Sigenergy’s C&I Solution, SigenStack SHANGHAI and SCHORNDORF, Germany, April 10, 2026 /PRNewswire/ — Sigenergy, a leading energy innovator, is entering the market for utility-scale photovoltaic systems in Europe. Together with Baden-Württemberg/Germany based PV specialist Arausol, and the European distributor Memodo, it is building Germany’s largest PV plant with decentralized storage systems that operate on direct current (DC). The project in Weissach im Tal is currently under construction and will have an installed peak PV capacity of 11.6 MWp and a battery capacity of 20 MWh. This capacity is distributed across 1,660 Sigenergy battery modules, each with a capacity of 12 kWh, which are securely installed in stackable SigenStacks. Unlike large-scale batteries, they are deployed in a decentralized manner. Installing SigenStacks on Arausol mounting structure—similar to PV module racks—is quick, easy, and safe, requiring no complicated cabling or the use of cranes or other heavy equipment. The solution thus avoids soil sealing, which is common in projects involving large central batteries housed in containers. DC Coupling and AI: more power, more renewable electricity and higher revenues Compared to AC-coupled systems, the system eliminates the need for multiple conversions between DC and AC. Instead, excess photovoltaic DC power is fed directly into the batteries and converted to AC via the inverters only when it is time to feed power into the grid. DC coupling thus increases the overall system’s efficiency—by at least 4%. It also eliminates the need for duplicate inverter infrastructure. The DC mode also allows Arausol to increase the output of the PV system, further enhancing the project’s economic viability. Less Need for Grid Expansion In comparison, AC-coupled systems have technical limitations. As a result, consistent use of DC coupling for large-scale PV projects would allow for a smaller-scale expansion of the power grids required for Germany’s energy transition. This would also help keep costs low for electricity customers. In addition to storage systems and inverters, Sigenergy is supplying Arausol with other electrical components, such as medium-voltage transformer stations equipped with pre-installed low-voltage connections. Memodo ensures reliable procurement through its delivery capability and market expertise. Arausol is responsible for the construction and project management, in addition to providing the substructures from its own facilities. Grid connection is scheduled for July 2026. Partnership for cutting-edge technology “This project sends a clear message: DC coupling enables utility-scale energy systems to be built faster, smarter, more efficiently, and in a more environmentally friendly way,” explains Emanuel Spahrkäs, Senior Account Manager at Sigenergy. “By combining Sigenergy’s unique DC-coupled solution with a decentralized battery architecture and Arausol’s easy-to-install mounting system, we achieve faster commissioning, higher performance, and lower operating costs.” Jaime Arau, CEO and founder of Arausol, said: “As a leading systems integrator and project developer for photovoltaic systems, we are committed to implementing the latest technology. Thanks to its innovative DC coupling, Sigenergy is an ideal partner for realizing this goal.” Memodo emphasized its strategic role in the project, highlighting early-stage collaboration and technology alignment. The company worked closely with the customer to define the system architecture and position Sigenergy as a suitable partner. “Our strength lies in actively bringing innovations to the market and supporting projects across the entire value chain,” said Jonas Hollweg, Head of Sales at Memodo. “The project underlines the potential of close and strategic cooperation between manufacturers, project developers and distributors in delivering advanced energy solutions.” View original content to download multimedia:https://www.prnewswire.com/news-releases/sigenergy-arausol-and-memodo-realize-germanys-largest-dc-coupled-pv-plant-with-decentralized-storage-302739155.html SOURCE Sigenergy Originally published on the BLOX Digital Content Exchange. Your browser is out of date and potentially vulnerable to security risks. We recommend switching to one of the following browsers: This site is for CNHI, LLC employees only. Please enter your cnhinews.com credentials to access this site. If you have any questions please contact help@cnhionline.com
By the People, for the People News Green energy project aims to power 400,000 homes, but locals worry about impact Apr. 10, 2026 at 2:45am Got story updates? Submit your updates here. › A proposed 6.2-mile solar farm in north Nottinghamshire is under review by planning inspectors, with the project’s developer touting its potential to provide renewable energy for the entire county, while local residents express concerns about increased traffic, flooding risks, and the overall impact on the rural landscape. The solar farm is a nationally significant infrastructure project, meaning the final decision rests with the national government. The outcome will have major implications for the region’s energy future and the balance between green energy goals and local community interests. The Great North Road Solar and Biodiversity Park would create a ring of ‘solar islands’ around villages like Caunton and Ossington, with the eastern edge running alongside the A1 highway. While developers claim the project could power 400,000 homes, a recent public consultation found 54% of respondents opposed the plans, citing concerns about increased HGV traffic, potential damage to historic homes, and the overall impact on the rural landscape. The company developing the Great North Road Solar and Biodiversity Park, which has reduced the land required for the project by 30% and implemented a biodiversity enhancement plan. The executive chairman of Elements Green, who highlights the company’s efforts to engage with the local community and address concerns. A resident of Egmanton who remains concerned about increased HGV traffic and potential structural damage to historic homes. A nearby resident who raises concerns about the potential for increased flooding from the Moorhouse Beck stream. “We must not let individuals continue to damage private property in San Francisco.” — Robert Jenkins, San Francisco resident “Fifty years is such an accomplishment in San Francisco, especially with the way the city has changed over the years.” — Gordon Edgar, grocery employee The judge in the case will decide on Tuesday whether or not to allow Walker Reed Quinn out on bail. This case highlights growing concerns in the community about repeat offenders released on bail, raising questions about bail reform, public safety on SF streets, and if any special laws to govern autonomous vehicles in residential and commercial areas. We keep track of fun holidays and special moments on the cultural calendar — giving you exciting activities, deals, local events, brand promotions, and other exciting ways to celebrate.
The country added around 44.6 GW of new PV capacity in fiscal year 2026, according to new figures released by JMK Research. The AMP Energy Bhadla Solar Power Plant in India Image: Sarvajanik Puralekh, Wikimedia Commons, CC BY-SA 2.0 From pv magazine India A new report by JMK Research reveales that India added around 44.6 GW of solar and 6 GW of wind capacity in fiscal year 2026. Solar and wind installations increased 87.2% and 45.6%, respectively, year-on-year. Fiscal year 2026 refers to the period from April 1, 2025 to March 31, 2026. With these additions, India’s total installed renewable energy capacity reached 275 GW as of March 31, 2026. Solar power accounts for around 55% with 150.26 GW of total renewable energy capacity, followed by wind at 20%, large hydro at 19%, biomass at 4%, and small hydro at 2%. Between April 1, 2025 and March 31, 2026, India added around 34.8 GW of ground-mounted solar capacity, a 106% year-on-year increase. The report attributes the sharp rise primarily to the commissioning of projects awarded under the Ministry of New and Renewable Energy’s (MNRE) 50 GW annual bidding trajectory launched in 2023. The commercial and industrial (C&I) open access segment also contributed significantly during the period. In addition, the inter-state transmission system (ISTS) waiver deadline of June 30, 2025 prompted developers to accelerate project execution in the first half of fiscal year 2026. Rooftop solar installations reached about 8.7 GW, up 69% year-on-year, driven primarily by the PM Surya Ghar scheme, which boosted residential adoption. Under the scheme, about 2.6 million homes have been covered, with nearly INR 14,771 crore ($1.8 billion) disbursed as central financial assistance, accelerating deployment across the country. Rajasthan led capacity additions in fiscal year 2026 with 12,140 MW (35%) of large-scale solar installations, followed by Gujarat with 8,952 MW (26%) and Maharashtra with 6,177 MW (18%). In rooftop solar, Maharashtra recorded the highest installations at 2,144 MW (25%), followed by Gujarat at 1,777 MW (20%) and Tamil Nadu at 600 MW (7%). In the off-grid solar segment, Maharashtra led with 614 MW (58%) of additions, followed by Gujarat with 77.9 MW (7%). This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Uma Gupta Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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April 10th, 2026 In 2026, most Australian solar households get more value from using or storing their solar power than from exporting it for a low feed-in tariff. Grid electricity often costs 30–50c/kWh, while feed-in tariffs commonly sit around 2–10c/kWh. That gap means your next solar dollar should focus on self-consumption, battery storage and panel quality, not just chasing the highest FiT. Quick Answer – Self-consumption is the new hero Self-consumption now drives most of the savings from rooftop solar. Feed-in tariffs used to be generous enough that “set and forget” exports could carry a big slice of your bill reduction, but most current guides now describe FiTs as a shrinking bonus, not the main benefit of going solar. The highest returns in 2026 usually come from: Battery vs export is not a simple yes or no question. It is about understanding where each kilowatt-hour does the most financial work for your home. What changed with feed-in tariffs? Feed-in tariffs started as high incentives that often paid more per kilowatt-hour than the price of grid electricity. That made exporting a strong revenue stream in the early days of rooftop solar. Over time, as more solar has flooded the grid in the middle of the day, wholesale prices during sunny hours have fallen and regulators and retailers have steadily reduced FiTs. By 2026: Industry articles now openly describe FiTs as “dead” in the sense that they are no longer a major profit centre. Instead, they are a modest credit sitting alongside much higher usage charges. That is the context for the shift toward self-consumption and batteries. Battery vs export: which gives better value per kWh? From a value-per-kilowatt-hour perspective, batteries and self-consumption usually beat exports in 2026. In an export-only scenario, you might export surplus solar for 3–10c/kWh and then buy electricity back in the evening at 35–50c/kWh. In a battery scenario, instead of exporting, the same surplus solar is stored in a battery and used at night, avoiding the 35–50c/kWh grid charge, minus some round-trip efficiency losses. That means each kilowatt-hour you shift from export to stored self-consumption can improve your position by roughly tens of cents per kilowatt-hour in many parts of Australia, before factoring in battery hardware and installation costs. Over thousands of kilowatt-hours each year, that gap is what funds the battery payback period. Export-only often suits: Solar plus battery suits: Three ways your solar can save you money in 2026 Every rooftop solar system can deliver value in three main ways: Because FiTs are now low while retail tariffs remain high, self-consumption and battery-shifted solar are both far more valuable per kilowatt-hour than exports. Why panel technology matters more in a low-FiT market Panel choice matters more when FiTs are low and self-consumption is king. A low-FiT market rewards systems that produce more usable energy from limited roof space over more years, because most of that energy is now being consumed or stored rather than exported. For homeowners with shading constraints, smaller roofs or higher daytime loads, higher-efficiency modules can noticeably increase total self-consumed energy and strengthen long-term savings. AIKO’s proprietary copper interconnection is designed to reduce the microcrack and bonding issues associated with traditional silver-based approaches, which supports better durability and lower long-term performance loss. This matters in Australian conditions, where panels face repeated thermal cycling, strong UV exposure and mechanical stress over many years. When homeowners are no longer being rewarded for bulk daytime export, preserving more generation over the life of the system becomes commercially critical, because every extra kilowatt-hour is used or stored rather than sold cheaply back to the grid. Why this matters for homeowners For homeowners, the key change is that self-consumption is now the main economic engine of a solar system. This shift affects both new and existing solar owners. Households with older systems may need to revisit how and when they use energy during the day. Households shopping for solar should prioritise generation quality, long-term yield and compatibility with batteries or smart load shifting instead of thinking only about FiT rates. What homeowners should do now? 1. Check your current retailer plan The first step is to make sure your plan still makes sense. Not every retailer pays the bare minimum, and some still offer premium FiT rates for a limited daily export volume or under specific tariff structures. That means the plan you choose now matters more than ever. Two households with identical solar can see very different outcomes depending on their retailer, FiT and usage tariffs. Start by: If your FiT is low and your usage tariffs are high, simply changing plans or retailers can boost your outcomes before you touch any hardware. 2. Shift more usage into solar hours The next step is to use more of your solar in the middle of the day. Running high-load appliances during the day is one of the easiest ways to improve solar economics. Because self-consumed energy avoids paying a high rate for grid electricity, every kilowatt-hour you move into daylight can deliver many times the value of exporting it for a few cents. Practical changes include: Some 2026 guides suggest that lifting self-consumption from around 30 percent to 50–60 percent through load shifting alone can dramatically increase annual savings compared with exporting that energy at 2–7c/kWh. 3. Reassess the case for a battery Once you understand your usage and exports, reassess whether a battery makes sense for your home. As daytime export value falls, the value of storing daytime solar for evening use increases. A battery does not create extra energy, but it takes solar that would have been exported cheaply and moves it into the evening peak, where each kilowatt-hour is worth the full retail rate you avoid paying. Batteries tend to make more sense when: Australian payback studies in 2026 suggest that for these households, batteries can now sit in a typical mid-single to low-double digit year payback window, especially when incentives are included. For very low-usage or short-stay households, export-only solar may still win on simplicity and cost. 4. Think harder about panel quality Finally, look at the quality of the panels themselves. If exported energy is worth very little, each extra kilowatt-hour you can use yourself matters more. That puts more emphasis on panels that deliver high efficiency, good temperature performance and stable long-term output, rather than simply chasing the lowest upfront module price. Key technical factors that now have direct financial impact include: Over 25 years, small differences in annual degradation and temperature performance can translate into thousands of extra kilowatt-hours, which is increasingly important when those kilowatt-hours are being used or stored, not sold cheaply. What installers should say now For most households, the customer story has shifted from “earn money from exports” to “keep more of your solar and buy less from the grid”. That is a simple message that matches what people see on their bills. A smart installer conversation now does three things: Framing the discussion this way helps customers look past “what is the cheapest system?” and focus on “what will work best for my home over the long term?”. That is better for the homeowner, who gets a system that actually meets their needs, and better for installers, who can design higher-quality jobs and build ongoing relationships instead of one-off price fights.
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The Colorado Sun Telling stories that matter in a dynamic, evolving state. Renewable energy made up at least 43% of electricity generated in Colorado in 2025, U.S. Energy Information Administration data shows. That’s roughly 24.7 million megawatt hours. Of that amount, wind power accounted for 69%, industrial or personal solar panels for 23% and conventional hydropower for 7.5%. Less than 1% came from biomass. Historically, coal-burning power plants generated most of Colorado’s electricity, but coal use has declined in recent years. In 2001, coal powered more than 75% of Colorado’s electricity generation. Last year, it accounted for just under 25%. Colorado has six remaining operational coal plants, all slated to close by 2031. The growth of solar and wind energy largely fueled Colorado’s turn toward renewables. Wind energy increased from approximately 14% of the state’s electricity overall in 2015 to 30% last year. Solar power increased from 0.5% overall to nearly 10% in the same period. See full source list below. This fact brief is responsive to conversations such as this one. The Colorado Sun partners with Gigafact to produce fact briefs — bite-sized fact checks of trending claims. Read our methodology to learn how we check claims. Get Fact Briefs in your email twice a week. Sign up here. Let us know what you want fact-checked by submitting a tip! Please note that claims that are partially true or false cannot be checked. In addition, we are unable to check claims for which there are no credible, available sources to verify or dispute them. Checks a specific statement or set of statements asserted as fact. Cassis Tingley is a Denver-based freelance journalist. She’s spent the last three years covering topics ranging from political organizing and death doulas in the Denver community to academic freedom and administrative accountability at the… More by Cassis Tingley The Colorado Sun is an award-winning news outlet based in Denver that strives to cover all of Colorado so that our state — our community — can better understand itself. The Colorado Sun is a 501(c)(3) nonprofit organization. EIN: 36-5082144 (720) 263-2338 Got a story tip? Drop us a note at tips@coloradosun.com
An international study has demonstrated that utility-scale solar PV paired with hydraulic hydro storage (HHS) could reach an LCOE as low as $0.022/kWh in select U.S. regions. The system could provide GWh-scale, cost-competitive, and highly reliable long-duration storage, capable of powering large commercial districts with minimal environmental impact. A schematic illustration of the system Image: University of Waterloo From pv magazine Global An international research team has found that combining utility-scale solar PV with gravity-based hydraulic hydro storage (HHS) could deliver a levelized cost of energy (LCOE) as low as $0.022/kWh in select U.S. locations. The study analyzed 936 sites across the country using multi-objective capacity optimization to assess the techno-economic viability of integrating PV and HHS at gigawatt-hour scale. “This represents the first comprehensive geospatial benchmark for giga-scale HHS paired with utility-scale solar PV,” co-author Mohamad T. Araji told pv magazine. “Previous research focused mainly on sub-100 MWh systems or single-site conceptual models.” Muhammed A. Hassan, another co-author, noted that the study systematically models PV-HHS operation while accounting for nighttime power demand, spatial load variability, solar resources, and regional costs. “Multi-objective optimization allows us to define the precise conditions under which this technology can move from theory to grid-scale reality,” he said. The system consists of three elements: a PV array as the primary generation source, an HHS unit serving as the energy buffer, and an aggregated commercial district load representing 2,000 buildings. When PV output exceeds demand, surplus electricity powers a reversible pump-turbine, lifting a rock piston and storing energy as gravitational potential. During discharge, the piston’s weight drives pressurized water through the same turbine to generate electricity. Construction leverages standard mining techniques, including cutting the piston from solid bedrock and installing a rolling membrane seal. Storage capacity scales with the fourth power of the piston’s radius, enabling GWh-scale storage sufficient to power a city for a day. Unlike conventional pumped hydro, HHS does not rely on elevation differences, widening its potential deployment, the scientists stressed. PV panels were modeled with a 20.3% efficiency, facing south at a tilt equal to local latitude. The HHS system was assumed to have 80% round-trip efficiency and eight hours of storage. Load profiles were derived from TMY3 weather data, and MATLAB optimization balanced low LCOE against high reliability, measured by loss of load probability (LOLP). Araji highlighted that in high-potential regions such as New Mexico, Nebraska, and Maine, the LCOE can reach $0.022/kWh because revenue from surplus solar exports offsets capital and operational costs. “The system can achieve extremely high self-sufficiency at district scale, with a levelized cost of storage (LCOS) below $0.166/kWh, competitive with utility-scale batteries for long-duration applications,” he said. Across climates, storage requirements ranged from 1.012 GWh to 4.232 GWh, with PV capacity typically lower in southern latitudes (0.626–2.305 GW). Around 75% of locations achieved an asset-level LCOE below $0.093/kWh, and most maintained an LOLP under 3.2%, demonstrating robust performance despite varying weather conditions. “The feasibility of these giga-scale projects is highly influenced by local policy,” the academics emphasized. “The state-specific power purchase agremeent (PPA) structures and regional PV capital costs are the primary determinants of the system’s relative performance. For gravity storage to reach its full potential, site selection must prioritize a convergence of favorable geological conditions and supportive electricity market designs.” The research findings were presented in “Techno-economic analysis of utility-scale photovoltaic plants with hydraulic hydro gravity storage for self-sufficient cities,” published in Energy Conversion and Management. Researchers from Canada’s University of Waterloo and Egypt’s Cairo University have participated in this work. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Lior Kahana Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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The consent follows a detailed examination process which, since the application was submitted in November 2024, has included extensive consultation with local communities, stakeholders and statutory bodies. The feedback gathered during this process helped shape the project, which has been jointly developed by EDF power solutions UK and Luminous Energy. EDF power solutions UK’s Director of Storage, Solar and Private Wire Matthew Boulton said: “This decision is an important step forward for Springwell Solar Farm. We welcome the Government’s approval following a thorough review of the project. “I would like to thank everyone who took part in the public examination process and consultations. As the project moves forward, we remain committed to working collaboratively with local communities and partners to reduce the impacts of construction while delivering long-term benefits for the region.” Located between Sleaford and Lincoln in Lincolnshire, Springwell will make an important contribution to the UK’s future energy mix by providing enough renewable, secure energy to power over 180,000 homes* every year once operational. The Springwell project also includes 12km of new footpaths, over 15km of proposed new hedgerows and a community growing area which would be available for public use. A community benefit fund would provide £400 per megawatt of installed capacity to support local projects. EDF power solutions and Luminous Energy will now consider the details of the consent and programme for delivery of the project, before engaging with stakeholders and the community on our plans to bring this project forward into construction. Once this is complete, it is planned for Springwell to export electricity to the national grid from 2029.
The California Public Utilities Commission approved a new community renewable energy program that critics argue favors investor-owned utilities while failing to create a workable market for distributed resources. Image: Pexels / Los Muertos Crew The California Public Utilities Commission (CPUC) has issued a new proposed decision in the ongoing community solar proceeding, a move that the Solar Energy Industries Association (SEIA) says “virtually ensures” that no new community solar projects will be developed in the state. CPUC’s proposed decision rejects the solar industry-backed Net Value Billing Tariff (NVBT), a rate designed to base electric grid export compensation on the hourly value of the energy produced. Advocates say NVBT is essential for securing private financing, particularly for community projects serving low-income subscribers. However, the CPUC has rejected this in favor of a structure based on the Avoided Cost Calculator (ACC). The metric estimates what the utility “avoids” paying to buy power elsewhere. Solar industry advocates argue the ACC rate is far too low and volatile to support new construction, essentially killing the market before it begins. The proposed decision issued by Administrative Law Judge Valerie Kao comes at a time when California is facing record-high energy prices. Industry advocates voiced hopes this round of the proceeding would correct flaws in the 2024 framework, but SEIA argues the commission has “wasted a golden opportunity” to provide relief to low-income residents. The decision maintains the commission’s focus on avoiding “cost-shifting” to non-participating ratepayers, a position heavily supported by investor-owned utilities (IOUs) like PG&E, SCE, and SDG&E. The debate around community solar program structuring mirrors the proceedings of rooftop solar grid compensation rates. Utility-backed analysis said that rooftop solar caused an $8 billion cost to non-solar customers in 2024, while independent analysis found a $1.5 billion net benefit to California electric ratepayers. Critics of the proposed decision say it also relies too heavily on one-time federal funding, specifically the $249 million Solar For All grant awarded to California by the EPA. The Coalition for Community Solar Access (CCSA) argued this is an unworkable substitute for a market model that leverages private capital. Instead of a new market-based tariff, the commission opted to modify existing utility-led “Green Tariff” programs. The decision also consolidates existing programs and discontinues the Community Solar Green Tariff (CSGT) for new projects, transferring its capacity to the Disadvantaged Communities Green Tariff (DAC-GT), while requiring that 51% of program capacity be dedicated to low-income subscribers. “With this proposed decision that crushes any chance of a viable community solar program in the state, the CPUC has doubled down on its past bad decisions at the behest of monopoly utilities,” said Stephanie Doyle, California State Affairs Director for SEIA. “The state legislature made it clear in passing AB 2316 that it wants a robust program… instead, the CPUC has issued a decision that virtually ensures no projects will be built.” The proposed decision is not yet final. It is scheduled to be heard at the Commission’s May 14, 2026, Business Meeting at the earliest. Until then, solar advocates say they will continue to push for a model that allows renters and those without suitable roofs to access the benefits of clean energy. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Ryan Kennedy we need to lower our soft costs in the US. and when we do that, we say to hell with the CPUC and the utilities. We don’t need military subsidies, we don’t need international tax subsidies, we don’t need the f**kin utility subsidies. We will compete against rising utility bills (30% minimum, likely 100% over the next 5 years, you know I’m right!) with our solar solutions at our delivered cost of 3-5 cents. And we can deliver that value in less than 18 months for the largest of projects. Keep the utility focused on improving their shit grid. We will be easily taking over 50% of demand over the next 10 years without them. WE DON”T WANT YOUR SAUDI MONEY!! Yes sir. Power to the people. Not utilities. Great news I am not a scientist, engineer or business person… Sometimes, I’d wonder if a newest great renewable “energy” find, “for all”, helping “all”, while preventing harm or loss, is doing some other general harm I didn’t know about, – and I would have to rethink and rearrange what I do, to avoid contributing to that – maybe, giving up some one? green part of it… — Then I think of the Energy Business, which sometimes is uncertain it can find more ways to continue to be a monopoly. The concern raised in this decision is valid—if the program structure makes projects uneconomic, the market simply won’t respond. Capital flows where there is clarity, bankability, and speed to execution. That said, this moment may actually highlight a more scalable path forward for California: Behind-the-meter distributed energy + microgrids Rather than relying solely on front-of-the-meter community solar constructs that depend heavily on utility program design, California should lean into localized, customer-sited generation paired with storage—especially where it directly offsets load and avoids grid friction. Where this works immediately: Residential subdivisions (new construction) Pre-integrated solar + storage microgrids Reduces interconnection complexity at scale Aligns with builder-driven deployment models Schools and universities Large daytime loads + public benefit alignment Ideal for resiliency hubs and emergency backup Hospitals and critical infrastructure Reliability is non-negotiable Microgrids provide energy security + cost control Commercial centers (malls, logistics, campuses) High load density with available rooftop/parking canopy space Strong economics with storage arbitrage Industrial users Demand charge reduction + operational continuity Increasing interest in energy independence Why this model works: Bypasses program dependency Projects are driven by customer economics, not tariff uncertainty Eliminates interconnection bottlenecks (in many cases) Especially when structured as load-serving microgrids Financeable today With tools like C-PACE, tax credit transfers, and long-term energy service agreements Aligns with grid realities Reduces strain on transmission while adding local resiliency The opportunity: If California wants distributed energy to scale, the focus should shift from program design → deployment enablement: Streamline permitting for microgrids Clarify rules for multi-meter / multi-tenant behind-the-meter systems Enable aggregation of distributed assets into market participation (RA, DR, etc.) Support standardized financing structures Bottom line: If the current community solar structure isn’t viable, the solution isn’t to pause deployment—it’s to pivot toward models that are already working. California doesn’t have a resource problem. It has a structure and execution problem. Behind-the-meter microgrids can bridge that gap—today. There is no reason to subside home solar. It causes poor people to pay more and subsidizes the rich. It s morally bankrupt If community solar programs are not producing financeable outcomes, the solution is not to pause deployment—it’s to shift toward models that deliver the same benefits through infrastructure. Multi-tenant behind-the-meter microgrids and community-scale microgrids can provide shared access to clean energy, bill savings, and resiliency—without reliance on complex tariff structures. In practice, these systems function as “physical community solar,” but with stronger economics, faster deployment timelines, and greater reliability. Cost shifting , free ride on others? These are my main reasons for opposition to “green” technologies. I have worked on economically viable solar installations in remote locations. To truly replace fossil fuels, solar costs must include storage. If you depend on the grid every night, you need to pay for that reliability, it does not come free. Let’s get balcony solar passed. At least it’s a start for low income renters and people who can’t afford rooftop solar Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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In a new weekly update for pv magazine, Solcast, a DNV company, reports that Saharan dust caused widespread panel soiling in southern Europe in early May, while much of central and eastern Europe benefitted from above-average solar resources. Favourable conditions broke down later in the month as cold polar air and a powerful storm system brought cloud, snow and damaging winds to many parts of the continent. Image: Solcast March in Europe brought a complex mix of atmospheric conditions for solar, according to analysis using the Solcast API. An early outbreak of Saharan dust temporarily reduced solar resource and caused widespread panel soiling in southern Europe, while much of central and eastern Europe benefited from persistently settled conditions and above‑average solar resource. Late in the month, this favourable pattern broke down as cold polar air and a powerful storm system brought cloud, snow and damaging winds to many parts of the continent, reducing solar resource once again. A significant Saharan dust outbreak in early March reduced irradiance across southern Europe while also introducing soiling risks for PV systems. Dust plumes originating over Saharan Africa were transported northwards, reaching Portugal, France and Italy within the first week of the month. This event coincided with the development of windstorm Regina over Portugal, which brought widespread cloud cover and rainfall. The rainy conditions not only further reduced irradiance but also deposited airborne dust onto panel surfaces in the form of “blood rain.” This combination of light reduction from atmospheric particles and surface soiling would have temporarily suppressed PV performance across affected regions, with elevated particulate concentrations confirmed by CAMS PM10 estimates during this period. The local impact is visible in Madrid, where Solcast-modelled clear-sky GHI dropped significantly from the morning of 3 March through to 5 March as the Saharan dust plume attenuated sunlight through the atmosphere. Dust losses exceeded 15% at peak irradiance on 3 March, with effects persisting through to 5 March before conditions began to normalise. In contrast, central and eastern parts of Europe benefited from sustained periods of clear and settled weather. Prevalent high-pressure systems dominated from northern France through Germany and into Eastern Europe for much of the month. This pattern suppressed cloud formation and enabled irradiance to exceed long-term averages by a notable margin — around 15% in northern France, 10% in Germany, and up to 25% in Poland, with northern Ukraine also seeing gains of approximately 20%. The persistence of this ridge was reinforced by a strongly positive North Atlantic Oscillation, which steered low-pressure systems further north toward Scandinavia, leaving much of continental Europe under clearer skies than typical for March. Conditions deteriorated sharply in the final week of the month as a deep trough pushed from the Atlantic into western Europe, drawing in a cold polar airmass. This shift brought widespread cloud, lower temperatures and late-season snowfall, all of which reduced available solar resource and introduced the risk of snow-related soiling. Concurrently, Storm Deborah developed near Italy on 25 March and tracked eastward, producing severe weather across southern and southeastern Europe. Deborah produced hurricane-force winds, heavy rain and thunderstorms across southern and southeastern Europe. Power infrastructure was disrupted, with around 18,000 people in Croatia losing power during the event. Italy, in particular, experienced compounded impacts from both the early-month dust and late-month storm activity, with monthly solar resource in some areas reduced to around 3 kWh/m²/day, well below the long-term average of approximately 3.7 kWh/m²/day. The cold outbreak has been linked to a breakdown of the polar vortex, allowing frigid air to extend into mid-latitudes. Solcast produces these figures by tracking clouds and aerosols at 1-2km resolution globally, using satellite data and proprietary AI/ML algorithms. This data is used to drive irradiance models, enabling Solcast to calculate irradiance at high resolution, with typical bias of less than 2%, and also cloud-tracking forecasts. This data is used by more than 350 companies managing over 350 GW of solar assets globally. The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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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: 10336 (2026) Cite this article 1620 Accesses Metrics details Accurate photovoltaic (PV) power forecasting is essential for grid operation but remains difficult due to nonlinear multi-scale dynamics and seasonal distribution shifts. This work presents MKAN-iTransformer, a cascaded framework that integrates two existing components—the Multi-Scale Kolmogorov–Arnold Network (MKAN) for scale-aware temporal representation learning and iTransformer for variable-wise attention and inter-variable dependency modeling—under a 15-minute single-step setting. Experiments on a real-world 30 MW PV plant dataset from the Chinese State Grid Renewable Energy Generation Forecasting Competition use chronological splits within each season. MKAN-iTransformer achieves the best overall performance in spring, autumn, and winter. In spring, it reaches MSE=2.892, RMSE=1.701, MAE=0.864, and ({R^{2}}=0.947), improving over LSTM by 23.5%/12.5%/20.5% (MSE/RMSE/MAE). In autumn, it attains MSE=2.884, RMSE=1.698, MAE=0.774, and ({R^{2}}=0.962), reducing errors vs. iTransformer by 16.5%/8.7%/12.4%. In winter, it achieves MSE=1.721, RMSE=1.312, MAE=0.443, and ({R^{2}}=0.969), yielding 81.6%/57.1%/71.9% error reductions vs. Transformer. Ablation further confirms the complementarity between MKAN and iTransformer and shows that direct KAN integration can be unstable under winter shifts (KAN-iTransformer: MSE=7.082, ({R^{2}}=0.872)). Amid escalating global climate change, transforming energy structures and accelerating renewable energy adoption have become shared priorities worldwide1. The growing electricity demand drives increasing renewable power requirements2, motivated by the carbon neutrality and eco-friendliness of renewable energy sources (RESs) compared to fossil fuels3. Empirical studies report negative associations between carbon emissions and renewable energy consumption, indicating emissions decrease as per-capita renewable energy use increases4,5. According to the International Energy Agency (IEA), renewables are expected to supply 42% of global electricity generation between 2023 and 2028, with solar and wind contributing 25%6. Photovoltaic (PV) generation, as representative green technology, has experienced rapid expansion. Global PV installed capacity continues growing with steadily rising power system share7. While large-scale PV integration brings substantial environmental benefits, it introduces new operational challenges8. The primary challenge stems from PV output variability. PV generation is highly sensitive to meteorological conditions—irradiance, temperature, humidity, and wind speed—whose nonlinear and time-varying nature creates pronounced fluctuations and uncertainty9. This uncertainty complicates grid dispatch, increases storage and flexibility requirements, and affects electricity market operations10,11. Therefore, accurate PV power forecasting is essential for secure and efficient power system operation12 and supports downstream decision-making including operational management and demand response13. These observations motivate a design that (i) captures multi-scale temporal dynamics of PV series and (ii) models cross-variable dependencies among meteorological inputs and historical power, while supporting transparent analysis. We develop MKAN-iTransformer, integrating Multi-Scale Kolmogorov-Arnold Networks (MKAN)14 with iTransformer15. MKAN provides multi-scale temporal representation learning with explicit functional structure, while iTransformer models inter-variable dependencies through variable-wise attention, together targeting robust and interpretable PV forecasting. Contributions. Our main contributions are: Cascaded forecasting framework. We develop MKAN-iTransformer, cascading multi-scale temporal representation learning with variable-wise attention for 15-minute single-step PV power prediction. Season-wise chronological evaluation. Beyond overall test splits, we evaluate models within each season using chronological splits, making seasonal robustness and failure modes explicit. Comprehensive KAN-enhanced baseline construction and evaluation. We systematically construct KAN/MKAN-augmented variants of recurrent and attention-based baseline architectures and establish unified benchmarking framework with consistent preprocessing, training, and evaluation protocols, enabling fair comparison and demonstrating the broad applicability of interpretable neural components in PV forecasting. Interpretability analysis. We provide multi-scale time-frequency decomposition, learned KAN function inspection, and attention visualization for transparent explanations. PV power forecasting has progressed from physics-driven and classical statistical models to modern machine learning and deep learning pipelines, largely driven by the need to handle nonlinearity, non-stationarity, and regime shifts. Physical and statistical models. Early forecasting relied on physical simulation using meteorological inputs and device characteristics, which can be physically meaningful but often requires high-quality inputs and detailed plant specifications, limiting scalability in practice9,12. Classical statistical models (e.g., ARMA/ARIMA and regression families) exploit temporal correlations and can perform well under relatively stable conditions; for instance, regression combined with numerical weather prediction has shown robust hourly forecasting16. However, abrupt ramps and distribution shifts common in PV generation challenge these assumptions and motivate more flexible nonlinear approaches. Machine learning approaches. Conventional ML methods improved nonlinear mapping from weather variables to PV output, including linear regression and ensemble methods such as random forests and gradient boosting17,18. Support vector regression has also been adopted for high-dimensional nonlinear forecasting17,19. Despite progress, many ML pipelines rely on handcrafted features and can degrade under seasonal and weather-regime shifts, encouraging end-to-end deep architectures with better representation learning. RNN-based models. LSTM and GRU variants have been widely used to capture temporal dependencies in PV forecasting20,21,22. Performance gains have been reported via parallel structures, feature selection, CNN integration, and attention augmentation20,23,24,25. Nevertheless, RNN-based components remain sequential and may become computational bottlenecks for long contexts, while their ability to explicitly model cross-variable interactions is often limited. Hybrid CNN-RNN architectures. CNN-LSTM and related hybrids seek to combine local pattern extraction and temporal modeling26, with variants replacing standard CNN blocks by temporal convolutional networks to improve receptive fields and parallelism27. Attention mechanisms are frequently introduced for feature weighting and fusion; for example, dual-stream CNN-LSTM with self-attention has been reported to improve accuracy on PV datasets28. However, these hybrids may still struggle to represent multi-scale behaviors spanning intra-hour variability to seasonal cycles, and they often treat heterogeneous meteorological variables as homogeneous inputs without an explicit variable-wise dependency mechanism. Transformer-based architectures. Transformers have enabled stronger long-range dependency modeling for time series, with forecasting-oriented variants targeting efficiency and inductive biases. Informer reduces attention complexity via ProbSparse attention with (O(L log L)) behavior29; Autoformer and FEDformer incorporate decomposition and frequency-aware mechanisms to better capture trend/seasonality30,31. In PV-specific contexts, multi-scale and hybrid designs combine Transformers with CNNs/GRUs or decomposition modules32,33,34, and domain-enhanced Transformers inject domain knowledge or nonlinear dependency modeling to improve robustness35,36. The iTransformer introduces an inverted design that treats variables (rather than time steps) as tokens, enabling efficient variable-wise attention for cross-variable dependency modeling15, which is particularly relevant for PV forecasting where meteorological drivers and historical power jointly determine future output. Multi-scale pattern recognition. PV generation exhibits multi-scale dynamics (diurnal cycles, intra-hour fluctuations, and weather-driven ramps), motivating multi-resolution modeling through decomposition, frequency-aware transformations, or multi-scale feature extraction. Interpretable deep learning pipelines have been proposed to disentangle multi-scale solar radiation variations while retaining predictive accuracy (e.g., reporting (R^2=0.97))37. Yet, many approaches increase architectural complexity and do not always provide transparent, component-wise explanations that remain stable across operating regimes. Interpretability requirements in energy systems. For energy applications, interpretability supports operational decision-making and stakeholder trust, but many deep models remain black boxes; moreover, attention weights alone do not guarantee faithful explanations. This motivates exploring model families with more explicit functional forms. Kolmogorov–Arnold Networks (KAN) for interpretable learning. KANs parameterize multivariate mappings via sums of learned univariate functions, often implemented with spline-based learnable functions, offering a potentially more inspectable representation than standard MLP layers38. Recent surveys summarize rapid development of KAN variants and applications (e.g., TKAN, Wav-KAN, DeepOKAN) and discuss their empirical strengths38,39. Theoretical extensions such as KKANs further improve robustness and approximation behavior40. For temporal data, KAN-based time series modeling has been explored, including general demonstrations and targeted work on bridging accuracy and interpretability in time series settings41,42. KAN integration with dynamical systems has also been studied via KAN-ODEs43. More recently, multi-scale KAN variants (MKAN) have been proposed to better capture mixed-frequency behaviors in temporal signals14. Despite these developments, systematic integration of KAN-style multi-scale representations with state-of-the-art variable-wise attention, and task-specific interpretability validation for PV forecasting, remains limited. Many PV forecasting studies emphasize aggregate metrics, which can obscure failure modes under seasonal regime shifts. Seasonal variability changes irradiance, temperature, and daylight duration, making season-wise evaluation important for deployment9,12. However, evaluation protocols and baselines are often inconsistent across model families, hindering fair comparison and limiting insights into robustness under regime transitions. The above literature motivates four gaps addressed in this work: Architectural integration gap: limited evidence on combining multi-scale temporal representations with explicit variable-wise attention for PV forecasting14,15. Interpretability integration gap: insufficient task-specific validation of interpretability when integrating KAN-style components with attention-based architectures38,39. Evaluation methodology gap: limited systematic assessment under seasonal regime shifts9,12. Benchmarking consistency gap: inconsistent protocols across model families impede fair comparison and understanding of when interpretable neural components help21. Building on existing components14,15, our MKAN-iTransformer focuses on principled integration of MKAN-style multi-scale representation learning with variable-wise attention, accompanied by systematic seasonal evaluation and interpretability-oriented analyses to clarify both strengths and limitations under different regimes. The photovoltaic (PV) power forecasting task aims to predict the near-future output power of a PV plant based on historical multivariate time series observations. Let the historical observation sequence be where (x_t in mathbb {R}^d) denotes the d-dimensional feature vector at time step t. In this study, we consider single-step forecasting with a 15-minute horizon (sampling interval = 15 minutes). Therefore, the forecasting horizon is (h=1), and the prediction target is the PV power at the next time step: The input variables include total solar irradiance, direct normal irradiance, global horizontal irradiance, air temperature, atmospheric pressure, relative humidity, and historical PV power. The target variable is the PV plant output power at the next 15-minute step. The Multi-Scale Kolmogorov-Arnold Network (MKAN) module is designed to efficiently capture complex, multi-scale temporal dependencies in multivariate time series forecasting. The overall structure is illustrated in Fig. 1 and consists of the following key components. Overall architecture of the Multi-Scale Kolmogorov-Arnold Network (MKAN) module. The left part shows the hierarchical residual structure with stacked TimeKAN blocks, each extracting features at different scales through multi-scale patching (MSP) modules. The right part details the patching, encoding, KAN-based transformation, decoding, and unpatching process within each MSP block. Cumulative addition and subtraction operations are used to aggregate both local and global temporal features. Multi-scale patching: Given an input sequence (X in mathbb {R}^{T times d}), we divide it into S sets of patches at different temporal scales, where the s-th scale consists of (N_s) patches of length (l_s): Patch encoder: Each patch is mapped to a latent embedding via a learnable encoder: where (operatorname {Enc}_s) denotes the patch encoder for scale s. KAN-based Transformation: Each scale has a dedicated Kolmogorov-Arnold Network (KAN) block to transform the encoded patch embedding: where (operatorname {KAN}_s) is the KAN subnetwork for the s-th scale. Patch decoder: The transformed embeddings are decoded back to the temporal domain: Feature aggregation: The reconstructed patches are reassembled to form multi-scale feature maps, which are then aggregated (e.g., by summation or concatenation) to obtain the final sequence representation: where (operatorname {Agg}) denotes the aggregation operation across scales. Forecasting head: The aggregated features are passed to a forecasting head to generate the final prediction: The overall output of the MKAN module can be summarized as a weighted sum of KAN transformations across all scales: where (phi _{s,n}(cdot )) represents the output of the KAN subnetwork for the n-th patch at scale s, (alpha _{s,n}) are learnable weights, and b is a bias term. A major advantage of the MKAN module is its interpretability. Each KAN block is inherently symbolic and can be visualized or analyzed, allowing for direct inspection of the learned temporal features at each scale. In summary, the MKAN module integrates multi-scale patching with expressive KAN transformations, providing a transparent and effective solution for multivariate time series forecasting. The iTransformer module is designed to efficiently model multivariate time series forecasting by leveraging an inverted Transformer architecture. The overall structure is illustrated in Fig. 2 and consists of the following key components. Overall architecture of the iTransformer module. The framework consists of independent variable-wise embedding, temporal layer normalization, multivariate self-attention, feed-forward transformation, and aggregation for final forecasting. The left and right parts of the figure detail the embedding and feed-forward processes, respectively. Variable-wise embedding: Given a multivariate input sequence (X in mathbb {R}^{T times N}), where T is the sequence length and N is the number of variables, each variable’s time series (X_{:,n}) is independently embedded into a latent representation: where (operatorname {Embedding}) is a learnable mapping from (mathbb {R}^T) to (mathbb {R}^d). Temporal layer normalization: Each variable embedding is normalized along the temporal dimension to reduce scale and distribution discrepancies: where (mu _n) and (sigma _n) are the mean and standard deviation of the n-th variable embedding. Multivariate self-attention: All variable embeddings are jointly processed by a self-attention mechanism to capture inter-variable dependencies: where Q, K, V are linear projections of the variable embeddings. The detailed structure of the multivariate self-attention mechanism is shown in Fig. 3. Detailed structure of the multivariate self-attention mechanism in the iTransformer module. The input is first projected to Q, K, and V, then split into multiple heads for independent attention computation. The results are merged and projected to form the final output. Feed-forward network: Each variable embedding is independently transformed by a shared feed-forward network to extract nonlinear features: where (operatorname {FFN}) denotes a two-layer MLP with activation and dropout. Stacked blocks and aggregation: The above operations are stacked for L layers, and the final output embeddings are aggregated for forecasting: where (operatorname {TrmBlock}) denotes one iTransformer block, and (operatorname {Projection}) maps the final embedding to the prediction space. The overall output of the iTransformer module can be summarized as: where (operatorname {Head}) is typically a linear layer for regression or forecasting. A major advantage of the iTransformer module is its variable-centric design. By treating each variable’s time series as an independent token, the model can explicitly capture inter-variable correlations and global temporal patterns, while maintaining efficient parallel computation and interpretability of learned representations. In summary, the iTransformer module integrates variable-wise embedding, normalization, and attention-based transformation, providing a simple yet powerful backbone for multivariate time series forecasting. The hybrid architecture adopts a cascaded design, where the Multi-Scale Kolmogorov-Arnold Network (MKAN) module first extracts multi-scale temporal features from the input sequence, and the resulting representations are subsequently processed by the iTransformer module to model inter-variable dependencies. The overall structure is illustrated in Fig. 4. Overall architecture of the cascaded MKAN-iTransformer framework. The pipeline consists of sequential MKAN and iTransformer modules, followed by a forecasting head. The left part details the multi-scale patching and KAN transformation, while the right part illustrates variable-wise attention and prediction. Multi-scale feature extraction (MKAN): Given an input sequence (X in mathbb {R}^{T times N}), the MKAN module extracts multi-scale temporal features: where (Z_{text {MKAN}}) encodes rich temporal dependencies across different resolutions. Inter-variable modeling (iTransformer): The multi-scale features (Z_{text {MKAN}}) are fed into the iTransformer module, which captures global dependencies among variables via self-attention mechanisms: where (Z_{text {iTrm}}) denotes the variable-attentive feature representation. Forecasting head: The final representation is passed to a forecasting head to generate the prediction: This cascaded hybrid design enables the model to: Efficiently extract multi-scale temporal patterns using the MKAN module, which models complex dynamics at various time resolutions. Explicitly capture inter-variable relationships through the iTransformer, which leverages attention to integrate information across variables. Produce robust and interpretable representations for accurate multivariate time series forecasting. In summary, the cascaded MKAN-iTransformer architecture unifies multi-scale temporal feature extraction and variable-wise attention modeling, forming a transparent and powerful backbone for multivariate time series forecasting. In this study, real-world operational data from a 30 MW photovoltaic (PV) power plant are utilized for experimental evaluation. The dataset contains records from 2019 and 2020, with a sampling interval of 15 minutes. The input features include total solar irradiance, direct normal irradiance, global horizontal irradiance, air temperature, atmospheric pressure, and relative humidity. The target variable is the output power of the PV power plant. Details are shown in Table 1. The quality of the dataset has a decisive impact on the accuracy of forecasting models. Therefore, it is particularly important to pay attention to missing value handling and dataset partitioning during the process of model optimization. To ensure the overall trend and consistency of the data, this study first employs linear interpolation to impute missing values during the data preprocessing stage. For outliers in each column, reasonable value ranges are defined based on actual physical meanings. Values exceeding these ranges are clipped to the valid interval, thereby improving the reliability of the data and the prediction accuracy of the model. Due to the fact that the operational intensity of photovoltaic systems is almost negligible during nighttime, the dataset contains sparse and uninformative data points for these periods. Such sparsity is detrimental to the performance of forecasting models. To address this issue, all nighttime data points were excluded from the dataset in this study. Specifically, only data collected between 6:00 AM and 8:00 PM were retained for subsequent experiments.For the 15-minute single-step setting, we align inputs and targets by shifting the PV power series by one step: the target at time t is the PV power at (t+1). This alignment is performed after nighttime filtering, and no future information is included in the model inputs. A total of 70,177 sampling points were collected from two years of photovoltaic data. The data were divided into four seasons according to the following scheme: spring (March to May), summer (June to August), autumn (September to November), and winter (December to February). The number of sampling points for each season was 17,666, 17,378, 17,467, and 17,666, respectively. To explore the relationships between meteorological and operational features and photovoltaic (PV) output power, this study employs the Pearson Correlation Coefficient for all numerical variables. The Pearson correlation coefficient measures the degree of linear correlation between two variables, with possible values in the interval ([-1,1]), where a value closer to 1 or (-1) indicates a stronger correlation. A positive value indicates a positive correlation, while a negative value indicates a negative correlation. Note that the correlation analysis is conducted for interpretability and exploratory understanding, rather than for feature selection. In particular, we retain all physically meaningful variables to support the subsequent variable-wise attention visualization of the iTransformer and to avoid excluding variables that may contribute through nonlinear interactions. The calculation formula for the Pearson correlation coefficient is as follows: where (x_i) and (y_i) denote the i-th observations of the two variables, (bar{x}) and (bar{y}) are their respective means, and n is the total number of samples. The correlation among features is visualized in the form of a heatmap, as shown in Fig. 5. Furthermore, the Pearson correlation coefficients between the main meteorological features and PV output power are listed in Table 2. Heatmap of Pearson correlation coefficients among main features. As shown in Fig. 5 and Table 2, PV output power (Power, MW) has the strongest correlation with total solar irradiance (Total solar irradiance, W/m(^2)), with a coefficient as high as 0.95. It also shows strong positive correlations with direct normal irradiance (Direct normal irradiance, W/m(^2)) and global horizontal irradiance (Global horizontal irradiance, W/m(^2)), with coefficients of 0.89 and 0.64, respectively. This indicates that irradiance is the dominant factor affecting PV output power. Air temperature ((^circ)C) has a correlation coefficient of 0.26 with output power, indicating a weak positive correlation. Relative humidity (%) shows a negative correlation with output power, with a coefficient of (-0.35). Atmospheric pressure (hPa) exhibits a very low correlation with PV output power, suggesting a limited linear association. Overall, irradiance-related features are the primary factors influencing PV output power, while temperature, humidity, and pressure provide complementary meteorological information. Final input features. In the forecasting experiments, the model inputs include total solar irradiance, direct normal irradiance, global horizontal irradiance, air temperature, atmospheric pressure, relative humidity, and historical PV power, while the prediction target is the PV plant output power at the next 15-minute step. This subsection describes the compared models and the unified hyperparameter tuning protocol used to ensure fair and reproducible evaluation. Compared models. We evaluate multiple forecasting backbones and their KAN/MKAN-augmented variants for 15-minute single-step PV power forecasting. KAN and MKAN are adopted from prior work; we implement their integrations with different backbones to form the compared variants. Specifically, we consider LSTM/GRU/BiLSTM/Transformer/xLSTM/iTransformer and their corresponding KAN- and MKAN-augmented versions (i.e., KAN-LSTM and MKAN-LSTM; KAN-GRU and MKAN-GRU; KAN-BiLSTM and MKAN-BiLSTM; KAN-Transformer and MKAN-Transformer; KAN-xLSTM and MKAN-xLSTM; KAN-iTransformer and MKAN-iTransformer). All models are trained and evaluated under the same input–output setting. Chronological split. To avoid look-ahead bias in time-series forecasting, we split the data in chronological order into a training set (80%), a validation set (10%), and a test set (10%). Specifically, the earliest 80% of samples are used for training, the subsequent 10% for validation, and the latest 10% for testing. The same temporal rule is applied within each seasonal subset. Grid search protocol. Hyperparameters are tuned on the validation set using a grid search with the following candidate values: learning rate in ({1times 10^{-2}, 5times 10^{-3}, 1times 10^{-3}, 5times 10^{-4}}), hidden dimension in ({32, 64, 128}), number of skip connections in ({1, 2, 3}), number of attention heads in ({2, 4, 8}), and convolution kernel size in ({3, 5, 7}). This yields (4times 3times 3times 3times 3 = 324) configurations. For model components where a hyperparameter is not applicable (e.g., attention heads for purely recurrent architectures), we keep that component at its default setting while tuning the remaining applicable parameters. The same tuning criterion (minimum validation loss) and training budget are applied to all models. Training and selection. Each configuration is trained for up to 100 epochs with early stopping based on the validation loss (patience = 10), and the checkpoint with the best validation loss is selected. The best hyperparameter setting is chosen according to the validation loss. Using the selected hyperparameters, we retrain the model on the union of the training and validation sets and report the final performance on the held-out test set. All experiments are conducted with a fixed random seed (seed = 42) to reduce randomness. All models are implemented in PyTorch and trained using the same pipeline to ensure a fair comparison. The input features are standardized using statistics computed on the training split only, and the same transformation is applied to the validation and test splits. The PV power target is kept in its original scale (i.e., no target normalization is applied). We optimize all models using the Adam optimizer and minimize the mean squared error (MSE) on the training set. The initial learning rate and other hyperparameters are selected via the validation-based grid search described above. We use mini-batch training with a batch size of 64. To improve training stability, gradient clipping is applied with a maximum norm of 1.0. Early stopping is performed based on the validation loss with a patience of 10 epochs, and the checkpoint with the lowest validation loss is selected. After hyperparameter selection, each model is retrained on the combined training and validation sets using the selected configuration, and the final performance is reported on the held-out test set using MSE, RMSE, MAE, and (R^2). All experiments are conducted with a fixed random seed (seed = 42) to reduce randomness. To comprehensively evaluate the prediction performance of the proposed MKAN-iTransformer and baseline models, four commonly used regression metrics are adopted: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination ((R^2)). The definitions are as follows: Mean Squared Error (MSE): Root Mean Squared Error (RMSE): Mean Absolute Error (MAE): Coefficient of determination ((R^2)): where (bar{y}) is the mean of the true values. A lower value of MSE, RMSE, and MAE indicates better model performance, while a higher (R^2) value (closer to 1) implies a better fit between predictions and actual values. In this section, we present and analyze the experimental results of the proposed MKAN-iTransformer model and various baseline methods on photovoltaic power forecasting tasks. The experiments are conducted under different seasonal. The performance of all models is evaluated using the metrics introduced previously. To systematically evaluate the impact of seasonal variations on model performance, we adopted the conventional monthly division method to classify the dataset into four seasons: spring (March–May), summer (June–August), autumn (September–November), and winter (December–February). This classification enables a more detailed analysis of the predictive capabilities of MKAN-iTransformer and baseline models under different seasonal conditions. Typical daily PV power curves for each month in 2019 and 2020. As shown in Fig. 6, the typical daily power curves for each month in 2019 and 2020 exhibit significant seasonal variations. The power output is higher in spring and summer due to abundant sunlight, while it is relatively lower in autumn and winter. These seasonal differences provide a solid foundation for the subsequent model performance analysis based on seasonal classification. To validate the effectiveness of MKAN-iTransformer, we conducted a detailed analysis of model prediction results and error distributions across different seasonal conditions. This section presents a comparative evaluation of MKAN-iTransformer and baseline models, highlighting both the accuracy and robustness of the proposed approach. Spring: single-day prediction curves and prediction error distribution. Summer: single-day prediction curves and prediction error distribution. Autumn: single-day prediction curves and prediction error distribution. Winter: single-day prediction curves and prediction error distribution. Figures 7, 8, 9 and 10 present typical-day forecasting results for spring, summer, autumn, and winter, respectively. For each season, the upper subfigure compares the predicted PV output power with the ground-truth measurements (black curve) over the daytime period (6:00–20:00 at 15-minute intervals), while the lower subfigure summarizes the corresponding prediction error distribution of each model. This season-wise “curve fitting + error distribution” layout allows an intuitive assessment of both temporal tracking ability (shape, peak timing, and ramping behavior) and statistical error characteristics (bias, dispersion, and tail behavior). In the spring case (Fig. 7), most models can capture the overall diurnal pattern, but noticeable deviations appear around rapid ramping segments and local peaks. The proposed MKAN-iTransformer shows a closer alignment with the ground truth during the main rising stage and peak region, and its error histogram is more concentrated around zero, suggesting reduced dispersion and fewer large-magnitude errors. For summer (Fig. 8), PV output typically exhibits a smoother and higher plateau under stronger irradiance conditions, making the dominant daily trend easier to learn. Accordingly, multiple models achieve relatively good tracking performance. Nevertheless, differences remain in reproducing sharp changes (e.g., abrupt drops and recoveries), where MKAN-iTransformer tends to maintain smaller deviations. The error distribution in summer is comparatively narrower for several models, indicating that the forecasting task is less challenging than in transitional or winter conditions. In autumn (Fig. 9), the ground-truth curve shows more frequent fluctuations and irregular ramps, likely due to increased variability in meteorological conditions. Some baseline models display either lagged responses or oversmoothing, leading to larger deviations during abrupt changes. MKAN-iTransformer provides more stable tracking across multiple fluctuation segments, and its error distribution shows reduced spread relative to many baselines, implying improved generalization to more volatile patterns. Winter results (Fig. 10) are the most challenging, as reflected by larger mismatches in several baselines and a visibly broader error spread in the histogram. The seasonal difficulty may be attributed to lower sun angles, shorter effective generation windows, and more frequent rapid variations (e.g., due to clouds and atmospheric conditions), which amplify both bias and variance in predictions. In contrast, MKAN-iTransformer remains closely aligned with the ground truth for most time intervals, and the error distribution remains comparatively concentrated, indicating stronger robustness under adverse seasonal conditions. Overall, across all four seasons, MKAN-iTransformer consistently achieves closer curve fitting and more compact error distributions, demonstrating improved accuracy and robustness. These observations are consistent with the quantitative seasonal metrics reported in Table 3, where MKAN-iTransformer achieves competitive or best performance on MSE/RMSE/MAE and high (R^2) in multiple seasons. Using MKAN-iTransformer as the main benchmark, we compare it with representative baselines (LSTM, GRU, BiLSTM, Transformer, xLSTM, and iTransformer) as well as KAN/MKAN-augmented variants on seasonal datasets. The quantitative results in Table 3 indicate that MKAN-iTransformer achieves the most consistent and competitive performance across seasons. In particular, it attains the best overall results in spring, autumn, and winter (covering MSE, RMSE, MAE, and (R^2)), while in summer it delivers the lowest MSE/RMSE and remains highly competitive in (R^2), although the best MAE is achieved by KAN-GRU. In spring, MKAN-iTransformer achieves the best performance across all four metrics, with MSE = 2.892, RMSE = 1.701, MAE = 0.864, and (R^2) = 0.947. Compared with LSTM, it reduces MSE/RMSE/MAE by 23.5%, 12.5%, and 20.5%, respectively, and improves (R^2) by 1.7%. Relative to GRU, MKAN-iTransformer reduces MSE by 13.7%, RMSE by 7.1%, and MAE by 13.4%, while increasing (R^2) by 0.9%. Against Transformer, the reductions are 7.4% (MSE), 3.7% (RMSE), and 11.5% (MAE), with a 0.4% gain in (R^2). These improvements demonstrate that MKAN-iTransformer better captures springtime ramping and peak behaviors, yielding both lower average error and improved goodness-of-fit. Summer exhibits different characteristics: MKAN-iTransformer achieves the lowest MSE (3.962) and RMSE (1.991) among all compared models, while the best MAE is obtained by KAN-GRU (0.951), and the highest (R^2) is achieved by xLSTM (0.924). Compared with LSTM, MKAN-iTransformer decreases MSE and RMSE by 3.3% and 1.6%, and slightly increases (R^2) (0.921 to 0.923). Compared with iTransformer, it yields a clear reduction in MSE (9.5%) and RMSE (4.8%) and improves (R^2) from 0.915 to 0.923. Although its MAE is not the best in summer, the advantage in MSE/RMSE suggests MKAN-iTransformer is particularly effective at suppressing larger deviations (which are weighted more heavily by MSE), while some models (e.g., KAN-GRU) achieve smaller absolute errors on average. In autumn, MKAN-iTransformer again provides the best results across all metrics (MSE = 2.884, RMSE = 1.698, MAE = 0.774, (R^2) = 0.962). Compared with LSTM, it reduces MSE/RMSE/MAE by 24.9%, 13.4%, and 27.4%, respectively, and improves (R^2) by 1.4%. Relative to GRU, it reduces MSE by 9.4%, RMSE by 4.8%, and MAE by 11.6%, with (R^2) increasing from 0.958 to 0.962. Compared with iTransformer, MKAN-iTransformer reduces MSE by 16.5%, RMSE by 8.7%, and MAE by 12.4%, while improving (R^2) from 0.954 to 0.962. These results indicate stronger adaptability to autumn’s higher variability and more frequent fluctuations. Winter is the most challenging season for many baselines, yet MKAN-iTransformer achieves the strongest overall performance with MSE = 1.721, RMSE = 1.312, MAE = 0.443, and (R^2) = 0.969. Compared with LSTM, it reduces MSE/RMSE/MAE by 71.4%, 46.5%, and 66.6%, respectively, and improves (R^2) from 0.891 to 0.969 (an 8.8% relative increase). Against Transformer, the reductions are 81.6% (MSE), 57.1% (RMSE), and 71.9% (MAE), with (R^2) increasing from 0.831 to 0.969. Compared with iTransformer, MKAN-iTransformer remains slightly better in error-based metrics (e.g., MSE from 1.730 to 1.721 and RMSE from 1.315 to 1.312) while maintaining the same (R^2). Overall, these results demonstrate that MKAN-iTransformer offers strong robustness under winter conditions, substantially reducing both average errors and large-error events relative to most baselines. This work focuses on evaluating the effectiveness of combining an iTransformer backbone with KAN-based modules. Note that KAN and MKAN are borrowed from prior work and are not proposed in this paper; our goal is to investigate whether integrating these modules with iTransformer yields complementary gains and improved robustness across seasonal distributions. Model variants. We compare four variants: (1) iTransformer, the backbone baseline; (2) KAN-iTransformer, which integrates a KAN-based (ekan) module into iTransformer; (3) MKAN-iTransformer, which combines MKAN with iTransformer (our main combination model); and (4) MKAN, the standalone MKAN model without iTransformer, included to distinguish the effect of MKAN alone from the fusion setting. All variants are trained and evaluated under the same experimental protocol. Metrics. We report MSE, RMSE, and MAE (lower is better) as well as (varvec{R^2}) (higher is better). To examine distribution shifts, results are presented for Spring, Summer, Autumn, and Winter. Results and discussion. As shown in Table 4, MKAN-iTransformer delivers the most consistent improvements across seasons. In Spring, it achieves the best results on all metrics, indicating clear complementarity between MKAN and iTransformer. In Autumn, MKAN-iTransformer again obtains the best overall performance, slightly outperforming KAN-iTransformer, suggesting that the multi-scale design provides additional benefit beyond directly integrating KAN. In Summer, MKAN-iTransformer yields the lowest MSE/RMSE and the highest (R^2), while iTransformer attains the lowest MAE. This indicates a trade-off between reducing larger errors (more reflected by squared-error metrics) and minimizing average absolute deviation; nevertheless, the improved RMSE and (R^2) suggest a better overall fit for MKAN-iTransformer. In Winter, iTransformer and MKAN-iTransformer are nearly identical, implying that the iTransformer backbone already captures the dominant winter dynamics and that MKAN integration does not introduce degradation. By contrast, KAN-iTransformer shows a pronounced performance drop in winter (MSE=7.082, (R^2)=0.872), indicating that this integration may be more sensitive to seasonal distribution shifts. Overall, these results support that MKAN-iTransformer is a robust and effective combination, whereas the gains from KAN-iTransformer are less stable across seasons. To explain the seasonal performance differences observed in the previous sections, we conduct an interpretability analysis of MKAN from three complementary perspectives. First, we decompose the PV power signal into hierarchical temporal components to isolate fast ramps, intermediate variations, and slow diurnal trends, and validate the separation in both time and frequency domains. Second, we inspect the learned KAN edge functions to understand how MKAN adapts its nonlinear transformations across seasons. Third, we visualize the inverted attention mechanism over features to quantify seasonal changes in feature importance, attention dispersion, and cross-feature interaction pathways. Together, these analyses form a consistent evidence chain from signal dynamics (multi-scale decomposition), to nonlinear representation (KAN activations), and finally to decision routing (feature-wise attention), clarifying why the model behaves differently under distinct seasonal atmospheric regimes. To capture PV dynamics from fast cloud-induced ramps to slow diurnal trends, the MKAN module decomposes the 15-min PV power series into three additive temporal components using hierarchical moving-average (MA) operators and residual (difference) bands. This formulation yields a physically consistent separation of high-, mid-, and low-frequency behaviors while preserving approximate additivity. Let P(t) denote the normalized PV power at 15-min resolution and let (textrm{MA}_m(cdot )) be an m-step moving average (centered window for analysis/visualization). We define: Thus, where (epsilon (t)) mainly captures boundary effects and minor mismatch. Figure 11 illustrates the multi-scale decomposition of PV power on a representative summer day. The decomposition separates the observed signal into three time-scale components, which helps interpret variability sources and motivates using scale-aware features in forecasting. High-frequency (45 min and below): rapid ramps and short-term fluctuations dominated by transient clouds and local turbulence, critical for short-horizon forecasting. Medium-frequency (90–180 min): intra-day variability related to evolving weather regimes and smooth changes in solar geometry. Low-frequency (180 min trend): slowly varying baseline reflecting the dominant diurnal envelope and seasonal irradiance level. Multi-scale temporal decomposition of PV power on a representative summer day. From top to bottom, the panels show the original signal and its high-, medium-, and low-frequency components. The low-frequency term captures the smooth diurnal envelope, the medium-frequency term reflects intra-day regime changes, and the high-frequency term highlights fast fluctuations. To validate that the proposed multi-scale decomposition indeed separates variability across time scales, we conduct a frequency-domain check using Welch’s power spectral density (PSD). Figure 12 reports the PSD characteristics of the decomposed components: the high-frequency residual (P_{text {high}}), the medium-frequency component (P_{text {mid}}), and the low-frequency trend (P_{text {low}}) for a representative summer day. We partition the frequency axis into three bands (in cycles/hour) to summarize spectral energy: Low-frequency band:(f < 0.1) (dominant diurnal/slow envelope and baseline variations). Mid-frequency band:(0.1 le f le 0.5) (intra-day variability and regime transitions). High-frequency band:(f > 0.5) (fast ramps and short-term fluctuations). For each component, the band energy percentages are computed by integrating its PSD over the corresponding frequency band and normalizing by the component’s total spectral energy: where denotes the Welch PSD estimate and is one of the three bands above. As shown in Fig. 12, (P_{text {high}}) allocates a larger portion of energy to higher frequencies, while (P_{text {low}}) concentrates energy in the low-frequency region consistent with the diurnal envelope. The medium-scale component (P_{text {mid}}) mainly captures intermediate-band energy, supporting the intended multi-scale separation. Frequency-domain validation (summer). The left column shows Welch PSD for (P_{text {high}}), (P_{text {mid}}), and (P_{text {low}}). The top-right panel compares PSD curves across scales, and the bottom-right panel summarizes the energy distribution over the predefined low/mid/high frequency bands. Overall, this hierarchical MA residual decomposition provides interpretable temporal bands and supports MKAN’s multi-branch design, reducing interference between fast ramps and slow trends. The seasonal consistency of this separation is further confirmed in Table 5. KAN replaces fixed activation functions (e.g., ReLU, GELU) with learnable univariate edge functions, making nonlinear transformations explicit and interpreable. We analyze learned activation patterns and relate their shapes to PV forecasting behavior across seasonal regimes. For an input feature (x_i) and output node (y_j), KAN learns an edge function (phi _{i,j}(cdot )) using cubic B-splines: where (B_k(x)) are spline basis functions, (c_{i,j,k}) are learnable coefficients, and K denotes the number of spline control points. A KAN layer aggregates edge functions as: This formulation allows each connection to learn a data-driven nonlinear mapping tailored to a specific input-output relation. Figure 13 illustrates representative learned KAN activations and their differences from standard fixed activations. In PV forecasting, asymmetric nonlinear responses are useful: suppressing low-power noise (e.g., dawn/dusk or heavy haze) while preserving sensitivity during normal operating conditions. Comprehensive analysis of KAN activation functions. The figure compares fixed activations with representative learned KAN activations and highlights how learnable nonlinearities adapt to different data regimes. Figure 14 shows season-specific learned activations, indicating that KAN adapts its nonlinearity to seasonal PV dynamics. Seasonal adaptation of learned KAN activation functions. Each panel shows a representative learned activation from seasonal data (colored) compared with a fixed baseline (gray). Shaded regions indicate deviation, highlighting season-specific nonlinear adaptation. We quantify seasonal differences using three metrics over a fixed input range: Table 6 indicates stronger nonlinear adaptation in more challenging regimes, supporting KAN interpretability: learned activation shapes reflect seasonal PV generation characteristics. MKAN adopts an iTransformer-style inverted attention mechanism operating over the feature dimension, enabling dynamic feature-to-feature interaction modeling. We visualize seasonal feature importance, attention distributions, and cross-feature attention pathways to interpret how meteorological variables contribute under different atmospheric conditions. Figure 15 presents normalized feature importance by season. Table 7 reports the corresponding scores (normalized to the maximum within each season), revealing clear seasonal reweighting between irradiance-driven and atmosphere-driven predictors. Seasonal comparison of feature importance scores. Bars show normalized importance of each meteorological feature within a season. Across seasons, DNI dominates in spring and winter, while GHI becomes most important in summer, reflecting stronger scattering/cloud effects. Autumn shifts toward atmospheric pressure and historical power, suggesting increased reliance on synoptic conditions and temporal persistence during transitional weather. Winter vs. summer shift: Compared with summer, winter assigns substantially higher importance to RH (+0.5742) and DNI (+0.4962), and also increases reliance on historical power (+0.3096). In contrast, GHI becomes less dominant in winter (–0.1831), consistent with reduced diffuse-driven regimes and stronger sensitivity to beam irradiance availability. Figure 16 shows attention weight distributions across features and seasons. Wider distributions indicate more frequent reallocation of attention, typically associated with more volatile atmospheric conditions. Seasonal attention weight distributions across features. F1: Total solar irradiance, F2: Direct normal irradiance, F3: Global horizontal irradiance, F4: Air temperature, F5: Atmospheric pressure, F6: Relative humidity, F7: Power. We summarize attention dispersion using entropy computed from mean attention weights: where (bar{w}_i) is the mean attention weight of feature i. Table 8 reports the attention entropy and the seasonal prediction performance (RMSE in MW). Higher entropy indicates more distributed attention (i.e., no single dominant feature), reflecting more frequent reallocation of attention across variables under volatile atmospheric conditions. Figure 17 visualizes seasonal cross-feature attention matrices. To highlight dominant interaction pathways, Table 9 lists the top-3 attention pairs (query (rightarrow) key) per season. Seasonal cross-feature attention matrices. Rows are query features, columns are key features. F1: Total solar irradiance, F2: Direct normal irradiance, F3: Global horizontal irradiance, F4: Air temperature, F5: Atmospheric pressure, F6: Relative humidity, F7: Power. These pathways are physically plausible: summer emphasizes humidity–irradiance coupling (cloud formation and scattering), while winter concentrates multiple queries onto DNI, indicating that beam irradiance penetration becomes a key bottleneck signal under haze/fog conditions. We further quantify attention matrix structure using diagonal dominance: and interaction diversity: Table 10 confirms a seasonal shift between distributed attention (higher I, lower D) and focused attention (higher D, lower I), consistent with changes in atmospheric conditions and feature reliability. This study has several limitations that should be acknowledged when interpreting the results. Single-site evaluation. All experiments are conducted on data from a single PV plant. While the seasonal split provides a meaningful within-site distribution-shift test, the cross-site generalization of MKAN-iTransformer (e.g., different climates, terrains, PV technologies, and sensor configurations) is not verified here. Daytime-only forecasting protocol. Nighttime samples are excluded (06:00–20:00) because PV generation is near-zero and the series becomes sparse and less informative for learning daytime dynamics. This choice improves training stability and focuses the evaluation on operationally relevant generation periods, but it limits applicability to round-the-clock settings. In particular, behavior during dawn/dusk transitions and full-day forecasting is not evaluated. Dataset size and coverage. The dataset covers two years and yields a moderate number of samples after filtering and seasonal partitioning. Although sufficient for 15-minute single-step forecasting, larger multi-year and multi-site datasets may expose additional failure modes, especially rare extreme-weather ramps. Lack of uncertainty quantification. This work reports point forecasting metrics (MSE/RMSE/MAE and ({R^{2}})) only. For grid operation and risk-aware scheduling, probabilistic forecasts (e.g., prediction intervals or quantiles) and calibration analyses are often required. Uncertainty quantification is not addressed in this paper. These limitations motivate future work on cross-site evaluation, round-the-clock and multi-horizon forecasting protocols, and probabilistic forecasting with calibrated uncertainty estimates. This paper studies robust and interpretable PV power forecasting under seasonal regime shifts and proposes MKAN-iTransformer, a cascaded hybrid framework that combines MKAN-based multi-scale temporal representation learning with iTransformer-style variable-wise attention for cross-variable dependency modeling. The model is evaluated under a unified protocol for 15-minute single-step forecasting with consistent preprocessing, hyperparameter tuning, and chronological splits within each seasonal subset. Seasonal accuracy and robustness. Season-wise benchmarking (Table 3) shows that MKAN-iTransformer achieves consistent and competitive performance across all four seasons. It delivers the best overall results in spring, autumn, and winter across MSE/RMSE/MAE and ({R^{2}}), and remains highly competitive in summer with the lowest squared-error metrics. The typical-day prediction curves and error histograms further support these findings by showing closer tracking during ramps and peaks and more concentrated error distributions, indicating fewer large-deviation events under seasonal variability. Component contribution validated by ablation. The ablation study (Table 4) isolates the effects of MKAN and iTransformer and confirms that their combination is beneficial. Comparing iTransformer, MKAN, and MKAN-iTransformer demonstrates that neither multi-scale temporal modeling nor variable-wise dependency modeling alone fully explains the observed improvements; rather, the gains arise from their complementarity. In addition, the inclusion of KAN-iTransformer reveals that not all KAN-style integrations are equally stable: KAN-iTransformer exhibits a pronounced degradation in winter, suggesting sensitivity to seasonal distribution shifts, whereas MKAN-iTransformer remains robust. Interpretability evidence. Beyond performance, we provide a coherent interpretability analysis from three perspectives: (i) a multi-scale temporal decomposition aligned with MKAN branches and validated in the frequency domain, clarifying how fast ramps, intermediate variations, and slow diurnal trends are separated; (ii) inspection and quantification of learned KAN univariate edge/activation functions, showing season-dependent nonlinear adaptations; and (iii) feature-wise attention visualization, demonstrating seasonal reweighting of meteorological drivers and physically plausible cross-feature interaction pathways. Implications. Overall, MKAN-iTransformer offers an effective balance among accuracy, seasonal robustness, and model transparency for short-horizon PV forecasting. The results indicate that coupling scale-aware temporal feature extraction with explicit inter-variable modeling is a practical strategy to mitigate seasonal degradation commonly observed in baseline architectures. Future directions. 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Kan-odes: Kolmogorov–arnold network ordinary differential equations for learning dynamical systems and hidden physics. Comput. Methods Appl. Mech. Eng.432, 117397. https://doi.org/10.1016/j.cma.2024.117397 (2024). ArticleMathSciNet Google Scholar Download references This research was funded by the National Natural Science Foundation of China, grant number 51967004. This research was funded by the National Natural Science Foundation of China, grant number 51967004. College of Electrical Engineering, Guizhou University, Guiyang, China Linjie Liu, Min Liu, Zhuangchou Han & HaiQiang Zhao North Alabama International College of Engineering and Technology, Guizhou University, Guiyang, China Min Liu Guizhou Provincial Key Laboratory of Power System Intelligent Technologies, Guiyang, China Min Liu Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar L.L. (first author) conceived the research idea, developed the MKAN-iTransformer model, implemented all experiments, and wrote the initial draft of the manuscript. M.L. (corresponding author) supervised the entire research process, provided key guidance on model design and result analysis, and substantially revised the manuscript. Z.H. and H.Z. contributed to data preprocessing, experimental support, and manuscript review. All authors have read and approved the final version of the manuscript. Correspondence to Min Liu. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Liu, L., Liu, M., Han, Z. et al. Interpretable ultra-short-term photovoltaic power forecasting with multi-scale temporal modeling and variable-wise attention. Sci Rep16, 10336 (2026). https://doi.org/10.1038/s41598-026-39797-6 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-026-39797-6 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Learning to Forecast for Better Scheduling: A Regret-Aware Framework for PV-BESS Optimization
view more Learning to Forecast for Better Scheduling: A Regret-Aware Framework for PV-BESS Optimization Credit: GREEN ENERGY AND INTELLIGENT TRANSPORTATION Researchers have developed a decision-aware forecasting framework for photovoltaic-battery energy storage systems, or PV-BESS, that improves how solar generation is predicted for day-ahead scheduling. Instead of treating forecasting and operational decision-making as two separate steps, the new approach trains the forecasting model to account directly for downstream scheduling objectives such as economic return and output smoothness. The result, according to the study, is a system that not only predicts well, but also helps the storage system operate more profitably and more stably. PV-BESS systems are becoming increasingly important as grids absorb more solar power. Pairing photovoltaics with battery storage can help offset the intermittency of solar generation, smooth power output, and support more reliable dispatch. But operating such systems well depends heavily on forecasting. If the forecast is inaccurate, the charging and discharging plan can be suboptimal, reducing arbitrage value and increasing fluctuations. In many existing systems, forecasting and optimization are still handled in a predict-then-optimize sequence, where the forecasting model is trained only to minimize statistical error and does not directly consider the practical impact of those errors on later scheduling decisions. The authors of the new study argue that this separation creates a structural weakness. A forecast that looks good according to conventional error metrics does not necessarily produce the best operational decision. In other words, the model may be optimized for prediction accuracy in a narrow mathematical sense, while the energy system itself cares about a different outcome: revenue, smooth dispatch, and reduced operational regret. To address that mismatch, the researchers designed a decision-aware training strategy that couples day-ahead photovoltaic power forecasting with downstream scheduling objectives. At the center of the framework is a surrogate decision model called the Regret Network, or R-Net. The purpose of R-Net is to estimate decision regret from simulated forecast-decision data and then feed that information back into model training in a differentiable form. This allows the forecasting system to learn not only from how close it is to the measured solar output, but also from how costly its forecasting errors become when translated into scheduling decisions. In effect, the forecasting model is taught to care about the real operational consequences of being wrong. The forecasting backbone itself is based on a Transformer architecture enhanced with numerical weather prediction information. The researchers then optimized the model using a hybrid loss function called ReMix, which is designed to balance two targets at once: statistical forecasting accuracy and decision performance. The highlights of the paper also note that R-Net is built as a CNN-LSTM surrogate model, allowing decision loss to be approximated efficiently rather than requiring a full downstream optimization loop at every training step. Together, these elements create a learning framework in which forecasting and scheduling are more tightly aligned. The study tested the approach on two real-world datasets, one from a centralized photovoltaic station in Daqing and another from a distributed system in Ningbo. According to the paper, the method reduced decision regret by as much as 19% and increased daily revenue by 3.8% while still maintaining high forecasting accuracy. These results matter because they suggest that the gains are not limited to abstract modeling improvements. Instead, they translate into measurable operational benefits, improving the economic value of PV-BESS scheduling while also contributing to more stable system behavior. The broader significance of the study lies in its challenge to a long-standing workflow in energy AI. For years, many machine learning systems in energy have been developed as if prediction and decision-making were separate problems. This paper suggests that for applications like solar-plus-storage scheduling, that separation can be inefficient. A model trained to minimize decision regret may deliver more useful operational outcomes than one trained only to minimize forecast error. That insight could influence not only PV-BESS scheduling, but also a wider range of energy applications in which forecasts are ultimately used to drive control actions, market bids, or dispatch plans. Further research will still be needed to understand how well the framework generalizes to additional market settings, battery configurations, and weather regimes. But the study provides a clear example of how decision-aware machine learning can improve both economic and operational performance in renewable energy systems. As solar deployment grows and storage becomes more tightly integrated into grid operations, methods that learn forecasting and decision-making together may become increasingly valuable tools for extracting more stability and value from clean energy infrastructure. Reference Author: Dayin Chen a b d, Guotao Wang a b d, Xiaodan Shi e g, Mingkun Jiang h, Shibo Zhu a b d, Haoran Zhang f, Dongxiao Zhang c d, Yuntian Chen c d, Jinyue Yan a b Title of original paper: Learning to Forecast for Better Scheduling: A Regret-Aware Framework for PV-BESS Optimization Article link: https://www.sciencedirect.com/science/article/pii/S2773153725001355 Journal: Green Energy and Intelligent Transportation DOI: 10.1016/j.geits.2025.100385 Affiliations: a Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China b International Centre of Urban Energy Nexus, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China c Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of Technology, 315200, Ningbo, China d Ningbo Institute of Digital Twin, Eastern Institute of Technology, 315200, Ningbo, China e School of Business, Society and Technology, Mälardalens University, Västerås, 72123, Sweden f School of Urban Planning and Design, Peking University, Shenzhen, China g Center for Spatial Information Science, the University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan h PV Industry Innovation Center, State Power Investment Corporation, 710061, Xi’an, Shaanxi, China Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system. Media Contact Ning Xu Beijing Institute of Technology Press Co., Ltd xuning1907@foxmail.com
WEATHER ALERT – ALL UH CAMPUSES ON OʻAHU CLOSED ON APRIL 10 All UH campuses on Oʻahu are closed Friday, April 10, due to severe weather conditions. Please monitor your @hawaii.edu email and UH RAVE Alerts for updates. Stay safe. A $14-million investment is set to transform the University of Hawaiʻi–WestOʻahu through the design and construction of a large-scale photovoltaic (PV) system paired with battery storage. This major renewable energy initiative will significantly expand the campus’s sustainability efforts while securing its energy future. The project will feature solar panel canopies installed over existing parking areas, transforming them into dual-use infrastructure that generates clean energy while providing shaded parking for the campus community. Planning and design are currently underway, with construction anticipated to begin in August 2026. Once completed, the system is projected to produce approximately 2.38 million kilowatt-hours of electricity annually, enough to power the equivalent of about 270 Hawaiʻi homes each year. The project is expected to support the UH System’s broader net-zero energy goals, contributing to both systemwide sustainability targets and supplying an estimated 50% of the net-zero energy needed to power the UH West Oʻahu campus. The next phase of the university’s efforts to become fully net-zero is the replacement of its chillers with new high efficiency units and control systems. That upgrade is planned for fiscal year 2027. The solar canopy will span multiple parking lots across campus and include an industrial-scale battery storage system designed to enhance operational resilience. In the event of a power outage, the system will be capable of supporting critical campus functions, an especially important feature within Hawaiʻi’s isolated island electrical grid. “The new PV system is designed to offset 100% of the campus cooling load, significantly reducing our dependence on imported fossil fuels while lowering greenhouse gas emissions,” said Miles Topping, director of energy management for the UH System. “Producing clean energy while providing shade just makes sense, it’s the right thing to do, and it also strengthens our resilience as a community.” All campus buildings at UH West Oʻahu are LEED-certified and incorporate energy-efficient systems, each supported by approximately 100-kilowatt solar installations. The campus also utilizes rainwater catchment systems for irrigation and benefits from proximity to public transportation, including on-campus bus and rail service. The project is being delivered through a combination of funding sources, including roughly one-third campus funding, one-third state capital improvement program funds, and one-third federal tax incentives. Project management is led by the UH Office of Project Delivery and the UH West Oʻahu Office of Planning and Design. The team also includes local industry partners Elite Pacific Construction and RevoluSun. UH Alumna and Designer Heading Back to Merrie Monarch Have a story idea or a question? Contact news@hawaii.edu If required, information contained on this website can be made available in an alternative format upon request. Get Adobe Acrobat Reader About Calendar COVID-19 Updates Directory Emergency Information For Media MyUH Work at UH English Gagana Samoa Kapasen Chuuk Lea faka-Tonga – Tongan Tiếng Việt ภาษาไทย Ilokano Tagalog Cebuano Kajin Majôl 简体中文 繁體中文 日本語 한국어 Español ʻŌlelo Hawaiʻi
Solar photovoltaics (PV) has long been heralded as a cornerstone of the global energy transition. With solar panels now a ubiquitous sight on rooftops and in open fields, it might seem that the technology has already reached maturity. However, as noted by professor Rebecca Saive of the University of Twente in the Netherlands, the author of a recent paper on the future of PV research, this perception is far from accurate. Get Premium Subscription In fact, as she and her co-authors argue, “fundamental” research in PV remains essential to address “unresolved” challenges in areas such materials science, device design, characterisation, reliability, recyclability and PV’s integration into the wider energy system. Without this continued focus, the industry risks stagnation at a time when its role in global sustainability and energy security has never been more critical. The paper, ‘The need for fundamental photovoltaics research to ensure energy security’, published earlier this month in the journal Progress in Photovoltaics, grew out of a discussion among members of the international PV research community at the 53rd IEEE Photovoltaic Specialists Conference in Montreal last year. A key motivation for their thinking, Saive explains, was recognition of an apparently declining interest in fundamental PV research. “We see that attendance and interest in PV conferences that address fundamental topics is declining (apart from the perovskite community),” she says to PV Tech Premium. This trend is compounded by challenges in securing funding, as agencies often question the relevance of fundamental research when solar is already so commercially well established. “The feedback is often, ‘Well, but we have solar panels, how is your research making them better, and why does this matter?’” Saive explains. This perception of PV as “old science” has also led to waning student interest, as they are increasingly drawn to emerging fields such as quantum technology. Yet, as Saive emphasises, this view is misguided. PV is far from a solved problem, the paper argues, and fundamental research is crucial to overcoming the bottlenecks that could hinder its future growth. The paper highlights several areas where fundamental research is urgently needed to ensure the continued growth and sustainability of the PV industry. Among these, the issue of critical raw materials stands out as particularly pressing for Saive. “We need to reduce our consumption and reliance on critical raw materials since this will be the ultimate bottleneck for a mainly solar-driven future and already causes supply chain risks at this point,” Saive says. This challenge requires a dual focus on increasing efficiency and yield while developing alternative materials to replace scarce or environmentally harmful materials. Other unresolved challenges include improving device design and characterisation to enhance performance and reliability, as well as addressing recyclability and system integration. This might include, for example, efforts to reduce the mismatch between solar production and seasonal demand, hybridise generation with agriculture, connect PV to long-duration storage and improve the resilience of energy systems. In the paper, Saive and her fellow authors use the historical analogy of the early days of electrification and communication. Then, power and data transmission lines were visibly strung across streets, whereas today energy and communication systems are largely hidden or delivered wirelessly. “In the same way, PV could evolve toward seamless, aesthetically integrated forms embedded into buildings, vehicles, and public infrastructure,” the paper says. “Realising this vision demands not only engineering progress, but also new scientific insights into materials, form factors, and system-level dynamics.” Such ideas, the paper says, illustrate the significant scope for further foundational PV research and the field’s continued relevance in shaping resilient and secure energy systems. One of the most promising avenues for innovation in PV research lies in interdisciplinary collaboration. “Technology leaps often come about from looking outside of your own research bubble and seeing if concepts from different fields can be married and create high-performance hybrid offspring,” Saive explains. She points to examples such as thermal photovoltaics, which are now being developed at an industrial level, as evidence of the potential for cross-disciplinary approaches to drive breakthroughs. The paper discusses other areas where PV intersects with different disciplines, such as agriculture (agrivoltaics), water conservation (floating solar), the circular economy (recycling and reuse) and social sciences (public perception and adoption). “These cross-cutting domains require a foundational understanding to enable transformative, context-aware solutions,” it notes. PV’s relevance to priorities beyond simply addressing climate change will also be a key message for its advocates to convey in today’s shifting landscape of funding and policy priorities, where the prevalence of stakeholders unconvinced of the climate emergency is growing. In some regions, government support for renewables research is being scaled back or re-prioritised, creating uncertainty for researchers and industry stakeholders alike. To counter this, Saive argues that the narrative around PV must emphasise its critical role in energy security. “Had we fully committed to transitioning 40 years ago, then no country could take a trade route hostage and cause global chaos to the extent it’s happening now,” she observes, referencing the global energy shock caused by the US-Israeli war with Iran. Highlighting the economic advantages of PV is another key strategy for securing sustained support. While fully depoliticising PV research may be impossible—given that energy security is inherently a political issue—framing it as an essential component of modern society can help ensure its prioritisation. With this in mind, the paper stresses the need for continued strategic support from funding bodies, recognising the critical role “foundational inquiry” plays in driving PV innovation. It highlights observations from attendees at the IEEE conference that 20% of fundamental effort can yield 80% of the key insights needed to inform the remaining engineering and implementation work. “This emphasises the absolute necessity for fundamental understanding to achieve progress. It is the same logic that drives the world’s most successful companies. Governments should adopt the same approach, recognising that sustained support for fundamental PV research is a strategic necessity rather than an optional expense,” the paper notes. The stakes for PV research could not be higher. Without progress in the areas outlined in the paper, the consequences could be severe. “The consequences are continuing to be at the mercy of energy prices and experiencing volatility and subsequent economic crises; to run out of materials or at least being exposed to severe supply chain risks; to suddenly create a society that does not possess the workforce to understand and operate existing technology … let alone advance technology to the next needed level,” Saive warns. Moreover, the failure to advance PV technology would accelerate climate change, a reality that, while politically contentious, cannot be ignored. Conversely, if the research community rises to the challenge, the potential benefits are transformative. “PV is globally the clean energy source with the biggest potential, followed by wind and, in some areas, hydro,” Saive notes. It’s spatially distributed nature makes it inherently resilient and equitable, offering a sustainable path to energy security for both developed and developing countries. The future of PV depends on the willingness of researchers, policymakers and industry leaders to invest in fundamental research and ensure PV technology can meet the demands of a rapidly changing world. By addressing the unresolved challenges outlined in this paper, the PV community can secure its place at the forefront of the global energy transition, driving progress toward a more sustainable and equitable future. As Saive concludes: “Basic research will ensure that we do this in a resource mindful way, that we diversify technologies to find the best match for each ecosystem, and probably most importantly: that we train the talent to understand, operate and advance our energy system.”
Searching for your content… In-Language News Contact Us 888-776-0942 from 8 AM – 10 PM ET Apr 10, 2026, 05:45 ET Share this article SHANGHAI and SCHORNDORF, Germany, April 10, 2026 /PRNewswire/ — Sigenergy, a leading energy innovator, is entering the market for utility-scale photovoltaic systems in Europe. Together with Baden-Württemberg/Germany based PV specialist Arausol, and the European distributor Memodo, it is building Germany’s largest PV plant with decentralized storage systems that operate on direct current (DC).
The project in Weissach im Tal is currently under construction and will have an installed peak PV capacity of 11.6 MWp and a battery capacity of 20 MWh. This capacity is distributed across 1,660 Sigenergy battery modules, each with a capacity of 12 kWh, which are securely installed in stackable SigenStacks. Unlike large-scale batteries, they are deployed in a decentralized manner. Installing SigenStacks on Arausol mounting structure—similar to PV module racks—is quick, easy, and safe, requiring no complicated cabling or the use of cranes or other heavy equipment. The solution thus avoids soil sealing, which is common in projects involving large central batteries housed in containers. DC Coupling and AI: more power, more renewable electricity and higher revenues Compared to AC-coupled systems, the system eliminates the need for multiple conversions between DC and AC. Instead, excess photovoltaic DC power is fed directly into the batteries and converted to AC via the inverters only when it is time to feed power into the grid. DC coupling thus increases the overall system’s efficiency—by at least 4%. It also eliminates the need for duplicate inverter infrastructure. The DC mode also allows Arausol to increase the output of the PV system, further enhancing the project’s economic viability. Less Need for Grid Expansion In comparison, AC-coupled systems have technical limitations. As a result, consistent use of DC coupling for large-scale PV projects would allow for a smaller-scale expansion of the power grids required for Germany’s energy transition. This would also help keep costs low for electricity customers. In addition to storage systems and inverters, Sigenergy is supplying Arausol with other electrical components, such as medium-voltage transformer stations equipped with pre-installed low-voltage connections. Memodo ensures reliable procurement through its delivery capability and market expertise. Arausol is responsible for the construction and project management, in addition to providing the substructures from its own facilities. Grid connection is scheduled for July 2026. Partnership for cutting-edge technology “This project sends a clear message: DC coupling enables utility-scale energy systems to be built faster, smarter, more efficiently, and in a more environmentally friendly way,” explains Emanuel Spahrkäs, Senior Account Manager at Sigenergy. “By combining Sigenergy’s unique DC-coupled solution with a decentralized battery architecture and Arausol’s easy-to-install mounting system, we achieve faster commissioning, higher performance, and lower operating costs.” Jaime Arau, CEO and founder of Arausol, said: “As a leading systems integrator and project developer for photovoltaic systems, we are committed to implementing the latest technology. Thanks to its innovative DC coupling, Sigenergy is an ideal partner for realizing this goal.” Memodo emphasized its strategic role in the project, highlighting early-stage collaboration and technology alignment. The company worked closely with the customer to define the system architecture and position Sigenergy as a suitable partner. “Our strength lies in actively bringing innovations to the market and supporting projects across the entire value chain,” said Jonas Hollweg, Head of Sales at Memodo. “The project underlines the potential of close and strategic cooperation between manufacturers, project developers and distributors in delivering advanced energy solutions.” SOURCE Sigenergy Nach der Inbetriebnahme seines 136.000 Quadratmeter großen Smart Energy Centers hat Sigenergy offiziell seinen ersten PV-Wechselrichter für… Après la mise en service de son Smart Energy Center de 136 000 mètres carrés, Sigenergy a officiellement lancé son premier onduleur photovoltaïque…. Utilities Electrical Utilities Construction & Building Environmental Products & Services Do not sell or share my personal information:
India has recently updated its carbon emissions goals. The latest policy plans to reduce the emissions intensity of its GDP as of 2005 by 47 percent by 2035. Its Nationally Determined Contribution — a figure every signatory to the 2015 Paris climate accords is expected to provide on a regular basis — now calls for 60 percent of its electric power capacity to come from non-fossil sources by 2035 and targets net-zero emissions by 2070. That is a far more ambitious target than a former world leader has set — or chosen to ignore entirely. Targets are all well and good, but achieving them is where the heavy lifting begins. In its latest report, Ember says “solar and battery storage can meet as much as 90 percent of India’s electricity demand at lower LCOE than the average power purchase costs in most states.” Kostantsa Rangelova. a global electricity analyst at Ember, said, “The dramatic improvement in battery economics over the past two years has delivered the missing piece that turns sunshine into reliable electricity day and night. For solar-rich countries like India, this makes the case for becoming a global solar superpower. The question is no longer whether solar can power India’s electricity system, but how quickly it can scale.” According to its analysis, Ember claims that plunging costs for battery energy storage systems (BESS) mean India could meet 90 percent of India’s electricity needs with solar and storage “at a competitive INR 5.06/kWh ($56/MWh).” Solar already plays a large and growing role in India’s power system, Ember says. It accounted for 9.4 percent of electricity generation in 2025, nearly double what it was in 2022. “Solar plays an important role during the day, meeting up to a quarter of demand during the sunniest hours of the day but none at night. “Installed solar capacity reached 143 GW in FY2025-26, up from less than 5 GW in FY2014-15, contributing to India’s broader goal of 500 GW of non-fossil capacity by 2030. Solar could play an even larger role in India’s electricity system over the longer term — especially with the help of cheap batteries.” In a report dated April 7, 2026, IEEFA said India’s target to reach 500 GW of renewable energy by 2030 and 60 percent non-fossil fuel in its energy mix by 2035…..will depend as much on structure of debt finance as on technology or policy.” The report finds that “India’s credit markets are already differentiating between clean and thermal energy assets, with consequences for company balance sheets that are hard to ignore. “India’s dependence on imported fossil fuels — for crude oil and LNG for power — leaves its economy acutely exposed to geopolitical shocks and supply disruptions, reinforcing the urgent need to accelerate transition. “The credit divergence between renewable and thermal assets is already visible across key financial metrics. Renewable-focused utilities enjoy stronger margins, thanks to zero fuel costs, broader access to offshore and international financing, and stronger interest from global institutional lenders. “Meanwhile, thermal-linked credits are being progressively shut out of international capital markets. All outstanding USD-denominated bonds from Indian power utilities are linked to renewable or hydro assets.” In a recent post on Substack entitled Night Into Day, Bill McKibben described the speed of the transition to battery storage technology. It begins with a chart by Nicholas Fulgham of Ember showing California’s source of electricity on March 29, 2026. McKibben wrote, “The huge yellow blob in the middle represents solar generation, the absolutely dominant source of supply from about 8 a.m. to 6:30 p.m. when it drops very quickly to zero. This is a phenomenon called sunset, which used to be the main argument against solar power. “But now look at the purple blob to its right. That’s battery storage coming online as the sun goes down. Those batteries spent the afternoon soaking up [cheap] sunshine and now they are distributing it back to the grid. “As Californians get home from work, turn on lights, cook dinner, start charging their EVs…..batteries are providing most of the power, outstripping imported power (much of which is renewable too), natural gas, and other sources like nuclear. You will notice wind picking up too, as the onshore breezes start to blow from the Pacific. “This is entirely different from how this graph would have looked even a year or two ago. Here’s how Fulghum explained it on Linked In: At 7pm, batteries reached 12.3 GW of output, meeting 42.8% of grid demand! “To put that kind of output during peak demand hours into perspective, it’s equivalent to the output from: “And it’s not just a short peak anymore. Batteries stayed above 20 percent of grid demand from 5.50 pm to 9.35 pm, almost four hours, and above. And here’s the thing: this has all happened in the blink of an eye. More than 90% of California’s battery fleet was built in the last five years. Total deployment is now over 17 GW, up from just 1.3 GW in 2020,” McKibben said. McKibben cites a report by Ben Payton of Reuters who says the race is on for “round-the-clock” solar power. He uses the example of a big project in the UAE, which is “combining the solar array with a massive amount of battery capacity. The aim is to store enough power generated during daylight hours so that a minimum of 1 GW of electricity is available 24 hours a day, 365 days a year. Sharp-eyed readers will note that the UAE is a petrostate, yet it is leaning heavily on renewables for the future. Chile is also pursuing large scale battery storage. It has 9 GW of storage capacity in operation, construction or testing, with a further 27 GW in the development pipeline, according to the industry association ACERA. “Chile is a very long country, so we rely very much on transmission to move energy from the north, where we have a lot of solar, and also from the very south, where we have a lot of wind,” said María Teresa Ruiz-Tagle, executive director of the Corporate Leaders Group for Climate Action (CLG) Chile. “So, to have battery storage projects in different points of the country could also help the system.” She added that storage is key to tackling the problem of the electricity grid being unable to absorb solar and wind power at times of peak generation. In 2024, 19 percent of all solar and wind electricity generated in the country had to be curtailed, which is a polite way of saying “wasted.” McKibben ends by saying, “Battery storage gives us some hope of liberating ourselves from that old energy storage medium — the barrel of oil — before more people die in the ugly wars being fought over its ownership. But of course that would challenge the power of the richest people in America, which is why our current government will keep funneling money to [the defense industry] instead. We have to make a huge choice about where to point our intelligence, our technology, our hopes. November 3 can’t come fast enough.” Amen to that. CleanTechnica’s Comment Policy Steve writes about the interface between technology and sustainability from his home in Florida or anywhere else The Force may lead him. He is proud to be “woke” and believes weak leaders push others down while strong leaders lift others up. You can follow him on Substack at https://stevehanley.substack.com/ but not on Fakebook or any social media platforms controlled by narcissistic yahoos. Steve Hanley has 6629 posts and counting. See all posts by Steve Hanley
You are currently accessing BusinessGreen via your Enterprise account. If you already have an account please use the link below to sign in. If you have any problems with your access or would like to request an individual access account please contact our customer service team. Phone: +44 (0) 1858 438800 Email: [email protected] Search BusinessGreen Search BusinessGreen You are currently accessing BusinessGreen via your Enterprise account. If you already have an account please use the link below to sign in. If you have any problems with your access or would like to request an individual access account please contact our customer service team. Phone: +44 (0) 1858 438800 Email: [email protected] Credit: Aldi Aldi has today announced plans to install solar panels at a further 62 of its UK supermarkets this year, which it said would mean more than 500 of its sites will have their own on-site clean power up and… To continue reading this article… In just a few clicks you can start your free BusinessGreen Lite membership for 12 months, providing you access to: Join now Login Study: EV charging offers 'lucrative opportunity' for commercial property owners 'The first thing I did was network – in person': How I shifted my career from tech startups to nature conservation
A new report by JMK Research says India installed around 44.6 GW of solar power capacity in FY 2026, an 87.2% increase year-on-year. NTPC REL’s Khavda project NTPC REL A new report by JMK Research says India installed around 44.6 GW of solar and 6 GW of wind capacity in FY 2026. Solar and wind installations increased by 87.2% and 45.6%, respectively, compared to the previous year. With this, India’s total installed renewable energy (RE) capacity reached 275 GW as of March 31, 2026. Solar energy accounts for around 55% (150.26 GW) of the total RE capacity. Wind energy follows with a 20% share, while large hydro contributes 19%. Bio power and small hydro contribute 4% and 2% of the total, respectively. From April 1, 2025 to March 31, 2026, India added around 34.8 GW of new ground-mounted solar capacity, marking a 106% increase compared to the previous year. The report attributes the significant increase in ground-mounted solar capacity primarily to the completion of projects initiated under the MNRE’s 50 GW annual bidding trajectory starting in 2023. The commercial & industrial (C&I) open-access segment (off-site C&I solar) made a substantial contribution to capacity additions during this period. The ISTS waiver deadline of 30 June 2025 also encouraged many developers to fast-track project execution in the first half of FY 2026. In the rooftop solar segment, about 8.7 GW was installed, marking a 69% YoY increase, primarily driven by the PM Surya Ghar scheme boosting residential adoption. Under the scheme, more than 26 lakh households have been covered and nearly INR 14,771 crore disbursed in central financial assistance, significantly accelerating adoption across the country. Rajasthan led the FY 2026 capacity addition with significant large-scale solar installations (12,140 MW, 35%), followed by Gujarat 8,952 MW (26%) and Maharashtra 6,177 MW (18%). Maharashtra recorded the highest rooftop solar installations with 2,144 MW (25%), followed by Gujarat 1,777 MW (20%) and Tamil Nadu 600 MW (7%). Maharashtra registered the highest off-grid solar capacity addition at 614 MW (58%), followed by Gujarat 77.9 MW (7%). This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Uma Gupta Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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Categories Original Shows About Categories Original Shows About The new solar farm marks the 25th nationally significant clean energy project approved by the UK since July 2024. The UK government just signed off on a significant new energy project that will change how the large, predominantly rural county of Lincolnshire gets its power. The Springwell Solar Farm officially received approval this Wednesday, and it’s a major deal for the country’s energy goals. Once it’s up and running, it will be the largest power-producing solar farm in the UK. To put its size into perspective, the developers say it could power over 180,000 homes every year. That is roughly half of all the homes in Lincolnshire. This project is the 25th major clean energy project approved by the government since July 2024. Combined, these projects represent enough electricity to power more than 12.5 million homes across the country. Advertisement There’s a practical reason behind this push for solar power. Recent global instability has shown how risky it is to rely on fossil fuel markets that Britain doesn’t control. When international markets get contentious, energy bills usually go up. Solar is currently one of the cheapest ways to generate electricity. By building more of it here, the goal is to create a more stable system that isn’t at the mercy of global price spikes. The government is also looking at other ways to get solar into the mix, like making panels a standard feature on new homes and speeding up auctions for future renewable projects. “We are driving further and faster for clean homegrown power that we control to protect the British people and bring down bills for good,” Energy Minister Michael Shanks explained. “It is crucial we learn the lessons of the conflict in the Middle East – solar is one of the cheapest forms of power available and is how we get off the rollercoaster of international fossil fuel markets and secure our own energy independence.” Explore More From Ammonia is projected to account for 46% of global marine fuel by 2050. The research could solve the mystery of the young age of Saturn’s rings and Titan’s unusual orbit. The design submission by Kengo Kuma and Associates was called “exemplary” and awarded the highest available score. George sends Greg to the ‘World of Innovation’ to explore the cutting-edge evolution of cardiac medicine, tracing the journey from invasive open-chest surgeries to the revolutionary pulsed field ablation system. MMXXIIITomorrows World Today ®| Developed by Flying Cork.
Bailiwick Express News Guernsey BE in the know – the latest news for Jersey and Guernsey The States of Guernsey are prepared to align with the UK’s government on the introduction of ‘Plug-in Solar’ units. The UK’s government recently announced that “plug-in” solar panels will be available in shops within months to help households reduce energy bills. Retailers like Lidl and Iceland are working to bring easy-to-install, plug-and-play solar kits to the UK market for use on balconies and small outdoor spaces. These systems connect directly to standard mains sockets, meaning there’s no need for any professional installation. The UK’s government claims the measure will help bolster the country’s energy security, and lower costs by reducing reliance on global fossil fuel markets. It’s a move which could be mirrored in Guernsey, with the Committee for the Environment & Infrastructure interested in the idea. But the committee President said it’s an idea which will have to wait for the UK Government to make the leap first. “In line with Guernsey’s Electricity Strategy, we’re supportive of any changes that can help increase the supply of electricity through solar PV and other renewables,” said Deputy Adrian Gabriel. “Plug in solar panels represent a lower cost, albeit at lower capacity, alternative to fixed installations. “Currently, plug in solar panels are not compliant with UK regulation, which is why they can’t currently be installed locally. However, once they are permitted in the UK they will be permitted in Guernsey as well.” The UK’s government is also pushing ahead with plans to introduce a mandate for most new-build homes in England to include solar panels and clean heating as standard. They’re also introducing a trial in Scotland, and in the east of England there’ll be discounted electricity on offer on windy days to utilise excess renewable energy that would otherwise be wasted. Locally, the President of the Development & Planning Authority says his team are actively removing barriers, recently eased rules for air source heat pumps, and are currently updating guidance to make solar installations even more straightforward. “As I mentioned in my recent update speech in the Assembly, we are working with other Committees and stakeholders on planning policy changes to encourage the provision of solar PV infrastructure and will be able to announce more on this shortly,” said Deputy Neil Inder. “At the moment, there are planning exemptions in place which enable solar PV and other renewable energy infrastructure in specified circumstances without the need for planning permission. Our intention is to make the installation of solar PV a mandatory requirement in the future when carrying out certain forms of development. “The DPA will update its published guidance within the coming weeks to provide clarity. This aligns with previous States’ Resolutions and reflects the direction of government policy elsewhere.” editor@bailiwickexpress.com
A decrease in FII (Foreign Institutional Investor) shareholding typically indicates that foreign investors are reducing their exposure to a particular stock. This may suggest a lack of confidence in the stock’s future performance, concerns about market conditions, or a shift in investment strategy. Based on shareholding disclosures available so far for the March 2026 quarter, we have highlighted nine stocks in the Nifty 500 segment that recorded a quarter-on-quarter decline of over 100 basis points in FII shareholding in Q4, according to StockEdge’s shareholding scan.
FII shareholding declined to 32.61% in the March 2026 quarter from 36.24% in December 2025.
FII shareholding declined to 44.05% in the March 2026 quarter from 47.67% in December 2025. FII shareholding declined to 13.38% in the March 2026 quarter from 15.1% in December 2025. FII shareholding declined to 2.45% in the March 2026 quarter from 3.97% in December 2025.
FII shareholding declined to 13.54% in the March 2026 quarter from 14.81% in December 2025. FII shareholding declined to 8.94% in the March 2026 quarter from 10.07% in December 2025. FII shareholding declined to 8.6% in the March 2026 quarter from 9.61% in December 2025.
FII shareholding declined to 42.62% in the March 2026 quarter from 43.54% in December 2025. FII shareholding declined to 7.31% in the March 2026 quarter from 8.19% in December 2025.
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Tokyu Corp. and partners will supply Tokyu Railway with newly built solar power under a corporate power purchase agreement (PPA), covering about 30% of traction electricity demand. Image: Snowscat, Unsplash Tokyu Corp. and partners have revealed plans to supply Tokyu Railway with solar power under a corporate PPA, in a move that underscores the shift toward new-build renewable procurement in Japan’s corporate power market. Tokyu Railway will procure electricity from around 98 MW (DC) of newly built solar power plants under a corporate PPA structure, with supply set to begin in fiscal 2026 and continue for 25 years. The transport and property group said the share of traction electricity Tokyu Railway expects to source from newly built solar plants under the procurement framework represents the highest adoption ratio among Japan’s major private railway operators, based on its own assessment as of March 2026. The projects will be developed by multiple special purpose companies (SPCs) backed by Tokyu Corp. and other partners at sites across Japan. The electricity will be used to support train operations on several train lines in the Tokyo metropolitan area, including the Toyoko, Meguro, Tokyu Shin-Yokohama, Den-en-toshi, Oimachi, Ikegami, and Kodomonokuni lines. The solar plants are scheduled to come online in phases from April 2026 through the end of fiscal 2027. By fiscal 2028, the company expects about 30% of its annual traction power consumption – approximately 110 million kWh of 370 million kWh – to be sourced from newly developed renewable energy assets under the PPA. The company has operated all train lines on 100% renewable electricity since April 2022 through retail electricity products. It said the new initiative will further support the build-out of additional renewable energy capacity and strengthen its role in supporting power infrastructure. Tohoku Electric Power Co. and Tokyu Power Supply Co. will jointly manage electricity procurement and supply under the arrangement. Tokyu Group and Tohoku Electric said they plan to expand collaboration on offsite corporate PPAs, solar development, and battery storage to support the growth of additional renewable energy and contribute to decarbonization. Japan’s railways are also testing perovskite solar on infrastructure. Central Japan Railway and Sekisui Chemical announced plans in January to pilot flexible perovskite solar panels on Tokaido Shinkansen noise barriers, signaling broader ambitions to integrate renewable generation across rail networks beyond procurement alone. Japan’s corporate PPA market has rapidly moved from niche to mainstream in recent years, with more than 500 publicly disclosed deals and over 2.5 GW of capacity by 2025. High fossil-fuel import costs, decarbonization pressure, and Japan’s shift from feed-in tariff (FIT) subsidies toward market-based mechanisms – including the feed-in premium (FIP) scheme – are pushing companies toward long-term procurement. Recent deals spanning rail, telecoms, retail, and heavy industry signal that large-scale and hybrid structures are now the market’s center of gravity. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Brian Publicover Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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Renewables Now is a leading business news source for renewable energy professionals globally. Trust us for comprehensive coverage of major deals, projects and industry trends. We’ve done this since 2009. Stay on top of sector news with with Renewables Now. Get access to extra articles and insights with our subscription plans and set up your own focused newsletters and alerts.
Apr 10, 2026 The Renegade Solar Energy Center is seen in October 2025. The facility, built by Invenergy and owned by UMERC, is now operational. (Courtesy photo) ST. NICHOLAS — A multi-year project to prepare land and install solar panels in rural part of the central Upper Peninsula is complete and now online, supplying a large amount of power to the electric grid. The Renegade Solar Energy Center became active and “officially reached commercial operations last week,” a representative for Invenergy told the Daily Press on Wednesday. Invenergy LLC, headquartered in Chicago, developed the project. The new facility belongs to Upper Michigan Energy Resources Corporation (UMERC), a subsidiary of WEC Energy Group that serves around 42,000 energy customers in Michigan’s Upper Peninsula. Solar power is an emissions-free mode of generating electricity, one of the ways companies are producing “clean” energy. UMERC previously took steps toward harnessing cleaner energy sources by providing natural gas-fueled generating stations in Negaunee and Baraga in 2019. “Having a mix of domestic power sources, including solar, helps keep costs stable and the lights on,” Invenergy said. Plots in Maple Ridge, Baldwin and Ewing Townships were identified by Invenergy as suitable for a solar farm when the area was scouted a handful of years ago. After finding landowners in favor of the project and ensuring legislation to allow solar farming in the area, the Renegade project began, using spaces both leased and purchased from local farmers. During development, Barton Malow was the primary solar construction contractor, MJ Electric worked on the substation, and Roy Ness Contracting & Sales worked on the operations and maintenance building. “A majority of the construction crew were Michiganders, including a significant UP workforce,” Invenergy told the Press. “Construction leadership included hometown talent from Eben Junction and Spalding areas.” In peak construction, Renegade aprovided 125 jobs. Now that the facility is operable, “the site will employ three full-time staff members and continue to serve as a long-term community partner,” a press release from Invenergy stated. Two of the three now employed at the station were local hires, while one moved to the area for the job. “Thanks to UMERC and our construction partners, we’re proud to have brought the Renegade Solar Energy Center online and ready to meet the ever-growing energy needs of Michigan communities,” said Sam Heagney, Invenergy developer. “Invenergy is excited to support American energy independence and we look forward to continued partnership in the Upper Peninsula.” Ewing Township Supervisor Dave Hall said, “Invenergy has been an exemplary partner, and I appreciate their team’s responsiveness and willingness to work with our community to bring Renegade Solar to life. The project is already helping bring in revenue for local improvements that will better our quality of life, which is especially important for a small town that doesn’t always have new sources of funding. I have been honored to support this team effort and look forward to being a part of Michigan’s energy future.” The three townships involved, as well as Delta County, received grants through the Michigan EGLE Renewable Ready Communities program totaling nearly $500,000 for a variety of community enhancements. In 2025, In 2025, Invenergy contributed more than $10,000 to local organizations including the Tri-Township Fire Department (which serves Ewing, Maple Ridge, and Turin Township), Rock Lions Club, Mid-Peninsula Wolverine Circuit Breakers (FIRST Robotics Team 7782) and Gladstone Area Public Schools, Invenergy shared. Renegade Solar Energy Center is a 100-megawatt facility that generates enough electricity to power more than 27,000 homes using American-made solar panels manufactured at Invenergy’s Illuminate facility in Ohio. “Michigan, along with most of the Midwest, is facing growing electricity demand from new industries and electrification of transportation and daily life,” said an Invenergy spokesperson. “Renegade Solar will contribute affordable, domestic power that helps meet that demand with no fuel costs, little to no water use, and no emissions.” Invenergy is also developing Superior Solar Energy Center in Marquette County.
All Things Considered is the most listened-to, afternoon drive-time, news radio program in the country. Each show consists of the biggest stories of the day, thoughtful commentaries, and insightful features brought alive through sound. From switching to LED light bulbs, to turning down your hot water heater, here are a few things you can do to save on your electricity bill. A meter shows energy produced by a photovoltaic system on the roof of a home in West Philadelphia. (Sophia Schmidt/WHYY) This story is part of the WHYY News Climate Desk, bringing you news and solutions for our changing region. From the Poconos to the Jersey Shore to the mouth of the Delaware Bay, what do you want to know about climate change? What would you like us to cover? Get in touch.
Electricity rates are high and expected to keep rising. PECO just asked state regulators to increase rates by 12.5%. A recent settlement by PPL Electric would increase rates by almost 5% this summer if approved by the Pennsylvania Public Utility Commission. A number of factors have combined to send electricity bills soaring in Pennsylvania, including the planned increase in data centers, rising supply costs as demand increases and infrastructure expansion. While ratepayers have little control over most of these factors, there are a few things consumers can do.
WHYY News’ senior climate reporter Susan Phillips joined WHYY’s Morning Edition host Jennifer Lynn to share quick tips to lower your bills. Jennifer Lynn: I like this topic a lot. I’m the kind of person who puts the shades down tight in the summer to block out the summer heat. You know, let’s start with some of this easy stuff. Our bills are going up. What can we do? Susan Phillips: First of all, if you still have incandescent light bulbs, it’s a good idea to switch to LED light bulbs. The Department of Energy says that this can save the average household $225 a year in electricity costs. Second, check the thermostat on your hot water heater. It should be set at 120 degrees Fahrenheit. So if it’s electric and set at 140 degrees, for example, the DOE says if you turn it down 20 degrees, that will save you up to $400 a year. JL: Well, that’s pretty good. What about heat and air conditioning? SP: So the DOE recommends a setting of 68 degrees Fahrenheit when you’re awake and 58 to 60 degrees when you’re asleep or not at home. Again, this is if you have electric heat. Most Pennsylvanians actually use gas to heat their homes. For air conditioning, the recommendation is 74 to 76 degrees, and a way to make things cooler is to add a fan. A cheap box fan that can circulate the air and cool things down further. JL: There are often the family wars, the temperature wars, right? We all know about that. So you got to navigate that too. What is the top user of electricity in my house? SP: Your refrigerator, which you can’t do much about because it has to run. However, experts say if your appliances like your refrigerator, washer, dryer or dishwasher are at the end of their life, it may be worth it to get a new one that is high efficiency. They encourage you to get as high-efficiency of an appliance as you can afford. And to do that, of course you look for that Energy Star rating. JL: It’s worth it if my appliance is really old and ready to just conk out. SP: That’s right. There is one exception though. If you have a window air conditioning unit, those will last forever, but apparently they become really inefficient over time. So it may be worth looking for a higher efficiency window unit, even if the one you have is not about to die. Your PECO bill could increase by $20 each month starting in 2027
The utility wants to raise its rates for electricity customers and suburban natural gas users. The CEO of PECO’s parent company, Exelon, made more than $15.6 million in 2025. 2 weeks ago JL: Let’s talk about weatherization, making sure the heat and the air conditioning are not leaking from the house. SP: Yes, very important. So you can hire a home energy auditor to assess your house, or Pennsylvania provides one for free if you are under a certain income level. Making sure there’s insulation in the crawl space below your roof is really important. And for the summer, especially in Philly, if you’ve got a row home, painting your rooftop white or silver helps a lot because those black tar roofs really absorb the sun’s heat. JL: So I know I have two different parts of my bill, Susan. There’s distribution and there’s supply. Is there anything I can do about the cost of supply? SP: Yes, Pennsylvania does allow customers to shop around for supply to try to get a better price than what you would get from your utility. Right now, the supply part of your bill, that supply cost, is also rising, and it’s simply supply and demand.
JL: OK, I’ve never shopped for supply. How does that work? SP: So one thing to note is that public and consumer advocates don’t recommend shopping for a cheaper supplier, and that’s because it’s easy to get ripped off. Suppliers may have things in the fine print that you didn’t read. So they may offer you a cheaper rate than PECO or PPL, for example, but it could be a variable rate. Then the next month it might jump up, and they might charge you a fee if you try to switch. I have spoken to people who do shop on the PA Powerswitch marketplace. They say the key is to use a calendar alert to make sure you go back to the online marketplace every month or so to check and see if what you signed up for is still a good deal. They have saved money this way, but they caution not to accept a variable rate and not to choose a supplier that has cancellation fees. JL: What’s another good thing to think about? SP: The best way to get rid of high bills is rooftop solar, which can have a high upfront cost and so it is prohibitive to many of us. But over time, rooftop solar can save you lots of money on your bills, obviously. Also, you can sign up for what is called time-of-use pricing as long as you have a smart meter. The way it works is you can save money by agreeing to run your high energy-use appliances like the dishwasher, the washer, the dryer during off-peak hours, and most utilities offer this. JL: I like that since I’m usually all about off-peak hours. I do a lot of things earlier and later than some people because of these crazy hours in the morning. Get daily updates from WHYY News! The free WHYY News Daily newsletter delivers the most important local stories to your inbox. WHYY is your source for fact-based, in-depth journalism and information. As a nonprofit organization, we rely on financial support from readers like you. Please give today. Philly-area MACH2 hydrogen ‘hub’ gets greenlight from Biden administration
India has become the third-largest country by installed renewable energy capacity, reaching 274.68GW, with over 150GW of solar PV capacity, according to statistics from the Ministry of New and Renewable Energy (MNRE). The country is now only behind China and the US in terms of cumulative total renewable energy capacity, and continues to expand. International Renewable Energy Agency (IRENA) statistics for 31 December 2025 said India had 250.62GW of total renewables generation capacity, while MNRE figures show a further 24.68GW added in the three months to 31 March. Get Premium Subscription The MNRE said it is still aiming to achieve Prime Minister Modi’s pledge to reach 500GW of renewable energy and nuclear capacity on India’s grid by 2030. Total solar capacity has increased by 53.28 times since 2014, the MNRE said, rising from 2.82GW in March 2014 to over 150GW in March 2026. It said that the current 150.26GW includes 110.43GW of utility-scale solar, 25.73GW of rooftop and 14.10GW of KUSUM & off-grid projects that support agricultural operations. The financial year (FY) 2025-26 saw a number of landmarks, the ministry said. It marked the highest solar capacity addition of any single year (44.61GW), more than double the previous record in 2024-25; this included the highest capacity additions in a single month (6.6GW) and the biggest expansion of distributed and KUSUM-backed solar projects (16.31GW). This expansion meant that non-fossil fuel sources produced 29.2% of India’s electricity over the course of FY 2025-26, with a peak of 51.5% in July 2025. A report from energy thinktank Ember published this week showed that co-located solar and energy storage projects could theoretically meet 90% of India’s power demand by using around one third of the total possible capacity the country could deploy. Last year also saw big expansions in India’s solar manufacturing sector. The MNRE said that around 98GW of solar module manufacturing capacity was added in FY 2025-26, bringing the cumulative total to 172GW. Indeed, module production facility announcements have come thick and fast in recent months, with expansions from major players like Waaree and Premier Energies alongside less recognised names. Market research firm Mercom puts India’s module production capacity even higher than the MNRE, estimating around 210GW of total module manufacturing capability. As a result, the MNRE said that Indian imports of solar modules have declined threefold between 2024-25 and 2025-26, from over US$2,152 million worth of products to around US$758 million. There is also growing upstream capacity. Mercom said cell production capacity reached 27GW in 2025, and efforts are planned to begin producing silicon wafers and ingots. Last month, Waaree began construction on a 10GW wafer and ingot plant in the state of Maharashtra, and the MNRE said it plans to begin an incentive scheme for ingot and wafer production under its Approved List of Models and Manufacturers (ALMM) programme in June 2026.
A new 238-acre solar farm capable of generating 40 megawatts of clean energy is coming to the North Alabama community of Triana following a unanimous vote of approval from the Huntsville City Council. Upon completion, the solar farm is expected to be the largest of its kind in the state's northern region. Huntsville's director of Urban and Economic Development, Shane Davis, provided detailed information on the property and solar farm during Thursday’s council meeting. "What we would do in partnership with Madison County and Huntsville Utilities is enter into a ground lease, so we're not selling the property," explained Davis. "The public remains the owners of the property of 237 acres to generate a new 40 megawatt solar facility. The lease term would be for 35 years. At the end of that term, we would have two options. A future council and a future county commission could renew, extend and continue to generate power there. If we chose not to do so, all the solar facility that would be on the property would have to be removed, and the property returned to its natural state. So there is a revenue stream attached to that." After the project's initial due diligence period is complete, it will enter its development phase. "Once construction starts, they have 14 months to complete and begin generating power," noted Davis. "During that construction period, we have asked for a partial payment of the annual lease in the amount of $23,796, and then upon completion and it generating power, there is an annual lease payment of $237,690 per year. We will split those proceeds 50-50 with the county." Chris Jones, Huntsville Utilities chief operating officer, spoke about the solar farm's projected positive impact on regional energy costs. "Energy costs that we will purchase will be lower than what we purchased from TVA," Jones revealed. "We also experienced demand savings. So we expect this will have some capital investment to build a substation, but we expect to pay back to be probably two years or less, and then it will be money savings after that." The Madison County Commission approved the solar farm project on Wednesday. The construction phase of the solar farm is expected to take under 14 months. To connect with the author of this story or to comment, email [email protected]. Don't miss out! Subscribe to our newsletter and get our top stories every day.
Please enable JavaScript to properly view our site. A portion of the 12,000 solar panels at a solar farm at Susquehanna University in Selinsgrove sits in the sun on Thursday, April 9, 2026. Sheep from Owens Farm in Sunbury arrive each spring, with two to three dozen grazing the site until late fall when they return home. The practice keeps the grass trimmed without the need for mowers. Support local journalism.Click here to learn more about the role the Lancaster County Local Journalism Fund plays in Lancaster County and to make a tax-deductible donation. Your browser is out of date and potentially vulnerable to security risks. We recommend switching to one of the following browsers:
0 Powered by : India-based solar cell and module manufacturer Premier Energies has secured new Q4 FY26 orders worth INR 2,577 crore for the supply of 1.6 GW. The report said the contracts were awarded by domestic independent power producers, module manufacturers, and EPC contractors in India, and execution has been scheduled over FY27 and FY28. The company said the fresh wins have been added to its existing order book as it continues expanding its manufacturing base. Premier Energies expects its solar cell capacity to climb to 10.6 GW by September 2026, while its module manufacturing capacity has recently reached 11.1 GW.
Through its official eufy Amazon storefront, Anker is currently offering its more advanced SoloCam S340 Wireless Outdoor Solar Security Camera for $119.99 shipped, matching the price directly from the brand’s website. Normally fetching $200 at full price, with some multi-cam bundle kits available on the same page at higher rates, this single-cam package has been regularly dropping to $160 over the last year, with occasional falls further. With the deal here you’re getting an even better 40% markdown from the going rate that saves you $80 at the all-time lowest price we have tracked, making it a great option to add to existing setups or to cover a very specific section of your property for added peace of mind. Head below to learn more about it and some of the other solar security solutions we’ve spotted this week. This Anker eufy SoloCam S340 security camera comes as a more advanced option from the popular SoloCam S220 cameras (currently down at $65), upgraded with an adjustable integrated solar panel that allows for greater sunlight absorption to constantly recharge its internal battery. It provides 3K to 4K video feeds alongside dual feed viewing, 360-degree pan/tilt, 8x zooming, and it can activate AI tracking when a HomeBase 3 is added to the mix. Installation is far less of a hassle, thanks to its small size and wireless design, only taking 7 minutes at most, according to Anker, as well as local feed storage without monthly fees or hidden costs. We’ve also seen two solid multi-camera packages benefiting from discounts this week, starting with the Baseus S1 Pro Wireless Outdoor Solar Security 2-Cam Kit that comes with a 16GB hub and sun-tracking solar panels to follow it throughout the day – down at a new $100 low. We also spied Autel’s 4-cam Outdoor Wireless Solar Security Camera kit falling to a new $180 low right now, too. FTC: We use income earning auto affiliate links.More. Subscribe to the 9to5Toys YouTube Channel for all of the latest videos, reviews, and more!
Kosol Energie has used air freight to sustain PV module production, as supply constraints pressure timelines across utility-scale and commercial and industrial (C&I) segments. Image: Kosol Energie From pv magazine India Kosol Energie has airlifted around 100 metric tons of solar cells to India, using a chartered cargo aircraft to support module manufacturing and project delivery timelines. The shipment arrived at Sardar Vallabhbhai Patel International Airport in Ahmedabad, Gujarat, as the company sought to avoid disruptions linked to global supply volatility and extended shipping timelines. The company said it opted for air freight over conventional sea transport despite higher logistics costs, prioritizing delivery speed and project execution. It added that the move was intended to maintain production continuity and meet customer commitments across utility-scale and C&I segments. Kosol Energie said the approach helped preserve commissioning schedules and reduce the risk of delays tied to supply chain uncertainty. The manufacturer and engineering, procurement, and construction (EPC) provider has executed 2.5 GW of projects across utility-scale and C&I segments and installed more than 40,000 solar systems. Kosol Energie is currently working on several projects, including a 250 MW solar installation for NLC India Ltd in Tamil Nadu, a 145 MW project for Coal India, and 109 MW under the PM-KUSUM program. It said it has supplied more than 3 GW of PV modules in India and international markets. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Uma Gupta Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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A solar farm has been granted a 15-year extension despite concerns it will impact the look of the neighbouring countryside. The site in Northamptonshire, located on a field to the south of the Eckland Lodge Business Park between Braybrooke and Desborough, was originally granted a 25-year limited lifespan. Despite concerns about the visual impact, North Northamptonshire Council's planning committee approved the extension. It means the site will run until 2057, instead of 2042, at which point it will be decommissioned. The site was first approved in 2015 and became operational two years later. Eckland Lodge Business Park submitted plans for the extension with conditions that the land be reinstated when the site is decommissioned, reports the Local Democracy Reporting Service. The council's planning committee heard the original time limit was imposed as the operational life of the solar panels was incorrectly thought to have been around 25 years. The applicant has now said that, with proper maintenance, the site could be operational for 40 years and that decommissioning it any earlier would be "wasteful". However, Braybrooke Parish Council objected, saying that extending the life of the solar park would be a "material shift from temporary to long-term land use — prolonging a non-agricultural, industrialised visual character". They also raised concerns that the extension could set an "unintended precedent" that long-term energy infrastructure is acceptable in rural locations. Do you have a story suggestion for Northamptonshire? Contact us below. Follow Northamptonshire news on BBC Sounds, Facebook, Instagram and X. Dame Andrea Jenkyns is "deeply disappointed" the government approved Springwell Solar Farm. The Springwell solar farm in North Kesteven would cover an area the size of 1,700 football pitches. If approved, the solar panels will generate up to 735 kW of electricity, enough to power hundreds of homes. The plan for Withington, near Hereford, includes solar panels, a substation and a weather station. A States vote means the default position on solar farms on agricultural land will be to refuse them. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking.
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