Solar Now

An IEA-PVPS report finds that solar power above 60° North is not only viable but rapidly expanding, driven by cold-climate performance gains, bifacial technologies, and rising energy security needs. While challenges like extreme seasonality, snow, permafrost, and scarce data remain, Arctic PV is emerging as a critical—and technically distinct—frontier for global solar deployment.
Image: Firat University, Case Studies in Thermal Engineering, CC BY 4.0
From pv magazine Global
For decades, the Arctic has been dismissed as a solar dead zone. Long winters, heavy snow loads, and extreme cold seemed to rule out photovoltaics as a serious energy option for communities above the 60th parallel. A new report from the IEA Photovoltaic Power Systems Programme (Task 13) challenges that assumption, arguing that solar PV is not just viable in the Arctic, but increasingly essential to the region’s energy security.
The 77-page report, titled “Photovoltaics and Energy Security in the Greater Arctic Region“ and authored by researchers across the US, Canada, Sweden, Norway, Denmark, and Finland, arrives at a moment when Arctic PV capacity is growing at rates of 46 to 145% per year in some regions. Total installed capacity above 60°N now stands at roughly 1,400 MWp as of 2023 — still a tiny fraction of global capacity, but the trajectory is unmistakable.
First and foremost, when planning a PV project at higher latitudes, the starting point must be considering seasonality: near the summer solstice in June, high-latitude regions receive large amounts of solar radiation. In contrast, near the winter solstice in December high-latitude regions receive little solar radiation (or not at all above the Arctic Circle at 66.56°N).
Bridging the gap between the intensity of summer and the scarcity of winter is the defining integration challenge for Arctic PV systems, and one that is addressed at length throughout the report.
The report’s central argument rests on a counterintuitive insight: cold is not the enemy of solar panels. It’s often an advantage.
Silicon PV cells produce more power at lower temperatures because the semiconductor bandgap widens, boosting voltage. The report cites data from a south-facing system in Alaska, where the median module temperature during daylight hours was just 15°C, which is far below the 25°C standard test condition at which panels are rated. In cold climates, modules may also degrade more slowly, with a median performance loss rate of just -0.37%/year measured across 16 systems above 59°N, compared to -0.75%/year for systems across the continental United States.
Snow, meanwhile, is a double-edged factor. It can block panels and stress racking systems, but it also dramatically raises ground albedo, potentially boosting the rear-side gain of bifacial modules to levels unseen in lower latitudes. The report notes that bifacial gain increases with latitude precisely because of long-lasting snow cover, increased diffuse light, and low solar elevation angles. The recommendation is clear: bifacial modules should be the default technology choice for Arctic deployments.
One of the report’s more striking practical findings concerns system orientation. East-west facing vertical bifacial arrays show particular promise above 60°N. Their near-90° tilt sheds snow naturally, avoiding the extended zero-production periods that plague tilted fixed-tilt systems in winter. They also produce power earlier and later in the day, better matching electricity demand curves and reducing the “cannibalization effect” that depresses midday wholesale prices.
Field data from a vertically-mounted agrivoltaic system in Sweden (59.55°N) illustrates the point. In December 2023, the vertical system outperformed its south-facing fixed-tilt neighbor on 28 out of 31 days, averaging 6.1 kWh/kW/month versus just 1.32 kWh/kW for the tilted array. On 14 of those days, the tilted system produced nothing at all due to snow coverage.
However, there is one section of the report that deserves special attention from developers: the discussion of frost heave and permafrost. Two detailed case studies — a 699 kW system in Luleå, Sweden, and a 563 kW array in Fairbanks, Alaska — document costly structural failures caused by ground freezing that installers failed to adequately anticipate.
In Luleå, perforated C-profile piles allowed the clay substrate to grip the racking, causing visible deformation within the first winter. The entire racking system had to be replaced with deeper, non-perforated piles. In Fairbanks, helical piles in a historically filled slough zone were jacked out of the ground and sank, breaking modules and requiring partial disassembly and reinstallation at 5.5 m depth.
The lesson from both cases: standard geotechnical surveys designed for construction and road work are not adequate for PV racking in frost-prone soils. Developers must commission surveys with PV-specific methodology, and should factor in the less obvious effect of the array itself.
In permafrost regions, the problem compounds further. Monitoring data from an array in Kotzebue, Alaska, shows that snow drifts accumulating behind solar rows are warming the permafrost, potentially destabilizing foundations over time. According to the report, solar arrays in these environments can act as snow fences, and the long-term structural consequences remain poorly understood.
For developers seeking to bankroll Arctic projects, the report identifies a persistent obstacle: the almost total absence of high-quality irradiance data above 60°N. Geostationary satellites degrade in accuracy beyond 65° latitude. Polar-orbiting satellites struggle to distinguish snow from cloud cover. Ground-based measurement networks are sparse, and those that exist face unique maintenance challenges, such as rime ice forming on radiometer domes, malfunctioning tracker mechanisms, and limited site access in winter.
As a result, energy yield assessments for Arctic projects carry substantially higher uncertainty than those at lower latitudes, which leads to complicated financing. The authors call for investment in heated, ventilated measurement instruments, rigorous maintenance protocols, and expanded ground-station networks across high-latitude regions.
The country-level data in the report paints a picture of a region moving fast despite the obstacles. Norway’s PV capacity above 60°N reached 173 MW in 2023, growing at 145% annually, with the country targeting 8 TWh of solar generation by 2030. Finland crossed 1 GW nationally and projects up to 9.1 GW by 2030. Arctic Sweden’s installed base hit 350 MW with a five-year mean growth rate of 58%/year, and utility-scale ground-mounted parks are now entering the permitting pipeline at gigawatt scale.
In North America, the story is different but equally dynamic. Alaska’s total PV capacity reached roughly 30 MW at end-2023, with the largest single facility at 8.5 MW and a 45 MW project announced for the Railbelt grid. More than 150 isolated diesel-dependent rural microgrids are receiving funding for solar-plus-storage systems, with some already capable of 100% renewable operation during favorable conditions.
The overarching message of this report is that the Arctic solar market is real, it is growing, and it has specific technical requirements that the global PV industry has not yet fully addressed. Bifacial vertical arrays, PV-specific geotechnical standards, Arctic-grade snow loss modeling, and expanded irradiance datasets are not nice-to-haves, but rather the foundations on which a credible high-latitude solar industry must be built.
Author: Ignacio Landivar
To access the full “Photovoltaics and Energy Security in the Greater Arctic Region,” you can download it here.
IEA PVPS Task 13 focuses on international collaboration to improve the reliability of photovoltaic systems and subsystems. This is achieved by collecting, analyzing, and disseminating information about their technical performance and durability. This creates a basis for their technical evaluation and develops practical recommendations to increase their electrical and economic efficiency in various climate regions.
 
The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.


Δ
By submitting this form you agree to pv magazine using your data for the purposes of publishing your comment.
Your personal data will only be disclosed or otherwise transmitted to third parties for the purposes of spam filtering or if this is necessary for technical maintenance of the website. Any other transfer to third parties will not take place unless this is justified on the basis of applicable data protection regulations or if pv magazine is legally obliged to do so.
You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled.
Further information on data privacy can be found in our Data Protection Policy.
pv magazine USA offers daily updates of the latest photovoltaics news. We also offer comprehensive global coverage of the most important solar markets worldwide. Select one or more editions for targeted, up to date information delivered straight to your inbox.
Δ
Legal Notice Terms and Conditions Privacy Policy © pv magazine 2026
Δ
Welcome to pv magazine USA. This site uses cookies. Read our policy. ×
The cookie settings on this website are set to “allow cookies” to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click “Accept” below then you are consenting to this.
Close

source

Europe Today
Euronews' flagship morning TV show with the news and insights that drive Europe, live from Brussels every morning at 08.00. Also available as a newsletter and podcast.
The Ring
The Ring is Euronews’ weekly political showdown, where Europe’s toughest debates meet their boldest voices. In each episode, two political heavyweights from across the EU face off to propose a diversity of opinions and spark conversations around the most important issues of EU affairs and the wider European political life.
No Comment
No agenda, no argument, no bias, No Comment. Get the story without commentary.
My Wildest Prediction
Dare to imagine the future with business and tech visionaries
The Big Question
Deep dive conversations with business leaders
Euronews Tech Talks
Euronews Tech Talks goes beyond discussions to explore the impact of new technologies on our lives. With explanations, engaging Q&As, and lively conversations, the podcast provides valuable insights into the intersection of technology and society.
The Food Detectives
Europe's best food experts are joining forces to crack down on fraud. Euronews is following them in this special series: The Food Detectives
Water Matters
Europe's water is under increasing pressure. Pollution, droughts, floods are taking their toll on our drinking water, lakes, rivers and coastlines. Join us on a journey around Europe to see why protecting ecosystems matters, how our wastewater can be better managed, and to discover some of the best water solutions. Video reports, an animated explainer series and live debate – find out why Water Matters, from Euronews.
Climate Now
We give you the latest climate facts from the world’s leading source, analyse the trends and explain how our planet is changing. We meet the experts on the front line of climate change who explore new strategies to mitigate and adapt.
Europe Today
Euronews' flagship morning TV show with the news and insights that drive Europe, live from Brussels every morning at 08.00. Also available as a newsletter and podcast.
The Ring
The Ring is Euronews’ weekly political showdown, where Europe’s toughest debates meet their boldest voices. In each episode, two political heavyweights from across the EU face off to propose a diversity of opinions and spark conversations around the most important issues of EU affairs and the wider European political life.
No Comment
No agenda, no argument, no bias, No Comment. Get the story without commentary.
My Wildest Prediction
Dare to imagine the future with business and tech visionaries
The Big Question
Deep dive conversations with business leaders
Euronews Tech Talks
Euronews Tech Talks goes beyond discussions to explore the impact of new technologies on our lives. With explanations, engaging Q&As, and lively conversations, the podcast provides valuable insights into the intersection of technology and society.
The Food Detectives
Europe's best food experts are joining forces to crack down on fraud. Euronews is following them in this special series: The Food Detectives
Water Matters
Europe's water is under increasing pressure. Pollution, droughts, floods are taking their toll on our drinking water, lakes, rivers and coastlines. Join us on a journey around Europe to see why protecting ecosystems matters, how our wastewater can be better managed, and to discover some of the best water solutions. Video reports, an animated explainer series and live debate – find out why Water Matters, from Euronews.
Climate Now
We give you the latest climate facts from the world’s leading source, analyse the trends and explain how our planet is changing. We meet the experts on the front line of climate change who explore new strategies to mitigate and adapt.
The case for green energy looks stronger than ever, as the war on Iran continues to highlight the widespread risks of fossil fuel dependency.
Brent crude, the worldwide benchmark for oil prices, has soared more than 50 per cent since the conflict began in the Middle East, hitting $116 (around €100.92) a barrel in early trading today (30 March).
Much of the volatility has been attributed to the effective closure of the Strait of Hormuz, one of the world’s biggest fossil fuel chokepoints that carries around one-fifth of global oil supplies. That’s around 20 million barrels being blocked every day.
Europe is already feeling the consequences, with the benchmark Dutch TTF natural gas price surging around 70 per cent – putting March 2026 on course to be the highest monthly increase for European gas prices since September 2021.
As rising energy prices threaten to hit struggling Europeans, several nations have witnessed a noticeable shift to green technology.
The UK, which has historically had one of Europe’s worst uptakes, has seen heat pump sales in the first three weeks of March increase by 51 per cent compared to the same period the month before – according to energy firm Octopus Energy.
Solar sales have also increased by 54 per cent, as homeowners “supersize” systems with 12 panels instead of the usual 10, while electric vehicle (EV) charger sales have climbed by 20 per cent.
“We’re seeing a massive shift as people stop just asking and start acting. British families are tired of being held hostage by global fossil fuel prices,” says Rebecca Dibb-Simkin of Octopus Energy.
“By switching to solar and heat pumps, they are becoming their own power stations – locking in low costs and protecting their wallets for the long term.”
European Commission data shows the average cost of petrol has risen across the EU by 12 per cent to €1.84 per litre from 23 February to 16 March.
This has triggered huge interest in Electric Vehicles (EVs), with French online used-car retailer Aramisauto witnessing its EV sales almost double between the middle of February and 9 March.
According to Reuters, Amsterdam-based Olx says customer enquiries for EVs have jumped across its marketplaces in France, Romania, Portugal and Poland, with growth “accelerating consistently week-over-week across all markets”.
In Norway, Finn.no – the country’s largest used-car marketplace – EVs have actually overtaken diesel models as the site’s best-selling fuel type.
German renewable energy firm Enpal BV tells Bloomberg that inquiries for solar panels and heat pumps have risen by around 30 per cent since the start of the US-Israel war on Iran, while solar firm 1KOMMA5° GmbH has also reported an almost doubling of interest in solar.
In the UK, energy firm E.ON has found interest in solar rose by 23 per cent between 23 February and 1 March, and surged a further 63 per cent between 2 and 8 March.
“It’s more important than ever that we help people take control of their energy use and lower their bills,” says Chris Norbury, Chief Executive of E.ON UK.
“Consumers are showing strong interest in solar and battery as a solution, and this product adds to the savings that can be achieved by generating and storing energy at home.”
Amid the boom in green technology, calls to double down on fossil fuels have gotten louder.
Earlier this month, British tabloid the Daily Express printed a frontpage story headlined ‘Get Drilling To Stop Soaring Bills’ – urging the UK to open up drilling licences in the North Sea.
However, an analysis from the University of Oxford found that a UK fully powered by renewable energy could save households up to £441 (€510) a year on their energy bills.
In comparison, maximising oil and gas extraction from the North Sea would only save households £16 (€19) to £82 (€95) per year – and this would rely on tax revenues being distributed to households to offset their energy bills.
Dr Anupam Sen, co-author of the analysis, said the idea that “draining” the North Sea would make the UK more energy secure and significantly cut household bills is “sheer fantasy”.
Multiple experts have also pointed out that oil and gas prices are set by global markets, not discounted for British consumers – and gas extracted from UK waters can be exported to the highest bidder – meaning increasing domestic production won’t significantly lower costs.
In contrast, Spain’s renewables revolution has been helping to keep energy bills low – even as gas prices soar.


Browse today's tags

source

Comprising six photovoltaic PV plants, the large-scale Noor Atlas solar PV scheme is under way.
Tagged with:
Power
Morocco
Subscriber only content
An active subscription is required to view this article
Subscribe to African Energy
View subscription options
Issue 541 – 29 March 2026
Power, Strategy & risk, Finance & investment
Issue 540 – 25 March 2026
Resources, Gas, Mid & downstream oil & gas, Upstream oil & gas, Strategy & risk, Finance & investment, Politics & security
Issue 540 – 20 March 2026
Ghana
Resources, Gas, Mid & downstream oil & gas
Contact Alex Wark to see an in-person demo of the platform and explore subscription options.
We can answer any questions you may have and discuss how the platform can be best used to help your business.
Tel: +44 1424 721667
Or request a 30 min platform demo
How we source our data
African Energy is a brand of
©Copyright 2026 African Energy
Head Office
4 Bank Buildings
Station Road
Hastings
TN34 1NG
United Kingdom

T: +44 (0)1424 721667

source

Apartment residents should be treated as part of the mainstream energy transition, not as an afterthought
Get our breaking news email, free app or daily news podcast
Most Australians now understand the basic promise of rooftop solar: lower power bills, cleaner electricity and, for some households, the option to charge an electric vehicle at home for far less than the cost of petrol.
But that promise was built around a particular kind of housing – the detached house with a privately controlled roof, a private meter board and a driveway or garage where the owner can install whatever equipment they need.
If you live in an apartment, unit or townhouse, the story is often very different.
That’s becoming a national problem.
Apartments made up 16% of Australian dwellings in the 2021 census and rooftop solar supplied 14.2% of Australia’s electricity in the second half of 2025, according to the latest Clean Energy Council report.
Yet apartment-specific solar programs are only now starting to appear. In New South Wales, the government says fewer than 2% of apartment buildings currently have solar.
Victoria and NSW have both started to respond. Victoria’s current Solar for Apartments round offers rebates of up to A$2,800 per apartment.
NSW’s Solar for Apartment Residents program offers grants of up to $150,000 for eligible shared systems.
That is overdue progress. It suggests apartment residents are finally being treated as part of the mainstream energy transition, not an afterthought.
But rebates alone will not solve the problem.
Australian research on apartment solar and strata solar and battery projects shows the main barriers are usually not the panels themselves.
They are the complications that come with shared buildings, including:
roof access
strata approvals
common-property rules
metering arrangements
switchboard upgrades
network constraints
and how benefits are shared across residents.
Newer research on power-sharing between tenants points in the same direction.
In a detached house, one household can make one decision. In a multi-owner building, the same decision can require committee approval, engineering advice, retailer coordination and agreement on who pays and who benefits.
Smart meters (which can send data on electricity use to your retailer, so you don’t need manual checks) will help, and governments are right to speed up their rollout. National rules now aim to deliver smart meters across the National Electricity Market by 2030.
But a smart meter on its own does not solve all the problems.
This is no longer only about electricity bills. It’s also about transport.
Federal guidance says most EV charging happens at home.
NSW says an estimated 80-90% of EV owners will charge where they live, including in apartment buildings.
That matters because home charging is usually the cheapest and most convenient way to run an EV, especially when households can use off-peak power or rooftop solar.
For people in detached houses, the long-term pathway is fairly clear: solar, a home charger and perhaps a household battery.
For people in apartments with no EV-ready infrastructure, that pathway may not exist at all.
Governments are starting to notice. NSW has funded EV-ready retrofits for residential strata buildings and Queensland has issued guidance for bodies corporate dealing with EV charging.
But if apartment buildings cannot support electrified living, a growing share of Australians will miss out.
The answer is both – but applied differently.
For existing apartment stock, governments need carrots. That means:
co-funding for common-property electrical upgrades
support for feasibility studies
simpler approvals and
trusted one-stop advice for owners corporations, body corporates and strata committees.
In many buildings, the real upfront cost is not the solar panel – it is the enabling infrastructure around it.
For new apartment developments, governments also need a stick. It makes little sense to keep approving buildings that are not solar-ready, EV-ready or set up for modern metering and shared energy services. Retrofitting later is usually slower, more expensive and more contentious.
And whatever model is used, consumer protection matters.
If apartment residents are asked to rely more on shared systems, they also need clearer rights, fairer disclosure and real recourse when something goes wrong.
Australia should not let rooftop solar, batteries and home EV charging become advantages available mainly to people who own detached houses.
This is partly a climate issue and partly an engineering issue. But it is also a cost-of-living issue and, increasingly, a housing equity issue.
NSW’s apartment solar program explicitly says renters should be able to benefit, not just owner-occupiers.
The Social Housing Energy Performance Initiative in NSW and Victoria’s Energy Efficiency in Social Housing Program show governments are also starting to treat energy access as a fairness question, not just a technology question.
The next phase of Australia’s energy transition is not about proving rooftop solar works. We already know it does.
It is about deciding whether people in shared buildings can participate on fair terms.
If governments get this right, apartment buildings can become more than passive consumers of electricity. They can host shared solar, smarter demand management, batteries and EV charging.
If governments get it wrong, many Australians will keep watching the energy transition from the sidelines.
Saman Gorji is an associate professor of renewable energy and electrical engineering at Deakin University. Alireza Ganjovi is a researcher in energy systems and applied physics at Deakin University. This article was originally published in the Conversation

source

By the People, for the People
News
Firefighters responded to the blaze within 5 minutes but found the roof and solar panels already ablaze.
Mar. 31, 2026 at 3:03am
Got story updates? Submit your updates here. ›
The roof and installed solar panels of a home in Arlington Heights, Illinois caught fire on Monday afternoon, according to the Arlington Heights Fire Department. Officials said the first-arriving fire crews found the fire coming from the second-story roof area, involving both the roof structure and the solar panels. One occupant had safely exited the residence before emergency personnel arrived.
Residential fires involving solar panels are a growing concern as more homes adopt renewable energy sources. This incident highlights the importance of proper installation, maintenance, and safety protocols to mitigate the risk of such fires, which can pose a threat to both property and human life.
Firefighters responded to a reported residential structure fire behind the 1000 block of Rand Road at around 1:12 p.m. Arlington Heights police were able to identify the fire as being in the 1200 block of Kelly Street. Officials said the first-arriving fire crews were on scene within five minutes and found a fire coming from the second-story roof area, involving both the roof structure and installed solar panels. Damage to the residence was limited primarily to the roof, which sustained an approximately two-foot hole, along with minor water damage to the interior. The home remains habitable, and the occupants can stay in the residence.
The local fire department that responded to and investigated the residential fire.
This incident underscores the importance of proper installation, maintenance, and safety protocols for residential solar panel systems to mitigate the risk of fires and protect both property and human life.
We keep track of fun holidays and special moments on the cultural calendar — giving you exciting activities, deals, local events, brand promotions, and other exciting ways to celebrate.

source

An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
This photograph features Greg Nielson, a project leader at Sandia National Laboratoies. He holds a solar cell test prototype with a microscale lens array fastened above it. Together, the cell and lens help create a concentrated photovoltaic unit. The t…
Integrated Energy Systems Office
This photograph features Greg Nielson, a project leader at Sandia National Laboratoies. He holds a solar cell test prototype with a microscale lens array fastened above it. Together, the cell and lens help create a concentrated photovoltaic unit. The tiny cells could turn a person into a walking solar battery charger if they were fastened to flexible substrates molded around unusual shapes, such as clothing. The solar particles, fabricated of crystalline silicon, hold the potential for a variety of new applications. They are expected eventually to be less expensive and have greater efficiencies than current photovoltaic collectors that are pieced together with 6-inch- square solar wafers.
Committed to Restoring America’s Energy Dominance.
Follow Us

source

An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
SETO presented on the current challenges and opportunities of VIPV. Download the slides. 
PV in Motion 2023 – VIPV Presentation
On July 14, 2022, the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) and Vehicle Technologies Office (VTO) released a request for information (RFI) on technical and commercial challenges and opportunities for vehicle-integrated photovoltaics (VIPV) or vehicle-added (or attached) PV (VAPV) systems. DOE has supported research, development, demonstration, and commercialization (RDD&C) efforts on vehicle photovoltaics (PV) via a variety of programs. The purpose of this RFI was to solicit feedback from various stakeholders, such as industry, research laboratories, academia, government agencies, regulators, and other experts, on issues related to VIPV/VAPV technologies and markets. 
The RFI received responses from organizations representing VIPV/VAPV stakeholders including product manufacturers, vehicle fleet operators, research institutions, national laboratories, consultants, and individuals.
Respondents addressed questions in five different categories, spanning the current state of the industry, product requirements, key barriers, RDD&C needs and opportunities, and stakeholder engagement. Respondents framed their responses based on specific questions in each category, though some of them outlined their answers differently around themes of interest spanning various categories and providing more general comments. This summary document is organized around the categories identified in the RFI and the individual questions.
The market segments most frequently cited as promising for VIPV/VAPV are:
 
Two primary use cases were identified for the role of PVs in vehicles:
(1) propulsion in electric vehicles
(2) supporting auxiliary loads
 
Commercial trucks and trailers were generally viewed as the largest market opportunity because they:
 
Other respondents viewed the passenger vehicles market segment as the largest market opportunity, due to the large fleet size and relative maturity of EV technology.
 
 
Use cases and value propositions of VIPV/VAPV systems:
 
In addressing what market segments or subsegments are most promising for vehicle PV systems, respondents identified three primary factors: the available area for PV, the curvature of vehicle surfaces, and the size of the segment (e.g., size of fleets and frequency of use). The market segments most frequently cited by respondents as promising for VIPV/VAPV are:
Two primary use cases were identified for the role of PV in vehicles: (1) propulsion in electric vehicles, and (2) supporting auxiliary loads. When used in conjunction with electric vehicles (EVs), PV systems could provide additional energy to the battery to increase vehicle range. Respondents noted that this could increase the autonomy of EVs and reduce the risk of stranded vehicles due to lack of charge. Solar charging of EVs could also enable use of EVs as emergency responses vehicles. The role of PV systems in active battery thermal management in EVs was also mentioned.
Vehicle PV systems could also support auxiliary loads in vehicles, such as refrigeration, heating/cooling, or electronics expanding the market opportunity for VIPV/VAPV beyond EVs. Multiple respondents identified transport refrigeration units (TRUs) as a promising market segment for PV integration. PV integration into TRUs was identified as particularly attractive because of the need to replace diesel fuel in TRUs. Further, TRUs have a duty cycle amenable to solar charging. Recreational vehicles (RVs) were also frequently identified as an opportunity to use VIPV/VAPV to support auxiliary loads – in this case, to reduce generator use associated with RVs.
Respondents expressed divergent views about the potential for PV integration into light-duty passenger vehicles. Some suggested that passenger vehicles represent a promising market for PV, citing that passenger vehicles are the most mature EV market and that the efficacy of VIPV reduces significantly as vehicle weight increases. Consumer interest in solar passenger vehicles may help drive this market. However, others noted that the curved surfaces in passenger vehicles creates an integration challenge and that the first markets to address are vehicles offering large, flat surfaces.
Respondents also expressed divergent views about the opportunity for PV integration into trucks and truck trailers. Generally, medium- and heavy-duty trucks were viewed as attractive for VIPV/VAPV because of the high potential for space utilization and flat surfaces amenable to PV integration. However, respondents also noted the need to differentiate between single-unit and combination trucks when evaluating VIPV/VAPV opportunities. They expressed concerns about the feasibility of applying PV systems on the truck trailers or shipping containers of combination trucks, despite the benefit of a large, flat surface, and suggested that single-unit trucks are better suited to VIPV/VAPV.
Other markets that were mentioned to be promising for VIPV/VAPV include:
Most respondents suggested that commercial trucks and trailers present the largest market opportunity for VIPV/VAPV. The respondents’ decision factors and views of use cases for VIPV/VAPV are similar to those noted above. Commercial vehicles are used frequently and are exposed to ample sunlight (e.g., not parked in garages); therefore, commercial vehicles, particularly those that move high-value products, are most likely to adopt VIPV in the near term. Commercial trucks offer high utilization of VIPV since they are part of large fleets driven during daylight hours. Commercial trucks or trailers typically consist of large, flat surfaces, making them compatible with PV integration. They also have more standardized vehicle designs and shapes than passenger vehicles. The grocer market and long-haul transport in the southern United States were referenced as specific VIPV/VAPV market opportunities.
Several market opportunities in addition to commercial trucks were also mentioned by respondents:
Respondents expressed strong interest in achieving domestic manufacturing of vehicle PV systems. Domestic manufacturing of VIPV/VAPV products could help quickly meet future domestic demand, particularly given the high number of TRUs in the United States. Respondents also referenced the importance of both manufacturing and installation of vehicle PV systems for domestic job support. Overall, lightweight modules for VIPV/VAPV applications were considered a better match to domestic manufacturing capabilities than stationary modules in terms of both the PV technology and manufacturing volume required. Further, lightweight modules were considered less price sensitive than the stationary PV market which could bolster domestic manufacturing.
A common theme in response to this question was the opportunity for domestic manufacturing of thin-film PV modules for vehicle integration, particularly high efficiency, conformal modules. Thin-film PV technologies offer flexibility, light weight, and less capital-intensive manufacturing processes which makes them amenable to vehicle integration. Keeping in mind that responses were received prior to passage of the Inflation Reduction Act of 2022, some respondents expressed doubt about vehicle-integrated silicon PV in domestic manufacturing, commenting that VIPV/VAPV will not create enough of a new market opportunity in silicon PV compared to stationary PV to drive domestic manufacturing.
Stakeholders also discussed components required for VIPV/VAPV systems where the United States could lead manufacturing:
Similar to prior questions, respondents emphasized several use cases and value propositions of VIPV/VAPV systems:
Other respondents approached this question by considering what information is needed to determine the most effective use of vehicle PV systems. A reliable source is needed to define system requirements, considering both vehicle performance and cost. VIPV/VAPV applications face a challenge distinct from grid-tied PV that the value of VIPV/VAPV is more than simply the energy generation provided; however, no common assessment exists to determine and communicate this value and examine the different product designs that will be most beneficial to different markets.
Vehicle PV products: Focus on VAPV products today, applied via adhesives or bracket-mounted
 
Customer market segments:
 
The primary list of key products requirements for VAPV/VIPV applications was identified as:
 
 
 
 
 
 
Some respondents approached this question from the market segment perspective and others focused on the type of VIPV/VAPV technology. Respondents commented that most vehicle PV products available today are VAPV products, such as rigid, flexible, or semi-flexible modules added to vehicles, rather than solar products integrated into the body of vehicles. These VAPV products are typically applied to vehicles via adhesives, in the case of flexible or semi-flexible mat-style systems, or bracket mounted systems in the case of rigid framed glass panels. Flexible modules typically consist of crystalline silicon (c-Si) or copper indium gallium diselenide (CIGS), and rigid, flat modules are commonly made of c-Si and encased in glass.
Available vehicle PV products were also discussed in terms of market segments:
Respondents also noted that some additional VIPV/VAPV products were listed in the RFI document.
The primary list of key products requirements for VAPV/VIPV applications was identified as:
While most of these factors are important to some extent in each VIPV/VAPV market segment, the prioritization of these product requirements changes based on the market segment. For passenger vehicles, aesthetics is considered a high priority, and a low VIPV/VAPV system weight is also critical to avoid negating the additional driving range provided. Alternatively, factors such as reliability and supply chain integration are viewed as a higher priority in medium- and heavy-duty vehicles. In commercial vehicles, SAE and International Organization for Standardization (ISO) standards for environmental and electrical applications are often included in validation testing, so VIPV/VAPV products for this segment need to be compliant with those standards.
For some of the product requirements listed above, respondents provided detail on how to define these requirements and optimize them for VIPV applications. For example, silicon cells could be cut in half to better fit the limited available area of a vehicle and increase voltage. Respondents emphasized the importance of cell performance in various lighting conditions and suggested that integration of bypass diodes between cells could mitigate power loss under partial shade. Panel cleaning should also be a key component of maintenance requirements to maintain cell efficiencies. With regard to reliability and product lifetime, VIPV/VAPV product lifetime may be hindered by the weathering and motion deterioration innate to the vehicle environment. Safety of VIPV/VAPV systems should include secure installation to prevent systems from detaching while in motion, potentially causing injury and property damage. Environmental safety was also referenced, with respondents suggesting the use of non-toxic materials for VIPV/VAPV systems and establishing safeguards to prevent environmental contamination.
Other product requirements mentioned by respondents include:
When evaluating the best PV cell technologies for VIPV applications, respondents commented on a variety of factors and trade-offs that informed their decisions. The most frequent factor cited was the limited area available on vehicle surfaces and resultant importance of high efficiency PV cells, leading several respondents to favor silicon cell technology. However, flexibility could also be an important factor, depending on the form factor of the vehicle, and it may be worth trading off some efficiency gains for increased flexibility. The flexibility offered by thin-film absorbers (e.g., CIGS, OPV, perovskites) could be attractive for VIPV, but respondents expressed the need to address challenges such as lower efficiency, poor shade tolerance, and electrical hysteresis impacting some thin-film PV technologies. Other important factors were lightweight, aesthetics, and transparency. Cadmium telluride was not specifically mentioned by respondents as a promising technology for VIPV.
As the VIPV sector matures, stakeholders will develop an improved understanding of use conditions of vehicles, standards, testing, costs, manufacturing processes, and other factors that can help inform which PV cell technologies are best suited to VIPV.
Respondents highlighted two main themes when considering vehicle PV integration requirements and challenges: (1) the importance of designing systems to enable vehicle maintenance and repair, and (2) the safety of occupants and first responders in the event of a crash. If these two items are not sufficiently addressed, the adoption risk of VAPV/VIPV will likely be too high to justify the investment. Several key integration requirements and considerations were identified by respondents, including:
Five key integration challenges with respect to structural integration and electrical systems integration were identified:
Respondents commented that some existing metrics and standards should be modified for vehicle PV integration while other metrics and standards should not change. In the case of PV performance and reliability, existing standards may require adjustment for vehicle PV systems because of the different operating conditions of the vehicle environment compared to stationary solar. Further, safety standards may need to address the vehicle environment specifically to ensure passenger safety and prevent risk of shock. Respondents commented that components such as cables, wires, and charge controllers should follow the codes or standards of the auto parts industry. Respondents also noted that VIPV/VAPV systems will be subject to a wide variety of operating conditions and that comprehensive metrics do not yet exist that account for these conditions. Further, metrics such as durability and lifetime will be more challenging to predict for vehicle PV systems than for stationary PV due to variable road and climate conditions.
Multiple respondents commented on the importance of considering end-of-life opportunities, challenges, and regulations, potentially through future research, development, and demonstration (RD&D). While VIPV/VAPV system lifetime will likely differ from stationary PV lifetime based on the environment and PV materials used, stationary PV modules today have estimated useful lifetimes longer that those of most vehicles, potentially presenting opportunities for reuse of modules on multiple vehicles. No regulations currently exist for recycling of PV modules or waste materials recovery. Further, integrating PV modules into vehicles may complicate the established vehicle waste stream processing; depending on how VIPV recycling and disposal processes are designed, integrating PV into vehicles may introduce hazardous materials (e.g., lead and cadmium) into vehicle waste streams.
Finally, respondents commented on the end user expectations surrounding vehicle PV system cost. If end users base their expectations for system cost on the relatively lower-cost stationary PV modules, customer adoption of VIPV/VAPV systems may be low since VIPV/VAPV may not be cost-competitive with utility-scale PV.
Respondents noted that existing standards and performance requirements are tailored to stationary systems and not aligned with vehicle PV applications. For example, irradiance variability on a moving vehicle differs significantly from that of a stationary PV module, necessitating alternative performance metrics. Further, current characterization standards are designed for flat panels and not curved PV modules. New standards for vehicle PV systems are needed to ensure quality and safety and encourage adoption. Lessons from space solar could be used to establish new performance requirements, such as the ability of space solar to resist g-forces and impacts. A few respondents mentioned that they are directly developing or aware of others developing standards for specific vehicle PV segments or technologies.
Respondents stressed the importance of being able to inspect, diagnose, and safely repair system components, especially considering the long lifetimes of commercial vehicles. VIPV/VAPV systems should be removable and replaceable in the event a car is damaged, such as via thin films applied to vehicles with an adhesive. Respondents also noted that VIPV/VAPV systems should be designed to allow for recyclability or simple disposal at end of life. Repair of individual cells in VIPV/VAPV systems – rather than replacement of an entire module – was viewed as highly beneficial for simplifying operations and maintenance (O&M) and reducing maintenance costs.
Vehicle use patterns and unique elements of the vehicle environment and value chain necessitate consideration of factors for VIPV/VAPV systems that may not be relevant for traditional PV systems. For instance, maintenance and repairs are often avoided or deferred in vehicles, so one respondent suggested that vehicle PV systems be designed to accommodate a similar repair schedule (or lack thereof) and offer long-term reliability and durability. Also, vehicle drivers and operators will require education about any VIPV system operation or maintenance that they will be responsible for. Cell technology selection should consider possibilities such as vehicle collisions or fires, where hazardous materials could be released. Further, hazardous cell materials could present new challenges for vehicle repair shops not trained or equipped to handle them.
Regarding considerations for vehicle insurance, respondents noted that VIPV/VAPV systems may necessitate special collision insurance options. Additionally, insurance companies should recognize the value of and offer specialized rates for vehicle PV systems.
 
 
 
Barriers to adoption and commercialization of VIPV/VAPV technologies were addressed from the technical and market perspectives. These barriers include manufacturing-line integration, immature supply chains, uncertain reliability, maintenance concerns, and aesthetics. Respondents focused on four major themes in their discussion of these barriers:
Barriers to collaboration and partnering between the solar and vehicle industries on vehicle PV technologies and businesses range from unfamiliarity and risk aversion to lack of important data. Respondents generally agreed that cross-sector partnering via technology and manufacturing integration will be critical to the success of vehicle PV. It is particularly important for PV and vehicle manufacturers to partner to integrate VIPV/VAPV products into the vehicle design and supply chains, rather than focusing largely on retrofit vehicle PV systems as is done today.
Several respondents commented that one or both industries may be resistant to entering the vehicle PV market or with working with the other industry. In particular, traditional solar manufacturers may not be eager to enter the VIPV/VAPV market while it remains relatively unproven. Trust between the two industries, particularly when players of different sizes are involved and power dynamics come into play, was also viewed as a barrier to sharing ideas and technology. Further, validated system data and tools need to be readily available to demonstrate the value of vehicle PV systems, particularly when attempting to partner with those in another industry.
In addition to barriers previously discussed, several adoption barriers were commented or expanded on in response to this question. Repair and replacement of the vehicle PV systems or components were considered a large barrier given the likelihood of general wear and tear, damage from the environment or inclement weather (e.g., hail or tree branch damage), vehicle crashes, and potentially vandalism and theft. Warranty and insurance processes and costs to consumers should be designed to be straightforward and analogous to common vehicle repairs and replacements.
Regarding VIPV/VAPV installation, respondents expressed concern that the upfront cost of installation could be an adoption barrier without subsidies or other financing mechanisms. Further, challenges with available installation options, such as methods to safely adhere panels to vehicle roofs, need to be addressed. Additionally, PV installation could cause problems with vehicle warranties if holes are drilled in the vehicle roof, making it preferable to integrate PV into directly into vehicle design and manufacturing.
Finally, a comprehensive market assessment was suggested to better understand and overcome existing market barriers and demonstrate the role of vehicle PV in commercial and general consumer markets as a value-added product.
 
 
 
 
 
 
Respondents reported that limitations exist in current modeling of energy yields, installed system costs, and system integration, including understanding of ancillary benefits, for vehicle PV technologies and systems.
The majority of respondents focused on current evaluations and standardized calculations for vehicle PV energy production in response to this question. Suggested inputs to vehicle PV energy production calculations include local weather tables across the year, to provide monthly energy production estimates, and energy required to cool vehicles. One respondent suggested that current calculations do not consider that solar car roofs may cause vehicles to heat up when parked in the sun more than traditional car roofs, though data supporting this claim was not cited. Respondents again noted the limitation of existing calculations to determine PV yield for a given driving route and the importance of predictability for realizing the value propositions of VIPV/VAPV (e.g., extended battery range).
Limitations in energy production data collection and monitoring were also addressed by several respondents. Access to vehicle PV system energy production data is important for troubleshooting issues and learning how the panels are charging; however, access to this data is currently not possible with all VIPV/VAPV systems. Respondents also noted a lack of standardization and ownership for measuring parasitic power consumption (e.g., telematics, controller, etc.), which is critical to accurately model the energy needed from a PV system to offset that load and maintain battery health.
Several respondents discussed the need for performance evaluations and standardized calculations to consider the variable angles of incidence likely in VIPV/VAPV systems. Calculations would need to consider how PV is integrated into the vehicle and the integrated energy throughout the day based on the changing angle of the PV modules to the sun. An integrated software tool was specifically suggested that included the PV performance across 90 degrees of incident sunlight along with a computer aided design of the vehicle. Respondents also cautioned that calculations based on current solar technologies may not provide accurate efficiency data if applied to VAPV/VIPV systems; new solar technologies may exhibit improved performance at wide angles of incidence. Thin-film PV technologies were particularly mentioned as resilient to off-angle performance degradation.
Finally, it is important to understand how to accurately calculate factors in addition to energy production, including the impact of VIPV on range, power electronics, and peak power tracking. A data-driven analysis based on information from private industry could enable standardized calculations for the impact of VIPV/VAPV systems on vehicle performance, energy production, and carbon emission reduction.
Most respondents addressed research and development needs; however, it was noted that field demonstrations are key to identify changes needed and that the marine market is advanced enough to begin demonstrations and start uncovering issues. Research and development in several areas was highlighted:
Other suggested research topics include:
Respondents discussed several challenges to demonstrating and validating the durability and performance of vehicle PV technologies and systems.
Several strategies to mitigate these challenges were also discussed. Overall, broad market acceptance and adoption of VIPV/VAPV will drive understanding of the benefits of vehicle PV systems. Further, lessons learned from other material changes in vehicles, such as the use of aluminum and composite vehicle bodies, can inform how the vehicle industry can successfully shift from established products to more complex technologies.
Respondents addressed the challenges in mobile solar combined with energy storage systems from the perspectives of both specific mobile solar-as-storage challenges and general vehicle-as-storage challenges.
Other challenges noted include:
 
 
 
 
Respondents focused on five major themes when identifying vehicle PV information and knowledge gaps:
In response to which stakeholder groups should be involved in conversations on VIPV/VAPV product requirements, barriers, and RDD&C needs, most respondents named types of stakeholder groups. However, it was also suggested to include stakeholders across the value chain of VIPV/VAPV products, including raw materials and end-of-life stakeholders. Respondents also expressed the need for a U.S.-based consortium with PV expertise that leverages industry-leading automotive consortia to advance the VIPV/VAPV industry. Specific stakeholder group mentioned are:
Respondents addressed an array of stakeholder engagement needs, ideas, and specific engagement opportunities in response to this question. Three general strategies were suggested for DOE to engage stakeholders:
Finally, specific avenues for stakeholder engagement were suggested, which could provide platforms for discussion and enable collaboration:
Committed to Restoring America’s Energy Dominance.
Follow Us

source

0
By clicking the button, I accept the Terms of Use of the service and its Privacy Policy, as well as consent to the processing of personal data.
Don’t have an account? Signup
Powered by :
Perovskite solar cells, long the holy grail of solar innopvation with breakthroughs in commercial manufacturing promised every few years, may finally be coming close to hitting markets. After earlier progress by Oxford PV researchers including licensing deals with manufacturing giants like First Solar and Trina , now researchers in Germany claimn to have solved a key issue with these cells. Since perovskites use a special class of crystalline materials to convert sunlight into electricity more efficiently, their sensitivity to temperature swings and faster degradation therefore has been an issue preventing progress. Now, researchers at the Technical University of Munich (TUM) and the Cluster of Excellence e-conversion have now identified why these promising materials lose their performance – and how they can be stabilized.
Working with partners from the Karlsruhe Institute of Technology (KIT), DESY (Deutsches Elektronen-Synchroton), and the KTH Royal Institute of Technology in Stockholm, the team claims to have uncovered the mechanisms behind the deterioration of the material through temperature swings and developed a strategy to prevent it. Their approach is builot around stabilizing the fragile crystal structure with specially designed molecular “anchors”.
The team claims that their new design strategies will make the top layer of tandem solar cells more robust, enabling them to withstand temperature variations as seen in the real world. Tandem solar cells are made up of stacked solar cells (two in minimum) and therefore make better use of sunlight.
In a study published in Nature Communications, lead author Dr. Kun Sun from the TUM Chair of Functional Materials and the team investigated High-Efficiency Wide-Bandgap cells – the upper cells in a tandem solar cell. Using high-resolution X-ray measurements at DESY, the team watched the material “breathe” in real-time during rapid temperature changes; the lattice periodically expanded and contracted in response to rapid temperature fluctuations.
They discovered that degradation happens in a massive initial “burn-in” phase, where cells can lose up to 60% of their relative performance. “We revealed that a microscopic tug-of-war triggers this loss,” explains Dr. Kun Sun. “Tensions arise inside the material and its structure changes – this costs power.” This finding gave engineers a clear target: if they could eliminate the burn-in, long-term stability woiuld follow that could deliver the decades of performance required of solar cells.
In a second paper published in ACS Energy Letters, the researchers reported how to stabilize the sensitive crystal material. They used special organic molecules that act as spacers, holding the structure together – like a molecular scaffold.
By comparing different spacers, the researchers found the answer: while common spacers led to structural breakdown, the bulkier organic molecule PDMA acted as a superior anchor producing a more robust solar cell that remains stable even under the mechanical stress of rapid heating and cooling.
Perovskite based solar cells are widely seen as an opportunity for newer firms outside China to find space inside the crowded solar manufacturing market dominated by Chinese firms, and every relevanmt patent or innovation outside China will give hope that the possibility is increasing.   
We are India’s leading B2B media house, reporting full-time on solar energy, wind, battery storage, solar inverters, and electric vehicle (EV)
Quick Links
© 2025 Saur Energy. All Rights Reserved.

source

Sponsored By
ADVERTISEMENT
ADVERTISEMENT
WORTHINGTON — A public hearing on a proposed solar farm and battery energy storage system for up to 200 megawatts, planned by Summit Lake Storage, LLC — a subsidiary of Geronimo Power — is planned from 6 to 9 p.m. Monday, April 6, at the Worthington Event Center.
The meeting is hosted by the Minnesota Public Utilities Commission, and is to take public comment on the joint site permit application for the project.
ADVERTISEMENT
The project is proposed to be built on approximately 1,416 acres in Elk and Summit Lake townships in Nobles County.

ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT

source

POET Technologies Reports Fourth Quarter 2025 Financial Results
RH Reports Fourth Quarter and Fiscal Year 2025 Results
SEALSQ Announces FY 2025 Audited Financial Results, Reaffirms Q1 …
NUBURU Wins Counter-Drone Directed-Energy Order from Tier-One Gov…
Datavault AI and Coppercore Inc. Announce Tokenization of High-Gr…
Rekor Systems Reports Full Year 2025 Results
POET Technologies Reports Fourth Quarter 2025 Financial Results
RH Reports Fourth Quarter and Fiscal Year 2025 Results
SEALSQ Announces FY 2025 Audited Financial Results, Reaffirms Q1 …
NUBURU Wins Counter-Drone Directed-Energy Order from Tier-One Gov…
Datavault AI and Coppercore Inc. Announce Tokenization of High-Gr…
Rekor Systems Reports Full Year 2025 Results
onsemi (NYSE:ON) won a design contract with Sineng Electric to supply its latest FS7 IGBT + EliteSiC hybrid power integrated modules (PIMs) for Sineng’s 430 kW liquid‑cooled energy storage systems and 320 kW utility‑scale solar inverters.
Benchmarks show up to 0.1% efficiency gains, 32% higher power density, and reduced switching losses and thermal resistance, enabling higher system ratings without increasing footprint.
ON was down 4.61% with key peers also weak: GFS -4.84%, STM -4.96%, UMC -3.37%, ASX -5.45%, CRDO -7.69%. Despite broad peer softness, the momentum scanner did not flag a coordinated sector move.
Recent ON news, including earnings and technology collaborations, has typically produced modest single-digit price moves, with no clear pattern of selling on positive announcements.
Over the last six months, ON has combined financial execution with strategic technology moves. Q4 2025 results showed revenue of $1,530.1M and full-year revenue of $5,995.4M, alongside a new $6B repurchase authorization. The company also announced a GaN collaboration with GlobalFoundries aimed at higher-efficiency power devices. Other items included scheduling the Q4/2025 earnings release and a future Financial Analyst Day. Today’s design-win news with Sineng Electric continues the theme of expanding ON’s power solutions footprint in renewable and AI-related infrastructure.
This announcement highlights a design win that places ON’s FS7 IGBT and EliteSiC-based hybrid modules at the core of Sineng’s 430 kW ESS and 320 kW solar inverters. System-level gains include up to 0.75% higher round-trip efficiency and 32% better power density. In recent months, ON has paired such technology milestones with solid financial results and capital return plans. Investors may watch future design wins, execution in renewable and AI power markets, and ongoing regulatory filings for additional context.
AI-generated analysis. Not financial advice.
EliteSiC technology boosts efficiency, power density and long-term reliability for Sineng’s high-power energy storage and solar inverter solutions
Summary
onsemi announced a new design win with Sineng Electric, which will feature onsemi’s latest‑generation hybrid power integrated module (PIM) in two utility‑scale renewable energy platforms. The PIM features onsemi’s FS7 insulated‑gate bipolar transistor (IGBT) and EliteSiC technology and will be used in Sineng’s next‑generation 430 kW liquid‑cooled energy storage systems (ESS) and 320 kW utility‑scale solar string inverter. Using onsemi’s technology, the Sineng solutions will deliver improved efficiency, higher power density, lower switching losses and improved thermal performance, advancing performance standards in utility-scale renewable energy applications. In benchmark testing against competing power modules, onsemi’s FS7‑based hybrid PIM delivered a 0.07% efficiency improvement and reduced losses by 225 W in a 320 kW solar inverter configuration.
News Highlights

SCOTTSDALE, Ariz., March 31, 2026 (GLOBE NEWSWIRE) — onsemi announced today that its hybrid power integrated modules (PIMs) will be featured in Sineng Electric’s next-generation 430 kW liquid-cooled string energy storage systems (ESS) and 320 kW utility-scale solar inverter. The design win builds upon the longstanding collaboration between onsemi and Sineng to deliver high-performance, future-ready solutions in the growing renewable energy and AI infrastructure markets.
Industry-Leading Power Module Technology
At the core of Sineng’s new platforms, onsemi’s latest-generation Field Stop (FS7) insulated-gate bipolar transistor (IGBT) and silicon carbide (SiC) hybrid PIMs in the F5BP package are engineered to boost the power output of utility-scale solar string inverters and energy storage systems (ESS). Compared to previous generations, the modules offer 32% increased power density with 0.1% higher efficiency within the same footprint to increase the total system power of a solar inverter from 320 kW to 350 kW.
Setting New Standards for Efficiency and Reliability
onsemi’s hybrid F5BP PIMs integrate the company’s FS7 IGBT and EliteSiC diode technologies, reducing power dissipation by up to 8% and switching losses by 10% compared to previous generations. Their advanced direct bonded copper (DBC) substrate design minimizes stray inductance and lowers thermal resistance to the heat sink by 9.3%. Together, the reduction in switching losses and thermal resistance enables up to 32% higher system power at the same weight and density compared to prior‑generation designs. The modules also feature an optimized electrical layout and an innovative baseplate design that enhance thermal management. This combination enables superior system performance and enhanced long-term reliability.
Compared to previous-generation modules, onsemi’s latest FS7‑based hybrid PIM combines lower switching losses and reduced thermal resistance, enabling the following system‑level improvements in Sineng’s new 430 kW string ESS:
Enabling a More Stable and Reliable Renewable Grid
“Utility‑scale operators are laser‑focused on squeezing more kilowatts from the same footprint while cutting lifecycle costs. By integrating onsemi’s F5BP package hybrid modules into our 430 kW ESS and 320 kW inverter platforms, we’re addressing two industry imperatives at once: higher power density that uplifts system ratings, and conversion efficiency gains that compound at gigawatt scale. Those incremental improvements translate into real savings for product development and a more stable, dispatchable renewable grid.” – Jianfeng Sun, General Manager of Research and Development, Sineng Electric
“Developers need solutions that fit existing layouts, simplify thermal design, and reduce energy losses. By pairing FS7 IGBTs with EliteSiC diodes in our F5BP modules, we deliver higher conversion efficiency and power density without increasing the footprint, enabling significantly higher power ratings at the same system size. This combination allows utility‑scale solar and storage sites to increase output from existing installations, while improving reliability and lowering lifetime operating costs.” – Sravan Vanaparthy, VP & GM of IGBT Power Division, onsemi
More Information:
Product Pages: NXH500B100H7F5SHG; NXH600N105L7F5P2HG; NXH600N105L7F5S2HG; NXH600N105L7F5SHG; Si/SiC Hybrid Modules
Datasheet: NXH500B100H7F5SHG
Solution Guides: Solar Inverter; Energy Storage
White Papers: BRD8095 – Overcoming the Challenges of Silicon Carbide to Ensure Application Success; TND6386 – Topologies for Commercial String Solar Inverter
About onsemi
onsemi (Nasdaq: ON) delivers intelligent power and sensing technologies that enable electrification, energy efficiency, safety, and automation across automotive, industrial, and AI data center end‑markets. With a highly differentiated and innovative product portfolio, onsemi helps customers solve complex challenges to achieve higher efficiency, improved performance, and lower system cost, while supporting a safer, cleaner, and more energy‑efficient world. The company is part of the S&P 500® index. Learn more at www.onsemi.com.
onsemi and the onsemi logo are trademarks of Semiconductor Components Industries, LLC. All other brand and product names appearing in this document are registered trademarks or trademarks of their respective holders. Although the Company references its website in this news release, information on the website is not to be incorporated herein.
Contact:
Michael Mullaney
michael.mullaney@onsemi.com
+1 838-289-7314
Continue reading with these related stories
© 2020-2026 StockTitan.net – Your Edge is Information
Information only — not investment advice.

source

The companies say their integrated software platforms will provide solar installers with a way to offer a holistic customer experience that carries through all parts of the solar sales and installation process, and beyond.
Image – RAZE Solar on Unsplash
Florida-based fintech company OneEthos has announced its acquisition of Bodhi, a customer experience platform that provides software-as-a-service products to solar installers across the country.
The acquisition will pair Bodhi’s software tools with the financial products offered by OneEthos and its affiliate, Climate First Bank, which work together to offer residential and commercial solar loans in all U.S. states.
The companies say the combination of transparent financing and automated communication will help installers foster sustainable revenue growth in a market facing challenges from the end of the Section 25D tax credit and an industry-wide decrease in public trust following well-publicized problems of unethical sales practices from some solar companies.
“Installers need new kinds of partnerships that help them win with both economics and execution,” said Scott Nguyen, CEO and founder of Bodhi. “Bringing together solar financing and customer experience is a practical way to help more installers grow while delivering a better experience for homeowners.”
Combining finance and software
Bodhi’s platform provides automation and artificial intelligence tools designed to improve communication between solar installers and homeowners. The platform provides a white-labeled installer portal through which homeowners can know what’s going on with their project and get all their questions answered.
Until now, installers have used Bodhi to manage post-sale communication with customers, but Nguyen says the integration with OneEthos will allow the platform to help installers implement what he calls a “holistic, full customer journey” that carries through every part of the process, from sale to installation and on down the road.
“As we start to integrate our two platforms, more of the financing information is now able to be easily displayed to the customer in a single channel. That limits confusion for the homeowner,” Nguyen said in comments to pv magazine USA. “If homeowners have less confusion and less anxiety, that means a lot less operational headaches for the installer.”
Notably, neither OneEthos nor Climate First Bank offer the kind of third-party ownership (TPO) financing products that have become popular in 2026. These kinds of agreements, such as prepaid leases and PPAs, are a way for solar installers to help customers to capture some of the remaining benefits of the Section 48E tax credit, which remains available for commercial businesses for installations which either begin construction by July 4 of this year or are completed by the end of 2027.
Nguyen says the reason for this is the companies made a conscious choice to offer transparent financing that is easy for residential customers to understand. 
He added that the simple approach to financing also helps installers keep things less “operationally complex,” meaning that dealing with the TPO providers adds work for installation crews and opens installers to risk that the companies won’t pay them in a timely fashion. Reducing this complexity helps to keep costs down for consumers.
OneEthos calls itself a ”mission-driven fintech company,” and operates as a Certified B Corp. The company says it is one of the only fintech enterprises that specializes in climate finance in the U.S. that is regulated by the Federal Reserve Bank.
Climate First Bank, which also operates as a B Corp, is among the fastest-growing lenders in the solar industry. The bank has appeared as one of the top three leading providers of solar installation financing in recent reports from solar marketplace EnergySage.
More information about the Bodhi onboarding process for OneEthos-approved installers can be found on the Bodhi website.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
More articles from Ben Zientara
Congratulations to the Bodhi team!
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.


Δ
By submitting this form you agree to pv magazine using your data for the purposes of publishing your comment.
Your personal data will only be disclosed or otherwise transmitted to third parties for the purposes of spam filtering or if this is necessary for technical maintenance of the website. Any other transfer to third parties will not take place unless this is justified on the basis of applicable data protection regulations or if pv magazine is legally obliged to do so.
You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled.
Further information on data privacy can be found in our Data Protection Policy.
pv magazine USA offers daily updates of the latest photovoltaics news. We also offer comprehensive global coverage of the most important solar markets worldwide. Select one or more editions for targeted, up to date information delivered straight to your inbox.
Δ
Legal Notice Terms and Conditions Privacy Policy © pv magazine 2026
Δ
Welcome to pv magazine USA. This site uses cookies. Read our policy. ×
The cookie settings on this website are set to “allow cookies” to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click “Accept” below then you are consenting to this.
Close

source

Proposed energy center would have over $3k-acre project area.
Around 50 area people provided testimony for the Ohio Power Siting Board during a public hearing last week regarding Sloopy Solar Energy’s proposal.
Invenergy, a renewable energy developer, is planning to develop, construct and operate a 180-megawatt energy center in Harmony Twp. between South Charleston and South Vienna. The project would occupy about 1,879 acres within a 3,152 acre project area.
The proposed facility would consist of large arrays of photovoltaic modules, also known as solar panels, ground-mounted on a tracking rack system, according to the OPSB. It would include associated facilities such as access roads, underground electric collection lines, inverters and transformers, and a collector substation.
A seven-foot-tall perimeter fence would secure the facility with access through gated entrances. Solar modules would be set back a minimum of 300 feet from non-participating residences, 150 feet from the edge of pavement of roads, 150 feet from non-participating property boundaries and 50 feet from active drinking water wells, according to the OPSB.
Ryan Van Portfliet, senior director of development of Invenergy, thanked everyone who participated in the hearing and shared their perspectives.
“Sloopy Solar represents an opportunity to deliver reliable, Ohio-generated energy to meet rapidly increasing energy demands with affordable energy, support the region’s economic growth, and respect property rights,” he said. “We remain committed to being a responsive partner to the Clark County community and look forward to the Ohio Power Siting Board’s continued, thorough review of the project.”
Manette Asta, Zachary Clark and Will Brailer, administrative law judges in the board’s legal department, conducted the hearing. About 50 community members spoke at the hearing, including those who are union members, farm and land owners, coalition members and students.
Members of local unions were all in support of the project, stating the opportunities it would bring related to apprenticeships, students and young workers, benefits and wages.
“This solar project will open the door to high-paying, family-sustaining careers with benefits and ongoing training, ensuring that Clark County residents can build lasting careers in construction,” said Vincent Irvin, Ohio Laborers’ Training & Apprenticeship Program. “Large projects like this are critical to introducing new workers through registered apprenticeship and the construction industry, strengthening Clark County and Ohio’s workforce and supporting the skilled members of my craft.”
Phillip Hooten said Sloopy Solar developers “made a commitment” to the local union that the work on the project would be done by locals.
“When our members are on these projects, they earn stable pay and benefits that allow us to buy homes and put children in extracurricular activities and stay involved in local community activities,” he said. “When this project comes to town, they’ll do more than just add jobs. They’ll boost the local economy … The cost of saying no to this project is just too high.”
Others in support included lifelong Clark County resident Eric Bradley who said “change is coming whether they like it or not.”
Robert Seman of Springfield said it’s a good opportunity.
“Things could be much worse … I believe in live and let live. What a person likes to do with their land is okay by me. Solar may have its downside, but the opposite may be true,” he said. “I personally think that if landowners want the solar on their properties, they should be allowed to do so.”
“There’s continuous contributions through the lifetime of the project to the local community … There’s not a lot of industry coming into Clark County. This is a great opportunity to spur that economic activity to enable us to educate our children,” Seman said.
Residents who are against the project raised concerns such as losing farmland, toxic chemicals, water, wildlife, traffic congestion and road safety, and more.
“It concerns me that we’re seeing all of our land disappear, not just for solar projects, but for housing and other things. I know there’s a necessity but we’re losing our farmland and you can’t get that back,” said Mary Adkins. “I’m not against solar. I’m against solar on farms. There’s plenty of other places that those power lines are available that they could put solar panels and still benefit the company, Clark County, the laborers, but not take our farm land away from us.”
Stephanie Ramsey of South Vienna said local leadership and elected officials are opposed to the project. She said, “It would be a disservice to our community to not listen to us and our leaders. We respectfully asked for you to deny this project as it certainly does not serve the needs of our community or land.”
Courtney Hoffmaster of South Charleston lives directly across the road from where the project would be and is concerned about heavy traffic, drainage, soil and severe weather.
“This land is classified as prime farmland, some of the most fertile in the country. We do not know yet if land used for large-scale solar can truly be restored … There’s only so much prime farmland left. We should not risk losing it through an industrial experiment,” she said.
Jordan Flax, a junior at Southeastern schools, said the solar facility would be right next to his family’s farm and is worried he won’t get to fully work on the farm after he graduates.
“If they start construction on this solar project, I won’t be allowed to help move equipment or deliver stuff like I do now. My parents said there will be too much traffic … I don’t think it’s fair. I’m going to live here my whole life and farm. I don’t think it’s right that they want to do this and I’ll have to always farm and live next to this,” he said.
The project is partially grandfathered in, according to the OPSB, because they received a system impact study and paid fees before October 2021. It was already in motion before passage of Senate Bill 52 in the fall of 2021, which allows a board of county commissioners to prohibit the construction of utility-scale wind or solar facilities altogether or in certain designated zones in unincorporated areas.
Clark County commissioners will not have any control over this project, though they had the ability to appoint Charles Patterson as an ad hoc board members to represent the commissioners on the OPSB while that agency has oversight of the project.
OPSB staff investigated the application for the proposed solar facility and recommended it be denied, according to the staff report. If the OPSB approves the certificate for the proposed facility, staff recommends 64 conditions for the board’s consideration.
An evidentiary hearing is scheduled for 10 a.m. April 16, at the offices of the Public Utilities Commission of Ohio in Columbus. During this hearing, the applicant, OPSB staff and intervening parties will offer expert testimony and evidence regarding the proposed facilities.
Once the evidentiary process is complete, the OPSB will schedule the project for a decision at a future monthly board meeting.
{{description}}
Email notifications are only sent once a day, and only if there are new matching items.
Your browser is out of date and potentially vulnerable to security risks.
We recommend switching to one of the following browsers:

source

Saatvik Solar wins ₹638.26 cr G12R solar cell order  Construction Week India
source

Our initiatives

Versol Solar has positioned Argentina as a core market within its Latin American growth strategy, aiming to strengthen its footprint over the coming years.
“2026 has a single name for us: consolidation,” said Humberto Di Pasquale, LATAM Regional Director at Versol Solar, outlining the company’s roadmap for the Argentine market during an interview at the Future Energy Summit (FES) Argentina.
According to the executive, the company has already completed an initial phase of market entry and internal structuring. The next stage will focus on executing and delivering on its strategic plans.
“We went through an initiation phase last year where we defined our plans, operating model and strategies. Now, 2026 is the year to act and bring those plans into reality,” he noted.
Watch the full interview: https://youtu.be/z0Ba11Py1o0

In this context, the company is working to deepen relationships with key stakeholders across the solar ecosystem—including project developers, EPC (engineering, procurement and construction) contractors, and technology suppliers—in order to position itself as a relevant player in Argentina’s solar PV market.
This push comes amid strong sector growth. Data from CAMMESA (the Argentine wholesale electricity market administrator) shows that installed solar PV capacity has reached 2,583 MW, while total renewable energy capacity stands at 7,980 MW, excluding hydropower plants above 50 MW.
Between 2025 and early 2026 alone, solar capacity added 910 MW, highlighting sustained expansion that continues to attract investment in renewables and international technology providers.
Versol Solar’s strategy in Argentina combines technological innovation with local capacity building.
The company is focusing on structural solutions for solar tracking systems, which currently account for a significant share of demand in utility-scale solar PV projects.
“We work with both 1P and 2P trackers, and we are known for adapting to different terrain conditions. We also optimise performance through artificial intelligence, enabling systems to respond to changing weather conditions and adopt protective positions when needed. Today, technology and AI are fundamental to the development of new solutions,” Di Pasquale explained.
These innovations are also shaped by Argentina’s demanding technical environment. The country has some of the highest wind speeds in Latin America, requiring robust structural design standards.
“From a regulatory standpoint, Argentina presents the highest wind speeds in the region. This is a major challenge for manufacturers, as we must ensure product stability over a 25–30 year lifespan,” he added.
At the same time, the executive highlighted the rapid professionalisation of the local market.
“One of the most significant changes in Argentina over the past four years has been the level of professionalisation—it is now among the highest in the region,” he stated.
In line with this trend, Versol Solar is evaluating the creation of a training centre to support the development of new talent and strengthen the technical capabilities of the local renewable energy sector, potentially in partnership with universities and technical institutions.
“We want to continue investing in local professionals—not only by training them, but by becoming a strategic partner, both professionally and academically. That is key to deepening our presence in Argentina,” Di Pasquale concluded.
by Strategic Energy Keep reading
The Spanish Hydrogen Association has signed five agreements with European and Latin American organisations to boost global cooperation, innovation and deployment of hydrogen as a key pillar of the energy transition.
by Lucia Colaluce Keep reading
Esteban Kieper, consultant at the Latin American Energy Organization (OLADE), analysed Argentina’s energy transition. The country has reached nearly 19% renewable generation and is entering a phase where renewables begin to compete with natural gas, alongside new storage tenders and transmission projects.
by Lucia Colaluce Keep reading
As the country’s solar PV sector expands, storage begins to gain traction, with global manufacturer JA Solar targeting utility-scale and C&I opportunities after reaching 14% global market share and nearly 300 GW in shipments.
by Strategic Energy Keep reading
The Spanish Hydrogen Association has signed five agreements with European and Latin American organisations to boost global cooperation, innovation and deployment of hydrogen as a key pillar of the energy transition.
by Lucia Colaluce Keep reading
Esteban Kieper, consultant at the Latin American Energy Organization (OLADE), analysed Argentina’s energy transition. The country has reached nearly 19% renewable generation and is entering a phase where renewables begin to compete with natural gas, alongside new storage tenders and transmission projects.
by Lucia Colaluce Keep reading
As the country’s solar PV sector expands, storage begins to gain traction, with global manufacturer JA Solar targeting utility-scale and C&I opportunities after reaching 14% global market share and nearly 300 GW in shipments.
A leading media group in digital marketing, strategic communication, and consultancy specialized in renewable energy and zero-emission mobility, with a presence in Latin America and Europe. We focus on helping companies position their brand in key markets, connecting with the main decision-makers in the energy transition.

source

Global solar PV continues its rapid growth, reaching around 650 GW in 2025, with record solar irradiation extremes across regions such as East Asia, India, and Latin America. With current production capacity and emerging technologies like perovskite-silicon tandem modules, PV is poised to surpass all other electricity generation technologies combined by the end of the decade.
Reprinted with permission from https://solargis.com/resources/blog/solargis-news/2025-solar-resource-overview-in-maps
Image: ISES

Photovoltaics have been the fastest-growing electricity generation technology for a decade now. Variations in the solar radiation resource availability directly affect both the expected supply and the economics of this technology, which will be dominant by the end of the current decade. In 2025, global solar irradiation reached both positive and negative extremes all over the world, with the highest positive deviations reaching as much as 20% above the long-term average (LTA). In East Asia, solar irradiation was between +15 and +20% above LTA, while Central America and parts of Latin America experienced the most pronounced shortcomings, with solar irradiation -7 to -14% below the LTA. Southeastern Australia and New Zealand recorded above-average solar radiation, generally in the range of +3% to +10%. India experienced strong negative anomalies, especially along the west coast, with up to -10% irradiation below LTA.
With another record year in 2025, global solar PV installations reached around 650 GW. At a 20% annual growth rate, by the end of the current decade, there will be more PV capacity installed worldwide than the sum of all the other electricity generation technologies combined.
Image: ISES

The current geopolitical scenario calls for even larger growth rates for renewable, decentralized and affordable energy generation technologies such as solar and wind. Solar PV has a lot of room for further growth, and production capacity is available to welcome more demand. PV uptake could nearly double immediately with the current and planned polysilicon, wafer, cell and module assembly capacity as shown below. With several companies beginning to commercially offer perovskite-Si tandem PV modules and the promise of stable efficiencies in the 30% range by the end of the decade, the PV landscape will undergo a new phase of development.
Image: ISES

The 2025 Global Horizontal Irradiation difference maps, recently published by Solargis, show significant differences in global solar irradiation in comparison with the long-term average LTA values. Solar dimming and brightening and PV power plant output performance issues are becoming common events, with consequences on large-scale PV power plant project development and financing. Global solar irradiation experienced both positive and negative extremes last year, with the highest positive deviations reaching up to 20% above the LTA. The solar resource overview in maps, which was previously presented with a standard colour code with a variation of -12% to +12%, had to have the range expanded to -14% to +14% due to an extreme anomaly in China. Solargis presented the following highlights for these variations:
It is still too early to assume these anomalies will become normal, but a trend is emerging. The world map below shows the differences in GHI 2025 from the long-term averages.
Image: ISES

Comparing the 2025 GHI (kWh/m².year) with the 2025 GHI difference (percentage deviation from the long-term average), sunbelt regions – where most of the new large-scale PV capacity is being installed – are at the same time the sunniest regions on Earth, and where most positive deviations from the LTA were measured in 2025, with the exception of India and South Africa.
Image: ISES

Authors: Prof. Ricardo Rüther (UFSC), Prof. Andrew Blakers /ANU
Andrew.blakers@anu.edu.au
rruther@gmail.com
ISES, the International Solar Energy Society is a UN-accredited membership NGO founded in 1954 working towards a world with 100% renewable energy for all, used efficiently and wisely.
The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website


Δ
By submitting this form you agree to pv magazine using your data for the purposes of publishing your comment.
Your personal data will only be disclosed or otherwise transmitted to third parties for the purposes of spam filtering or if this is necessary for technical maintenance of the website. Any other transfer to third parties will not take place unless this is justified on the basis of applicable data protection regulations or if pv magazine is legally obliged to do so.
You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled.
Further information on data privacy can be found in our Data Protection Policy.
By subscribing to our newsletter you’ll be eligible for a 10% discount on magazine subscriptions!
Δ
Legal Notice Terms and Conditions Privacy Policy © pv magazine 2026
Δ
This website uses cookies to anonymously count visitor numbers. To find out more, please see our Data Protection Policy. ×
The cookie settings on this website are set to “allow cookies” to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click “Accept” below then you are consenting to this.
Close

source

0
Powered by :
Andhra Pradesh has announced that DISCOMs will purchase surplus electricity generated by beneficiaries under the PM Surya Ghar Muft Bijli Yojana for domestic and agricultural purposes. The Energy Minister of Andhra Pradesh, Gottipati Ravi Kumar has made the announcement in Bhimavaram during the inauguration of a rooftop solar panel at a beneficiary’s residence. According to the report, Mr. Ravi Kumar said that, under the PM Kusum scheme, farmers will receive 9 hours of quality solar power during daytime, and tenders have been invited for the initiative. The report added that the State has set a target of providing 2,000 solar connections to SC and ST families and 6,000 connections to families belonging to Backward and Other Castes in each Assembly constituency. Union Minister of State for Steel and Heavy Industries Bhupathiraju Srinivasa Varma accompanied the Energy Minister at the event.

Subscribe to our Newsletter!

source

JinkoSolar has formally terminated its 4 GW Hai Ha solar cell project in Vietnam, citing US antidumping duties that undermined its export economics. The company will maintain other Vietnamese operations while shifting focus to localized supply chains and Asia-Pacific and Middle East markets.
Image: JinkoSolar
JinkoSolar’s Hai Ha solar cell project in Vietnam was formally terminated on March 11, after the investor requested to halt development and the Quang Ninh Provincial Economic Zone Management Board approved the request under Vietnam’s Investment Law. The authority also revoked the project’s investment certificate, No. 3282186668.
The project had been scheduled for development by Jinko Energy (Vietnam) Intelligent Manufacturing Co., Ltd., a vehicle set up by one of JinkoSolar’s Hong Kong subsidiaries, at Hai Ha Industrial Park in Quang Ninh province. The facility was planned to reach 4 GW of solar cell capacity and 3 GW of module capacity. The project initially carried a total planned investment of $1.5 billion when filed in October 2023, later revised to around $294.2 million in January 2024.
The termination followed a formal notice submitted by the company to the Quang Ninh administrative service center on Feb. 26, 2026. Vietnamese official reporting noted that the shutdown was the investor’s decision, rather than a government-mandated closure.
A key factor behind the termination was the worsening economics of exporting Vietnam-made PV products to the United States. In June 2025, the US Department of Commerce issued antidumping duty orders on crystalline silicon PV cells from Vietnam, following affirmative final determinations by both Commerce and the US International Trade Commission. Under the amended final determination, Jinko Solar (Vietnam) Industries Co., Ltd. was assigned an estimated weighted-average dumping margin of 125.91% and an adjusted cash deposit rate of 120.38%.
The U.S. trade measures appear to have undermined the strategic rationale for the Hai Ha project, which had been part of JinkoSolar’s Southeast Asian manufacturing footprint aimed in part at the US market. The termination reflects not only a project-level decision but also a broader reassessment of Southeast Asian solar manufacturing economics amid rising US tariffs.
The move does not signal a full withdrawal from Vietnam. JinkoSolar continues to operate other manufacturing facilities in the country, including solar cell and wafer plants, with around 10 GW of integrated n-type capacity remaining online. The company is also reportedly pivoting its global strategy toward more localized supply chains and greater focus on markets such as the Middle East and the wider Asia-Pacific region.
JinkoSolar is now expected to complete the project’s termination process, including handling construction in progress, tax and social insurance obligations, and land lease matters. The case highlights how trade policy is reshaping overseas manufacturing for Chinese solar companies, pushing investment decisions toward profitability, market access, and regional diversification rather than simple capacity expansion.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
More articles from Vincent Shaw
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.


Δ
By submitting this form you agree to pv magazine using your data for the purposes of publishing your comment.
Your personal data will only be disclosed or otherwise transmitted to third parties for the purposes of spam filtering or if this is necessary for technical maintenance of the website. Any other transfer to third parties will not take place unless this is justified on the basis of applicable data protection regulations or if pv magazine is legally obliged to do so.
You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled.
Further information on data privacy can be found in our Data Protection Policy.
Legal Notice Terms and Conditions Data Privacy © pv magazine 2026
Δ
This website uses cookies to anonymously count visitor numbers. View our privacy policy. ×
The cookie settings on this website are set to “allow cookies” to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click “Accept” below then you are consenting to this.
Close

source

Please enable JavaScript to proceed.

source

When operational, Hannacroix Solar will contribute to the Power Authority’s Renewable Energy Access and Community Help (REACH) program, which provides bill credits to low-income New Yorkers.
“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,” said New York Power Authority President and CEO Justin E. Driscoll. “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. This agreement reflects progress in the Power Authority’s efforts to strengthen New York’s grid with emissions-free generation resources.”
In September 2025, Governor Kathy Hochul directed state entities to fast-track shovel-ready renewables projects to take advantage of expiring federal tax credits. To that end, NYPA is exploring project partnership and acquisition opportunities across New York State and is leveraging its resources to accelerate renewables project development. NYPA has entered into exclusivity agreements for projects totaling more than 350 MW and will launch the process to develop a new biennial renewables strategic plan later this year.
Under the terms of this deal, NYPA will provide initial financing to advance Hannacroix Solar through its remaining pre-construction development activities. Pending final due diligence and customary closing conditions, the Power Authority will finance the project’s construction later this year, fully acquiring it in 2027. The agreement was negotiated between Teichos Energy, the project’s developer, and the New York Renewable Energy Development Holdings Corporation (NYRED)—a wholly-owned NYPA subsidiary created to facilitate the Power Authority’s renewable efforts.
Teichos Energy CEO Stephen Voorhees said, “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.”
Hannacroix Solar is expected to break ground later this year and will begin operating in late 2027. The project is included in the NYPA Renewables Updated Strategic Plan, which details the Power Authority’s efforts to develop, own, and operate renewable generation and energy storage projects.

source

PowerBank Advances Pennsylvania Community Solar Project as Key Regulatory Decision Looms  TipRanks
source

Distributed solar accounted for 15% of all new U.S. power capacity in 2025 as residential and community projects reached record installation levels.
Image: Institute for Local Self Reliance
The U.S. solar market reached a new milestone in the final quarter of 2025 as distributed capacity surged to a record 1.9 GW of new installations. Data from the Institute for Local Self-Reliance (ILSR) shows this quarterly peak capped a year where solar generation accounted for 78% of the 46 GW of new power capacity added to the national grid.
While utility-scale projects dominated installed capacity at 63%, the 15% contributed by residential and community solar projects indicates strength in the distributed energy segment. ILSR and the Energy Information Administration define distributed or “small solar” as less than 1 MW in nameplate capacity, and generally connected to the grid behind-the-meter.

Market analysts suggest the Q4 surge was driven in part by a rush to secure the 25D residential energy efficient property tax credit. The incentive, which provided a 30% credit for solar electric property, began its scheduled phase-down at the end of the year, prompting homeowners to finalize interconnections before the deadline.
The cumulative impact of these behind-the-meter assets is building a case for a decentralized grid by bypassing the multi-billion dollar bottlenecks of traditional transmission expansion. This growth is further bolstered by the 15 GW of new energy storage deployed in 2025. Approximately 14% of that storage capacity was installed at the distributed level to provide essential grid balancing.
Despite persistent regulatory hurdles, the closing months of 2025 demonstrated that small-scale solar remains a driver of U.S. energy resilience. The technical and economic data suggests the decentralized model is challenging the century-old centralized utility paradigm.
Distributed resources continue to offer unique advantages to the grid beyond simple capacity additions. These benefits range from reduced land-use requirements and lower transmission losses to increased local job creation and price predictability for consumers.
(Read: Ten reasons why behind-the-meter solar is a benefit.)
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
More articles from Ryan Kennedy
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.


Δ
By submitting this form you agree to pv magazine using your data for the purposes of publishing your comment.
Your personal data will only be disclosed or otherwise transmitted to third parties for the purposes of spam filtering or if this is necessary for technical maintenance of the website. Any other transfer to third parties will not take place unless this is justified on the basis of applicable data protection regulations or if pv magazine is legally obliged to do so.
You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled.
Further information on data privacy can be found in our Data Protection Policy.
pv magazine USA offers daily updates of the latest photovoltaics news. We also offer comprehensive global coverage of the most important solar markets worldwide. Select one or more editions for targeted, up to date information delivered straight to your inbox.
Δ
Legal Notice Terms and Conditions Privacy Policy © pv magazine 2026
Δ
Welcome to pv magazine USA. This site uses cookies. Read our policy. ×
The cookie settings on this website are set to “allow cookies” to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click “Accept” below then you are consenting to this.
Close

source

It begins quietly—almost invisibly. A thin film layered across a window, catching sunlight not just to illuminate a room, but to power it. No bulky panels. No industrial installations. Just a subtle transformation of everyday surfaces into energy sources.
For years, solar energy has been associated with rigid, heavy panels perched on rooftops. But now, a new chapter is unfolding—one where energy generation blends effortlessly into daily life. At the heart of this transformation lies the Organic Solar Cell Market, a rapidly evolving space redefining how we think about renewable power.

The Technology That Changes Everything
Organic solar cells are fundamentally different from traditional silicon-based photovoltaics. Instead of rigid wafers, they rely on carbon-based materials—polymers or small molecules—that can be printed onto flexible surfaces. This allows them to bend, stretch, and even remain semi-transparent. Imagine solar panels that can wrap around curved buildings, integrate into car roofs, or be embedded into wearable devices. This adaptability opens doors to applications that were previously impossible.
What makes this technology particularly compelling is not just its flexibility, but its potential for low-cost manufacturing. Unlike conventional panels that require energy-intensive processes, organic solar cells can be produced using printing techniques similar to newspapers—fast, scalable, and cost-efficient.
According to Mordor Intelligence, the

Organic Solar Cell Market is projected to grow at a 12.50% CAGR, driven by increasing demand for lightweight, portable, and environmentally friendly energy solutions. This growth reflects a broader shift toward decentralized and integrated energy systems.
Organic Solar Cell Market Growth, Share, and Emerging Demand
The organic solar cell market size is steadily expanding as industries recognize the value of flexible energy solutions. Unlike traditional solar markets dominated by large-scale installations, this segment thrives on versatility and innovation. One of the most interesting dynamics is the distribution of the organic solar cell market share. Instead of being controlled by a handful of major corporations, it is currently shaped by a mix of startups, research institutions, and niche technology firms. This creates a highly competitive and innovation-driven environment.
Several factors are fueling this growth:
Regions like Europe and Asia-Pacific are emerging as key contributors, supported by strong environmental policies and investments in renewable technologies. As urban spaces become more energy-conscious, the ability to integrate solar power into infrastructure without altering aesthetics is becoming a significant advantage.
Everyday Integration: Energy Becomes Invisible
Perhaps the most fascinating aspect of organic solar cells is their ability to disappear into the background. Unlike traditional panels that demand dedicated space, these cells integrate directly into objects we already use.
Picture a future where:
This shift represents more than technological progress—it signals a cultural change. Energy is no longer something we “install”; it becomes something we “experience” seamlessly. The implications are profound. By embedding power generation into everyday materials, organic solar cells could reduce reliance on centralized energy systems and bring power generation closer to the point of use.
Challenges on the Road Ahead
Despite their promise, organic solar cells are not without limitations. Efficiency levels still lag behind traditional silicon panels, and long-term durability remains a concern. Exposure to moisture and UV radiation can degrade performance over time.
However, ongoing research is rapidly addressing these issues. Improvements in material stability, encapsulation techniques, and hybrid technologies are pushing the boundaries of what organic solar cells can achieve. The current trajectory suggests that while they may not replace traditional solar panels entirely, they will complement them in ways that expand the overall solar ecosystem.
A Future Written in Light
The story of solar energy is no longer just about efficiency percentages or installation capacity. It is about accessibility, adaptability, and integration. Organic solar cells embody this shift by transforming how and where energy can be generated.
As the Organic Solar Cell Market continues to grow, it challenges us to rethink the role of energy in our lives. Instead of being confined to rooftops and solar farms, power generation becomes part of the fabric of our environment-quiet, constant, and almost invisible.
And perhaps that is what makes this revolution so powerful. It doesn’t demand attention. It simply works—silently capturing sunlight and turning it into possibility. So here’s the question: if every surface around you could generate energy, how would it change the way you live?

How does it work?
There are no comments for this story
Be the first to respond and start the conversation.
More stories from

efingutthomas and writers in Journal and other communities.
It doesn’t make headlines. It doesn’t trend on social media. Yet, it touches billions of lives-every single day. From a newborn’s first diaper to essential healthcare products used in hospitals, there’s an invisible force holding modern hygiene together.

By

efingutthomas

about 14 hours ago in

Journal
If you would’ve asked me 20 years ago did I know I’d become a writer and an author, I would’ve said “nope, ain’t happening”. As fate would have it I did become an author and I can honestly say I’m loving it so far. It really does feel good to be a writer. I’ve learned a lot on this journey and I feel like with even me being as new to this world as I am, there’s some wisdom I need to share with every other aspiring author.
By Joe Patterson20 days ago in Journal
The discomfort begins quietly. A dull ache in the lower back. A hesitation when drinking water. A subtle fear that something isn’t quite right. For many, these are the first whispers of a urinary tract obstruction—something small, yet potentially serious.
By DAISY2 days ago in Journal
There's a cat in my room. I cannot find the broom. I Think it ran in my closet. I shut the door, But maybe I should lock it.
By HandsomelouiiThePoet (Lonzo ward)5 days ago in Poets
© 2026

Creatd, Inc. All Rights Reserved.

source

A mix of clouds and sun with gusty winds. A stray shower or thunderstorm is possible. High around 80F. Winds SW at 25 to 35 mph..
Showers and thunderstorms likely. Potential for severe thunderstorms. Low 42F. Winds NNE at 10 to 20 mph. Chance of rain 100%. Rainfall may reach one inch.
Updated: March 31, 2026 @ 2:58 pm
U.S. Secretary of Agriculture Brooke Rollins announced on August 19 that the USDA will cease funding solar panels on productive farmland and prohibit the use of panels manufactured by foreign adversaries in USDA projects. This decision means solar farms will no longer receive federal loans or grants from the USDA, likely slowing the development of these operations. Developers will also need to source panels domestically due to the new restrictions.
In a statement, Rollins said, “It has been disheartening to see our beautiful farmland displaced by solar projects, especially in rural areas that have strong agricultural heritage. One of the largest barriers of entry for new and young farmers is access to land. Subsidized solar farms have made it more difficult for farmers to access farmland by making it more expensive and less available.”
Recent data indicates that solar farms occupy nearly 28,000 acres of land in Illinois, suggesting that many local farmers may welcome this announcement. However, the decision may not be favorable for everyone, as some view solar farms as a valuable source of clean energy that supports local economies.
Currently in Champaign
Your browser is out of date and potentially vulnerable to security risks.
We recommend switching to one of the following browsers:

source

Superyachts
Published on Mar 31, 2026 at 11:17 AM (UTC+4)
by Jason Fan
Last updated on Mar 30, 2026 at 3:47 PM (UTC+4)
Edited by Emma Matthews
A solar-powered yacht harnessing clean energy was slicing through the Mediterranean waves, and its captain couldn’t have been more excited.
YouTuber True North Yachts has been sharing live updates of his unique vessel over the past months, and the yacht has been steadily upgraded over the journey.
After months of tinkering and engineering, the vessel was headed from Port Dubuc in France toward Spain, with sunny skies and calm seas providing the perfect backdrop.
If you thought sailing had to be slow and tedious, this solar-powered marvel proved otherwise.
Enter our competition to win a stunning 2006 Ford GT or $400,000 cash!
The journey began with a quick hands-on tweak.
Using a 12-millimeter aluminum sheet and a few screws, the YouTuber bolted a rudder straight onto the motor, transforming the yacht’s handling and stability.
Acting like an extra fin, the addition balanced out the forward keel and kept the boat tracking smoothly through waves and headwinds.
After just five minutes on the water, the rudder held firm without adjustment, a promising start for what looked like a high-speed solar adventure.
The vessel glided silently and required very little input.
There were no sails to manage and no slippery decks to worry about, just occasional steering to stay on course.
Powered by a 2,500-watt system, the yacht maintained a steady six knots even against a 6 m/s headwind.
By mid-morning, the battery had climbed from 40 percent to 70 percent thanks to continuous solar charging.
With about 25 nautical miles ahead, the ship managed to hit 100 percent battery before the end of the day’s cruise.
According to the YouTuber, the goal wasn’t simply to travel efficiently, but also to show that solar-powered boats could make traditional marina stops largely unnecessary.
Beyond the engineering, the trip highlighted a different kind of lifestyle.
With minimal maintenance required and solar panels designed to last for decades, one person could operate the yacht with ease.
He even envisioned scaling the concept up to a 50-meter (164-foot) fully self-sufficient superyacht, while keeping the same simplicity and freedom.
As the solar-powered yacht moved past the Petite Ron River and continued toward Spain, it offered a glimpse of both stunning coastal scenery and the future of boating.
With a full day of cruising behind him, the YouTuber wrapped up his latest update.
If you’re curious to learn more about what clean energy travel is like, you can check out the full video below:
DISCOVER SBX CARS: The global premium car auction platform powered by Supercar Blondie
# Tags – Clean energy, Solar, Solar power, solar-powered explorer yacht, superyachts
Jason joined the editorial team at Supercar Blondie in April 2025 as a Content Writer. As part of the growing editorial team, he helps keep the site running 24/7, injecting his renowned accuracy, energy, and love for all things supercar-related into every shift.
Supercar Blondie finds and covers the coolest cars, tech, luxury and gaming in the world.
Copyrights ©2026 SB Media Holding Limited

source

UNSW researchers developed a chemically selective, nitrate-based, single-sided accelerated ageing method for TOPCon solar cells that replicates the mildly acidic environment inside EVA-encapsulated modules. The proposed approach enables rapid, physically meaningful screening of front-side metallisation stability, reliably predicting module-level degradation and reducing development time and costs, according to its creators.
Electroluminescence images of TOPCon solar modules
Image: UNSW
A research team from the University of New South Wales (UNSW) has developed a new cell-level accelerated ageing method for TOPCon solar technologies.
“Conventional solution-based accelerated tests like cetic acid soaking impose chemically unrealistic conditions and often fail to reproduce degradation trends at the module level,” the research’s lead author, Bram Hoex, told pv magazine. “We introduced a chemically selective, pH-controlled, nitrate-based cell-level aging method that replicates the mildly acidic environment within EVA-encapsulated modules.”
The scientists explained that, to accelerate the assessment of TOPCon cell and module stability, solution-based ageing methods, particularly immersion in acetic acid (CH₃COOH), have been widely used. While these approaches provide valuable mechanistic insights, they have limitations, as both sides of the cell are exposed simultaneously, and the chemical conditions are often harsher than the mildly acidic environment that develops inside EVA-encapsulated modules.
Alternative methods, such as spraying salts like sodium chloride (NaCl) or sodium bicarbonate (NaHCO₃) onto cell surfaces, fail to replicate these realistic conditions. Nitrate species, however, are naturally occurring and can produce tunable acidic environments depending on the cation used, making them well suited for chemically relevant accelerated ageing.
“Building on these insights, we develop a nitrate-based, single-side ageing method in which controlled-pH contaminants are applied to the front surface before damp-heat exposure,” said Hoex. “This approach enables targeted evaluation of front-side metallisation stability and reliably reproduces module-level degradation trends, providing a framework for chemically realistic accelerated testing of TOPCon solar cells.”
The research team conducted the tests on TOPCon solar cells measuring 182 mm × 183.75 mm and based on n-type Czochralski silicon wafers with two front-contact variants: conventional silver/aluminum (Ag/Al) paste and a low-Al Ag paste processed using a laser-assisted firing technique (Ag/LAF). All cells were half-cut to form 144-cell modules, encapsulated with EVA – UV-blocking on the front and UV-transparent on the rear – and completed with a transparent backsheet featuring a white grid.
Module-level damp-heat testing was conducted according to the IEC TS 62782 standard, with electrical output measured and electroluminescence imaging used to identify degradation. At the cell level, accelerated stress tests included immersion in 0.1 M CH₃COOH or CH₃COONa at 85 C, as well as spray application of salt solutions with controlled pH, followed by damp-heat testing at 85 C and 85% relative humidity (DH85). Solution pH was determined at 25 C, and relative acidity trends were maintained during high-temperature ageing.
The electrical performance was measured before and after tests using a LOANA system, while photoluminescence and series resistance maps were acquired with a BT Imaging R3 system. All experiments included multiple replicates to ensure reproducibility, enabling comprehensive assessment of front-side metallisation stability and module-level degradation fingerprints.
The tests showed that the Ag/Al and Ag/LAF cells showed different degradation behaviors, with Ag/LAF contacts exhibiting higher sensitivity to acidic conditions and pronounced losses in efficiency and fill factor due to front-contact delamination.
Scanning electron microscopy (SEM) and focused ion beam-scanning electron microscopy (FIB-SEM) analyses revealed that Ag/Al contacts rely on Al spikes and high glass frit content, which provide mechanical robustness and slower degradation, while Ag/LAF contacts depend on silver nanoparticles (AgNPs) with thin lead oxide (PbO)-rich glass frit layers that dissolve under acidic or chloride-rich conditions.
Furthermore, single-sided salt treatments highlighted the role of solution pH and specific ions in front-contact corrosion, showing severe degradation under aluminium nitrate (Al(NO₃)₃) and chloride (Cl⁻) salts. Neutral salts were found to cause minor effects, whereas acidic nitrate solutions accelerated PbO glass-frit dissolution. Moreover, Cell-level DH85 with zinc nitrate (Zn(NO₃)₂) demonstrated consistent trends with module-level performance, with fill factor loss as the dominant factor.
“Our approach provides a fast and physically meaningful screening tool to identify reliability risks at the solar cell stage, before committing to full module assembly and long-term damp-heat testing exceeding 1,000 hours,” Hoex stated. “It enables rapid optimization of metallization and bill-of-materials (BOM) choices, reducing development time and costs while avoiding misleading conclusions that can arise from overly aggressive or non-representative accelerated tests. By establishing a clear link between cell-level testing and actual module degradation mechanisms, this method enhances the predictive capability for long-term performance.”
The new methodology was presented in “Bridging accelerated cell-level degradation to module-relevant failure mechanisms in TOPCon solar cells and modules,” published in the Chemical Engineering Journal. “In essence, this work demonstrates that well-designed, chemically relevant cell-level tests can significantly accelerate reliability assessment, while still capturing the key degradation pathways observed at the module level,” Hoex concluded. 
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
More articles from Emiliano Bellini
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.


Δ
By submitting this form you agree to pv magazine using your data for the purposes of publishing your comment.
Your personal data will only be disclosed or otherwise transmitted to third parties for the purposes of spam filtering or if this is necessary for technical maintenance of the website. Any other transfer to third parties will not take place unless this is justified on the basis of applicable data protection regulations or if pv magazine is legally obliged to do so.
You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled.
Further information on data privacy can be found in our Data Protection Policy.
Legal Notice Terms and Conditions Data Privacy © pv magazine 2026
Δ
This website uses cookies to anonymously count visitor numbers. View our privacy policy. ×
The cookie settings on this website are set to “allow cookies” to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click “Accept” below then you are consenting to this.
Close

source

Extensive testing validates reuse of 23-year-old second-life polycrystalline solar modules  pv magazine International
source

Humanitarian | Founder | Chairman | CEO | Blockchain & Transformation Engineer
Humanitarian | Founder | Chairman | CEO | Blockchain & Transformation Engineer
Humanitarian | Founder | Chairman | CEO | Blockchain & Transformation Engineer
The Alpha Engineer's Endgame – Becoming a Nobody
Humanitarian | Founder | Chairman | CEO | Blockchain & Transformation Engineer
Edwin Liava'a
Jan 07, 2025
Mark Milton
Aug 23, 2018
MBApes Academy
Mar 20, 2022
BlockEx
Mar 07, 2018
BlockEx
Feb 05, 2018
BlockEx
Jun 01, 2018
Edwin Liava'a
Jan 07, 2025
Mark Milton
Aug 23, 2018
MBApes Academy
Mar 20, 2022
BlockEx
Mar 07, 2018
BlockEx
Feb 05, 2018
BlockEx
Jun 01, 2018

source

Two solar farms, self-storage expansion proposed in Clarence  The Business Journals
source

Renewable
By commenting, you agree to the Prohibited Content Policy
By commenting, you agree to the Prohibited Content Policy
News
See whats happening in Energy sector right now
Exclusive
Read and get insights from specially curated unique stories from editorial
Leaders Speak
Business leaders sharing their insights
Events
Explore and discuss challenges & trends in India’s leading B2B events
Awards
Recognise work that not only stood out but was also purposeful
Webinars
Join leaders & experts for roundtables, conferences, panels and discussions
Subscribe to our Daily Newsletter

By continuing you agree to our Privacy Policy & Terms & Conditions
Advertise With Us
We have various options to advertise with us including Events, Advertorials, Banners, Mailers, etc.
Download ETEnergyworld App
Save your favourite articles with seamless reading experience
Get updates on your preferred social platform
Follow us for the latest news, insider access to events and more.
About Us
Contact Us
Newsletters

source

EIB Group to guarantee EUR 200m of Polish loans for EVs, solar panels  Renewables Now
source

0
By clicking the button, I accept the Terms of Use of the service and its Privacy Policy, as well as consent to the processing of personal data.
Don’t have an account? Signup
Powered by :
Can Solar Survive an Earthquake? GameChange Says Yes After Extreme Shake Test Photograph: (Archive)
GameChange Solar said on Tuesday it has completed what it described as the solar industry’s first full-scale seismic shake table testing of a utility-scale tracker system, as developers increasingly target earthquake-prone regions.
The U.S.-based supplier of solar tracker and fixed-tilt racking systems said the tests were conducted at the University of California, Berkeley’s Pacific Earthquake Engineering Research (PEER) Center, in line with IEEE 693 seismic design standards.
The programme simulated high-intensity earthquake conditions, including scenarios comparable to those in the New Madrid Seismic Zone, one of the most seismically active regions in the United States.
A full-scale Genius Tracker system equipped with photovoltaic modules was subjected to progressively stronger seismic inputs, including broadband excitation and testing up to 2.5g spectral acceleration, the company said.
GameChange Solar said the system showed no structural damage to key components such as torque tubes, bearings, posts and actuators during the tests. It added that its proprietary lateral capture mechanism functioned as designed, redistributing loads under stress conditions.
The company also reported no significant microcracking in solar modules and less than 1% performance degradation, while the tracker motor remained fully operational after testing.
“As solar expands into seismic-risk regions like California and Chile, the industry has had limited real-world data on how modern utility-scale trackers perform in earthquakes,” Chief Engineer Scott Van Pelt said in a statement.
Independent validation by the Renewable Energy Test Center (RETC) in Fremont, California, confirmed that the system remained in sound physical and operational condition following the testing campaign, the company said.
The findings have been detailed in a technical white paper and were also presented in a webinar earlier this month, according to the company.
Solar assets in high seismic zones face risks including module damage, prolonged outages and increased repair costs. Industry participants say empirical performance data could help developers, insurers and asset owners better assess and mitigate such risks.
GameChange Solar, which ranks among the top global providers of solar tracker solutions, said it has delivered more than 58 GW of systems worldwide for utility-scale and distributed generation projects.
We are India’s leading B2B media house, reporting full-time on solar energy, wind, battery storage, solar inverters, and electric vehicle (EV)
Quick Links
© 2025 Saur Energy. All Rights Reserved.

source

When operational, Hannacroix Solar will contribute to the Power Authority’s Renewable Energy Access and Community Help (REACH) program, which provides bill credits to low-income New Yorkers.
“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,” said New York Power Authority President and CEO Justin E. Driscoll. “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. This agreement reflects progress in the Power Authority’s efforts to strengthen New York’s grid with emissions-free generation resources.”
In September 2025, Governor Kathy Hochul directed state entities to fast-track shovel-ready renewables projects to take advantage of expiring federal tax credits. To that end, NYPA is exploring project partnership and acquisition opportunities across New York State and is leveraging its resources to accelerate renewables project development. NYPA has entered into exclusivity agreements for projects totaling more than 350 MW and will launch the process to develop a new biennial renewables strategic plan later this year.
Under the terms of this deal, NYPA will provide initial financing to advance Hannacroix Solar through its remaining pre-construction development activities. Pending final due diligence and customary closing conditions, the Power Authority will finance the project’s construction later this year, fully acquiring it in 2027. The agreement was negotiated between Teichos Energy, the project’s developer, and the New York Renewable Energy Development Holdings Corporation (NYRED)—a wholly-owned NYPA subsidiary created to facilitate the Power Authority’s renewable efforts.
Teichos Energy CEO Stephen Voorhees said, “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.”
Hannacroix Solar is expected to break ground later this year and will begin operating in late 2027. The project is included in the NYPA Renewables Updated Strategic Plan, which details the Power Authority’s efforts to develop, own, and operate renewable generation and energy storage projects.

source

GameChange Solar tracker withstands high-intensity seismic testing  Renewables Now
source

Spanish renewable energy developer Ecoener has secured 15-year power purchase agreements (PPAs) to build two solar PV projects totalling 200 MWp in Guatemala. 
The portfolio comprises the 140 MWp Cocales and 60 MWp La Hulera solar PV projects, marking a significant milestone as the first in Guatemala to integrate battery energy storage systems (BESS).  
Get Premium Subscription
Cocales will feature a BESS with 20 MW/80 MWh of storage capacity, while La Hulera will include a 10 MW/40 MWh system, enhancing grid flexibility and supporting renewable integration. Both plants are slated to commence operations in early 2028. 
The contracts were secured through Guatemala’s largest power auction, awarded via an international tender that received 51 technical bids for around 4.7 GW of capacity under the government’s Power Generation Expansion Plan to meet burgeoning domestic energy demand. 
“This contract award reaffirms the strength of Ecoener’s international position and our capacity to compete successfully in highly competitive environments. It is also a relevant stage in the incorporation of storage solutions that deliver greater operational efficiency and long-term value to the company, as well as stability to the electricity system,” Luis de Valdivia, chairman of Ecoener, said. 
With the commissioning of the Cocales and La Hulera projects, Ecoener will bring its operational capacity in Guatemala to 362MW. The company is entering an expansion phase after its largest single-year increase in 2025, when 253MW came online, taking its total capacity to 680 MW. Including the projects currently under construction, Ecoener’s total installed capacity globally will reach 815MW. 
Beyond Guatemala, Ecoener has a presence in 13 other markets across the Americas – including Colombia, the Dominican Republic, Honduras, and Panama – as well as in Europe, including its home market of Spain and Greece, and in Asia. 
Last year, the firm secured a US$43.1 million loan from Proparco, the French Development Agency-backed financier, to fund its 60 MW Payita 1 solar PV project in the Dominican Republic’s María Trinidad Sánchez province. 

source

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Nature Energy (2026)Cite this article
The upper stability limit of formamidinium–caesium (FACs) lead iodide perovskite solar cells (PSCs) under thermal and light stress is poorly understood. Now, analysis of the photothermal stability of hundreds of FACs PSCs reveals distinct temperature-dependent degradation modes. On the basis of the mechanistic insight, stabilizing strategies are proposed to mitigate the degradation pathways.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
USD 39.95
Prices may be subject to local taxes which are calculated during checkout
Yi, C. et al. Entropic stabilization of mixed A-cation ABX₃ metal halide perovskites for high performance perovskite solar cells. Energy Environ. Sci. 9, 656–662 (2016). This paper reports that mixed FACs perovskites are entropically stabilized, enabling high efficiency and excellent long-term stability.
Article  Google Scholar 
Fei, C. et al. Strong-bonding hole-transport layers reduce ultraviolet degradation of perovskite solar cells. Science 384, 1126–1134 (2024). This paper presents the high-temperature photostability of FACs PSCs.
Article  Google Scholar 
Wang, M. et al. Ammonium cations with high pKa in perovskite solar cells for improved high-temperature photostability. Nat. Energy 8, 1229–1239 (2023). This paper presents the high-temperature photostability of FACs PSCs under 85 °C 1-sun illumination.
Article  Google Scholar 
Burlingame, Q. C., Loo, Y.-L. & Katz, E. A. Accelerated ageing of organic and perovskite photovoltaics. Nat. Energy 8, 1300–1302 (2023). This comment advocates for the adoption of accelerated ageing testing for perovskite photovoltaics.
Article  Google Scholar 
Ciammaruchi, L., Penna, S., Reale, A., Brown, T. M. & Di Carlo, A. Acceleration factor for ageing measurement of dye solar cells. Microelectron. Reliab. 53, 279–281 (2013). This paper reports the application of the Arrhenius model to fit the experimental data from solar cells subjected to thermal stress under illumination.
Article  Google Scholar 
Download references
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Wang, M. et al. Decoupling cation segregation and volatile loss in formamidinium–caesium metal halide perovskite solar cells under high-temperature operating conditions. Nat. Energy https://doi.org/10.1038/s41560-026-02011-y (2026).
Reprints and permissions
Degradation pathways of FA_xCs_1−xPbI₃ perovskite solar cells. Nat Energy (2026). https://doi.org/10.1038/s41560-026-02012-x
Download citation
Published: 31 March 2026
Version of record: 31 March 2026
DOI: https://doi.org/10.1038/s41560-026-02012-x
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Nature Energy (Nat Energy)
ISSN 2058-7546 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source

Climate Change Influence on Solar Photovoltaic Energy Production and Its Associated Drivers in CMIP6 Ensemble Projections  agupubs.onlinelibrary.wiley.com
source

Power off-take deal signed for 100-MW solar-wind hybrid in Zambia  Renewables Now
source

Subretinal Photovoltaic Implant to Restore Vision in Geographic Atrophy Due to AMD  NEJM
source

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

source

Next Upcoming
Virtual
By Canary Media

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

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

Jeff St. John is chief reporter and policy specialist at Canary Media. He covers innovative grid technologies, rooftop solar and batteries, clean hydrogen, EV charging, and more.
Clean energy
Wind
Electrification
Clean energy
© 2026 Canary Media

source

We use cookies to improve your experience and for marketing. Read our cookie policy or manage cookies.
Search across reports, market insights, and blog stories.
Lithuania’s solar power capacity reached 3,040 megawatts by the end of the previous year, according to a report from the International Energy Agency’s Photovoltaic Power Systems Programme. The nation added roughly 600 megawatts of solar capacity during that year.
Solar generation in Lithuania accounted for 14.2 percent of total national electricity consumption in the previous year, producing 1.79 terawatt-hours. The country ranks highly among European Union member states for solar generation per person.
Market growth has been largely driven by approximately 170,000 solar prosumers, who were responsible for about 70 percent of total solar electricity production. These participants have benefited from a net-metering scheme, though commercial entities have since moved to a net-billing system. Public investment support has also been available, typically covering a portion of installation costs for households and small-to-medium enterprises.
While technical permits have been granted for an additional 4 gigawatts of solar capacity, grid congestion is now a primary constraint. A research director contributing to the IEA-PVPS report indicated the market is nearing saturation due to limited grid capacity, noting that solar and wind already exceed national strategy targets for renewable electricity share.
Consequently, future solar market expansion is expected to be strongly linked to integration with storage technologies. Nearly 2 gigawatts of battery energy storage system facilities received technical permits in the previous year, and a recent tender procured significant storage capacity. Support schemes exist for hybrid solar-plus-storage farms and standalone storage systems. The integration of thermal energy storage is also increasing.
Lithuania’s National Energy Independence Strategy, adopted in 2024, targets 100 percent of electricity from renewable sources by 2030.
This report provides a comprehensive view of the solar cells and light-emitting diodes industry in Lithuania, tracking demand, supply, and trade flows across the national value chain. It explains how demand across key channels and end-use segments shapes consumption patterns, while also mapping the role of input availability, production efficiency, and regulatory standards on supply.
Beyond headline metrics, the study benchmarks prices, margins, and trade routes so you can see where value is created and how it moves between domestic suppliers and international partners. The analysis is designed to support strategic planning, market entry, portfolio prioritization, and risk management in the solar cells and light-emitting diodes landscape in Lithuania.
The report combines market sizing with trade intelligence and price analytics for Lithuania. It covers both historical performance and the forward outlook to 2035, allowing you to compare cycles, structural shifts, and policy impacts.
This report provides a consistent view of market size, trade balance, prices, and per-capita indicators for Lithuania. The profile highlights demand structure and trade position, enabling benchmarking against regional and global peers.
The analysis is built on a multi-source framework that combines official statistics, trade records, company disclosures, and expert validation. Data are standardized, reconciled, and cross-checked to ensure consistency across time series.
All data are normalized to a common product definition and mapped to a consistent set of codes. This ensures that comparisons across time are aligned and actionable.
The forecast horizon extends to 2035 and is based on a structured model that links solar cells and light-emitting diodes demand and supply to macroeconomic indicators, trade patterns, and sector-specific drivers. The model captures both cyclical and structural factors and reflects known policy and technology shifts in Lithuania.
Each projection is built from national historical patterns and the broader regional context, allowing the report to show where growth is concentrated and where risks are elevated.
Prices are analyzed in detail, including export and import unit values, regional spreads, and changes in trade costs. The report highlights how seasonality, freight rates, exchange rates, and supply disruptions influence pricing and margins.
Key producers, exporters, and distributors are profiled with a focus on their operational scale, geographic footprint, product mix, and market positioning. This helps identify competitive pressure points, partnership opportunities, and routes to differentiation.
This report is designed for manufacturers, distributors, importers, wholesalers, investors, and advisors who need a clear, data-driven picture of solar cells and light-emitting diodes dynamics in Lithuania.
The market size aggregates consumption and trade data, presented in both value and volume terms.
The projections combine historical trends with macroeconomic indicators, trade dynamics, and sector-specific drivers.
Yes, it includes export and import unit values, regional spreads, and a pricing outlook to 2035.
The report benchmarks market size, trade balance, prices, and per-capita indicators for Lithuania.
Yes, it highlights demand hotspots, trade routes, pricing trends, and competitive context.
Making Data-Driven Decisions to Grow Your Business
A Quick Overview of Market Performance
Understanding the Current State of The Market and its Prospects
Finding New Products to Diversify Your Business
Choosing the Best Countries to Establish Your Sustainable Supply Chain
Choosing the Best Countries to Boost Your Export
The Latest Trends and Insights into The Industry
The Largest Import Supplying Countries
The Largest Destinations for Exports
The Largest Producers on The Market and Their Profiles
Instant access. No credit card needed.
Online access to 2M+ reports, dashboards, and tables. Trusted by Fortune 500 teams.
IndexBox, Inc.
2093 Philadelphia Pike #1441
Claymont, DE 19703, USA
Contact us
© 2026 IndexBox, Inc
Instant access. No credit card needed.
Online access to 2M+ reports, dashboards, and tables. Trusted by Fortune 500 teams.

source

Molecular additive boosts silicon-perovskite tandem solar cell efficiency to 32.76%  Tech Xplore
source

From daily news and career tips to monthly insights on AI, sustainability, Aerospace, and more—pick what matters and get it in your inbox.
Access expert insights, exclusive content, and a deeper dive into engineering and innovation.
Engineering-inspired textiles, mugs, hats, and thoughtful gifts
We connect top engineering talent with the world's most innovative companies.
We empower professionals with advanced engineering and tech education to grow careers.
We recognize outstanding achievements in engineering, innovation, and technology.
All Rights Reserved, IE Media, Inc.
Follow Us On
Access expert insights, exclusive content, and a deeper dive into engineering and innovation.
Engineering-inspired textiles, mugs, hats, and thoughtful gifts
We connect top engineering talent with the world's most innovative companies
We empower professionals with advanced engineering and tech education to grow careers.
We recognize outstanding achievements in engineering, innovation, and technology.
All Rights Reserved, IE Media, Inc.
The project marks a step toward cheaper and faster solar energy.
An Australian electricity and gas provider has been using autonomous robots at its 250-megawatt solar farm, where nearly 500,000 panels are being installed ahead of schedule with the help of AI-powered machines.
The robots, designed to speed construction and improve safety, are being trialed at ENGIE’s photovoltaic Goorambat East Solar Farm. The site is located less than a mile south-east of the Goorambat township in Victoria.
“It will have a generating capacity of up to 250 MW, which is enough to power up to 105,000 average Victorian homes,” Justin Webb, ENGIE site representative said.
Developed by US-based Luminous Robotics, the LUMI S4 fleet uses AI-driven pick-and-place technology to autonomously lift and position solar modules onto racking structures.
Human crews then complete the securing process, cutting down on repetitive manual labor while increasing efficiency and reducing injury risks.
To develop the robots, Luminous Robotics secured USD 4.9 million in funding from the Australian Renewable Energy Agency (ARENA). The funds were part of its USD 10 million Solar ScaleUp Challenge.
The machines autonomously lifted and placed panels onto racking structures, at the Goorambat East site, located near the city of Benalla, approximately 130 miles northeast of Melbourne.
This division of labor reportedly reduces one of the most physically demanding aspects of solar farm construction. It also improves both safety and efficiency while allowing the human workforce to focus on skilled tasks.
The system, first used for pilings and now for panels, marks Luminous’ debut of its LUMI robots outside the US, showcasing the future of solar farm construction.
“The intended higher productivity of these autonomous systems will reduce the cost of renewable energy projects and enable projects to be built in less time – which will bring down energy costs for consumers and potentially allow more solar farms to be built,” Webb continued.
The solar farm will have a capacity of up to 250 MW once complete. It will supply electricity to more than 100,000 average homes. Commissioning has already begun, with first energization expected before the end of October 2025 and full operation targeted for mid-2026.
Webb revealed that the robots require skilled technicians to help upskill the renewable workforce and boost productivity. He noted they could also benefit solar projects in remote, harsh regions where conditions are unsafe for workers.
“In the longer term, with continued development, robots like these will also enable a reduction in health and safety related risks from construction projects, for example reducing the manual handling of heavy solar panels,” he explained.
Jay M. Wong, Luminous Robotics CEO and founder elaborated that the Australian deployment of the LUMI fleet provided vital data, performance insights and real-life results to support global adoption.
“Our LUMI robots exceeded our target production rate and fueled by support from the Australian Renewable Energy Agency (ARENA), we’re keen to accelerate our next phase where we fine tune the LUMI fleet’s capabilities,” Wong said.
Meanwhile, Luminous and ARENA are preparing to release and open source solar construction’s largest, most comprehensive robotics dataset in the coming months. “We believe this is the honest approach to truly democratize solar for humanity,” Wong concluded.
Based in Skopje, North Macedonia. Her work has appeared in Daily Mail, Mirror, Daily Star, Yahoo, NationalWorld, Newsweek, Press Gazette and others. She covers stories on batteries, wind energy, sustainable shipping and new discoveries. When she's not chasing the next big science story, she's traveling, exploring new cultures, or enjoying good food with even better wine.
Exclusive content, expert insights and a deeper dive into engineering and tech. No ads, no limits.
Exclusive content, expert insights and a deeper dive into engineering and tech. No ads, no limits.
Premium
Follow

source

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Scientific Reports volume 16, Article number: 8197 (2026) Cite this article
1418 Accesses
1 Altmetric
Metrics details
Accurate forecasting of photovoltaic performance is essential for improving solar energy management, optimizing operational schedules, and supporting investment decisions. This study proposes a structured data-driven forecasting framework that integrates standalone learners with a hybrid boosting–aggregation strategy to predict two critical photovoltaic performance indicators: the optimal peak operating time (NOPT) and the power conversion efficiency (PCE). The methodology involves systematic data preprocessing, feature normalization, model training using both single and hybrid learners, and performance validation under identical experimental conditions. Multiple data-driven algorithms were examined using comprehensive statistical metrics, including R², RMSE, and U95. Among all models, the hybrid XGBA framework demonstrated superior predictive performance, achieving R² values of 0.9954 for NOPT and 0.9970 for PCE, and consistently low errors across all evaluation criteria. Model robustness and generalization were further assessed through uncertainty-based evaluation metrics. Sensitivity analyses highlight key influential parameters such as Emin Emax, and Ap, revealing their substantial contributions to model outputs. The proposed hybrid model provides a robust and highly accurate predictive tool that can reduce operational uncertainties, enhance energy yield, and support data-driven decision-making for photovoltaic plant operators and energy sector stakeholders.
The global transition to sustainable energy has highlighted photovoltaic (PV) technology as a pivotal solution for reducing greenhouse gas emissions and dependence on fossil fuels¹. Over the past decades, PV research has focused on enhancing power conversion efficiency (PCE), reducing production costs, and incorporating environmentally friendly materials, such as thin-film polymers and perovskite tandems². The integration of PV systems into diesel-based energy infrastructures, including microgrids, remote power stations, and hybrid vehicles, presents a hybrid solution that can improve fuel efficiency, reduce emissions, and extend engine lifespan^3,4,5. Such integration specifically aligns with several Sustainable Development Goals (SDGs), notably SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), by encouraging the use of renewable energy and decreasing the use of diesel^6,7,8.
One of the significant breakthroughs in PV technologies is organic photovoltaics (OPVs) that have been the focus of renewed interest in the last couple of years, a fact that can be related to the successful implementation of non-fullerene acceptors (NFAs) allowing single-junction devices to achieve power conversion efficiencies (PCEs) over 18%^9,10,11. Nevertheless, there will always be inefficiency-limiting processes in NFA-based systems, which are the main stumbling block for the understanding of these systems that, in turn, obstruct the rationality of human-computer-aided design of new donor–acceptor materials. It has been proven that the quadrupole moment of acceptors is the factor that most strongly influences the interfacial energetics, and high internal quantum efficiencies (IQEs) are generally observed when ionization energy offsets over 0.5 eV are used for exciton dissociation¹². Research on material modifications can be exemplified by the end-group engineering effected by fluorination and chlorination that, besides allowing charge transport, also makes recombination less likely, resulting in the ‘device’ overall performance ameliorating^13,14,15.
Machine learning (ML) methods are a significant factor in advancing research in renewable energy, especially in forecasting and optimizing photovoltaic (PV) systems^16,17,18,19. Keddouda et al.²⁰ developed artificial neural network (ANN) and regression models using meteorological data and operating temperature as inputs, achieving high predictive accuracy with R² values reaching 0.998. Kumari and Toshniwal²¹ conceived extreme gradient boosting with deep neural network (XGBF-DNN), which essentially integrates extreme gradient boosting forests and deep neural networks by the vehicle of ridge regression, thereby soaring not only the security but also the accuracy of a wide variety of climatic conditions. The use of such ensemble strategies underscores the viability of hybrid ML frameworks for addressing the unpredictability of PV system outputs in the real world.
Nonetheless, interpretability remains a major stumbling block, despite progress in predictive capabilities. Some XAI (Explainable AI) techniques, like SHAP²² and LIME²³, can provide an account of feature importance and develop local explanations; however, they are still largely untapped in PV research. Chen et al.²⁴ pointed out the difficulties related to terminology, cross-task evaluation, and the range of existing interpretability techniques; therefore, they suggested that more research should be carried out to enhance the transparency of the processes. Scott et al.²⁵ examined the use of benchmark machine learning algorithms to forecast photovoltaic power generation for building-scale renewable energy systems. Several models, including random forest, neural networks, support vector machines, and linear regression, were compared using operational data from a university campus to evaluate forecasting accuracy across different dataset sizes and prediction horizons. The results showed that random forest achieved the lowest average error, although no single algorithm consistently outperformed the others under all conditions. The study highlighted the importance of dataset characteristics and model usability when selecting forecasting approaches for integration into building management systems. Bhutta et al.²⁶ investigated the use of hybrid machine learning models to improve the prediction accuracy of solar power generation within smart grid systems. Hybrid deep learning architectures, including convolutional–recurrent, convolutional–LSTM, and convolutional–GRU networks, were applied to forecast key solar plant parameters such as power production, plane-of-array irradiance, and performance ratio. The results demonstrated that the hybrid convolutional–LSTM model achieved the highest predictive accuracy, yielding the lowest RMSE and MAE values across all evaluated variables. The findings indicated that hybrid machine learning approaches were effective in enhancing the efficiency and reliability of solar power generation forecasting in intelligent energy networks. Ridha et al.²⁷ proposed a hybrid photovoltaic power prediction framework integrating singular spectrum analysis, an adaptive beluga whale optimization algorithm, and an improved extreme learning machine. Singular spectrum analysis was applied to preprocess long-term PV time-series data, while the adaptive beluga whale optimization method was used to enhance exploration–exploitation balance and optimize model hyperparameters. The improved extreme learning machine further refined output weight estimation to enhance prediction accuracy. Comparative evaluations using benchmark functions and real-world PV data demonstrated that the proposed hybrid model outperformed existing optimization and hybrid learning approaches across multiple statistical performance metrics.
Although ML and hybrid models have achieved high accuracy in photovoltaic forecasting, existing studies mainly focus on single performance indicators and accuracy-driven evaluation. The simultaneous prediction of optimal operating time and efficiency, along with uncertainty-aware validation and robustness assessment, remains largely unexplored. Moreover, despite advances in hybrid learning, model interpretability and sensitivity-based physical insight are insufficiently integrated into PV forecasting frameworks. These limitations highlight the need for a unified, transparent, and decision-oriented modeling approach that balances accuracy, reliability, and practical applicability. In addition, the proposed XGBA model addresses the methodological gap in existing hybrid PV forecasting approaches by enabling simultaneous multi-target prediction, improving robustness and uncertainty-aware performance, and integrating sensitivity-based interpretability for enhanced operational insight.
The primary task in this research is to formulate a hybrid machine learning system capable of accurately predicting solar energy parameters, such as the number of optimal peak operating times (NOPT) and power conversion efficiency (PCE). The accurate forecasting of these targets can lead to better management of solar energy resources, higher-quality service, and easier financial planning. Standard single models often fail to capture the complex relationships between environmental variables and energy outputs. To address this problem, the paper presents the concept of cooperation models, the result of the successful interaction of multiple learning paradigms. Such a combination is based on integrating tree-based algorithmic predictability with metaheuristic optimization strategies, e.g., simulated annealing or genetic algorithms. The most important part of the proposed method is the role the Bat Algorithm (BAT) plays as a tuner. The BAT optimizer is a metaheuristic approach inspired by the echolocation behavior of bats. The advantages include effective exploitation and space searching, fast convergence, and high adaptability to the problem. Moreover, the possibility of balancing global search and regional refinement allows it to be used for adjusting the parameters of hybrid machine learning models. In effect, the performance of different targets, i.e., NOPT and PCE, is improved. In addition to this, this paper also relies on a few sensitivity analysis techniques systematically to explore the effects the input variables exert on the model outputs. The FAST sensitivity methodology gives a comprehensive interpretation of the variable importance. In contrast, the Accumulated Local Effects (ALE) method measures the effect of each input on the predicted outputs regardless of the underlying model. Besides that, post hoc statistical tests such as Dunn’s test are applied to support the significance and independence of model predictions. The joint use of multiple sensitivity tools enables the proposed models not only to be accurate but also interpretable, allowing the identification of critical factors affecting solar energy performance. Figure 1 shows the process of the study.
Process of the present study.
The RBF network, a member of the Artificial Neural Networks (ANNs) family, links input and output components without the use of mathematical formulae. Instead, it infers the model’s structure and unknown parameters only from the data²⁸. The RBF network consists of three layers: input, hidden, and linear output. As input vectors pass through the hidden layer, they undergo transformations that result in radial basis functions. These procedures use an activation mechanism based on the Gaussian distribution and have a solid basis in the properties of the Gaussian function. According to the literature, the Gaussian basis function ((:{mathcal{G}}_{j})) is defined by two essential parameters: width and center²⁹. The following is an expression for the function:
The width and center of the Gaussian basis function are denoted by (:{omega:}_{j}) and (:{gamma::}_{j}), respectively, while (:x) is the input pattern. The output neuron is commonly represented by:
Here, (:{U}_{j}) is the weight factor that connects the (:j)th hidden neuron to the output neuron, (:mathfrak{B}) is the bias coefficient, and (:n) is hidden neuron’s numbers. Figure 2 shows how the RBF model works using a flowchart.
Flowchart of the RBF.
XGBoost, a supervised learning technique, was used to train models for forecasting missing laboratory test data. Because of its effectiveness in model training, the extended distributed gradient boosting package XGBoost was chosen³⁰. This approach employs an adaptive binary splitting algorithm to iteratively select the optimal split at each stage, thereby producing an ideal model. Model selection procedures are enhanced by XGBoost’s resistance to overfitting and outliers due to its tree-based structure. Equation (3) defines the normalized goal of the XGBoost model during the (:s)th training phase. The loss function (:mathcal{L}mathcal{f}left({{x}^{left(sright)}}_{mathcal{p}},:{x}_{gt}right)) measures the difference between the predicted value (:{{x}^{left(sright)}}_{mathcal{p}}) and the corresponding ground truth (:{x}_{gt})
(:left| {omega :} right|^{2}) represents the (:mathcal{L}mathcal{f}2) norm of all leaf scores. The regularizer (:Omega :left( {f_{k} } right) = gamma :T + frac{1}{2}lambda :left| omega right|:^{2}) represents the complexity of the (:q)th tree. The parameters control the accuracy of the tree search, (:gamma:) and (:lambda:). Moreover, Fig. 3 shows the flowchart of the XGBR model.
Structure of the XGB model.
Averaging predictions from hundreds or even thousands of decision trees is how the random forest algorithm, an ensemble approach, creates multiple trees for regression. Each tree is derived from the Classification and Regression Tree (CART), which was first presented by Breiman et al.³¹. Data complexity shapes the learning process that each tree goes through. A decision tree is made up of decision and leaf nodes. According to Eq. (4), the input vector (:X={{x}_{1},{x}_{2},dots:,{x}_{m}}) maps to a scalar output (:Y) using a training set of (:n) observations ((:{R}_{n})).
By splitting the input data at each node until it reached a terminal leaf or satisfied stopping conditions, like a minimum sample size or maximum depth, the algorithm optimized split functions during the training phase. A prognostic function (:widehat{H}=(X,{R}_{n})) that can forecast results was created by this process. An ensemble of tree-structured base classifiers (:H=(X,{varTheta:}_{K})) was developed in Random Forest Regression³², where each (:{varTheta:}_{K}) denoted a random vector that identified a bootstrap sample of the training data or a subset of features. To ensure an equal selection probability, bootstrap sampling entailed drawing n observations from (:{R}_{n}) with replacement. This process was repeated across several bootstrap sets by the bagging procedure, producing a separate prediction tree for each set. The result was a set of (:q) trees (:widehat{h}left(X,:{S}_{n}^{{{Theta:}}_{1}}right),:…,:widehat{h}left(X,:{S}_{n}^{{{Theta:}}_{q}}right)). In contrast to a single decision tree, the outputs from all trees were averaged to produce the final predicted value, (:widehat{Y}), which improved accuracy and decreased variance³³.
The output of the (:l)th tree is denoted by (:{widehat{Y}}_{l}), where (:l) takes values between 1 and (:q).
By integrating bagging with ensembles of unpruned decision trees, Random Forest (RF) regression improves model robustness^32,33. RF is a computationally efficient method because it doesn’t require pruning, unlike other approaches. Only two parameters need to be adjusted for it to be simple: the number of trees ((:{n}_{tree})) and the number of randomly chosen predictors for every split ((:{m}_{try}))³⁴. In general, adding more trees increases accuracy and stability, but eventually, there comes a point at which more trees are no longer able to reduce error. Typically, a standard value of (:{n}_{tree})=500 is used. In addition to strengthening trees, increasing (:{m}_{try}) also makes trees more correlated with one another³⁵. Approximately two-thirds of the original dataset is included in each of the (:{n}_{tree}) bootstrap samples that are created during the RF process. To ensure diversity among trees, the optimal split is determined at each node using a random subset of predictors ((:{m}_{try})). While out-of-bag (OOB) samples, which are data not included in bootstrap sets, are used for validation to lower the risk of overfitting, predictions are aggregated through averaging for regression tasks. Figure 4 illustrates the application of the RF regression framework for prediction.
Flowchart of the RF model.
The echolocation method used by wild bats to find food served as the model for the BAT search algorithm. It was first presented by Yang^36,37,38,39 and is used to solve several optimization issues. Every virtual bat in the original population updates its position using echolocation in a homologous fashion. Bats use a perceptual mechanism called echolocation, which produces echoes by releasing a sequence of loud ultrasonic waves. Bats can identify a particular prey by using the delays and different sound levels that these waves return. A few guidelines are being researched to expand the BAT algorithm’s structure and take advantage of bats’ echolocation traits^40,41,42,43.
(a) Every bat uses echolocation features to differentiate between obstacles and prey; (b) Every bat flies at random with loudness (:{E}_{0}) and velocity (:{k}_{i}) at position (:{x}_{i}) with a fixed frequency (:{f}_{min}) varying wavelength (:lambda:) to find prey; it controls the frequency of its released pulse and modifies the rate of pulse release (:r) in the range of [0,1], depending on how close its aim is; (c) Every bat varies its frequency, loudness, and pulse release rate; (d) The loudness (:{E}_{m}^{iter}) shifts from a significant value (:{E}_{0}) to a minimum constant value (:{E}_{min}); Throughout the optimization process, each bat’s position (:{x}_{i}) and velocity (:{v}_{i}) should be specified and updated; the new solutions (:{x}_{i}^{t}) and velocities (:{k}_{i}^{t}) at time step (:t) are carried out by the following Eqs.^44,45:
Where (:phi:) is a random vector selected from a uniform distribution and falls between 0 and 1, after analyzing all of the positions among all (:n) bats, the current global best location is (:{x}^{*}). One may use either (:{f}_{i}) (or (:{lambda:}_{i})) to adjust the velocity change while setting the other component, as the velocity increment is the product of (:{lambda:}_{i}) and (:{f}_{i}). Each bat is given a frequency at random for implementation, which is uniformly selected from ((:{f}_{min}),(:{f}_{max})). Following the selection of one of the existing top solutions for the local search, a random walk is used to produce a new solution for every bat locally.
Where (:t) is the average loudness of all bats at this time step and (:epsilon:) is a random value that falls between 1 and 1. The volume may be set to any convenient number since, after a bat has located its prey, the loudness typically falls while the rate of pulse emission rises. Considering that (:{E}_{min}=0) indicates that a bat has just discovered its victim and has momentarily stopped making noise, one obtains:
Where (:gamma:) is a positive constant and (:beta:) is a constant in the interval [0,1]. The loudness tends to be zero as time approaches infinity, and (:{r}_{i}^{t}) equals (:{gamma:}_{i}^{0}).
This article provides details on various statistical metrics that account for the accuracy of predicting peak times (NOPT) and power conversion efficiency (PCE) in solar energy systems. One of these metrics is the coefficient of determination (R²), which is the measure of agreement between actual and predicted values, where a number close to one indicates a strong match. For instance, a solar module with an actual PCE of 18.5% and an expected value of 18.3% will exhibit a high R², indicating a perfect match between the two values. Besides this, the root mean square error (RMSE) gives the average size of differences between the values. If we consider an example where 49 NOPT is predicted instead of the actual 50, this will have a minimal impact on RMSE. The 95% confidence level uncertainty (U95) indicates prediction stability and helps to ensure that long-term forecasts are reliable. Correspondingly, MRAE and MDAPE are measures of error in percentage that are normalized and robust. At the same time, the prediction interval coverage probability (PICP) is a criterion that checks whether the actual NOPT or PCE values fall within the model’s predicted bounds. The mathematical formulations of the employed evaluation metrics are presented in Eqs. (11) to (16).
Where, (:{t}_{i}) is observed (actual) solar energy value at instance (:i), (:{p}_{i}) is predicted solar energy value at instance (:i), (:stackrel{-}{t}) and (:stackrel{-}{p}) are the mean of observed and predicted values, respectively. (:n) denotes the total number of observations. (:left[{low}_{i},:{up}_{i}right]) are lower and upper prediction interval bounds for the (:i)th prediction, and (:{k}_{i}) demonstrates the indicator variable, equal to 1 if the observed value lies within the prediction interval, and zero otherwise.
The hybridization strategy adopted in this study is designed to enhance nonlinear pattern learning by combining the complementary strengths of different learning paradigms rather than relying on a single-model structure. Single machine learning models, such as kernel-based learners or tree-based algorithms, are effective in capturing specific types of relationships; however, they are inherently limited in representing the full complexity of photovoltaic system behavior, which is governed by highly nonlinear, nonstationary, and interacting environmental and operational variables. In the proposed hybrid framework, gradient boosting models act as strong base learners capable of capturing high-order nonlinear interactions and abrupt regime changes, while the adaptive aggregation mechanism integrates multiple weak and strong predictors to reduce bias and variance simultaneously.
This fusion enables the model to learn both global trends and localized nonlinear responses, which are common in PV systems due to fluctuating irradiance, temperature-dependent efficiency, and extreme energy generation. Hybridization improves learning performance by mitigating the weaknesses of individual models. While single models may overfit local patterns or underperform in extrapolation regions, the fusion strategy stabilizes predictions through ensemble averaging and adaptive weighting, thereby improving generalization and robustness. This is particularly important for small-to-moderate datasets, where individual learners may exhibit high variance. Furthermore, the hybrid framework enhances error correction, as other models in the ensemble can compensate for mispredictions from a single model. This mechanism explains the observed reductions in RMSE and uncertainty bounds, as well as the consistent performance across the training, validation, and test datasets. Compared to single-model baselines, the hybrid approach demonstrates superior capability in learning complex nonlinear relationships while maintaining interpretability and stability, making it especially suitable for simultaneous forecasting of NOPT and PCE. As a result, the hybridization strategy directly addresses the limitations of standalone models and provides a more reliable and scalable solution for real-world photovoltaic system forecasting.
All data preprocessing procedures, hybrid machine learning model implementations (including the XGBA framework), training scripts, and evaluation workflows used in this study were custom-developed and implemented in Python. To ensure transparency and reproducibility, the complete source code, including model configurations, parameter settings, and execution instructions, is available from the corresponding author upon reasonable request. Requests for access can be directed to: asifmmd1in@gmail.com. The code is provided for academic and research purposes without restriction.
The dataset in this research includes 305 records with seven input variables, namely Ap, Amin, Amax, Ep, Emin, Emax and nyield, and the targets for prediction are the number of peak times (NOPT) and the power conversion efficiency (PCE), expressed as percentages. The dataset, obtained from⁴⁶, was partitioned into training (70%), validation (15%), and testing (15%) subsets. To ensure reproducibility and prevent data leakage, the dataset was explicitly partitioned into three mutually exclusive subsets: 70% (214 samples) for model training, 15% (46 samples) for validation, and 15% (45 samples) for independent testing. This splitting strategy was selected to provide sufficient samples for learning model parameters while reserving adequate data for unbiased hyperparameter tuning and final performance assessment. The validation subset was used exclusively for model selection and hyperparameter optimization, whereas the test subset remained completely unseen during the training and tuning phases. This strict separation ensures that reported test results reflect true generalization performance rather than memorization effects. Moreover, data splitting was performed in a deterministic, reproducible manner, and the same partitions were consistently applied across all single and hybrid models to ensure fair, transparent comparisons. This structured training–validation–testing workflow minimizes the risk of optimistic bias and aligns with best practices in machine learning–based photovoltaic performance modeling.
According to Table 1, the variables characterize various operational and environmental conditions related to solar energy systems. Specifically:
Ap expresses the peak absorption wavelength measured under standard test conditions (nm).
Amin and Amax represent the minimum and maximum absorption wavelength during the measurement period, reflecting daily and seasonal variations in sunlight exposure.
Ep denotes the peak emission wavelength (in nm) produced under the measured irradiance conditions.
Emin and Emax indicate the minimum and maximum emission wavelength across different operating regions, capturing fluctuations due to environmental and system variations.
is the absolute emission quantum yield, ranging from 0 to 100, calculated as the ratio of actual energy output to the available solar resource, reflecting system performance efficiency.
The target variables quantify predictive objectives:
NOPT, with a maximum value of 12.91%, indicates the number of optimal peak operating times for the PV system.
PCE, with a maximum of 4.36%, measures the efficiency with which solar irradiance is converted into electrical energy.
All measurements were collected using calibrated pyranometers for irradiance and standard energy meters for electrical output, ensuring accurate representation of environmental and operational conditions. Hence, this dataset not only signifies environmental variations but also captures system performance metrics, providing a solid foundation for building and validating predictive models. Before training and evaluating the predictive models, the raw dataset underwent a systematic preprocessing workflow to ensure data quality, consistency, and compatibility with machine learning algorithms. First, all input variables were normalized using min–max scaling to map values to 0–1, preventing features with larger numerical ranges from dominating the learning process. Second, missing values were handled using a two-step approach: records with minor missing entries (< 5% of the dataset) were imputed using linear interpolation based on neighboring temporal values, while records with substantial missing information were excluded to avoid introducing bias. This ensured that the final dataset retained meaningful variability without compromising integrity. Third, noise filtering was applied to smooth transient fluctuations in energy and irradiance measurements. A moving average filter with a window size of 3 was applied to the input features , , , , , and to reduce measurement noise while preserving significant trends relevant to model training.
Figure 5 shows a scatter matrix, which displays the distributions and pairwise relationships of the features in the dataset. On the diagonal, each panel represents distributions of individual variables, whereas off-diagonal plots show possible correlations and grouping between pairs of features. The nopt values are distributed mainly between 0 and 4, so most samples are within this range. Likewise, the PCE values are primarily concentrated between 0 and 4.4, consistent with their distribution in the dataset. By and large, the matrix delineates variable ranges, uncovers potential relationships (linear or nonlinear) and regions of concentration, thus giving a brief indication of feature behavior, which is handy for exploratory data analysis.
Scatter matrix plot for the distribution and relationships within the dataset across different feature subsets.
The computational complexity and training time of the proposed models were systematically analyzed to assess their practical feasibility and scalability. The runtime results clearly demonstrate the computational trade-off introduced by BA–based optimization across all models and both target variables (NOPT and PCE). In all cases, incorporating BA increased execution time by approximately 3–5 times compared with the corresponding base models, attributable to the iterative population-based search mechanism and the repeated fitness evaluations inherent to metaheuristic optimization techniques. Among the evaluated models, RBF consistently exhibited the lowest computational cost, both in its base configuration and when coupled with BA. For NOPT prediction, the RBF model required 9.47 s in the base form and 48.29 s with BA optimization, while for PCE prediction, the runtime remained similarly low (10.36 s in the base form and 52.87 s with BA). This behavior reflects the simpler mathematical structure and lower training complexity of kernel-based models, making RBF computationally efficient even under optimization. The XGBoost-based models showed moderate computational demand, with base runtimes of 16–18 s, increasing to 62–66 s after BA optimization. The additional overhead primarily stems from repeated tree construction, gradient boosting iterations, and hyperparameter evaluations during the optimization process.
In contrast, Random Forest exhibited the highest computational cost, particularly in its optimized form, with runtimes reaching 80–84 s, due to the large ensemble size, bootstrap sampling, and repeated evaluation of tree-based structures across BA iterations. From a scalability perspective, the observed computational trends indicate that training time grows approximately linearly with dataset size for RBF and near-linearly to moderately superlinearly for tree-based ensemble models. While BA-based hybridization introduces additional overhead, this cost is incurred offline during model development and optimization, whereas online inference remains computationally lightweight, enabling real-time deployment in photovoltaic monitoring systems. Regarding scalability to larger PV datasets and different climate zones, the proposed hybrid framework is inherently extensible. Larger datasets are expected to improve generalization while increasing training time proportionally, particularly for ensemble models. However, the modular design of the hybrid approach allows parallelization of BA fitness evaluations and tree construction, making it suitable for high-performance or cloud-based computing environments. Moreover, the data-driven nature of the models enables adaptation to diverse climatic conditions, provided that representative environmental and operational data from different regions are included during training.
3D wall plot illustrating the convergence behavior of the optimization process across iterations or parameters.
The random search procedure was conducted using predefined hyperparameter ranges that were selected based on model-specific constraints, prior literature, and preliminary sensitivity trials to ensure both computational feasibility and sufficient exploration of the solution space. For kernel-based hybrid models (RBBA), the length scale was sampled from a continuous logarithmic range of [10⁻³, 10¹], while the lower and upper bounds of the length scale were drawn from [10⁻⁵, 10⁻²] and [10³, 10⁶], respectively, allowing the model to capture both smooth and highly nonlinear functional relationships. For hybrid models (RFBA and XGBA), the number of estimators was randomly sampled from the interval [20, 1000], enabling evaluation of ensemble sizes from small to large. The maximum tree depth was explored within the range [5, 1000] to assess the trade-off between model expressiveness and overfitting risk, while the minimum number of samples required to split a node was sampled from [2, 150] to regulate tree granularity and stability. For boosting-based hybrids (XGBA), the learning rate was sampled from the continuous range [0.01, 0.9] to balance convergence speed and generalization performance. In addition, the column sampling rate per tree (colsample_bytree) was varied within [0.5, 1.0] to enhance feature diversity and reduce correlation among trees, and the number of leaves was sampled from [10, 100] to control the complexity of individual boosting trees.
From a computational standpoint, the random search was executed for a fixed budget of 200 independent hyperparameter evaluations per model–target pair, ensuring consistent and fair optimization across all frameworks. Each candidate configuration was trained on the training subset and evaluated exclusively on the validation subset using RMSE and R² as the primary selection criteria. Table 2 summarizes the hyperparameters optimized for the hybrid models used to predict solar energy targets, specifically NOPT and PCE. The hyperparameters are length scale, length scale bounds (lower and upper), number of estimators, maximum tree depth, minimum samples required to split a node, learning rate, colsample by tree for NOPT, and number of leaves for PCE. The model’s flexibility, complexity, and learning dynamics are controlled by these parameters, which in turn aim to achieve prediction accuracy as the ultimate goal. For example, the RBBA model has a length scale of 3.9516 for NOPT and 2.1531 for PCE, indicating the degree of smoothness of the underlying regression function. In tree-based hybrid models, the number of estimators for RFBA and XGBA are 321 and 246 for NOPT, and 846 and 21 for PCE, respectively, so that the differences in ensemble size and their effects on predictive performance are clear. All experiments were conducted under identical computational settings to ensure fair model comparison and reproducibility. The implementations were executed on a workstation equipped with an Intel^® Core™ i7 processor, 32 GB RAM, and a 64-bit Windows operating system. The models were implemented using Python (v3.9) with Scikit-learn, XGBoost, and NumPy libraries, which are widely adopted in ML research.
To address concerns regarding potential overfitting due to the small dataset size (305 samples), a 5-fold cross-validation procedure was implemented on three representative single models: RBF, RF, and XGB. Table 3 shows the 5-fold cross-validation results for the single models. The 5-fold results demonstrate consistent performance across folds, indicating robust generalization ability.
Table 4 summarizes the comprehensive performance of both single and hybrid models in forecasting the NOPT and PCE. The evaluation used a suite of statistical indicators, including R², RMSE, PICP, U95, MRAE, and MDAPE, to ensure a rigorous assessment of predictive accuracy and reliability. Among the single models, the XGB framework consistently outperformed RBF and RF, yielding the lowest error rates across RMSE, MRAE, and MDAPE, which highlights its superior ability to approximate the underlying solar energy dynamics. Nevertheless, the hybrid configurations markedly advanced the prediction quality beyond that of the standalone models. In particular, the XGBA model achieved exceptional results, with R² values of 0.9954 for NOPT and 0.9970 for PCE, thereby capturing nearly all variability observed in the actual system behavior. Furthermore, its minimal uncertainty values (U95 = 0.5346 for NOPT and 0.1526 for PCE) underscore the robustness and stability of its forecasts. These outcomes demonstrate that the XGBA model not only minimizes deviation from ground truth but also ensures reliable and consistent estimations, which are indispensable for effective scheduling, energy resource allocation, and risk reduction in solar energy management. Collectively, the results affirm the superiority of hybrid learning strategies, particularly XGBA, in providing both accuracy and resilience for practical decision-making in renewable energy systems.
Figure 7 shows a comparative graphical representation of the effectiveness of each model using the evaluation metrics, and it identifies the gap between single and hybrid models in terms of NOPT and PCE forecasting. With the value of the metric R² taken as an example, we can determine that the RF model is shown by having the lowest correlation; hence, it has a lower predictive ability; in short words, the respective model’s predictions deviate more from the actual peak operating times and PCE measured in real solar energy systems. As for RMSE, all the models yield lower errors for PCE than for NOPT, indicating that power conversion efficiency is a more accurate predictor than the number of peak times. The same direction can be drawn from the U95 values, which reveal that the predictions are more stable for PCE. On the other hand, MRAE and MDAPE scores are higher for PCE, which signifies that the values of relative and percentage errors are greater for peak time predictions. As for PICP, RBF, XGBA, and XGB are the models that allow the highest coverage for PCE, while XGB and RBBA are the best performers for NOPT, which indicates that these models offer the most reliable probabilistic forecasts in real-world solar energy applications.
Performance evaluation of the developed models using key statistical metrics. The best-performing models were selected based on their superior accuracy and reliability.
Figure 8 compares the scatter plots for NOPT and PCE predictions, which depict the degree to which the models’ predictions are accurate. The points representing the hybrid XGBA model are very close to the best-fit line. They are mostly located within the ± 15% deviation lines, showing that there is a good correlation between the predicted and actual values. In NOPT, this means that the model can accurately predict the optimal peak operating times. With PCE, the forecast is close to the actual power conversion efficiency of solar modules. From a financial perspective, such dependable projections enable solar farm managers and investors to schedule energy production more accurately, thereby making better use of resources and allowing for a higher level of confidence in revenue estimation. Correct predictions of peak times and efficiencies become the basis for making operational decisions that involve the organization of maintenance, energy trading, and capacity planning, all of which lead to a reduction in the economic risk and an increase in the overall profitability.
Scatter plot of predicted versus actual values on the test dataset, showing the performance of the selected models.
Table 5 provides an overview of the statistical comparisons of the best hybrid models for both NOPT and PCE targets in the testing phase. The values of NOPT that were measured vary from 0.1 (min) to 10.12 (max), with 4.3182, 3.8950, and 2.8322 being the mean, median, and standard deviation, respectively. The XGBA model is closest to these statistics, indicating that it not only captures the most frequent but also the extreme variations in the number of optimal peak operating times. The range of measured PCE values is from 0.15 to 4.1, with mean, median, and standard deviation being 1.7836, 1.7250, and 0.9239, respectively. All models offer minimum predictions that are in agreement. In contrast, the XGBA model achieves the maximum value (4.0875), which is closest to the measured maximum, indicating good model performance under peak efficiency conditions. The outcome of this study is that the hybrid models are helpful for solar energy systems as they not only depict the normal performance but also the peak outputs, and therefore, the operators can use the energy scheduling to achieve maximum revenue and minimize financial uncertainty by being able to predict the periods of high energy generation and efficiency.
Figures 9 and 10 show the significant prediction errors for each model and the NOPT and PCE targets. The XGBA model has the narrowest line through the origin, suggesting that almost all of its predictions are very close to the actual values. To be more specific, in Fig. 9, the error of the XGBA model is very close to zero, which is the range of -5 to + 5, while other models have errors in much wider ranges. This high accuracy enables the prediction of any number of peak operating times and power conversion efficiency with surprising accuracy. From an investor’s point of view, such accurate predictions are extremely valuable: they enable solar power investors and managers to estimate likely energy output and efficiency with a high degree of confidence, enabling them to better allocate resources, plan maintenance activities more effectively, and predict revenues more accurately. As a result, models such as XGBA can reduce financial risk, increase profit potential, and enhance decision-making in solar energy projects.
Histogram showing the distribution of prediction errors for the selected models.
Line plot of prediction errors for the selected models.
Table 6 presents the results of Dunn’s post hoc test for pairwise model comparisons alongside the Durbin–Watson (DW) statistics to assess the reliability and independence of model residuals. Dunn’s post hoc test is a non-parametric method used for multiple pairwise comparisons following a Kruskal–Wallis test and was selected because the performance metrics, such as RMSE and R², do not necessarily follow a normal distribution. This test evaluates whether the differences in model performance are statistically significant. The Durbin–Watson statistic measures autocorrelation in the residuals, with values ranging from 0 to 4; values close to 2 indicate no significant autocorrelation, values below 2 suggest positive autocorrelation, and values above 2 indicate negative autocorrelation. In this study, the XGBA model shows DW values of 1.9274 for both NOPT and PCE, which is very close to 2, confirming that the residuals are statistically independent. This independence implies that the model predictions are reliable and not biased by systematic correlation in the data. In contrast, several single or hybrid models exhibit DW values substantially below or above 2, indicating residual correlation and potentially less reliable predictions. Together, Dunn’s post hoc test and DW statistics provide a rigorous assessment of model validity: the former confirms that XGBA’s performance differences are statistically robust, while the latter demonstrates that the residuals are independent, supporting the model’s robustness and generalization capability.
Table 7 presents the confidence intervals (CIs) for RMSE and MDAPE across the training, validation, and testing phases for all single and hybrid models, for both target variables, NOPT and PCE. These intervals provide an explicit measure of prediction uncertainty and offer insight into the statistical stability and reliability of each modeling framework beyond pointwise performance metrics. During the training phase, all models exhibit relatively narrow confidence intervals, indicating stable learning and limited dispersion in prediction errors. For NOPT prediction, the hybrid models—particularly XGBA—show comparatively tighter RMSE and MDAPE intervals, suggesting more consistent error distributions than single-model counterparts. A similar trend is observed for PCE, where hybrid models demonstrate reduced uncertainty bounds, reflecting improved robustness during model fitting. During the validation phase, the confidence intervals slightly widen across all models, as expected, since predictions are evaluated on unseen data used for hyperparameter tuning.
Nevertheless, hybrid models maintain narrower CI ranges than single models for both RMSE and MDAPE. This behavior indicates enhanced generalization capability and reduced sensitivity to data variability, reinforcing the effectiveness of hybridization strategies in controlling prediction uncertainty. In the testing phase, confidence intervals widen further, reflecting realistic uncertainty under fully unseen data conditions. Despite this, the XGBA model consistently exhibits balanced, relatively compact CI ranges for both NOPT and PCE, demonstrating reliable performance and controlled error dispersion. The comparable CI widths across training, validation, and testing subsets indicate the absence of severe overfitting and confirm the statistical stability of the proposed hybrid framework.
Figure 11 shows the Taylor diagram for the difference between measured and predicted values. The RBF-based models outperform the other approaches for both nopt and PCE, achieving the highest correlation coefficients and standard deviations closest to the measured data, which results in the lowest overall error. Tree-based and ensemble models (RF, XGB, and their variants) capture general trends but show noticeable variance mismatch and reduced correlation, especially for PCE. Overall, the Taylor diagrams confirm the RBF model’s superior robustness and generalization, particularly in representing the system’s nonlinear behavior.
Taylor diagram for the difference between measured and predicted values.
Figures 12 and 13 present a combined sensitivity analysis assessing the impact of the input variables on the output variables NOPT and PCE, respectively. As per Fig. 12, the FAST sensitivity analysis identifies as the variable with the most significant influence on NOPT, exhibiting an 1 value of 1.45, while is the leading variable for PCE predictions with an 1 of 2.2. This indicates that the extreme values of generated electrical energy strongly govern both the optimal timing of peak operation and the PV system’s efficiency. In physical terms, reflects periods of minimal energy generation, which critically limit the identification of optimal peak times, whereas corresponds to the highest achievable energy output, directly affecting power conversion efficiency. Furthermore, the accumulated local effects (ALE) study portrayed in Fig. 13 reveals the possible influence of each variable on the model outputs, along with lower and upper confidence intervals for NOPT and PCE predictions. These analyses highlight that, in addition to and , variables such as also significantly contribute, reflecting the direct impact of solar irradiance on system performance. Physically, higher irradiance levels increase energy production and efficiency, while variations in the minimum and maximum energy values determine the system’s operational window and efficiency ceiling.
The different ranking of feature importance between FAST and ALE arises from their distinct perspectives: FAST captures global variance contributions, while ALE highlights local and conditional effects. For example, shows the greatest influence on NOPT in FAST because variations in minimal energy generation dominate overall prediction variance, whereas ALE indicates that has stronger local effects on PCE, reflecting its direct impact on peak conversion efficiency under high irradiance conditions. These findings not only unveil the most sensitive parameters but also provide actionable insights for PV system operators: by understanding which energy extremes and irradiance levels most strongly affect system performance, resource allocation, system design, and maintenance schedules can be optimized to maximize energy yield. This connection between model sensitivity and real-world PV behavior enhances the interpretability and practical relevance of the predictive framework.
FAST Sensitivity analysis depicting the effect of input variables on the model output.
Sensitivity analysis for the impact of input variables on the model’s output based on the ALE method.
Despite the high predictive accuracy and robustness of the proposed hybrid model, several limitations exist. First, the study primarily relies on historical PV systems and meteorological data, which may limit model performance under entirely new climatic scenarios or rapidly changing environmental conditions. Second, while the hybrid framework demonstrates strong accuracy and interpretability, exploring more advanced deep learning architectures, such as Transformers or Graph Neural Networks, was beyond the current scope. Third, uncertainty quantification was performed using standard evaluation metrics, but probabilistic forecasting and real-time adaptive prediction were not fully addressed.
Future work includes:
Integration of advanced reinforcement and deep learning-based hybrid models (e.g., Transformers, GNNs) to capture complex temporal and spatial dependencies in PV systems.
Development of probabilistic and real-time adaptive forecasting approaches to improve reliability under dynamic environmental conditions.
Expansion of the framework to include larger and more diverse PV datasets, enhancing generalization and practical applicability.
Further exploration of explainable AI techniques to deepen physical insight and improve transparency for operational decision-making.
These directions aim to enhance both the predictive performance and practical deployment of hybrid PV forecasting models in real-world energy systems.
In addition, the current dataset and modeling framework do not explicitly account for environmental disturbances, such as dust accumulation, humidity, partial shading, or soiling, which are known to influence photovoltaic system performance in real-world deployments. The absence of such factors may limit the generalizability of the predictions to field conditions where these disturbances occur. Nonetheless, the selected input variables, including , , , , , , and , indirectly reflect cumulative environmental effects on system performance. For example, variability in irradiance and energy output may partially capture the influence of transient shading or atmospheric conditions. To enhance applicability in operational settings, future studies should integrate additional environmental monitoring data, including humidity levels, dust deposition rates, soiling factors, and shading patterns. Incorporating these features into hybrid machine learning models can improve predictive robustness, reduce uncertainty under extreme or variable conditions, and increase the reliability of NOPT and PCE forecasts for real-world PV systems. This limitation does not diminish the current study’s contribution, as the framework provides a robust baseline for forecasting PV system performance under nominal environmental conditions and can readily be extended to include more complex environmental variables in subsequent research.
Beyond numerical accuracy, the proposed forecasting framework can be directly integrated into the operational workflow of real PV plants as a decision-support tool. In a practical deployment scenario, the trained model can be embedded within a plant energy management system to provide day-ahead or intra-day predictions of NOPT and PCE based on real-time or forecasted environmental inputs. Specifically, NOPT predictions enable operators to identify time windows during which the PV system operates at maximum effectiveness, supporting informed scheduling of load management, grid interaction, and energy storage charging or discharging. Accurate PCE forecasting allows continuous assessment of system health and performance degradation, facilitating early detection of faults, soiling, or suboptimal operating conditions. When predicted PCE deviates from expected values under similar irradiance and energy conditions, maintenance actions can be prioritized proactively.
Furthermore, the sensitivity analysis results provide actionable physical insight for system optimization. The dominance of variables such as and indicates that energy extremes critically influence both operational timing and efficiency, suggesting that operational strategies should focus on mitigating low-energy periods and maximizing utilization during high-energy intervals. This information can guide inverter control strategies, energy storage dispatch, and plant design adjustments, such as panel orientation or capacity planning. From an economic perspective, integrating the proposed model into PV plant operation can reduce uncertainty in energy yield forecasting, improve scheduling efficiency, and support more reliable participation in energy markets. The framework is scalable and adaptable to different plant sizes and climatic regions, making it suitable for both utility-scale PV plants and distributed solar installations. As a result, the proposed approach bridges the gap between high-accuracy data-driven modeling and practical, real-world PV system management.
Table 8 compares the proposed XGBA model with recent hybrid PV forecasting studies. Xu et al.⁴⁷ combined EEMD decomposition, XGBoost, LSTM, and Snake Optimization for PV power prediction, achieving reduced errors but focusing only on power series without addressing optimal operating points or efficiency. Renold et al.¹⁹ integrated TCN, LSTM, and GRU networks for short-term PV forecasting, improving accuracy and computational efficiency. Wang et al.⁴⁸ applied a stacking strategy of gradient-boosted and deep networks for solar irradiance and generation, achieving R ≈ 0.99. Tanyıldızı and Ağır⁴⁹ combined LSTM with SVM for very short-term PV forecasting, reporting R ≈ 0.9823 and RMSE ≈ 0.0300, demonstrating hybridization benefits over single models. The proposed XGBA model surpasses these approaches, achieving R² up to ~ 0.997 and very low RMSE for both NOPT and PCE. Unlike prior studies, it forecasts both optimal peak times and power conversion efficiency, offering broader applicability, robust generalization, and low prediction uncertainty.
This study introduced a framework to accurately predict solar energy parameters, including the number of optimal peak operating times (NOPT) and power conversion efficiency (PCE), using hybrid machine learning models optimized through the Bat Algorithm (BAT). Based on hyperparameter tuning, the performance of each model, including Radial Basis Function (RBF), eXtreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR), was improved by exploring the parameter space and achieving optimal predictive results with fewer iterations of the algorithm. The dataset consisted of 305 records with seven features, including solar irradiance (Ap, Amin, Amax), electrical energy output (Ep, Emin, Emax), and normalized energy yield (nyield), which collectively represented the environmental and operational conditions that influence solar energy systems. Numerical evaluation of the hybrid models highlighted the superiority of the XGBA model. Specifically, XGBA reduced the RMSE of the single XGB model in predicting NOPT by 40.155% and decreased the U95 value for PCE by 135.58%, demonstrating that this model was more accurate, stable, and robust across both targets. These enhancements suggested that the hybrid system was capable of predicting both average and extreme weather conditions, supporting effective management and scheduling of solar energy. In addition, three sensitivity analysis procedures were used to determine the effects of input variables on the models’ outputs. The FAST sensitivity analysis identified as the most crucial variable for NOPT, whereas for PCE, the highest first-order effect (1) corresponded to and the highest total effect (ST) to . These outcomes provided valuable insights regarding the drivers predominantly affecting solar energy performance and enabled informed decision-making. In general terms, the union of hybrid modeling, BAT optimization, and rigorous sensitivity analysis provided a stable, understandable, and highly performing system for predicting solar energy parameters and supporting strategic planning for solar energy projects.‎.
Data will be provided upon reasonable requests, and codes can be accessed in the GitHub repository (https://github.com/AsifMd-10/Accurate-Forecasting-of-Photovoltaic-Optimal-Points-and-Efficiency).
Photovoltaic
Organic photovoltaics
Power conversion efficiencies
Artificial neural network
Explainable AI
Local interpretable model agnostic explanations
Power conversion efficiency
Accumulated local effects
Radial basis function
Classification and regression tree
Coefficient of determination
95% Confidence level uncertainty
RBF + BAT
XGBR + BAT
Lowest Energy Generation
Normalized energy yield
Sustainable development goals
Non-fullerene acceptors
Internal quantum efficiencies
Machine learning
SHapley additive explanations
Number of optimal peak operating times
Bat algorithm
Fourier amplitude sensitivity testing
eXtreme gradient boosting regression
Random forest regression
Root mean square error
Prediction interval coverage probability
RFR + BAT
Efficient electrical energy produced
Highest energy generation
Direct solar irradiance
Zhang, W. Main Contributions, Applications and Future Prospect of PV, In MATEC Web of Conferences, vol. 386, p. 3012. (2023).
Castellano, N. N., Salvador, R. M. G., Rodriguez, F. P., Fernandez-Ros, M. & Parra, J. A. G. Renewable energy: the future of photovoltaic energy, In Living with Climate Change, Elsevier, 373–396. (2024).
El-Sheekh, M. M., El-Nagar, A. A., ElKelawy, M. & Bastawissi, H. A. E. Bioethanol from wheat straw hydrolysate solubility and stability in waste cooking oil biodiesel/diesel and gasoline fuel at different blends ratio. Biotechnol. Biofuels Bioprod. 16 (1), 15 (2023).
Article  CAS  PubMed  PubMed Central  Google Scholar 
El-Din, H. A., Elkelawy, M. & Yu-Sheng, Z. HCCI engines combustion of CNG fuel with DME and H 2 additives. SAE Tech. Paper, (2010).
El Shenawy, E. A., Bastawissi, H. A. E. & Shams, M. M. Enhancement of the performance and emission attributes for the diesel engine using diesel-waste cooking oil biodiesel and graphene oxide nanofluid blends through response surface methodology. Mansoura Eng. J. 49 (5), 8 (2024).
Google Scholar 
Elkelawy, M., El Shenawy, E. A., Bastawissi, H. A. E., Mousa, I. A. & Ibrahim, M. M. A. R. Analyzing the influence of design and operating conditions on combustion and emissions in premixed turbulent flames: A comprehensive review. J. Eng. Res. 8 (1), 34 (2024).
Google Scholar 
Aboubakr, M. H., Elkelawy, M., Bastawissi, H. A. E. & El-Tohamy, A. R. A technical survey on using oxyhydrogen with biodiesel/diesel blend for homogeneous charge compression ignition engine. J. Eng. Res, 8, 1, (2024).
Elbanna, A. M., Cheng, X., Yang, C., Elkelawy, M. & Bastawissi, H. A. E. Investigative research of diesel/ethanol advanced combustion strategies: A comparison of premixed charge compression ignition (PCCI) and direct dual fuel stratification (DDFS). Fuel 345, 128143 (2023).
Article  CAS  Google Scholar 
Cui, Y. et al. Over 16% efficiency organic photovoltaic cells enabled by a chlorinated acceptor with increased open-circuit voltages. Nat. Commun. 10 (1), 2515 (2019).
Article  ADS  PubMed  PubMed Central  Google Scholar 
Yuan, J. et al. Single-junction organic solar cell with over 15% efficiency using fused-ring acceptor with electron-deficient core. Joule 3 (4), 1140–1151 (2019).
Article  CAS  Google Scholar 
Liu, Q. et al. 18% efficiency organic solar cells. Sci. Bull. 65 (4), 272–275 (2020).
Article  CAS  Google Scholar 
Karuthedath, S. et al. Intrinsic efficiency limits in low-bandgap non-fullerene acceptor organic solar cells. Nat. Mater. 20 (3), 378–384 (2021).
Article  ADS  CAS  PubMed  Google Scholar 
Chen, M. et al. Influences of non-fullerene acceptor fluorination on three-dimensional morphology and photovoltaic properties of organic solar cells. ACS Appl. Mater. Interfaces. 11 (29), 26194–26203 (2019).
Article  CAS  PubMed  Google Scholar 
Wang, X. et al. Tuning the intermolecular interaction of A2-A1-D-A1-A2 type non-fullerene acceptors by substituent engineering for organic solar cells with ultrahigh V Oc of ~ 1.2 V. Sci. China Chem. 63 (11), 1666–1674 (2020).
Article  CAS  Google Scholar 
Du, X. et al. Efficient polymer solar cells based on non-fullerene acceptors with potential device lifetime approaching 10 years. Joule 3 (1), 215–226 (2019).
Article  MathSciNet  CAS  Google Scholar 
Şahin, F., Işik, G., Şahin, G. & Kara, M. K. Estimation of PM10 levels using feed forward neural networks in Igdir, Turkey. Urban Clim. 34, 100721 (2020).
Article  Google Scholar 
Sahin, G., Isik, G. & van Sark, W. G. Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression. Energy Rep. 10, 2837–2849 (2023).
Article  Google Scholar 
Hamad, S. A., Ghalib, M. A., Munshi, A., Alotaibi, M. & Ebied, M. A. Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems. Sci. Rep. 15 (1), 10750 (2025).
Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 
Renold, A. P., Sinha, N. & Gao, X. Z. Hybrid machine learning approach for improved short-term PV power forecasting accuracy. Results Eng. 28, 107374. https://doi.org/10.1016/j.rineng.2025.107374 (2025).
Article  Google Scholar 
Keddouda, A. et al. Solar photovoltaic power prediction using artificial neural network and multiple regression considering ambient and operating conditions. Energy Convers. Manag.. 288, 117186 (2023).
Article  Google Scholar 
Kumari, P. & Toshniwal, D. Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance. J. Clean. Prod. 279, 123285 (2021).
Article  Google Scholar 
Lundberg, S. M. & Lee, S. I. A unified approach to interpreting model predictions. Adv Neural Inf. Process. Syst., 30, (2017).
Ribeiro, M. T., Singh, S. & Guestrin, C. ‘ Why should i trust you?’ Explaining the predictions of any classifier, In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. (2016).
Chen, Z., Xiao, F., Guo, F. & Yan, J. Interpretable machine learning for Building energy management: a state-of-the-art review. Adv. Appl. Energy. 9, 100123 (2023).
Article  Google Scholar 
Scott, C., Ahsan, M. & Albarbar, A. Machine learning for forecasting a photovoltaic (PV) generation system. Energy 278, 127807 (2023).
Article  Google Scholar 
Bhutta, M. S. et al. Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction. Sci. Rep. 14 (1), 17101 (2024).
Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 
Ridha, H. M. et al. A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive Beluga Whale optimization, and improved extreme learning machine. Renew. Energy. 256, 123878 (2026).
Article  Google Scholar 
A. H., Alavi, A. H., Gandomi, M., Gandomi & Sadat Hosseini, S. S. Prediction of maximum dry density and optimum moisture content of stabilised soil using RBF neural networks. IES J. Part. Civ. Struct. Eng. 2 (2), 98–106. https://doi.org/10.1080/19373260802659226 (2009).
Article  Google Scholar 
Heshmati, R. A. A., Alavi, A. H., Keramati, M. & Gandomi, A. H. A radial basis function neural network approach for compressive strength prediction of stabilized soil, In Road Pavement Material Characterization and Rehabilitation: Selected Papers from the 2009 GeoHunan International Conference, pp. 147–153.https://doi.org/10.1061/41043(350)20 (2009).
Ke, G. et al. Lightgbm: A highly efficient gradient boosting decision tree. Adv Neural Inf. Process. Syst, 30, (2017).
Breiman, L., Friedman, J., Olshen, R. & Stone, C. Classification and regression trees–crc press. Boca Raton Florida, (1984).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Article  Google Scholar 
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M. & Chica-Rivas, M. Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol. Rev. 71, 804–818 (2015).
Article  Google Scholar 
Liaw, A. & Wiener, M. Classification and regression by randomforest. R News. 2 (3), 18–22 (2002).
Google Scholar 
Peters, J. et al. Random forests as a tool for ecohydrological distribution modelling. Ecol. Modell. 207, 304–318. https://doi.org/10.1016/j.ecolmodel.2007.05.011 (2007).
Yang, X. S. A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer, 65–74. (2010).
Yang, X. S. Bat algorithm for multi-objective optimisation. Int. J. Bio-Inspired Comput. 3 (5), 267–274 (2011).
Article  Google Scholar 
Bora, T. C., Coelho, L. & Lebensztajn, L. Bat-inspired optimization approach for the brushless DC wheel motor problem, IEEE Trans. Magn., vol. 48, no. 2, pp. 947–950, (2012).
Yang, X. & Hossein Gandomi, A. Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29 (5), 464–483 (2012).
Article  Google Scholar 
Taha, A. M. & Tang, A. Y. C. Bat algorithm for rough set attribute reduction. J. Theor. Appl. Inf. Technol. 51 (1), 1–8 (2013).
Google Scholar 
Rakesh, V., Aruna, S. B. & Raju, T. D. Combined economic load and emission dispatch evaluation using BAT algorithm. Int. J. Eng. Res. Appl. 3 (3), 1224–1229 (2013).
Google Scholar 
Ramesh, B., Mohan, V. C. J. & Reddy, V. C. V. Application of Bat algorithm for combined economic load and emission dispatch. Int. J. Electr. Eng. Telecommun.. 2 (1), 1–9 (2013).
Google Scholar 
Yang, X. S. & He, X. Bat algorithm: literature review and applications. Int. J. Bio-inspired Comput. 5 (3), 141–149 (2013).
Article  Google Scholar 
Biswal, S., Barisal, A. K., Behera, A. & Prakash, T. Optimal power dispatch using BAT algorithm, In 2013 International conference on energy efficient technologies for sustainability, pp. 1018–1023. (2013).
Kheirollahi, R. & Namdari, F. Optimal coordination of overcurrent relays based on modified BAT optimization algorithm. Int. Electr. Eng. J. 5 (2), 1273–1279 (2014).
Google Scholar 
Ferreira, R. A. S. et al. Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning. Sci. Rep. 14 (1), 4160 (2024).
Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 
Xu, Y., Ji, X. & Zhu, Z. A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model. Sci. Rep. 15 (1), 30177 (2025).
Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 
Wang, J., Zhang, Z., Xu, W., Li, Y. & Niu, G. Short-Term photovoltaic power forecasting using a Bi-LSTM neural network optimized by hybrid algorithms. Sustainability 17 (12), 5277 (2025).
Article  ADS  Google Scholar 
Gao, J., Cao, Q., Chen, Y. & Zhang, D. Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting, arXiv Prepr. arXiv2406.03808, (2024).
Download references
Department of Electronics and Communication Engineering, GLA University, Mathura, 281406, India
Anjan Kumar
Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, India
Md Asif
College of Engineering, Applied Science University, Al Eker, Kingdom of Bahrain
Malak Naji
Department of Electrical and Electronics Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India
B. Spoorthi
Department of Electronics & Communication Engineering, Siksha ’O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India
Badri Narayan Sahu
Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
S. Radhika
College of Technical Engineering, the Islamic University, Najaf, Iraq
Marwea Al-hedrewy
College of Technical Engineering, the Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
Marwea Al-hedrewy
Department of General Science, Mamun University, Khiva, Uzbekistan
Egambergan Khudaynazarov
Faculty of Technology, Urgench State University, Urgench, Uzbekistan
Hayitov Abdulla Nurmatovich
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
A.K. conceived the research idea, designed the methodology, and supervised the overall study. M.A. (corresponding author) managed the project administration, coordinated data collection, and led the manuscript preparation. M.N. performed data preprocessing, feature engineering, and contributed to model development. S.B. implemented the machine learning algorithms, carried out the computational experiments, and validated the results. B.N.S. conducted the statistical analyses, model evaluation, and contributed to the interpretation of findings. S.R. prepared the figures, visualizations, and supported the development of the sensitivity analysis. M.A.-h. contributed to literature review, background formulation, and technical editing of the manuscript. E.K. supported the experimental design, reviewed the technical content, and contributed to refining the methodology. H.A.N. contributed to result interpretation, proofreading, and preparation of the final draft. All authors reviewed and approved the final manuscript.
Correspondence to Anjan Kumar.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissions
Kumar, A., Asif, M., Naji, M. et al. Accurate forecasting of photovoltaic optimal points and efficiency using advanced hybrid machine learning models. Sci Rep 16, 8197 (2026). https://doi.org/10.1038/s41598-026-39031-3
Download citation
Received: 24 November 2025
Accepted: 02 February 2026
Published: 10 February 2026
Version of record: 05 March 2026
DOI: https://doi.org/10.1038/s41598-026-39031-3
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Scientific Reports (Sci Rep)
ISSN 2045-2322 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

source

French independent power producer (IPP) Qair has secured PLN350 million (US$94 million) in funding from Bank Gospodarstwa Krajowego (BGK) to build renewable energy projects with a combined capacity of 203 MW in Poland. 
The funding will enable Qair to construct three renewable energy projects: an 80MW solar project in Wierzbica, a 105MW solar plant in Grudziądz, Kuyavian-Pomeranian Voivodeship, and an 18MW wind facility in Jenkowo, Lower Silesian Voivodeship.  
Get Premium Subscription
Construction is scheduled to start in Q2 2026, with commissioning expected in 2027. The financing comes through Poland’s Energy Support Fund, a key pillar of the country’s National Recovery and Resilience Plan, backed by the EU’s Recovery and Resilience Facility. 
In other news, the French IPP has brought commisioned its 14.6 MW Beehive solar portfolio, comprising thirteen small-scale photovoltaic farms strategically distributed across the country.  
The portfolio combines multiple sites into an integrated system, optimising local energy production. The company said the project will strengthen the national grid while providing clean electricity to surrounding communities. 
As of early 2026, Qair has installed nearly 500MW of solar capacity. Across all markets, the firm has 1.7GW of renewables either in operation or under construction, with a 35GW development pipeline spanning 20 countries. 
Qair recently signed a power purchase agreement (PPA) with Brazilian LPG distributor Ultragaz for its 192MW Bom Jardim solar PV project in Ceará, northeastern Brazil. Construction on the plant began in 2024, and it reached commercial operations earlier this year. 

source

11,000+ projects in Latin America.
24,000+ global companies doing business in the region.
83,000+ key contacts related to companies and projects
Analysis, reports, news and interviews about your industry in English, Spanish and Portuguese.

source

Learn about EMA’s leadership, milestones and accomplishments.
Explore the career, scholarship and internship opportunities available in EMA.
VISION, MISSION AND VALUES
Forging Towards a Clean Energy Future for Singapore
CAREERS
Learn How You Can Be A Part of the Exciting Energy Sector
Discover how the Singapore Energy Story sets the vision towards a net-zero energy future.
Gain insights on the power behind Singapore’s economy and our daily lives.
Discover ways to enhance energy efficiency and lower your carbon footprint.
Explore how EMA ensures a reliable and secure energy supply for everyone.
Learn about the intricacies of Singapore’s energy market structure and operations.
FEATURE STORY
A Look Inside Singapore's Electricity Market
REGIONAL POWER GRIDS
Importing Low-Carbon Electricity
ENERGY GRID
Find Out More About VirtuaL Power Plants
Get tips on buying electricity and protecting your family from electrical hazards.
Learn about purchasing gas and safeguarding your family against gas hazards.
Access information on installing solar panels at your home and selling excess electricity to the national grid.
MEDIA RELEASES
New Residential Demand Response Pilot Programme
BUYING ELECTRICITY
Learn About Your Options When Buying Electricity
ELECTRICAL SAFETY
All Homes Must Have A Residual Current Circuit Breaker
Stay up-to-date with the latest regulations, policies and frameworks governing the energy sector.
Learn about the licences that EMA issues to different stakeholders in the energy sector.
Read about the Codes of Practice and Circulars that EMA publishes to regulate the energy sector.
FRAMEWORKS
How Do Vesting Contracts Work?
WORKER LICENCES
Applying for an Electrical Worker Licence?
Get all the latest news and announcements by EMA.
Stay informed about EMA’s flagship events.
 
FEATURE STORY
Hazel's Year with the International Energy Agency in Paris
FEATURE STORY
Growing with Data: An Intern's Energy Story
MEDIA RELEASE
Enhanced Safeguards for Residential Electricity Contracts with Auto-Renewal
Collaborate with EMA in co-creating innovative solutions for the energy sector.
Give your comments and feedback on EMA’s policies and regulations.
Discover how EMA works with stakeholders to catalyse new and innovative digital technologies.
Learn about EMA’s efforts in nurturing talent and cultivate interest in the energy sector.
CONSULTATIONS
Enhancements to Auto-Renewal Practice in Retail Electricity Market
CONSULTATIONS
Power Sector Carbon Capture and Storage Grant Call
GRANT CALLS
Award of Second Energy Storage System Grant Call
Get quick access to EMA’s services for application of worker licences, scholarships and more.
Government officials will NEVER ask you to transfer money or disclose bank log-in details over a phone call. Call the 24/7 ScamShield Helpline at 1799 if unsure. For more information on how to protect yourself against scams, please visit www.scamshield.gov.sg.
There were 899 new grid-connected solar PVs installed in the fourth quarter of 2025, bringing the total number of installations in Singapore to 14,625.
Residential accounted for majority of the total installations (47.3% or 6,912 installations), followed by town councils & public housing common services (34.6% or 5,061 installations) and private (15.9% or 2,321 installations) sectors. Public service agencies constituted the remaining (2.3% or 331 installations) of total installations.
For more information, please download the Solar Photovoltaic Report for Q4 2025.
Download
Share
© {{current}} Government of Singapore

source

60MW data center colocated with solar and battery storage planned in Finland  datacenterdynamics.com
source

How Are Tensions in Iran Causing a Rise in UK Solar Demand?  Sustainability Magazine
source

Sign in
Sign Up
As countries across the region accelerate efforts to balance food security with environmental sustainability, Oman is stepping up initiatives to modernise its agricultural sector while reducing its carbon footprint. In line with these goals, the Ministry of Agriculture, Fisheries, and Water Resources has introduced a new programme aimed at integrating renewable energy into farming practices.
The initiative, titled ‘Solar Energy – Sustainable Harvest’, focuses on deploying solar power systems across agricultural projects in Oman. It is designed to ease the financial burden of rising electricity costs for farmers and investors while supporting national efforts to cut carbon emissions.
Launched in collaboration with Nafath Renewable Energy and the Development Bank, the programme aims to accelerate the adoption of clean energy solutions, particularly for farms that rely heavily on electricity to operate irrigation systems, greenhouses, and other essential activities.
Officials said the initiative reflects the government’s broader strategy to promote sustainability, expand the use of renewable energy, and strengthen the agricultural sector’s contribution to national food security.
Under the scheme, eligible farms can receive financing of up to RO15,000 to install solar systems. The package offers zero interest for full-time farmers and a reduced rate of 3% for part-time farmers. It also includes a grace period of up to 1 year, with repayment terms of up to 7 years.
Priority will be given to farms involved in producing key fruits and vegetables under the national food security programme, particularly those using greenhouses, advanced irrigation systems, or projects that directly enhance domestic food production.
The ministry outlined several requirements for applicants, including officially registered agricultural land, either owned or under a valid usufruct agreement, and an agricultural electricity meter. Applicants must also provide ownership documents and a land survey map.
Additional conditions include holding a valid agricultural certificate, membership in a relevant agricultural association, and access to at least 100 square metres of open, unshaded land for the installation of solar panels.
Officials noted that the initiative is expected to reduce operating costs, lower dependence on conventional energy, and encourage wider adoption of renewable technologies, while supporting Oman’s long-term environmental and climate objectives.
More
How brands must navigate marketing, innovation, and disruption in 2024
News
News
News
News
News
How the Middle East is using tourism to create jobs in rural villages
No panic, no fire sales. Why is Dubai’s property market playing the long game?
How strategic oil reserves work and why they matter now
A $57 billion pipeline is driving Abu Dhabi’s vision for future cities
WHOOP targets GCC expansion after $575 million raise and $10.1 billion valuation
Dubai’s next growth phase takes shape with AED 1 billion stimulus and steady GDP expansion
How architects design airports to handle superlong security lines
Ex‑SpaceX engineer unveils an $80 plastic‑free coffeemaker as microplastic health risks rise
Google Maps and Apple Maps are making parking harder than it has to be
There are more women in the workforce than men—again
Psychological safety is the first step. Most companies forget the second
Everything you’re doing about work stress is wrong. Here’s what to do instead

With a delivery-first approach and rising global participation, the summit positions Abu Dhabi as a testbed for scalable, future-ready cities.

As part of its expansion strategy, WHOOP plans to establish its first international research and development facility, WHOOP Labs, in Doha.

From SME relief and GDP reforms to worker welfare and digital trade, Dubai is layering policy to future-proof its economic model.
[pmpro_login show_menu=”true” show_logout_link=”true”]
Create an Account
You can see how this popup was set up in our step-by-step guide: https://wppopupmaker.com/guides/auto-opening-announcement-popups/
 
cancel anytime
Monthly Digital Access

$1.99 / month

Subscribe

Two Year Digital Access

Best Value

$34.99 / year

Subscribe

Annual Digital Access

$19.99 / year

Subscribe

Student subscriptions

Gift subscriptions

Group subscriptions

Have an inquiry? Visit our
Help Center
 
You can see how this popup was set up in our step-by-step guide: https://wppopupmaker.com/guides/auto-opening-announcement-popups/
You can see how this popup was set up in our step-by-step guide: https://wppopupmaker.com/guides/auto-opening-announcement-popups/
[pmpro_levels]
You can see how this popup was set up in our step-by-step guide: https://wppopupmaker.com/guides/auto-opening-announcement-popups/

source