V}_{out}=1text{% of }48text{ V}). The calculated capacitance was approximately 470 µF.
These design parameters ensure efficient converter operation under the dynamic duty cycle adjustments provided by the FFMO algorithm, thereby enhancing reproducibility for hardware implementation.
The Fig. 3 illustrates the operational flow of the Fuzzy Fishier Mantis Optimizer (FFMO) applied to MPPT in a PV solar system. The process begins with the PV Solar System, which continuously generates electrical energy based on available sunlight. The next step involves Voltage and Current Sensing, where sensors measure real-time electrical parameters from the PV array to monitor its performance. Algorithm 1 presents the proposed Fuzzy Fisher Mantis Optimizer (FFMO)-based MPPT approach, which integrates fuzzy logic with the Fisher Mantis Optimizer to dynamically adjust the duty cycle of the DC–DC converter, ensuring fast convergence, reduced oscillations, and accurate tracking of the MPP under varying environmental conditions. The FFMO algorithm integrates fuzzy logic with the Fisher Mantis Optimizer (FMO) to achieve fast and stable MPPT. The process begins with initializing a population of candidate duty cycle solutions in the range of 0–0.9. Real-time PV voltage and current measurements are collected to compute power, which is then used to determine the error (E) and the change in error (ΔE). These inputs are processed by the fuzzy logic controller (FLC) through a predefined rule base to provide an initial duty cycle adjustment. In parallel, the FMO algorithm emulates the hunting strategy of mantis shrimp, where each candidate solution explores the search space through a combination of random exploration (Eq. 14) and directed movement to optimal positions (Eq. 15). The walk parameter (Eq. 11) gradually decreases the step size across iterations, transitioning the search from global exploration to local refinement and ensuring convergence toward the MPP. At the same time, the FMO optimizes the FLC membership function to minimize oscillations around the MPP. The optimized duty cycle is then applied to the DC–DC converter, and the iterative process continues until the power change falls below a defined threshold, signifying that the MPP has been reached. This structured pseudocode enhances clarity, reproducibility, and guidance for researchers implementing the FFMO algorithm.
Fuzzy Fishier Mantis Optimizer (FFMO) for MPPT.
These values are then fed into a Fuzzy Logic Controller (FLC), which acts as an intelligent decision-making unit. The FLC interprets the error (difference between previous and current power outputs) and the change in error using a predefined rule base and fuzzy membership functions. The outputs from the FLC are used as inputs for the Fishier Mantis Optimization (FMO) process.
The FMO mimics the predatory behavior and sensitivity of mantis shrimps to optimize critical fuzzy parameters such as membership function shapes, scaling factors, or control rules. This optimized decision-making process is encapsulated within the FFMO Algorithm, which outputs an improved duty cycle to maximize the power extraction.
The next block is a decision node that evaluates whether the current operating point has reached the Maximum Power Point (MPP). If the MPP is reached (i.e., the power change is below a threshold), the system maintains the current control parameters. If not, it continues to Apply the Updated Duty Cycle to the DC-DC converter (usually a boost or buck-boost converter), adjusting the PV module’s operating voltage to move closer to the MPP.
This loop continues dynamically in real-time, adapting to changes in solar irradiance and temperature, ensuring that the system always operates at or near its peak efficiency.
In this paper, a clustering model based on the Fishier Mantis Optimizer (FMO) algorithm is used to find the optimized Proportional Integral-based voltage controller. First, the FMO algorithm is formulated based on the hunting behavior of this insect, and in the second part, this behavior is used to find the optimized Proportional Integral-based voltage controller.
The mantis is an insectivorous carnivore. It is green and locust-like, with long legs, large heads, and two sets of wings (Fig. 4a). Mantises live mainly in the tropics, but some are found in temperate zones. Female mantises often kill and consume the males after mating. Some mantises can turn their heads 180 degrees to look at their surroundings. They primarily live on trees and camouflage themselves as twigs to quickly approach prey. One kind of mantis hunts fish. These mantises disguise themselves underwater for days, catching up to nine fish in five days. Figure 4b shows a fish-eating mantis camouflages its prey.
(a) mantis and (b) fish-eating mantis.
The fishier mantis displays intelligent predator behavior by assessing different situations and moving towards its prey. Its goal is to reach the optimum place where the prey is. It can either prepare to strike or choose to give up the hunt.
In the FMO algorithm, all fishier mantises explore random positions in the problem space, treating each as a candidate solution. These positions are evaluated using the objective function to find the one nearest to the optimal solution, as described in Eqs. (3) and (4).
({X}_{ij}) represents j_th component of the solution of i_th solution. A random set of solutions is generated in the first iteration as illustrated in Eq. (2)³⁰:
The equation (Mantis) defines as a matrix of possible solutions. The (F(Mantis)) represents their performance value. Each solution ({X}_{i}) consists of d dimensions (({X}_{i1},{X}_{i2},{X}_{i3},dots ,{X}_{id})). Random solutions are provided by Eq. (5).
Here, rand (0,1) shows the random vector between zero and one. L and U are the lower and upper limits of the search space.
In the FMO algorithm, moths choose a new position to hunt, place themselves in that position, and camouflage themselves. Mantis can memorize several different states, and these optimal states in a matrix are defined as m. The state is m < n and is defined according to Eq. (6):
The states matrix holds states. It is assumed that the value of optimality is proportional to the maximum of solutions. It is assumed that the mantis keeps these limited conditions in its memory and hunts mainly in these areas.
Each time the status matrix is updated, the mantis has better conditions to locate in the matrix. A mantis can randomly select an optimal situation and move towards it, as in Eq. (7), and take a position in it:
({X}_{i}) is the current position of a mantis, ({X}_{i}^{new}) is the new position of a mantis, (States(j)) is a random state and j is a random member calculated from Eq. (8):
The walk is the size of the mantis’s step towards the desired solution. The value of the walk parameter is reduced by the iteration of the FMO algorithm because it is assumed that the mantis reduces step size. In contrast, the mantis closes the prey or the optimal solution. To change the walk parameter of relation (9), it is suggested:
In this respect, it is the current iteration number. The value of the MaxIt is the final iteration number of the algorithm. To apply a more random behavior, the Chebyshev random function is used, and the criterion of the desired function is according to Eq. (10). The step relation is formulated as Eq. (11):
The maximum number of iterations of the proposed algorithm is 100. The step reduction changes the nature of the search from global to local search in terms of the algorithm iteration.
Any solution or mantis can ignore the previous optimal situations and look for a random position. Random position selection improves the algorithm’s global search and reduces the likelihood of convergence to local optimizations. To modeling this behavior, Eq. (12) be used:
The value of r is a random number between zero and one.
Each solution or mantis can consider the previous optimal conditions and use the knowledge of all of them. It also searches for the space between the mean and the optimal state it has achieved so far, as in Eq. (13):
In this equation, (overline{States }) is the average number of optimal solutions and is calculated like Eq. (14):
In the proposed method, by increasing the iteration counter and approaching the mantis to the prey, the number of situations is reduced based on the iteration of the algorithm, such as Eq. (15). The value of m is the number of initial states and (mleft(tright)) is the number of states in iteration t:
Clustering is one of the most important data mining techniques in discovering hidden patterns. In this data mining technique, each data be clustering according to its similarity to other data. In clustering methods, unlike classification methods, data labels are not used to separate the data, and clustering is performed based on the similarity of the data with the centers of the clusters.
To find the optimized Proportional Integral-based voltage controller, there are several answers with a tune value that linearized the. The values are according to Eq. (16) in a set:
In this regard, I is related to values that obtain from the system. Value of ({X}_{i}) is also the optical information of the ith pixel. The purpose of clustering is to place the specimens within k of the cluster (Eq. (17)) so that the objective function of Eq. (18) is minimized:
In the objective function, the weight value ({w}_{ij}) is assessing according to the condition of Eq. (19):
In the proposed method, the fish-eating mantis optimization algorithm is using to minimize the clustering objective function. Each mantis is considered as a vector such as Eq. (18), which is a set of cluster centers. A number of these random cluster centers are created as populations of Fishier Mantis Optimizer algorithms and attempts are made to optimize them by this algorithm.
To ascertain the objective function, it might be necessary to minimize various parameters. The cost function for modules is expressed as Z = 1/P, where obtaining the inverse of the average power is crucial for determining the maximum power value. Equations (14) and (15) elucidate the relationship between the cost function and power.
This equation indicates that the cost function utilized in the optimization process is inversely proportional to the average power. The cost function serves as a measure of solution quality in the optimization algorithm. The symbol ∝ (proportional to) signifies a direct relationship, implying that the cost function is directly linked to the inverse of the average power, represented as 1/(Power Average).
The cost function outlined in this paper is defined by the equation in (16):
Here, the variable cost function Z is represented, with P denoting the average power acquired at each stage of the FMO algorithm procedure. This algorithm continually seeks the smallest value for the boost converter before attaining maximum power output, with X symbolizing the duty cycle of the FMO algorithm. At the core of the primary cost function, cost is depicted as 1/P.
According to this equation, high power yields low Z, resulting in minimal cost. Figure 5 presents the depiction of the MPP calculated using the FMO technique.
Flowchart for the suggested procedure.
The specified parameters to include are: (alpha), (beta), (lambda), upper bound (0, 1), and lower bound (0.9) for both the FMO and the population of the FMO.
Establishing the initial population of FMO, denoted as (n). The fitness value attains its maximum when the power reaches its peak.
The fitness function, represented as Cost Function ∝ 1/(Power Average), as shown in Eq. (23).
In the evaluation of brightness, direct all FMO towards the brighter ones. During the position update, relocate all FMOs to a more advantageous position.
Due to the utilization of FFMO in the simulation, the solution will be derived after four generations.
The FFMO algorithm combines fuzzy logic with a proportional-integral (PI) controller to enhance MPPT in photovoltaic (PV) systems. Its control framework consists of three main components: (1) a fuzzy logic controller (FLC) that investigates current (I_pv) and real-time voltage (V_pv) to calculate the error (left(E=Pleft(tright)-Pleft(t-1right)right)) and the error change (ΔE), (2) the FMO algorithm which optimizes both the FLC’s membership functions and the PI controller gains, and (3) a DC-DC boost converter using the optimized duty cycle. The FLC uses a rule base of 25 rules (such as, IF E is positive large AND ΔE is negative small, THEN ΔD is positive medium) to generate an initial adjustment of the duty cycle (ΔD). The FMO algorithm refines the shapes and limits of the membership functions, such as triangular functions for E and ΔE, by treating parameters like peak and width as optimization variables, as defined by Eq. (20). Additionally, FMO optimizes the proportional (K_p) and integral (K_i) gains of the PI controller to minimize voltage fluctuations across the load, using the cost function Z = 1/P (Eq. 16). This dual-optimization approach ensures that the duty cycle (left({D}_{opt}={D}_{prev}+Delta Dright)) quickly converges to the maximum power point while maintaining a stable voltage output.
This article discusses two distinct scenarios of partial shading. It examines situations involving series-connected PV panels under partial shading conditions and parallel-connected PV panels under partial shading conditions.
The photovoltaic module under consideration has a maximum power output of 220.5 W and comprises 60 solar cells. Its open-circuit voltage (V_oc) is 36.8 V, while the short-circuit current (I_sc) measures 8.08 A. At the maximum power point, the voltage (V_mp) is 30 V and the current (Imp) is 7.35 A. The module’s performance is influenced by temperature, with a temperature coefficient of − 0.3364%/°C for V_oc and 0.038465%/°C for I_sc. The light-generated current (IL) is 8.1108 A, and the diode saturation current (I0) is extremely low at 1.1169 × 10⁻¹⁰ A, indicating minimal leakage under reverse bias. The diode ideality factor is 0.9567, which reflects the quality and recombination characteristics of the diode. Additionally, the module’s internal resistive losses are characterized by a shunt resistance (R_sh) of 83.699 ohms and a series resistance (R_s) of 0.3192 ohms, both of which play critical roles in determining the efficiency and fill factor of the PV module.
The simulation results in a minimal value for the cost function of 0.0000613735 after 30 iterations, which can vary depending on the simulation outcome and is not constant. For a serial connection of 6 PV panels, the fourth iteration yielded the optimal solution (Duty Cycle) of 0.3852 and a maximum power value of 16,293.6671. The processing time for this simulation was 21.87 s on a personal computer equipped with MATLAB 2022a version and a 6 GHz Core i7 processor.
In contrast, different values were obtained for a parallel connection of 4 PV panels. After 4 iterations, the simulation reached a minimal cost function value of 0.0000295833. Similar to the serial connection scenario, this value is subject to the simulation outcome and is not fixed. In the fourth iteration of the parallel 4PV panel configuration, the optimal solution (Duty Cycle) was found to be 0.21088, resulting in a maximum power value of 33,802.89 watts. The optimization process and model running time took 25.46 s. This simulation was also conducted on a single personal computer running MATLAB 2024a with a 8 GHz Core i7 processor. The aforementioned results are visually represented in Table 1.
During this phase, power will be determined by analysing voltage and current, while the duty cycle will be derived from the cost value generated by the FFMO technique. Figure 6 demonstrates the objective function curve for series-connected PV panels.
Power mean is 16,293.6671 Watt, for 0.3852 duty cycle.
Best solution is equal to 0.3852.
Best objective is 0.0000613735 (Z = 1 / P)
The function is evaluated 15 times.
Computation time is represented by e = 21.8705 s
Objective function outputs for a series-connected solar panel.
Figure 7 illustrates the (a) current–voltage (I-V) and (b) power-voltage (P–V) characteristics of a series-connected PV system with six panels. The I-V curve shows a non-linear relationship with the x-axis labelled as Voltage (V) ranging from 0 to 220.8 V (6 × V_oc = 6 × 36.8 V) and the y-axis as Current (A) from 0 to 8.08 A (I_sc). The P–V curve highlights the maximum power point (MPP) at approximately 180 V (6 × V_mp = 6 × 30 V) and 16,293.6671 W, with the x-axis as Voltage (V) and the y-axis as Power (W).
I-V and P–V characteristics of series-connected solar system.
The simulation outcomes for the series-connected photovoltaic system are presented in Table 2.
Figure 8 illustrates the results of the simulation for the solar system connected in parallel. It reveals an average power of 33,802.89 W corresponding to a duty cycle of 0.21088. The optimal target achieved is 0.0000295833, with a total of 15 function evaluations and a calculation time of 25.46 s.
The average power is 33,802.89 Watts with a duty cycle of 0.21088.
The optimum solution corresponds to a duty cycle of 0.21088.
The best objective value achieved is 0.0000295833 (Z = 1 / P).
A total of 15 function evaluations were conducted.
The result of simulation for parallel connected solar panel.
The computation time is recorded as 25.46 s.
Note: The reported maximum power of 33,802.89 W for the parallel-connected PV system corresponds to a simulation with a larger array configuration, approximately equivalent to 153 PV modules (33,802.89 W ÷ 220.5 W ≈ 153). For consistency with the four-panel configuration described, the expected maximum power is 882 W (4 × 220.5 W), and the voltage range is limited to 36.8 V (Voc per module). The higher power value reflects an extended array simulation, which was not explicitly specified in the initial setup.
Figure 9 illustrates the (a) current–voltage (I-V) and (b) power-voltage (P–V) characteristics of a parallel-connected PV system with four panels. The I-V curve shows the x-axis as Voltage (V) ranging from 0 to 36.8 V (V_oc per module) and the y-axis as Current (A) from 0 to 32.32 A (4 × I_sc = 4 × 8.08 A). The P–V curve peaks at the maximum power point (MPP) at approximately 30 V (V_mp) and 882 W (4 × 220.5 W), with the x-axis as Voltage (V) and the y-axis as Power (W).
I-V and P–V characteristics of parallel connected solar panels.
The results of simulation for parallel connected photovoltaic panel are depicted in Table 3.
Figure 10 Comparison of (a) current–voltage (I-V) and (b) power-voltage (P–V) curves for series (6 panels, blue) and parallel (4 panels, red) PV configurations. For the series configuration, the x-axis (Voltage, V) ranges from 0 to 220.8 V, and the y-axis (Current, A) ranges from 0 to 8.08 A. For the parallel configuration, the x-axis (Voltage, V) ranges from 0 to 36.8 V, and the y-axis (Current, A) ranges from 0 to 32.32 A. The P–V curves show MPPs at 16,293.6671 W (series) and 33,802.89 W (parallel), with the x-axis as Voltage (V) and the y-axis as Power (W). The blue curve shows 6 PV panels in series; the red shows 4 PV panels in series–parallel.
I-V and P–V curves of series and parallel connected solar panels.
Table 4 outlines the environmental and operational specifications for four different case studies involving photovoltaic (PV) panels, specifically focusing on irradiation levels across four modules and the corresponding ambient temperature conditions.
The simulation setup was implemented in MATLAB-Simulink 2022a using the Power GUI toolbox, modeling a PV system with 60-cell modules with details provided in Sect. 4 (({V}_{oc}=36.8left(vright),{text{rm I}}_{sc}=8.08left(Aright),{V}_{mp}=30left(vright),{text{rm I}}_{mp}=7.35left(Aright))) The load model consisted of a resistive load of 10 Ω for series-connected modules and 5 Ω for parallel-connected modules, representing practical applications such as battery charging or grid-connected inverter inputs. The DC-DC boost converter was designed for an input voltage of 30–36.8 V and an output voltage of 48 V, with a switching frequency of 20 kHz, an inductance of 1.2 mH, and a capacitance of 470 µF. These parameters were selected to minimize ripple and ensure stable operation. Irradiance and temperature profiles for the four case studies are summarized in Table 4, replicating realistic. Table 4, replicating realistic partial shading and thermal variations. The selected parameters guarantee consistency across simulations and align with standard PV system configurations, thereby facilitating reproducibility.
Each case simulates a realistic and varying solar exposure scenario to test the robustness and adaptability of maximum power point tracking (MPPT) algorithms. In Case 1, the PV system is subjected to highly non-uniform irradiation levels, with Module 1 receiving only 100 W/m² and Module 4 exposed to full sunlight at 1000 W/m², under a relatively cool ambient temperature range of 10–25 °C. This case simulates a partially shaded condition where the performance of MPPT techniques under low and uneven irradiance is critically examined. In Case 2, the irradiation is slightly more balanced but still non-uniform, ranging from 100 to 900 W/m², with a constant high temperature of 45 °C. This scenario evaluates the thermal stress on PV modules and how increased temperature negatively impacts efficiency and power output. Case 3 represents a moderately varying irradiation pattern from 200 to 1000 W/m², with a relatively narrow and moderate temperature window of 20–30 °C, representing typical mid-day solar conditions. Finally, Case 4 models high irradiation levels across all modules, ranging from 300 to 1000 W/m², combined with a high temperature range of 30–40 °C, mimicking harsh summer conditions in arid regions. These diverse case studies serve as comprehensive benchmarks to validate the performance, convergence speed, and accuracy of various MPPT algorithms under a wide range of realistic operating conditions, including partial shading, thermal variation, and uneven irradiation distribution across PV arrays.
The significance of taking into account both irradiance and temperature factors in the design, operation, and optimization of PV systems is illustrated by these case studies. In addition, they underscore the necessity of employing suitable technologies and strategies to reduce the effects of adverse conditions on the efficiency and overall performance of the solar power generation system.
Table 5 presents a comprehensive comparison of simulation results for four different cases using five prominent metaheuristic optimization algorithms: Grey Wolf Optimizer (GWO), Cuckoo Search (CS), White Shark Optimization (WSO), Harris Hawks Optimization (HHO), and the proposed Fuzzy Fishier Mantis Optimizer (FFMO). In future work, additional advanced MPPT optimizers such as APSOLF, self-pollination infused APSOLF, and self-adaptive PSO will be implemented under the same test conditions to further extend the comparative statistical analysis of the proposed method. However, to ensure a fair and reproducible comparison, these optimizers will be evaluated after implementing their original parameterization settings and aligning them with the same PV array configuration, irradiance/temperature profiles, and stopping criteria used in this study.
In all instances, FFMO consistently outperforms the other methods in terms of optimum power output, settling time, converging time, and overall efficiency. In Case 1, FFMO achieves the fastest convergence at 0.1625 s and the shortest settling time of 0.0977 s, yielding a maximum power of 97.07 kW and an impressive efficiency of 98.78%, significantly higher than the second-best HHO algorithm. Case 2 further confirms FFMO’s dominance, demonstrating a converging time of only 0.0406 s and a minimal settling time of 0.0735 s while delivering 97.5135 kW at 95.73% efficiency. This marks a substantial improvement over traditional methods like GWO and CS, which show longer response times and lower efficiencies. In Case 3, the FFMO again demonstrates superior behavior, reducing convergence time to 0.0663 s and settling time to 0.1464 s while producing 94.745 kW at 95.32% efficiency, surpassing all other algorithms which hover around 84–88% efficiency. Lastly, Case 4 reiterates the robustness of FFMO, achieving a power output of 94.32 kW with the highest efficiency recorded at 97.98%, while its competitors demonstrate moderate performance in both power output and convergence behavior. These results clearly validate that the FFMO method offers the fastest and most reliable tracking performance under varying simulation conditions, making it a highly effective solution for real-time maximum power point tracking in PV solar systems.
The differences in maximum power outputs observed across Cases 1–4 in Table 5 (i.e., 97.07 kW in Case 1 versus 94.32 kW in Case 4 for FFMO) are primarily attributed to variations in irradiance profiles and temperature conditions, as summarized in Table 4. Case 1 was involved highly non-uniform irradiance levels (100–1000 W/m²) combined with lower ambient temperatures (10–25 °C), which minimized thermal losses and slightly improved power generation. In contrast, Case 4 exhibited higher irradiance levels (300–1000 W/m²) along with elevated temperatures (30–40 °C), resulting in higher thermal losses and a corresponding reduction in output power. These findings align with the realistic behavior of PV systems under various environmental conditions. Notably, FFMO consistently maintained high efficiency (95.32–98.78%) across all cases, whereas competing algorithms displayed greater variability in power outputs (like GWO ranging from 79.25 to 91.24 kW), underscoring FFMO’s robustness and adaptability.
To further evaluate the robustness of the FFMO algorithm under dynamic and complex partial shading operating conditions, two additional case studies were carried out. Case 5 simulated a dynamic irradiance transition in which Module 1’s irradiance increased linearly from 100 to 1000 W/m² in 10 s, while Modules 2–4 remained constant at 500, 700, and 900 W/m², respectively, at a uniform temperature of 25 °C. Case 6 considered a complex shading scenario created by intermittent cloud cover: Modules 1 and 2 experienced a sudden drop from 1000 to 200 W/m² at t = 5 s, recovering to 800 W/m² at t = 8 s, whereas Modules 3 and 4 were kept at 1000 W/m², with temperatures ranging between 20 and 30 °C. In Case 5, the FFMO algorithm achieved a convergence time of 0.0582 s and a settling time of 0.1124 s, and a maximum power output of 96.45 kW with 97.15% efficiency. In Case 6, FFMO attained a convergence time of 0.0617 s, a settling time of 0.1198 s, and a maximum power of 95.82 kW with 96.89% efficiency. Compared with the HHO algorithm—which required 0.1423 s to converge and achieved only 89.67% efficiency in Case 5—FFMO clearly demonstrates superior adaptability to dynamic irradiance variations and complex shading conditions.
To validate the practical applicability of the FFMO algorithm, a small-scale experimental prototype was developed using a 220.5 W PV module (specifications in Sect. 4) connected to a DC-DC boost converter, with an Arduino Mega 2560 microcontroller implementing the algorithm. The prototype was tested under controlled partial shading, where irradiance levels of 200 W/m² and 800 W/m² were applied on two sections of the module at an ambient temperature of 25 °C. The FFMO algorithm dynamically adjusted the duty cycle in real time, achieving a maximum power output of 95.12 W with 96.45% efficiency. In comparison, a standard PSO-based MPPT yielded 88.76 W with 90.23% efficiency under identical conditions. These findings confirm the experimental feasibility of the FFMO algorithm and align with the simulation outcomes presented in Table 5, underscoring its strong potential for real-world PV system applications.
The developed experimental setup was primarily designed to validate the real-time implementation feasibility and MPPT capability of the proposed FFMO algorithm under partial shading conditions. A more comprehensive hardware validation including synchronized voltage and current waveform acquisition will be considered in future work to further assess the transient and steady-state tracking behavior of the proposed method.
To evaluate the performance of the FFMO algorithm, statistical measures such as convergence rate, tracking efficiency, and root mean square error (RMSE) were analyzed across the four case studies summarized in Table 5. The convergence rate, defined as the inverse of convergence time, was 6.15 s⁻¹, 24.63 s⁻¹, 15.08 s⁻¹, and 18.62 s⁻¹ for Cases 1–4, respectively, with FFMO. In comparison, the second-best HHO algorithm achieved 5.97 s⁻¹, 20.83 s⁻¹, 6.44 s⁻¹, and 9.09 s⁻¹. Tracking efficiency, defined as the ratio of extracted power to the theoretical maximum, reached 98.78%, 95.73%, 95.32%, and 97.98% for FFMO in Cases 1–4, substantially outperforming HHO, which achieved 97.12%, 90.62%, 88.86%, and 83.21%. The RMSE of power tracking, calculated as: (RMSE=sqrt{frac{1}{N}} {{sum }_{i=1}^{N}left({P}_{actual,i}-{P}_{MMP,i}right)}^{2}) was 0.012 kW, 0.015 kW, 0.018 kW, and 0.011 kW for FFMO, whereas HHO produced higher errors of 0.087 kW, 0.092 kW, 0.095 kW, and 0.089 kW. These findings confirm the superior speed, accuracy, and stability of FFMO in maximum power point tracking under different operating conditions.
The development and evaluation of the Maximum Power Point Tracking (MPPT) algorithm, which is based on the FFMO method, represent a substantial advancement in the field of photovoltaic (PV) solar systems to conclude. The integration of the FFMO method with MPPT algorithms has been shown to be a promising approach to improving the efficacy and performance of PV systems in this study. The maximum power point (MPP) can be rapidly and accurately tracked under dynamic operating conditions due to the remarkable adaptability of the FFMO-based MPPT algorithm to variable environmental conditions. Its ability to swiftly converge towards optimal solutions, even in the presence of partial shading and varying solar angles, underscores its robustness and effectiveness in real-world applications. Moreover, the computational efficiency and simplicity of implementation make the FFMO-based MPPT algorithm well-suited for deployment in PV systems, offering a practical solution for maximizing energy harvesting and system reliability. By reducing dependency on system parameters and improving convergence characteristics, this algorithm addresses key challenges faced by conventional MPPT techniques, thus contributing to the overall advancement of renewable energy technologies. Looking ahead, further research and development efforts can explore enhancements and refinements to the FFMO-based MPPT algorithm, with a focus on scalability, integration with advanced control strategies, and validation through extensive field testing. In the end, the accelerated transition to a sustainable energy future and the pervasive adoption of solar energy will be significantly influenced by the ongoing evolution of MPPT algorithms, such as the one proposed in this study.
The Matlab code and Simulink model is available in following link: ([https://www.mathworks.com/matlabcentral/fileexchange/183597-fuzzy-logic-based-fishier-mantis-optimization-for-mppt-in-pv](https://www.mathworks.com/matlabcentral/fileexchange/183597-fuzzy-logic-based-fishier-mantis-optimization-for-mppt-in-pv)).
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Department of Electrical and Electronics Engineering, Karabuk University, Karabuk, Turkey
Hamza Elamouri Elwaer & Selçuk Alparslan Avci
Department of Electrical and Electronics Engineering, Istanbul Topkapi University, Istanbul, Turkey
Javad Rahebi
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Non-Chinese manufacturers gain ground as trade barriers reshape global solar supply chains
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LONGi Green Energy ranked first in Wood Mackenzie’s Global Solar PV Module Manufacturer Ranking 2026, as shifting trade policies and continued oversupply reshaped competition across the global solar manufacturing sector.
Wood Mackenzie’s ranking evaluated 48 module manufacturers across ten countries, representing 65% of global production capacity and 83% of global shipments. Wood Mackenzie assessed suppliers across ten criteria, including capacity utilisation, technology maturity, financial health, supply chain resilience, ESG, reliability standards, Research & Development and so on. 
Nine of the twelve manufacturers sharing top-ten positions are headquartered in China, reinforcing the country’s continued dominance across the global solar supply chain. However, suppliers targeting protected and high-barrier markets gained momentum, with India’s Adani Solar ranking sixth, Singapore-based ELITE Solar eighth and South Korea’s Qcells tenth. 
“Chinese manufacturers continue to lead globally on manufacturing scale, technology advancement and operational efficiency. However, severe financial pressure from ongoing oversupply is accelerating structural change across the sector,” said Yana Hryshko, Head of Solar Supply Chain Research at Wood Mackenzie. 
TOPCon modules accounted for more than 80% of shipments among the top 10 manufacturers in 2025, confirming that the transition to N-type technology is now effectively complete among leading suppliers. Mainstream TOPCon module efficiency reached 24.8% during the year. 
Despite strong shipment volumes, persistent global oversupply continued to pressure profitability across the sector. Leading Chinese solar manufacturers recorded a combined loss of US$5.5 billion in 2025, while most non-Chinese manufacturers remained profitable due to stronger pricing conditions in protected high-barrier markets. 
Average capacity utilisation among the top 10 manufacturers rose to 70% in 2025, up from 67% in 2024, signalling improving demand conditions. Meanwhile, manufacturers continued diversifying production footprints outside China in response to rising trade tensions and localisation requirements. Nine of the top 10 manufacturers now operate facilities in at least two countries.  
Wood Mackenzie awarded 25 manufacturers its Grade A status for 2026, recognising suppliers that meet the industry’s highest benchmarks for operational strength, reliability and transparency.  
The Grade A classification provides an independent benchmark for procurement teams, developers and asset owners evaluating module suppliers. Manufacturers qualify by meeting at least five of Wood Mackenzie’s benchmark criteria, reflecting best-practice standards across leading global suppliers.  
The assessment is based on extensive industry survey data and evaluates manufacturers against current market conditions and supplier qualification requirements. The designation identifies manufacturers considered low-risk and high-reliability partners based on Wood Mackenzie’s assessment methodology. 
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When those incentives change, projects can be delayed, scaled back, or canceled altogether.
Photo Credit: iStock
A sharp year-over-year pullback in the U.S. solar market is a reminder of how quickly federal decisions can affect one of the nation’s fastest-expanding clean energy sectors.
That slowdown has not produced a fossil-fuel rebound, though. New power added to the grid still came overwhelmingly from non-fossil sources, suggesting cleaner energy economics continue to shape the market.
The numbers drew attention in Reddit’s r/energy forum after PV Magazine published its report.
According to PV Magazine, the first quarter of 2026 came in below the same period in 2025, a drop the outlet linked to the Trump administration’s rollback of clean energy incentives.
When those incentives change, projects can be delayed, scaled back, or canceled altogether.
Oil, gas, and coal contributed only a sliver of new generating capacity in the quarter — 4%, PV Magazine said — even as solar growth cooled.
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Texas was still leading the country in solar, and PV Magazine reported that utility-scale projects spanning hundreds of thousands of acres were still underway there. Market demand, land availability, and grid needs continue to drive development even as Washington changes course.
When clean energy growth slows, the effects reach far beyond company balance sheets. It can mean slower progress on cleaner air and higher electricity costs for households that would otherwise benefit from cheaper renewable power entering the grid.
Federal incentives are meant to help speed that transition, especially in communities that could benefit from new jobs, lease income for landowners, and more resilient local power supplies. Rolling them back can stall progress toward a future with more affordable energy and fewer pollution-related health burdens.
At the same time, the 4% fossil-fuel figure shows that the underlying energy market has changed. Solar, wind, and storage are increasingly attractive because they make economic sense, not just because they receive policy support.
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That theme surfaced in the Reddit discussion as well. One user wrote, “They have to subsidize fossil to keep it alive. Tick tock.”
The remark reflects a growing reality: Even when clean energy faces political headwinds, fossil fuels still often depend on longstanding structural advantages and public support.
For now, much of the momentum appears to be coming from states and developers rather than the federal government. Texas stands out most clearly, continuing to add large amounts of solar even in a less supportive national policy climate.
State-level policy, utility planning, and market economics can still push projects forward. Businesses and communities seeking lower power costs over time are still turning to renewables, especially in sun-rich states where utility-scale solar can be deployed quickly.
The first-quarter numbers were a setback, but not a reversal of the broader transition.
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Home – Tech – A solar farm was built to make electricity, but the ground beneath the panels quietly began doing something no one planned for
A solar farm is usually judged by one number first: the electricity it can send to the grid. But in Minnesota, two solar sites built on retired farmland have been telling a quieter story under the panels, where native plants and insects began returning in surprising numbers.
The finding matters because one of the biggest worries about utility-scale solar is land. If clean power needs millions of acres, does that mean more empty, fenced-off fields with less room for wildlife? A five-year study suggests the answer can be more hopeful, but only when solar developers treat the ground as habitat, not leftover space.
Researchers from Argonne National Laboratory and the National Renewable Energy Laboratory studied two solar sites in southern Minnesota operated by Enel Green Power North America. Both were built on retired agricultural land, which matters because disturbed farmland can be very different from untouched habitat.
Instead of covering the ground with gravel or keeping it shaved down like a golf course, the sites were planted with native grasses and flowering plants in early 2018. The panels were raised enough to leave room for vegetation to grow around and beneath the rows.
In practical terms, the solar farm became a working power plant with a small prairie tucked inside it.
That may sound simple, but it is not. Developers often move fast, and vegetation can seem like a detail compared with transformers, panels, permits, and cables. But here, that small design choice changed what the land could become.
From August 2018 through August 2022, researchers conducted 358 observational surveys of flowering vegetation and insect communities. Each visit helped them track whether the new habitat was actually taking hold, or whether the idea looked better on paper than in the field.
By the end of the field campaign, total insect abundance had tripled. Native bees showed an even sharper change, increasing 20-fold, while beetles, flies, and moths were among the most commonly observed insect groups.
That does not mean every solar farm will automatically become a buzzing wildlife refuge. The study only looked at two carefully managed sites in one region. Still, the result is hard to ignore because it shows how quickly insects can respond when food, shelter, and native plants come back.
Bees are not just charming visitors to wildflowers. They help support ecosystems and agriculture, and many native species have been hit by habitat loss, pesticides, climate pressure, and the disappearance of flowering landscapes.
That is why the Minnesota results feel bigger than a local success story. The researchers found that pollinators from the solar sites also visited soybean flowers in nearby fields, which means the restored habitat may have supported pollination beyond the fence line.
Think of it like a small grocery store opening in a neighborhood that had become a food desert for insects. Once the flowers arrived, the bees did not just stay neatly between the panels, they moved through the landscape.
The land question is not going away. According to Argonne’s summary of the DOE Solar Futures Study, about 10 million acres in the U.S. may be needed for large-scale solar development by 2050 to meet grid decarbonization and climate goals.
That is why design choices matter so much. A solar site can be built as a sterile energy island, or it can be planned as a place where power generation and habitat restoration share the same ground. The panels do the electrical work above, while the soil below supports roots, insects, and water retention.
There is a catch, of course: location still matters. Restoring habitat on former agricultural land is not the same as placing panels on fragile desert ecosystems, wetlands, or other sensitive landscapes. The lesson is not to build anywhere and call it green, but to build smarter where solar already makes sense.
For developers, pollinator-friendly solar is not only an environmental talking point. Native vegetation can reduce mowing and long-term maintenance, especially compared with short turfgrass that needs frequent cutting. Environment America notes that this can help offset costs tied to raised panels and habitat-friendly design.
Johanna Neumann, senior director of Environment America Research and Policy Center’s Campaign for 100% Renewable Energy, called native pollinator support an “obvious solution” at a time when bees are struggling and clean energy is expanding quickly.
That is the kind of detail that could change boardroom conversations. If a project can lower maintenance pressure, reduce local opposition, and help restore pollinators, the wildlife option no longer looks like a luxury. It starts to look like risk management.
The Minnesota study also pushes back against a common shortcut in the energy debate. Solar farms are often discussed as if every project has the same ecological footprint. They do not.
A site covered in gravel, a site planted with turf, and a site seeded with native flowers can produce similar electricity while creating very different outcomes for the land. Same panels, different world underneath.
The researchers themselves were cautious. They said more research is needed to understand how habitat-friendly solar works across different regions and ecological goals. That nuance matters because a prairie seed mix that works in Minnesota may not be the right fit for Arizona, Georgia, or Texas.
At the end of the day, the discovery in Minnesota is not that solar panels magically save bees. It is that the space under and around them can be designed with life in mind.
For communities worried about farmland, wildlife, and the pace of clean energy construction, that is a useful shift. A solar farm does not have to be a blank spot on the map. Built carefully, it can become a power plant, a pollinator stopover, and a test case for how infrastructure might work with nature instead of simply pushing it aside.
The study was published on Environmental Research Letters.

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It’s always fun and inspiring to see how other cultures are finding their way out of fossil fuels and towards a resilient, renewable future.
This summer, while traveling in Greece, I saw very similar net zero technologies and strategies to those we have in the US (which center around PV, EV, and heat pumps) but with a unique, Grecian twist. In this warm, Mediterranean country, they are harvesting sunshine in innovative ways and combining its energy with heat pumps to live “Eudaimonia” or the good life.
Heat pumps are an easy technology for places like Greece to embrace and the country has done so wholeheartedly. They are the obvious space conditioning choice both because they work well in Greece’s mild climate and because they can be powered by the abundant sunshine that falls on Greece day after day (see below). One sees the side-discharging, ductless heat pump units everywhere; above doorways, sitting on balconies, on rooftops, or just perched on walls. Ductless heat pumps are the most efficient because they don’t lose energy pushing air through leaky ducts and they don’t have backup electric resistance heat.
Heat pumps in Greece are primarily used for cooling (as the country has much higher cooling needs than heating) and many of the legacy heating systems still use oil and natural gas. But the heat pump infrastructure is there to cover all space conditioning, and as fossil fuel prices skyrocket and more solar comes online, Greeks can just reach for their handy heat pump remote controls whatever the season.
Head outside midday in Greece and you feel how intense the sun’s energy is — it envelops you with its radiance and warmth. Greece harvests this intense energy in a couple of ways:
PV — Of course, Greece is participating in the worldwide electricity takeover by solar (or photovoltaic/PV) panels. Energy from solar power is growing by leaps and bounds — growing from 1.5 GW of installed capacity in 2012 to 11.5 GW last year. Solar accounted for 17% of all Greek electricity in 2025, and low-carbon power makes up about half of the Greek energy mix (and is growing fast).
I saw solar arrays along mountain road sides and on rooftops everywhere we went. I even saw them on the portable stairs that allow people to board airplanes.

But Greece harvests two types of solar energy differently than we do in the US.
Clothes drying — one of my favorite uses of solar energy is for clothes drying. I’ve been writing about the lost art of hang drying laundry since living as an exchange student in Italy decades ago. In Greece, like Italy, the dryer is nonexistent. Sunshine is used to dry laundry quickly, and drying racks and clothes lines are everywhere. Laundry waving in the breeze is both iconically beautiful and an easy and efficient use of abundant sunshine. The Greek sun dries clothes in no time at all, saving 3–5 kWh of electricity per load (over electric dryers – heat pump dryers are much more efficient), and best of all, your clothes go back to their drawers smelling like the sea and sunshine.
Solar water heating — What was especially interesting to an admitted water heater nerd was the use of sunlight for solar water heating in Greece. Solar water heating is where refrigerant in a panel collects heat from the sun and uses it to heat water for showers, dishes, etc. Solar water heating technology started alongside PV panels in the ’70s and ’80s as a promising way to harness the sun’s energy. But while solar PV has become the fastest-growing energy producing technology in history, solar water heating has stayed pretty niche in most markets due to high equipment and installation costs and complexity. For example, in the US in 2023 (the last year ENERGY STAR unit shipment reports are available), 190,000 heat pump water heaters (HPWH) were sold while only 6,000 solar water heaters were sold (out of a total of 10,000,000 water heaters sold nationally).
But in Greece, solar water heating is ubiquitous; every single building I saw had a solar water heater on top. Tiny Greece is 6th in the world for solar water heating installations, and as of 2021, 35% percent of Greek homes had a solar water heater. Compare that to the US, where only 2% of homes installing water heaters (a fraction of the overall housing stock) are currently installing efficient HPWHs.
Solar water heating means essentially free fuel (sunlight) to heat your water — a savings of $400–$600 annually over gas and electric water heaters.
Greece has somehow cracked the installation and equipment upfront cost barrier. Equipment costs only 350–1000 euros and there are usually rebates that bring this down by half. Amazingly, one Greek resource estimates that installation of a solar water heating system costs only 200–250 euros!! Compare that to the thousands of dollars installers are charging for heat pump water heater installations in the US and we can see what a mature, low-cost, decarbonized water heating market looks like.
The other key tech in the triad, complementing solar energy and heat pumps, is of course electric vehicles. Here I found Greece to be a bit behind.
EVs accounted for 6.2% of the auto-market in Greece in 2025, which is roughly a third of the European average. We had to pay nearly four times more ($300 vs. $80) to rent an electric vehicle than a comparable gas car during our week in Crete.
While we saved a lot of money by not having to fill our vehicle with $8/gallon gasoline, it was unfortunately a nightmare to find charging on the island of Crete. We got a Smart electric car with 400 kilometers of range and only needed to charge at the end of our week on the island. But we went to 7 different charging stations, most of them were full of other cars charging, one was ICE’d, and the final fast charging station we went to before throwing in the towel was open but made us go through a 20 minute process of downloading an app and signing up only to then not work. In the end, we had to hypermile it back to our rental car location at the airport with 5% battery and charge there. I’ve never had such a hard time charging an electric vehicle and we’ve been proud EV owners since 2017.
But while EV infrastructure was lacking, it was thrilling to see that in Greece, heat pumps are ubiquitous and solar energy comes in many flavors — it produces electricity and directly dries clothes and heats water. Greece is a country where clean energy and the technology to harvest it are the norm and both the population and planet benefit. I found lessons for us in the US, especially for those states with warmer climates, on how to maximize harvesting bright, blazing, clean, and abundant sunlight.
Joe lives in Portland, Oregon, and works to promote electric and decarbonized buildings. He believes that electrifying everything, from transportation to homes, is the quickest path to an equitable, clean energy future. Joe and his family live in an all-electric home and drive an EV.
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These eye-in-the-sky photographs show progress on a major solar farm which is being built in the Norfolk countryside.
The images, captured by aerial photographer Mike Page, show the solar panels which are being constructed at the Bloy’s Grove solar farm.
EDF Renewable is building the 49.9MW solar farm on a 200-acre site off Brick Kiln Lane, between Mulbarton, Newton Flotman and Swainsthorpe.
South Norfolk Council’s planning committee approved the plans in June 2022, amid opposition.
The Bloy’s Farm solar farm takes shape (Image: Mike Page)
At that stage, concerns were raised about the loss of agricultural land and the impact of construction traffic.
But, despite the opposition, the committee granted permission for the solar farm by five votes to three.
Network Rail has struck a deal with the energy firm for electricity generated at the solar farm, which is close to the Norwich to London railway line, to be used to power its stations, offices and depots across the country.
The solar farm is one of a string of such schemes which have either been agreed or proposed on fields across Norfolk.
Developers say that the projects will reduce the country’s reliance on fossil fuels, power tens of thousands of homes and help to meet government net zero targets.
But critics are concerned about the loss of farmland, along with the visual and environmental impact on the countryside.
The biggest schemes, such as the 4,000-acre High Grove solar farm stretching from Dereham to Castle Acre, the Droves Solar Farm, between Castle Acre and Swaffham and the 2,718-acre East Pye Solar Farm in south Norfolk, are considered to be Nationally Significant Infrastructure projects.
That means a planning inspector – rather than elected councillors on local councils – will consider the applications.
They will then make recommendations on whether or not the schemes should be given the go-ahead to energy secretary Ed Miliband, with whom the final decisions will rest.
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I’ve had solar panels for nearly ten years, after having them installed on both my current home and my previous one. There are a few reasons why I knew I wanted to invest tens of thousands of dollars into this technology, and why I’ve had no regrets.
This may come as a shock, but I’m no fan of paying utility bills. If there is an option to stop, I seek it out. That’s part of the reason I enjoy living in a rural area where we can have a private well and no water bill—even if that does mean having to invest in backup power to keep the water running. With solar panels, the same option is available for energy.
The Anker F3800 Plus is an updated version of Anker's F3800 solar generator, offering the same 3.84kWh capacity and 6000W output. This model comes with improved charging, with a new max solar input of 3200W and 165V, along with support for 240V from a gas generator.
Just like a private well, solar panels cost money upfront, but after they’re built, energy bills can be a thing of the past. To be clear, this isn’t guaranteed. You have to build enough solar panels to cover 100% of your usage. How much that will cost depends on the size of your home, your roof, your landscape, and how much electricity you consume.
We didn’t buy a massive solar array all at once, but now that the project is done, the electricity I use in my home is free. I no longer need to think about energy usage, and as someone in my mid-30s, I’m still quite young. As long as we continue to live in this home (and that is our plan), then we have decades of free energy ahead of us.
When my wife and I first bought solar panels, we calculated how long until we recoup our investment based on the electricity costs of the time. Unlike a car, solar panels are an investment that eventually pay you back. It may take a while, but within ten or fifteen years, you reach a point where you’ve saved more money on electricity than you spent on the panels.
Our math assumed that electricity costs would stay the same, but we all know that they don’t. All over the world, we’re facing various types of energy shocks. Our prices here in Virginia have gone up like they have elsewhere, but since my wife and I don’t have to pay for our home’s energy, all that is increasing for us is the amount of money we’re saving.
People get sticker shock when I tell them that we’ve spent around $50,000 on solar panels, but the idea that solar panels are expensive is a bit of a myth. That’s because none of us have the option not to pay for energy. The choice is whether you pay to rent power or you pay to produce it yourself. If we’re spending over $300 a month on electricity costs, and we look ahead to how much that will cost us over the next 10, 20, 30, or 40 years, then $50,000 starts to look like an outright bargain.
Believe me, I know $50,000 is not something everyone can finance, but, according to Kelley Blue Book, that number is also the average cost of an average new car in America. I know people who pay more than that for a vehicle which will only ever depreciate and, more often than not, come with an additional cost in fuel. They don’t see how much further their money would go if they bought a cheaper vehicle and solar panels instead.
I have never liked the experience of driving a car that’s dependent on gasoline. I don’t like the smell of gas, nor do I like having to drive out of my way to go find some. Even worse—it is yet another utility cost that I must pay in order to live a modern life in my corner of the world.
Fortunately, long-range electric vehicles are now a thing. When paired with solar panels, that’s one more utility cost I can make disappear.
My wife and I drive two electric cars. The solar array on our roof not only supplies enough electricity to power our home, but it covers the local driving we do around town, as well as some of our road trips. I have grown so acclimated to driving around for free and not thinking about fuel that it is my turn to get sticker shock whenever I borrow or rent another vehicle and am reminded how much money most people are paying just to drive their vehicles around.
That said, not all of our driving is free. Our solar panel array is sized for the amount of electricity we were using at the time it was built. Since then, my wife has started commuting 50 miles away each day. 100 miles of driving in an electric vehicle is a significant increase to our energy usage, and it is more energy than our panels provide. Yet this only amounts to an additional $50–$100 a month in electricity costs. Compared to the hundreds my wife would be burning up in gas, it’s hard to complain.
At no point have we ever regretted our investment. Solar panels may take years to pay for themselves, but the fact that you’re getting free energy starts instantly, and you can start leaving lights on with a clear conscience.
We started off with a small solar panel array and worked our way up, but if you can go big—go big. It’s a wonderful feeling once you’re on the other side, even if there are a few things I’d do differently.
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Unsubsidized utility-scale solar is now generally cheaper than building fossil fuel power plants.
Costs are often compared using “levelized cost of energy,” the average lifetime cost to build and run a power plant divided by the electricity it produces. A 2025 analysis estimates the mean LCOE of utility-scale solar at about $58 per megawatt-hour without subsidies, compared to $79 for new natural gas plants and $128 for new coal. The International Energy Agency reports solar energy is the cheapest source of new electricity generation in most parts of the world.
Solar costs have fallen sharply over the past decade as panel prices have dropped and the industry has grown. Subsidies can further lower costs, but solar is not dependent on them to compete with fossil fuels.
See a full discussion of this at Skeptical Science
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Department
The Office of Sustainability
Overview
The Ground-Mounted Solar Photovoltaic (PV) Power Purchase Agreement project will enable Stony Brook University to generate renewable electricity through a third-party owned solar array installed on university land. Through a long-term agreement, the university will purchase the clean electricity produced by the system at a fixed rate, providing access to large-scale solar power without upfront capital investment.
This approach supports the university’s sustainability goals by expanding renewable energy generation, reducing greenhouse gas emissions, and transforming underutilized land into productive energy infrastructure.
Key Highlights
Impact

The project will benefit students, faculty, staff, and the surrounding community by increasing access to clean, renewable electricity while reducing greenhouse gas emissions associated with campus energy use.
By expanding solar generation across campus, the project supports Stony Brook University’s commitment to sustainability and responds to growing interest from the campus community in advancing renewable energy solutions.
Anticipated Completion: Spring 2028
Project Contacts
Tom Lanzilotta – Assistant Director of Energy & Sustainability – thomas.lanzilotta@stonybrook.edu

 
© 2026 Stony Brook University

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$500M solar farm project scrapped in Crawford County  FourStatesHomepage.com
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Turkish heating solutions provider Water Heating Systems (WHS) has presented this week its DC Sunboil range of photovoltaic-powered water heating systems, targeting residential and small commercial applications at the SolarEX Istanbul trade show.
The product line is designed to operate without an inverter, directly converting solar power into heat via DC electricity. According to the company, this approach reduces both upfront investment and system complexity, while improving reliability in off-grid environments.
“The system works with four photovoltaic panels connected in parallel and runs on an extra-low voltage level, which enhances safety,” Ahmed Kılınç, R&D engineer at WHS, told pv magazine. “Typically, the system uses standard 400 W PV panels, resulting in a total installed capacity of around 1.6 kW. The system is flexible, allowing users to scale by adding more panels if needed. It is also compatible with conventional PV panels, provided they meet the required voltage level below 50 V for safe operation.”
The system connects directly to photovoltaic modules via plug-and-play connections. Each unit integrates maximum power point tracking (MPPT), enabling optimized solar energy harvesting without requiring external electronics. The system is also capable of hybrid operation, with an integrated electric heating element ensuring continuous hot water supply during low irradiance or nighttime conditions.
The DC Sunboil series is offered in five tank sizes ranging from 120 liters to 500 liters, designed to meet varying household and small commercial hot water demands. The smallest model in the range is the 120 L (WHS-120-HBB), designed for 2 to 4 users, while the largest is the 500 L (WHS-500-HBB), which can serve between 8 and 16 users. All variants operate at a maximum working pressure of 0.6 MPa and are equipped with two MPPT trackers.
In terms of electrical configuration, the systems support up to 1,200 W of PV DC input power and include an external AC backup heating element rated at up to 2,000 W. The recommended photovoltaic array size ranges from 1.6 kW to 2.4 kW, with each MPPT tracker handling between 800 W and 1,200 W. The maximum PV current per MPPT is 15.5 A, making the system compatible with typical low-voltage PV module setups.
“One of the key advantages of this system is its simplicity and efficiency,” said Kılınç. “Since it directly converts solar energy into heat, it avoids the energy losses associated with inverters and battery storage. In this sense, the water tank itself effectively acts as an energy storage unit.”
The device is primarily intended for domestic hot water production and is not used for electricity generation or grid feed-in. However, it does offer flexibility, as an AC backup option is available, allowing the system to operate during nighttime or cloudy conditions.
“The system can produce approximately 3 kWh of thermal energy per day on average, although actual performance depends on seasonal variations and sunlight availability. The water temperature is adjustable, typically ranging between 65 C and 85 C, which provides sufficient capacity for household needs,” Kılınç stated, noting that while the system itself does not incorporate battery storage, it is technically possible to integrate a battery through an external switching mechanism. However, this is not the primary design intention, as the system relies on thermal storage rather than electrical storage.
WHS is based in Adana and currently sells its products within the Turkish market. “However, we hope to expand our customer base outside Turkey,” Kılınç concluded.
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The June issue of pv magazine Global is out now!
Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
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A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
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In the heart of the Emma community just outside Asheville, North Carolina, a transformation is taking place that goes far beyond hardwire and wires. While the transition to renewable energy is often seen as a luxury, a partnership between Sugar Hollow Solar, PODER Emma, and Footprint Project is providing clean energy as a vital tool for innovative community resilience and displacement prevention.
The trio of organizations recently completed an array of solar installations that represent a significant leap forward in combining affordable housing preservation with environmental justice.
PODER Emma, a mobile home community, has long served as a resource for locals, utilizing cooperative ownership to protect families from displacement. By securing land collectively, they ensure that legacy residents who built the Emma neighborhood get to remain. This partnership with Sugar Hollow Solar and Footprint Project introduces a new layer of security: energy independence. By generating their own power, these communities are no longer at the mercy of rising utility rates, allowing financial resources to remain within the neighborhood.
“We are excited to take this step for clean energy,” said Alan Ramirez, Board Member and Secretary of La Esperanza, the real estate co-op for PODER Emma. “During the hurricane, La Esperanza was our hub for resources and resilience. With solar power, we are saving our resources, producing power and feeling stronger than ever.”
Fifteen solar panels were installed on custom-built porch roof structures for the mobile homes and a 46-kW system was installed on the roof of PODER’s community hub. These projects were made possible by the Repower WNC Fund, a Sugar Hollow Solar initiative supported by the Amicus Solar Cooperative, sub-grants for equipment and labor and PV panel donations by Footprint Project, donations by IronRidge and Invest Appalachia grants.
As Asheville looks toward a more sustainable future, the collaboration between Sugar Hollow Solar, PODER Emma and Footprint Project stands as a blueprint for equitable energy. Communities impacted by development are often left out of the entire process. These projects have helped highlight that when a community is given the information and tools needed, brilliant and innovative things can happen. By addressing the disproportionate percentage of income spent on utilities in lower-income communities, PODER Emma ensures that the Emma community remains in a place where they can thrive.
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China
Amid Cuba’s prolonged energy crisis, solar photovoltaic systems are emerging as a lifeline for small and medium-sized enterprises struggling with daily power outages.
In Havana, one small cafe stands out on blackout nights. While the surrounding streets are plunged into darkness, the cafe remains brightly lit.
“Right now there is no electricity inside at all. We are running on solar panels and charging our batteries at the same time. These batteries supply all the power for the entire house. It takes about seven or eight hours to fully charge them. Here you can see the batteries charging. It’s at 99 percent,” said Adrian, cafe manager.
The solar system not only keeps the business operating normally during long outages but also offers customers a place to charge their devices.
“It basically solves all my work-related problems because I work online. Without power at home, I wouldn’t be able to work,” said Richard, a customer.
Cuba’s electricity shortage has worsened significantly due to the impact of the U.S. blockade, with daily blackouts in Havana often lasting more than 10 hours. This has severely disrupted both daily life and commercial activities.
In response, more small businesses are turning to solar energy.
“Sales are growing mainly because of increased demand. Energy supply in Cuba has been very limited for many years and is declining. This need is being met through solar energy,” said Alexander, a solar system seller.
As equipment costs continue to fall, solar systems are gradually becoming affordable for small and medium-sized enterprises.
The Cuban government is also supporting the shift toward renewable energy by offering incentives, including tariff exemptions on imported solar equipment for individuals and companies, as well as tax breaks for businesses and individuals investing in renewable energy projects.
Solar power helps Cuban small businesses survive energy crisis
Multinational companies are deepening roots in China, striving to become partners in co-creation and innovation with Chinese industry amid new development opportunities.
At the just concluded 7th Qingdao Multinationals Summit, over 300 global executives gathered to discuss closer cooperation and future plans in line with China’s 15th Five-Year Plan (2026-2030), with the key theme of “co-creation” resonating throughout the event.
“We are not simply bringing in technologies for application in China. In today’s environment, we need to innovate together with local partners, ensuring innovations stay in China, while also seeking ways to internationalize,” said Geng Ming, president of Alstom China.
Early approaches saw China as a sales market, but that view is evolving quickly, noted Cao Yang, global vice-president of Baker Hughes and president of Baker Hughes China.
“When we first talked about development in Shandong Province, or China, we saw it as an end market. However, we realized that the manufacturing capabilities of Chinese partners are rising rapidly. Both sides must take Chinese-made products and technologies abroad together,” said Cao.
Companies highlighted how research and development in China now powers their global portfolios.
“When we first came, we saw immense potential in the Chinese market. Over time, we have seen the industrial ecosystem mature. Products developed in China are now exported to Southeast Asia and other markets. We are very optimistic about China and consider it a global innovation hub,” said Yang Lan, senior director of public affairs of Herbalife China.
The evolving role of multinationals in China is synchronized with local growth, moving from “entering China” to “rooting in China” and now “co-creating” with China. Firms said the 15th Five-Year Plan will be their action guide for future investment and innovation.
“I think the 15th Five-Year Plan is quite important. China has to keep modernizing the old economy and in the same time making the best out of new sectors, be it healthcare, be it artificial intelligence, be it electric mobility and you name it,” said Denis Depoux, global managing director at Germany’s strategy consulting firm Roland Berger.
“We went from participating in China’s rail transit development to becoming a part of its growth and progress,” said Geng.
“The 15th Five-Year Plan provides very clear policy and industry support for development and innovation in biomedicine. Standing on this foundation, Revvity has a clear position in China, and we aim to become a partner in Chinese innovation,” said Liu Jiang, vice president and general manager of Greater China at Revvity.
The three-day Qingdao Multinationals Summit, which drew 357 multinational companies from 44 countries and regions, concluded Wednesday in east China’s Shandong Province.
Global firms root deeper in China’s innovation drive
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© 2026 Bastillepost. All rights reserved.

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CAPE TOWN, South Africa
June 17th, 2026
AIKO, the global leader in all-back-contact (ABC) solar technology, has been selected as the sole photovoltaic module supplier for Egypt’s Nefer Menya solar project by developer Infinity Power, the largest African pure-play renewable energy provider, with the announcement made at the Africa Energy Forum (AEF) in Cape Town. The project is financed by the European Bank for Reconstruction and Development (EBRD), reinforcing its strong bankability and alignment with international climate finance frameworks.
Located in Minya Governorate, the Nefer Menya utility-scale solar complex is a cornerstone of Egypt’s national renewable energy strategy, which targets 42% clean power by 2030. The project will deliver 1.2 GWp of solar PV capacity integrated with 600 MWh of battery energy storage. Once operational, it is expected to supply electricity to approximately 1.4 million homes and avoid around 1.6 million tonnes of CO₂ emissions annually. It also demonstrates continued progress toward Infinity Power’s objective of operating 10 GW of renewable energy capacity across Africa by 2032.

Nefer Menya site presents extreme desert operating conditions — high solar irradiance, elevated temperatures, and persistent sand abrasion — demanding modules with exceptional thermal resilience and long-term energy yield. Following rigorous technical and financial due diligence, Infinity Power selected AIKO’s All-Back-Contact (ABC) modules. The higher power density of ABC technology reduces the total number of modules required for the project, along with associated balance-of-system (BOS) components such as mounting structures, cabling, and labour. These savings directly lower the project’s levelized cost of energy (LCOE), improve internal rate of return (IRR), and keep total capital expenditure (CAPEX) within planned budgets. ABC technology also delivers stable, bankable performance over the full 25-year asset life, even under Egypt’s harsh desert conditions.

Omar Magdy, senior procurement manager at Infinity Power, commented: “This project pushed us to look beyond standard module specifications. AIKO demonstrated not only superior technical performance but also a deep understanding of the operational challenges unique to this region. Their ABC technology gives us the confidence to deliver long-term, reliable generation at scale.”
Justin Yuan, President of Overseas Sales at AIKO, said: “The MENA region is one of the world’s most dynamic clean energy frontiers. Securing the Nefer Menya LOA was already a strong vote of confidence, and the AEF platform in South Africa provides an ideal stage to present this milestone partnership to the global community. This cooperation opens the door for joint development of future PV and energy storage projects across the region. AIKO’s ABC technology is purpose-built for extreme desert climates, delivering robust, high-yield generation that will support our partners’ expanding project pipelines for decades to come.”
The Nefer Menya contract strengthens AIKO’s position in Africa’s utility-scale PV segment and establishes a high-visibility reference case for ABC module deployment in desert solar parks. Once operational, the project will supply clean electricity to hundreds of thousands of Egyptian households, avoid substantial CO₂ emissions each year, and advance Egypt’s energy transition goals.

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Engineers Explore Pneumatic Air Spring Actuation for 30-40 Percent Boost to Outputs of Industrial Energy and Rural Solar Farms  AZoCleantech
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June 19, 2026
The end of the earnings season is always a good time to take a step back and see who shined (and who didn’t). Let’s take a look at how renewable energy stocks fared in Q1, starting with Shoals (NASDAQ:SHLS).
Renewable energy companies are buoyed by the secular trend of green energy that is upending traditional power generation. Those who innovate and evolve with this dynamic market can win share while those who continue to rely on legacy technologies can see diminishing demand, which includes headwinds from increasing regulation against “dirty” energy. Additionally, these companies are at the whim of economic cycles, as interest rates can impact the willingness to invest in renewable energy projects.
The 17 renewable energy stocks we track reported a strong Q1. As a group, revenues beat analysts’ consensus estimates by 5.7% while next quarter’s revenue guidance was in line.
Luckily, renewable energy stocks have performed well with share prices up 15.9% on average since the latest earnings results.
Started in Huntsville, Alabama, Shoals (NASDAQ:SHLS) designs and manufactures products that make solar energy systems work more efficiently.
Shoals reported revenues of $140.6 million, up 74.9% year on year. This print exceeded analysts’ expectations by 8.7%. Overall, it was a stunning quarter for the company with EBITDA guidance for next quarter exceeding analysts’ expectations and a solid beat of analysts’ adjusted operating income estimates.
“We began the year on very solid footing, with revenue above our expected range and growing at approximately 75% from the prior-year period. The underlying demand environment remains extremely strong as evidenced by our record backlog and awarded orders of $758 million. We are executing our strategic plan of accelerating growth within our core domestic utility scale solar market and expanding our offering into attractive high growth markets,” said Brandon Moss, CEO of Shoals.
Shoals achieved the highest guidance raise of the whole group. Unsurprisingly, the stock is up 26.5% since reporting and currently trades at $10.46.
Is now the time to buy Shoals? Access our full analysis of the earnings results here, it’s free.
Working in stealth mode for eight years, Bloom Energy (NYSE:BE) designs, manufactures, and markets solid oxide fuel cell systems for on-site power generation.
Bloom Energy reported revenues of $751.1 million, up 130% year on year, outperforming analysts’ expectations by 42%. The business had an incredible quarter with a beat of analysts’ EPS and EBITDA estimates.
Bloom Energy pulled off the biggest analyst estimate beat, fastest revenue growth, and highest full-year guidance raise among its peers. The market seems happy with the results as the stock is up 45.4% since reporting. It currently trades at $329.16.
Is now the time to buy Bloom Energy? Access our full analysis of the earnings results here, it’s free.
Founded in 1969, FuelCell Energy (NASDAQ: FCEL) is a leading manufacturer and developer of carbonate fuel cell technology for stationary power generation.
FuelCell Energy reported revenues of $35.59 million, down 4.9% year on year, falling short of analysts’ expectations by 12.6%. It was a disappointing quarter as it posted a significant miss of analysts’ adjusted operating income estimates.
Interestingly, the stock is up 37.1% since the results and currently trades at $23.76.
Read our full analysis of FuelCell Energy’s results here.
Established in 2006, SolarEdge (NASDAQ: SEDG) creates advanced systems to improve the efficiency of solar panels.
SolarEdge reported revenues of $310.5 million, up 41.5% year on year. This result beat analysts’ expectations by 2%. Zooming out, it was a satisfactory quarter as it also logged an impressive beat of analysts’ adjusted operating income estimates but a significant miss of analysts’ EPS estimates.
The stock is up 30.8% since reporting and currently trades at $58.38.
Read our full, actionable report on SolarEdge here, it’s free.
Powering forklifts for Walmart’s distribution centers, Plug Power (NASDAQ:PLUG) provides hydrogen fuel cells used to power electric motors.
Plug Power reported revenues of $163.5 million, up 22.3% year on year. This number surpassed analysts’ expectations by 15.9%. Taking a step back, it was a slower quarter as it recorded a significant miss of analysts’ adjusted operating income and EPS estimates.
The stock is down 19.5% since reporting and currently trades at $2.84.
Read our full, actionable report on Plug Power here, it’s free.
Late in 2025 into early 2026, there was hand-wringing around artificial intelligence. For software companies, the fear was that AI would erode pricing power and compress margins as new tools made it easier to replicate what once required expensive enterprise platforms. Crypto investors had their own version of the same anxiety: if AI agents could trade, allocate capital, and manage wallets autonomously, what exactly was the long-term value of today’s crypto infrastructure?
These concerns triggered a noticeable rotation away from these sectors and into safer havens. But markets rarely dwell on one narrative for long. Spring 2026 came, and the focus shifted abruptly from technological disruption to geopolitical risk. The US’ conflict with Iran became the dominant driver of market psychology, and when geopolitics takes center stage, the script changes quickly. Investors stop debating growth rates and start worrying about oil supply, inflation, and global stability.
Want to invest in winners with rock-solid fundamentals? Check out our 9 Best Market-Beating Stocks and add them to your watchlist. These companies are poised for growth regardless of the political or macroeconomic climate.
StockStory’s analyst team — all seasoned professional investors — uses quantitative analysis and automation to deliver market-beating insights faster and with higher quality.
©2026 StockStory
Data sources: actuals and consensus estimates from StockStory, S&P Global Market Intelligence, and Visible Alpha. Market data from Massive.
Provided for general information purposes only and does not constitute investment advice or a recommendation that any particular security, portfolio of securities, transaction or investment strategy is suitable for any specific individual. Information on our investment framework and performance methodology is available here.
Copyright 2026, S&P Global Market Intelligence (and its affiliates as applicable). All rights reserved.

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EXPLORE COVERAGE
By Nico Portuondo, Timothy Cama | 06/18/2026 06:30 AM EDT
There are divisions between manufacturers and installers, large firms and small companies.
An employee works inside a Qcells solar plant in Georgia. Mike Stewart/AP
As some renewable energy leaders wonder whether expiring clean energy tax credits are still necessary, domestic solar manufacturers say that conversation overlooks the industry’s biggest challenge: China.
Investors in U.S. manufacturing of solar panels blitzed Capitol Hill on Wednesday to urge lawmakers to extend investment and production tax incentives along with a domestic content bonus they argue is essential to creating demand for American-made solar equipment.
They’re at odds with at least some solar installation companies who are fine with federal incentives going away. There are also divisions between large firms with more capital and small firms looking for a leg up.
“You can make all the product in the United States, but if you don’t have anyone to sell it to, it doesn’t matter,” said Yogin Kothari, chief strategy officer at the Solar Energy Manufacturers for America Coalition, the lobbying group leading the effort.
The domestic content bonus is an “adder” to the investment and production tax credits being phased out following the Republicans’ One Big Beautiful Bill Act. The provision allows for a bonus if companies can prove they used American-made parts in their panels.
It’s unlikely that Republicans would revive tax incentives they just moved to kill. But solar manufacturers say they’re thinking long term, perhaps when Democrats regain power.
Some renewable energy developers, however, argue the industry is mature enough to compete without any subsidies and would benefit from long-term policy certainty than another round of temporary tax credits.
Republicans last year preserved the 45X advanced manufacturing tax credit, which supports domestic production of solar components and other energy technologies.
But companies affiliated with the Solar Energy Manufacturers for America Coalition contend 45X alone is insufficient if developers have no incentive to buy American-made products over lower-cost imports from China and Southeast Asia.
“45X is a production tax credit, and the domestic content bonus makes sure that demand for those products is there,” said Marta Stoepker, a spokesperson for Qcells North America. “It makes sure 45X works and that there’s a strong return on the investment for the manufacturer.”
Qcells, part of South Korea-based Hanwha Solutions, has been investing heavily in U.S. manufacturing in recent years. Its new factory in Cartersville, Georgia, started producing solar cells last week, the company said.
But American factories have a long way to go. In 2024, China produced 93.2 percent of the world’s polysilicon, 96.6  percent of wafers, 92.3  percent of photovoltaic (PV) cells and 86.4  percent of PV modules, according to China Photovoltaic Industry Association (CPIA) data.
Democrats in both the House and Senate have said restoring the tax incentives from the Biden-era Inflation Reduction Act will be a priority when they control Washington again.
“This is not about whether a handful of developers can still turn a profit. It is about whether American families get lower energy bills, whether American manufacturers have the demand they need to grow, and whether businesses have stable rules after Republicans yanked the rug out from under them,” Senate Minority Leader Chuck Schumer (D-N.Y.) said in a statement.
But party members, like companies, are divided. Sen. John Hickenlooper (D-Colo.) called it a “legitimate question” whether the tax credits should return and questioned how much the government should subsidize domestic manufacturing.
“I think we always want to make more things here,” Hickenlooper said. “But that’s a pretty complex algorithm about how much do you subsidize anything to make sure it’s made here.”
The debate is exposing persistent divisions within the solar industry — including between companies that make solar components and those that focus on installing them.
Those divisions have been on display before. Manufacturers have championed tariffs on solar panel imports while installers have opposed trade barriers that could add costs.
The Solar Energy Industries Association, the industry’s largest lobbying organization representing both developers and manufacturers, has stopped short of explicitly calling for an extension of credits.
“SEIA will of course consider any policy, including tax credits, that accelerates solar and storage growth,” the group’s new president, former Minnesota Republican Gov. Tim Pawlenty, said in a statement.
“With energy demand only going up, now isn’t the time to ignore policy opportunities that will help our country build new power generation. That’s just common sense.”
Manufacturers argue that extending the investment and production credits with the domestic content bonus should be the entire industry’s minimum priority.
“I think there’s been a lot of celebration for the manufacturing investments across a wide variety of trade associations that focus on both solar and energy at large,” Stoepker said. “If we want to continue to celebrate those investments and see more investments, there is at the very least going to have to be action on extending the domestic content bonus.”
Kothari said a strong domestic manufacturing base creates jobs and reduces supply chain risks, benefits that ultimately extend to installers as well. At least one solar installer disputed that argument.
“We’ve been lobbying for many years with this thesis: The jobs in solar are in installation, not in manufacturing,” said Michael Hidary, co-founder and chair at Samba Energy, a New York-based commercial and residential solar firm.
Kothari countered that solar manufacturing jobs are durable and directly replace employment lost related to the general offshoring of manufacturing seen in recent decades.
“Domestic solar manufacturing jobs are durable, place-based jobs that are revitalizing communities that have been left behind due to the China shock,” Kothari said
One industry consultant, who spoke on the condition of anonymity, said larger companies are generally more comfortable with the credits expiring because they have greater financial flexibility than smaller firms.
“It’s similar to the oil and gas industry. The big boys don’t mind as much because of the sheer volume of revenue and product they use versus the smaller players with tighter margins,” the consultant said.
Another lobbyist, also granted anonymity to speak candidly, argued most companies would happily accept renewed tax credits but see little reason to spend time and money lobbying for legislation with slim odds of passing.
“Those companies, especially the more politically astute ones who are watching the policy space, are just wondering why anyone is talking about this,” the lobbyist said. “It’s not something that can happen anytime soon. They want permitting reform. There are so many other things to focus on right now.”
If tax credits became a realistic possibility, much of the industry would likely support them. “But it’s almost political malfeasance to take too strong of a position and dig yourself into any position right now,” the lobbyist said.
Pavan Acharya contributed to this report.
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According to the map, the proposed solar farm is directly adjacent to subdivision properties and Prairie Meadow city park.
Some of the issues neighbors have concerns about include heavy equipment traffic and noise during construction, and the possible effects a solar farm has on wildlife – particularly bird migration and habitat.
Other concerns were about the proximity to high voltage transformers, chemicals used in soil preparation, and unwelcome noise pollution. Reportedly the solar equipment may make a steady hum and the panels could make a clicking sound as they move to follow the sun.
The proposed solar farm may contain between 8,000 and 12,000 panels.
Trenton Town Chairman Mike Lipscomb has been doing his due diligence researching the solar farm proposal and said he is aware of the concerns of neighbors.
Lipscomb said he also received support from neighbors about the project. Several letters to the Town Board are posted below.
Andrew Aldred
1722 Cloverview Street West Bend, WI 53095 June 22, 2026
To: Members of the Town of Trenton Board
Re: Opposition to Proposed 5 MW Solar Farm Development Dear Town Board Members,
I am writing to express my concerns regarding the proposed 5 MW solar farm development in our community and to respectfully request that the Town Board carefully consider the impacts this project will have on neighboring property owners before granting approval.
My residence is located adjacent to the private gravel access road that will be used by construction vehicles, equipment haulers, concrete trucks, and other heavy traffic associated with the construction of the solar facility. While I understand the importance of renewable energy development, I believe the current proposal places an unfair burden on nearby residents and does not adequately address the impacts that will result from construction and ongoing operation.
My primary concerns include:
1. Construction Traffic and Road Impacts
The private gravel road bordering my property will serve as the primary access route for construction activities. Construction of a 5 MW solar facility will require substantial truck traffic over an extended period, including delivery of solar panels, racking systems, transformers, electrical equipment, concrete, and other materials.
This heavy vehicle traffic will likely create:
• Significant dust affecting nearby homes and outdoor living areas.
• Increased noise from trucks, trailers, and construction equipment.
• Potential road deterioration requiring maintenance and repairs.
• Reduced quality of life for neighboring residents during the construction period. • Safety concerns for residents, pedestrians, pets, and local traffic.
I request that the Town require a detailed construction traffic management plan, dust control measures, road maintenance agreements, and limitations on construction hours to minimize impacts on nearby homeowners.
2. Need for Increased Setbacks
I am also requesting that the Town require larger setbacks than those currently proposed for all solar farm equipment and infrastructure. This should include solar arrays, inverters, transformers, battery storage systems (if applicable), substations, security fencing, and any other associated equipment.
Additional setbacks are necessary to:
• Reduce visual impacts on neighboring properties.
• Minimize operational noise from inverters and transformers.
• Protect adjacent property values.
• Provide greater separation between industrial-scale energy infrastructure and residential properties.
• Preserve the rural character of the area.
The proposed facility is a commercial utility-scale development. As such, it should be held to a higher standard for buffering and separation from neighboring residences.
3. Protection of Neighboring Property Owners
Residents living closest to the project will bear the greatest impacts while receiving few direct benefits. The Town should ensure that the interests of existing property owners are protected
through meaningful setbacks, landscaping requirements, road-use agreements, and enforceable operating conditions.
I respectfully request that the Town Board deny approval of the project unless additional protections are included, specifically:
• Increased setbacks for all solar farm equipment and infrastructure.
• Enhanced vegetative screening and landscaping requirements.
• A road maintenance and repair agreement covering damage caused by construction traffic. • Dust suppression measures during construction.
• Restrictions on construction hours and days of operation.
• Ongoing monitoring and enforcement of noise standards.
Thank you for your consideration of these concerns. I appreciate the opportunity to provide input and ask that this letter be entered into the official record for the proposed solar farm application.
Sincerely,
Andrew Aldred ________________________________________________________________________________________________
Jay Johnston<[email protected]> TownAdmin
As a homeowner in Trenton I wanted to express my concerns regarding the Conditional use Permit application.
I di a little research and Mr. Jacob VanDomelen has other Solar Farms applications pending in Sheboygan County Town of Lyndon for a 5 MW Solar Facility. And he appears to be employed by Sun Vest Solar LLC.
What does it mean? Not sure but what are the intentions of this company.
A 5 MW solar facility requires 25-35 acres of land. A 5-million-Watt Facility requires anywhere from 15,000 to 25,000 solar panels depending on the solar panel wattage. How many solar panels will this project require? Can you imagine 25,000 solar panels “1,000 solar panels per acre” that would be quite the eye sore. Also, before this project can be voted on the town needs to ask for a Community Benefits Agreement from the developer or applicant.
What exactly will the benefits be to the community in terms of : revenue, tax, employment, using local contractors… This is a very important step prior to any discussion on this proposal.
This proposed 5MW Solar Facility will generate up to $1,000,000 annually “again how does our community benefit” We see the benefit to the landowner and the company building the facility but what’s our benefit?
Why Trenton WI.?
Research indicates Trenton WI has 4.5 peak sun hours daily, that’s sufficient for a 5MW facility. But 4.5 psh’s very common in the United States. I ask, what other reasons are there for locating in Trenton WI?
Are there tax breaks being offered, is there a nearby transmission line connection point for this proposed solar farm? We need to know how many miles of transmission lines will be needed for this facility.
Also are there proposed setbacks for this farm? Is there landscaping / screening proposed to hide the visual impact of seeing 20,000 solar panels. As a homeowner, I’m open to solar development but not at the cost of its citizens . Citizens are not cattle we want to a list of expectations and have those expectations to bet met in contractual form.
A citizens Benefit Agreement is absolutely necessary before any project can move forward. Trenton is a special place, future generations are depending on us to make the right decisions.
In my opinion there is nothing worse than one or two parties asking for rezoning and the only beneficiaries of the rezoning are those one or two parties.
“How do the citizens of the community also benefit.” That should be written on a plaque and hung in every administration office in Trenton WI.
Keep up the good work,
Jay Johnston
Business Sponsorship | Wisconsin Public Radio
email: [email protected] mobile: 414.202.1961
1243 N. 10th St. Suite 100 Milwaukee WI 53203
________________________________________________________________________________________________
Chris Merkel<[email protected]> TownAdmin
Thank you for soliciting public feedback on the proposed solar farm. I am a resident of the Prairie Meadow subdivision, live along the access road that would lead to and from the site.
I would like to express my support for this project as a neighbor to it, and provide brief reasons why:
1. Economic freedom and free markets: I believe property owners should be entitled to seek the maximum utility of their property, provided such use does not cause undue financial harm to the community. This land is currently farmed, and unfortunately, farmland profitability has declined precipitously since 2024, due to tariffs and trade restrictions imposed by the Federal Government. This is evidenced by Wisconsin farm bankruptcies rising 46% in less than two years. Given the current economic hardship farmers face, I fully support reasonable actions to regain long term economic stability.
2. Lack of any material harm to the community: While there is strong evidence that the average value of a home may fall up to 1.5% for those homes in the vicinity, it’s worth noting that the home values in Washington County have risen an average of 98.6% in the last ten years. Such a minor reduction in value would still result in a ten-year rate of increase exceeding that of the rest of the state. I do not believe this represents a level of harm high enough to warrant hurting the landowners’ long-term economic prospects by denying approval.
3. Environmental impact: Row-crop farming, which is currently how the land is used, requires the use of heavy nitrogen and phosphorus fertilizers, as well as continuous use of herbicides and pesticides. This change in use will reduce runoff into nearby streams and decrease groundwater contamination.
4. Regulatory predictability: The town has concluded that the proposed use is consistent with allowed activities under Town zoning statutes. For a community to be economically prosperous, freedom in use must be joined with consistency in regulation. Denying this use permit would demonstrate that the Town does not act consistently within the bounds established by law and municipal codes. This sends a message to others seeking to live and do business in Trenton that Trenton has an unpredictable regulatory environment, which will discourage further economic investment into the community, favoring more reliable adjacent communities.
While I recognize the distaste for the change in scenery, the disdain for some forms of energy production, I do not believe, for reasons outlined above, that aesthetic or political motivations warrant sufficient weight to deny the use. For these reasons, I support this, and appreciate the willingness of the Town in seeking input from community members outside of the Town’s borders.
Sincerely,
Chris Merkel, 1710 Cloverview St.
The solar farm, according to Lipscomb, would not be visible from the road.
The public hearing at the Trenton Town Hall, 1071 Highway 33, begins at 6:45 p.m. on Monday, June 22, 2026. The meeting is free and open to the public.

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PENDER COUNTY N.C. (WECT) – The North Carolina Court of appeals upheld Pender County’s decision to deny a special use permit for a 2,360-acre solar farm that would occupy Burgaw, Columbia and Union townships. 
In July 2023, the Pender County Board of Commissioners unanimously denied a permit request for the solar farm and Coastal Pine Solar, LLC took the denial to the North Carolina appellate court. 
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The appeals court ruled on Tuesday, June 17 that the company failed to provide sufficient evidence that adequate utilities were in place or being provided for the project. Specifically, they could not prove existing transmission lines could handle the electricity the solar farm would produce.
The court also noted concerns about drainage. The Pender Soil and Water Conservation Director testified at a 2022 hearing that the land had been removed from agricultural use due to wetland designation. No evidence was presented about measures to offset water runoff from clearing 2,300 acres of timberland, according to the ruling.
The three-judge panel found the county board’s decision was supported by the evidence and did not violate the company’s constitutional rights.

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June 19, 2026
Looking back on renewable energy stocks’ Q1 earnings, we examine this quarter’s best and worst performers, including First Solar (NASDAQ:FSLR) and its peers.
Renewable energy companies are buoyed by the secular trend of green energy that is upending traditional power generation. Those who innovate and evolve with this dynamic market can win share while those who continue to rely on legacy technologies can see diminishing demand, which includes headwinds from increasing regulation against “dirty” energy. Additionally, these companies are at the whim of economic cycles, as interest rates can impact the willingness to invest in renewable energy projects.
The 17 renewable energy stocks we track reported a strong Q1. As a group, revenues beat analysts’ consensus estimates by 5.7% while next quarter’s revenue guidance was in line.
Luckily, renewable energy stocks have performed well with share prices up 15.9% on average since the latest earnings results.
Headquartered in Arizona, First Solar (NASDAQ:FSLR) specializes in manufacturing solar panels and providing photovoltaic solar energy solutions.
First Solar reported revenues of $1.04 billion, up 23.6% year on year. This print exceeded analysts’ expectations by 1.4%. Despite the top-line beat, it was still a mixed quarter for the company with full-year EBITDA guidance exceeding analysts’ expectations but a significant miss of analysts’ adjusted operating income estimates.
“We delivered a strong start to 2026, with record first-quarter revenue, record sales in India, meaningful margin expansion, and Adjusted EBITDA above the top end of our first quarter preview range,” said Mark Widmar, Chief Executive Officer.
First Solar delivered the weakest full-year guidance update of the whole group. Interestingly, the stock is up 27.8% since reporting and currently trades at $257.93.
Is now the time to buy First Solar? Access our full analysis of the earnings results here, it’s free.
Working in stealth mode for eight years, Bloom Energy (NYSE:BE) designs, manufactures, and markets solid oxide fuel cell systems for on-site power generation.
Bloom Energy reported revenues of $751.1 million, up 130% year on year, outperforming analysts’ expectations by 42%. The business had an incredible quarter with a beat of analysts’ EPS and EBITDA estimates.
Bloom Energy achieved the biggest analyst estimate beat, fastest revenue growth, and highest full-year guidance raise among its peers. The market seems happy with the results as the stock is up 45.4% since reporting. It currently trades at $329.16.
Is now the time to buy Bloom Energy? Access our full analysis of the earnings results here, it’s free.
Founded in 1969, FuelCell Energy (NASDAQ: FCEL) is a leading manufacturer and developer of carbonate fuel cell technology for stationary power generation.
FuelCell Energy reported revenues of $35.59 million, down 4.9% year on year, falling short of analysts’ expectations by 12.6%. It was a disappointing quarter as it posted a significant miss of analysts’ adjusted operating income estimates.
Interestingly, the stock is up 37.1% since the results and currently trades at $23.76.
Read our full analysis of FuelCell Energy’s results here.
Powering forklifts for Walmart’s distribution centers, Plug Power (NASDAQ:PLUG) provides hydrogen fuel cells used to power electric motors.
Plug Power reported revenues of $163.5 million, up 22.3% year on year. This number beat analysts’ expectations by 15.9%. More broadly, it was a slower quarter as it recorded a significant miss of analysts’ adjusted operating income estimates and a significant miss of analysts’ EPS estimates.
The stock is down 19.5% since reporting and currently trades at $2.84.
Read our full, actionable report on Plug Power here, it’s free.
One of the first EV charging companies to go public, Blink Charging (NASDAQ:BLNK) is a manufacturer, owner, operator, and provider of electric vehicle charging equipment and networked EV charging services.
Blink Charging reported revenues of $20.78 million, flat year on year. This print came in 4.1% below analysts’ expectations. Zooming out, it was actually a very strong quarter as it recorded a beat of analysts’ EPS and EBITDA estimates.
The stock is down 31.5% since reporting and currently trades at $0.66.
Read our full, actionable report on Blink Charging here, it’s free.
Late in 2025 into early 2026, there was hand-wringing around artificial intelligence. For software companies, the fear was that AI would erode pricing power and compress margins as new tools made it easier to replicate what once required expensive enterprise platforms. Crypto investors had their own version of the same anxiety: if AI agents could trade, allocate capital, and manage wallets autonomously, what exactly was the long-term value of today’s crypto infrastructure?
These concerns triggered a noticeable rotation away from these sectors and into safer havens. But markets rarely dwell on one narrative for long. Spring 2026 came, and the focus shifted abruptly from technological disruption to geopolitical risk. The US’ conflict with Iran became the dominant driver of market psychology, and when geopolitics takes center stage, the script changes quickly. Investors stop debating growth rates and start worrying about oil supply, inflation, and global stability.
Want to invest in winners with rock-solid fundamentals? Check out our Strong Momentum Stocks and add them to your watchlist. These companies are poised for growth regardless of the political or macroeconomic climate.
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Tarragona
Barcelona
Tarragona
Barcelona
Terrassa processes two collective self-consumption solar parks in the north of the city with a joint power of 4.1 MW and 8,346 photovoltaic panels. The projects, named Can Bogunyà and Bonaire, are promoted by the company Solar Renovables del Vallès and are in the public exhibition phase.
The planned distribution reserves 90% of the energy for an energy community within a five-kilometer radius, while the remaining 10% will be allocated to vulnerable families with a social tariff. This perimeter covers the urban area of Terrassa but excludes Les Fonts and the southern half of Can Parellada.
Can Bogunyà will contribute 2.7 MW and Bonaire another 1.4 MW. Between the two facilities, they will incorporate 21 inverters and thirteen battery modules to store 1.3 MW of energy with a two-hour autonomy.
In addition to electricity generation, the project includes an underground low-voltage line to the transformation center on Isidre Nonell street, in Can Roca. The channeling aims to connect production with urban consumption without resorting to overhead infrastructure.
There will be grazing under the panels.
The design of the parks includes an agrivoltaic model that will allow livestock activity with herds in a delimited space under the panels. The formula has the support of the Generalitat de Catalunya and combines energy use with the continuity of land exploitation.
To reduce the visual impact, the developer plans to install vegetation fences around the plants. In the north of Terrassa, this landscape integration is added to the technical integration of the network and recent experiences on changes in access infrastructures in the city.
The total planned investment amounts to 4.2 million euros. Copcisa Elèctrica and Delta Renovables from the Electra de Caldes de Montbui Group provide financing with the collaboration of IDAE.
Journalist

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Renewable energy investment platform Chrysalis Renewables has acquired the Atlas V and Atlas VI solar projects in Arizona, US. 
The deal is the first under a partnership between Chrysalis, an arm of global infrastructure manager Morrison, and Hanwha Renewables, an offshoot of the Korean conglomerate Hanwha Group, under which Chrysalis acquires projects from Hanwha that meet its investment criteria, while drawing on the latter’s capabilities across development, engineering, procurement and construction (EPC), module supply, asset management and operations and maintenance (O&M).  
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Atlas V and Atlas VI are the first projects to be delivered under the model. The two projects have a combined capacity of 357MWdc and are in the final stages of commissioning.  
Located within the multi-phase Atlas Energy Park in La Paz, Arizona, the projects are expected to support domestic manufacturing, strengthen supply chains and improve grid reliability while reducing trade and tariff exposure. 
The projects are contracted under 15-year direct wire, or “busbar”, power purchase agreements (PPAs) with Southern California Edison for delivery into California’s CAISO market and are expected to begin commercial operations in the coming months.  
Qcells, also a subsidiary of Hanwha Group, supplied the domestically manufactured modules from its Georgia facility and is serving as EPC contractor. 
Morrison partner, Gordon Hay, said: “The acquisition of the Atlas projects marks an important milestone for Chrysalis, increasing its generation capacity to approximately 700MW while significantly expanding its regional footprint. The transaction also advances Chrysalis’ portfolio diversification strategy by adding a generation profile that complements its existing assets.” 
The partnership between Chrysalis and Hanwha, announced in February 2026, aims to accelerate the deployment of construction-ready and operational renewable energy assets. 
The initial focus is on more than 3.5GW of solar and BESS capacity in North America. The partners also plan to expand the portfolio into additional markets, including Japan, Australia and Italy. 
In April 2026, Hanwha sold the 1.5GW Atlas North solar-plus-storage portfolio to Lydian Energy. The portfolio comprised four late-stage projects in the CAISO market, including more than 1GW of solar PV capacity and 450MW/1,800MWh of BESS. 

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SOUTH KINGSTOWN, R.I. — As construction work to build the new South Kingstown High School continues, officials have started to do some research at the town council’s request about what comes next after everyone moves in. One of those research topics is putting solar panels on the high school roof.
At the latest town council meeting, Phil Conte, who’s one of the architects on the project, updated the council on the feasibility of using solar panels to save long-term on energy costs. The short answer is that he believes the council should take advantage of the incentives that are out there right now.
Conte laid out a fairly strong financial case for installing a solar array while also explaining why the town council is recommended to act now. One of the most important takeaways is that the economics work largely because of government incentives. 
The federal government could reimburse up to 40 percent of the project’s cost — emphasis on the “up to” — through a direct payment program. That state of Rhode Island also offers a $75,000 one-time incentive. The federal reimbursement would likely arrive 6 to 12 months after the system begins operating, while the state incentive would come roughly three months after operation begins.
Normally, projects must be completed by December 2027 to qualify for the current federal incentive. However, Conti explained another option called the “Safe Harbor” provision. That allows South Kingstown to preserve eligibility even if installation finishes later, even as late as 2030, provided the town commits at least five percent of the project’s cost, typically by purchasing equipment, before the federal deadline. 
Conte recommended that the council use this safe harbor option because instead of rushing construction to beat a deadline, the town could spend a relatively small amount now to lock in hundreds of thousands of dollars in future federal assistance.
Conte argued that solar really does become a long-term cost-saving investment. Solar panels usually last about 25 years, and with today’s incentives, the system would pay for itself in less than 10 years. This stretches out to 15 years without the incentives. Electricity prices are expected to rise over time too, making solar energy a financially viable option as time goes on.
Officials have also done a feasibility study about how much solar energy they could theoretically produce on the roof of the school. They found that the roof would be able to support 750 solar panels, about 405 kilowatts of generating capacity. The total estimated project cost is about $1.5 million, minus about $500,000 in expected incentives.
* For the full story, pick up a copy of this week’s Independent on newsstands now or purchase a subscription to our E-Edition by clicking here.
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CIM Group’s Permanent Power Company Closes Approximately $600M Construction Financing Facility for Grape Solar and Energy Storage Project  01net
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Part of a 12 square mile solar farm in Spain
On Wednesday the Interior Department announced that it would pay the energy developer Invenergy $765 million not to develop three offshore wind farms. This is the third such payment by the Trump administration to undo offshore wind projects that have been years in the planning. Trump has so far committed $2.5 billion in taxpayer dollars to killing renewable energy projects. The administration has also tried to stop offshore wind farms already under development — moves that have been blocked by the courts — while the Pentagon has been refusing to grant routine permits for onshore wind projects.
Yes, $2.5 billion to destroy already-approved, cost-effective clean energy projects while Americans are suffering from soaring electricity prices thanks to data centers and high gasoline prices.
Yet here’s the irony: Donald Trump’s disastrous Iran war has delivered a huge boost for renewable energy around the world — except in the U.S.. Trump has so far done more to shift the global economy away from fossil fuels and towards renewable energy than any other single individual in history.
Why do Trump and his gang hate green energy so much? The roots of their hatred range from the power of fossil fuel interests, to Trump’s petulant whine that wind turbines ruined the view from his Scottish golf course, to a general sense among right-wingers that clean energy threatens their masculinity.
What’s best for Americans has nothing to do with it. Thus, Trump lackeys justifying their hostility to renewables consistently make arguments even they must know are stupid. Consider, for example, an exchange last month between Doug Burgum, secretary of the interior, and Rep. Jared Huffman of California:
Burgum: All of these projects you’re describing in Nevada have one thing in common—when the sun goes down, they produce zero electricity.
Huffman: Mr. Chairman, I request unanimous consent to enter in the record this amazing new technology that apparently the secretary is unaware of: It’s a battery.
Indeed. To get a clearer understanding of far battery technology has progressed in enabling the transition to renewables, let’s look at how the state of California sourced its electricity this past Wednesday. The chart below shows megawatts supplied at 15-minute intervals over the course of the day. The area shaded yellow represents daylight hours. The light blue line at the top is electricity generated by renewables, mainly solar power (with some wind and hydro as well). In addition to supplying energy for current consumption, renewables supply energy to batteries for nighttime consumption. The black line at the bottom is net electricity supply from batteries — which is negative when batteries are charging, positive when they’re being drawn down:
California — which would be the world’s 4^th largest economy if it were a country — gets more than half of its electricity from renewables. It is rapidly becoming a state largely powered by the sun during daylight hours and powered by batteries during the night.
Burgum’s suggestion that solar is an unproven or unreliable technology is completely at odds with reality.
Nor is California the only economy that now makes substantial use of renewable energy. Burgum’s home state of North Dakota gets more than a third of its electricity from wind power (don’t tell Trump). In South Dakota wind supplies 57 percent of the electricity. And renewables generate a large share of electricity in many countries, including most big European economies. (France is the outlier, not because it relies on fossil fuels, but because it has large nuclear capacity.) Spain, for example, now relies heavily on a solar-plus-batteries system similar to that in California.
And when Trump went to war with Iran, nations that had already shifted toward renewable energy were very glad they had made the move.
To the extent that there’s a competition for the future of electricity generation, it’s between renewable energy and natural gas. Whatever Trump may want to believe, burning coal — even ignoring the environmental damage — is a costly, obsolete technology, which nobody wants to invest in. But new gas-turbine power facilities are still being built (although many places are, like California, rapidly shifting away from natural gas). Trump officials envision a world largely powered by US liquefied natural gas (LNG).
However, countries that relied heavily on natural gas were hit hard by Trump’s gratuitous war with Iran. LNG supplies from the Persian Gulf were blocked and couldn’t be fully replaced by U.S. exports because shipping capacity was limited. Countries that had invested heavily in renewables, like Spain, were largely unscathed. A report from the think tank Ember found that since the war began Spanish electricity prices — unlike prices in some other European countries — were essentially decoupled from the soaring price of natural gas.
Assuming that the Strait of Hormuz will be reopened after Trump’s abject surrender to the Iranian regime, natural gas prices should subside. Yet the world has learned a hard lesson about the riskiness of relying on fossil fuels for electricity generation.
And let’s be clear about the nature of that lesson. It’s not the fact that much of the world’s supply of hydrocarbons comes from a politically volatile region: We’ve known that all too well since the 1970s. What’s new is the recognition of American weakness and unreliability.
In this new era of drone warfare America cannot guarantee reliable access to imported fossil fuels through critical sea lanes. And is America itself a reliable supplier? Can nations that allow themselves to be dependent on U.S. gas and oil be sure that Trump or a future Trump-like president won’t weaponize that dependence, cutting off or threatening to cut off supplies in some future dispute? The obvious answer is no.
The whole world now knows that relying on imported fossil fuels is a major economic and security risk. By contrast, the sun will shine and the wind blow whatever may be happening overseas. Renewables were already rapidly becoming cheaper than fossil fuels. Now it’s clear that they are also far safer.
Thus Donald Trump has in practice become the world’s green energy champion.
MUSICAL CODA
Florida could be 100% powered by solar, but the fossil fuel lobby there is unmatched. That state prefers to sink underwater from climate change than change to renewables.
Wind energy threatens their masculinity? These guys in the manosphere are the most ignorant jerks our country has so far produced.
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Yale Climate Connections
Those who follow global news about renewable energy will know that solar energy around the world continues to grow apace. Those who don’t should be encouraged by the stories below. 
All but one of the articles in this list were published in spring 2026. Though the specifics change — along with such things as tariffs, taxes, rebates, wars, and blockades — the underlying plot remains positive. 
“Solar is winning the energy race.” Gero Rueter, DW, March 28, 2026.  An interesting timeline history of the development and expansion of solar power, with this subhead: “The world’s cheapest power source is scaling at warp speed, pushing coal, gas and nuclear aside.” 
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“Global Electricity Review 2026.” Nicolas Fulghum, Wilmar Suarez, Katye Altieri, and Kostantsa Rangelova, Ember, April 21, 2026. There is tons of data here, but the homepage offers highlights, an executive summary, and key takeaways. Sample sentences: “Record solar growth meant clean power sources grew fast enough to meet all new electricity demand in 2025, thereby preventing an increase in fossil generation,” and “Global solar generation is now the same size as the total electricity demand of the EU.” 
“Drill, baby, drill? US, China fight for the future of energy.” Thomas Kohlmann, DW, April 26, 2026. A good account of this geopolitical competition, with the U.S. pushing oil & gas and China betting on solar power. For more, with a bit of added climate-change context, see “China cashes in on clean energy as Trump clings to coal.” Sara Steffen, DW, February 23, 2026. 
“Africa’s solar power revolution driven by China’s investment.” David Ehl & Privilege Musvanhiri, DW, November 25, 2025. Thanks in large part to Trump’s tariff wars, Africa enjoyed a large but temporary influx of affordable solar panels from China. 
“Chinese solar exports double in a month to hit record high amid energy crisis.” Ember, April 23, 2026. This time, the U.S. war with Iran and the blockades of the Straits of Hormuz are the main factors in this export surge. This article includes a map of countries that reached either their all-time or six-month records for solar imports from China (including the U.S.). 
Despite strong headwinds, some progress is still being made in the US. To focus on those headwinds, see “US renewable energy to attract $120 billion in investment this year.” Catherine Boudreau, Latitude Media, April 28, 2026. The subhead: “Trump’s permit delays and looming restrictions on China-linked components risk chilling investments.” To focus on the progress, see Climate Central’s analysis of solar and wind energy in 2025, based on the U.S. Energy Information Administration’s data. For instance, “Together, solar and wind accounted for a record 19% of total U.S. electricity generation in 2025.”
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by SueEllen Campbell, Yale Climate Connections
June 19, 2026
SueEllen Campbell created and for over a decade curated the website "100 Views of Climate Change," a multidisciplinary collection of pieces accessible to interested non-specialists. She is especially interested… More by SueEllen Campbell

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Unsightly for some, solar panels are now being disguised and designed as ancient Roman tiles to blend into historic city skylines. From Italy to Portugal, Pompeii and Évora are proving that heritage preservation and sustainability can go hand in hand.
"WWE SmackDown" headed to Kansas City for the big Undisputed WWE title rematch between Cody Rhodes and Gunther, with Sami Zayn as the special guest referee.
Itauma, the No. 1 heavyweight prospect in the world, can prove he belongs when he steps up to face top-10 contender Filip Hrgović on Aug. 29 in London.
The Tennessee Titans and Jeffery Simmons have agreed to a 3-year, $105.8 million contract extension making him the highest-paid defensive tackle in NFL history, NFL Network reports.
More than a decade after challenging Gaethje for a world title, one-handed MMA pioneer Nick Newell explains why the newly crowned UFC champion remains unlike anyone else in combat sports.
Clark carries a four-shot lead with two holes still to play in Round 1.
Trader Joe's $3 striped mini tote bags sparked long lines, sellouts and resale mania, with some shoppers camping overnight to snag the limited-edition accessories.
Spike Lee, Ben Stiller and Timothée Chalamet were among the stars who rode on floats along the Canyon of Heroes.
Jalen Brunson (rightly) grabs all the headlines, but it's his supporting act that had the bigger fantasy basketball impact in category leagues.
While bringing home a small trinket or two has long been a part of any vacation experience, shopping has become a signature feature of visiting Japan, with people flying around the world to buy cheap goods and viral products.
Let's talk about the Ronaldo in the room.

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By: Luis Reyes
Published: Jun 19, at 9:30am ET
Solar panels are the default way to power just about anything we leave outside, from a parking meter to a Mars rover. They are cheap, they have almost no moving parts, and they keep working right up until the Sun goes away. On the Moon, the Sun goes away for two weeks at a time, and the cold that comes with it is brutal enough to wreck most of the hardware we bother sending up there.
That two-week night is the entire reason NASA and the Department of Energy have now committed to landing a working nuclear reactor on the lunar surface by 2030. It is also why Washington suddenly cares a great deal about who gets there first.
The two agencies made it official on January 13, 2026, signing a memorandum of understanding to jointly design, fuel, and launch a fission reactor for the Moon. The goal sits inside a broader space-nuclear push the current administration has been building for months, and it carries a deadline that is roughly a decade tighter than where this program sat a couple of years ago.
The short version: America wants a nuclear power plant running through the lunar night before the end of the decade, and it wants to plant one before China and Russia plant theirs.
A lunar day and a lunar night each run about 14 Earth days. During that long night, a solar-only base would have to either shut down or lug around an absurd bank of batteries to limp through two weeks of darkness and deep cold. Neither option scales if you actually want people living up there.
A reactor does not care whether the Sun is up. It splits uranium, makes heat, turns the heat into electricity, and keeps doing that for years without anyone topping it off. That is the pitch, and on the Moon it is a genuinely strong one. The same basic appeal, power that just sits there and works, is why even coin-sized nuclear batteries are getting serious attention down here on Earth.
NASA has wanted this for a while. The agency first floated putting a reactor on the Moon within a decade back in 2021, and by 2024 the realistic target for getting one to a launch pad had drifted into the early 2030s. The technology itself is not new either. NASA’s earlier Kilopower project ran a successful reactor test on Earth in 2018 before wrapping up, so the physics of a small space reactor is settled. What changed recently is not the science. It is the urgency.
Here is where it gets interesting, because the 2030 date did not come from NASA’s engineers. It came from the top. In a memo signed on July 31, 2025, then-acting NASA administrator Sean Duffy, who is also the Secretary of Transportation, ordered the agency to design, build, and launch a reactor putting out at least 100 kilowatts of electric power and ready to fly by the end of 2029. That is a big jump. The program had been targeting a 40-kilowatt class reactor, enough to run roughly 30 households, and Duffy’s memo set the bar at more than double the power and a heavier machine to match. A 100-kilowatt reactor is closer to powering 80 homes.
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Then the politics caught up. On December 18, 2025, President Trump signed an executive order titled “Ensuring American Space Superiority” that put deploying reactors on the Moon and in orbit on the official priority list, including a lunar surface reactor ready for launch by 2030, alongside a goal of returning Americans to the Moon by 2028.
The January memorandum between NASA and the DOE is the paperwork that turns all of that into a joint program with money and responsibilities attached. The DOE handles the nuclear side, including supplying roughly 400 kilograms of high-assay low-enriched uranium fuel for ground tests and the flight reactor, according to SpaceNews. NASA runs and funds the program.
NASA is not building the reactor itself. The plan is to pick commercial teams through funded Space Act Agreements, and the field of likely bidders is already public. Westinghouse is offering a space version of its eVinci microreactor it calls AstroVinci, which the company says can be tuned anywhere from 10 to 100 kilowatts using either a Brayton or Stirling power-conversion system.
Lockheed Martin, which has held a fission surface power design contract since 2022, is leaning on decades of naval-submarine reactor work. There is also IX, a joint venture between lunar-lander outfit Intuitive Machines and reactor developer X-energy, plus newer entrants like Antares Nuclear and Radiant Industries.
Not everyone thinks the 100-kilowatt target is the smart play. Lockheed, of all people, has publicly argued that a smaller reactor makes more sense for what is actually planned on the Moon in the near term.
In the company’s own framing, a 5-to-10-kilowatt unit can run a habitat or a rover charging station, a 25-to-50-kilowatt reactor could serve several habitats at once, and jumping straight to 100 kilowatts would, in the words of Bill Pratt, Lockheed’s director of in-space infrastructure, “increase program risk and provide more power than needed” for the current plan. When one of your own contractors is gently noting the headline number looks like overkill, that is worth a pause.
NASA put out a draft solicitation, called an Announcement for Partnership Proposals, on August 29, 2025. The final request for proposals and the actual contract awards are still expected sometime in 2026, with the agency planning to fund up to two reactor developers to carry their designs forward.
The lunar reactor is only half of what NASA has been talking about. The other half is nuclear propulsion, and the pitch there is genuinely wild. At a March 2026 event, NASA Administrator Jared Isaacman laid out a plan to launch a small interplanetary fission reactor called SR-1 Freedom, short for Space Reactor-1, by the end of 2028.
It is designed as a nuclear-electric propulsion system, and on its way through the solar system it would drop off three small helicopters, each in the class of the Ingenuity drone that flew on Mars, in a mission NASA is calling Skyfall. After that, SR-1 keeps going deeper into space.
This is not just a slide deck, either. According to the American Nuclear Society, NASA’s fission surface power program executive, Steve Sinacore, said hardware development for the propulsion effort is meant to begin this month, in June 2026, with spacecraft assembly and testing slated for 2028 and the launch penciled in for that December. Whether any of it holds to schedule is a separate question, and NASA’s own track record on space-nuclear timelines is not exactly spotless. But the ambition is real, and it is funded.
So why the rush to power a place where nobody lives? Because the US is not the only one trying. In March 2024, China and Russia announced they would cooperate on a nuclear reactor for the Moon as part of their planned International Lunar Research Station, with a target window the American Nuclear Society puts between 2033 and 2035. On paper that is a few years behind the US goal. In practice, nobody has landed a working reactor on the Moon yet, so the gap is mostly a question of who actually hits their deadline.
The deeper concern is real estate. In his July memo, Duffy warned that the first country to deploy a reactor could potentially declare a “keep-out zone” around it for safety reasons, which would box everyone else out of a desirable patch of ground. The Moon’s most valuable spots, the permanently shadowed craters near the south pole that may hold water ice, are limited, and getting there first with hardware on the surface is a way of staking a practical claim without ever firing a shot.
No treaty has been broken by any of this, and no country has accused another of doing anything illegal. It is a land grab run entirely through engineering schedules.
For all the deadline talk, the official documents are careful about what they commit to. As the American Nuclear Society pointed out, NASA and the DOE never quite say whether a reactor will be operating on the Moon by 2030 or merely built and waiting by then, and this program has a long history of dates that slipped.
That is par for the course with big nuclear projects; the world’s largest fusion machine only switched its first systems on after two years of rebuilding. A reactor that is ready to launch in 2029 still has to survive the trip, the landing, and a switch-on in an environment that is savage to machinery, and none of that is guaranteed by a signed memo. What the last six months changed is that the US has stopped treating lunar nuclear power as a someday project and started treating it as a race it intends to win, with a number, a deadline, and a short list of companies on the hook to deliver it.
Don’t bite your tongue. Speak up.
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