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Australian scientists swapped solar panels for plastic mirrors and heated them to 754°F — Then something unexpected happened  energiesmedia.com
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“The lungs are particularly vulnerable.”
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New research has uncovered the dangerous health consequences of breathing in tiny plastic particles. 
To date, little is known about the health effects of inhaling microplastics. Now, a University of Technology Sydney-led study suggests that inhaling these tiny plastic particles can lead to lung inflammation and damage. The scientists say this could increase the risk of respiratory diseases such as lung cancer, chronic obstructive pulmonary disease, asthma, and pulmonary fibrosis.
“The lungs are particularly vulnerable to microplastic damage due to their large surface area and limited ability to clear particles, particularly smaller ones that travel deep into the lungs,” lead author Dr. Keshav Raj Paudel said in a press release. “… Different plastics also have varying degrees of toxicity. For example, polystyrene microplastics can stick to the lungs’ protective coating, disrupt air sac function and trigger chemical reactions that may damage lung tissue.”
This is one of a handful of studies that have analyzed the health impacts of airborne microplastics. For instance, one group of scientists recently spoke out about the potential for airborne microplastics to serve as virus carriers. 
Meanwhile, another study helped to quantify the number of microplastics floating around — those scientists estimated that land sources of plastic release about 600 quadrillion (600,000,000,000,000,000) particles into the atmosphere every year, which is 20 times more than the particles contributed to oceans.
Other researchers are delving into how microplastics might affect us once they enter our bodies. So far, studies suggest ties to cancer, reproductive issues, dementia, and other serious health problems.
In order to help slow down the number of new microplastics that are entering the environment, and ultimately, our bodies, it’s important to cut down on single-use plastics. This can be accomplished by making simple changes, such as carrying a reusable water bottle or bringing your own takeout containers when you eat out. 
Scientists have also discovered a few promising methods to remove what’s already out there. One group of researchers used egg whites to remove microplastics from ocean water.
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EDP Renewables North America brings 150 MW Pleasantville solar facility in Illinois into completion  Energies Media
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This summer, more than a dozen Burrowing Owls hatched in an unusual habitat: in plastic underground tunnels within the footprint of a sprawling, 10,000-acre solar energy complex outside of Phoenix, Arizona. The owlets played with clods of dirt and were curious about the camera that monitored their movement. Their parents—around the size of small bread loaves atop stiletto legs—scared away kit foxes, coyotes, and roadrunners that attempted to steal the nutritious mice that a human overseer delivered each day.
Installed by the company Longroad Energy and the raptor rehabilitation center Wild at Heart, the setup was an experiment to help a struggling population. In March, nine owl pairs (and one bachelor) were caravaned from a housing development about 50 miles away, where the colony’s natural tunnels were slated to be wiped out, to 40 artificial burrows on the solar farm. Their question, says Greg Clark, Burrowing Owl habitat coordinator at Wild at Heart, was: Would the relocated birds successfully reproduce in this new locale, surrounded on all sides by solar panels?
While such a place might not seem like an ideal breeding site, these birds of prey are running out of options. Since the 1960s, their numbers have dropped by more than a third in the United States, and the U.S. Fish and Wildlife Service lists them as a species of conservation concern. In the West, the owls typically occupy tunnels dug and abandoned by small mammals like ground squirrels and prairie dogs, but housing and agricultural development, habitat fragmentation, and other threats have made such sites increasingly scarce. 
When a Burrowing Owl colony is about to be paved over or otherwise disturbed, scientists and conservation groups like Wild at Heart have established practices for trapping and moving the birds to safer areas. Typically, Clark will choose relocation sites near irrigated farmland, where the birds can hunt insects and rodents that scurry between crops. But agricultural land in the West is also waning as it’s bought up for more profitable development. 
“We’ve run up against the limit now,” Clark says. Today, he ends up driving owls to sites 60 to 100 miles from where they originally nested—a task he says is too costly and time-consuming to sustain in the long term. But Burrowing Owls aren’t too picky about where they live and don’t require a lot of space, so even small portions of land are helpful. “We can do this at spotty locations,” he says. 
That’s where the collaboration with Longroad Energy came in. In recent years, utility-scale solar energy has rapidly expanded across Arizona’s flat, sunny expanses. On one hand, the industry’s growth adds to the challenge of dwindling habitat for sensitive desert wildlife, says Tice Supplee, Audubon Southwest’s former bird conservation director and a current consultant. On the other, she says, renewable energy projects that help reduce emissions can slow climate change—a threat that put hundreds of U.S. bird species at risk. Many conservationists are looking to help companies find a balance: a way to build clean energy, while also making space for wildlife.
“That’s a pretty big land use change, and it’s important to see where we can maximize energy production, but also maximize benefits to wildlife, their habitats and ecosystems,” said Josh Ennen, senior scientist for solar at the nonprofit Renewable Energy Wildlife Institute (REWI). 
Arizona law requires a wildlife survey before energy projects are built. At the Sun Streams 2 project site, which Longroad Energy acquired from the company First Solar in 2021, the survey revealed eight Burrowing Owls, says Deron Lawrence, Longroad’s vice president of environment. They called Wild at Heart, which ferried the owls to a farm to keep them safe—and that’s when Lawrence also learned Wild at Heart was running low on relocation space. 
After construction was complete, Lawrence wondered if they could welcome the species back. Longroad offered the group around 250 acres nestled between Sun Streams 2 and Sun Streams 3 for its Burrowing Owl relocation work. Clark jumped at the opportunity. He had rehomed owls to solar farms before—but never at this scale. Both hoped to show that a solar site with the right habitat could be a safe place for a colony. They got to work installing burrows and placing a breeding pair in each one (at first surrounded by netting so they wouldn’t fly back to their former home). They also set up lights to attract insects and fed the owls defrosted mice daily as they adjusted to their new digs. 
For this experiment, the company funded monitoring and feeding through the breeding season. (Normally, Wild at Heart only has the capacity to do this for 30 days.) One month in, all the females had laid eggs. “This is working,” Clark said. By late June, 36 chicks hatched. Within three months, 29 owlets had fledged, and Clark slowly weaned the birds off their meal deliveries to encourage the birds to start hunting on their own. He began seeing insect carcasses in their scat—an encouraging sign. At the end of August, Wild at Heart stopped feedings altogether.
In building the Sun Streams solar complex, comprised of several projects, energy developers also had to consider threats to declining desert songbirds called thrashers. Knowing the area overlapped habitat for four thrasher species—Bendire’s, Sage, Crissal, and LaConte’s—conservation groups including Audubon and the Maricopa Bird Alliance, a local Audubon chapter, advocated for setting aside thrasher habitat during its planning. In 2021, however, Maricopa County denied the Alliance’s request to protect key areas through zoning restrictions, since none of the species were endangered. Nevertheless, the Audubon chapter continued to work on behalf of the birds. The group partnered with field biologists to conduct a thrasher survey in 2021 and presented the results to First Solar, the owners of the site at the time. 
Although the Alliance didn’t get all the land set aside that they wanted, says Mark Horlings, the chapter’s former board member, the ultimate project included dedicated wildlife corridors with habitat for thrashers, as well as openings in its fences to let small animals pass through and protections for nests during construction. Unofficial data since then have found thrashers still present, although the group hasn’t carried out a more formal survey. 
As solar energy expands in the Southwest, scientists, companies, and government officials are also looking at the bigger picture to help solar developers minimize their impacts—for example, by avoiding key habitat or migratory routes, creating vegetation between clustered solar arrays, and avoiding construction during breeding season. Arizona is drafting solar development guidelines to help protect wildlife like Burrowing Owls, “so that they can remain on the landscape and not be moved super large distances,” says Kenneth Jacobson, raptor management coordinator at Arizona Game and Fish. 
In a 2025 study, Arizona State University researchers mapped out where ideal utility-scale solar sites—flat, sunny areas with easy plug-in to local energy grids—overlap with important habitat for sensitive species throughout California, Nevada, Utah, Colorado, Arizona, New Mexico, and Texas (the study found, for example, prime solar sites have a 13 percent overlap with Burrowing Owl habitat in these states). The nonprofit REWI is also currently developing a database to help the solar industry access information on biodiversity when deciding where to site projects. Meanwhile, a research and advocacy coalition, the Desert Thrasher Working Group, has also created guidelines for solar design to mitigate impact to thrashers. 
At the Longroad relocation site, the Burrowing Owls still appear to be doing well. Most of the young have fledged and dispersed, which is typical, while three pairs of adults remain. Clark has a grander vision going forward: working with solar developers to actively grow vegetation to attract insects and rodents for Burrowing Owls to eat, providing long-term habitat. As of now, Lawrence says he doesn’t have plans to do that, but he is talking with Clark about a potential design for a solar panel that would capture water from the humidity in the air to help grow vegetation. 
Clark hopes he can work with more solar companies, which have funding and motivation to help Burrowing Owls, to continue to protect the birds he loves. “If we can convince enough solar farms to buy in on this,” he says, “that’s what we’re gonna do.”
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Sunday, April 5, 2026
A company headed up by one of the legends of Australian solar research and development has won more than $150 million in federal Solar Sunshot funding to build a commercial-scale PV panel manufacturing plant in one of the nation’s biggest coal hubs – the New South Wales Hunter Valley.
The federal and NSW Labor governments on Tuesday announced a $171 million co-investment in the Hunter Valley Solar Foundry project, an initiative of the Sunman Group, to build a factory that will produce Australian-made PV modules for local and export markets.
Sunman’s founder, Zhengrong Shi, is a graduate of Australia’s University of New South Wales and was once dubbed the “Sun King” for his key role in PV innovation as well as in founding Suntech – once one of the world’s biggest solar companies before its collapse.
For the past decade, Shi has been working on the commercialisation of his new company’s flexible solar panels, a lightweight and bendy form of solar PV using a polymer-based “skin”, sold by Sunman under the brand of “eArc” panels.
Sunman already has two large manufacturing facilities – a 1 GW facility in China unveiled in 2022, and a smaller 500 MW facility that is under construction in Indiana in the US. But last year, Shi revealed his company’s plans to build a big manufacturing centre in Australia.
“We went to China to build first company Suntech, and there were many comments about why Australia didn’t support Dr Shi and let him go to China,” Dr Shi told Renew Economy in an interview almost exactly one year ago.
“Perhaps it was too early to do it here. But I think now it is time to come back and build this.”
This time around, support is forthcoming, with $151 million in conditional funding under the federal government’s $1 billion Solar Sunshot program and $20 million from the New South Wales government’s Net Zero Manufacturing program.
Arena, which administers Solar Sunshot grants, says the money will go towards building a 500 megawatt (MW) per annum manufacturing facility that will produce Sunman’s lightweight solar panels, as well as glass solar modules, using local materials and suppliers.
The Hunter Valley Solar Foundary, as the facility is being called, will also provide production capability to other manufacturers and is expected to produce a total of 800,000 solar modules a year.
It will also create around 200 jobs during construction and more than 100 ongoing positions once the plant is at full capacity. An advanced manufacturing training program is also being explored in collaboration with TAFE NSW, alongside a First Nations recruitment strategy and scholarship program. 
Image supplied, Hunter Valley Solar Foundry
“It’s right and proper that the Hunter, which has powered Australia for so long, will be centre of our Future Made in Australia,” federal energy minister Chrid Bowen said on Tuesday at the site where the new facility will be built as part of the Hunter Business Park at Black Hill.
“We invented the modern solar panel. We store solar panels. We’ve been missing, by and large, the middle part of manufacturing solar panels.”
Arena CEO Darren Miller said the project is a “clear demonstration” of Solar Sunshot’s mission, but also marks a homecoming for Dr Shi who began his solar journey in Australia.
“Solar Sunshot is about building on Australia’s world-leading solar research to expand manufacturing capacity, strengthen supply-chain resilience and grow local jobs and skills,” Miller said on Tuesday.
“The Hunter Valley Solar Foundry project reflects these goals, bringing together advanced technology, local workforce development and long-term economic benefits for regional communities.
“Building our manufacturing capabilities will help ensure that our supply chains are resilient and Australian innovations are supported as we accelerate the rollout of solar PV.”
Dr Shi said he was “proud” to bring commercial-scale renewable manufacturing to Australia.
“Once established, the Hunter Valley Solar Foundry will be the largest manufacturer of solar photovoltaic modules in Australia, and the only one in NSW, delivering world-leading products to residential, commercial and utility customers around the country,” he said on Tuesday.
“As a proud Australian and a solar expert trained at the University of NSW, it has been my long-held ambition to establish solar module manufacturing in Australia, and it is my hope that over time the Foundry supports the foundation of a vertically-integrated solar supply chain in Australia. 
“This is an important milestone in Australia’s energy transition,” Shi said.
Image supplied, Hunter Valley Solar Foundry 
Federal Labor first announced its Solar Sunshot policy in March 2024, promising $1 billion in production subsidies and grants to build a solar supply chain on Australian soil.
Australia “should not be the last link in a global supply chain built on an Australian invention,” prime minister Anthony Albanese said in a speech delivered from the then recently closed Liddell coal-fired power station in the New South Wales Hunter region.
Round 1A – launched in September 2024 and now closed – dedicated up to $500 million to support module manufacturing, while Round 1B set aside $50 million for feasibility and engineering studies, and remains open until November 2026.
A second round, is offering a share in $150 million to manufacturers of module frames, glass, junction boxes and deployment technologies, after opening to submissions in September.
Currently in Australia, domestic solar panel manufacturing is limited to one company: South Australia-based Tindo Solar, which in August won $34.5 million in the first round of Solar Sunshot funding to help deliver a huge scale-up in production – from 20 megawatts (MW) a year to 180 MW.
The funds, delivered via a Manufacturing Production Credit (MPC) and a capital grant, are being used to renovate and ramp up production at Tindo’s Mawson Lakes factory and expand its product range to include premium N-type modules. They will also support a feasibility study for the development of a future Gigafactory, capable of producing up to 1 gigawatt (GW) of modules a year. 
The money for Tindo was part of a $45.5 million tranche of funding from the Sunshot Program, with a further $11 million awarded to support feasibility studies for upstream solar manufacturing.
Of the $11 million, $4.7 million was awarded to Stellar PV to test the waters on building a 2 GW low-emissions polysilicon ingot pulling and wafering facility close to Townsville in Queensland.
Another $5 million was granted to Solquartz for its Townsville Green Polysilicon Feasibility Study into a 100,000 tpa low-emission, solar-grade polysilicon production facility, also close to Townsville.
Another of Australia’s biggest domestic solar manufacturing hopes, SunDrive, is yet to get a share in the Sunshot funds, but last month won another $25 million in separate Arena funding to scale and commercialise its copper-based PV technology and edge closer to the goal of ultra-low cost solar.
SunDrive in 2024 applied for a share in the first round of Sunshot funding in partnership with Chinese PV giant Trina Solar, putting forward a proposal to set up a module manufacturing plant in Western Sydney, with an initial production capacity of 1.2 gigawatts (GW).
Bowen told reporters on Tuesday that said Arena was still in discussions with SunDrive on the companies’ Sunshot application.
“We’ll have more detailed announcements when they’re ready. Quite separate to today’s announcement,” the minister said.
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Sophie is editor of Renew Economy and editor of its sister site, One Step Off The Grid . She is the co-host of the Solar Insiders Podcast. Sophie has been writing about clean energy for more than a decade.
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The layout of the proposed Globemallow solar project near Stanley.
The location of the proposed Globemallow solar facility in Santa Fe County.
A map showing the location of the proposed Globemallow project.

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The layout of the proposed Globemallow solar project near Stanley.
The location of the proposed Globemallow solar facility in Santa Fe County.
Santa Fe County is weighing another utility-scale solar and battery energy storage development, which with more than 700,000 solar panels, would be one of the largest projects ever proposed in the state.
A Thursday meeting on Linea Energy’s Globemallow project just north of Stanley in southeastern Santa Fe County showed much less resistance and interest than the Rancho Viejo solar project. Only a handful of people attended a Sustainable Land Development Code hearing officer meeting about the proposal. By contrast, the controversial Rancho Viejo project saw hundreds of people turn out to marathon meetings.
The San Francisco-based company plans to sell its power to Public Service Company of New Mexico and aims to produce a stunning 350 megawatts — enough electricity to power an estimated 93,000 homes annually, Linea Energy officials have said — on some 2,000 acres of agricultural and ranch land. The proposal has drawn excitement from clean energy advocates.
“Projects like this show that we don’t have to choose between economic development and protecting Mother Earth because we can do both,” said Emmet Yepa of the Semilla Project, a nonprofit advocacy and leadership development organization.
Globemallow would not be the largest solar farm in the state, but it would be close. The Atrisco solar and battery energy storage project in Rio Rancho generates about 365 megawatts of energy.
The Globemallow project would be 250 to 300 containers as part of the battery energy storage system, along with 761,904 solar panels, Linea officials wrote in a previous email.
Detractors of solar arrays with battery energy storage systems often argue such projects pose risks of thermal runaway fires and affect property values. However, proponents say new technology dramatically reduces risks posed by such facilities.
The Globemallow project did not receive any direct opposition Thursday, but some Stanley-area residents raised concerns about water issues facing those who live in the troubled Estancia Basin.
“As a Stanley resident, my concern is water,” said Cindy Golden Arnold. “We have people whose wells are going dry, and we have no water systems to hook up to where we are at. … If we don’t have water, it doesn’t matter what energy we have.”
The Globemallow Solar Project is estimated to use 67.4 acre-feet of water during construction of the project, according to Linea Energy’s conditional use permit application. That’s the equivalent of about 22 million gallons of water.
The application points out various potential sources for the water, including Entranosa Water Association potable water stations and the Santa Fe County bulk water dispensing facilities.
“Water during construction will be used for dust control, compaction, equipment washing, and general construction activities,” the application reads.
Linea Energy officials noted they are not seeking to obtain any new water rights for the property, at 4234 N.M. 41, other than what is tied to it currently.
Consideration of the new project follows the Santa Fe County commissioners’ August approval of the controversial Rancho Viejo Solar Project. Opponents of that project — most notably an Eldorado-based group known as the Clean Energy Coalition — have appealed the commissioners’ decision in District Court. Rancho Viejo Solar plans to generate 96 megawatts of power and roughly 45 megawatts of battery storage, developers have said.
The Globemallow proposal comes as New Mexico’s Energy Transition Act, passed in 2019, lays out an ambitious timeline of renewable energy goals for the state’s power grid, requiring electric utilities to generate 50% of their power from renewable sources by 2030 and 80% by 2040. Investor-owned utilities must reach 100% renewable sources by 2045 and rural electric cooperatives by 2050.
In a recent email to members, the Clean Energy Coalition of Santa Fe County, a group with over 2,000 members that opposed Rancho Viejo, said it would attend the Thursday meeting to provide comments and express recommendations about Globemallow. Several members of the group spoke, in some cases raising concerns about thermal runaway fires stemming from battery energy storage facilities and scrutinizing aspects of Linea Energy’s application.
“Given our current court case regarding the AES project, we’ve decided neither to fully support or oppose the Linea one,” the organization said in an emailed message to its members.
Andrew Davidson, a senior associate with Linea Energy, said company leaders expect construction to begin in 2027. PNM has transmission line infrastructure on the property for the development, he said.
Following a recommendation from a Santa Fe County hearing officer, the Planning Commission will next consider the conditional use permit application for the project.
A map showing the location of the proposed Globemallow project.
The new proposal, just north of Stanley, comes after commissioners recently approved the Rancho Viejo Solar project, opposed by some Eldorado residents.
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REMBERT, S.C. — Residents in Sumter County will soon have another chance to weigh in on a proposed solar farm that could cover more than 1,700 acres.
The White Palmetto Solar Project is being proposed by TOCE SC Solar One LLC and would be built near Borden Road and Black River Road, about 12 miles northwest of the City of Sumter near the Lee County line.
According to the developer, the project would produce clean energy while generating about $765,000 in tax revenue each year.
The proposal has faced strong opposition from some residents.
In May of last year, the Sumter City-County Board of Zoning Appeals unanimously denied the company’s request for a special exception after community members raised concerns about the project.
The developer has since appealed that decision to the state, which will now review the case.
A new public hearing will give residents the opportunity to share their concerns directly with the South Carolina Public Service Commission.
Some residents say they believe local governments should prioritize protecting communities when considering projects like this.
“So I believe that the, you know, what we expect, the citizens of the county, is for our local government whether it’s city or county council to make decisions that first and foremost protect us, and I believe that that will be something that will be, you know, in jeopardy it would if this, if this goes through, it will allow anyone to come in with any industrial type facility,” said Sumter resident Traci Rogers.
Others say they are encouraging more residents to learn about the proposal and get involved in the process.
“We’re making sure that the community is aware, getting the community involved, not only in this area of Rembert, but the Bordon area, where this is expected, but to make the whole town aware because it could be coming to their back door next,” said resident Serena Cook.
State leaders say they are aware of the concerns raised by residents and are monitoring the situation as the state review process moves forward.
“What I am going to do, and this is my promise to everyone is to stay out of it and just watch and learn this process the best I can so that on the other side of it we can craft legislation to make sure that sufficient legislation protections are in place across the state and then look out for them and build up on them if they so choose,” said Sen. Jeff Zell.
A public hearing will be held Wednesday, March 18 at 6 p.m. at Patriot Hall in Sumter. Another in-person hearing is scheduled for March 25 in Columbia, along with virtual options for residents who want to provide comments.
After the hearings conclude, the Public Service Commission is expected to issue a final decision by June 1.

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“It’s almost like … there is a lot of money put into making people still do things even if they’re inefficient or costly.”
Photo Credit: TikTok
Renewable energy is being developed around the world at an astounding rate, yet some critics are seemingly oblivious. 
Former U.S. climate negotiator and TikTok creator Lia Newman (@liaandtheworld) debunked detractors’ claims that solar and wind aren’t cheaper than alternatives like coal, oil, and gas. 
“Ninety-one percent of new renewable power projects delivered electricity at a lower cost than the cheapest fossil fuel alternative last year,” Newman said. 
It’s a sentiment echoed by experts from multiple fields. Analysts from the New York Financial advisory firm Lazard reported that solar and wind are the cheapest, fastest energy sources to deploy for grid-scale work. And, while U.S. energy policy has shifted to favor coal, oil, and gas, government data from January noted that their share of supply is still expected to fall. 
“We expect the combined share of generation from solar power and wind power to rise from about 18% in 2025 to about 21% in 2027,” the report added. 
Global energy think tank Ember has the U.S. renewable share at 24.4%. It’s nearing a global 30% milestone marked in 2024, and evidence that developers are seeing the value in the cleaner energy sources. Unlike nonrenewables, solar and wind don’t produce harmful air pollution when they generate electricity.
What’s more, renewable power is poised to continue its rise. The International Renewable Energy Agency reported that the cleaner energy sources accounted for more than 90% of “total power expansion globally” in 2024. It’s a trend that Newman said will continue through the end of the decade. 
California is a case study for success. The state has invested heavily in solar power and large-scale battery storage. The state ranks low on a nationwide long-term electricity risk assessment by the North American Electric Reliability Corporation. 
In the meantime, U.S. energy prices are widely reported to be rising faster than inflation, partly due to surging data center power demand. 
Newman’s post showed that the right information can aid your advocacy for policy that impacts your home. And unlike many energy sources, solar can also be quickly leveraged at households to help lower energy bills. 
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One TikToker likened detractors’ claims to cigarette use. 
“It’s almost like people aren’t fully rational, and there is a lot of money put into making people still do things even if they’re inefficient or costly,” they commented. 
“Don’t forget … how fast it is to install renewables,” another person chimed in.
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Scientific Reports volume 16, Article number: 9107 (2026) Cite this article
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This paper introduces an improved current-sensorless maximum power point tracking (MPPT) approach, coupled with a battery charging unit, specifically designed for single-phase standalone photovoltaic (PV) power systems. An interleaved hybrid DC-DC boost converter with high voltage gain, previously developed by the authors, is used to boost the low and non-linear voltage output of the PV array to the usable DC grid voltage level. Since the energy yield of PV systems is highly sensitive to variations in solar irradiance, fast and accurate tracking of the maximum power point (MPP) is essential. Unlike conventional MPPT techniques that rely on both voltage and current measurements, the proposed method estimates the input current using only the inductor voltage observed during the switch ON-state, thus removing the need for direct current sensing. This sensorless approach simplifies hardware design and reduces implementation costs, particularly in experimental environments where current sensors may introduce complexity and noise susceptibility. In addition, the proposed system includes a battery charging unit which ensures effective energy transfer to the battery in isolated operating conditions for single-phase AC off-grid power applications. The control structure regulates charging dynamics based on voltage behavior and operating constraints, contributing to stable performance under changing environmental conditions. The system’s effectiveness is verified through MATLAB/Simulink simulations under dynamic irradiance profiles. Predicted and actual current values are compared to validate estimation accuracy. Furthermore, experimental validation using a digital signal processor (DSP) demonstrates reliable real-time operation, confirming the practical applicability of the proposed method in cost-sensitive, off-grid solar energy systems.
The rapid increase in global energy demand, coupled with the urgent need to mitigate environmental degradation, has accelerated the shift towards clean and sustainable energy sources. While fossil fuels still play a significant role in energy production, their limited reserves and contribution to greenhouse gas emissions make them an unsustainable long-term solution. In this context, renewable energy sources—particularly solar, wind, and hydrogen—emerge as clean, inexhaustible, and environmentally friendly alternatives^1,2,3. Among these, solar energy stands out as a key player in the development of sustainable energy systems due to its widespread availability, scalability, low operational costs, and minimal environmental impact^4,5,6.
A PV-based single-phase standalone power system typically consists of PV arrays, a DC-DC converter for voltage regulation and MPPT, a battery bank energy storage system, and an inverter to supply AC loads. The DC-DC converter ensures that the PV array operates at its maximum power point, regardless of irradiance and temperature conditions. The AC output is provided through a single-phase inverter, which is often controlled to maintain voltage and frequency within standard limits (e.g., 230 V, 50 Hz)^7,8,9. Despite the reliability and modularity of such systems, two major challenges persist: (i) maintaining energy harvesting efficiency under dynamic environmental conditions and (ii) ensuring safe and effective management of the energy storage unit. The former is typically addressed through MPPT algorithms, whereas the latter requires a battery management system (BMS).
The rapid advancements in PV panel manufacturing have propelled solar energy technology forward, making it a viable alternative to fossil fuels due to its clean, safe, and sustainable energy supply capabilities. This progress is evident in the dramatic increase in installed solar capacity, which has increased at least tenfold in the last decade¹⁰. However, current PV technology still faces significant challenges, such as a loss of up to 25% of generated energy due to inefficiencies and dependence on varying climatic conditions¹¹. To address this, the MPPT technique plays a crucial role in optimizing energy production from PV arrays. MPPT techniques aim to continuously match the PV panel’s output load by controlling a DC voltage converter, ensuring maximum power transfer under varying environmental conditions^11,12,13. These methods typically rely on real-time monitoring of voltage and/or current generated by the PV modules. Using a controller device and an algorithm, the MPPT system dynamically adjusts the duty cycle or switching frequency of the converter to maintain the optimal power output^14,15,16. In doing so, MPPT ensures that PV systems generate the highest possible energy output, enhancing overall power transfer efficiency.
The MPPT technique, which is crucial for extracting maximum energy from PV systems, has been extensively studied in literature with various algorithms. These include Incremental Conductance (IC), Perturb and Observe (P&O), Artificial Neural Networks, Fuzzy Logic Controllers and adaptive approaches such as FOCV algorithm, metaheuristic algorithms^{9,17,18,19,20,21,22,23,24,25,26,27}. Among these techniques, the P&O method is preferred due to its simplicity, ease of implementation, and relatively low computational cost^9,14. However, under rapidly changing weather conditions, its performance may degrade due to oscillations around the maximum power point. All MPPT methods require accurate voltage and current measurements from the PV array; however, traditional sensing circuits can increase system costs and introduce susceptibility to noise^15,18,20. This is particularly problematic in low-power, standalone systems. Despite their varying levels of complexity, these methods aim to optimize energy extraction from PV arrays while minimizing system costs and enhancing efficiency.
In traditional MPPT implementations, current and voltage sensors are employed to determine the instantaneous power output of the PV array. However, these sensors not only add to the overall system cost but also introduce complexity in signal conditioning and noise filtering—especially in harsh outdoor environments^20,21. To mitigate these issues, sensorless MPPT techniques have emerged as a promising alternative^22,23. These techniques rely on estimation algorithms to infer the PV current or voltage using known electrical relationships within the power converter topology.
While most sensorless methods in the literature focus on eliminating current sensors, voltage sensors are typically retained due to the difficulty of accurately modeling PV voltage behavior under dynamic load and irradiance changes. However, there remains a research gap in developing low-cost MPPT methods that also minimize voltage sensing requirements, particularly for embedded systems with limited computational resources^22,23. In addition, some recent studies have focused on improving the performance of photovoltaic MPPT algorithms under dynamic environmental conditions while reducing system complexity and sensor dependency^28,29,30. These advanced and adaptive MPPT techniques have been proposed to enhance tracking efficiency, accelerate convergence speed, and mitigate steady-state oscillations compared to conventional methods. These approaches emphasize robustness against irradiance and temperature variations, as well as practical feasibility for real-time implementation. Experimental and simulation-based validations consistently demonstrate improved dynamic response and stable operation, highlighting the effectiveness of adaptive and sensor-reduced control strategies in modern PV energy conversion systems. Table 1 presents a qualitative comparison of the proposed MPPT method with representative conventional P&O, sensor-based, and some sensorless type MPPT approaches reported in the literature. The comparison is conducted in terms of required sensing hardware, algorithmic complexity, robustness, and practical feasibility, which are some key criteria for real-world photovoltaic applications. Conventional P&O methods are characterized by their simplicity and widespread adoption but remain sensitive to measurement noise due to their reliance on current sensing. Sensor-based MPPT techniques generally achieve improved robustness and tracking performance at the expense of increased hardware complexity and cost. Recent sensorless approaches reduce sensing requirements; however, many rely on computationally intensive observers or estimation algorithms, which may limit their practical deployment. In contrast, the proposed method offers a balanced trade-off by minimizing sensor dependency and implementation complexity while maintaining robustness and ease of integration into low-cost embedded PV systems.
The role of boost type DC-DC power electronic converters is extremely important in on-grid or off-grid PV systems. A boost converter operating in continuous conduction mode (CCM) steps up the low and non-linear voltage generated by the PV array to a higher, more usable level at its output. This is particularly critical in stand-alone and off-grid systems where the PV voltage is often well below the required load voltage. Boost type DC-DC converters are used not only to regulate the DC voltage required for the inverter used in PV systems but also to enable the implementation of MPPT algorithms. Among the different boost type DC-DC topologies, the classical single-switch DC-DC boost circuit is one of the most widely used due to its cost-effectiveness^31,32. However, in cases where high voltage gain is required, the classical single-switch boost circuit is insufficient^33,34. In this case, interleaved and hybrid interleaved boost circuits with high voltage gain have been developed. Interleaved boost converters offer significant advantages over classical boost circuits, especially in high-power and wide-output PV systems. Thanks to developing control systems and power electronic components, the use of this topology in PV systems is increasing.
In standalone PV systems, batteries serve as critical components for maintaining energy supply during periods of low or no solar irradiance. However, battery performance is highly sensitive to charging and discharging profiles, ambient temperature, aging, and operational stress. To ensure safety, reliability, and longevity, a BMS is employed to monitor and control key battery parameters, including voltage, current, temperature, rates of charge/discharge and state-of-charge (SOC)^17,18. Advanced BMS architectures incorporate several layers of protection: overvoltage and undervoltage cutoff, overcurrent protection, thermal management, cell balancing, and predictive maintenance algorithms. Additionally, in PV-powered systems, the BMS often plays a role in coordinating with the MPPT controller to optimize energy flow between the PV source, the battery, and the load. Improper battery charging can lead to reduced capacity, shortened lifecycle, or in severe cases, thermal runaway and safety hazards^19,35. Recent literature also emphasizes the integration of smart battery management with renewable energy forecasting and adaptive control. Techniques such as fuzzy logic, neural networks, and model predictive control have been explored to improve the responsiveness and intelligence of battery management in PV-based systems^36,37.
The Full Bridge Isolated DC-DC converter is a crucial component in BMS, offering a robust and efficient solution for power conversion in energy storage systems. By providing electrical isolation between the input and output stages, it ensures optimal power transfer and protection for the battery-powered devices. Its high efficiency and ability to manage bidirectional power flow make it an ideal choice for applications that require reliable voltage and current regulation during charging/discharging. These isolated topologies play a vital role in safeguarding the system, ensuring the battery operates within its safe limits and enhancing the overall performance and longevity of energy storage systems.
This study presents an improved current-sensorless MPPT control strategy, integrated with a battery charger specifically designed for single-phase off-grid PV systems. The key contribution of this study lies in the development of a simplified sensorless MPPT approach which, in contrast to many existing methods that depend on complex observers, optimization routines, or computationally intensive algorithms, employs a lightweight current estimation strategy integrated with conventional control logic. This design significantly reduces implementation complexity while enhancing robustness against sensor noise and sensor-related failures. Moreover, the proposed framework facilitates the seamless integration of MPPT and battery management functions within a single microcontroller, making it particularly suitable for low-cost embedded photovoltaic systems. The subsequent sections of the paper introduce the proposed current-sensorless MPPT method, which is based on an interleaved hybrid DC-DC boost converter with a high voltage gain, a technique previously developed by the authors. Additionally, a BMS utilizing an isolated full-bridge DC-DC converter is also introduced. The simulation results of the entire proposed system are then analyzed using the MATLAB/Simulink environment. Lastly, experimental results, controlled via a DSP, are presented, and related comparisons are provided.
MPPT control algorithms are employed to identify the optimal operating point at which PV panels can extract the maximum possible power under varying solar irradiance conditions. These algorithms enable PV systems to maximize energy generation despite fluctuations in environmental conditions³⁸. In conventional MPPT methods, both the input current and voltage of the PV system must be measured and sampled to accurately determine the point of maximum power. However, in this study, only the input voltage (PV voltage) is measured, while the input current (PV current) is neither measured nor sampled. By eliminating the need for current sensing, the associated circuit components required for current measurement are removed from the implementation, resulting in a reduction in the overall system cost. The proposed improved current-sensorless P&O MPPT control method, developed for a grid-unconnected PV system, is illustrated in Fig. 1.
The proposed current-sensorless P&O MPPT method by using hybrid interleaved DC-DC boost converter.
CCM current waveforms of the hybrid interleaved DC-DC boost converter.
Since the classical and interleaved-type DC-DC boost converter circuits cannot have high voltage gain, they are exposed to high current stresses while producing the high DC bus voltage required for inverters in PV systems. This problem can be solved by an improved hybrid interleaved DC-DC boost circuit developed by the authors³⁹as given in Fig. 1. The average circuit model of the proposed hybrid interleaved DC-DC circuit used for MPPT is obtained from the equivalent circuits depending on the switching states of the converter. The voltage gain and related average-model equations of the 180° phase shifted hybrid interleaved DC-DC boost with the current waveform in CCM mode as shown in Fig. 2 are given in Eqs. (1), (2) and (3)³⁹.
where, L₁ = L₂=2 L; C_b3=C_b4=2 C; VC_b3=VC_b4=V_DC/2; I_PV=I_L1+I_L2.
As stated before, a modified version of the conventional P&O MPPT algorithm was implemented to determine the maximum power point for an off-grid PV system in this study. The P&O algorithm operates by scanning the power-voltage (P-V) curve and adjusting the operating point accordingly to converge on the maximum power point. As seen from Fig. 3, the voltage is incrementally perturbed: if a positive change in power is observed, the voltage is increased from point A toward the MPP; conversely, if a negative power change occurs, the voltage is decreased from point B toward the MPP. When the change in power approaches zero, the system is considered to have reached the MPP^40,41.
P-V curve of the classical proposed P&O method.
The corresponding flowchart outlining the operation of the improved current-sensorless P&O MPPT algorithm is provided in Fig. 4. In the conventional P&O MPPT method, both the voltage and current of the PV system must be measured. Consequently, the output voltage and output current of the PV array are sampled and transferred to the digital controller, such as a DSP. However, employing both voltage and current sensors increases the overall system cost and complexity. Therefore, reducing the number of measured signals is crucial for a cost-effective implementation. When utilizing digital controllers like DSPs, it is feasible to eliminate the need for either current or voltage sampling. In the improved MPPT method presented in Fig. 4, the current sensing circuit is eliminated by leveraging the known values of the passive components used in the designed converter. Instead of directly measuring the current, the output current of the PV system (I_PV) is estimated through Eqs. (4) and (5), by using inductor value and the switching period generated by the DSP. Once the output current of the PV system is computed, the instantaneous output power of the PV array is determined using the measured voltage and the estimated current. In the proposed MPPT algorithm, a reference voltage (V_ref) is generated to adjust the duty cycle (d) accordingly. This reference voltage dynamically varies based on the comparison between the instantaneous and previously calculated PV output power. Moreover, maintaining a stable DC bus voltage is critical for the operation of the DC-DC converter interfacing the PV array with the load. The generated reference voltage is therefore used as an input to a proportional-integral (PI) controller for regulating the DC bus voltage to the desired level.
The flow chart of the proposed current-sensorless P&O MPPT method.
The proposed current-sensorless MPPT method is implemented in a PV-battery hybrid power supply system designed for stand-alone AC loads, as illustrated in Fig. 5. In this system, the battery group functions as a secondary power source, acting as a charge/discharge unit to balance power flow within the hybrid configuration. A hybrid interleaved DC-DC boost converter is employed for MPPT implementation, while a classical isolated full-bridge bidirectional DC-DC converter is utilized to integrate the battery group into the DC bus. The DC electrical energy obtained from the PV panels or stored in the batteries can be directly used for DC applications. Alternatively, it can be converted to AC using a suitable DC-AC inverter for powering AC loads.
Bidirectional converters are essential for both storing electrical energy in batteries and utilizing the stored energy when needed. These converters are generally categorized as either isolated or non-isolated. Non-isolated bidirectional converters are typically preferred in low and medium voltage bus applications, whereas isolated ones are favored for higher voltage applications due to their safety benefits. The use of transformers in isolated converters provides galvanic isolation between the battery and the main circuit components, ensuring both equipment and user safety. Bidirectional converters employing high-frequency transformers operate in two distinct modes: buck mode for charging the batteries, and boost mode for discharging them. Unlike low-frequency transformers, which increase in physical size with power rating, high-frequency switching in the isolated full-bridge bidirectional DC-DC converter allows the use of smaller transformers.
Overall schematic of the proposed PV based standalone system.
The designed bidirectional converter operates within a power range of 100–800 W, with high-voltage side outputs ranging from 280 to 400 V, and low-voltage side outputs 48 V. In this topology, switching elements connected to the primary winding of the transformer generate an AC square wave, which is then rectified by output diodes. The rectified voltage is filtered using an output inductor (L_bd1) and battery capacitor (C_bd). The ideal relationship between input and output voltages for the isolated full-bridge bidirectional DC-DC converter, as shown in Figs. 5 and 9, is given by Eq. (6), which involves the primary winding (N_p), secondary winding (N_s), and the duty cycle of the full-bridge converter (d_fb).
For buck mode operation, switches Q_bd1-Q_bd3 and Q_bd2-Q_bd4 must be switched with a 180° phase shift and a maximum duty cycle of 50%. Meanwhile, Q_bd5 and Q_bd6 should remain off (cut-off state), and the diodes connected in parallel should conduct or block current based on the polarity of the voltage from the transformer. Under these conditions, the 400 V V_bd voltage is stepped down to 48 V (V_battery), thereby charging the battery group. Conversely, for boost mode operation, Q_bd5 and Q_bd6 are switched with a 180° phase difference and a duty cycle greater than 50%. At the same time, Q_bd1-Q_bd3 and Q_bd2-Q_bd4 are turned off, and the parallel-connected diodes conduct or block based on the transformer’s output voltage polarity. In this mode, the energy stored in the batteries is delivered to the system.
In hybrid PV–battery standalone systems, real-time power management is critical to ensure system stability, efficient energy utilization, and battery health. The proposed system incorporates a BMS that dynamically adjusts its charging or discharging behavior based on the instantaneous power flow between the PV array, load, and battery bank. In the proposed study, the BMS operates by referencing both the instantaneous power demand of the load and the maximum available power from the PV system. Based on the power difference between the PV system and the load, the BMS determines the appropriate charging or discharging mode of the battery bank. This decision mechanism directly influences the operational mode boost or buck of the isolated full-bridge DC-DC converter integrated within the BMS. BMS operates using two reference signals: the maximum available power from the PV system, P_PV, obtained via the MPPT algorithm, and the load power demand, P_L. The power balance condition for the system can be expressed as:
where, P_bd is positive when discharging and negative when charging.
Meanwhile, the hybrid interleaved boost DC-DC converter interfacing the PV source is actively controlled to extract maximum power through MPPT and to regulate the DC bus voltage. The primary function of the system controller is to maintain power balance across the entire hybrid energy system, enforce automatic battery charge/discharge management, and continuously operate the PV array at its maximum power point. For instance, if the load power (P_L) exceeds the maximum power output of the PV array, the BMS enables the battery bank to compensate for the power deficit. In this scenario, the isolated full-bridge converter transitions to boost mode, and the battery discharges energy into the system. Conversely, when the load demand is lower than the PV system’s available power, the PV array alone supplies the load. The surplus energy is diverted to charge the battery bank, during which the converter operates in buck mode.
Six distinct operational scenarios given belove have been defined to illustrate the dynamic behavior of the BMS under different power conditions. These scenarios capture all possible interactions between the PV array, the battery bank and the load.
Scenario-1 If the power produced by the PV Panel system is greater than the sum of the load and battery power; the power required by the load is met by the PV system, and if the battery is not fully charged, the excess power produced by the PV system is transferred to the battery. The system is operated by PI₁, PI₂, PI₃, and MPPT controllers.
Scenario-2 PV greater than battery, PV panel charges the battery. The MPPT controller operates PV panel to harvest the maximum available solar energy. PI₁, PI₃, and MPPT controllers are manage the power flow.
Scenario-3 PV Maximum Power feed the load, the load demand matches exactly with the PV’s maximum power output. In this condition, the entire load is supplied by the PV array operating at the MPP. PI₁, PI₂, and MPPT controllers are active.
Scenario-4 PV panel generates insufficient power for the load requires. PV and battery combined to meet the energy demands for load. PI₁, PI₂, PI₄, and MPPT controllers work together to feed the load.
Scenario-5 Zero PV Output, Load Exceeds Battery Discharge Limit; in low-irradiance conditions (e.g., at night), the PV output approaches zero, and the load demand exceeds the battery’s maximum discharge capacity. The system must either shut down or perform load shedding to prevent over-discharge and protect the battery. PI₂, and PI₃ controllers provide enough power for the load.
Scenario-6 Zero PV Output, Load and battery; The system is not operated. All controllers are passive.
These six scenarios form the foundation for the real-time decision-making logic of the proposed power management strategy, ensuring optimal energy distribution, safe battery operation, and maximum utilization of solar energy under diverse operating conditions. A concise overview of the various operating conditions described above is provided in Table 2, which categorizes the system’s behavior under different power generation and load scenarios. Furthermore, the logical sequence of the proposed power management and control strategy is illustrated through the flow diagram shown in Fig. 6, providing a clear visualization of the decision-making process employed by the digital controller.
Flow diagram of the proposed BMS.
In this section, the implementation and simulation of the proposed current-sensorless MPPT control method and BMS are conducted using the MATLAB/Simulink platform. Initially, a hybrid interleaved boost-type DC-DC converter was modeled according to the design parameters³⁹. The current-sensorless MPPT control strategy was then integrated into this converter structure within the simulation environment. To estimate the PV system output current without employing a physical current sensor, a dedicated computational block was developed in Simulink. This block calculates the current by sampling the input voltage of the boost converter, which is equivalent to the output voltage of the PV array. The calculation relies on the known inductance value (L), the duty cycle (d) generated by the DSP, and the sampled voltage. These parameters are used in conjunction with (4) to estimate the PV current. During each switching cycle, the inductor current exhibits a ripple that varies depending on the switching state. When the switch is conducting (ON state), the current ramps up; when the switch is open (OFF state), it ramps down. The PV output current corresponds to the average of the inductor current over a full switching period. Therefore, the simulation block computes the average of the peak and valley current values in each cycle to represent the PV output current accurately. This estimation process is executed continuously during the simulation, allowing the MPPT algorithm to operate effectively without requiring real-time current measurements. The recalculated average current is updated each switching period, enabling the controller to respond dynamically to changing irradiance and load conditions.
The hybrid interleaved boost-type DC-DC converter, powered by the PV system, was evaluated under the proposed current-sensorless MPPT control strategy through MATLAB/Simulink simulations. The system was tested at an ambient temperature of 25 °C under 400 W resistive load and varying irradiance levels of 800, 1000, and 900 W/m². The proposed MPPT algorithm, based on the P&O technique, operates without direct current measurement. Instead, it estimates the PV output current and generates a reference voltage, which is then utilized by a PI controller to regulate the DC bus voltage. This control scheme ensures that the PV array consistently operates at its maximum power point, while the DC bus voltage is maintained within the desired limits under all tested irradiance conditions. As illustrated in Figs. 7 and 8a, the system successfully tracked the MPP across all irradiance scenarios while supplying the load. Although the current input and PV voltage varied with irradiance changes, the output voltage remained virtually constant, validating the effectiveness of the PI controller in maintaining voltage regulation.
The simulation results of power, voltage and current for input and output under variable irradiances.
To validate the performance of the proposed current-sensorless MPPT method, both the measured and estimated output current values of the PV system were analyzed. The comparative results are illustrated in Fig. 8a. As expected, the measured current exhibits an analog waveform due to the continuous nature of physical sensing, while the estimated current—derived through the algorithmic computation within the DSP—presents a discrete or digital profile. Although the two current signals are not perfectly time-synchronized due to sampling and processing delays, the comparison demonstrates a high degree of agreement. The deviation between the measured and estimated PV output currents remains within an acceptable range of approximately 1% to 3%, confirming the reliability and accuracy of the proposed estimation-based MPPT strategy under real-time operating conditions. Steady-state oscillations are directly related to the amplitude of power fluctuations induced by the MPPT algorithm in the vicinity of the maximum power point. As observed in the presented results (Fig. 8b), both the PV power and the load power exhibit bounded and periodic oscillations around the MPP, without any divergence or amplification that could jeopardize system stability. Quantitatively, the steady-state ripple of (:{P}_{PV})is limited to approximately 4–5 W peak-to-peak, corresponding to about ± 0.5% of the 500 W power. In contrast, the oscillation amplitude of the load power further reduced to approximately 2–3 W, i.e., ± 0.3–0.4%, indicating effective attenuation of MPPT-induced fluctuations by the power conversion stage. This reduction in load-side power ripple demonstrates the algorithm’s ability to suppress oscillations and minimize steady-state power losses. In the existing literature, such low-amplitude steady-state oscillations are widely recognized as a hallmark of a well-tuned MPPT scheme, reflecting an appropriate trade-off between fast tracking dynamics and stable steady-state operation. Overcurrent, overvoltage, and SOC limits were determined in simulation and experimental studies.
It is worth noting that in systems utilizing MPPT, power is delivered to the load in accordance with the load’s instantaneous demand. Consequently, the operating point of the PV array and hence the MPP may shift with changing irradiance or load conditions. Despite these variations, the proposed method maintained accurate MPPT performance under both continuous conduction mode and discontinuous conduction mode. Moreover, the method correctly predicted system behavior in open-circuit scenarios where irradiance was present but no load was connected. In such cases, no current was generated, as expected. Conversely, whenever a load was connected under irradiance, current was successfully produced, and the control system responded accordingly. These results confirm that the proposed MPPT approach has accurate performance across diverse operating conditions.
The simulation results for, (a) I_PV and the predicted I_PV, (b) P_PV-P_L.
The entire PV based standalone system by using proposed current-sensorless P&O MPPT method and BMS.
Following the validation of the proposed current-sensorless MPPT method using the hybrid interleaved boost DC-DC converter, comprehensive simulation studies were conducted by modeling the entire power conversion system, as depicted in Fig. 9, within the MATLAB/Simulink environment. This full-system simulation enabled the evaluation of the integrated operation of the PV array, battery management system, power converters, and digital control algorithms under various operating conditions. The nominal parameters of the key system components used in the MATLAB/Simulink-based simulation model are summarized in Table 3. The selection of the parameters for the converters and the determination of the coefficients for the controllers are obtained through detailed design studies. These design studies are described in detail in references^41,42. The values given in Table 3 were also calculated based on the design studies in the references given above. Here, Ziegler–Nichols tuning method was used to determine all PI controller coefficients. In this method, integral and derivative gains are initially set to zero and the proportional gain is increased until sustained oscillations are observed. The corresponding ultimate gain (Ku) and oscillation period (Tu) are then used to calculate the proportional and integral parameters⁴².
During the simulation studies, system behavior was analyzed under various operating scenarios defined for the BMS. The DC bus voltage controller was configured to regulate the voltage within the predefined limits of 380 V to 420 V. Additionally, the inverter and filter components for the single-phase AC load were designed to deliver an output voltage of 220 V rms. Since the DC bus voltage and the AC output voltage remained unaffected by changes in BMS operating modes, their corresponding simulation results are presented for a representative operating condition only. As an example, Scenario-1 represents a condition in which the PV array generates sufficient power to meet both the demand of a single-phase AC load and to charge the battery bank, assuming the battery is not yet fully charged. Under standard test conditions—specifically, an irradiance of 1000 W/m² and an ambient temperature of 25 °C—the PV panels successfully delivered the necessary power to supply a 400 W AC load at 220 V rms, while concurrently charging the battery bank at a current of 4 A with an initial SOC of 10%. The key performance indicators under this scenario, including PV array voltage, current, and power; battery charging parameters; and inverter output characteristics are depicted in Figs. 10 and 11, which demonstrates the effective coordination of the MPPT controller and the battery management system within the proposed hybrid power architecture.
The simulation results of power, voltage and current under 400 W output load; (a) for PV system, (b) for battery, (c) for output.
In the proposed system, when operating under Scenario-1, the BMS transitions into the charging mode. In this state, switch S₄ is turned OFF, while the remaining switches are conducting. The PI controller block associated with the hybrid interleaved boost converter remains inactive during this operation. Under these conditions, the isolated full-bridge bidirectional DC-DC converter operates in buck mode to facilitate energy transfer from the PV array to the battery bank. Specifically, the Q_bd5 and Q_bd6 switches remain OFF, and current conduction occurs through the corresponding body diodes D_bd5 and D_bd6, enabling battery charging. Meanwhile, the Q_bd1–Q_bd4 switches are actively switched to regulate the charging process, and their associated freewheeling diodes D_bd1–D_bd4 protect the circuit against reverse current flow. Additionally, to ensure proper inverter operation and prevent reverse current damage, all inverter switches (Q_i1–Q_i4) and their corresponding diodes (D_i1–D_i4) remain active. This coordinated switching configuration enables safe and efficient power flow from the PV array to both the AC load and the battery bank during Scenario-I operation.
The simulation results under 400 W output load; (a) V_DC, I_DC, (b) I_DC, I_L and I_bd, (c) V_bd, I_bd, (d) V_DC, I_L.
In the proposed system, the DC bus voltage, which corresponds to the output voltage of the hybrid interleaved boost converter, must be equal to both the input voltage of the isolated full-bridge bidirectional DC-DC converter and the input voltage of the single-phase inverter. This common DC link voltage is regulated within the range of 380–420 V DC, a condition achieved by the implementation of the proposed MPPT control algorithm. Furthermore, for proper power balance and system integrity, the output current of the boost converter must be equal to the sum of the input currents drawn by the inverter and the bidirectional DC-DC converter. This current continuity ensures that the entire power delivered by the PV array is accurately distributed between the AC load and the battery charging system.
The voltage and current waveforms observed at the output of the inverter, along with the total harmonic distortion (THD) value of the output voltage, are presented in Fig. 12. Since the THD values of both the output voltage and current were found to be identical under the given operating conditions, only the voltage THD is reported for brevity. In the simulation studies, the performance of the entire system was evaluated under six predefined operating scenarios corresponding to the BMS control logic. These scenarios were specifically designed to assess the system’s power control capability under various irradiance, temperature, and load conditions. The resulting system responses are depicted in Fig. 13. The detailed operating conditions for each time interval shown in Fig. 13 are as follows:
0–1 s: The PV array supplied both a 350 W battery charging load and a 400 W single-phase AC load under 1000 W/m² irradiance at an ambient temperature of 25 °C. 1–2 s: The PV panels continued to charge the 350 W battery bank at reduced irradiance of 400 W/m² and an ambient temperature of 30 °C. 2–3 s: The PV array solely supplied a 600 W AC load under irradiance conditions of 750 W/m² at 35 °C. 3–4 s: At 600 W/m² irradiance and 25 °C, both the PV array and the battery bank jointly supplied a 700 W AC load. 4–5 s: The battery bank independently powered a 500 W AC load without contribution from the PV system. 5–5.5 s: No power transfer occurred, simulating a no-load and no-generation condition.
The voltage and current waveforms observed at the output of the inverter, along with THD.
The power, voltage and current results of the entire system under various irradiance, temperature, and load conditions for each time interval.
These test scenarios effectively demonstrate the flexibility and robustness of the proposed system under dynamic real-world conditions, validating the coordination between the MPPT controller and the BMS under varying generation and load profiles. The simulation results confirm that the proposed current-sensorless MPPT algorithm, the implemented BMS, and the associated system components operate in full accordance with the predefined BMS operating scenarios. Notably, the simulation also reveals that the battery current exhibits transient high-frequency oscillations for a brief duration, particularly during periods when the battery bank supplies power to the load. This behavior is primarily attributed to the combined effects of the leakage inductance of the transformer used for galvanic isolation in the isolated full-bridge bidirectional DC-DC converter, and the parasitic capacitance of the DC bus capacitor. The interaction between these inductive and capacitive elements creates a resonant circuit, which induces temporary current oscillations until the system stabilizes under steady-state conditions.
Following the successful validation of the proposed system through simulation studies, experimental investigations were conducted using a 400 W resistive load, as depicted in Fig. 14. The components utilized in the power converters for the experimental setup including their nominal ratings, sources, and load characteristics were selected to match exactly those used in the simulation studies. This ensured consistency between simulation and experimental conditions, facilitating a direct comparison of system performance across both validation environments. The control algorithms, initially developed and tested in the MATLAB/Simulink environment, were deployed onto a LAUNCHXL-F28379D DSP microcontroller platform for real-time implementation. The experimental hardware included power electronic converters designed by us, specifically: a hybrid interleaved boost DC-DC converter, an isolated full-bridge bidirectional DC-DC converter, and a single-phase full-bridge inverter. For the PV energy source, three series-connected SK125 × 125-M-72–195 W model PV panels were employed, while the energy storage system consisted of four 12 V, 100 Ah batteries connected in series to match the required system voltage levels. This experimental configuration enabled comprehensive testing of the integrated PV–battery hybrid power generation system under realistic conditions. brevity.
As previously discussed, the proposed current-sensorless MPPT method eliminates the need for direct measurement of the PV output current. In simulation studies, the accuracy of the estimated PV current was validated by comparison with the actual current values generated by the system model. For experimental validation, the accuracy of the current estimation algorithm implemented on the DSP controller was assessed by measuring the PV output current with a physical current sensor and comparing it to the DSP-calculated current values. Using integrated visualization and monitoring tools available within the Code Composer Studio (CCS) environment, both the measured and estimated current values were simultaneously displayed numerically and graphically on the DSP interface. As illustrated in Fig. 15, the comparison demonstrates a strong correlation between the measured and calculated PV current values, confirming the high accuracy and reliability of the proposed estimation technique under practical operating conditions.
The picture of the experimental setup.
The measured and calculated PV current values.
The proposed hybrid interleaved boost DC-DC converter, which integrates the current-sensorless MPPT method, incorporates both phase-interleaved power stage paralleling and hybrid converter characteristics. Owing to its hybrid topology, the converter achieves approximately twice the voltage gain compared to a conventional single-phase boost converter. As illustrated in Fig. 16, the input current of the converter—equivalent to the output current of the PV source—is the sum of the inductor currents from the two parallel power stages, denoted as I_L1 and I_L2. The phase-shifted switching operation of the two power stages causes the current in one stage to increase while the current in the other decreases, resulting in a significant reduction of the input current ripple when the two currents are combined. Moreover, the ripple frequency characteristics of the input current differ from those of the individual power stage currents. While each power stage operates with a switching frequency of 20 kHz, the interleaving effect effectively doubles the ripple frequency of the combined input current to 40 kHz. This frequency doubling leads to improved dynamic performance, reduced input filtering requirements, and enhanced electromagnetic compatibility (EMC) characteristics of the overall converter system.
The experimental results of hybrid interleaved boost converter; gate signal and I_L1, V_PV-V_DC, I_L1-I_L2, I_PV-I_L1.
The output voltage of the boost converter also serves as the DC bus voltage for the system. As depicted in Fig. 16, the system maintained a stable DC bus voltage of approximately 407 V under varying irradiance levels from the PV source. Across different MPPT operating conditions, minor fluctuations were observed, with the DC bus voltage ranging between 390 V and 410 V, demonstrating the effectiveness of the proposed control scheme in maintaining voltage stability despite dynamic changes in input power.
An LCL-type output filter was designed and integrated into the single-phase full-bridge inverter, which represents the final stage of the proposed system architecture. As illustrated in Fig. 17, the inverter output voltage derived from the DC bus was measured both before and after filtering to evaluate the filter’s effectiveness. Despite fluctuations in the DC bus voltage between 390 V and 410 V due to dynamic MPPT operation, the inverter consistently delivered a stable 220 V rms AC output. The THD of the inverter output voltage was measured to be within the range of 1% to 2%, confirming that the LCL filter effectively attenuated high-frequency components and ensured high-quality power delivery to the load. Furthermore, as shown in Fig. 18, the voltage-current characteristics of both the DC bus and the inverter output were recorded under a nominal load condition of 400 W. The single-phase inverter, operating at a switching frequency of 40 kHz, demonstrated a measured efficiency of approximately 94% at the rated power level.
Unlike in simulation studies, it is not feasible to simultaneously capture all operating states of the BMS in real time on the oscilloscope during experimental validation. Nevertheless, comprehensive experimental tests were conducted under all predefined BMS operating conditions, and the system consistently exhibited stable and reliable performance.
The experimental results of the inverter output voltage.
The experimental results of; input voltage/current of the inverter and output voltage/current of the inverter for a 400 W load.
The overall system efficiency was found to fluctuate depending on the battery bank’s charging/discharging status and whether the battery system was active or inactive. As a result, a consolidated system efficiency curve was not presented. However, the individual conversion efficiencies of the primary power stages were measured. Specifically, the hybrid interleaved boost DC-DC converter achieved an efficiency of 96% at a nominal power output of 400 W, while the single-phase full-bridge inverter recorded an efficiency of 94% under the same load condition.
This paper presented the design, simulation, and experimental validation of an improved current-sensorless MPPT control method, integrated with a BMS, for single-phase standalone PV-battery hybrid power systems. The proposed system utilizes a hybrid interleaved boost DC-DC converter for enhanced voltage gain and reduced input current ripple, significantly improving dynamic response and efficiency over conventional boost converter topologies. By eliminating the need for direct current measurement, the current-sensorless MPPT algorithm reduces system complexity, cost, and susceptibility to sensor-related noise, making it highly attractive for off-grid and cost-sensitive applications.
The effectiveness of the proposed approach was validated under both simulation and experimental conditions. MATLAB/Simulink simulations demonstrated accurate power tracking and stable DC bus voltage regulation across varying irradiance levels and load conditions. Experimental studies, conducted with a 400 W resistive load and real-time DSP implementation, confirmed the robustness and practicality of the system. The hybrid interleaved boost DC-DC converter achieved an efficiency of 96% at a nominal power output of 400 W, while the single-phase full-bridge inverter recorded an efficiency of 94% under the same load condition. The THD of the inverter output voltage was measured to be within the range of 1% to 2%, confirming that the LCL filter effectively attenuated high-frequency components and ensured high-quality power delivery to the load. As illustrated in Figs. 7 and 8, following a step change in irradiance from 800 to 1000 W/m², the proposed MPPT algorithm converges to the new maximum power point within approximately 50–100 ms. This response time is significantly faster than that reported for conventional P&O-based MPPT methods, while maintaining limited transient overshoot and stable DC-side operation. The results confirm the effectiveness of the proposed method under dynamic operating conditions. Quantitatively, the steady-state ripple of P_PV is limited to approximately 4–5 W peak-to-peak, corresponding to about ± 0.5% of the 500 W power. The average tracking efficiency is estimated to be around 99.4%, indicating that the proposed approach can extract nearly all the available PV power with minimal oscillatory losses. These results confirm both the dynamic effectiveness and steady-state robustness of the proposed MPPT scheme.
Additionally, the BMS successfully coordinated energy flow between the PV array, battery bank, and load, ensuring continuous operation under diverse power generation and consumption scenarios. The system maintained operational stability even under transient conditions, such as load switching and irradiance fluctuations.
Future work will focus on extending the control strategy to incorporate advanced battery state-of-charge (SOC) estimation algorithms and improving the efficiency of the whole system by applying soft-switching methods.
The datasets generated and/or analysed during the current study are not publicly available because a standalone dataset was not created for the simulation outputs; however, the simulation data can be provided by the corresponding author on reasonable request. The experimental results are reported as oscilloscope outputs, and no separate dataset is available for these.
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This research was funded by THE SCIENCE COMMITTEE OF THE MINISTRY OF SCIENCE AND HIGHER EDUCATION OF THE REPUBLIC OF KAZAKHISTAN under Grant No. AP23488947.
Faculty of Engineering, Electrical and Electronics Engineering Department, Yalova University, Yalova, Turkey
Naci Genc
Electrical Engineering Department, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Kazakhstan
Naci Genc & Zhansaya Kalimbetova
Faculty of Engineering, Electrical and Electronics Engineering Department, Van Yuzuncu Yil University, Van, Turkey
Hasan Uzmus
Department of Electrical and Energy, Agri Ibrahim Cecen University, Agri, Turkey
Mehmet Ali Celik
Department of Physics, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Republic of Kazakhstan
Sherzod Ramankulov
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Conceptualization, H.U. and N.G.; methodology, H.U., N.G. and M.A.C.; software, H.U., N.G., Z.K.; validation, H.U. and M.A.C.; formal analysis, N.G., Z.K.; investigation, H.U., N.G., Z.K.; writing—S.R., Z.K.; visualization, S.R. and Z.K.; funding acquisition, S.R. All authors have read and agreed to the published version of the manuscript.
Correspondence to Zhansaya Kalimbetova.
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Genc, N., Uzmus, H., Kalimbetova, Z. et al. Current sensorless MPPT method with battery management for PV based single phase standalone system. Sci Rep 16, 9107 (2026). https://doi.org/10.1038/s41598-026-40097-2
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A legend of Australian solar research and development has won more than $150 million in federal Solar Sunshot funding to build a commercial-scale PV panel manufacturing plant in one of the nation’s biggest coal hubs – the New South Wales Hunter Valley.
The federal and NSW governments announced a joint $171 million investment in the Hunter Valley Solar Foundry.
Sunman founder Dr Zhengrong Shi, a UNSW graduate and pioneer of modern solar PV, will lead the project.
Photographed with former directors of the Sydney Theatre Company following the installation of its new solar PV system, Shi has spent the past decade commercialising Sunman’s lightweight, flexible eArc panels.
Sunman already operates large factories in China and the US, but confirmed last year it planned to return manufacturing to Australia.
Solar Sunshot and NSW funding will support a 500 MW-per-year facility producing lightweight and glass modules using local suppliers.
The Foundry will make about 800,000 modules annually, create roughly 300 jobs, and support training programs with TAFE NSW and First Nations groups.
Bowen and ARENA said the project strengthens supply chains, boosts regional jobs and marks a homecoming for Shi’s Australian-led solar innovation.
Shi said the Foundry will be Australia’s largest solar module manufacturer and hopes it will anchor a full domestic solar supply chain.
The project joins broader Solar Sunshot initiatives supporting companies like Tindo Solar and ongoing bids from SunDrive, with further funding rounds still underway.

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WATERTOWN, New York (WWNY) – A rare ecosystem could be at risk if plans for a new solar farm continue as is.
The habitat is what’s called an “Alvar community,” a place where unique plants and animals prosper.
The town of Lyme is home to one, but wildlife experts say it could be in danger if plans for a new solar farm move forward.
“At the globally rare level, Alvar plant communities are designated as globally imperiled, which is the most rare condition,” said Todd Bittner, Natural Areas Director for Cornell Botanic Gardens.
You’d find one such Alvar community in Jefferson County. Just outside of Chaumont in the town of Lyme.
While not in season right now, at certain times of the year, this Alvar ecosystem has plant life that’s rare to find.
Plants like the Indian Paintbrush.
“It has some kind of, it looks like a flame coming off the top of a plant from a distance, and if you’re not in an Alvar plant community, you’re not going to find it,” said Bittner.
It’s plant life like that that nature groups are concerned would be destroyed if energy company AES built its proposed Limestone solar farm. Construction would overlap with the ecosystem.
It’s not just plant life that’s rare here; some animals are too.
Retired DEC wildlife biologist and resident birder Irene Mazzocchi says very rare birds like the short-eared owl often come here for the winter, and occasionally nest here.
Mazzocchi says that for all wildlife, they like to have 80 to 100 acres for breeding and winter habitats.
“To put these solar projects in these big open fields, I really have a concern with. I am not against solar at all. I’ve got solar panels at my house. I just believe they need to be placed in an area that makes sense,” said Irene Mazzocchi.
Where the short-eared owl and other local wildlife would go if the solar farm were built is impossible to say. AES was going to submit plans for state approval last September, but at a meeting last night, a member of Lyme’s Responsible Solar committee says AES officials said the plans would be submitted this Summer. We reached out to AES for comment, but did not hear back.
Copyright 2026 WWNY. All rights reserved.

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Topic: U.S. residential solar photovoltaics  Statista
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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 increases^4,5.
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 operations^10,11. 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 practice^9,12. 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 boosting^17,18. Support vector regression has also been adopted for high-dimensional nonlinear forecasting^17,19. 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 forecasting^20,21,22. Performance gains have been reported via parallel structures, feature selection, CNN integration, and attention augmentation^20,23,24,25. 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/seasonality^30,31. In PV-specific contexts, multi-scale and hybrid designs combine Transformers with CNNs/GRUs or decomposition modules^32,33,34, and domain-enhanced Transformers inject domain knowledge or nonlinear dependency modeling to improve robustness^35,36. 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 strengths^38,39. 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 settings^41,42. 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 deployment^9,12. 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 forecasting^14,15.
Interpretability integration gap: insufficient task-specific validation of interpretability when integrating KAN-style components with attention-based architectures^38,39.
Evaluation methodology gap: limited systematic assessment under seasonal regime shifts^9,12.
Benchmarking consistency gap: inconsistent protocols across model families impede fair comparison and understanding of when interpretable neural components help²¹.
Building on existing components^14,15, 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. Future work will extend the evaluation to multi-site datasets and round-the-clock settings, generalize the framework to multi-horizon forecasting, and incorporate uncertainty quantification to produce calibrated prediction intervals suitable for risk-aware operational decision-making.
The dataset used in this study is sourced from the State Grid Corporation of China New Energy Power Generation Forecasting Competition (^{?}). Comprehensive experiments were conducted on this authoritative dataset to verify the superior performance of the proposed model under different seasonal conditions. The dataset is publicly available and can be accessed at the following link: https://www.nature.com/articles/s41597-022-01696-6#citeas.
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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
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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.
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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 Rep 16, 10336 (2026). https://doi.org/10.1038/s41598-026-39797-6
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SUMTER, S.C. — A proposed solar farm project in Sumter County has been withdrawn, bringing an end to a contentious debate that drew a strong community response.
In a new filing with the Public Service Commission of South Carolina, TOCE SC Solar One requested to withdraw its application to build and operate a 170-megawatt solar facility in the county.
The decision follows a recent public hearing at which residents voiced concerns about the project, particularly its potential impact on farmable land and the local decision-making process.
“I don’t want to share my planet with a solar farm,” Tanya Eddins said.
Eddins was among those who spoke out against the proposal during the March 18 meeting.
“It was the right thing to do. I’m not, I’m gonna kind of twist it a little bit to look at it from their perspective because they don’t, the company, which is a multinational global corporation headquartered in Australia, they don’t have any ties to this area,” she said. “They kept trying to say that, you know, we want to be good neighbors. But all of their actions showed that they really didn’t.”
State lawmakers say the issue could lead to broader discussions about how solar projects are regulated across South Carolina.
“If we want to build a society that all economic levels can participate in, that, you know, all folks of whether you’re rural or urban can breathe clean air and drink clean water, but also have dignified work that is suitable to raise a family, I don’t think we, I don’t think those two things are, are in competition to one another,” South Carolina State Senator Jeff Zell said. “And so, I think the more we talk about this, the more advocacy that the media, you know, brings to it and light they shine on it, the more the public is involved and demands these things, the more you’ll see legislators focus in on it.”
The withdrawal effectively ends the current proposal, though discussions about solar development and regulation in the state are expected to continue.

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A new study titled ‘Closing the UV-Induced Photodegradation Gap Through Global Scale Modelling of Fixed Tilt and Tracking Photovoltaic Systems,’ has highlighted the growing impact of ultraviolet radiation on photovoltaic performance, warning that current industry testing standards may significantly underestimate long term degradation risks, particularly in high irradiation regions.
Ultraviolet radiation is known to accelerate wear at both cell and module level, reducing efficiency and shortening operational life. However, the study finds that widely used testing frameworks such as IEC 61215 photovoltaic module standard fail to replicate real world exposure conditions. In some regions, standard test doses of 15 kWh per m2 can be reached in less than 50 days, raising concerns about the adequacy of existing reliability benchmarks.
Researchers developed a high precision model to estimate UV radiation on tilted solar panels, incorporating solar position, atmospheric conditions and system design. The model demonstrated strong accuracy, with deviation below ±4.28% when compared with observed data and even lower bias of under 1.6% against simulation and radiometer measurements.
The findings show significant global variation in UV exposure, ranging from below 30 W per m2 in high latitude regions to above 80 W per m2 in arid zones. This variation has direct implications for system design and performance. Notably, single axis tracking systems consistently recorded higher UV exposure than fixed tilt installations, resulting in approximately double the degradation rates in arid and semi-arid climates.
The study also confirms that environmental conditions play a decisive role. Tropical, arid and semi-arid regions experience higher UV driven degradation due to a combination of elevated temperatures, strong irradiation and, in some cases, humidity effects. Even in dry regions, clearer skies and higher temperatures contribute to intensified UV stress on solar modules.
One of the most significant findings is that UV induced degradation alone can account for nearly 25% of total annual performance loss in monocrystalline silicon modules operating in high exposure environments. This could shorten system lifetimes by 7 to 10 years, increasing maintenance requirements and negatively affecting the levelized cost of electricity.
The research underscores that identical solar technologies deployed in different regions may perform very differently over time. As a result, the authors call for regionally adaptive testing protocols and more climate specific reliability assessments to better reflect actual operating conditions.
For developers, manufacturers and investors across high solar resource regions such as Africa, the implications are clear. Improved modelling of UV exposure and degradation will be critical for accurate lifetime forecasting, material selection and overall project bankability as the continent continues to scale up solar deployment.
Author: Bryan Groenendaal






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A solar farm on property containing a former Xerox landfill in Webster received another approval in a split Town Board vote on April 2.
The industrial use permit for the solar farm was approved by a 3-2 vote, with Town Supervisor Alex Scialdone, Deputy Town Supervisor Nick Hunter and Councilwoman Jennifer Wright in favor. The vote came after a lengthy public comment period where the solar project, Webster Solar Garden, was a topic of discussion, with some for and some against. The approval comes with several contingencies: approval of a host community agreement, decommissioning plan and an amended site plan approval by the town’s planning board.
The host community agreement and decommissioning plan will both have to come before the Town Board for approval.
The site plan for the solar project was previously amended to remove solar panels from the capped landfill on the property north of Caracas Drive. As a result, the 5-megawatt project will have about 20% less capacity. The project is being developed by Montante Solar, a Buffalo-based company.
The design calls for no ground penetration to secure the panels, which will be weighted and ballasted to limit soil disturbance.
Any trees removed will be cut flush with the ground to avoid disturbing ground on the site. Some additional trees will be planted along the north of the leading edge of the site. The newly planted trees will be a minimum of 10 feet tall when planted in the spring, likely eastern red cedar or Norway spruce. Controls will be put in place to control soil erosion on the site during and after installation of the panels, which will remain in place for as long as 35 years.
Councilman Garrett Wagner floated the idea of pushing the approval vote until the board’s second April meeting, but representatives for Montante Solar pushed back, arguing the company had already provided all of the relevant information.
Steve Howe reports on suburban growth, development and environment for the Democrat and Chronicle. An RIT graduate, he has covered myriad topics over the years, including public safety, local government, national politics and economic development in New York and Utah.

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The Attorney General of Texas, a US state legal authority, has launched an initiative targeting alleged fraud and deceptive practices in solar panel system sales. According to the statement, Civil Investigative Demands have been issued to Freedom Forever, LLC, SunRun, Inc., Lone Star Solar Services LLC, and CAM Solar Inc. The source stated that over 100 complaints were filed with the Office of the Attorney General (OAG) against these companies, while thousands more complaints were available online. The investigation has focused on possible violations of the Deceptive Trade Practices-Consumer Protection Act, including alleged misrepresentations related to electricity bill savings, system efficacy, equipment implementations, and company terms and policies. The demands have also sought records on bill tracking, warranties, service plans, marketing materials, and contract information, while the statement added that legal action may follow against corporations found to have broken the law.

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Sunday, April 5, 2026
A material touted as a candidate for next-generation solar technology is showing more promise, with Australian scientists behind its success.
The next-generation of solar technology could be cheaper, more efficient and a step closer to reality after advances by a team of Australian scientists.
The group, from the University of NSW, published evidence they had improved performance from a promising solar cell material this week and broke international records doing so.
But the achievement, verified by the CSIRO, will need to be followed by additional research before it can be deployed on rooftops or even windows.
The announcement comes after Australia set a record for solar energy generation last year, becoming responsible for more than 12 per cent of the nation’s power, and after more than 4.2 million households installed rooftop solar panels.
The researchers, from UNSW’s School of Photovoltaic and Renewable Energy Engineering, investigated ways to boost the efficiency of a material called antimony chalcogenide that has been tipped for use in future solar technology.
The findings, published in the journal Nature Energy, showed the group was able to boost the power conversion efficiency of the material to 11.02 per cent in a university lab and to a rate of 10.7 per cent as certified by the CSIRO.
The world-leading result could keep antimony chalcogenide in the running as a candidate for more efficient solar technology, UNSW Professor Xiaojing Hao said.
The next generation of solar panels would be designed with tandem cells, she said, in which two or more solar cells were stacked on top of one another.
“What researchers around the world are trying to work out is what material is best to use as the top cell in partnership with a traditional silicon cell,” she said.
“Antimony chalcogenide is one of those and (seems) very positive, especially given its distinct properties.”
The material is abundant and inexpensive to use, is more stable than other candidates, and can be deployed in a layer much thinner than a human hair to improve energy efficiency.
The researchers identified a barrier to its use in the uneven distribution of sulfur and selenium, and Dr Chen Qian said they addressed it by adding sodium sulphide during the manufacturing process.
“It was like driving a car up a steep slope,” he said
“If you do that, you need more fuel to get to the end, whereas if the road is flat it’s more efficient to reach there.”
Further research would involve adding chemical treatments to the material to improve its output, Dr Qian said, and research would continue over the “next few years”.
“We believe an achievable aim is to increase the efficiency up to 12 per cent in the near future by addressing the challenges that still remain, one step at a time,” he said.
Further breakthroughs could be used in the university’s spin-off company, Sydney Solar, which is developing transparent solar stickers that promise to generate energy from windows.
AAP

Journalist covering technology, transport, AI and renewable energy at AAP
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Sunday, April 5, 2026
South Australia’s Liberal Party has unveiled a $20 million policy pledge to deliver discounted rooftop solar systems for rental properties in Australia’s most renewable state, in a last-minute pitch to voters ahead of the March election.
The SA Liberal Party and its new leader Ashton Hurn on Sunday promised $40 million to fund two solar funding programs, one for renters and one for small businesses, in an effort to “make life more affordable.”
The announcement, published solely on Hurn’s Facebook account, says a $20 million fund would target landlords, offering a $2,000 grant plus a $2,000 interest free loan to install solar panels on their residential properties.
A second $20 million pool of funding would go to small businesses, offering access to up to $50,000 for purchasing and installing rooftop solar panels and batteries.
Hurn’s Liberals are facing bleak polling ahead of the March 21 poll where they will face off against the incumbent Labor Party, under the leadership of Peter Malinauskas, who has been in office since the 2022 election, and a resurgent One Nation Party.
Like the Queensland Liberals, the SA Libs’ pledge to drive rooftop solar uptake on rental properties is overshadowed by a distinct change of tone on renewables, aligning more closely with federal Liberal policy.
Since taking the helm in December, Hurn has suggested the party will abandon South Australia’s target of 100 per cent net renewables by 2027 – a target first set under the Marshall Liberal government in 2019.
South Australia, the country’s most advanced renewables grid, has averaged around 75 per cent renewables over the last 12 months and regularly hits levels of more than 100 per cent renewables.
The state government has an official target of reaching an average 100 per cent “net” renewables by the end of 2027, helped by the completion of a major new transmission link to NSW, and its growing fleet of big batteries which should reach around 20 by that time.
“Net” renewables means that the state will export surplus power to neighbouring states when needed (the output below zero in graph above), and also import power from those states at times (the purple blobs). Big batteries also play a prominent role (in blue), and account for up to 40 per cent of state demand at times in the evening peak.
Nevertheless, a policy promising to boost the rollout of solar on rental properties in the state has been welcomed, including by Solar Citizens, who called on South Australia’s other political parties to match or better the pledge.
“Right now one third of Australians who rent are currently locked out of the cost of living relief and carbon reduction benefits that cheap, clean rooftop solar power (backed by storage) provides,” said Heidi Lee Douglas, Solar Citizens CEO.  
“A four-person household with rooftop solar saves around $1,400 per year on their electricity bill.
“It’s encouraging that the SA Liberals are in step with the Queensland Liberal Government – who launched their Supercharged Solar for Renters scheme late last year. That scheme offers eligible landlords rebates of up to $3,500 to install solar on their rental properties.”
Queensland’s Liberal-National Party government launched its Supercharger Solar for Renters scheme in December – two days after they dumped the state’s renewable energy targets and pledged billions of dollars to prop up coal power.
With the South Australian Liberals also stepping up to the plate to advocate for solar for renters and businesses, the logic behind the Liberal Party’s efforts to hinder renewable energy development continues to unravel.  
So far, the SA Liberals have not followed their federal counterparts’ in abandoning net zero. Hurn, however, also announced last month that a state Liberal government would “secure gas power generation for the future” under the guise of keeping “power bills under control”.
The duelling policy promises serve to highlight the current division in the Liberal Party in Australia and its inability to serve both its fossil fuel masters and the needs of Australians.
While welcoming the policy promise, Solar Citizens nevertheless thinks the SA Liberal Party can go further.
“The SA Liberal policy would be even more effective if it included rebates and loans for energy efficiency upgrades, and policy support for Mandatory Minimum Energy Efficiency in rental homes,” added Lee Douglas.
“This is an important policy gap that should be addressed this state election. South Australia does not have legislated, comprehensive, and mandatory minimum energy efficiency standards for rental properties.”
Solar Citizens’ Lee Douglas also highlighted that South Australia’s current policies and promises focus only on free-standing rental homes but not apartments.  
“That’s why we are calling for South Australian political parties to commit to a solution for people seeking solar for apartments: Urban Renewable Energy Zones (UREZ) pilots,” said Lee Douglas.
“This would activate big roofs in our town and cities by installing medium-scale rooftop solar and batteries on large commercial, industrial and public buildings, so this cheaper, clean energy can be shared locally to nearby rental homes and apartments.  
“Solar Citizens urges State, Local and Federal levels of government to work together to develop (UREZ) pilots, including in South Australia.”
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By Miriam Raftery
Photo:  proposed Starlight Solar site, via County of San Diego
April 4, 2026 (Boulevard) – Should a 588-acre solar project with one of the state’s largest battery energy storage sites be built near rural Boulevard, a small town located in a very high fire hazard zone?  The County Planning Commission may vote May 8 on the Starlight Solar project, which supporters say is needed to help meet state and county green energy mandates.

Opponents have launched a No Starlight Solar website. They’re urging concerned residents to contact county supervisors, as well as attend an April 15 town hall meeting at 6 p.m. at the Boulevard Resource Center (39919 Ribbonwood Road) with Andrew Hayes, a representative from Supervisor Joel Anderson’s office.
“Boulevard’s dark skies, safety, and desert quiet are under threat,” the No Starlight Solar site states.  “We support clean energy — but not projects that put rural communities at risk or concentrate industrial energy infrastructure in one small town.”
The site adds, “We are not anti-solar. Renewable energy is essential. What residents are questioning is the pattern: Boulevard, Jacumba, and Campo have increasingly become the de facto dumping grounds for regional energy infrastructure. Projects like the nearby JVR Energy Park in Jacumba show how rural East County is being rapidly industrialized while most of the region’s energy demand lies elsewhere… Residents deserve the same level of safety, transparency, and environmental protection that would be expected anywhere else in San Diego County,” the No Starlight Solar site asserts.
The project’s photovoltaic solar arrays could generate up to 100 megawatts (MW) of electricity.  It would also include inverters, transformers, an onsite substation, gen-tie line connecting to Boulevard’s substation, and most controversially, a 217.4 MW battery energy storage system (BESS).  Similar battery energy storage systems have caused numerous fires, sometimes burning for days and forcing evacuations of nearby residents and businesses, also potentially leaking toxic materials. The project’s own safety documents acknowledge major uncertainties regarding lithium battery fire response.
The site, situated south of Old Highway 80 and north of the international border, is less than a mile from Clover Flat Elementary School, near key evacuation routes, and encompasses land that is home to sensitive species.  The land is owned by the Haagen Company.
In a March 2026 KPBS article, San Diego City College assistant professor John Bathkey coined the term “green colonialism” to describe the foisting of industrial-scale renewable energy projects on disadvantaged communities and Native American tribes.
The fears are not unjustified. Some green energy projects already built in East County and adjacent Imperial County have posed threats to residents, including wind turbines bursting into flames and sparking fires, multi-ton blades hurled off, chemical leaks, stray voltage, noise and visual blight.
Former Boulevard Community Planning Group Chair Donna Tisdale described the region as an “energy sacrifice zone” after the county approved numerous massive wind and solar projects across the rural southeastern part of the county. Tisdale recently moved out of state, after unsuccessfully fighting to oppose projects approved by the County, including a huge wind project adjoining her own ranch. Community concerns over noise, fire danger, visual blight, and potential depletion of ground water among others were largely ignored.
The Boulevard Planning Group, now under leadership of Earl Goodnight, has since approved the Starlight Solar project, but with conditions. The group has asked for a $7 million community benefit fund and increased setbacks from homes and roads. But their vote is purely advisory, not binding on county planners or ultimately, supervisors if the planning commission’s decision is appealed.
Goodnight has said the community planners believed the county would likely approve the project, so opted to try and get as many benefits for the county as possible, noting, “When a project gets to this point, then there’s little that can be done,” KPBS reported.
But Jim Whalen, the land use consultant hired to lobby for the project, says Starlight Solar’s proposal does not include a community benefit fund for Boulevard, though he adds that Haagen would refurbish Boulevard’s Backcountry Resource Center.
The project, which would create glare off a sea of solar panels, also threatens Boulevard’s efforts to pursue International Dark Sky Community designation, which Borrego Springs and Julian have already attained. It also raises concerns over Native American cultural resources and whether adequate tribal consultation has occurred.
Perhaps most significantly, the concentration of multiple massive industrial-scale energy projects is changing the region’s rural character—the quiet, opens spaces, natural landscaping and starry night skies that attracted residents to the area, some of whom have lived here for generations.
Why the push for the project
California has enacted a mandate to produce 100% of the state’s power from renewable resources by 2045. San Diego County has a similar goal to attain zero carbon emissions by 2045. The City of San Diego and San Diego Community Power, which provides much of the region’s energy, have an even more ambitious goal of producing all of its electricity from clean, renewable sources a decade sooner, by 2035.
Those mandates and goals are driven by climate change, which is rapidly heating the planet, fueling record-breaking heat worldwide as well as unprecedented severe storms, dry conditions across the Southwest and increasingly severe wildfires.
What opponents want
Residents opposed to the project are urging people to come show solidarity at the April 15 meeting.
They’re also urging the public to contact County Supervisors and ask each Supervisor to oppose the project or, if the project is approved, to assure that battery storage is moved away from Jewel Valley Road, a key evacuation route. Opponents also want a second evacuation route added, an alarm system to warn residents of a BESS fire, a fire truck dedicat4ed to the Starlight Solar project, expanded on-site water storage, as well as limits on construction hours, expanded setbacks from homes, roads and property lines, and a $7 million community investment benefits package, among other things.
In the Pine Valley Neighbors forum on Facebook, Minty Stars urged area residents to attend the April 15 meeting in support of neighboring Boulevard’s residents.  Of the solar project’s owners, she wrote, “they are trying to make as much money as possible and hurt anyone/any land and any resources in the process.” 
 

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From efficiency and power output to warranty cover and long-term performance, this guide compares the best solar panels for UK homes in 2026
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With energy bills still a concern for many households, more people are now asking the same question: are solar panels worth it? But choosing the best solar panels isn’t always straightforward. Once you’ve decided to invest, the next question is which models are actually worth paying for. Is higher efficiency worth the extra cost? Which brands have a strong track record? And which panels are built to keep performing for decades?
To help answer those questions, I reviewed the best solar panels for UK homes in 2026, looking beyond headline wattage and marketing claims to focus on what matters in practice. I compared leading models on efficiency, power output, cost, warranty cover and long-term degradation, while also considering installer feedback and real-world use. The result is a practical guide to the panels best suited to British homes, budgets and long-term savings.
There are no major solar panel manufacturers currently producing at scale in the UK, but the models included here are among those most widely recommended by UK solar panel installers and best suited to British homes, climate and light levels.
The result is a practical ranking designed to help you find the best solar panels for your home, your budget and your long-term savings potential, without overpaying for features you may not need or taking a chance on untested brands.
This updated guide brings all those insights together to help you choose a system that fits your budget and energy goals. Alongside our rankings, you’ll find a breakdown of the cost of solar panels in the UK today, plus expert advice to ensure your investment pays off for decades to come. Answer a few quick questions above to compare free solar panel quotes in your area.
Most UK homes are fitted with monocrystalline solar panels, which are typically the most efficient and best suited to limited roof space. You may also come across polycrystalline panels, though these are now less common, and thin-film panels, which tend to be used in more specialist or commercial settings rather than on standard homes.
The right option depends on your roof size, budget and how much electricity you want to generate. In most cases, homeowners comparing the best solar panels will be choosing between different types of monocrystalline panel, with variations in efficiency, appearance and warranty cover. You can read our full guide to the different types of solar panels for a more detailed breakdown of how each one works and which is best for your home.
Panel
Price per kW, installed
Efficiency
Wattage per panel
Type
Made in
Degradation
Warranty
SunPower Maxeon 7
£1,250
24 per cent
475W
N-Type
Malaysia and the Philippines
90.8 per cent after 30 years
25 years
DMEGC Infinity
£795 to £1,195
23 per cent
440W to 460W
N-Type
China and Indonesia
87.4 per cent after 30 years
25 years
REA Fusion R Solar Panel
£700 to £820
23 per cent
460W
N-Type
Australia
90.3 per cent after 30 years
25 years
Perlight Black Grid
£895 to £1,195
26 per cent
500W
China
87.4 per cent after 30 years
30 years
Aiko Neostar
£1,250
23 per cent
460W
N-Type
China
88.90 per cent after 30 years
25 years
Jinko Tiger
22 per cent
440W
N-Type
China, Malaysia, the US, and Vietnam
87.4 per cent after 30 years
25 years
What sets the Maxeon 7 apart is its Interdigitated Back Contact (IBC) cell design. Unlike conventional panels, all electrical contacts sit behind the cell, reducing resistance and improving durability. This design also makes the panel more resistant to micro-cracks, corrosion, and heat-related efficiency loss – key factors over a 30–40 year lifespan.
Degradation performance is among the best we’ve seen. After three decades, the Maxeon 7 is expected to retain more than 90 per cent of its original output, meaning it continues generating meaningful savings long after many panels have declined.
The high output per panel makes it suitable for homes with limited roof space, while its relatively light weight simplifies installation. In real-world use, installers consistently cite reliability and consistency as standout strengths.
Installers and homeowners regularly praise SunPower panels for long-term reliability and low fault rates. Reviews tend to highlight peace of mind, consistent generation, and strong aftercare support when installed through approved partners.
For those seeking top-tier solar solutions, Maxeon Solar Technologies stands as a steadfast leader in the PV sector, backed by a dedicated network.
Pros
Cons
Why SunPower Maxeon 7 is best overall:
It scores highest across efficiency, durability, and warranty strength. While expensive, no other panel offers the same level of long-term certainty for homeowners who want the most dependable option available.
Read the full SunPower Maxeon 7 solar panel review
The Infinity’s key strength is its balanced degradation profile. While it doesn’t quite reach the 90 per cent benchmark of the very top performers, retaining more than 87 per cent output after 30 years is still well above industry averages.
Its N-type cells slow long-term performance loss and reduce light-induced degradation — a common issue with older P-type panels. Combined with a robust frame, anti-glare coating, and solid heat tolerance, the Infinity is well suited to long-term UK use.
Installers often highlight the panel’s consistency rather than any single standout metric, which is exactly why it works so well for a wide range of homes.
DMEGC panels tend to be reviewed positively when installed by reputable UK installers, with customers noting steady generation and few post-installation issues. Feedback commonly reflects satisfaction rather than flashiness.
Being able to trust in the longevity of a company that has been around as long as DMEGC, its skills in electrical manufacturing and as a Tier-1 solar manufacturer, gives us peace of mind that we are supplying our customers with quality.
Pros
Cons
Why DMEGC Infinity is best for longevity:
It offers dependable performance over decades without the premium price tag of ultra-long warranties, making it a sensible long-term investment for most households.
Read the full DMEGC Infinity solar panel review now
Its standout feature is the combination of N-type cells with Heterojunction Technology (HJT). This pairing improves low-light performance and slows degradation, allowing the panel to retain more than 90 per cent of its output after 30 years — exceptional at this price point.
Optional micro-inverter compatibility adds further value, allowing each panel to operate independently. This improves resilience to shading and boosts generation during cloudy conditions.
The bifacial design can also increase output by capturing reflected light, particularly on lighter-coloured roofs.
Customers installing REA panels through major UK installers often comment on value for money and unexpectedly strong performance, with fewer complaints than typically seen in budget-tier products.
REA Fusion panels combine advanced N-Type and HJT cell technologies, offering the best of both worlds: N-Type cells minimise long-term efficiency degradation, while HJT (Heterojunction Technology) enhances performance in low-light conditions. This powerful combination makes the Fusion 2 the go-to choice for the UK market, delivering maximum generation, even in our famously gloomy climate.
Pros
Cons
Why REA Fusion R is best budget option:
It delivers premium-grade degradation and modern cell technology at a genuinely affordable price.
Read the full REA Fusion R solar panel review
The key differentiator here is power density. With both high efficiency and high wattage, the Black Grid produces more electricity per square metre than any other panel listed, which makes it a favourite among many of the best solar panel installers.
Its bifacial construction and reinforced frame improve resilience, while the long 30-year warranty adds confidence. Although degradation is slightly weaker than some rivals, overall lifetime output remains strong due to the high starting efficiency.
Perlight panels are frequently mentioned positively by installers for build quality and output. Consumer reviews tend to reflect satisfaction with the generation rather than brand loyalty.
A 30-year product warranty is hard to come by in any product, but Perlight offers one of the longest warranties available in the market.
Why Perlight Black Grid is best for efficiency:
It enables homeowners to generate the maximum energy possible from their roof, even if they’re short on space.
Read the full Perlight Black Grid solar panel review
The Aiko Neostar delivers high output in a compact, lightweight format, making it ideal for space-constrained rooftops. These panels deliver the best combination of compact design and high power density, with each panel producing 460W at 23 per cent efficiency while maintaining a relatively light and slim build. The panels also boast excellent durability, retaining almost 89 per cent of its output after 30 years.
With new generation All Back Contact (ABC) technology, Aiko Neostar panels supply excellent efficiency, and their entirely black design is aesthetically pleasing and looks great on any rooftop. They represent a premium option built for homeowners in search of the latest in tech and the best performance from a small-scale system.
The panels also feature cell-level partial shade optimisation, which improves energy yield even when parts of the array are shaded. This means a consistent output will be delivered, despite changing skylines or nearby trees.
Durability is another strength. Aiko highlights its micro-crack resistance technology, ensuring panels withstand impacts from hail, branches, or debris. This robustness, coupled with its sleek all-black aesthetic, makes the Neostar both practical and visually appealing.
At just 21.5kg, it’s lighter than many rivals, which reduces strain on roofs and simplifies installation – another advantage for smaller properties.
Why Aiko Neostar is best for small roofs:
With strong per-panel output, lightweight construction, and shade optimisation, the Aiko Neostar is the best choice for homes with limited space. It helps smaller rooftops achieve big solar gains without compromising on quality or longevity.
Read the full Aiko Neostar solar panel review now
The Jinko Tiger is optimised for consistent generation in weak or diffuse light, making it particularly well suited to the UK climate.
The panels have an advanced N-type cell construction to maintain higher energy output even under weak sunlight, whether early morning, evening, or cloudy UK days, ensuring steadier performance throughout the year. So, for the UK’s famously overcast skies and shorter winter days, the Jinko Tiger is the standout choice. N-type cells are also slower to degrade and are resistant to salt corrosion, making them a great pick for coastal properties.
Jinko Tiger panels deliver reliability and performance with robustness and high energy output in weak light conditions. They’re a great choice for customers who want quality, affordability, and performance from one of the best solar manufacturers in the world.
These panels are also mid-weight and relatively small compared with some other options, making them a practical fit for most UK rooftops.
Jinko is one of the most frequently reviewed solar brands globally, with homeowners often citing their reliability and steady year-round output.
Why Jinko Tiger is best for UK weather:
With its emphasis on low-light performance and proven global reliability, the Jinko Tiger is the most dependable option for homeowners who want to generate energy in Britain’s cloudy climate.
Read the full Jinko Tiger solar panel review
Choosing the best solar panels that UK homeowners can trust means balancing technical performance with real-world experience. To create this guide, we developed a clear scoring system and combined it with expert insight and real consumer feedback.
Every solar panel was rated across five core factors, each on a simple scale of one to five:
Each factor was given equal weight to produce an aggregate score out of 25. Panels that scored consistently high across multiple categories were ranked more favourably than those that excelled in just one area.
Panel
Efficiency
Cost
Wattage
Warranty
Degradation
Total (out of 25)
DMEGC Infinity
3
4
4
3
3
17
SunPower Maxeon 7
4
2
5
5
5
21
REA Fusion R
3
5
4
3
5
20
Perlight Black Grid
5
3
5
4
3
20
Aiko Neostar
3
2
4
3
4
16
Jinko Tiger
2
2
2
3
3
12
Numbers only tell part of the story. By combining technical specifications, expert recommendations, and consumer sentiment, our methodology ensures that this guide reflects both the science of solar panels and the lived experience of UK homeowners.
Read more: My honest review of Sunsave solar panels
To get a thorough understanding of the best solar panels, we also looked at customer satisfaction through Trustpilot reviews, Google reviews, and independent forums. We also spoke directly to consumers who have purchased solar panels. This feedback helped us understand how different solar panel brands and installers perform when it comes to reliability, service, and post-installation support.
One consumer we spoke with was Justin Webb, a graphic designer and founder of Judmedia. He had solar panels installed more than two years ago. When choosing, he prioritised a clear like‑for‑like kit spec (panels, inverter, battery), long warranties with an MCS‑accredited installer who did an in‑person survey, and a simple single‑brand ecosystem so everything works well together.
While cost was a major consideration for him, he says consumers shouldn’t be too concerned about the return on investment (ROI) in solar. “People always talk about ROI with solar panels, but often forget that there’s no ROI on paying your energy bill, or your gas bill, or your mortgage. You just pay it and it’s gone,” he says. “With solar, I’m fixing my energy price instead. There are so many variables in life that you can’t fix, but energy is one of them. Solar allowed me to fix that cost and future-proof myself against energy price rises.”
And while we’re all concerned about costs, Webb offered the following considerations:
Finally, he suggests not forgetting the basics. Do an energy mini‑audit before you choose your solar panels. Before purchasing panels, he switched all of his lights to LEDs, calculated what his fridge and oven actually draw. The goal, he says, is to reduce waste so that your solar and battery combination can go further and save you the maximum amount of money.
We interviewed leading solar installers, including Glow Green and Solar4Good, and considered guidance from industry bodies such as the Microgeneration Certification Scheme (MCS) and the Energy Saving Trust. These conversations highlighted the importance of installer reputation, aftercare, and real-world durability.
According to Lloyd Greenfield, founder of Glow Green, which sits in our guide to the best solar panel installers, the three factors that should top every homeowner’s checklist are warranty, manufacturer reputation, and cell technology. “There’s a big difference between a panel guaranteed for 30 years and one that only lasts 15,” he says. “You also want a manufacturer with a strong track record, not a new entrant whose panels haven’t been tested in the UK over decades.”
Greenfield warns against focusing solely on price or raw efficiency figures. Many households are quoted for lower-wattage panels to bring down upfront costs, but that can backfire. “If you can get 515W panels instead of 450W in the same roof space, you’ll generate more power and better long-term payback,” he explained. Spending a little more upfront, he argues, often means greater savings over the system’s lifetime.
The choice of installer is just as important as the panel itself. Greenfield advises looking for MCS and NIC accreditations and whether the company uses the Energy Performance Validation Scheme (EPVS). “It’s almost like an insurance policy,” he says. “They check the installer’s design assumptions – things like shading, pitch, and orientation – to make sure the numbers are realistic.” He also recommends checking Trustpilot ratings, years in business, and whether deposits are protected under an insurance-backed scheme.
A big trend in the market, he added, is pairing your array with a battery. Greenfield says that almost every customer now chooses battery storage, with more than 95 per cent of Glow Green’s clients opting for a battery and around 10 per cent coming back within a year to add a second. Falling panel prices and smarter tariffs are making storage even more attractive.
And while Greenfield admits some bias, his personal recommendation is Perlight’s Black Grid panel, thanks to its sleek all-black design, 30-year warranty, bifacial technology, and strong reputation. “In our view, it’s one of the best solar panels in the UK market,” he said.
The Independent has been reporting on green energy and climate matters since it was founded in 1986. Since then, we have written hundreds of reviews and news stories on energy matters, including the best solar installers and various other guides on green power. Jeff Meyer is The Independent’s energy editor. He has written extensively on everything from how you can earn money from solar panels to a guide on whether solar panels are actually worth it. His experience is why you can trust his verdict on the best solar panels. Jeff has conducted extensive research, including consulting industry experts and customers, to gain a thorough understanding of which brands are making the best solar panels.
After you have filled out our form above, you will receive three quotes from solar fitting companies local to you. They may be able to quote based on the information you have given them, or they may need more information, or to visit your home. Once you have these quotes, you can decide if you wish to proceed.
Join thought-provoking conversations, follow other Independent readers and see their replies
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John Palsgrave, a linguist and tutor in Henry VIII’s court, illustrated a point by writing, “He cracked afore we came hyther that he wolde do marvaylles, but nowe he is shronke asyde no man can tell whyther.” The word “cracked” was often used in the Middle Ages to mean “boast,” as in “he cracked on about his battlefield achievements.” 
That archaic meaning gave us the modern expression “not all it’s cracked up to be,” used when something or someone fails to meet high expectations. Davy Crockett once said, “Martin Van Buren is not the man he is cracked up to be.”
The expression is now being used in reference to renewable energy, but not for the reasons you might think. It isn’t that public support has waned or policymakers have weakened. On the contrary, it’s that wind and solar generation is nowhere near the capacity people were promised. Promises like, “We’ve just added 1,000 megawatts of solar capacity.” The actual generation, especially during high demand, is often a small fraction of the announced capacity.
I have studied energy policy much of my life but have never run across any analysis of why “capacity” is measured differently for wind and solar installations than for coal or gas power plants. A brilliant new assessment by my friend Alex Epstein, popular author and speaker and founder of EnergyTalkingPoints.com, explains it. His article “Solar and wind aren’t real power sources, they’re intermittent fuel-savers” is transformational. 
You see, every power generator is rated for the maximum electrical output it is designed to produce under ideal, standardized conditions. The number (in kilowatts or megawatts) is stamped on the equipment’s factory nameplate and is thus called “nameplate capacity.” But it’s apples and oranges for different types of power plants.
For solar installations, that “nameplate capacity” assumes sunny skies, a specific sun angle, standard 75-degree temperatures and clean panels. In other words, a 100-megawatt-rated solar farm can produce 100 megawatts, but only around midday on a perfect day. Output is lower during mornings, evenings, winter, and cloudy days – and zero when it’s dark. 
For wind turbines, “nameplate capacity” assumes wind blowing at an ideal wind speed of 35 mph. So, a 3-megawatt-rated turbine provides 3 megawatts when the wind is in that range. Below that, the system produces less power, and above that it shuts down for safety.
By contrast, gas and coal plants are designed to run 24 hours a day – and can do so because the fuel supply is not intermittent like the sun or wind. So, the nameplate capacity is the actual capacity most of the time. Such plants run less than 100 percent only when cheaper power is available from a different generator. They can reduce power when demand is low and ramp up when demand rises, to supply all the power the grid demands. 
That is the only “capacity” that matters, the ability to generate all the electricity needed at a given time – 24/7/365. And that has always been the primary issue with wind and solar power. The sun doesn’t always shine and the wind doesn’t always blow, so there is always a need for dependable fossil fuel plants, too. That does not make solar and wind useless. In fact, they are sometimes the source of cheaper power that allows the traditional plants to power down, as mentioned above. In that sense, they save fuel during those times, with the resulting emission reductions. 
The cost of those reductions is controversial, compared to generally cheaper fossil fuels, though many Americans concerned about climate change think the higher energy cost is worth it. That is a debate for another column another day. The point of this one is that wind and solar save fuel but can never satisfy the need for energy that is always available.
That became obvious during the January storm that blanketed a third of North America. As Epstein notes, wind provided very little electricity during much of that storm. In one six-hour midday period in the Mid-Atlantic grid, wind provided barely 2 percent of its “capacity.” Solar power was essentially unavailable during times of highest demand, morning and evening. Even in Florida, the Sunshine State, solar installations provided nothing during the peak demand for three days. In New England, solar systems that were installed with great fanfare produced almost nothing for a week. 
Wind and solar can supplement, save fuel and reduce emissions. But if the promise is to replace fossil fuels, then they are not all they’re cracked up to be. 
Greg Walcher is an author and columnist, a fellow at the Common Sense Institute, former head of the Colorado Department of Natural Resources and former president of Club 20.
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Off-grid homeowner shares honest review five months after installing DIY solar panel array  Yahoo
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The UAE capital is expanding its solar self-supply policy to the residential sector, after first launching for the agricultural sector in February.
Image: Po-Hsuan Huang/Unsplash
Abu Dhabi’s Department of Energy (DoE) is launching the second phase of the city’s solar energy self-supply policy, expanding to the residential sector for the first time. 
The policy, which gives customers across the UAE capital the option to adopt solar for self-consumption, was first unveiled in February, with the first phase targeting the agricultural sector. 
The updated policy now allows villa owners and eligible residential buildings to generate and store electricity from rooftop solar systems, either with or without connected battery energy storage systems. It forms part of the DoE’s mandate to promote the adoption of smart and flexible solutions for energy production and consumption.
An update from DOE explains the policy expansion aligns with national objectives to meet increasing demand for energy through advanced solutions serving all sectors. It adds that the new phase will also focus on implementing a simplified regulatory framework that streamlines installation and grid connection procedures.
Abdulaziz Mohammed Al Obaidli, DoE’s Director-General of Regulatory Affairs, said extension to the residential sector will enhance energy consumption efficiency and support the integration of the power system.
The UAE added around 1 GW of solar in 2025, taking cumulative capacity to approximately 6.7 GW, with current deployments dominated by utility-scale installations. Forecasts from GlobalData expect the country’s total solar capacity to reach 20 GW by the end of the decade. 
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A new study titled ‘Closing the UV-Induced Photodegradation Gap Through Global Scale Modelling of Fixed Tilt and Tracking Photovoltaic Systems,’ has highlighted the growing impact of ultraviolet radiation on photovoltaic performance, warning that current industry testing standards may significantly underestimate long term degradation risks, particularly in high irradiation regions.
Ultraviolet radiation is known to accelerate wear at both cell and module level, reducing efficiency and shortening operational life. However, the study finds that widely used testing frameworks such as IEC 61215 photovoltaic module standard fail to replicate real world exposure conditions. In some regions, standard test doses of 15 kWh per m2 can be reached in less than 50 days, raising concerns about the adequacy of existing reliability benchmarks.
Researchers developed a high precision model to estimate UV radiation on tilted solar panels, incorporating solar position, atmospheric conditions and system design. The model demonstrated strong accuracy, with deviation below ±4.28% when compared with observed data and even lower bias of under 1.6% against simulation and radiometer measurements.
The findings show significant global variation in UV exposure, ranging from below 30 W per m2 in high latitude regions to above 80 W per m2 in arid zones. This variation has direct implications for system design and performance. Notably, single axis tracking systems consistently recorded higher UV exposure than fixed tilt installations, resulting in approximately double the degradation rates in arid and semi-arid climates.
The study also confirms that environmental conditions play a decisive role. Tropical, arid and semi-arid regions experience higher UV driven degradation due to a combination of elevated temperatures, strong irradiation and, in some cases, humidity effects. Even in dry regions, clearer skies and higher temperatures contribute to intensified UV stress on solar modules.
One of the most significant findings is that UV induced degradation alone can account for nearly 25% of total annual performance loss in monocrystalline silicon modules operating in high exposure environments. This could shorten system lifetimes by 7 to 10 years, increasing maintenance requirements and negatively affecting the levelized cost of electricity.
The research underscores that identical solar technologies deployed in different regions may perform very differently over time. As a result, the authors call for regionally adaptive testing protocols and more climate specific reliability assessments to better reflect actual operating conditions.
For developers, manufacturers and investors across high solar resource regions such as Africa, the implications are clear. Improved modelling of UV exposure and degradation will be critical for accurate lifetime forecasting, material selection and overall project bankability as the continent continues to scale up solar deployment.
Author: Bryan Groenendaal






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Sunday, April 5, 2026
Soon the one third of Australian renters and apartment dwellers may be able to do what people in Europe, India, South Korea and, oddly enough, Utah, already can: pop a solar and battery system onto their balconies and help power their homes.
The obstacles, as reported by Renew Economy last week, are not small. See: Balcony solar is powering apartments from Berlin to Barcelona. So why not in Australia?
But moves are afoot to correct what is seen by many as a market failure and bring power to the rest of the Australian people.
The technology on offer elsewhere in the world can work in Australia – it’s already being tested here for safety – but changing the rules needed to make regulators, networks and, crucially, conservative strata committees, feel it’s safe will take some effort. 
“The main component is that Australia and New Zealand have slightly different electrical standards than the rest of the world, in terms of the configuration of our networks and things like weathering systems and safety requirements for RCDs,” says Smart Energy Lab general manager Glenn Morris.
“So long as we can prove that none of this is being compromised, [plug in solar and batteries] shouldn’t be a problem.”
Morris is currently testing a 1.4 kilowatt hour (kWh) balcony battery from Anker Solix, a Chinese company keen to prepare for the currently non-existent plug-in opportunity in Australia. 
The Anker balcony battery Morris’ lab is currently testing. Image: Glenn Morris
His lab confirmed the maximum power injected into the circuit is less than 800 watts, or two panels, the maximum that German law allows and which could be a useful guide for Australia.
Morris says it also only works with extra-low voltage solar panels, and the system de-energises within milliseconds so if anyone touches the plug after pulling it out they can’t get shocked. 
Crucially, the battery can also provide 1.2 kWh of backup if the power goes out.
Morris’ work shows the units available overseas can technically work safely here, but others are beginning to push the concept forward from a legal and regulatory perspective. 
Race for 2030 is launching a project to investigate the legal, technical and regulatory barriers to plug-in balcony solar and batteries, and what it will take to change these.
Interestingly, the Victoria government is keen on the project — and it’s not the only government quietly interested in making plug-in energy devices possible. 
Anker Solix sales manager Phil Krok says he’s talking to everyone about the concept, to the point where energy minister Chris Bowen knows him as “the balcony guy”.
“I took a piece of kit down to Canberra in December last year and showed them the physical hardware and walked them through the unboxing, what it does, how it does it, and I talked them through some of the challenges,” he told Renew Economy.
“If the rules changed, I think demand would shoot through the roof, so we would just need to fix this… I bet in New South Wales (NSW) alone there would be appetite for thousands of them straight off the bat.”
Krok is pushing hard for those rules changes and says there is a working group in the federal Department of Climate Change, Energy, the Environment and Water (DCCEEW) looking at this issue.
He’s not the only one it’s talking to.
Davood Dehestani, the founder of 3.5 kWh battery-for-renters company Smartizer, says he also had some chats with people from DCCEEW who were keen to understand what regulatory changes might be needed to make plug-in solar-batteries feasible. 
A DCCEEW spokesperson declined to admit the department has any work on plug-in solar products underway, saying instead the federal government is “already” delivering other consumer energy equity initiatives.
Dehestani says the UK is looking to have plug-in consumer energy devices approved and available by the end of this year, and like Krok, hopes a similar outcome could happen for Australians soon. 
With Anker Solix pushing for change and homegrown companies emerging, when Australia throws the doors open to balcony solar there will be no shortage of competition or innovation. 
Just this week, Chinese company Growatt released its own balcony battery, a 5 kWh unit designed specifically to plug into a max 2.5 kilowatt (kW) solar system. 
It can run critical devices during a power cut, can work in temperatures as low as -30ºC, and comes with software that will tap into dynamic tariff pricing by exporting when prices are high and importing when low.
“Energy systems must evolve with how people live today,” Growatt CEO David Ding said in a statement.
Ding’s comment is one that has many Australians – including people spoken to by Renew Economy this week who work at the entities that make this country’s energy system function – baffled as to why they’re locked out of solar and batteries just because they live in an apartment or rent.
Energy consultant Gabrielle Kuiper points out that if Australia had a distributed energy resources (DER) technical standards regulator running now, as has been on the federal drawing board for years, it could have been working through the issues preventing plug-in devices.
“They could have been working through the challenges and opportunities for balcony solar from a technical perspective, and ensuring that we had the standards in place so millions of Australian households could have access to this incredibly affordable technology without requiring an electrician to wire it in which makes the payback a lot longer,” she told Renew Economy.
Indeed, one distribution network service provider (DNSPs) even told Renew Economy they’re happy for people to plug in, so long as the devices are legal. 
“We will approve CER products of any kind to connect to our networks, so long as they comply with the relevant Australian standards (AS/NZS 4777) and are approved for use by the Clean Energy Regulator,” says Powercor’s head of customer connections Dan Bye. 
“Once that happens, we will approve those products to connect in our network areas.”
But there are reasons why the dream is yet to be realised, as Renew Economy reported this week.
Letting people in Australia plug in where and when they wish is not simple: there are myriad versions of ancient technology still used to measure and manage electricity flows into apartment buildings in this country. 
And then, despite NSW’s ban on anti-green bylaws, conservative body corporate committees in the rest of the country tend to take a ‘no’ route out of fire fears.
Another DNSP told Renew Economy that, quite aside from any regulatory challenges, Australian Standards for electrical wiring in a residential home and battery installation rules would need to change.
Australian Standards, the technical specs for goods sold here, would also need to change to include plug in solar and batteries. 
But there is another way: a motivated government could override these by making new regulations, Morris points out.   
“The normal approach is that any electrical appliance that generates electricity has to meet an Australian Standard,” he says. 
“But there are other means to get approvals such as by government regulation. I have heard it mooted that this [plug in solar and batteries] could be a case where the government steps in to override the standards.”
So while it feels like the world is moving onto the next generation so solar and batteries without us, perhaps Australia won’t be left behind for much longer.
You can find out more about balcony solar in this interview with Brent Clark, the CEO of apartment-focused energy consultancy Wattblock,  on this episode of the SwitchedOn Australia podcast.
If you would like to join more than 29,000 others and get the latest clean energy news delivered straight to your inbox, for free, please click here to subscribe to our free daily newsletter.
Rachel Williamson is a science and business journalist, who focuses on climate change-related health and environmental issues.
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Geopolitical crises have repeatedly exposed Bangladesh’s excessive dependence on imported fossil fuels, driving up energy prices and depleting foreign exchange reserves. The ongoing war in the Middle East has once again highlighted this structural vulnerability. To mitigate such external shocks, Bangladesh must urgently expand its domestic renewable energy capacity. But to what extent does the government demonstrate genuine commitment to a renewable energy transition? And if such intent exists, are current policies and strategies being implemented effectively? The answer, unfortunately, appears to be no. While financing constraints, investment gaps, and taxation remain the primary barriers to renewable energy expansion in Bangladesh, another critical issue has received far less attention: barriers related to imported renewable energy components.
Renewable energy systems typically rely on high-quality components that can deliver efficient, stable, and durable performance under challenging environmental conditions. A robust solar components testing facility is, thereby, essential to ensuring that these products meet international safety and performance standards. Proper testing ensures optimal energy conversion and storage, and safe operation in challenging environmental conditions. It improves system efficiency, reduces failure risks, and ensures maximum return on investment for end users.
Bangladesh needs its own dedicated renewable energy components testing institute for a successful renewable energy transition. Countries currently leading in renewable energy transition—such as Germany, Italy, the US, China, and India—all have their own specialised testing institutes for renewable energy components. Even Pakistan, which has recently achieved a 46 percent share of renewable energy in its electricity generation mix as of September 2025, established a state-of-the-art solar panel testing facility in collaboration with South Korea in December 2025.
However, there is no single dedicated renewable energy components testing institute in Bangladesh. Neither does the country have its own comprehensive solar components testing facility, although approximately 78 percent of the total renewable energy capacity comes from solar power in Bangladesh. The Bangladesh Standards and Testing Institution (BSTI) has performed a limited role in this area, but its capacity remains significantly constrained. For instance, its capabilities for testing solar inverters are negligible, and it does not offer testing services for other solar components.
Normally, solar panels are certified based on internationally recognised standards, which are enforced by national testing authorities. Unfortunately, Bangladesh has not developed its own certification framework for solar panels. Instead, it adapts different international standards such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), etc. Moreover, BSTI’s testing system is mainly documentation-based. It is not based on direct physical testing. Usually, a company imports solar components and submits the certificates to BSTI, and then BSTI reviews them to determine compliance with the required standards. If approved, the product is listed in BSTI’s database. Once a specific model is listed, subsequent imports of the same model do not require further testing. However, bureaucratic delays often complicate the process. Sometimes, even with BSTI certifications, the Sustainable and Renewable Energy Development Authority (SREDA) conducts unnecessary and lengthy documentation checks that may take three to four months. As a result, importing companies cannot install solar panels for the customers on time, escalating project costs and diminishing efficiency.
Another major concern is that BSTI lacks adequate modern and advanced testing equipment for testing solar panels and solar inverters. Without proper testing infrastructures, imported solar panels and inverters often fail to meet their claimed quality and performance standards: for example, a solar panel advertised as having a capacity of 500 watts delivers only 300 watts. Therefore, the current government should urgently establish a separate testing institute for renewable energy components, if the funds for such a project are available. If not, the existing capacities of BSTI should be strengthened, modernised, and upgraded. Also, BSTI must introduce direct physical testing-based methods for all renewable energy components, rather than a purely documentation-based system. Another solution could be to establish port-based testing facilities that could significantly decrease delays by enabling rapid testing of imported products upon arrival. Otherwise, several months are wasted in bringing these components to Dhaka and distributing them among the city’s few testing labs.
As technology is changing rapidly around the world, BSTI’s testing equipment needs to be updated and modernised accordingly. In addition, the government should encourage the establishment of testing laboratories in the private sector to enhance capacity and encourage competition.
Md Razib is research associate at South Asian Network on Economic Modelling (SANEM). He can be reached at [email protected].
Views expressed in this article are the author’s own. 
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Solar Fabrik has introduced a 400 W red-brown glass-glass PV module designed to meet strict aesthetic requirements for historic and protected buildings, offering a 20.02% efficiency and compatibility with traditional tiled rooftops.
Image: Solar Fabrik
From pv magazine Global
Solar Fabrik has developed a red-brown solar module designed for use on historic buildings and in heritage-protected areas where strict aesthetic requirements apply.
The “Mono S4 Halfcut Chroma Orange” module is a colored panel intended to blend with traditional red-tiled roofs. The company cites an efficiency of 20.02% and a power output of 400 W.
It is a bifacial monocrystalline n-type glass-glass module with 96 tunnel oxide passivated contact (TOPCon) half-cells, measuring 1,762 mm × 1,134 mm × 30 mm and weighing around 24 kg.
The front glass is a 2 mm copper-red pane similar to RAL 8004 and features an anti-reflective coating, while the aluminum frame is also color-matched to a similar tone (RAL 8011).
The module is rated for a maximum system voltage of 1,500 V and operates in temperatures ranging from −40 C to 85 C, with a power temperature coefficient of −0.29% per degree Celsius.
In terms of durability, the module is designed to withstand hail impacts from ice balls up to 40 mm in diameter at speeds of up to 29.2 m/s, and snow loads of up to 5,400 Pa.
Solar Fabrik offers a 30-year product and performance warranty. Shipments to distributors are scheduled to begin in April 2026, while pricing has not been disclosed.
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According to the latest IndexBox report on the global AR Coated Film Glass market, the market enters 2026 with broader demand fundamentals, more disciplined procurement behavior, and a more regionally diversified supply architecture.
The global AR Coated Film Glass market is projected to experience robust expansion from 2026 to 2035, driven by the relentless pursuit of optical clarity and energy efficiency across multiple high-tech industries. This specialized glass, treated with thin-film coatings to minimize reflection and maximize light transmission, is becoming a critical component in enhancing the performance and user experience of electronic displays, solar panels, and advanced optical systems. The market’s trajectory is underpinned by the proliferation of high-resolution touchscreen interfaces, the global push for renewable energy, and the increasing integration of displays in automotive and architectural design. However, growth is tempered by challenges including the capital intensity of advanced coating processes, competition from alternative polymer films, and supply chain sensitivities for high-purity coating materials. This analysis provides a comprehensive outlook on the sector, detailing demand drivers, key application segments, regional dynamics, and the competitive landscape shaping the industry’s path through 2035.
The baseline scenario for the AR Coated Film Glass market from 2026 to 2035 is one of sustained, technology-driven growth. The market is expected to advance as a function of its adoption across its core end-use sectors, each with distinct innovation and replacement cycles. In display panels, the continuous consumer and commercial demand for larger, brighter, and more energy-efficient screens will necessitate higher-performance anti-reflective solutions. For solar energy, the incremental efficiency gains provided by AR coatings will remain a cost-effective method to boost panel output, supporting deployment in both utility-scale and distributed generation. The automotive sector’s shift towards digital cockpits and heads-up displays creates a new, high-value avenue for durable, optically superior glass. The market will be characterized by a bifurcation between high-volume, cost-optimized production for consumer electronics and solar, and lower-volume, high-specification production for specialized optical and medical applications. Regional manufacturing hubs in Asia-Pacific will continue to dominate supply, while demand growth will be most pronounced in regions aggressively deploying solar capacity and upgrading consumer device infrastructure. Pricing pressure will persist in commoditized segments, but premiumization in areas like automotive and architectural glass will support overall value growth.
This segment encompasses AR glass for smartphones, tablets, laptops, monitors, TVs, and public information displays. Current demand is anchored by the high-volume production of consumer electronics, where AR coatings are a standard feature for premium devices to improve sunlight readability and contrast. Through 2035, the trend towards larger screen sizes, higher pixel densities (4K/8K), and the integration of displays into more ambient environments will elevate the performance requirements for anti-reflective solutions. The demand story will be driven by unit shipments of consumer electronics, the penetration rate of AR coatings across device tiers, and the average coating area per device. The mechanism is direct: each new device generation seeking better optical performance creates a unit of demand for coated glass. The shift towards foldable and flexible displays presents both a challenge for coating adhesion and a new frontier for advanced, durable AR films. Current trend: Strong Growth.
Major trends: Adoption of AR coatings becoming standard across mid-to-high-tier smartphones and tablets, Growth in large-format commercial displays for retail and control rooms driving demand for large-area coated glass, Development of hybrid hard-coat AR solutions to withstand the abrasion of styluses on touchscreen devices, Integration of anti-fingerprint (AF) properties with AR coatings for enhanced consumer appeal, and R&D into AR coatings for emerging micro-LED and OLED display technologies.
Representative participants: Corning (Gorilla Glass with DX/DX+), AGC (Dragontrail), Nippon Electric Glass, Schott (Xensation Cover), and Taiwan Glass Industry Corp.
AR-coated glass is used as the front protective cover on photovoltaic (PV) modules to increase light transmittance and thus the panel’s power conversion efficiency. The current market is driven by utility-scale solar deployments and distributed rooftop installations, where even a 2-4% relative gain in output is economically significant. Through 2035, demand will be tightly coupled with global annual PV capacity additions. The key demand-side indicator is the levelized cost of electricity (LCOE); AR glass lowers LCOE by boosting energy yield per panel. The mechanism is efficiency-driven: as panel manufacturers compete on watts-per-dollar, integrating AR glass becomes a cost-effective performance upgrade. The trend towards bifacial modules, which capture light from both sides, may also increase the surface area requiring treatment. Demand will be strongest in regions with aggressive renewable energy targets and high solar irradiance. Current trend: Steady Growth.
Major trends: Standardization of AR coatings as a premium feature in mainstream PV module production, Development of textured or patterned AR glass to further trap light and increase efficiency, Focus on coating durability to withstand 25+ years of outdoor exposure to UV, abrasion, and environmental stress, Integration of anti-soiling properties to reduce maintenance costs in dusty environments, and Growth in building-integrated photovoltaics (BIPV) creating demand for architecturally pleasing, high-performance coated glass.
Representative participants: AGC (Sunbelt series), Saint-Gobain (SGG ALBARINO P), Guardian Glass (SunGuard SNX), Taiwan Glass, and Xinyi Solar.
This segment includes AR-coated glass for camera lenses, binoculars, microscopes, spectacles, and scientific instruments. The primary function is to eliminate stray reflections that degrade image contrast and cause lens flare. Current demand is characterized by high-value, low-to-medium volume production with stringent optical specifications. Through 2035, growth will be driven by the expansion of machine vision systems in industrial automation, the increasing complexity of multi-lens arrays in smartphone and automotive cameras, and the professional photography/videography market. The demand mechanism is precision-based: as optical systems incorporate more elements and operate in broader light spectra, the cumulative loss from reflections becomes unacceptable, necessitating high-performance AR coatings on multiple surfaces. Demand indicators include shipments of high-end imaging sensors and lenses for industrial and consumer applications. Current trend: Moderate, Technology-Led Growth.
Major trends: Demand for broad-spectrum (visible to near-IR) and ultra-low-reflection (V-coat) coatings for advanced imaging systems, Miniaturization of lenses for mobile devices pushing coating technology to smaller, more complex curvatures, Growth in augmented/virtual reality (AR/VR) headsets requiring lightweight, high-clarity optical elements, Automation in manufacturing driving demand for machine vision lenses with superior light throughput, and Increasing use of AR coatings in premium eyewear for both vision correction and blue-light filtering products.
Representative participants: Zeiss, Hoya, Schott, Ohara Corporation, Edmund Optics, and Abrisa Technologies.
This application covers AR glass for instrument clusters, center stack touchscreens, heads-up display (HUD) projectors, and potentially side/rear windows. The current market is focused on interior displays, where glare reduction is critical for safety and readability. Through 2035, the sector is poised for significant expansion as vehicles evolve into ‘computers on wheels.’ The demand story is linked to the electrification and digitalization of automotive cabins. Each new electric vehicle (EV) model typically features larger, more integrated digital displays. The mechanism is integration-led: the move towards panoramic glass roofs and larger windshield areas for augmented reality HUDs creates new, large-format substrates requiring high-performance optical coatings. Key demand indicators are automotive production volumes, the average number and size of displays per vehicle, and the penetration rate of AR-HUD technology. Current trend: High Growth.
Major trends: Rapid increase in size and quantity of touchscreens per vehicle, from luxury to mass-market segments, Adoption of augmented reality heads-up displays (AR-HUD) requiring complex, curved combiner glass with precise AR coatings, Exploration of AR coatings on side and rear windows to reduce glare and improve camera vision system performance, Demand for coatings that meet automotive-grade durability for temperature cycling, humidity, and abrasion resistance, and Integration with other functional layers, such as heating elements or transparent antennas, in smart glass solutions.
Representative participants: AGC, Saint-Gobain, Corning (AutoGrade Gorilla Glass), Fuyao Glass, and Nippon Sheet Glass.
This segment includes AR-coated glass for museum display cases, high-end retail storefronts, picture framing, and specialty architectural glazing. The primary value proposition is aesthetic enhancement—eliminating distracting reflections to make artwork, merchandise, or views more visible. Current demand is niche but high-value, driven by prestige projects in cultural and commercial construction. Through 2035, growth will be supported by the broader trend of architectural minimalism and the desire for ‘invisible’ glass in modern design. The demand mechanism is project-based and specification-driven. Architects and designers specify AR glass to achieve a specific visual effect. Demand indicators include spending on high-end commercial construction and museum/retail fit-outs. The trend towards smart buildings may also see AR glass integrated with dynamic glazing systems for optimal light and energy management. Current trend: Steady Growth.
Major trends: Growing use in museum and gallery display cases to protect artifacts while maximizing viewer clarity, Adoption in luxury retail storefronts and interior displays to enhance product visibility, Application in frameless glass balustrades and partitions in high-end residential and commercial architecture, Development of easy-to-clean and anti-graffiti properties combined with AR performance, and Experimentation with AR coatings on curved architectural glass for iconic building designs.
Representative participants: Saint-Gobain, AGC, Guardian Glass, Euroglas, and Pilkington (NSG Group).
Interactive table based on the Store Companies dataset for this report.
Asia-Pacific is the undisputed production and consumption hub, home to leading glass manufacturers, coating service providers, and the world’s largest electronics and solar panel assembly bases. China’s massive PV installation targets and dominant consumer electronics supply chain are primary demand drivers. Japan, South Korea, and Taiwan are centers for high-tech display glass and coating innovation. Southeast Asia is emerging as a growing manufacturing base, attracting investment in display and solar component production. Direction: Dominant and Growing.
North America represents a high-value market characterized by strong demand in automotive displays (especially with the EV transition), architectural specialty glass, and medical imaging equipment. The region is a key innovation center for advanced coating technologies and integrated smart glass solutions. Demand is driven by technological adoption in vehicles and buildings, as well as the reshoring of some high-tech manufacturing. The US solar market also provides a steady demand stream for AR-coated cover glass. Direction: Steady with Premium Focus.
Europe’s market is mature, with demand driven by stringent building energy regulations, a strong automotive premium segment (particularly in Germany), and a well-established solar sector. The region is a leader in high-performance architectural glass and automotive glazing solutions. Innovation focuses on sustainability, with AR coatings seen as an enabler for building-integrated photovoltaics (BIPV) and energy-efficient fenestration. Competition from Asian suppliers is intense in volume segments. Direction: Mature with Green Tech Emphasis.
Latin America is an emerging market where growth is primarily tied to the expansion of solar energy capacity, particularly in Brazil, Chile, and Mexico. Demand for consumer electronics is also rising with economic development, though much of the coated glass is imported as part of finished devices. Local manufacturing of AR glass is limited, making the region import-dependent. Growth prospects are tied to infrastructure investment and renewable energy policies. Direction: Emerging Growth.
This region presents a mixed picture. The Middle East, with its high solar irradiance and major investments in solar parks (e.g., Saudi Arabia, UAE), is a growing market for AR-coated solar cover glass. The region also has demand for high-end architectural glass in luxury developments. Africa’s market is nascent, with potential long-term growth linked to off-grid solar solutions and gradual urbanization, but currently constrained by limited local manufacturing and purchasing power. Direction: Niche with Solar Potential.
In the baseline scenario, IndexBox estimates a 7.2% compound annual growth rate for the global ar coated film glass market over 2026-2035, bringing the market index to roughly 195 by 2035 (2025=100).
Note: indexed curves are used to compare medium-term scenario trajectories when full absolute volumes are not publicly disclosed.
For full methodological details and benchmark tables, see the latest IndexBox AR Coated Film Glass market report.
This report provides an in-depth analysis of the AR Coated Film Glass market in the World, including market size, structure, key trends, and forecast. The study highlights demand drivers, supply constraints, and competitive dynamics across the value chain.
The analysis is designed for manufacturers, distributors, investors, and advisors who require a consistent, data-driven view of market dynamics and a transparent analytical definition of the product scope.
This report covers Anti-Reflective (AR) Coated Film Glass, a specialized optical glass treated with thin-film coatings to minimize surface reflection and increase light transmission. The coverage encompasses the product across its primary manufacturing forms and key application segments, including various coating technologies such as vacuum deposition, sol-gel, and plasma-enhanced processes applied to glass substrates for use in electronic displays, optical components, and energy applications.
The market data is classified according to the primary product forms and manufacturing stages of AR coated glass. This includes finished coated glass sheets and panels, as well as key upstream components like shaped glass elements ready for coating. The classification aligns with international trade codes covering worked glass, optical elements, and related plastic glazing materials that serve as functional substitutes or components in AR glass applications.
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The analysis is built on a multi-source framework that combines official statistics, trade records, company disclosures, and expert validation. Data are standardized, reconciled, and cross-checked to ensure consistency across time series.
All data are normalized to a common product definition and mapped to a consistent set of codes. This ensures that comparisons across time are aligned and actionable.
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Major supplier of specialty glass
Gorilla Glass, advanced optics
Broad AR glass portfolio
Key supplier for displays
Major architectural & specialty glass
SageGlass, specialty coatings
AR, AG coatings on glass
AR coatings via subsidiaries
Manufactures coated optics
AR coatings for various glass
Produces AR glass products
AR coated glass elements
AR coatings on glass/plastic
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AR coating capabilities
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Bird Proofing Clips HOMTOO 100-Pack Guard Clips – 304 Stainless Steel J-Hook Fasteners For Bird Wire Mesh, 25mm Washers Solar Panel  ruhrkanal.news
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The pressure to shift to solar energy is enormous. Small units are struggling with the switch

S
heikh Imtiaz Ahmed, a textile mill owner in the industrial area of Khurrianwala, switched to solar energy five years ago to cope with rising electricity tariffs and frequent load-shedding.
His factory now operates a one-megawatt solar power system, which supplies electricity to over 100 power looms, a stitching unit and offices. The mill also draws around 700 kilowatts of electricity from the FESCO to run its processing unit.
Ahmed says he initially installed a 200-kilowatt solar system. It was later expanded to reach its current capacity.
“We currently generate around 4,000 units of electricity daily from solar panels. This comes to nearly 120,000 units per month and nearly 1.44 million units annually,” he says.
He says the use of solar energy has enabled the mill to save over Rs 5.4 million per month in electricity bills. The initial investment has already been recovered.
Ahmed intends to run the entire operation on solar energy. He says limited rooftop space is the only constraint.
He also highlights the environmental benefits of solar energy. “According to environmental experts, our one-megawatt solar project is equivalent to planting 25,700 trees. It is helping reduce approximately 650 tonnes of carbon dioxide emissions annually.”
Azizullh Goheer, the secretary general of the Pakistan Textile Exporters’ Association, says the survival of the textile industry is increasingly dependent on the adoption of solar energy. He says energy tariffs for the industry have risen by more than 100 per cent over the past 15 years.
“In India, Bangladesh and Sri Lanka, electricity costs do not exceed 7 to 9 cents per unit; in Pakistan, these tariffs have reached 15 cents,” he says.
Goheer says high interest rates—10 to 11 per cent—make it difficult for industries to manage operational costs. Solar energy is one of the few viable options to cut production costs.
He says the high upfront cost of installing large-scale solar systems remains a major barrier for small and medium-sized textile units.
Recent changes in government policy replacing net metering with the new “prosumer” framework have created additional uncertainty.
According to a March 2026 report, titled Impact of NEPRA Prosumer Regulations 2026: A Case Study for the Textile Sector, published by Alternative Development Services, future investment decisions in the industry will depend more on settlement mechanisms and energy storage rules than on the cost of solar panels themselves.
The report warns that these changes could increase the overall cost of transitioning to solar energy, potentially slowing down adoption in the industrial sector. According to the report, recent changes in the net metering policy are likely to have significant implications for Pakistan’s textile sector, particularly for industries that have already made substantial investments in solar energy to cope with rising power costs.
Under the revised framework, reduced compensation for surplus electricity supplied to the grid and stricter regulatory conditions may extend the ROI period, potentially discouraging further investment in solarisation.
As a result, the pace of new solar installations may slow down. Existing users could face increased financial pressure. This, in turn, may raise production costs and undermine the competitiveness of Pakistan’s textile exports in global markets. The report says that policy uncertainty could negatively affect investor confidence, further slowing the transition to renewable energy.
To mitigate these challenges, the report recommends that the government introduce a balanced long-term policy framework that preserves the incentives for industrial users. This could include revisiting net metering tariffs to ensure reasonable returns, as well as introducing alternative mechanisms such as net billing or hybrid models. In addition, access to low-interest financing, tax incentives and the promotion of energy storage solutions are identified as critical measures. Continuous consultation between the government and industry stakeholders is also essential to develop policies that stabilise the energy sector while maintaining export competitiveness.
It is important to note that Pakistan’s largest textile export markets – the United States and the European Union – are increasingly enforcing regulations related to renewable and clean energy. This has accelerated the textile sector’s shift toward solar energy. In Faisalabad, an estimated 15-20 per cent of textile units have partially transitioned to solar power.
Currently, solar energy is primarily used in stitching units, offices and other low-energy operations. Heavy machinery still relies on grid electricity. In the past, during periods of severe load-shedding, industries had installed gas-based captive power plants. However, the rising cost of gas has made their option prohibitive.
Some industries are now considering coal or biomass (wood) as alternative energy sources. However, these options are environmentally unsustainable.
According to the Economic Survey 2024-25, the textile sector accounts for around 55 per cent of Pakistan’s exports. Faisalabad alone contributed more than 60 per cent of textile exports. In this context, the transition to solar energy is not only critical for cost reduction but also for sustaining and increasing export volumes.
Energy shortages continue to hamper production. A study conducted by Haider Ali, a PhD scholar at the Pakistan Institute of Development Economics, based on 125 textile mills in Faisalabad, found that power outages result in production losses ranging from 23 to 65 per cent in an eight-hour shift and 21 to 60 per cent in a ten-hour shift.
According to the study, the hosiery and ready-made garments sector has suffered the highest production losses due to the power outages.
The report notes that as far back as 2013, around 65 per cent of the textile industry relied on diesel- and petrol-powered generators as an alternative source to ensure uninterrupted electricity supply.
However, continuous increase in the prices of petroleum products, gas and electricity has significantly raised operational costs, prompting a rapid shift toward solar energy in recent years.
Data from the Power Planning and Monitoring Company shows that Pakistan’s installed solar net metering capacity has grown from 201 megawatts in 2021 to approximately 6,975 megawatts.
In terms of net metering adoption, Lahore accounts for 24 per cent of the consumers, followed by Multan (11 per cent), Rawalpindi (9 per cent), Karachi (7 per cent), Faisalabad (6 per cent) and Islamabad (5 per cent).
Another research study conducted by Alternative Development Services last year across 80 textile mills in Faisalabad and Multan highlighted the urgent need for the industrial sector to transition to renewable energy in order to reduce carbon emissions and meet environmental compliance requirements in global markets. This transition, the report suggests, is critical for maintaining and enhancing export competitiveness.
According to ADS chief executive officer Amjad Nazeer, evolving global carbon reduction targets, changing buyer expectations and stricter supply chain compliance standards are already reshaping industrial production.
He says industries that adapt quickly to these changes are likely to gain significant economic advantages; those that delay the switch risk missing out on emerging opportunities.
It is worth noting that a joint report by the Government of Pakistan and the World Bank (2018) estimated that installing solar panels on just 0.071 per cent of the country’s land area could meet its entire electricity demand. The report also said that by increasing the share of solar and wind energy by at least 30 per cent by 2030, Pakistan could save up to $5 billion in fuel costs over the next two decades.
To achieve this target, Pakistan will need to install approximately 24,000 megawatts of solar and wind energy projects by 2030. Currently, the country’s total installed solar capacity stands at around 18,000 megawatts, indicating both substantial progress and a significant gap.
Mounting pressure from international markets to comply with clean energy standards adds urgency to this transition. A stable and supportive policy environment, coupled with targeted incentives and investment in energy infrastructure, will be critical to ensuring that the industry not only sustains its global competitiveness but also contributes to a more resilient and sustainable energy future for Pakistan.

The writer has been associated with journalism for the past decade. He tweets @naeemahmad876

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Australia: installed capacity of rooftop solar PV 2024  statista.com
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How do photovoltaic solar panels work?  iberdrola.com
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Photovoltaic cells: discover their evolution, their different types and the latest innovations  iberdrola.com
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About one in three Australian households have rooftop panels. (ABC News: Michael Lloyd)
Australians are expected to throw away 90,000 tonnes of solar panel waste in 2030, a federal government hearing has been told.
A $24.7 million, three-year solar panel recycling pilot is aiming to develop a national plan for safely and economically handling solar waste.
The pilot, which aims to collect up to 250,000 solar panels from 100 sites nationwide, is expected to begin by July.
The annual level of solar panel waste generated in Australia is expected to grow by 30,000 tonnes by 2030, according to forecasts from a federal government department working to create a national recycling plan.
In January, a $24.7 million, three-year solar panel recycling pilot aimed at developing a national plan for handling solar waste was announced by the federal government.
Australia is on the brink of a solar panel waste crisis. Experts say there's much to do before a national recycling program can stand on its own two feet.
The pilot aims to collect up to 250,000 solar panels from 100 sites across the country to gather data on how best to recycle them.
In a parliamentary committee hearing today, officials from the Department of Climate Change, Energy, Environment and Water explained some of the challenges the pilot faced.
Cameron Hutchison, from the department's packaging, stewardship and investment branch, said the recycling issue might be bigger than the federal government anticipated.
"At the end of last year around 60,000 tonnes of solar panels in Australia reached their end of life and that — on our current projections — could end up being around 90,000 tonnes by 2030," he said.
"There's some industry work there though that starts to bring that number into question, as maybe a bit of an underestimate.
"Some of that is predicated on the fact that we think solar panels are used for around 20 years."
Mr Hutchison said this life span was "shortening" with the emergence of new technology and better panels.
"People are replacing them more quickly … the problem is there and known and possibly even bigger than we anticipate," he said.
Up to 50,000 solar panels could end up in landfill by 2035, the federal government has forecast. (ABC News: John Gunn)
James Tregurtha, from the department's circular economy branch, told the committee the logistics of moving discarded solar panels to waste facilities had prevented many of them being recycled.
"They're heavy, they can break if they're not handled properly, if they break they are less able to be recycled due to the shards of glass," he said.
"Getting intact panels from in situ to a recycling facility in a way that maintains their recyclability is, from our perspective, one of the key challenges."
Many solar panels are not recycled because they are damaged while being removed or discarded. (ABC News: John Gunn)
About one in three Australian households have rooftop panels, making the country one of the highest users of the technology, according to the federal government.
End-of-life solar panel waste forecasts released by the International Energy Agency in 2016 showed Australia could generate as much as 145,000 tonnes by 2030.
Early government forecasts compiled from self-reported industry data have suggested up to 50 million solar panels may become waste by 2035, equating to around 1 million tonnes of waste.
Australia does not yet have a national dataset for tracking solar panel waste and the number of products that enter landfill or are recycled.
"A lot of challenges are around the logistics of getting them to the right places in the right condition," Mr Hutchison said.
"We understand that in the right condition with the right system, about 90 per cent of a panel can be recycled.
"We need to do more work to understand how we can set that system up in Australia and support that system."
Many Australians have been discarding rooftop solar panels so they can replace them with newer, cheaper and more efficient models, according to Mr Tregurtha.
"Over a period of the last five to 10 years, your top end panel … would have been 330 kilowatts. It's now 450, so it's like 25 per cent better and it's cheaper," he said.
"The reason it's cheaper is effectively because of … the vast scaling up of production, particularly across some Asian nations.
"As people seek to self-supply more of their own electricity off their own roofs or, in the case of commercial venture, how do you improve your bottom line. By getting those more efficient panels that will generate more power for a cheaper investment."
Australia's renewable energy push has resulted in a surge in household solar power. But what does that mean for private solar farms?
Mr Tregurtha said there was limited evidence showing Australians were choosing to re-use their old solar panels.
"Generally what we're finding is once the panels come off the roofs and are taken into a stream of disposal, the re-use proposition is probably uneconomic given the amount of testing [and] maintenance that you would have to do," he said.
"People have suggested things like using them for community housing or for facilities, voluntary organisations [and] scout halls.
"The issue there is one of making sure they're safe because you are then taking a whole system apart and putting it back together somewhere else."
The federal government pilot would also aim to assess best practice for reducing toxicity and electrical safety risks related to solar panel waste, Mr Tregurtha said.
The government was still in the early stages of finding an organisation to run the pilot, the committee heard.
"We anticipate that we would have the pilot up and running this side of the end of the financial year," Mr Hutchison said.
"From there we expect the pilot to run for 12 to 18 months, where we're really extracting the data and learnings.
"We're really at the forefront of this. Australia is a leader in the uptake of solar."
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Tongwei is partnering with GS-Solar and Golden Solar to develop a large-scale manufacturing facility for hybrid heterojunction back-contact (HBC) solar cells that combine heterojunction passivation, tunneling oxide and polysilicon structures used in TOPCon designs, and the grid-free front-side architecture typical of back-contact technologies.
Image: Tongwei
Tongwei has signed a strategic cooperation agreement with Gold Stone (Fujian) Energy Co Ltd (GS-Solar) and Golden Solar (Quanzhou) New Energy Technology Co Ltd to develop a mass-production facility for hybrid heterojunction back-contact (HBC) solar cells.
The agreement was signed on March 18, 2026, at Golden Solar’s headquarters in Quanzhou. The partners plan to collaborate across the full value chain, including technology development, manufacturing, and process optimization. The factory’s location and planned capacity have not been disclosed, but the companies said it is intended for large-scale commercialization.
Under the agreement, Tongwei Solar, a wholly owned subsidiary of Tongwei, will provide manufacturing capacity, production facilities, supply chain resources, and operational management. GS-Solar will act as the technology provider, contributing its hybrid HBC cell design, GW-scale integrated equipment, and mass-production process solutions. Golden Solar will provide patents, commissioning experience, and process support.
The companies said they will establish a coordination mechanism to optimize production processes, reduce manufacturing costs, and improve conversion efficiency as they move toward industrial deployment.
At the center of the partnership is GS-Solar’s hybrid HBC technology, which combines multiple cell concepts. The design builds on HBC architecture and integrates heterojunction passivation from HJT cells, tunneling oxide and polysilicon structures associated with TOPCon, and a grid-free front-side design typical of back-contact technologies. The approach aims to balance high efficiency with lower production costs and simplified processing.
GS-Solar reported a laboratory conversion efficiency of 27.08% for the technology in March 2023, rising to 27.62% in November 2024, according to the company.
The collaboration brings Tongwei, one of the world’s largest solar cell manufacturers, into the hybrid HBC segment and could accelerate the transition from laboratory-scale development to industrial production. It also expands Tongwei’s technology portfolio, which includes TOPCon, HJT, BC, and TBC-related approaches.
GS-Solar has previously announced similar partnerships. In September 2024, a GS-Solar-linked entity and Golden Solar (Quanzhou) partnered with Longi to upgrade four PERC production lines in Xi’an to HBC technology. In April 2025, GS-Solar and Golden Solar (Quanzhou) agreed with a subsidiary of JA Solar in Yiwu to develop a 4 GW hybrid HBC upgrade project using existing PERC capacity. The companies said the JA Solar-linked project is among the largest planned HBC mass-production projects globally.
These partnerships indicate GS-Solar is positioning itself as a technology and equipment supplier for hybrid HBC commercialization, combining intellectual property, manufacturing equipment, and process expertise.
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The world added 510GW of new solar PV capacity in 2025, the most of any electricity generation source, as global cumulative renewable energy capacity now exceeds 5,000GW.
This is the key takeaway from ‘Renewable Capacity Statistics 2026’, the latest in a series of annual reports from the International Renewable Energy Agency (IRENA). Of the 5,149GW of renewable energy capacity in operation as of the end of 2025, solar accounted for 2,391GW, the most of any energy source and almost double the 1,291GW of wind capacity in operation.
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Renewable energy, more broadly, was the driving force behind new electricity generation capacity in 2025, accounting for 85.6% of all new energy capacity additions. Solar, and solar PV in particular, were key contributors to this change.
The IRENA report notes that solar PV accounted for 510GW of the 511GW of solar added in 2025. The other 1GW was concentrated solar. Solar as a whole accounted for 75% of the 692GW of new renewable energy capacity additions made last year.
“This impressive, consistent growth reflects the strength of the economic case for the energy transition; the competitiveness and resilience of renewable power have pushed additions to new records almost every year since the turn of the millennium,” wrote IRENA director-general Francesco La Camera in his foreword to the report.
However, he noted that “significant disparities” remain in deployment, highlighting that China, the US and the EU accounted for 79.5% of new renewable energy capacity installed in 2025. Considering the scale of solar capacity already in operation in these regions—as illustrated in the graph below, where China alone accounted for half of the world’s operational solar capacity at the end of 2025—the world’s solar deployments are becoming increasingly concentrated in a few parts of the globe.
There is encouraging growth in some parts of the world, however, and IRENA draws attention to the Middle East, in particular, as an example of a region with significant growth in renewable energy capacity. The region’s renewable additions increased by 28.9% year-on-year in 2025, with more than 12GW of new solar capacity installations between 2024 and 2025.
This is the most new solar capacity that has ever been added in the region in a year, and means that the operational solar capacity in the Middle East has increased by almost 24 times since 2016.
Saudi Arabia, in particular, has been a driver of this change, boasting 11.9GW of cumulative operational solar capacity, and having added more than 5GW in 2024. Earlier this year, Egyptian firm Elsewedy Electric commissioned a 349MW solar PV project in the country, and the Saudi Arabian solar sector has benefitted from agreements with overseas players, including US tracker manufacturer GameChange Solar and the Turkish government.

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MADISON, Wis. (WMTV) – A fire that started at a Rock County solar farm burned through about 10 acres of grass, Lakeside Fire & Rescue confirmed Tuesday.
When Lakeside Fire & Rescue arrived just after 12:30 p.m. Monday to North Rock Solar Farm, firefighters spotted grass burning underneath the solar panels.
Firefighters quickly got the fire under control to keep it from spreading.
Lakeside Fire & Rescue said the location of the fire and and strong winds prompted them to call for more backup.
Evansville and Footville fire departments brought in brush trucks to help put out the flames.
After firefighters put out most of the flames, they made sure all hotspots were put out.
Crews fought the fire for about two hours.
The fire department is still investigating the cause of this brush fire.
No one was hurt.
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Copyright 2026 WMTV. All rights reserved.

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30W Portable Solar Panel Heater With Battery Box – 17×7.9 Inch Panel For Greenhouse, Shed, Camping & Pet Houses  ruhrkanal.news
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Office: Solar Energy Technologies Office 
FOA Number: DE-FOA-0003289 
Link to Apply: Apply on EERE Exchange
FOA Amount: $50.5 million
On June 6, 2024, the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) announced the FY24 Solar Energy Supply Chain Incubator funding opportunity, which will provide up to $50.5 million for research, development, and demonstration (RD&D) projects that de-risk solar hardware, manufacturing processes, and software products across a wide range of solar technology areas. The FOA also seeks projects that provide outreach, education, or technology development for software that delivers an automated permit review and approval process for rooftop solar photovoltaics (PV) with or without energy storage.
This funding opportunity announcement (FOA) aims to increase U.S. domestic manufacturing across the solar energy supply chain and expand private investment in the country’s solar manufacturing sector. These investments will help accelerate the growth of the solar industry, identify emerging opportunities, and drive down costs for our domestic energy market, positioning the United States on the leading edge of solar industry advances.
Technologies of interest include PV, systems integration, and concentrating solar-thermal power (CSP) technologies, as well as those that connect solar with storage or electric vehicles and dual-use PV applications like agrivoltaics and vehicle-integrated PV. Read the FOA for the full list of relevant areas.
This topic area focuses on R&D projects at for-profit companies to de-risk new solar components and/or manufacturing processes, while developing and validating a realistic pathway to commercial success.
This topic area focuses on RD&D projects at established companies or startups for pilot-scale and/or prototype demonstration of solar products. Successful applicants for this topic area will have an existing prototype that requires further testing, engineering work, or demonstration in a controlled environment. 
This topic area focuses on outreach, education, or software technology development activities for permitting software that automates code compliance checks and permitting for rooftop solar PV, rooftop solar PV with energy storage, or residential and commercial electric vehicle supply equipment (EVSE). The permitting software must be designed for use by solar or EVSE installers to submit rooftop solar or residential and/or commercial EVSE permit applications, and by local governments to automate their review and approval. Projects can be led by for-profit or non-profit entities.
Do you have questions about putting together your concept paper or full application? Is this your first time considering a FOA application? SETO, through the American-Made Network, is providing Applicant Education Services available to you free of charge. 
You can engage with the following points of contact at ADL Ventures, Entrepreneur Futures Network (EFN), and the University of Arizona Center for Innovation (UACI) for more details: 
Note: Participation in the Application Education Services is not mandatory and will have no impact on the evaluation of your application by DOE.
DOE is compiling a Teaming Partner List to facilitate the formation of project teams for this FOA. The Teaming Partner List allows organizations that may wish to participate on a project to express their interest to other applicants and explore potential partnerships. 
The Teaming Partner List will be available on EERE eXCHANGE and will be regularly updated to reflect new teaming partners who provide their organization’s information.
SUBMISSION INSTRUCTIONS: View the Teaming Partner List by visiting the EERE eXCHANGE homepage and clicking on “Teaming Partners” within the left-hand navigation pane. This page allows users to view published Teaming Partner Lists. To join the Teaming Partner List, submit a request within eXCHANGE. Select the appropriate Teaming Partner List from the drop-down menu and fill in the following information: Investigator Name, Organization Name, Organization Type, Topic Area, Background and Capabilities, Website, Contact Address, Contact Email, and Contact Phone.
DISCLAIMER: By submitting a request to be included on the Teaming Partner List, the requesting organization consents to the publication of the above-referenced information. By facilitating the Teaming Partner List, DOE is not endorsing, sponsoring, or otherwise evaluating the qualifications of the individuals and organizations that are identifying themselves for placement on this Teaming Partner List. DOE will not pay for the provision of any information, nor will it compensate any applicants or requesting organizations for the development of such information.
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Canadian Solar’s energy storage solutions subsidiary, e-STORAGE, will deliver 420MWh AC of battery energy storage systems (BESS) across two projects for Drax Group.
April 1, 2026
Canadian Solar closed March with the announcement of two projects and a sale, as the renewable energy provider expands its UK footprint.
Canadian Solar’s energy storage solutions subsidiary, e-STORAGE, will deliver 420MWh AC of battery energy storage systems (BESS) across two projects for Drax Group. The projects will join the renewables company’s FlexGen portfolio.
The two installations include a 60MW / 120MWh AC installation in Marfleet, England, and a 150MW / 300MWh AC installation in Neilston, Scotland. The installations are expected to begin in Q3 of 2026 and early 2027, respectively.
Lee Dawes, chief operations officer of Drax Group, said: “This is our first investment in short-duration storage, and these assets will complement our existing generation portfolio.”
“As the UK network increases its reliance on intermittent renewables, these batteries will provide secure power and help keep the lights on when the wind isn't blowing and the sun isn't shining."
Related:Drax acquires UK-based AI-enabled asset optimisation platform Flexitricity
A fully integrated and commissioned BESS will be provided by e-STORAGE – including its SolBank 3.0 batteries – and it will also oversee operations under a long-term service agreement (LTSA). This will include monitoring, performance analytics, and preventative maintenance.
According to Canadian Solar’s announcement, the goal of the arrangement with e-STORAGE is to provide “consistent operational availability” throughout the lifecycle of the projects. The company explained that this will improve grid flexibility in their respective regions and contribute to the UK’s adoption of renewable energy sources.
Apatura, a UK-based energy infrastructure company, is developing both projects, as the company specialises in digital infrastructure and large-scale BESS. Giles Hanglin, CEO of Apatura, added: “By combining our development expertise with e-STORAGE's technology and Drax's operational capability, we are delivering assets that strengthen grid security and enable more renewable power to flow onto the system."
“This collaboration with Drax and Apatura reflects our shared commitment to advancing a more flexible and resilient energy system in the UK,” Colin Parkin, president of both Canadian Solar and e-STORAGE, commented.
“Leveraging the strong foundation and operational expertise we have established in this market, we are dedicated to delivering reliable system performance and service excellence to customers across Europe."
The projects in Marfleet and Neilston are not Canadian Solar’s only recent UK developments.
Related:Rolls-Royce begins construction on 43MW BESS project in Scotland
The company additionally closed March with movement on Project Higher Witheven – a 42.5MWp solar project in Cornwall, England – through its subsidiary Recurrent Energy. Earlier in the month, the ready-to-build site was sold to investment manager Downing. By the end of March, Higher Witheven had additionally secured a Contract for Difference (CfD) in the UK government’s Allocation Round 7 (AR7) auction.
In the company’s official announcement, Ismael Guerrero, CEO of Recurrent Energy, said: “The transaction of Higher Witheven highlights our ability to originate, develop, and successfully bring high-quality renewable energy assets to market.
“Securing a CfD in AR7 further reinforces the competitiveness of our UK pipeline.”
Recurrent Energy’s announcement stated that the site was designed with considerations for long-term environmental impact and management. As such, the project was developed with biodiversity and landscaping in mind.
Higher Witheven is predicted to generate over 46,000MWh of renewable energy per year, and has an anticipated completion date of Q4 2027.
Read more about:
Catie Owen
Contributing writer
Since 2019, Catie has been writing news, interviews, client content and editing magazines. In recent years, her interest in sustainability has led her to pursue renewable energy as her primary beat. Having written primarily about solar energy and storage, Catie also enjoys covering the positive human impact of renewable technology.
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‘We’re harvesting the sun’: A huge solar project grows in California  circleofblue.org
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10W 12V Portable Polycrystalline Solar Panel With Clip – For Charging 9-12V Batteries, Outdoor Use  ruhrkanal.news
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Cloudy skies. A stray shower or thunderstorm is possible. Low 41F. Winds S at 10 to 15 mph..
Cloudy skies. A stray shower or thunderstorm is possible. Low 41F. Winds S at 10 to 15 mph.
Updated: April 4, 2026 @ 10:53 pm
A solar farm can be seen from Mohr Road in the town of Florida in November 2024. A solar farm being built in New Baltimore will be acquired by New York state in 2027.

A solar farm can be seen from Mohr Road in the town of Florida in November 2024. A solar farm being built in New Baltimore will be acquired by New York state in 2027.
NEW BALTIMORE — The Hannacroix Solar farm in New Baltimore will be taken over by a state entity once it is completed.
The New York Power Authority on Tuesday announced an agreement that would result in the authority helping with the development of the five-megawatt solar farm on Railroad Avenue, with a full acquisition of the farm set for 2027.
“NYPA is providing the financing needed to move Hannacroix Solar through its remaining preconstruction development work,” Power Authority spokesperson Alex Chiaravalle said Tuesday. “After completing a final round of due diligence, the Power Authority will finance the project’s construction — expected to begin later this year — and fully acquire the project upon its completion in 2027.”
The project is being developed by Teichos Energy, a Seattle-based solar energy company.
“NYPA has long been recognized as an innovator in the electric-power industry and we welcome the opportunity to advance their goals of making New York’s electric power mix cleaner while stabilizing power costs,” Teichos Energy CEO Stephen Voorhees said in a statement.
The acquisition is part of the state’s Renewable Energy Access and Community Help program, designed to provide renewable energy credits to low-income New Yorkers, according to the state’s website.
The project is also part of the authority’s renewable-energy strategic plan, which outlines the state’s efforts to develop, own and operate renewable-energy projects, the announcement reads.
The credits are funded through renewable-energy production from properties developed by or for the authority.
Gov. Kathy Hochul authorized several state departments, including the Power Authority, to fast track renewable-energy developments before federal tax credits expired.
“Since NYPA was authorized less than three years ago to support large-scale renewable-energy development, it has worked diligently to stand up a program capable of accelerating clean-energy projects across New York State,” Power Authority President and CEO Justin Driscoll said in a statement. “Amid headwinds affecting the industry, NYPA established new business structures, assembled a team of seasoned professionals, and refined a portfolio of project opportunities. In 2026, those efforts will bear fruit.”
The five-megawatt facility will be developed on nearly 54 acres of land in New Baltimore. Construction is expected to begin later this year, Chiaravalle said.
The authority is also set to help finance the project’s preconstruction development and Gordian Energy Systems as the project’s engineering, procurement, construction and contractor to build the project, according to Bill Parkhurst, Teichos vice president of marketing and finance.
“We are excited that the New York Power Authority has stepped in as a strong financing and development partner,” Parkhurst said.
The New York Power Authority is the largest state power organization in the country, operating 17 facilities and more than 1,550 miles of transmission lines.
The solar farm is expected to go online in late 2027.
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Newsfrom Japan

Tokyo, April 3 (Jiji Press)–The Japanese government on Friday adopted a bill that requires operators of mega solar power plants to submit plans on how they will dispose of used panels.
The bill is aimed at getting mega solar farm operators to cooperate on recycling used panels, in order to reduce the volume of those that go to landfill.
The country is expected to see the amount of dumped used solar panels reach up to 500,000 tons around 2040, about six times the current volume.
The bill stipulates that operators set out in their plans the volume of panels to be disposed of, along with when and how they will be dumped, among other aspects.
Operators will be asked to dispose of used panels in ways that allow them to be recycled into materials.

[Copyright The Jiji Press, Ltd.]

Jiji Press

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Cloudy with periods of rain. Thunder possible. Low 39F. Winds W at 10 to 20 mph. Chance of rain 100%..
Cloudy with periods of rain. Thunder possible. Low 39F. Winds W at 10 to 20 mph. Chance of rain 100%.
Updated: April 4, 2026 @ 11:33 pm
Ed Sekerak, standing at left center, offers testimony regarding property values in the area surrounding the proposed Solar Flats project. The hearing Thursday lasted over four hours.

Ed Sekerak, standing at left center, offers testimony regarding property values in the area surrounding the proposed Solar Flats project. The hearing Thursday lasted over four hours.
Corry City Council convened a public conditional use hearing Thursday on a proposal by Solar Flats LLC to construct a 3-megawatt AC solar energy facility on approximately 16 acres of land along South Shady Avenue, owned by Troyer Growers Inc. The session drew a crowd of about 45, and featured hours of technical testimony, legal argument and public comment from residents who live within sight of the proposed installation.
Mayor Jeff Fike presided, joined by Councilmen William “Buzz” Hammond and William Roche and Councilwoman Charles “Chuck” Gray. Councilman Andrew Sproveri recused himself, citing concern over potential bias.
At the close of testimony, Council adjourned to executive session and announced it would reconvene at a later date to render a formal decision.
The project
Annika Schiffer-Delagard, project development associate for Greenwood Sustainable Infrastructure, described Solar Flats as a relatively modest commercial solar installation designed to feed power directly into the grid through Penelec. She said the project would have the capacity to power between 300 and 400 homes.
Schiffer-Delagard outlined what she characterized as the project’s low environmental footprint: no lighting, no signage, no smoke, no odor. She said maintenance would require only approximately three truck visits per year. She added that the panels themselves are silicon and glass, are UL-certified and pose no leaching risk to soil or groundwater.
Schiffer-Delagard said the company contracted for noise and glare studies ahead of the application. A noise analysis found all operational sound would fall at or below 45 decibels, comparable to a household dishwasher, and below ambient levels. A glint-and-glare study, performed by Fisher Associates engineer Steve Mellott, found minimal “green glare” — the lowest category, likened to sunlight reflecting off snow or a pond — at two locations, the first totaling 47 minutes of exposure annually, occurring in spring and fall near an agricultural barn at 340 Sample Flats Road; the second at the residence of Dr. Kurt Lund, totaling 16 minutes per year between May and June.
The project will include tracker panels — motors that rotate the arrays to remain perpendicular to the sun — powered by electricity generated by the project itself. Mellott testified the site plans include erosion and sediment controls, post-construction stormwater management and vegetative screening on three of four sides of the property. The north side, he said, is already bordered by existing woods.
Greenwood representative Drew Rogerson told Council that a 100-foot setback from residences is included and that the company maintains a 24/7 operations-and-maintenance team to respond to any damage, such as panels broken by hail. He also addressed concerns about site selection, assuring the audience that Corry was chosen because a willing landowner came forward and the utility interconnection proved feasible — not because the company believed the community would offer little resistance.
Legal arguments over zoning classification
Attorney Kevin Barley of Steptoe and Johnson in Pittsburgh, representing Solar Flats LLC and Greenwood Sustainable Infrastructure, argued that the application was filed at the city’s own direction. He said the city’s interim zoning officer, Samantha Vollentine, consulted with City Solicitor Lydia Caporosa in spring 2025 and instructed Solar Flats to apply under the “special residential and commercial projects” conditional use category in the R-1 single family residential district.
Barley reminded Council that a conditional use is presumed by the legislature to be consistent with the zoning plan and not inherently adverse to the public interest. He said the hearing was not about whether residents or officials favor or oppose solar energy, but whether the applicant meets the conditions set forth in the ordinance. He also noted that under the existing R-1 ordinance, uses with far greater potential impact — including mineral extraction, cemeteries, hospitals, nursing homes and funeral homes — are already permitted by right or conditional use.
Attorney Edward Betza of Elderkin Law Firm in Erie, representing objecting resident Kelly Goodsel and other South Shady Avenue neighbors by extension, offered a sharply different reading of the ordinance. He argued that the city’s zoning code, adopted in 1991 and never updated to address solar development, contains three distinct use categories: “planned commercial project,” “industrial use,” and “special residential and commercial project.”
Betza contended that a solar energy facility is plainly a planned commercial project, a category permitted only in I-1 and I-2 industrial districts, not R-1, or possibly an industrial use, which is similarly barred from the residential zone. Betza argued that the “special residential and commercial project” category requires both a residential and a commercial component, and that Solar Flats, having no residential element whatsoever, cannot legitimately claim that classification. 
“The word ‘and’ is significant,” he told Council. “I didn’t write it.”
Beyond the classification dispute, Betza argued that the applicant failed to meet its burden of proof under Section 605 of the zoning ordinance, pointing out that the testimony addressed neither fire protection nor electrical distribution disturbance, both required criteria. He also cited the ordinance’s provision that reflective materials or lighting “which produce objectionable, direct or reflected glare on adjoining properties shall not be permitted,” arguing the standard is absolute. 
“It says none — zero. Shall not be permitted,” Betza said.
Property values and decommissioning
Columbus resident Edward Sekerak, a state-certified general real estate appraiser who testified he has completed more than 10,000 appraisals over his career, said he found no evidence in available studies that a solar project of Solar Flats’ size and location would have a measurable negative effect on surrounding property values. He cited research from Virginia Tech and other institutions. Under cross-examination, however, Sekirak acknowledged he had not read the cited studies in their entirety.
On decommissioning, Schiffer-Delagard said the project’s anticipated operational life is 25 to 30 years. She said Solar Flats plans to secure a surety bond covering 110% of estimated decommissioning costs, calculated using 2026 pricing and an inflation rate. 
When asked by Councilwoman Gray whether the panels might simply be replaced and the facility continued beyond that horizon, Schiffer-Delagard said she could not commit to that scenario. 
When Mayor Fike asked how many sites Greenwood has decommissioned to date, she replied, “Zero.”
Public comment
Ten members of the public addressed Council, the large majority in opposition. Kelly Goodsel, whose property abuts the proposed installation, organized his objections into three categories: environmental concerns, including potential stormwater runoff into a nearby spring and existing wetlands to the north; property and home value impacts; and legal and ordinance compliance questions.
Goodsel also noted that property owners along Stewart Road in Concord Township, whose land would be directly affected by the project, received none of the informational mailings Greenwood sent to Shady Avenue residents in October 2025, and therefore had no advance notice of the proceedings. He said a portion of his own 20-acre property lies in Concord Township.
Lund echoed Goodsel’s testimony and raised additional concerns about glare hazards for motorists traveling roads near the project.
One speaker broke from the majority. Ed Spitman, who said he has lived near the area for 66 years, argued that the landowner has a right to use his property as he sees fit. 
“Everybody wants to make money, so everybody’s got to sacrifice a little bit,” Spitman said. “You’ve got to be open-minded to everybody.”
Planning commission previously split
The Corry Planning Commission previously voted 3–2 against recommending approval of the conditional use application, leaving the final determination to City Council. See the March 6 edition of The Journal for more information. 
Council has not announced a date for its decision.
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