Agrivoltaics has become one of those ideas that is simple enough to fit on a social media tile and complex enough to be mangled by one. The image that prompted this discussion showed a farmer kneeling beneath solar panels in front of vegetables, sheep, mountains, and an American flag, with the claim that America is proving solar panels and farming can share the same land and that crops grown under panels outperform crops grown in full sun. There is a useful truth buried in that image, but it is wrapped in the wrong flag and stated with too much confidence. Solar and farming can share land. In some climates, with some crops, in some configurations, partial shade from solar panels can improve crop performance, reduce water stress, lower evaporation, and cool the microclimate enough to improve solar panel output as well. That is not a fantasy. It has been demonstrated in field trials, especially in hot and dry conditions. But it is not a universal law of agriculture, and it is not mainly an American story if the question is deployment scale. China is the global scale leader in agrivoltaics by a wide margin. The United States is a meaningful participant, especially in research, sheep grazing, pollinator habitat, and demonstration projects, but American exceptionalism is misplaced again. The first problem is definition. Agrivoltaics sounds like one thing, but it is really a family of land and water co-use systems. It can mean vegetables grown under elevated panels, sheep grazing beneath standard utility-scale arrays, pollinator habitat planted around solar rows, panels over fish ponds, greenhouses with semi-transparent photovoltaic glass, orchards under protective solar canopies, or desert-edge restoration projects where shade reduces wind erosion and evaporation. All of these combine solar generation with agricultural or ecological production, but they are not interchangeable. A gigawatt of sheep grazing beneath conventional solar arrays is not the same thing as a gigawatt of elevated structures above broccoli. A fishery-solar project in eastern China is not the same as a Japanese solar-sharing installation over rice or a German orchard canopy. Comparing them as if they are identical leads to bad conclusions. The global capacity picture makes the point quickly. A 2026 Scientific Data paper assembled a national vectorized dataset for China and identified 1,678 agrivoltaic projects totaling 134.55 GW by the end of 2022. That figure uses a broad Chinese definition, including crop-based, fishery-based, greenhouse-based, husbandry, and other forms of co-use. It is not directly comparable to a narrow definition of vegetables under high-clearance racking. But even with that caveat, China is clearly in a different league. The United States, by contrast, reached about 10 GW of agrivoltaic capacity by November 2024 according to NREL’s InSPIRE and OpenEI tracking. That represented almost 600 sites and roughly 60,000 acres. It is a useful number and a real category, but it is less than one-tenth of China’s broad reported agrivoltaic capacity from two years earlier. It is also heavily weighted toward grazing, pollinator habitat, and vegetation management rather than crop production under purpose-built elevated arrays. Europe sits somewhere between scale and governance. SolarPower Europe’s agrisolar map listed more than 200 projects across 10 countries exceeding 2.8 GW as of 2024, but that map includes a broad mix of agrivoltaic and farm-integrated solar types. France, Germany, Italy, Spain, and the Netherlands all have serious activity, but permitting, agricultural rules, subsidy eligibility, and definitions remain uneven. Europe’s contribution may be less about raw capacity today and more about defining what counts as legitimate agrivoltaics: continuing agricultural production, crop performance, biodiversity, farmer participation, and protection against token farming. Japan is important for a different reason. It has thousands of solar-sharing sites and long experience with farming under panels, but it also learned that agrivoltaics can drift into “paper agriculture” if rules are weak. Some projects underperformed agriculturally or were poorly managed, which led to tighter requirements around cultivation plans, monitoring, and agricultural performance. If the farming is ornamental, the public-policy argument collapses. Agrivoltaics is supposed to preserve or improve agricultural value while adding clean electricity. A few weeds under panels are not food security. ASEAN and Africa have strong theoretical fit but much less transparent capacity data. Thailand, Vietnam, Indonesia, Malaysia, Kenya, Tanzania, Togo, and others have pilots or early projects, and some local claims are larger than the independent evidence supports. For these regions, the honest answer is that public capacity data is too thin for a clean regional GW number. The need is obvious. Many regions face heat stress, water stress, rural income pressure, weak grids, and land-use conflicts. Agrivoltaics could help in specific places, but the deployed base is not yet comparable to China, the United States, or Europe. China’s lead is not surprising when the full system is considered. China has the world’s largest solar manufacturing base, the world’s largest solar deployment machine, strong provincial implementation capacity, major land-use pressures, food security priorities, and desertification challenges. Agrivoltaics in China is not just “crops under panels.” It includes fishery-solar installations, greenhouse-solar systems, crop-solar projects, animal husbandry, tea plantations, orchards, and desert-edge vegetation systems. Photovoltaics become part of rural infrastructure, not merely an electricity asset placed beside a farm. The American story is still interesting, but it is different from the social media version. NREL’s InSPIRE program, university field trials, and dryland experiments in Arizona and elsewhere have generated useful evidence. The Arizona work is especially important because it explains why shade sometimes helps. In hot, dry conditions, full sun can exceed the useful range for many crops. Plants can close stomata to conserve water, photosynthesis can fall during the hottest part of the day, and irrigation demand rises. Partial shade can reduce thermal and water stress, allowing the plant to keep operating for more of the day. In those settings, less light can produce more useful growth. That is how some of the eye-catching results occur. Chiltepin peppers, tomatoes, jalapeños, leafy greens, berries, and forage crops can benefit in the right climates and layouts. Shade can reduce evaporation. Soil moisture can persist longer. Some crops can avoid sunscald or heat stress. Solar panels can run cooler because vegetation and transpiration reduce local temperatures, and solar panels lose efficiency as they heat up. It is a genuine food-water-energy interaction, but the word “some” is doing a lot of work. Corn, wheat, soy, canola, and many other full-sun commodity crops are not automatically improved by panels. They are often low-margin, highly mechanized, light-hungry, and managed with large equipment on tight schedules. Put posts, cables, rows, and overhead structures in the wrong places and the farm operation gets worse. Raise panels high enough and space them wide enough to preserve machinery access and the solar project becomes more expensive and less dense. Keep panels low and cheap and the site may work for sheep, pollinator habitat, or groundcover, but not for serious crop production. The strongest technical fit is hot, dry, high-radiation regions where crops are already stressed by too much heat and too little water. In those places, panels can act like productive shade infrastructure. They generate electricity while reducing some of the conditions that damage crops. A second strong fit is intensive horticulture where farmers already pay for protection. Orchards, vineyards, berries, and some vegetable systems already use shade cloth, hail netting, frost protection, windbreaks, trellises, or irrigation infrastructure. If photovoltaic structures can replace or supplement some of that infrastructure, the economics become more plausible. A solar canopy that reduces sunburn on apples, heat stress on vines, or water demand in berries may be doing two jobs. The electricity is not an add-on. It is part of a farm protection system. A third strong fit is grazing, especially sheep. This is not the Instagram version of agrivoltaics, but it may be one of the most commercially scalable forms in the United States, the United Kingdom, and parts of Europe. Sheep fit under standard solar arrays. They reduce mowing costs. They can lower fuel use and fire risk from unmanaged vegetation. They provide income to graziers and operational savings to solar owners. The system still requires good stocking density, water access, fencing, animal welfare practices, and vegetation planning, but it is much easier to integrate than combine harvesters under elevated panels. Pollinator habitat is another practical category. It is not crop production under panels, but it can matter if solar sites are planted with native flowering vegetation near pollination-dependent agriculture. A site designed for native plants, pollinators, soil cover, and runoff management has a different land impact than panels surrounded by gravel and mowed turf. Aquavoltaics and desert restoration broaden the frame again. Panels over ponds can generate electricity while moderating water temperatures and reducing evaporation. In desert-edge systems, panels can reduce wind speed, shade soil, lower evaporation, and support vegetation. China has treated solar in some arid regions as ecological control and rural development, not just generation capacity. The failures are just as important. Agrivoltaics struggles where shade reduces yield without reducing heat or water stress enough to compensate. It struggles where panels interrupt mechanized agriculture. It struggles when installation damages soil through compaction, trenching, roads, laydown yards, and construction traffic. It struggles when rainfall runs off panel edges into concentrated drip lines that create erosion, wet strips, dry strips, and weed pressure. Solar panels do not simply cast shade. They redistribute water. Irrigation design, soil protection, and erosion control all have to account for that microhydrology. This is why agrivoltaics works best when the agricultural system comes first. The design should begin with crop physiology, climate, machinery width, irrigation, soil, harvest logistics, water rights, pest management, and farmer economics. Only then should the solar layout be optimized. If a developer starts with a standard solar farm and adds a farmer at the end to improve permitting optics, the result is likely to be weak agriculture and awkward operations. A credible project has measurable agricultural output, farmer authority, and a design that supports ordinary farm operations. The farmer economics are central. Agrivoltaics is not credible if the farmer is only a permitting prop while the developer captures the value and controls the land. The contracts have to decide who gets lease revenue, who pays for crop losses, who controls access, who maintains roads and fences, who carries liability, and who has authority when farming and electrical maintenance conflict. A system that improves the solar developer’s permitting odds but leaves the farmer with lower yields, awkward access, and unmanaged risk is not a good agricultural system. It is a land-control strategy borrowing the language of farming. The crop-yield claim also needs better metrics. A project can be good even if crop yield per hectare falls, if electricity revenue, water savings, reduced risk, and improved land equivalent ratio make the farm system more productive overall. Land equivalent ratio is useful because it asks how much separate land would be needed to produce the same crop and electricity outputs independently. If one hectare of agrivoltaics produces the same combined value as 1.3 hectares of separate solar and farming, the system is doing something meaningful. But that is different from saying the crop always beats full sun. Policy should reward genuine dual use without pretending every solar project must become a farm. Some land should host solar because it is a good solar site. Some land should remain agriculture without panels. Some land is suitable for dual use. The point is to classify and design honestly, with crop agrivoltaics, grazing, pollinator habitat, greenhouses, aquavoltaics, and ecological restoration counted separately instead of blended into one flattering number. Policy also needs enforcement because solar development on agricultural land is politically sensitive. Credible dual use can reduce rural land-use conflict, but only if the agricultural use is visible, measurable, and economically meaningful. Japan’s experience is a warning. If agrivoltaic approval is easier than ordinary solar approval, developers will have an incentive to claim agriculture whether or not agriculture is serious. The cure is not endless bureaucracy, but clear rules: a cultivation plan, farmer access, crop or livestock performance expectations, annual reporting, soil and water management, and consequences if the farming disappears. The leading practices are becoming clear. Agrivoltaics should start with the farm, not the panels. The project should match shade to crop and climate, preserve machinery access, manage water deliberately, protect soil during construction, and give farmers real operational authority instead of decorative participation. It should monitor crop yield, water use, soil health, biodiversity, and PV output. Pollinator habitat is not vegetable production. Sheep grazing is not crop agrivoltaics. Token vegetation is not farming. The serious version is more interesting than the meme because it does not need a flag. Agrivoltaics is not proof that one country has discovered a trick the rest of the world missed. It is a test of whether energy systems, farm systems, water systems, and rural politics can be designed together. China has scaled the broad category. The United States is contributing research and practical niches. Europe and Japan are working through the governance problem. The next phase will belong to jurisdictions that stop treating agrivoltaics as a slogan and start treating it as infrastructure for farms, grids, water, and climate adaptation at the same time. CleanTechnica’s Comment Policy Michael Barnard works with executives, investors, and policymakers to navigate the pathways toward decarbonization. He helps make sense of complex transitions by combining insights from physics, economics, and human systems, turning them into practical strategies and clear opportunities. His work spans sectors from sustainable building materials and aviation fuels to grid storage and logistics, always with an eye on how they fit together in the larger picture of the clean economy. Informed by projects across North America, Asia, and Latin America, his perspective is both global and grounded in real-world application. Michael shares his thinking through regular publications on technology trends, innovation, and policy frameworks — not as final answers, but as contributions to an ongoing conversation about building a sustainable future. Michael Barnard has 1397 posts and counting. See all posts by Michael Barnard
Download our app for the optimal streaming experience This story is free to read because readers choose to support LAist. If you find value in independent local reporting, make a donation to power our newsroom today. Solar developers say they’re facing crippling losses and potential bankruptcy amid a stall in a state-funded solar power program. California’s “self-generation incentive program,” or SGIP, was reworked in 2024 to help low-income households install solar and battery-storage systems for free. But SGIP has been plagued by delays, bureaucracy, poor communication and stalled payments, according to five developers LAist spoke with. Small developers say they’ve been hit especially hard by a lottery system that they argue favors larger developers. And customers who stood to benefit the most from free installation of solar and battery storage — low-income households in the hottest and most fire-prone areas of the state — are in limbo, waiting months for the bill savings and energy reliability they were promised ahead of what is expected to be a record-hot summer. The issue highlights the challenges to expanding access to clean energy as fossil fuel pollution continues to accelerate climate change and is another hit to an industry that has faced significant setbacks in recent years from changes to state-level rooftop solar programs and the Trump administration’s cuts to clean energy incentives.
The state has offered incentives to large electric customers to install generation and backup systems since the energy crisis of the early 2000s. The latest version of the SGIP program aims to prioritize qualifying low-income residents. In 2024, the state allocated $280 million in state funds to install solar and batteries for free on qualifying homes and apartments. The program is administered through the state’s investor-owned utilities and the Los Angeles Department of Water and Power. It officially launched last summer. Here’s how it’s supposed to work: Developers identify projects they can take on, then apply for funding via a first-come-first-served reservation system. If requested funds exceed the total funding, then a lottery is triggered. If their project is approved, the developer does the work and covers the upfront costs of the installation with the understanding they’ll get paid back through SGIP within a year. As soon as the SGIP program launched last June, large developers quickly flooded the application system. Sunrun, one of the nation’s largest solar developers, submitted applications requesting as much as 97% of the total funds available in Los Angeles Department of Water and Power territory, according to public data reviewed by LAist. (Sunrun declined to be interviewed for this story. LADWP didn’t agree to be interviewed about the breakdown of applications.) LADWP said it is in the process of reviewing the 451 applications it received. So far, DWP officials have approved one: $28,000 for a single-family home project, the utility told LAist. Smaller developers told LAist they’re concerned that there is no cap on how much any single developer can receive through the program. General market versions of SGIP not targeted for low-income properties have developer caps of 20% of the incentive funds, according to the program’s handbook. “The purpose of the program, I believe, is not to just enrich the biggest players or to allow them to have free project financing,” said Aaron Eriksson, owner of Escondido-based Solar Symphony Construction, which applied for projects in LADWP territory. “We all got kind of left out in the cold on that one.” Robert Cudd, a research analyst with UCLA who has studied SGIP, said the program does incentivize developers lining up as many projects as possible ahead of time to “claim the largest possible share of that rebate pool.” That’s often the case for similar programs that aim to serve low-income customers. The state “is agnostic about who is doing this work,” Cudd said. “They just want to accelerate the energy transition.” Only a few large companies — including Sunrun and GRID Alternatives, as well as growing startup Haven Energy — have developed specialized expertise in these kinds of complex programs that have higher upfront costs. Delayed reimbursements have developers worried about projects in the works and about new paperwork requirements. In February, the California Public Utilities Commission — five governor-appointed regulators who oversee the program — abruptly paused SGIP. In their ruling, they said that projects submitted varied widely in costs, with many exceeding incentives “significantly.” The ruling flagged discrepancies such as the same wall battery reportedly costing as low as $8,600 and as high as $21,000. So the CPUC decided to require developers to submit additional receipts and documentation of their costs. But developers LAist spoke with said only a fraction of applications were at the state’s predicted costs. The developers argue costs have gone up due to inflation, tariffs and cuts to clean energy tax credits. Projects serving low-income households also often require upgrades because of the buildings’ age. Joshua Buswell-Charkow, deputy director of California Solar and Storage Association, a trade organization that represents more than 70 companies that participate in the SGIP program, said work is already underway in some cases. “Some of our contractors are out literally millions of dollars right now,” he said. “ I’m worried that we’re going to have folks go out of business because of this.” That could be the case for Eriksson’s company, Solar Symphony. More than 100 of the company’s applications to install solar and battery systems at no cost to qualifying customers were approved by Southern California Edison and San Diego Gas & Electric. Now, Eriksson said, they don’t know if they’ll be paid for projects they’ve already installed. “We were very excited by the potential to deliver truly no-cost, home-sited solar and batteries to California ratepayers,” Eriksson wrote in a statement to the public utilities commission. “The regulators effectively induced us to commit under one set of rules; we accepted and delivered — and now the terms are changing.” Eriksson told LAist he could be out of business by June if the state doesn’t release the payments. Other companies have indefinitely paused installing systems approved by program administrators. “We’ve signed contracts with hundreds of low-income families. We’ve purchased the equipment,” said Vinnie Campo, co-founder of Haven Energy, one of the state’s largest SGIP installers, at a Public Utilities Commission meeting in late April. “Our crews are ready to install, but systems sold in good faith to customers … are sitting in warehouses instead of on homes.” Seven representatives of solar companies, including a lawyer representing multiple companies in Southern California, expressed their concerns at that meeting. Lionel Rodriguez of Glendale-based Solar Optimum was one. “Many people are hurting,” Rodriguez said, “and it’s destroying the integrity of our company and also the customer’s trust.” In early May, in response to such concerns, the Public Utilities Commission released another ruling saying administrators can start paying developers when certain documentation has been submitted but that they still could audit any company that receives funds. Meanwhile, utilities have until the end of June 2028 to spend the funds, or else they’ll be returned to the state’s general fund. LAist is an independent, nonprofit newsroom that is also home to L.A.’s largest NPR station broadcasting at 89.3 FM. We center our coverage around people and communities, not institutions or policies. We hold power to account. We are unapologetically L.A.
Oswego School District 308 looks to save an estimated $61.6 million over 30 years by installing panels on seven schools, including Oswego High School. (Eric Schelkopf) Oswego School District 308 looks to save an estimated $61.6 million over 30 years by installing solar panels on seven schools. “As we know, electricity costs have been going up significantly, not just here, but all over the country,” District 308 chief financial officer and chief school business official Raphael Obafemi told school board members during their May 11 school board meeting. The district looked into the possibility of installing solar panels “as a way to mitigate that increase in electricity costs, especially with the installation of data centers in our area, which is driving up the cost of electricity,” he said. The board has not made a decision yet on installing the panels. Board members are expected to continue to discuss the issue at future meetings. As proposed, rooftop-mounted solar photovoltaic arrays would be installed on both Oswego and Oswego East high schools, along with five junior high schools: Bednarcik, Murphy, Plank, Thompson and Traughber. “We’re not gong to offset demand completely,” Kurt Hintz, business development manager for Performance Services, said during the meeting. “We’re going to shift your peak demand day from the highest, most expensive day to a much less expensive day.” Performance Services has done similar solar projects for several Illinois school districts, including Valley View Community Unit School District 365U in Romeoville and Glenbard Township High School District 87 in Glen Ellyn. The company is proposing that the school district purchase the solar panels. Maintaining the panels is estimated to cost about $15,000 a year, Hintz said. Board member Amy Murillo asked what would happen to the solar panels when a building’s roof needed repairs. Repairing the roof in sections would be an option, Hintz said “I will say, we have extended the life of many roofs, because now direct sunlight is not hitting the roof, it’s hitting the solar panels,” he said. With the installation of the solar panels, the district is expected to receive $1.51 million in ComEd incentives/rebates along with $4.89 million in renewable energy credit revenue and a federal inflation reduction act incentive of $8.15 million. In February, Oswego village trustees voted to become a part of a community solar program the village said will save an estimated $40,000 annually in electric costs. The Community Solar program allows the village to subscribe to a remote solar energy project instead of installing panels onsite. At the Feb. 17 Oswego Village Board meeting, trustees unanimously passed a resolution approving solar agreements with Solstice Power Technologies, LLC. Adam Hoover, director of strategic accounts at the Northern Illinois Municipal Electric Cooperative, told village trustees Community Solar is a program that is guaranteed to provide 10% savings while still finding the fixed rate savings that NIMAC brings. NIMEC serves as the broker and consultant for the village’s electrical aggregation program. Community Solar in ComEd territory allows residential and business customers to subscribe to a remote solar energy project instead of installing panels onsite, Oswego finance director Andrea Lamberg told village trustees “Participants receive monthly Community Solar aggregation credits on their ComEd electric bills based on their share of solar generation, which offsets the electricity supply portion of their bills,” she said. Oswego Village President Ryan Kauffman liked the fact that over the life of a 20-year contract, the village could save about $800,000. “If electric prices rise, it could be even greater than that,” he said. “It’s a good deal all around.” Eric Schelkopf, who is a Kendall County resident, writes for the Record Newspapers/KendallCountyNow.com, covering Oswego and Plainfield. Schelkopf, who is a Kendall County resident, started with the Kane County Chronicle in December 1988 and appreciates everything the Fox Valley has to offer, including the majestic Fox River.
The site of an in-progress solar array on Old Sharon Road in Jaffrey. Solar company ReVision Energy is partnering with the town to install over 2,000 panels, which, once completed in 2027, will provide energy for households in the area with low incomes.
The site of an in-progress solar array on Old Sharon Road in Jaffrey. Solar company ReVision Energy is partnering with the town to install over 2,000 panels, which, once completed in 2027, will provide energy for households in the area with low incomes. JAFFREY — A local town is transforming a trash dumpsite into the home of an energy resource generator, a clean energy developer announced recently. The town of Jaffrey is partnering with ReVision Energy, a New Hampshire-based solar company, to construct a solar array at the site of a former municipal landfill, according to a May news release. The project is set to be a win-win for the town, utility customers, and the planet, the release said. Construction is underway on the project, which, when completed in early 2027, will provide power at a cheaper rate to roughly 250 low- and moderate-income householdsthough New Hampshire’s Electric Assistance Program, the release said. The community solar array will deliver up to $2 million in bill credits during the life of the system, which will decrease participants’ electricity supply rates by roughly 25 percent in their utility bills, the release said. Comprising 2,266 solar panels, the community solar array is expected to generate more than 1.7 million kilowatt hours of electricity each year, offsetting over 930 tones of carbon pollution, per the release. “It’s the perfect use of land that can’t do anything else,” Town Manager Jon Frederick said in the release. “This project generates value for the town while supporting families who need energy savings the most.” Jaffrey is leasing the Old Sharon Road landfill — which is capped, meaning it has a cover over the trash that prevents the creation of contaminated liquid — to the solar company for the 1.34 megawatt solar array installation, the release said. The town is slated to receive annual lease payments of $10,000. Regional energy company Eversource is overseeing the state program’s selection process, which is guided by criteria put in place by the N.H. Department of Energy, the release said. The program will prioritize customers who have signed up or who are on the waitlist for the Electric Assistance Program within the project’s zip code, followed by eligible customers in neighboring areas. ReVision has already installed solar arrays on 11 landfills in New Hampshire and Maine, with another three scheduled to be completed by 2027, per the release. Last summer, the Town of Winchester announced ReVision was involved in an effort to build a solar array at the site of a former tannery. Mark Zankel, director of community solar for ReVision Energy, said in the release that the project is representative of the possibilities for collaboration between communities and clean energy developers. “By transforming a capped landfill into a source of clean power and directing 100 percent of the energy to households enrolled in the Energy Assistance Program, the Jaffrey Landfill community solar array will transform an underused site into meaningful, long‑term benefits for the community and for Granite Staters who need it most,” he said. Noah Diedrich can be reached at 603-355-8569, or ndiedrich@keenesentinel.com. {{description}} Email notifications are only sent once a day, and only if there are new matching items. Your comment has been submitted.
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AUSTRALIA’S NATIONAL SCIENCE AGENCY We have developed technology to accurately test solar panels. This helps to protect Australia’s solar energy supply. Share Australia is experiencing a solar energy revolution. In 2024, 36 per cent of Australia’s total electricity generation was from renewable energy sources with solar representing 18 per cent of this.
Dr Chris Fell and Dr Andre Cook inspect the new Sinus 3000 solar simulator which performs some of the most advanced photovoltaic module testing in the world.
Yet, despite our abundant sunshine and growing reliance on solar power, Australia has limited infrastructure to verify the performance of imported solar photovoltaic (PV) modules. CSIRO’s Solar PV Performance Laboratories are helping to close this gap—delivering trusted testing, research, and innovation to ensure solar panels perform as promised. Solar PV modules are sold based on a label power rating, but real-world conditions—such as shipping, handling, extreme weather, and long-term exposure—can significantly affect performance. With almost no domestic solar manufacturing, Australia relies heavily on imported panels, where warranties are often difficult to enforce. Accurate, independent testing is essential to protect consumers, support industry quality control, and ensure value-for-money. CSIRO operates three specialised solar PV measurement laboratories at its Energy Centre in Newcastle, NSW:
We have developed and demonstrated highly accurate testing of solar PV module output, along with software algorithms to extract key performance information from real-world outdoor testing.
Our solar cell measurements are accredited by the National Association of Testing Authorities (NATA) under the IEC17025 technical competency standard, ensuring internationally recognised quality and reliability. CSIRO’s solar scientists contribute strongly to both national and international PV standards committees. They collaborate with leading institutions such as the U.S. National Laboratory of the Rockies (NLR) and key European and Japanese organisations, focusing on solar module performance, indoor/outdoor testing, and shared research. This global engagement ensures CSIRO remains at the forefront of solar technology and brings best practices back to Australia. In addition to testing, CSIRO’s Solar Technologies group works with industry and academia to develop cheaper, longer-lasting, and more environmentally sustainable solar cells. This includes research into advanced materials, module durability, and recycling readiness. To further support the solar industry, CSIRO is developing a portable solar module testing solution. This innovation will allow performance assessments to be conducted directly at solar farms, reducing downtime and enabling faster diagnostics after extreme weather events, routine maintenance, or for establishing the re-use potential of un-installed solar panels. By providing independent testing of solar PV modules, CSIRO helps enforce a high Australian standard for solar performance. This protects consumers, supports industry growth, and ensures that Australia’s transition to clean energy is built on reliable, high-quality technology. CSIRO’s Solar PV Performance Laboratories offer the expertise, infrastructure and collaborative approach to support researchers, installers, manufacturers on our solar journey. Read more about how CSIRO measures decade of solar performance. Principal Research Scientist Last updated: 24 February 2026 Principal Research Scientist Last updated: 24 February 2026 We are committed to child safety and to the implementation of Child Safe principles and procedures. Thanks. You’re all set to get our newsletter We could not sign you up to receive our newsletter. Please try again later or contact us if this persists. CSIRO acknowledges the Traditional Owners of the land, sea and waters, of the area that we live and work on across Australia. We acknowledge their continuing connection to their culture and pay our respects to their Elders past and present. View our vision towards reconciliation. Find out how we can help you and your business. Get in touch using the form below and our experts will get in contact soon! CSIRO will handle your personal information in accordance with the Privacy Act 1988 (Cth) and our Privacy Policy. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply. Enter a valid email address, for example jane.doe@csiro.au A Country value must be provided First name must be filled in Surname must be filled in Please choose an option Organisation must be filled in Please provide a subject for the enquriy 0 / 100 We'll need to know what you want to contact us about so we can give you an answer 0 / 1900 We have received your enquiry and will reply soon. The contact form is currently unavailable. Please try again later. If this problem persists, please call us with your enquiry on 1300 363 400 or +61 3 9545 2176. We are available from 9.00 am to 4.00 pm AEST Monday – Friday.
Last month, North Carolina’s top utility regulator abruptly halted Duke Energy’s solar farm investments for the year — an unforeseen blow to an industry already reeling from tariffs and a hostile federal government. Now, clean energy businesses and advocates have filed a motion to cancel the order, which they argue is not just “arbitrary and capricious,” but also at odds with a 2021 state climate law requiring Duke, North Carolina’s predominant utility, to zero out its carbon pollution by midcentury. Because the freezing of solar contracts this year could upend Duke’s long-term plan to transition to carbon-free resources, critics of the order are asking for an expedited ruling on their reconsideration petition by May 19. The carbon-reduction plan is still pending with regulators. “Any kind of reconsideration should happen quickly,” said Mikaela Curry, a North Carolina–based Beyond Coal campaign manager with Sierra Club, one of the groups protesting the order. “If this gets dragged out and delayed, it still could have the same impact.” The April 23 ruling from Utilities Commission Chair William Brawley takes aim at the competitive bidding process by which Duke adds large-scale solar fields to its generation mix. It has been the main avenue for building solar in North Carolina since the 2021 climate law. To comply with that law, Duke must submit a carbon-reduction plan for approval by regulators every two years. According to the last blueprint, greenlit by commissioners in 2024, the utility was scheduled to solicit bids for 1.7 gigawatts of solar this year — including some paired with storage. Yet, in part because Congress curtailed tax credits for solar last summer, Duke proposed in March to procure just 770 megawatts in 2026. In his order, Brawley, a Republican former state legislator, seized on that 1-gigawatt deficit to argue that the company should procure no solar or storage at all. “Any modification of an existing procurement target for solar generation and battery energy storage,” Brawley wrote, should be based on a long-term plan approved by the commission, not a plan “unilaterally proposed” by Duke. Brawley’s directive defers Duke’s procurement plans for solar and storage until the company’s next carbon-reduction blueprint is ratified, an action due by Dec. 31. Clean energy advocates say that deadline makes the practical effect of the order unmistakable: eliminating solar procurement entirely this year — not just deferring it. “The Deferral Order’s prohibition on even the pre-issuance design and stakeholder discussion processes forecloses any possibility of completion in 2026,” wrote John Burns, general counsel for the Carolinas Clean Energy Business Association, in his organization’s motion for reconsideration. “The inevitable result is that no 2026 solar procurement will take place.”
By sambarker in Market news15th May 20260 Nine in 10 house hunters (93%) want their dream home to have green energy modifications like solar panels and EV charging, according to research from E.On Next. More than two-thirds (70%) of prospective buyers say energy technology is now a non-negotiable when choosing a property, while 68% say they would not have been looking for these features a year ago. Almost all of today’s buyers (93%) report growing energy uncertainty is shaping what they want in a home. Rising energy costs (52%) and a desire for greater energy security (29%) are driving this shift towards improved energy efficiency. Meanwhile 60% of those surveyed said the current energy market has prompted them to seek a property with solar panels, while 61% now want a home with EV charging capabilities. Furthermore, 70% say they are searching for a house with a heat pump for the first time ever, and almost two-thirds (63%) are on the lookout for a home battery. E.On Next head of future energy homes Richie Atkinson said: “The pace of change is striking – over two‑thirds of prospective buyers say features like this wouldn’t have been on their radar a year ago. “That shift is now shaping longer‑term buying decisions, and we’re seeing sales up from February this year by 182% for solar and home batteries, 129% for heat pumps and 18% for EV chargers as people factor energy efficiency into what they want from a future home during times of uncertainty.” E.On surveyed 1,000 adults between 24 and 28 April 2026 that are planning on buying a home. You must be logged in to post a comment. Paul Walton, General Manager – UK Lending, SBS Direct delivery of news and expert analysis to your inbox
As an independent publication, we rely on contributions from readers like you to fund our journalism. Thanks for your contribution! Sign up to our free newsletter to get the latest news delivered straight to your inbox. Noozhawk The freshest news in Santa Barbara County The Santa Barbara County Planning Commission voted 3-1 on May 10 to approve allowing up to 16,000 acres of agriculturally zoned land to be converted for solar projects — a figure county staff described as no more than 2% of available agricultural acreage. Staff also stated that the first 8,000 acres of solar development “should supply” 100% of the county’s power needs, raising questions about why the proposal allows enough acreage to potentially generate double the county’s current electricity demand.
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Noozhawk also reported that the Sierra Club Santa Barbara chairwoman said adding new solar farms would help the county move away from oil and gas. She added that solar panels on farms can “benefit crops, animals and workers by providing shade and protecting them from excessive heat” (wow). So, let’s make sure that these priorities aligned with all our good citizens of Santa Barbara County because our elected decision makers (Board of Supervisors) hope to see this item on their agenda sometime this summer! What’s under the hood? The 16,000 acres were calculated to help the county meet its climate goals, including generating sufficient local “renewable” energy to support California’s stated targets. The first half (8,000 acres of new solar farms) is planned for what’s called the “grid neutral” goal, whereas the total 16,000-acre figure is the true “gas replacement” goal. “Grid neutral” is based on the needs of everyone in the county’s current electrical load (lighting, computers, existing appliances). UC Santa Barbara’s “2035 Initiative” would argue that stopping at 8,000 acres of development would effectively lock the county into keeping natural gas for heating and evening power forever … i.e., not acceptable. This aligns with what our environmental nonprofit organizations, educational institutions, county staff and many local elected officials very much want: the elimination of all-natural gas used for any use in our homes, buildings, restaurants — anywhere and everywhere. But as we can now see, what’s not being presented well by these piecemeal actions and public meetings is akin to always moving the goal posts. Natural gas is primarily used for thermal energy (heat), not just electricity generation. Some 8,000 acres of solar panels can keep the lights on, but they cannot heat our county’s homes or power its industries unless that heating equipment is physically replaced with electric alternatives — a process that takes decades and requires far more power than the current grid supplies. This is called the electrification of energy to fully eliminate all natural gas usage in Santa Barbara County.
The city of Santa Barbara covers 16,640 acres — 24 square miles.
The city of Santa Barbara covers 16,640 acres — 24 square miles. The cost of a 16,000-acre buildout is a mind-boggling number. According to Gemini 3 artificial intelligence models, the breakdown is calculated based on a reasonable partitioning of multiple sizes of solar farms. The approved ag lands are considered nonprime, which is normally grazing land, poorer soil and lacking irrigation. Neither Santa Barbara County staff nor the agricultural industry has released a “master plan” that pre-assigns a specific number of acres to Tier 3 (community) versus Tier 4 (utility) projects. Tier 3 project estimates of entire life cycle costs, installation, maintenance, hooking up to the grid, battery storage, maximum years of operation before replacement and disposal costs amount to an estimated upfront investment of between $12.5 million and $14 million. There still is a 30% federal investment tax credit that remains active if built by 2028, which could drop the cost to $9 million. Estimates of a revenue stream (selling solar energy to the utilities is estimated by maximizing a power purchasing agreement and selling energy in the higher priced evening requirements. This can generate an estimated $780,000 a year with a breakeven of 11½ years, knowing that the economic life (inverters and batteries) is 15 years before disposal and replacement. Tier 4 projects are greater than 30 acres and could be as much as 100-acre solar farms. There are economies of scale that would reduce the overall costs if there is a blend of solar farms. It’s estimated that if all 16,000 acres were Tier 3, it would be a whopping $7.1 billion investment, while a blending of Tier 4 solar farms at 100-acre parcels possibly price out still at a fixed cost of $3.5 billion! Bottom line: That’s a lot of money for eliminating natural gas consumption in our county. And who’s the real buyer, i.e. the payer of this bill? It’s the retail consumer … you and me! But we’re not done. The final leg — once the 16,000-acre solar farms are producing the estimated green energy — requires us mortals to retire our gas furnaces in favor of heating with electric heat pumps, electric dryers, and electric stoves and ovens, including buying pots and pans specifically designed for an all-electrification home (and buildings). Subject matter experts (again summarized by Gemini AI models) emphasize that solar acres cannot replace gas power plants without massive storage. A gas plant runs at night; a solar farm does not. To actually retire the gas plants, the solar buildout must be oversized (likely two times the peak demand) to charge the batteries required for the overnight shift. The 8,000 acres cover the “net” annual usage, but fails the “24/7 reliability” test without the extra acreage for battery charging. The transition to a fully electrified Santa Barbara County household — utilizing the energy from the 16,000-acre solar buildout — requires an individual consumer capital investment of $18,000 to $28,000 (excluding possible incentives). Further it would shift monthly expenses from a “pay-for-usage” model to a “high fixed cost plus low usage rate” model. So while solar farm energy might reduce the cost of generating electricity, it does not pay for the “last-mile” grid upgrades or the appliances needed to use it. None of this grand plan eliminates the 30% of total energy that California needs because we are a part of the Western Electricity Coordinating Council. Without this connection we wouldn’t survive with only the energy we develop here. It consists of natural gas, hydro, coal, nuclear, solar (12%) and wind (10%) that balances the peaks and demands of 14 states, Baja Mexico and parts of Canada. And just to get a perspective of what 16,000 acres looks like, the city of Santa Barbara covers 16,640 acres — 24 square miles.
Michael Rattray is retired from a lifetime in the defense industry while continuing to support Friends of Goleta Beach Park and the Goleta Kelp Project. The opinions expressed are his own.
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0 Powered by : Greenvolt Next Romania, a subsidiary of Greenvolt Group, has signed an agreement with Sam Mills for a renewable energy project in Satu Mare, Romania. The project includes a 2 MW photovoltaic system paired with a 10.03 MWh battery energy storage system. The total investment reached €2.6 million, bringing renewable energy investments for Sam Mills above €2.8 million. The installation will include 3,660 solar panels and operate under a Power Purchase Agreement structure. Sam Mills expects the project to support more than 30% energy autonomy across its operations. Battery storage will allow surplus electricity to be stored and used during production demand periods. Greenvolt Next currently operates 22 projects totaling 7.2 MW across Romania’s North-West region.
The solar industry says nearly half a billion dollars of investments in solar projects across Hawaiʻi is in jeopardy after state lawmakers voted to phase out the renewable energy tax credit. The state RenewableEnergy Technologies Income Tax Credit helps to finance new residential and commercial solar systems. Senate Bill 3125 eliminates the RETITC in 2031, but changes to the incentive start before that. Moving forward, the state would cap spending on the credit at $40 million each year, which is less than half of what the state has spent on the credit in recent years. That cap goes into effect in 2027, meaning that projects that are already underway this year are suddenly in competition for a smaller pot of state funding. Rocky Mould, the executive director of the Hawaiʻi Solar Energy Association, said he’s been hearing from solar developers who are worried that they may have to cancel or refinance their current projects due to the uncertainty about the amount of state support those projects might receive. “We’re looking at $460 million of investment just this year that are at risk,” Mould said. Ted Peck is the president of the solar company Holu Hou Energy, which primarily develops systems for low and middle income residents living in multi-dwelling units. Peck said he has seven projects that would be affected by retroactive changes to the credit. “I literally, on Friday, had an investor in a project tell me he was out as the Legislature was signing off on this bill,” he said. This repeal of the state-level tax credit comes as the solar industry reels from the recent rollback of federal incentives for renewable energy projects. Congress eliminated a federal tax credit for residential solar at the start of this year. Rising Sun Solar CEO Matias Besasso said demand for household solar systems has since fallen off a cliff. “We were probably doing six to eight installations a week last year. We are doing about two installations a week this year,” he said. Besasso said revenue for his business has decreased by 65%. As a result, he’s had to lay off five members of his staff, some of whom have been with his company for a decade.“It’s all in an effort to keep the business that I have employing the people that I can employ given the new environment,” he said. Commercial solar installers have until July to break ground on new projects to ensure they qualify for remaining federal tax credits. If financing falls through for those projects at this point as a result of changes to the state tax credit, Mould said developers may miss that deadline. The Hawaiʻi Solar Energy Association wants lawmakers to convene a special legislative session to clarify the language in SB3125 and create safe harbor provisions for projects in development this year. A special session can either be called by the governor or by a two-thirds vote of lawmakers in the chambers seeking to reconvene. HPR reached out to the offices of the House speaker, the Senate president, and the chairs of each chamber’s finance committees to see if there was support for reconvening to address the solar industry’s concerns. As of Thursday morning, HPR had not received a response. State Rep. Nicole Lowen, who chairs the House Energy and Environmental Protection committee, raised concerns about how changes to the renewable energy tax credit may affect the solar industry during a floor vote for the bill last Friday. “This bill provides no safe harbor protections for projects already underway, and some projects that were financially viable just a few weeks ago now may have to shut down,” she said.Lowen told HPR she thinks that a special session is appropriate, and that time is of the essence to clarify financing for commercial projects trying to break ground by July. “It really is urgent that we do something as soon as possible,” she said. Gov. Josh Green could also veto SB 3125. Green passed an executive order in 2025 calling for 50,000 new rooftop solar systems by 2030. Last year, he vetoed another measure that would have sunset the renewable energy tax credit, noting its importance to the economy. But the governor is in a trickier position this year. The language about the credit is part of a much larger bill that preserves income tax cuts for Hawaiʻi households. He can’t veto one without vetoing the other. Gov. Josh Green recently praised the work of lawmakers on SB 3125 on Hawaiʻi News Now. HPR asked the governor’s office to comment on whether he would support a special session or would consider vetoing SB 3125. In response, the office stated that “the Governor remains committed to Hawaiʻi’s clean energy transition and lowering energy costs for local families,” but did not offer specifics on Green’s next move. Rocky Mould said the Hawaiʻi State Energy Office may push for a veto of the bill if a special session is not convened. “We’re reaching out to allies and legislators and the executive to try to figure something out and create a viable path forward,” he said. Josh Mason, the owner of the solar company Blue Sky Energy on Hawaiʻi Island, said that the renewable energy tax credit isn’t perfect. He believes it could be better designed to meet the needs of residents and local businesses while promoting the state’s clean energy goals. But Mason added that such abrupt changes to state support for solar amid ongoing rollbacks in federal incentives could “cripple” the industry. “The retroactive language and the $40 million cap are very problematic for an industry that’s already in turmoil, and it’s only going to create more panic, which is not going to be beneficial for anybody,” he said. Hawaiʻi Public Radio exists to serve all of Hawai’i, and it’s the people of Hawai’i who keep us independent and strong. Donate today. Mahalo for your support.
0 Powered by : Researchers from the University of Perugia, Università Mediterranea di Reggio Calabria, and ENEA investigated new applications for end-of-life photovoltaic waste. The study focused on recovering silicon from discarded PV panels and using it as a heterogeneous support material. Recovered silicon was combined with palladium nanoparticles to create catalytic systems for Mizoroki–Heck cross-coupling reactions. The catalyst delivered strong activity, low palladium leaching, and repeat-use performance comparable with established Pd catalysts. Researchers prepared 20 products, including methyl (E)-ferulate and intermediates linked to pharmaceutical synthesis. The work addressed projected PV waste growth, which could reach 8 million tons globally by 2030.
An official website of the United States government Here’s how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites. Funding Opportunities In early 2024, the U.S. Department of Agriculture (USDA) and U.S. Department of Energy (DOE) held American Farms, Rural Benefits virtual listening sessions to better understand the impact of renewable energy development on farmers and rural communities. Based on feedback, USDA and DOE recommitted to working together and developed an approach to addressing the needs of farmers and community priorities while also enabling a greater diversity of energy options. The plan includes: Aligning federal funds with local support and local benefits Promoting agricultural benefits at utility-scale projects Sharing public information on land and farmer revenue Expanding research on agrivoltaics Additional programs Conservation Considerations for Solar Farms Guide Farmer’s Guide to Solar Energy Innovative Site Preparation and Impact Reductions on the Environment (InSPIRE) Summary of Listening Sessions Committed to Restoring America’s Energy Dominance. Follow Us
A decision on plans for a solar farm near a village in Leicestershire has been deferred. Downing Renewable Developments wants to create a complex covering 81 acres (200.2 hectares) on land east of Waltham Road, near Freeby. The applicant said the scheme could generate enough renewable energy to power 10,000 homes. The firm's planning application was discussed by Melton Borough Council's planning committee on Thursday, but councillors said they needed more information on the project before they could make a decision. The application is expected to come back before the committee at a later date. Tony Gannon, from Downing Renewable Developments, told councillors the proposal would help counter the "increasing threat" to energy security nationally and that there was a "clear and urgent" need for solar schemes. The Local Democracy Reporting Service (LDRS) reported two tenant farmers would lose land as a source of income if the solar farm was built. The applicant said it would make a £200,000 capital contribution to Freeby Parish Council to help manage construction disruption. Listen to BBC Radio Leicester on Sounds and follow BBC Leicester on Facebook, on X, or on Instagram. Send your story ideas to eastmidsnews@bbc.co.uk or via WhatsApp on 0808 100 2210. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 16, Article number: 14016 (2026) Cite this article 1091 Accesses Metrics details This work introduces a metaheuristic (MH) optimization method, which is inspired by the red-tailed hawks’ predatory behavior is Improved Red-tailed Hawk (IRTHA) Algorithm. The algorithm uses a dynamic adjustment method which uses the combined effect of nonlinear decay and chaotic mapping to enhance the convergence efficacy and accuracy of outcomes. This enhancement affects the search radius of the algorithm and creates diversity in the dive speed of hawks, hence adaptively balancing exploration and exploitation, enhancing diversity and convergence. IRTHA’s efficacy is examined for single, double, and triple diode models of various photovoltaic (PV) cells and modules, such as RTC France, PVM 752, STP 120/36, STM 40/36, and Photowatt-PWP201. A comparative analysis of IRTHA with other advanced MH optimization techniques indicates that IRTHA exhibits considerably lower RMSE values: 7.72986E-04 for SDM-RTC France, 7.41918E-04 for DDM-RTC France, 7.34782E-04 for TDM-RTC France, 1.59243E-04 for PVM 752, 1.44508E-02 for STP 120/36, 1.72192E-03 for STM 40/36, and 2.05285E-03 for the Photowatt-PWP201 module, respectively. The reliability of IRTHA is futher validated by statistical analyses, including non-parametric tests (Friedman and Wilcoxon rank-sum tests), convergence curve assessments, and graphical representations with boxplots, which collectively confirm its potential to deliver robust and computationally efficient optimization. From the outcomes, it is observed that the IRTHA demonstrates superior performance compared to other existing MH algorithms. The results obtained by IRTHA show exceptional performance in PV system modeling and parameter estimation in solar PV applications. In a world increasingly preoccupied with climate change, and requiring a sustainable energy option. Solar Photovoltaic (PV) systems have become a leading renewable energy option, offering a clean and sustainable alternative to traditional fossil fuel-based power generation1,2. The PV systems convert sunlight into electrical energy and are widely used in applications ranging from off-grid setups to large-scale solar farms3,4. The performance and efficiency of PV systems depend on the accurate modeling and characterization of the fundamental PV cells and modules5,6,7. As the complexity of PV systems is increasing, there is an increasing need for detailed and comprehensive models. It may lead to considerable variations in the performance as well as energy generation of a PV system, affected by environmental factors like sun intensity, temperature, and shade conditions8. The PV module is an important component of the PV power generation. The PV module is a crucial element in PV power generation. The design of effective models and the acquisition of precise model parameters are essential for assessing and monitoring the real performance of simulated PV modules and forecasting PV power output9. The accurate and reliable estimation of parameters from PV models is important to improve their efficiency as well as maximize power generation. Recently, widely adopted photovoltaic models include equivalent circuit models for single, double, and triple diode models (SDM, DDM, and TDM), which are used to determine the I-V characteristics of the PV cell. The I–V characteristics provide an in-depth depiction of the PV cell, illustrating the correlation among its output characteristics. Nevertheless, the equivalent circuit model accurately demonstrates the internal characteristics of the PV cell. The solar energy production system has been considerably affected by several external environmental conditions, including temperature and radiation intensity. Therefore, optimizing the use of solar energy for maximum efficiency and the effective implementation of photovoltaic models is essential10,11,12. To address the challenge of parameter estimation in photovoltaic models, different approaches have been developed, that are primarily classified into two categories: deterministic as well as MH optimization approaches. Deterministic approaches are highly sensitive to initial solutions and assume that models demonstrate properties of convexity as well as differentiability. But the MH approach, inspired by the biological principle of survival of the fittest, can effectively mitigate these limits with more flexibility, resulting in superior accuracy and durability13,14,15. Many MH algorithm optimization methods have been studied for extracting unknown parameters of the solar PV model such as modified Exponential Distribution Optimization Algorithm (mEDOA), Modified electric eel foraging optimization (MEEFO), Enhanced differential evolution (EDE), Equilibrium optimizer-single candidate optimizer (EO-SCO), Grey Wolf Election-Based Optimization algorithm (GWEBO), Multi-strategy gaining-sharing knowledge-based algorithm (MSGSK), Improved Artificial Protozoa Optimizer (iAPO), Adapted human evolutionary optimization (AHEO), Hippopotamus optimizer (HOA), Multi-strategy Nutcracker Optimization Algorithm (EMNOA), Opposition-based Learning White Shark Optimizer (IWSO), Dynamic oppositional learning strategy and Sorting Teaching-Learning-Based Optimization (DSTLBO), Improved Walrus Optimizer (m WO), Frilled Lizard Optimization (FLO), Mean Differential Evolution with Newton-Raphson (MDE-NR), Differentiated Creative Search combined with Newton-Raphson (DCS-NR), Coati Improved Snow Ablation Optimization (CSAO), Chaotic Differential Variation Snake Optimization (CDVSO), Four Vector Intelligent Metaheuristic Differential Evolution (FVIM-DE), Leveraging the opposition-based Exponential Distribution Optimizer (OBEDO), Enhanced Artificial Hummingbird Algorithm (enAHA), Improved JAYA (Sjaya), Enhanced Prairie Dog Optimizer (En-PDO), Improved Simultaneous Heat Transfer Search (ISHTS), Modified version of Mountain Gazelle Optimization (MGPS), Multi-strategy-based Tree Seed Algorithm (MS-TSA), Modified RIME (MRIME), Robust Newton–Raphson method integrated improved Differential Evolution (RoNRIDE), Bio-dynamics Grasshopper Optimization Algorithm (BDGOA), Walrus optimization algorithm (WaOA), Chaos-inspired Invasive Weed Optimization (CIIWO), Adaptive Sine Cosine Particle Swarm Optimization Algorithm (ASCA-PSO), Improved Marine Predators Algorithm and Equilibrium Optimizer (IMPAEO), Enhanced Snake algorithm (ISASO), Hybrid Chaotic Particle Swarm Optimization and Slime Mould Algorithm (HCPSOSMA), Improved Crayfish Optimization Algorithm (ICOA), Modified Bare-Bone Imperialist Competitive Algorithm (MBB-ICA), Improved Kepler Optimization Algorithm (IKOA), Developed JAYA Algorithm (DIWJAYA), Hybrid Cuckoo Search-Gorilla Troop Optimization (CS-GTO), Tiki Taka Algorithm Mean Differential Evolution based on Weibull distribution (TTA-MDEW), Self-adaptive Enhanced Learning Differential Evolution (SaELDE), Weighted Velocity-Guided Grey Wolf Optimizer (WVGGWO), Artificial Hummingbird Technique (AHT), Enhanced Sine–Cosine Algorithm (ESCA), Nutcracker optimizer algorithm (NOA), Improved Snake Optimization Algorithm (ISOA), Growth Optimization (GO), Squirrel Search Algorithm (SSA), Chaos Game Optimization-Least Squares (CGO-LS), Fractional Henon Chaotic Harris Hawks Optimization (FCHHHO), Hybrid White Shark Optimize Artificial Rabbits Optimization (hWSO-ARO), Improved Moth Flame algorithm with Local escape operators (IMFOL), Northern Goshawk Optimization (NGO), and roved Archimedes Optimization Algorithm (IAOA) are given in Table 1. A detailed summary of the latest advancements in various MH algorithms for determining unknown parameters in solar PV cells/modules is presented in Table 1. The comparison has been conducted to highlight the methodological variation, modeling reliability, author names, journal title, publication year, objective functions, additional metrics, and validation approaches. In the literature, it is clearly demonstrates the applicability of several MH optimization methods in addressing the solar PV parameter estimation challenge. The no free lunch (NFL) theory claims that no optimization algorithm is globally superior for all engineering optimization scenarios16. It is essential to consider different MH techniques and frameworks for altering and enhancing solutions according to the challenge presented. However, there is potential to enhance the present frameworks rather than developing new ones. The NFL theorem motivated researchers to develop innovative MH optimization methods or enhance current ones, facilitating their application in solving real-world problems across several domains. The literature study indicates that the identification of solar PV parameters is a current research area. Moreover, recently developed MH algorithms must be evaluated for improved modelling related to error minimization, faster convergence, and enhanced statistical metrics. As a result, many MH algorithms have been studied in the literature to address the same problem and present an opportunity for the development of new methods that offer more accurate results. In this paper, a novel improved optimization approach for parameter extraction of PV models is applied, known as the Improved Red-Tailed Hawk Algorithm (IRTHA). The IRTHA is inspired by the predatory behavior and flight patterns of red-tailed hawks. In this study, the performance of IRTHA has been systematically compared against other established MH optimization techniques such as Horned Lizard Optimization Algorithm (HLOA)17, Pelican Optimization Algorithm (POA)18, Zebra Optimization Algorithm (ZOA)19, Hybrid Particle Swarm Optimization and Grey Wolf Optimizer (PSOGWO)20, Whale Optimization Algorithm (WOA)21, Pelican Optimization Algorithm (POA)18, Hippopotamus Optimization (HO)22, Osprey Optimization Algorithm (OOA)23, Harris Hawks Optimization (HHO)24, Grey Wolf Optimizer (GWO)25, and Coati Optimization Algorithm (COA)26 to evaluate its efficacy. The outcomes demonstrate that the IRTHA algorithm competes effectively with other MH algorithms across key measures like as convergence speed, accuracy, and robustness. These key points of the algorithm’s capability as an effective tool for parameter estimation in PV cell/models and other complicated optimization challenges. Using its advanced features, IRTHA presents a crucial step toward improving optimization methods for modern energy problems. The key contributions of the paper are summarized as follows. An enhanced MH optimization algorithm namely the Improved Red-tailed Hawk Algorithm (IRTHA), has been applied for estimating solar PV parameters by combining the features of the RTH algorithm with the Nonlinear Decay, Chaotic Map strategy, and the Newton-Raphson (NR) Method. The IRTHA is applied for parameter estimation of solar PV models, including RTC France (SDM, DDM, and TDM), Photowatt-PWP201, PVM-752-GaAs, and STM6 40/36 PV, STP6 120/36 panels. The outcomes of the IRTHA algorithm have been compared with 10 advanced MH optimization techniques, including HLOA, ZOA, PSOGWO, WOA, POA, HO, OOA, HHO, GWO, and COA, as well as additional parameter estimations of solar PV techniques reported in the literature. Furthermore, the accuracy and reliability of the IRTHA in PV parameter extraction is validated by using other statistical metrics, including Mean Absolute Error (MAE), Mean Square Error (MSE), Sum of Square Error (SSE), Individual Absolute Error (IAE), the Root Mean Square Error (RMSE), and the Friedman and Wilcoxon rank-sum test. The results demonstrate that the IRTHA algorithm exhibits the minimal difference between the observed and estimated values. This illustrates the efficacy of the IRTHA algorithm for the parameter estimation problem of Solar PV. This paper is structured into five subsections: Section “Mathematical modelling” presents the mathematical framework of solar PV systems, incorporating the Single, Double, and Triple Diode Model (SDM, DDM, and TDM), together with the corresponding objective function. Section “Improved red tailed hawk algorithm (IRTHA)” explains a detailed representation of the IRTHA algorithm, which includes a mathematical modeling, flowchart, and pseudocode of the algorithm. Section “Result and discussion” discusses the test results for parameter extraction of solar PV cells/modules, along with extensive validation to confirm the efficacy and robustness of the IRTHA algorithm from multiple perspectives. Finally, the last section of the paper outlines numerous conclusive outcomes, remarks, and observations, with the potential directions for future research. The equivalent model of solar PV SDM, comprising a current source ((I_{ph})) which is connected in parallel with a diode ((D_{1})), a parallel resistor to account for leakage current ((I_{sh})), as well as a series resistor to model losses from the load current (I) is depicted in Fig. 1. According to Kirchhoff’s Current Law (KCL), the output current (I) of the SDM is calculated by utilizing the following Eq. 181,82. Equations 2 and 3 present the mathematical formulations for (I_{d}) as well as (I_{sh}), respectively. The output current I is shown in the given Eq. 4. where (I_{sc}) denotes the reverse saturation current for SDM, the Kelvin temperature of the solar cell (T), the shunt resistance ((R_{sh})), the series resistance ((R_{s})), the charge of the electron ((q=1.60217646 times 10^{-19} , C)), the Boltzmann constant ((k=1.3806503 times 10^{-23} , J/K)), and the ideality factor of the diode (n) are used. Since current is not explicitly represented as a function of voltage in 4. A precise PV model can be constructed by extracting these parameters ((I_{ph}), (I_{sc}), (R_{sh}), (R_{s}), and n). The exact estimation of these factors directly influences the effectiveness of optimization as well as the maximum power point tracking of solar cells. Equivalent circuit of SDM of solar PV. Figure 2 illustrates the equivalent model for the photovoltaic double diode model (DDM). This model comprises a current source ((I_{ph})) in parallel with two diodes ((D_{1}) and ((D_{2})), a parallel resistor representing leakage current ((I_{sh})), and a series resistor to account for losses due to the load current (I). According to KCL, the output current of the DDM is given by the Eq. 59,83. Equations 6 and 7 present the mathematical formulations for (I_{d1}) and (I_{d2}), respectively. The output current I is illustrated in the given Eq. 8. where (I_{sc1}) and (I_{sc2}) represent reverse saturation currents for DDM, and diode ideality factors ((n_{1})) and ((n_{2})), are used. A precise PV model can be constructed by extracting seven unknown parameters such as (I_{ph}), (I_{sc1}), (I_{sc2}), (R_{sh}), (R_{s}), (n_{1}), and (n_{2}). Equivalent circuit of DDM of solar PV. Figure 3 illustrates the configuration of the triple diode model (TDM), where three diodes ((I_{d1}), (I_{d2}), and (I_{d3})) as well as a photo-generated current source ((I_{ph})) are arranged in parallel with a shunt resistor ((R_{sh})). Mathematically, the TDM solar PV is expressed as follows46,84,85. (I_{sh}) and (I_{d}) can be calculated using Eqs. 10 and 11, respectively. Finally, I can be determined using the following Eq. 12. where (I_{sc1}), (I_{sc2}), and (I_{sc3}) are the reverse saturation currents for TDM. From Eq. 12, there are 9 unknown parameters in TDM, such as (I_{ph}), (I_{sc1}), (I_{sc2}), (I_{sc3}), (R_{s}), (R_{sh}), (n_{1}), (n_{2}), and (n_{3}). The accuracy of the TDM model can be evaluated by accurately determining these unknown parameters. Equivalent circuit of TDM of solar PV. The identification of solar PV parameters generally requires minimizing the error between measured PV current (I_{k,measured}) (reference) and estimated PV current (I) determined utilizing the selected model (SDM, DDM, and TDM). In this work, the root mean square error (RMSE) is adopted as the objective function. This work examines three categories of models defined by Eqs. 4, 8, and 12. Each model is associated with a distinct set of parameters. This objective function is expressed in Eq. 1333,86,87. Subject to: Where C is the number of measured data samples. The RMSE is a measure to accurately model the solar PV cell/module by comparing the values calculated with the experimental results. The imposed boundaries limit the algorithm from exploring infeasible spaces, hence preserving computational time. The RTH algorithm draws inspiration from the hunting nature of red-tailed hawk’s88. The IRTHA utilizes a dynamic adjustment strategy or method, which includes a hybrid approach (nonlinear decay as well as a chaotic map)89. This method aims to achieve equilibrium between the exploration stage as well as exploitation stage, thereby enhancing the search process. Incorporating a hybrid methodology which integrates non-linear decay alongwith a chaotic map in the Transition Function Factor (TRF), the IRTHA can dynamically modify the hawks’ movement size of step. This adjustment improves the search radius of the algorithm and creates diversity in the hawk’s dive speed, thus influencing the convergence behavior of the IRTHA algorithm89. This algorithm is made up of three different phases: high soaring phase, low soaring phase, and stooping and swooping phase. The red-tailed hawk dives to significant elevations in search of optimal locations with abundant food resources. Equation 15 illustrates the mathematical formulation of this phase. The red-tailed hawk’s location at iteration t is indicated by the symbol X(t). (X_{best1}) denotes the optimum location obtained, while (X_{mean1}) signifies the mean of all positions. The distribution function (LevyF) utilised in the calculations outlined with Eqs. 16 and 17, while TRF(t) signifies the transition factor function derived from Eq. 17. Here, (p_1 = 0.01) signifies a constant valued, D specifies the problem’s dimension, (delta ‘_{01} = 1.5) is a constant set, while s and (r_1) are random numbers within the range of 0 to 1. The hybrid approach, including nonlinear decay along with a chaotic map, is an effective approach for enhancing the RTH algorithm’s performance by modifying the transition function factor (TRF). Incorporating both nonlinear decay and a chaotic map mechanism into the TRF to adaptively control sparsity over time. This methodology proves particularly advantageous for datasets that display seasonal patterns or in instances where the ideal level of sparsity fluctuates over time. The formula for the modified TRF of IRTHA can be determined by the subsequent equation 1989. where (r_1) stands for a constant parameter that regulates the growth rate and ranges from 0 to 4, (T_{max}) denotes the maximum number of iterations, whereas (T_{iter}) indicates the current iteration count. The hawk spirals downward towards their target while flying closest to the surface of the ground. T denotes a phase that can be depicted through the subsequent model. where (SS_1(t)) signifies the step size as well as the parameters (y_{11}) as well as (z_{11}), which indicate direction coordinates, that can be determined through the subsequent equations. where (R_0) denotes the initial radius value inside the interval of [0.5,3], (AG_1) denotes the spectrum of angel gain, which spans from 5 to 15, while (RG_1) signifies a random gain that can assume values between [0,1]. The variable (r_1) denotes a control gain, which may assume the values of 1 or 2. These variables facilitate the hawk’s movement surrounding the prey using spiral movements. Pseudocode of IRTHA Flowchart of IRTHA algorithm. In this step, the hawk rapidly drops as well as strikes the target from the optimal position attained at the low soaring stage. This phase may be denoted by the subsequent equation 23. The computation for every step size can be ascertained via Eqs. 24 and 25. The parameters (beta _1) and (G_1) denote the acceleration and gravitational factors, correspondingly. They can be described as follows. The symbol (beta _1) signifies the hawk’s acceleration, which rises with time to enhance the convergence speed, while (G_1) suggests the gravitational force, which reduces the exploitation diversity as the hawk or predator approaches its target. The pseudocode for IRTHA is presented in Algorithm 1. Also, the flowchart of the IRTHA algorithm is illustrated in Fig. 4. This section evaluates the performance of the IRTHA algorithm through solar PV parameter estimation challenges. For analysis, the five common types of solar PV cells/modules, such as RTC France (SDM, DDM, and TDM), Photowatt-PWP201, PVM-752-GaAs, STM6 40/36, and STP6 120/36 PV panels, are used. The performance of the IRTHA algorithm is compared with different MH algorithms like HLOA17, ZOA19, PSOGWO20, WOA21, POA18, HO22, OOA23, HHO24, GWO25, and COA26. Also, the parameter details of different MH optimization is given in the Table 2. The findings indicate that IRTHA exhibits superior accuracy, achieves faster convergence, and demonstrates computational efficiency. Table 3 indicate the search boundaries for each unidentified parameter of solar PV associated with the parameter extraction techniques9,90,91. The assessment of the algorithm’s efficacy is conducted through standard metrics including RMSE, MAE, IAE, SSE, MSE, along with the analysis of the convergence curve. Additionally, a low RMSE value signifies that the parameters have been effectively determined, as RMSE seeks to minimize the variance between observed as well as predicted data. The statistical robustness of the obtained results is evaluated through evaluating the standard deviation, as well as the worst and mean error values across 30 independent runs, in addition to minimizing the RMSE. A non-parametric Wilcoxon rank-sum test is also conducted to validate the accuracy of IRTHA’s outcomes over the other compared algorithms. Furthermore, the box plots and convergence graphs are presented to visually highlight the stability and accuracy of the IRTHA algorithm. All simulations were executed in MATLAB R2021a on a Windows 11 laptop featuring an Intel Core i5-1035G1 processor with 8GB of RAM. The algorithms employ a population size of 50, with a maximum of 1000 iterations for each of the five PV models. Each algorithm is carried out autonomously 30 times for every PV model. The IRTHA algorithm is tested using a single, double, and triple diode model (SDM, DDM, and TDM) of the RTC France PV cell under standard test conditions of 33 °C, 1000 W/(hbox {m}^2). Tables 4, 5, 6, 7, 8, 9, 10, 11, and 12 present the detailed analysis of the RTC France solar cell (SDM, DDM, and TDM). Tables 4, 7, and 10 display the measured as well as estimated data points of current for SDM, DDM, and TDM, respectively. Also, the Table 4, 7, and 10 present in-depth statistical measures like MAE, MSE, RMSE, SSE, MBE, and IAE values. A comparative analysis of the efficacy of eleven optimization algorithms is shown in Table 5, 8, and 11 respectively. Also, Table 5, 8, and 11 present a detailed overview of the best, worst, mean, min, standard deviation, and optimal values achieved by each MH algorithm across 1000 iteration and 30 run for SDM, DDM, and TDM, respectively. The results of these experiments indicate that the IRTHA algorithm shows outstanding results regarding both the mean objective function value and the best objective function value when compared to other algorithms. The optimal RMSE solutions for SDM, DDM, and TDM achieved with the IRTHA algorithm are 7.72986E-04, 7.41918E-04, and 7.34782E-04. The graphical characteristics demonstrated in Figs. 5a , 6a and 7a highlights the effectiveness of the IRTHA algorithm. The measured and estimated values on the I-V curves of SDM, DDM, and TDM align precisely, indicating that the model accurately reflects the performance of the RTC France PV cell. Also, Figs. 5b , 6b, and 7b present the effectiveness of the IRTHA algorithm. The measured and estimated values on the P-V curves of SDM, DDM, and TDM are in exact match, demonstrating that the model correctly represents the effectiveness of the RTC France PV cell. The data presented in these figures clearly indicate a strong connection between the experimental polarization curves and those derived from the identified model. Figures 5c , 6c, and 7c provide the behavior of convergence for 1000 iterations, in which IRTHA has a more rapid and stable convergence curve than other MH algorithms. Figures 5d , 6d, and 7d shows the comparative boxplot analysis achieved by IRTHA in comparison with other algorithms across the RTC France Solar PV (SDM, DDM, and TDM). Furthermore, Figures 5e , 6e , and 7e illustrate a radar chart which indicates the ranking of the 11 MH optimization methods for the RTC France Solar PV (SDM, DDM, and TDM). It has been noted that IRTHA exhibits the smallest shaded area, clearly illustrating its enhanced performance relative to the other algorithms. The shaded areas of POA and HO are positioned in 2nd and 3rd place, indicating that POA and HLOA are in close competition with the IRTHA algorithm. On the basis of the Wilcoxon rank test in Table 6, 9, and 12, IRTHA obtained the first rank for SDM, DDM, and TDM, hence highlighting IRTHA’s superiority in accuracy and convergence performance. This demonstrates that the effectiveness of the IRTHA algorithm, implemented as an optimization technique for parameter estimation from solar PV, significantly outperforms that of other algorithms. The RTC France SDM (a) I-V characteristic (b) P-V characteristic (c) convergence curve characteristic (d) Boxplot characteristic (e) Radarchart characteristic. The RTC France TDM (a) I-V characteristic (b) P-V characteristic (c) convergence curve characteristic (d) Boxplot characteristic (e) Radarchart characteristic. The IRTHA algorithm demonstrated exceptional performance in optimizing the PVM-752-GaAs thin film Solar PV, providing accurate parameter estimations, quick convergence, and dependable results. The precision and effectiveness of the IRTHA are evaluated by estimating the unknown model parameters of PVM-752-GaAs solar PV. Table 13 presents the measured outcomes for 44 voltage-current data points along with corresponding measured and estimated power. Additionally, the statistical metrics, such as MAE, MSE, RMSE, and MBE, are also presented in Table 13. A comparative analysis of the efficacy of eleven optimization algorithms, including IRTHA, HLOA, ZOA, PSOGWO, WOA, POA, HO, and OOA, is shown in Table 14. From the numerical simulation outcomes demonstrated in Table 14 it is observed that the proposed IRTHA achieves the lowest RMSE value of 1.59243E-04 in comparison with other MH algorithms. The I-V and P-V characteristic curves presented in Fig. 8a and b demonstrate a high correlation between the experimental and estimated data, thus validating the IRTHA performance in simulating the PVM752 module. Additionally, Fig. 8c illustrates the convergence curves of all algorithms used for comparison across the PVM-752-GaAs Solar PV, providing insight into the performance of algorithms. The figure shows that the convergence curves of the IRTHA algorithm demonstrate superior performance compared to other comparative algorithms. This simple and effective convergence illustrates IRTHA’s capability to rapidly and effectively optimize fitness values, a significant benefit in optimization. The Fig. 8d shows the comparative boxplot analysis achieved by IRTHA in comparison with other algorithms across the PVM-752-GaAs Solar PV. In addition, Fig. 8a presents a radar chart illustrating the ranking of the 11 MH optimization techniques for the PVM 752 GaAs thin-film cell. The results reveal that IRTHA exhibits the smallest shaded area, hence illustrating its greater efficacy relative to the other MH algorithms. The shaded portions of POA and HHO are positioned in (hbox {2}^{nd}) and (hbox {3}^{rd}) place, indicating that POA and HHO are in close competition with IRTHA. Finally, rank analysis is carried out using the Wilcoxon signed-rank test, which is illustrated in Table 15. The IRTHA ranked the highest among all the algorithms, followed by POA, HHO, and HO. Conversely, algorithms such as OOA, COA, and GWO exhibited inferior rankings, hence highlighting IRTHA’s superiority in accuracy and convergence performance. The comparison has been conducted by considering the evaluation of statistical parameters such as standard deviation, worst, mean, and minimum RMSE values, and other indicators that highlight convergence, and solution quality metrics. Interestingly, IRTHA exhibits significant stability and consistency as marked by its low standard deviation and low mean, min RMSE. The RTC France TDM (a) I-V characteristic (b) P-V characteristic (c) convergence curve characteristic (d) Boxplot characteristic (e) Radarchart characteristic. The PVM-752-GaAs (a) I-V characteristic (b) P-V characteristic (c) convergence curve characteristic (d) Boxplot characteristic (e) Radarchart characteristic. The STP-120/36 PV module was utilized to evaluate the efficacy of the proposed IRTHA algorithm in comparison to eleven sophisticated optimization techniques. Table 16 presents the measured outcomes for 24 voltage-current data points along with corresponding measured and estimated power. Furthermore, the statistical metrics, such as MAE, MSE, RMSE and MBE, are also presented in Table 16. Completing the simulation of the algorithms in Matlab, the statistical metrics of the final RMSE values derived from the MH optimization methods are shown in Table 17. Table 17 provides a detailed overview of the best, worst, mean, min, standard deviation, and optimal values achieved by each MH algorithm across 1000 runs. The results of these experiments indicate that the IRTHA algorithm shows outstanding results regarding both the mean objective function value and the best objective function value when compared to other algorithms. The best RMSE result obtained utilizing the IRTHA algorithm is 1.44508E-02. The graphical characteristics show in Fig. 9a , and b demonstrate the efficacy of the IRTHA algorithm. The measured as well as estimated values on the I-V and P-V curves match exactly, which demonstrates that the model accurately represents the performance of the STP-120/36 PV module. The figures demonstrate a clear correlation between the experimental polarization curves and the model-derived curves. Figure 9c shows a direct comparison of the algorithms’ effectiveness in improving their search methodologies to attain the minimal RMSE value. The IRTHA algorithm illustrates its superiority in Fig. 9c by attaining optimal fitness values. The optimization process of the IRTHA algorithm demonstrates constant performance, whereas other MH algorithms display either delayed convergence or unstable fluctuations, highlighting challenges with balancing the exploration as well as exploitation stages. Figure 9e shows the comparative boxplot analysis conducted by IRTHA compared to other MH optimization techniques for the PVM-752-GaAs Solar PV module. Additionally, Fig. 9e depicts a radar chart which displays the ranking of the 11 MH optimization algorithms for the STP6 120/36 PV module. The findings indicate that IRTHA displays the smallest shaded area, showcasing its exceptional performance compared to the other MH techniques. The shaded areas of POA and HO are in (hbox {2}^{nd}) and (hbox {3}^{rd}) positions, indicating that POA and HO are in close competition with the IRTHA algorithm. On the basis of Wilcoxon rank test in Table 18, IRTHA obtained the (hbox {I}^{st}) rank followed by POA and HO while OOA and COA exhibited inferior rankings, hence highlighting IRTHA’s superiority in accuracy and convergence performance.This highlights the efficacy of the IRTHA algorithm, implemented as an optimization technique for parameter identification from Solar PV, significantly outperforming other algorithms. The STP6 120/36 PV module (a) I-V characteristic (b) P-V characteristic (c) convergence curve characteristic (d) Boxplot characteristic (e) Radarchart characteristic. The parameter optimization of the STM6 40/36 PV module was carried out utilizing the IRTHA algorithm as well as was compared with ten other MH optimization algorithms, which include HLOA, ZOA, PSOGWO, WOA, POA, HO, OOA, HHO, GWO, and COA. The preciseness of the IRTHA optimized model has been demonstrated by comparing the measured and estimated values of current ((I_{m}) and (I_{e})) and power ((P_{m}) and (P_{e})) across various current densities (IE), as presented in Table 19. Furthermore, the statistical metrics, such as MAE, MSE, RMSE, and MBE, are also presented in Table 19. A comparative analysis of the efficacy of eleven optimization algorithms is shown in Table 20. Also, Table 20 provides a detailed overview of the best, worst, mean, min, standard deviation, and optimal values achieved by each MH algorithm across 1000 iterations and 30 runs. The results of these experiments indicate that the IRTHA algorithm shows outstanding results regarding both the mean objective function value and the best objective function value when compared to other algorithms. The optimal RMSE solution achieved with the IRTHA algorithm is 1.72192E-03. The I–V and P-V characteristic graphs of the STM6 40/36 PV module are illustrated in Fig. 10a, and b, demonstrating a significant similarity between the measured as well as simulated current-voltage values obtained through the IRTHA algorithm. This indicates that the IRTHA algorithm provides a significant level of accuracy in modelling the STM6 40/36 PV module. Figure 10cillustrates the convergence plots of the objective function (RMSE) achieved by the IRTHA algorithm in comparison with the other algorithms. The diagram illustrates that the IRTHA algorithm exhibits rapid and consistent convergence closer to the optimal solution when compared to the other algorithms. Figure 10dpresents the boxplot graphs for the STM6 40/36 PV module. It is evident that HO, POA, and HLOA exhibit a close relationship regarding the distribution range and fluctuations. It is evident that the data derived from the IRTHA algorithm exhibits narrower distribution ranges and upper/lower bands compared to the other MH algorithms. This indicates that the IRTHA algorithm can attain the lowest RMSE while maintaining the highest stability. Additionally, Fig. 10ashows a radar chart demonstrating the position of 11 MH optimization techniques for the STM6 40/36 PV module. The IRTHA presents the minimal shaded area, clearly highlighting its superior performance compared to other MH techniques. The shaded areas of HLOA and POA are positioned in (hbox {2}^{nd}) and (hbox {3}^{rd}) positions, indicating that HLOA and POA are in close competition with IRTHA. Finally, rank analysis is carried out utilizing the Wilcoxon signed-rank test, which is shown in Table 21.IRTHA achieved the top ranking among all algorithms, followed by HLOA, POA, HO, and PSOGWO. On the other hand, algorithms like HHO, COA, and WOA demonstrated lower rankings, hence demonstrating IRTHA’s superiority in accuracy as well as convergence performance. The comparison has been carried out by evaluating statistical parameters, including standard deviation, worst, mean, and minimum RMSE values, along with other indicators that demonstrate convergence and solution quality indicators. Interestingly, IRTHA exhibits significant stability and consistency as marked by its low standard deviation, mean, and min RMSE. The STM6 40/36 PV module (a) I-V characteristic (b) P-V characteristic (c) convergence curve characteristic (d) Boxplot characteristic (e) Radarchart characteristic. The IRTHA algorithm demonstrated exceptional performance in optimizing the Photowatt-PWP201 PV module, providing accurate parameter estimations, quick convergence, and dependable results. The precision and effectiveness of the IRTHA are evaluated by estimating the unknown model parameters of Photowatt-PWP201 PV module. Table 22 presents the measured outcomes for 25 voltage-current data points along with corresponding measured and estimated power. Additionally, the statistical metrics, such as MAE, MSE, RMSE, and MBE, are also presented in Table 22. Furthermore, Table 23 displays the optimal parameter values along with the RMSE. The experimental findings were recorded after the 30-time run of every optimizer. The findings reveal that the IRTHA optimization method surpasses other MH algorithms, as shown by its optimal RMSE performance presented in Table 23. Additionally, Fig. 11a and b shows the P-V and I-V characteristic curves, which are obtained from the optimal parameters determined by the IRTHA algorithm. The graphical representations present the relationship between estimated and actual measurements. The data indicates that the parameters obtained from the IRTHA algorithm achieve current and power levels that closely match the observed outcomes. Figure 11d illustrates a direct comparison of the algorithms’ effectiveness in optimizing their search methodologies to attain the minimal RMSE value. The IRTHA algorithm demonstrates its superiority in Fig. 11d by achieving optimum fitness values. Figure 11d presents a direct comparison of the algorithms’ efficacy in optimizing their search techniques to achieve the smallest RMSE value. The IRTHA algorithm reveals its superiority in Fig. 11dby attaining optimum fitness values. The optimization process for IRTHA algorithm exhibits consistent performance, while other MH algorithms reveal either delayed convergence or unstable variations, indicating challenges in balancing exploration as well as exploitation. The Fig. 11d presents the comparative boxplot analysis performed by IRTHA compared to other MH techniques for the Photowatt-PWP201 PV module. Additionally, Fig. 11a illustrates a radar chart that indicates the position of the 11 MH optimization algorithms for the Photowatt-PWP201 PV module. The findings indicate that IRTHA demonstrates the least shaded area, effectively highlighting its superior performance compared to the other algorithms. The shaded areas of POA and HLOA are positioned in the (hbox {2}^{nd}) and (hbox {3}^{rd}) positions, indicating that POA and HLOA are in close competition with the proposed algorithm. On the basis of Wilcoxon rank test in Table 24, IRTHA obtained (hbox {I}^{st}) rank followed by POA and HLOA while GWO and PSOGWO exhibited inferior rankings, hence highlighting IRTHA’s superiority in accuracy and convergence performance. This illustrates that the efficacy of the IRTHA algorithm, implemented as an optimization technique for parameter identification from Solar PV, significantly outperforms that of other algorithms. The Photowatt-PWP201 PV module (a) I-V characteristic (b) P-V characteristic (c) convergence curve characteristic (d) Boxplot characteristic (e) Radarchart characteristic. Table 25 presents the computational time complexity (in seconds) of all MH algorithms utilized for RTC France (SDM, DDM, and TDM), Photowatt-PWP201, STP6 120/36, PVM-752-GaAs, and STM6 40/36 PV panels. All simulations have been executed in MATLAB R2021a on a Windows 11 laptop containing an Intel Core i5-1035G1 CPU and 8GB of RAM. The algorithms have a population size of 50, with a maximum of 1000 iterations, and have been executed for 1 run for all the algorithms used for analysis. From the analysis, it is observed that the IRTHA continuously takes a higher execution time than other MH algorithms, such as ZOA, HO, HHO, WOA, POA, PSOGWO, OOA, COA, GWO, and HLOA. The IRTHA algorithm takes approximately 16.7799s, 18.5538s, 21.3759s, 18.6296s, 27.6161s, 18.0251s, and 16.8908s for the RTC France (SDM, DDM, and TDM), Photowatt-PWP201, PVM-752-GaAs, STP6 120/36, and STM6 40/36 PV panels, respectively. Despite the higher computational time complexity, IRTHA achieves the minimal RMSE values among all PV cells/modules. Although other MH algorithms exhibit lower time complexity, these MH algorithms fail to achieve optimal results. The RMSE achieved by IRTHA is 7.72986E-04 for SDM, 7.42740E-04 for DDM, 7.42631E-04for TDM, 2.05285E-03 for Photowatt-PW201, 1.59243E-04 for PVM-752-GaAs, 1.44508E-02 for STP6-120/36, and 1.72192E-03 for STM6 40/36, all of which are consistently lower than the results obtained using the other MH algorithms. The outcomes illustrate a distinct balance between computational complexity and estimation accuracy, with the IRTHA highlighting reliability and precision in solar PV parameter estimation. This paper presents an improved MH algorithm, known as the Improved Red-tailed Hawk Algorithm (IRTHA), which has been proposed for the estimation of solar PV parameters by integrating the properties of RTHA with the Nonlinear Decay Chaotic Map strategy and the Newton-Raphson Method. This article focuses on the modeling of solar PV, presenting simulation results that closely align with those observed in the experimental. The objective function of this work is the RMSE, representing the variation between calculated as well as measured voltages. Three distinct varieties of PEMFCs, specifically including the RTC France (SDM, DDM, and TDM), Photowatt-PWP201, STP6 120/36, PVM-752- GaAs, and STM6 40/36 PV panels, were utilized to illustrate the robustness of the IRTHA. The outcomes show that IRTHA demonstrated a superior RMSE value in comparison to various techniques, including HLOA, ZOA, PSOGWO, WOA, POA, HO, OOA, HHO, GWO, and COA, as well as additional parameter estimations of solar PV techniques reported in the literature, in addition to a more accurate model. A comparative analysis with other advanced MH optimization techniques illustrates that IRTHA demonstrates significantly lower RMSE values: 7.72986E-04 for SDM-RTC France, 7.41918E-04 for DDM-RTC France, 7.34782E-04 for TDM-RTC France, 1.59243E-04 for PVM 752, 1.44508E-02 for STP 120/36, 1.72192E-03 for STM 40/36, and 2.05285E-03 for the Photowatt-PWP201 module, respectively. Additionally, determine the reliability and effectiveness of the IRTHA in extracting PV parameters by employing various statistical metrics, including Mean Absolute Error (MAE), Mean Square Error (MSE), Sum of Square Error (SSE), Individual Absolute Error (IAE), Root Mean Square Error (RMSE), and the Friedman and Wilcoxon rank-sum tests. In addition, the convergence to the optimal values occurs rapidly as compared to the other MH algorithms. The statistical evaluation demonstrates the superior reliability of the calculated outcomes. The solutions demonstrate an optimal alignment between the calculated and measured I–V curves using the proposed IRTHA. Therefore, the findings validate that the IRTHA algorithm is promising and acts as an effective tool for extracting PV cell parameters, as it demonstrates superior performance in addressing the nonlinear equations of the analyzed challenge. 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School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India Pankaj Sharma, Asmita Ajay Rathod, Shubhi Shukla, Saravanakumar Raju & Balaji Subramanian Department of Electrical Engineering, National Institute of Technology, Andhra Pradesh, Tadepalligudem, Andhra Pradesh, India Pankaj Sharma & Asmita Ajay Rathod Department of Mathematics, School of Advance Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India Arun Choudhary Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Pankaj Sharma: Conceptualization, Methodology, Data curation, Writing-Original draft Preparation, Resources, Reviewing and Editing, Validation, Result and Discussion. Asmita Ajay Rathod, Shubhi Shukla, : Methodology, Writing-original draft preparation, Data curation, Software, Validation, Result and Discussion, Real-world Application. Arun Choudhary, Saravanakumar Raju, Balaji Subramanian: Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Investigation, Formal analysis. Correspondence to Balaji Subramanian. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. 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📢 Introduction Solar energy is becoming one of the most popular ways to reduce electricity bills and achieve energy independence. However, many users do not get maximum output from their solar panels due to improper usage, installation, or maintenance. Experts say small improvements can significantly boost solar efficiency. ⚡ 6 Ways to Improve Solar Panel Performance 🌞 1. Install Panels in the Right Direction The direction of solar panels plays a major role in power generation. 🧼 2. Keep Panels Clean Regularly Dust, bird droppings, and pollution can block sunlight. 👉 Dirty panels = reduced electricity generation 🌤️ 3. Avoid shadow Obstruction Even partial shade can reduce performance. 🔋 4. Use High-Quality Inverters The inverter converts solar DC power into usable AC power. 📊 5. Monitor Energy Output Regularly Tracking performance helps identify issues early. 🌡️ 6. Manage Heat Effectively Excess heat can reduce panel efficiency. 💡 Bonus Tip: Upgrade Technology New technologies like: can significantly improve overall efficiency. 📌 Conclusion To generate maximum electricity from solar panels, proper installation, regular maintenance, and smart monitoring are essential. With the right techniques, households can significantly reduce electricity bills and achieve long-term energy savings. Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of any agency, organization, employer, or company. All information provided is for general informational purposes only. While every effort has been made to ensure accuracy, we make no representations or warranties of any kind, express or implied, about the completeness, reliability, or suitability of the information contained herein. Readers are advised to verify facts and seek professional advice where necessary. Any reliance placed on such information is strictly at the reader’s own risk. Empowering 140+ Indians within and abroad with entertainment, infotainment, credible, independent, issue based journalism oriented latest updates on politics, movies.
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MACON, Georgia (41NBC/WMGT) – The Boys and Girls Club of Central Georgia is getting a major boost in sustainability, with 162 solar panels now covering the roof of the new Buck Melton Center. The installation, carried out by Cherry Street Energy, is expected to drastically reduce energy costs for the nonprofit. As Georgia temperatures rise and electricity costs increase, the panels offer long-term financial relief for the organization. Cherry Street Energy CEO Michael Chanin said the project reflects his commitment to the Macon community and its children. “The Boys and Girls Club is an incredible institution in Macon that supports children after school and in the summer,” he said. “We can help them control the cost of their electricity with solar panels that we installed at no capital expense.” Chanin described the project as his way of giving back to the community he loves. Cherry Street Energy will continue to support the panels for the next 20 years.
JinkoSolar has signed a strategic supply agreement for 2GW of its high-efficiency PV modules with global clean energy leader Masdar, under which it will supply its Tiger Neo series modules for RTC, the world’s first gigascale round-the-clock renewable energy project in Abu Dhabi. The signing ceremony was attended by Masdar CEO Mohamed Jameel Al Ramahi and his Jinko counterpart Charlie Cao, together with senior executives from both parties. RTC is the world’s first gigascale renewable energy project, integrating solar power and battery energy storage. Jointly developed by Masdar and the Emirates Water and Electricity Company (EWEC), the project integrates a 5.2GW solar photovoltaic (PV) plant with a 19 gigawatt-hour (GWh) battery energy storage system (BESS), the largest and most technologically advanced system of its kind in the world. RTC reimagines the potential of renewable energy by overcoming intermittency and, once operational, the project will produce gigascale baseload energy at a globally competitive rate for the first time, setting a new international benchmark and reaffirming the UAE’s leading position in renewable energy development. The signing of the agreement marks an important milestone for JinkoSolar’s development in the high-end Middle Eastern new energy market and the long-term strategic partnership with Masdar.
As solar panels lose their ability to generate electricity after sunset, one major challenge remains for renewable energy: how to store solar power for use later, whether during cloudy weather or overnight. Researchers at UC Santa Barbara believe they may have found an answer that avoids the need for massive battery systems or reliance on the electrical grid. Writing in the journal Science, Associate Professor Grace Han and her research team describe a new material capable of absorbing sunlight, storing that energy in chemical bonds, and later releasing it as heat whenever needed. The material is based on a modified organic molecule called pyrimidone and represents a new step forward in Molecular Solar Thermal (MOST) energy storage technology. "The concept is reusable and recyclable," said Han Nguyen, a doctoral student in the Han Group and lead author of the study. "Think of photochromic sunglasses. When you’re inside, they’re just clear lenses. You walk out into the sun, and they darken on their own. Come back inside, and the lenses become clear again," Nguyen continued. "That kind of reversible change is what we’re interested in. Only instead of changing color, we want to use the same idea to store energy, release it when we need it, and then reuse the material over and over." DNA-Inspired Solar Energy Storage The scientists drew inspiration from an unexpected source while designing the molecule: DNA. The pyrimidone structure resembles a component found naturally in DNA that can reversibly change shape when exposed to ultraviolet light. Using a synthetic version of that structure, the team engineered a molecule capable of repeatedly storing and releasing energy. To better understand why the molecule remained stable while holding energy for long periods, the researchers partnered with UCLA distinguished research professor Ken Houk. Computational modeling helped explain how the material could retain stored energy for years without significant loss. "We prioritized a lightweight, compact molecule design," Nguyen said. "For this project, we cut everything we didn’t need. Anything that was unnecessary, we removed to make the molecule as compact as possible." A Reusable "Sun Battery" Unlike standard solar panels that directly convert sunlight into electricity, this system stores energy chemically. The molecule behaves somewhat like a compressed spring. After absorbing sunlight, it shifts into a strained, high-energy form and stays in that state until activated. When exposed to a trigger — such as a small amount of heat or a catalyst — the molecule snaps back into its original form, releasing the stored energy as heat. "We typically describe it as a rechargeable solar battery," Nguyen said. "It stores sunlight, and it can be recharged." The molecule also delivers impressive energy density. According to the researchers, it stores more than 1.6 megajoules of energy per kilogram. By comparison, a conventional lithium-ion battery stores roughly 0.9 MJ/kg. The new material also outperformed earlier generations of optical energy-storage switches. New Material Can Boil Water Using Stored Sunlight A key milestone for the team involved turning the molecule’s high energy storage capacity into a practical demonstration. In experiments, the researchers showed that the material could release enough heat to boil water under ambient conditions, something that has been difficult to accomplish in this area of research. "Boiling water is an energy-intensive process," Nguyen said. "The fact that we can boil water under ambient conditions is a big achievement." The technology could eventually support a variety of real-world uses, including off-grid heating systems for camping or home water heating applications. Because the material dissolves in water, researchers say it may someday circulate through rooftop solar collectors during the day before being stored in tanks that release heat at night. "With solar panels, you need an additional battery system to store the energy," said co-author Benjamin Baker, a doctoral student in the Han Lab. "With molecular solar thermal energy storage, the material itself is able to store that energy from sunlight." The project received support from the Moore Inventor Fellowship, awarded to Han in 2025 to advance the development of these "rechargeable sun batteries." Story Source: Materials provided by University of California – Santa Barbara. Original written by Seren Snow. Note: Content may be edited for style and length. 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7-DAY UNLIMITED ACCESS By Shelby Webb | 05/15/2026 06:34 AM EDT The Electric Reliability Council of Texas said solar outproduced coal power in its region in 2025, while a new federal projection put 2026 as the first year that could happen. The growing role of solar power generation in Texas is receiving state and national attention. Mark Felix/AFP via Getty Images Texas’ main grid operator said Thursday that solar topped coal for power generation in its region last year, contradicting a new federal report projecting that breakthrough in 2026. On Wednesday, the U.S. Energy Information Administration published a forecast predicting that — for the first time on an annual basis — utility-scale solar could produce more electricity this year than coal in the region covering most of Texas. But Trudi Webster, a spokesperson for the Electric Reliability Council of Texas, said in a statement Thursday that ERCOT reports about fuel mix and energy demand in its region “confirm that solar surpassed coal on an annual basis in 2025.” The chasm between data from EIA and ERCOT on utility-scale solar power generation last year is enormous — a difference of more than 10 terawatt-hours. One terawatt can power about 250 million homes on a peak day in ERCOT’s region. Request a FREE trial to receive unlimited access to
The Pan India Solar Sector Association’s networking and business meet in New Delhi brought together EPC players, manufacturers, suppliers and industry experts to strengthen partnerships and promote collaboration across India’s expanding solar energy ecosystem. May 16, 2026. By News Bureau The Pan India Solar Sector Association (PISSA) has recently organised its Networking cum Business Meet. The event took place on May 9, 2026, at Hotel Pride Plaza, Aero City, New Delhi. It witnessed participation from more than 120 Renewable Energy Professionals representing various segments of the Renewable Energy, specifically Solar Sector. The event served as a platform for industry stakeholders: a mix of EPC service providers, Industry Professionals, Suppliers, Manufacturers etc. The platform was an opportunity for the participants to engage in meaningful discussions, strengthen business relationships and explore new opportunities for collaboration within India’s growing renewable energy ecosystem. The Highlights of the event were a key note address by the Chief Guest, Dr. Jeevan Kumar Jethani, Scientist ‘F’ at the Ministry of New and Renewable Energy (MNRE). During his session, Jethani interacted with the audience queries and gave a perspective to all the apprehensions of the stakeholders. The event also witnessed industry sponsors, including KAMPSOL INDUSTRIES – Manufacturer of LT Panels, ACDB, DCDB and solar mounting structures. GENNEX Transformers – Transformer Manufacturer, Nunam – Battery Manufacturer and Solvanta RPSG – Solar Panel Manufacturer. The gift sponsors for the event included, WELCO PRINTS –Printing services and SunCart. PISSA expressed that it remains committed to creating meaningful opportunities for networking, knowledge sharing and collaboration across the renewable energy ecosystem. Encapsulant Selection is a Strategic Reliability Decision: Avinash Hiranandani From Innovation to Execution, BESS is Now Central to Power Planning: Savek Dubey, Sungrow Mufin Green Finance's Gunjan Jain Bets on Premium Financing as India’s Next Credit Opportunity Grid Modernisation, Storage, and Hydrogen to Shape India’s Energy Future: Advait's Rutvi Sheth Energy Security Has Evolved into a Strategic Imperative for India: Hartek Singh
SLR Solar has inaugurated an 800 MW fully automated AI-driven solar PV module manufacturing facility in Kishangarh, Rajasthan, which will produce next-generation TOPCon glass-to-glass solar modules. The company plans to scale capacity to 3 GW and expand into EVA sheet and junction box manufacturing at a later stage. May 16, 2026. By Mrinmoy Dey Encapsulant Selection is a Strategic Reliability Decision: Avinash Hiranandani From Innovation to Execution, BESS is Now Central to Power Planning: Savek Dubey, Sungrow Mufin Green Finance's Gunjan Jain Bets on Premium Financing as India’s Next Credit Opportunity Grid Modernisation, Storage, and Hydrogen to Shape India’s Energy Future: Advait's Rutvi Sheth Energy Security Has Evolved into a Strategic Imperative for India: Hartek Singh
A recent study found that floating solar panels installed on Morocco’s dams could contribute to the country’s energy needs while also helping to reduce water loss from evaporation. Titled, “Techno-economic feasibility analysis of floating photovoltaic systems on 58 Moroccan dams: energy potential, economic viability, and water evaporation,” the research examined 58 dams across the country, analyzing water surface availability, evaporation rates, expected energy production, costs, and different technical configurations for floating platforms. The study was conducted by four Moroccan researchers, namely Abdelilah Mouhaya, Saad Motahhir, and Abdelaziz El Ghzizal from Sidi Mohamed Ben Abdellah University in Fes, and Aboubakr El Hammoumi from Abdelmalek Essaâdi University in Tetouan. It highlighted that Morocco benefits from abundant and consistent sunlight, making “photovoltaic solar energy a highly promising solution,” despite challenges like land scarcity and high temperatures that affect efficiency. “Installing solar photovoltaics on existing dams offers an attractive and sustainable alternative, as they enhance overall renewable energy production and reduce evaporation,” the source stressed. The study estimated that the monitored dams cover around 433 square kilometers of water surface, which collectively lose about 909.468 million cubic meters of water each year due to evaporation. The researchers also found that the optimal angle for maximizing energy production was a panel tilt of 31 degrees, but lower angles, such as 11 degrees, were also viable, providing a better balance between electricity generation and water conservation. The results also showed that floating solar panels installed on 1% of the total surface area of these dams could play an important role in meeting Morocco’s energy needs and provide a relatively quick return on investment. Subscribe now to the newsletter, to receive the latest news daily
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Around Cornell News directly from Cornell’s colleges and centers Renewable energy infrastructure is booming globally, driven by improved tech, rising oil prices and global energy instability. But large, utility-scale solar projects often raise community concerns about land-use changes. Researchers have developed a model that overlays areas suitable for solar development with prime farmland and habitats critical for protecting biodiversity in New York. The model could inform solar siting decisions across the state, helping communities identify critical areas to protect. The study, “Sustainability Trade-offs at the Nexus of Solar Energy, Agriculture and Biodiversity,” published April 22 in Geography and Sustainability. The team of researchers from Cornell, The Nature Conservancy (TNC), the U.S. Geological Survey (USGS) and Central Michigan University assessed the geography of New York state according to three competing land-use priorities: solar development at the lowest cost, farmland preservation and biodiversity conservation. Their overlapping maps based on those priorities identified potential low-conflict sites and hotspots where competing priorities could lead to tradeoffs of potential solar development. Read the full story in the CALS Newsroom. Get Cornell news delivered right to your inbox. Cornell Chronicle 120 Maple Ave. Ithaca, NY 14850 cunews@cornell.edu
0 Powered by : Meridian Energy, New Zealand power company, has received consent to develop the 120 MW Bunnythorpe Solar Farm north of Palmerston North in Manawatū, New Zealand. The project will be paired with an already approved battery energy storage system at the Bunnythorpe Energy Park. Around 250,000 solar panels are planned for the site, with annual generation expected to reach approximately 225 GWh. The output could supply electricity to around 30,000 average homes. The 280 ha site is located near Transpower’s Bunnythorpe substation between Ashhurst and Stoney Creek Roads. Meridian expects the project to create more than 100 local construction jobs and up to NZD 50 million in regional spending during construction. Final investment decision is expected in Q4 2027.
Fujiyama Power Systems Limited, one of India’s leading rooftop solar solution providers, today announced the commissioning of its 2,000 MW solar panel manufacturing facility at Ratlam, Madhya Pradesh, marking a major milestone in the company’s manufacturing expansion strategy. The Ratlam facility forms part of Fujiyama’s large-scale greenfield project aimed at strengthening its integrated solar manufacturing ecosystem and enhancing its position in India’s rapidly growing renewable energy sector. The facility has been designed with a planned manufacturing capacity of 2,000 MW each for solar panels, batteries and inverters. Operations at the solar panel line have commenced with an initial annualized production capacity of nearly 1,000 MW under a single-shift model. The company plans to progressively scale up operations through phased expansion and double-shift manufacturing to achieve full capacity utilization by the fourth quarter of FY27. Following the commissioning of the Ratlam plant, Fujiyama’s total solar panel manufacturing capacity has increased to 3,568 MW, significantly strengthening the company’s ability to cater to the fast-growing domestic rooftop solar market. The company also provided an update on its inverter and battery manufacturing lines being developed at the same facility. Fujiyama stated that commissioning timelines for these segments experienced delays due to the incorporation of advanced lithium-ion battery technologies aimed at enhancing product competitiveness and long-term market relevance. Additionally, certain geopolitical developments impacted equipment supply schedules during the project execution phase. The company said these challenges have now been substantially addressed. Fujiyama expects the inverter manufacturing line to become operational during the first quarter of FY27, with the required machinery already delivered to the facility. Orders for machinery related to the battery manufacturing line have also been placed, and commissioning is expected during the second quarter of FY27. Commenting on the development, Pawan Kumar Garg, Chairman and Joint Managing Director of Fujiyama Power Systems Limited, said the commissioning of the Ratlam solar panel facility represents a significant milestone in the company’s long-term growth journey. He stated that the expansion strengthens Fujiyama’s ability to serve the rapidly expanding rooftop solar segment through enhanced manufacturing scale, improved operational efficiencies and greater control across the value chain. The Ratlam project is expected to further improve backward integration capabilities, optimize supply-chain efficiencies and support cost competitiveness across product categories. With nearly three decades of operational experience, Fujiyama has established itself as a prominent player in India’s rooftop solar solutions market, offering a wide portfolio that includes solar panels, inverters, lithium and tubular batteries, chargers and power-electronics systems. The company operates multiple manufacturing facilities across Himachal Pradesh, Uttar Pradesh and Haryana, while continuing to expand its integrated renewable energy manufacturing infrastructure through the Ratlam project. Fujiyama’s predominantly B2C business model is supported by a widespread distribution and service ecosystem comprising more than 8,900 channel partners, including distributors, dealers, exclusive retail outlets and service engineers. The company has built a strong presence across Tier-2 and Tier-3 markets, enabling seamless product delivery, installation and after-sales service support. The company has also commissioned 1 GW of Mono PERC solar cell manufacturing capacity and is currently expanding its TOPCon solar cell capacity by an additional 1.2 GW. Fujiyama believes these investments position it strongly to benefit from India’s accelerating demand for Domestic Content Requirement-compliant solar panels under various government-supported renewable energy initiatives. The story of ‘MAKE IN INDIA’ has reached far and wide. But who are makers of ‘MAKE IN INDIA’? What is their story? ‘Machine Maker’ is a dedicated magazine that seeks to bring the incredible stories… Read more B-201, SPIREA, Wakad, Pune – 411057 [email protected] +91-703-093-2700 Contact Us Subscribe India’s Top
An unexpected error occurred. Please try again. The practices used in conventional farming help produce large amounts of food, which of course helps farmers stay in business. However these practices also contribute to climate change. The use of synthetic fertilizers and pesticides, plus planting the same crop over and over in the same field, leads to lower quality soil and increased vulnerability to pests. Managing a profitable farm and practicing good climate stewardship aren’t mutually exclusive ideas. Farmers have options that make it possible to grow large amounts of food and minimize their impact, including using regenerative agriculture techniques and installing agrivoltaic systems for solar power on their land. Let’s look at how both these paths improve a farm’s resilience and support the farmer’s economic well-being. According to the Regenerative Agriculture Foundation, regenerative farming is any practice, process or management approach that enhances the functioning of the systems on which it relies that makes the land, community and bottom-line healthier year after year. Regenerative farming practices include using natural fertilizers and cover crops, eliminating mechanical soil tilling, and integrating trees into farming. These methods help promote biodiversity and maintain healthy carbon levels in the soil; they can also reduce overall agricultural greenhouse gas emissions. Regenerative farming also has the potential to increase crop yield – and also revenue – depending on how and where it is used. Organic farming, or the practice of using natural fertilizers, shows an average yield gain of 16 percent in tropical countries in Africa. In subtropical and tropical regions, using agroforestry, or integrating trees into crop land, results in a 7-16 percent increase in crop yield. Farmers that planted cover crops like legumes in areas with course-textured soil and not much rain saw an overall yield increase of 14 percent. However even when crop yields are smaller, regenerative agriculture can still result in higher revenue through decreased operational costs. A 2025 study looking at regenerative farming in the Upper Midwest compared a five-crop rotation system to a conventional corn-soybean rotation and found that the regenerative system can be just as profitable, particularly over the long term. After three years of a five-crop rotation, the revenue produced was similar to that of the two-crop system, likely because of the need for less spending on pesticides, spraying, fertilizers, and tractor use. Prioritizing climate sustainability doesn’t have to mean an unprofitable farm. Regenerative agriculture can offer financial stability, but it’s not the only option farmers have. In the short term, switching to regenerative farming can be a financial risk due to unpredictable initial crop yields, so finding a secondary income source can be a lifesaver for farmers. Arivoltaics, or solar panels integrated into an agriculture system, allow farmers to earn money in two ways: from selling products and from producing or selling electricity. This extra energy income can help farmers become less affected by unpredictable crop price fluctuations and protect them from financial losses caused by extreme weather conditions. However, agrivoltaic systems can be expensive to install because they require taller, stronger, and more complex structures than regular ground-mounted solar panels. Some studies estimate the cost to be about 5 to 40 percent higher than conventional solar panel installations, though newer designs and distributed manufacturing may help lower costs and improve payback period, which is already less than 10 years on average. Even with the high installation costs, a 2025 systematic review found that agrivoltaic systems can provide several benefits beyond renewable electricity. These systems may improve water-use efficiency in hot climates by up to 150-300 percent through providing shade. They also enhance land-use efficiency by up to 200 percent, reduce the need for irrigation by 14 percent, and increase revenue by up to 15 times. In addition, shade from the panels helps reduce heat stress in livestock and the physical structure itself creates a shield, reducing potential wind damage to crops and the earth. However, these potential benefits don’t mean that agrivoltaics will work equally well everywhere. The success of these systems depends on the location of the farm, soil and crop type, and the layout of the solar panels. A 2026 PNAS study gives a good example of these mixed results. It found that agrivoltaics performed differently in locations across the U.S. Midwest. In the more humid, eastern Midwest, agrivoltaics reduced crop yields and farm profits, while in semiarid western Midwest locations, solar panel shade helped reduce water stress, improving crop growth. One lesson we can take from this study is that agrivoltaics may be most useful in hotter or drier climates. While regenerative agriculture and agrivoltaics have their trade offs, they offer farmers a hopeful path forward. Regenerative agriculture can rebuild soil health, reduce dependence on expensive fertilizers and pesticides, and even increase yields when matched to the right climate and crop, while agrivoltaics add another layer of economic support and provide shade and wind protection. Together these practices show that farming does not have to choose between profit and environmental responsibility. When carefully planned for local conditions, regenerative agriculture and agrivoltaics can help farmers earn money, protect their land, and build farms that actively fight against global warming. Check out our other articles on regenerative farming, soil health, and all things food. This article is available for republishing on your website, newsletter, magazine, newspaper, or blog. The accompanying imagery is cleared for use with attribution. Please ensure that the author’s name and their affiliation with EARTHDAY.ORG are credited. Kindly inform us if you republish so we can acknowledge, tag, or repost your content. You may notify us via email at [email protected]. Want more articles? Follow us on substack.
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