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13 Jun 2026, 20:45 GMT+
(260613) — KUNMING, June 13, 2026 (Xinhua) — Visitors look at a sand table demonstrating photovoltaic power generation at the Green Energy Pavilion during the 10th China-South Asia Expo in Kunming, southwest China’s Yunnan Province, June 13, 2026.
The 10th China-South Asia Expo kicked off on Thursday in Kunming. The event has put an emphasis on enhancing regional trade and industrial cooperation under the theme “Solidarity and Coordination for Common Development.”
The expo features 13 pavilions, covering sectors ranging from green energy, service trade and manufacturing to the coffee industry.
In the Green Energy Pavilion covering an area of about 10,000 square meters, visitors can learn about the innovations of energy science and technology such as digital inspection, drone patrol, intelligent operation and maintenance through digital sand tables, smart operation and maintenance scenarios, virtual experiences, etc. (Xinhua/Peng Yikai)
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IEA: Economic Weaponizing Of Clean Energy Trade – OpEd Eurasia Review
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Saturday, June 13, 2026 by Daniel Vasquez
Tele Turquino, the provincial television station in Santiago de Cuba, has completed the installation and activation of a photovoltaic system consisting of 13 solar panels. This initiative aims to ensure the uninterrupted transmission of its TV signal in light of the severe power outages crippling the eastern part of the country.
The project is designed to operate an alternative digital TV transmitter directly from the telecenter, counteracting the disruptions experienced by the main transmitter of Channel 38, located at the Boniato Transmission Center, as reported by Canal Caribe.
A Tele Turquino expert elaborated on the scope of this strategy: “Primarily, we are establishing an alternative to broadcast a digital TV transmitter that will remain in operation here at the telecenter. This temporary solution is intended to replace the Boniato Channel 38 digital TV transmitter, currently impaired.”
The technician further noted that the team is collaborating closely “with colleagues in Texas to ensure continuous power supply from this photovoltaic panel system in response to the ongoing electrical outage.”
This installation is part of a broader transition in the energy matrix of the radio and television system in Santiago, although official coverage admits that the solution will provide only “limited” high-definition terrestrial digital signal reception for viewers in the main municipality.
The Boniato transmitter has a history of failures linked to the energy crisis and a shortage of spare parts. On May 7, it was completely shut down due to generation deficits and fuel scarcity, leaving hundreds of thousands in Santiago without access to state broadcasting.
This wasn’t an isolated incident. The Boniato HD transmitter suffered a major breakdown on January 28, 2025, when the high-definition broadcast was out of service for over four months due to a lack of parts and components, affecting Santiago de Cuba, Palma Soriano, San Luis, Mella, Songo-La Maya, and Contramaestre.
The electrical situation prompting this measure is dire. On May 31, the Electric Company of Santiago de Cuba confessed, “We often can’t even provide two hours of service.”
Nationally, the electrical shortfall exceeded 2,010 MW during peak hours on June 11, with the eastern region among the hardest hit.
On Friday, the entire province of Guantánamo was left without electricity after the 110 kV line connecting Santiago to that province was disrupted, while Holguín’s electric company announced a schedule of just three hours of power for every 39 or 40 hours of blackout.
The implementation of the 13 solar panels in Tele Turquino represents an emergency response to a structural crisis that the Cuban government has failed to address following decades of underinvestment in energy and telecommunications infrastructure.
The solar panels are installed to maintain the TV signal transmission despite severe power outages in eastern Cuba.
The system operates a backup digital TV transmitter powered by solar energy, ensuring continued broadcast when the main transmitter fails.
The solution provides limited high-definition terrestrial digital signal reception for the main municipality viewers.
© CubaHeadlines 2026
Cypress Creek Energy has reached a big milestone in Arkansas for one of the largest solar-plus-storage projects in the US.
The company just closed financing on the first two phases of the Steel River Energy Center in Arkansas – a $3.5 billion deal that will fund construction and long-term operation of the project.
Phases 1 and 2 combined will add 1.63 gigawatts (GW) of solar and 1.9 gigawatt-hours (GWh) of battery storage to the regional grid. When all three phases are complete, Steel River is expected to deliver 2.45 GW of solar and 2.9 GWh of battery storage by 2029.
Steel River will use 100% US-made structural steel, with nearly all sourced from Mississippi County, Arkansas, and domestically manufactured solar panels from First Solar. It’s expected to create around 700 construction jobs onsite.
The financing attracted strong interest from lenders, which isn’t surprising given the strong demand for large-scale energy infrastructure right now. The deal was fully underwritten by four coordinating lead arrangers: Barclays, BNP Paribas, Santander, and Wells Fargo. Cypress Creek also closed tax equity financing with a major investor at the same time, and secured long-term power sales for both phases through a virtual power purchase agreement with an investment-grade corporate buyer.
CEO Kevin Smith said the financing reflects “strong support from the capital markets for high-quality energy infrastructure projects,” and that the project will help meet Arkansas’s and the US’s “rapidly growing electricity demand while delivering long-term economic benefits to local communities.”
Read more: Arkansas turns on its first-ever utility-scale wind farm
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Major technology conglomerate Meta is expanding its digital fingerprint with a new partnership which will bring forth a new solar plant in Freestone County, Texas.
The collaboration between Meta and Zelestra, a global, multi-technology, customer-focused renewable energy company, is part of a new power purchase agreement (PPA) for the 180 MWdc (140 MWac) Palmera solar plant. This is the latest proceeding in the continued energy agreement between both entities.
Amanda Yang, Meta’s Head of Clean and Renewable Energy, says: “Meta is committed to bringing new renewable energy to the grid and our expanding relationship with Zelestra is helping make that possible at scale.
“These projects aren’t just advancing our energy goals, they’re creating jobs and delivering long-term value in the communities where they operate and it shows what strong partnerships can achieve.”
Serving as a cornerstone of Meta’s sustainability mission, the Palmera solar plant is designed to bolster the electrical grid through the use of entirely carbon-free energy.
This latest development builds upon a history of successful infrastructure collaborations between Meta and Zelestra, including the 200 MWdc Reclamation Solar Project in Indiana and the 176 MWdc Skull Creek Solar Plant in Anderson County, Texas, both of which are supported by Meta’s PPAs.
These combined initiatives are projected to generate approximately 400 employment opportunities, augmenting the existing 81 MWdc Jasper County Solar Project located in Indiana.
Collectively, the synergy between Zelestra and Meta has yielded PPAs for approximately 1.4 GWdc of solar output spanning eight US projects, with a target operational date of 2028.
Phil North, Zelestra’s US CEO, adds: “Our partnership with Meta continues to translate ambition into delivery. In just a few months, we have brought Jasper County online, started construction on Skull Creek and Reclamation, and now added Palmera to the portfolio.
“Together, we are accelerating the delivery of new energy infrastructure that supports Meta’s decarbonisation goals while delivering long-term economic value in local communities.”
According to Texas electric company Chariot Energy, a solar farm is a large collection of photovoltaic (PV) solar panels which absorb energy from the sun, convert it into electricity and send the electricity to power grids for distribution and consumption. There are two types of solar farms: utility-scale and community.
Utility scale solar farms are large areas of lands where huge solar panels are installed. Hundreds of thousands of solar panels collect energy, generate an electric current and distribute it to high-voltage power lines. These power lines are connected to homes and businesses.
Community solar farms typically produce approximately 5 MW of power to serve local residential and commercial needs. Here’s how they operate:
In addition to solar farms, Meta has struck deals to start construction on multiple date centres across the US.
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Silicon valley tech giant Meta has signed another power purchase agreement (PPA) with RWE for a solar project in Texas.
Under the deal, Meta will buy the power produced at RWE’s 298MW Rabbit’s Foot solar project in Bowie County, northeast Texas. Construction began on the project earlier this year and it is expected to begin producing power by the end of 2027.
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RWE said the PPA would support Meta’s goal to “match” its operations with 100% clean energy. Those operations largely involve massive data centre expansion in the US and the development of AI infrastructure.
This is the fourth corporate PPA Meta and RWE have inked in the US. It builds on the deals signed in 2024 for two projects in Illinois and Louisiana with a combined 274MW of capacity and the 200MW PPA deal inked for another Texas project last year.
Including Rabbit’s Foot, the two companies have inked deals for 827MW of solar PV capacity to date.
Meta has invested heavily in Texas energy infrastructure, in particular, to power its planned data centre expansions and other operations in the state. In July 2025, Canadian energy developer Enbridge announced plans to build a 600MW solar PV project near San Antonio, Texas, backed by a PPA with Meta. Spanish IPP Zelestra also inked a 200MW offtake deal with meta for a Texas PV plant earlier this week.
LYONS, NEB. (RFD NEWS) — Solar energy projects are on the rise as electricity demand grows, prompting increased discussion around how farmland can be used to support both agricultural production and solar development.
Laura Priest with the Center for Rural Affairs (CFRA) joined us on Friday’s Market Day Report to take a closer look at strategies for balancing energy development and agricultural land use.
In her interview with RFD News, Priest discussed the recent increase in solar development and the factors that determine where projects are sited, including broader land-use considerations.
Priest also highlighted findings from a report on how land-use tax policy can incentivize dual-use practices, outlining key themes to encourage continued agricultural production alongside solar installations. She noted that land-use policies can vary by state and discussed how those differences can influence agrivoltaic and dual-use approaches.
She also addressed the advantages for farmers who want to keep land in active agricultural production while participating in solar energy projects.
Finally, she outlined the broader takeaway for the agriculture sector regarding planning and considerations for agrivoltaic development and dual-use solar strategies.
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Solar farm or higher taxes? Lee County’s vote pits 600 acres of farmland against the budget Yahoo
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One of the largest solar and battery storage facilities in the United States is taking a massive step forward right in Northeast Arkansas.
Mississippi County, AR – JonesboroRightNow.com – One of the largest solar and battery storage facilities in the United States is taking a massive step forward right in Northeast Arkansas.
Cypress Creek Energy announced June 11 that it has secured $3.5 billion in financing to fund the first two phases of the Steel River Energy Center, a large utility-scale energy project located in Mississippi County, specifically in Wilson.
The company said that when fully completed in 2029, the three-phase project will generate 2.45 gigawatts (GW) of solar capacity and 2.9 gigawatt-hours (GWh) of battery storage, reinforcing regional grid reliability by deploying stored power during peak demand. The newly announced $3.5 billion financial package specifically funds Phases 1 and 2, which will deliver 1.63 GW of solar and 1.9 GWh of storage.
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Beyond its national energy map scale, the Steel River Energy Center’s logistical footprint promises a massive economic boost for the region.
Cypress Creek Energy projects the site will generate nearly $300 million in local tax revenue over its lifetime. It is also expected to create roughly 700 on-site construction jobs, providing a substantial secondary boost to local hospitality and retail sectors.
The company said the Steel River Energy Center is prioritizing American-made materials, utilizing 100% U.S.-made structural steel—nearly all of which is being sourced directly from within Mississippi County. Additionally, the project will rely on 100% domestically manufactured solar panels supplied by First Solar.
According to Kevin Smith, CEO of Cypress Creek Energy, the facility is being built to help meet Arkansas’s growing need for reliable, affordable generation amid rising electricity demands across the country.
“We look for locations with the right combination of transmission infrastructure, available land, and strong local partners, and Mississippi County checks all those boxes,” Smith said in a statement sent to Jonesboro Right Now. “It’s also home to the nation’s leading steel manufacturing hub, which means nearly all the structural steel supporting this project is being produced right here in the community. That’s something we’re incredibly proud of. Just as important, we’re committed to being a long-term partner in Mississippi County by listening to local needs and investing in community priorities.”
Cypress Creek Energy projects the project will be completed by 2029.
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Jody Barker is a contributing writer at JonesboroRightNow.com. Email him at jbarkernews@gmail.com.
A Jonesboro man is behind bars after police said he attempted to break into a home, damaging it in the process, during a domestic disturbance.
It was a luau Friday afternoon as Best Manufacturing Inc. granted a wish to a Jonesboro girl to take a family trip to Hawaii.
Jonesboro police said a man was arrested after he repeatedly punched, kicked, and threatened a former partner during a domestic assault earlier this month.
Roads in downtown Jonesboro will be closed Saturday for multiple events.
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ZEELAND TOWNSHIP, Mich. — Zeeland Township neighbors opposing a proposed $330 million solar farm are working to secure their place in the state regulatory process after the developer’s legal team moved to remove some of them from the case.
WATCH: Zeeland Township neighbors fight to stay in solar farm approval process as legal battle takes shape
Residents in Zeeland and Jamestown townships have spent several months pushing back on RWE Americas’ Silver Maple Solar Farm proposal — a 200-megawatt project that would be built on 1,900 acres of agriculturally zoned land and generate enough electricity to power more than 34,000 homes.
RWE Americas officially filed an application with the Michigan Public Service Commission in April. A pre-hearing was held June 4.
A number of Zeeland Township neighbors have filed petitions through the MPSC to become intervenors in the case.
PRIOR COVERAGE: Zeeland Township board passes resolution opposing RWE solar farm application at special meeting
“Our entire north boundary shares with a field that is part of the design plan,” Valerie Driesenga, a Zeeland Township neighbor, said. “We’re an adjacent landowner, so we immediately have the right to be part of the case.”
Driesenga and more than 20 others secured intervenor status at the pre-hearing, alongside both Zeeland and Jamestown Charter Townships. However, neighbors who do not share an adjacent border with the project — including Cadence DeVree and Christi Meppelink — face a higher hurdle.
The day before the pre-hearing, RWE’s legal team filed an “omnibus opposition to permissive petitions to intervene” motion.
PRIOR COVERAGE: Neighbors, Zeeland Township take steps to oppose proposed solar farm in Ottawa County
“The attorney for the solar farm had filed a petition to dismiss extra interveners, because they said … our case would be handled by other interveners, and it was duplicative,” Meppelink said.
Administrative Law Judge James Varchetti gave Meppelink, DeVree, and 14 others one week to file a response to RWE in order to remain in the process.
“Then the attorneys for RWE get a week to respond to our responses, and then a week later, the ALJ judge will make a decision on whether or not we can stay in or not,” Meppelink said.
PRIOR COVERAGE: Developer behind proposed solar farm in Zeeland, Jamestown Twps, moves forward with state application
Varchetti is expected to issue that ruling on June 25.
In the meantime, the neighbors have banded together to retain legal representation.
“It would allow the attorneys to talk back and forth, whereas the attorney can’t, from the townships, can’t talk to us as individuals, so it would create an extra layer of assistance,” Meppelink said.
PRIOR COVERAGE: Neighbors in Zeeland Township weigh in on solar farm proposal
To fund that effort, the group is planning a pancake breakfast fundraiser at the Ottawa Executive Airport on June 20, with a goal of raising $35,000.
“From 8am to 11am – the pancake breakfast itself is by donation. We’ll have a bounce house for the kids,” Meppelink said. “Then we have somebody who’s donating their time as a pilot and their aircraft to provide airplane rides, which will have a set price with it, which will be purely fundraising money.”
PRIOR COVERAGE: Zeeland Township approves data center moratorium as solar farm debate continues
Fox 17 reached out to RWE regarding the pre-hearing and overall process. The company said in part:
DeVree said she is not giving up.
“I just hope everybody would see and come together and see how amazing our community is, and how precious it is to save and keep what we have going here,” DeVree said. “I’m still fighting as hard as I can.”
PRIOR COVERAGE: Proposed solar farm in Zeeland Township draws neighbor feedback at planning commission meeting
Varchetti confirmed the following schedule for the MPSC review process. The commission is expected to issue a final decision on the Silver Maple Solar project by April 3, 2027 — one year from when RWE filed its application.
This story was reported on-air by a journalist and has been converted to this platform with the assistance of AI. Our editorial team verifies all reporting on all platforms for fairness and accuracy.
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Fraunhofer Institute for Solar Energy Systems ISE, a Germany-based PV research institute, has achieved 34.4 percent efficiency for a III-V germanium PV module. The result broke its own module record through optimized solar cell interconnection using shingled matrix technology. In early 2026, the Vorfahrt team has developed an 833 sq cms module with 34.2 percent efficiency, which set the earlier world record. The module uses triple III-V germanium cells, which AZUR SPACE adapted from space applications to the terrestrial solar spectrum. In this shingle-matrix design, solar cells are cut into narrow strips, overlapped, and connected with electrically conductive adhesives. The structure enables direct contact between cells and avoids solder-coated copper ribbons. By eliminating shaded active cell areas, the architecture supported high area utilization and helped raise the module efficiency. Anti-reflective front glass coatings were provided by temicon, while the record module is being shown at Fraunhofer ISE’s booth A1.440 during Intersolar / The Smarter E 2026.
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Pacific Gas & Electric said the milestone comes during an industry shift from “a one‑way grid to an interactive system where customer energy resources are increasingly part of the solution.”
PG&E has invested in “grid automation, advanced forecasting, streamlined interconnection, and the growing integration of solar paired with battery storage,” the company said in its release. “These efforts signal a broader industry shift: from a one‑way grid to an interactive system where customer energy resources are increasingly part of the solution,” it said.
PG&E customers have access to California rebates like the Self-Generation Incentive Program, which subsidizes the installation of residential storage or solar-plus-storage, as well as credit rates for solar generation under the state’s net metering program. California is the top state for residential solar in the U.S. and for overall solar capacity, according to the Solar Energy Industries Association.
Residential solar can also offer relief from the state’s high electric rates, and solar plus storage can provide backup power during public safety power shutoffs, which the state’s utilities deploy to mitigate the risk from wildfires and extreme weather events. PG&E has used PSPS 31 times in the past seven years, the utility said in an April report.
In a 2019 report, SEIA said that increasing consumer interest in residential solar, and solar-plus-storage, was particularly strong in California due in part to “dissatisfaction with California utilities.”
“This disaffection has a long history but most recently stems from Public Safety Power Shutoffs (PSPS) which have left hundreds of thousands of utility customers without electricity, often for days at a time,” SEIA said. “These developments have combined to renew a latent demand in solar and resiliency options in California.”
Surpassing 1 million solar interconnections “underscores a broader transformation underway in California’s energy system: The grid of the future will be more distributed, more digital, and more participatory,” PG&E said. “And customer‑owned solar, especially when paired with storage and connected through virtual power plants, will play a central role in delivering clean, reliable energy at scale.”
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The Southwest Power Pool service aims to help data centers and other large loads get online quickly, but they can have their service cut when grid conditions are tight.
The grid operator urged states to develop rules to shield other ratepayers from data center-driven costs, but analysts said it remains unclear how a reliability auction’s costs could be allocated only to hyperscalers.
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The Southwest Power Pool service aims to help data centers and other large loads get online quickly, but they can have their service cut when grid conditions are tight.
The grid operator urged states to develop rules to shield other ratepayers from data center-driven costs, but analysts said it remains unclear how a reliability auction’s costs could be allocated only to hyperscalers.
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Bruce Blakeman, the Nassau County executive who is running against Kathy Hochul for governor, launched a digital ad campaign. In his newest ad, he calls out Hochul’s attempt to seize prime farmland for what he calls her “solar scam.”
The ad features country music star John Rich and Alexandra Fasulo, an ecopreneur, financial advocate and freelance writer. Fasulo said that the state’s Office of Renewable Energy Siting ORES “allows things in our community that no other state agency would be allowed to get away with.”
Among the things that ORES does is “allow foreign corporations to abandon their equipment in the soil at the three-foot mark. They said, ‘Oh, you’ll farm [the land] again.’ No, we won’t.”
Blakeman blasted Hochul for allowing ORES to override local zoning laws, stripping local control in order to install solar farms in upstate towns. At the same time, she’s targeting highly fertile, prime topsoils for heavy construction, which he said will permanently ruin drainage and soil structure.
“Kathy Hochul’s solar sprawl is cannibalizing our state’s most precious resource: our farmland,” Blakeman said.
According to Blakeman, New York lost 500 farms and 100,000 acres of land in 2024 and 2025; of that amount, 80% belonged to small, family-owned operations. He also said that experts are predicting that Hochul’s policies will wipe out an additional 450,000 acres of farmland by 2040.
Blakeman’s digital campaign urges New Yorkers to fight back, protect their food security, and demand the preservation of the state’s rural economy. It also highlights his plan to cut utility bills in half by eliminating Hochul’s energy mandates that he says drive up costs and exploring other energy sources like nuclear.
“We cannot sit by while Hochul allows the best soil in our state to be bulldozed for corporate profit and solar panels that don’t solve the state’s energy crisis,” Blakeman said. “I am drawing a line in the sand with a common-sense blueprint to save our farms and restore local democracy.”
In response, Hochul campaign spokesperson Ryan Radulovacki said, “As usual, Bruce Blakeman’s record is clear: While Governor Kathy Hochul is putting money back in New Yorkers’ pockets with energy rebates and tariff relief for farmers, Bruce Blakeman is against both, selling out this state and screwing over farmers all for Donald Trump and his MAGA agenda.”
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RWE commissions Emily Solar, a 273.6 MW project in Clark and Cumberland Counties, reaching a 1 GW operating energy capacity milestone in Illinois.
RWE commissioned its Emily Solar project on May 12, 2026, in Clark and Cumberland Counties, Illinois, simultaneously crossing the 1 gigawatt (GW) threshold of operating energy capacity in the state. The installation has a capacity of 273.6 megawatts (MW) and was developed and owned by RWE, then built by Blattner. The commissioning reflects the pace of solar deployment in Illinois amid growing electricity demand. The US renewable energy market continues to evolve, as illustrated by Renewable America’s recent sale of 33 MWdc of solar capacity and 31 MWh of storage in California.
RWE’s Illinois portfolio now comprises two solar projects and three wind farms. Together, these facilities produce, according to the company, enough electricity to power hundreds of thousands of homes and businesses across the state. RWE says it has operated in Illinois for 15 years, steadily expanding its footprint to meet local energy needs. Large-scale photovoltaic projects require advanced technical solutions, as demonstrated by the SCADA deployment on Scatec’s 225 MWac Grootfontein project in South Africa.
Emily Solar generated approximately 400 jobs at peak construction, according to RWE. The ribbon-cutting ceremony brought together local officials, union representatives, landowners and teams from RWE and Blattner. Hanson Wood, Chief Development Officer of RWE Americas, described reaching the 1 GW threshold as “an important milestone for RWE and for the communities we serve.”
Over the project’s lifetime, Emily Solar is expected to generate approximately $30 million in property tax revenues for Clark and Cumberland Counties, according to RWE’s projections. These funds are intended to support local public services, including schools, roads and emergency services. On the sidelines of the inauguration, RWE announced a $15,000 donation to Casey Youth Soccer, a local sports association active in the Casey area.
Tony Burchill, Director of Solar Construction at Blattner, highlighted the community dimension of the project and respect for ongoing land use. Joe Riley, President of LiUNA Local 159 — a local chapter of the Laborers’ International Union of North America (LiUNA) — and Downstate Illinois LECET Director, said the project had opened pathways to union trades for local workers not yet part of a union. The company did not disclose the total investment cost of Emily Solar.
RWE has operated in the state for 15 years and says it has steadily expanded its presence to meet growing electricity demand. The group did not specify the capacity of its second solar project, nor the respective capacities of its three wind farms in Illinois. Emily Solar remains the only installation whose installed capacity has been officially disclosed in RWE’s Illinois portfolio to date.
Eurowind Energy Romania has received construction permits for the Siminoc project, the company's first hybrid wind-solar park in Constanța County, with an estimated investment of €
Gamuda's Weasel Solar Farm (200 MW) and Cellars Hill Wind Farm (341 MW) in Tasmania have been selected under Australia's Capacity Investment Scheme, providing a 15-year government-
Cambodia's installed solar capacity reached almost 1.5 GW in 2025, surpassing national targets for 2030 and 2035 nearly a decade early, driven by utility-scale projects, according
Powering an Energy Revolution in Puerto Rico Earthjustice
source
Home – Tech – Solar energy is arriving faster than Ireland’s grid can handle, and the bottleneck is no longer the panels
Ireland just got a bright glimpse of its clean-energy future, and it came from the sky. During a sunny spell, utility-scale solar supplied a record 37.06% of national fuel demand at 2:30 p.m. on May 24, according to Green Collective data cited by Solar Ireland.
That should be great news for households, businesses, and anyone worried about imported fossil fuels. But Solar Ireland is warning that the country may be letting too much clean electricity slip away because grid upgrades, storage, flexibility, and day-to-day system operations are not moving as fast as solar generation itself.
For a country better known for wind than blazing sunshine, the new solar record is striking. Solar Ireland said utility-scale solar came within about 2% of imported gas use during the 24-hour period leading into that Sunday afternoon, while total wind and solar generation exceeded 46%, according to EirGrid figures cited by the group.
Ronan Power, CEO of Solar Ireland, called it “a fantastic week for solar in Ireland.” That line matters because it captures the speed of the shift, not just the number itself.
What does that mean in everyday terms? On a bright afternoon, solar farms are no longer a small extra on the edge of the system. They are becoming one of the main engines keeping lights, laptops, refrigerators, and factories running.
Herein lies the problem. Clean electricity is only useful if the system can take it, move it, store it, and deliver it when people need it.
Solar Ireland says rising renewable output is leading to more “dispatch down” and curtailment. In plain English, that means solar or wind generators can be told to reduce output even when the weather is perfect, because parts of the system cannot absorb everything being produced.
That can feel almost absurd. Imagine filling a rain barrel during a storm, then watching water overflow because the barrel was too small. Ireland’s solar moment is similar, except the overflow is clean electricity that could have helped cut fuel imports and energy costs.
Solar is grabbing headlines, but wind remains the heavy lifter in Ireland’s electricity mix. EirGrid said wind generated 38% of all electricity in April, making it the biggest contributor to the fuel mix that month.
Overall, renewables supplied 48% of Ireland’s electricity in April, including 6% from large grid-scale solar farms. That made April the third consecutive month in which renewable generation met roughly half of electricity demand, a sign that Ireland’s power system is changing quickly.
The solar numbers are moving especially fast. EirGrid reported that grid-scale solar passed 1 gigawatt for the first time in April, then set a new record of 1,133 megawatts on April 25, or 1.13 billion watts of output at that moment.
The pace of growth is hard to ignore. Ireland’s first large-scale ground-mounted solar farm, Millvale Solar in County Wicklow, supplied the national grid in 2022.
By November 2025, Solar Ireland said national solar capacity had passed 2 gigawatts, supported by more than 155,000 rooftop installations across homes, farms, and businesses. That is a dramatic jump in only a few years, and it helps explain why sunny afternoons are now showing up so clearly in national electricity data.
There is a business story here, too. More solar means more construction, more grid connection work, more maintenance jobs, and more demand for software, batteries, forecasting tools, and smart controls. In practical terms, the energy transition is becoming an infrastructure race.
Wasted renewable electricity is not just a technical headache. It has an environmental cost, because every clean unit that cannot be used may leave more room for imported gas or other backup sources.
It also has a consumer angle. Solar Ireland argues that every usable unit of renewable power can reduce reliance on imported fuels, improve energy security, and support consumers at a time when energy costs remain a concern.
That does not mean solar alone can solve Ireland’s energy challenge. The country still needs a balanced system, with wind, solar, storage, interconnectors, flexible demand, and backup capacity. But the cleaner the daytime supply becomes, the more pressure there is to modernize the machinery behind it.
Ireland has set a target of reaching 80% renewable electricity by 2030, according to the Sustainable Energy Authority of Ireland. That target is now close enough to make grid delays feel less like paperwork and more like a real climate risk.
EirGrid says current developments already allow up to 75% of electricity to come from variable renewable sources such as wind and solar at any one time. It also has a major work program underway to raise that level to 95%.
That jump is not small. It requires more than building panels and turbines. It means faster grid delivery, better forecasting, batteries that can respond quickly, and operating rules that help the system stay stable when fossil fuels are playing a smaller role.
Solar Ireland says the investment commitment is in place, but the next step is delivery. That means speeding up grid infrastructure, adding flexibility, improving storage, and making sure operational measures keep up with renewable deployment.
At the end of the day, Ireland’s solar record is both a success story and a warning light. The country has proved that solar can scale quickly, even in a climate where sunshine is not always guaranteed.
Now comes the harder part: Ireland has to make sure that when the sun does show up, the grid is ready to use every possible watt.
The press release was published on Solar Ireland.
Techy44 by okdiario is the space dedicated to technology within okdiario, where we analyze, explain, and anticipate the trends that are transforming the digital world.
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Almost 12 years ago, I wrote about the fast adoption of small-scale solar power in Bangladesh. A couple years later, I got to talk with an expert and leader in the industry. It was an interesting and inspiring story, but apparently it didn’t continue and grow as fast as desired. The country is now implementing some strong solar policies to try to see much faster growth.
Bangladesh has just introduced a 0% tax rate for the solar power sector that runs until 2035. Various duties and taxes are being removed on solar power components. To be specific, the import duty, regulatory duty, supplementary duty, and advance tax are all being cut to 0% on critical solar power components. Additionally, “Businesses that consume electricity generated from solar plants are also expected to receive incentives. Under the proposal, companies could claim a tax rebate equivalent to 5% of their solar electricity bill against their total payable income tax.”
Bangladesh aims to have renewable energy sources provide 20% of total electricity demand by 2030. The new policies are aimed at helping the country get to that target, as well as the next one — clean renewable energy providing 30% to 50% of electricity demand by 2050. Admittedly, that latter one is a pretty lame target. One would hope — and expect — the country would be at 100% renewable energy by then.
At the moment, Bangladesh has 1,797 MW of installed renewable energy capacity, most of which — 1,504 MW — comes from solar power. Overall, the country has about 29,000 MW of power capacity. That means renewables account for only about 6% of total power capacity. Unfortunately, renewables account for an even much less of the country’s actual electricity generation. “A recent report by IEEFA found that renewable energy contributes only 2.3% of Bangladesh’s grid-based electricity generation, significantly below the global average of 33.8%.”
Hopefully the big incentives for solar power lead to a surge in the sector and Bangladesh can get back to sharing inspiring solar stories.
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Note
Chinese overseas clean tech investment is growing but not as large as some headlines suggest, according to new data on FDI by Chinese companies abroad across the EV, solar PV, and wind turbine value chains.
China has emerged as a major global clean tech investor, but the lack of robust data complicates analysis. Rhodium Group’s new China Global Clean Tech Investment Dashboard provides detailed data on both announced and completed foreign direct investment (FDI) by Chinese companies abroad across the electric vehicle (EV), solar photovoltaic (PV), and wind turbine value chains. The highlights are:
China has emerged as a major global investor in clean energy technologies.1 Over the past decade, Chinese companies have aggressively invested in the production of clean energy technologies, bringing down prices and accelerating the global deployment of related goods.
China has also ramped up overseas investment in clean tech infrastructure and manufacturing, creating hopes that Chinese capital could rapidly accelerate the buildout of low-carbon infrastructure and clean tech manufacturing clusters. The initial wave of China’s low-carbon overseas capital mostly consisted of government finance and policy bank lending to relevant infrastructure and upstream assets, especially in middle income economies and countries associated with Beijing’s Belt and Road initiative. Since 2021, Chinese clean tech firms have stepped up outbound FDI, eliciting enthusiasm about the prospects of a “green energy tsunami” of private capital from China.
While decarbonization enthusiasm is real, there are concerns about Chinese capital that are rooted in the unique configuration of China’s economic and political system. China’s economic system has produced rampant overcapacity across clean tech sectors, and FDI could accelerate the spillover of subsidies and other market distortions. Moreover, Beijing continues to control capital outflows by private firms, which creates unique concerns about economic statecraft and national security implications. Changing domestic politics have also contributed to boom and bust cycles of Chinese FDI in the past two decades. These factors have led many governments to strengthen foreign investment reviews and design new regimes to reduce negative spillovers and ensure local benefits from Chinese FDI.
In the absence of robust government statistics, policymakers around the world must make important decisions about the right balance between maximizing opportunities and minimizing risk associated with Chinese investment without adequate data. Alternative datasets have become crucial to create transparency on the size, patterns, and impacts of Chinese outbound investment globally. Rhodium Group’s China Cross-Border Monitor (CBM) tracks China’s global outbound FDI across industries using rigorous and consistent methodologies developed over more than two decades. Our new China Global Clean Tech Investment Dashboard provides structured data on both announced and completed outbound FDI by Chinese companies across the three most important clean tech value chains: EVs, wind turbines, and solar PV.
This report presents data for 2014 through the end of 2025, with a focus on trends since 2021. Future updates will be available on the China Global Clean Tech Investment Dashboard. Complementary data on China’s global outbound FDI in other sectors is available on the China Cross-Border Monitor. Rhodium Group’s Clean Investment Monitor (CIM) provides complementary, quarterly-updated data on global clean tech investment by country, with future updates including detailed information on cross-border investment flows, the relative importance of foreign investors across economies, and project-level investment status and trends.
Government statistics on China’s global outbound FDI in clean tech sectors are not available. China’s official figures on overseas FDI by Chinese companies are heavily distorted by the country’s unique external economic configuration and shortcomings in its statistical data collection and provide no sectoral breakdown that would allow for isolating investment in clean technology value chains. Statistics by host economy governments are similarly distorted since they collect data on balance of payments principles, suffer from major time lags, and do not offer breakdowns by sector.2
Alternative trackers produced by think tanks and open-source researchers have been an indispensable resource to better understand Chinese outbound FDI. However, these efforts differ greatly in scope and methodology, resulting in very different estimates. First, alternative datasets often do not separate FDI from other types of investments (lending or portfolio flows) and sometimes mix it with construction contracts or even more abstract measures of “engagement.” Second, data collection efforts use different definitions for sectors or value chains. Some datasets include the full values of upstream investments, even though those minerals are used for a range of products rather than exclusively clean tech. Third and most importantly, most datasets apply different definitions and thresholds for counting announced investment and do not track transactions through the entire lifecycle to capture their progress and eventual completion.
Rhodium Group’s China Clean Tech Investment Dashboard was designed to provide high quality data on both announced and completed Chinese outbound FDI in clean technology value chains. Figure 1 shows three different measures of Chinese outbound FDI in three clean tech sectors in the past decade: An expansive approach that counts announced investments along the entire value chain at full value gets us to almost $400 billion of Chinese clean tech FDI. Applying a more conservative estimation method that only includes announced transactions that meet certain thresholds (like signed memoranda of understanding or legally binding documents) and pro-rates upstream investments to clean tech end-use results in less than half that amount ($173 billion). Most importantly, counting only realized or completed investment cuts that number in half again: Only $85 billion of Chinese clean tech FDI projects have been realized in the past decade.
Plotting both announced and completed FDI along a timeline illustrates that the gap is attributable to the long investment timelines typical of these technologies, as well as the cancelation, suspension, or scaling back of projects as Chinese investors adjust their capital expenditures to market and political realities. Chinese firms announced a raft of capital-intensive greenfield projects around the globe in 2022 and 2023, but actual investment expenditures have been lower and steadier (Figure 2).
Much of the growth in China’s announced overseas clean tech investment has been driven by investment in EV value chains, which has reached $108 billion since 2021, or 79% of the total (Figure 3). Solar PV followed at $25 billion (18% of total) as Chinese firms responded to higher tariffs and increased overseas demand. Announced FDI in both the EV and solar sectors has declined since 2023. Wind turbine manufacturing has only recently entered the picture, with announced investment surpassing $1 billion for the first time in 2024.
On a completed basis, the distribution over the past five years is similar: 74% EV, 25% solar, and 2% wind. Completed EV investment has risen steadily since 2022, while solar has stagnated at around $4 billion annually. This dominance is set to deepen: Only 45% of announced EV investment has been completed so far, compared to 61% for solar PV.
Chinese FDI in the EV value chain historically focused on acquiring raw material assets in resource-rich emerging economies (Argentina, the Democratic Republic of the Congo, or Indonesia), alongside strategic acquisitions of EV and battery manufacturers in high-income economies. Investment began expanding midstream toward the end of the 2010s, led by CATL’s plant in Germany in 2018, the first large-scale Chinese battery facility built overseas. Upstream investment remained modest despite a gradual transition toward mineral refining, driven by surging mineral demand and tightening local refining requirements (Figures 4 and 5).
The 2022–2023 period marked an inflection point. Announced investment surged to an average of $33 billion annually, driven by battery manufacturing. Europe captured more than half of the total, followed by Asia (19%) and North America (16%). This investment boom coincided with a surge in Chinese EV and battery exports as well as growing trade frictions and localization requirements (Figure 7 and 8). Yet FDI remained a secondary mode of expansion: More than 90% of EV investment by Chinese companies over the past decade has stayed in China, meaning foreign markets have been served overwhelmingly through exports rather than local production (Figure 6).
Since then, announced investment levels have moderated to around $15 billion per year, and completed investment has finally caught up, reaching a record $17 billion in 2025. The composition has shifted however: EV manufacturing and battery materials have overtaken battery cells as the leading investment categories, and the Middle East and North Africa (MENA) and Asia are displacing Europe as primary destinations. Chinese companies are also losing interest in the US market, with 60% of announced investment now canceled. But while new announcements have waned, completed investment has continued apace as local manufacturing becomes an increasingly important market entry requirement. By 2025, about 45% of total announced outbound investment had been completed.
Chinese FDI in solar PV manufacturing grew modestly through the mid-2010s. US and EU trade defense measures pushed Chinese firms to relocate module assembly to Southeast Asia, particularly in Vietnam, Malaysia, Cambodia, and Thailand. Overseas investment remained relatively low through 2018 before surging in 2019 and again from 2022 onward. Each wave closely followed new rounds of foreign trade barriers, and investment shifted toward Indonesia and Laos as earlier Southeast Asian hubs came under greater scrutiny (Figure 10).
From 2023, announced investment levels nearly tripled compared to the previous five years, rising from an average of $2 billion to $7 billion annually, driven by new rounds of tariffs and increased overseas demand. This surge coincided with a boom in Chinese domestic solar investment, peaking at around $120 billion, and a near-doubling of exports. As domestic margins compressed and trade barriers increased, manufacturers pushed for overseas investment: The share of overseas investment in total announced Chinese solar investment rose from just 4% in 2020–2023 to nearly 20% in the past two years (Figure 11). Chinese exports of solar PV equipment continued to grow but the collapse in prices led to declining exports in value terms (Figure 12).
Chinese overseas investment also shifted in composition. New local content requirements drove up investment in midstream segments—for example, silicon wafers—to around 60% of announced investment. Upstream FDI edged up to nearly 10% as rising polysilicon prices incentivized overseas production (Figure 9). Geographically, investment diversified as manufacturers sought to reduce tariff exposure and secure market access. MENA emerged as the largest destination from 2023 onward, accounting for 37% of total announced investment. Only around a quarter has been completed to date though, reflecting the high degree of uncertainty around announcements in the region, particularly in Gulf economies. The US captured close to a quarter of announced investment over the past three years, partly driven by access to local subsidies, but most large announced investments have since been delayed or canceled amid growing market access headwinds.
More recently, Ethiopia and the Philippines have emerged as new manufacturing hubs for tariff circumvention, attracting nearly a quarter of all new investment. This shift has recently prompted US solar manufacturers to file a complaint with the Department of Commerce alleging Ethiopia is being used to circumvent import duties on Chinese-made goods (Figure 10).
Chinese outbound FDI in wind turbine manufacturing has boomed over the past two years, with 80% of investment announced since 2023 (Figure 13). This marks a sharp departure from historical patterns, where nearly all investment was domestic (98%), and overseas activity was limited to a few acquisitions of European turbine makers. Unlike in solar PV or batteries, where Chinese companies dominate global supply, Chinese wind turbines have remained a small fraction of international installations. Bulky components make shipping less economical and western incumbents dominate market shares. China consumes most of its own production domestically, with exports averaging only $4 billion annually over the past five years (Figure 16).
Rapid growth in outbound FDI since 2023 has been driven by a combination of push and pull factors. Chinese manufacturers are squeezed by overcapacity and intensifying price competition at home, making overseas expansion more urgent. Local content requirements and other trade barriers in target markets further deteriorate the economics for exports. Together, these pressures have pushed annual announced outbound FDI from an average of $200 million before 2023 to nearly $3 billion in 2025.
That said, wind investment remains modest compared to FDI in other clean tech sectors and highly geographically concentrated. Most new projects involve manufacturing plants tied to specific wind farms, where local production of turbine components or fully integrated turbine systems is a contractual requirement. Alongside these project-linked investments, new manufacturing capacity is also emerging in third markets such as Morocco, targeting European export markets rather than local consumption (Figure 14).
This rapid expansion has drawn equally rapid regulatory scrutiny. In 2025, a turbine supplier for a German wind farm canceled its contract with Ming Yang amid mounting political scrutiny over security concerns related to Chinese participation in power grid infrastructure. In early 2026, the British government blocked the company’s $2 billion Chinese wind turbine manufacturing project in Scotland over national security concerns. The blocked project alone accounted for roughly 40% of all announced investment to date, underlining the fragility of this nascent boom. All in, only 25% of announced wind turbine investment has been completed so far.
The data presented in this report yields the following implications for policymakers:
First, China’s role as a global source of clean tech FDI is not as large as existing tallies of investment pledges suggest. Our data shows that Chinese companies are becoming more important investors in global clean tech manufacturing, but simply counting FDI pledges overstates the true extent of Chinese FDI. It is important that policymakers who are making bets on attracting Chinese investment understand the complex political economy that is shaping Chinese outbound investment and the boom-and-bust cycles it has produced throughout the past three decades.
Second, exports remain the dominant mode for Chinese clean tech producers to serve overseas markets. While overseas investment is growing, our data shows that Chinese producers remain primarily focused on domestic investment. Since the pandemic, domestic capital expenditure has exceeded overseas investment by a ratio of 10:1 and exports have jumped 432% (and even more if one were to account for price effects). Realized outbound FDI has accelerated but from such a low base that overseas manufacturing capacity remains marginal compared to domestic capacity and exports. The scale of China’s domestic market, its highly competitive manufacturing ecosystem, and access to industrial policy support are strong incentives for Chinese firms to keep and expand their manufacturing base within China’s borders.
Third, Chinese outbound FDI in clean tech manufacturing is correlated with the rise of trade barriers. Our data shows that the rise of tariffs and other trade barriers is an important factor catalyzing investment in offshore manufacturing capacity. However, this “tariff-jumping” FDI often leads to a buildout of capacity in third countries with advantageous external trade configurations and not necessarily local plants close to final demand. Policymakers that are eager to attract Chinese clean tech factories must take a holistic approach to trade defense (including safeguards against transshipping), possibly in combination with local content instruments. At the same time, expansive market access barriers—such as supply chain security measures in the United States—result in the withholding, cancelation, and withdrawal of Chinese investment.
Fourth, the evidence for the local impact of Chinese clean tech investments remains mixed. Our data on realized investments show that the track record of Chinese clean tech investments abroad is very short. Most large greenfield facilities are either still under construction or in early stages of production. Within that small sample, there are examples of successful investments that have created entirely new industry clusters (EV investments in Hungary). At the same time, there are data points that reinforce existing concerns about Chinese producers exporting weak labor standards (BYD in Brazil), crowding out other firms (nickel processing in Indonesia), and increasing supply chain dependence on inputs from China (solar PV manufacturing in Southeast Asia). More research is needed to clarify these patterns, but these initial data points confirm the importance of strong domestic institutions and civil society efforts to monitor Chinese FDI and mitigate negative local spillovers.
Looking forward, tough market conditions at home and growing barriers to accessing overseas markets through exports will create strong incentives for Chinese firms to continue expanding their overseas presence through outbound FDI. However, political headwinds will remain elevated. Beijing maintains administrative controls over outbound capital flows and is moving to further tighten its grip and explicitly align its outbound investment controls with geopolitical and economic statecraft goals. Meanwhile, host countries will continue to strengthen their defensive toolkits through investment reviews and next-generation FDI conditionality rules, in part in response to Beijing more actively weaponizing FDI for economic statecraft. Taken together, these factors make it likely that China’s global clean tech FDI will unfold in a more gradual and piecemeal fashion rather than a green energy tsunami.
The CBM China Global Clean Tech Investment Dashboard tracks Chinese outbound foreign direct investment since 2014 globally. It covers the three most important clean tech sectors: EVs, wind turbines, and solar PV. Investments across the entire clean technology value chain are categorized into three segments:
Investments in power generation assets, such as the acquisition of wind farms or the development of solar power plants, are not included.
The CBM China Global Clean Tech Investment Dashboard uses the proprietary transaction-based methodology of Rhodium Group’s China Cross-Border Monitor. It applies strict sourcing standards that only include robust FDI transactions that are verifiable while excluding rumors, unverifiable transactions, and investment projects without legally binding documentation. It also splits large multi-year transactions into quarterly increments and consistently monitors and updates transaction statuses, to ensure a robust measure of completed, delayed, dormant, or canceled investments.
Midstream and downstream investments are included only when the target asset can be clearly identified as primarily engaged in the development of covered clean technologies, or when the investor explicitly indicated at the time of the transaction that the asset would be integrated into a clean technology value chain. For example, Envision’s acquisition of AESC in Japan in 2018 is included under the EV value chain. On the other hand, Geely’s acquisition of Volvo in Sweden in 2010 and SAIC’s acquisition of MG in the UK in 2005 are excluded, because the target firms were not primarily focused on EVs at the time of the transaction. However, subsequent investments by Volvo and MG are tracked once their business strategy explicitly shifted toward primarily producing EVs. If target companies meet the criteria, investment values are recorded at full transaction value.
Upstream investments include transactions in the extraction or refining of minerals critical to clean technology manufacturing, such as copper, lithium, cobalt, silicon, nickel, manganese, graphite, and rare earths. These investments are attributed to specific technologies using the International Energy Agency’s (IEA) Stated Policies Scenario projections for end-use demand by mineral. Projections are applied with reference to the announcement year of each investment. Projected demand shares are then used to allocate investment figures by mineral proportionally across technologies. For example, if the IEA estimates that 67% of lithium demand in a given period came from electric vehicles, then 67% of each Chinese outbound investment in lithium is attributed to the EV value chain.
For the purposes of this report, “clean technologies” refers exclusively to electric vehicles, wind turbines, and solar PV. It does not include energy infrastructure deployment, such as wind farms or solar farms.
For more details on official government statistics, see Rhodium Group’s The Next Generation of China’s Outbound Investment: https://cbm.rhg.com/research-note/next-generation-chinas-outbound-investment
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Applause echoed through the halls of the Gage County courthouse. The county board had just approved new, more stringent wind energy regulations, and the overflow crowd of residents couldn’t contain themselves.
Few in the crowded courthouse that day in September 2020 beamed brighter than Larry Allder. The Cortland-area resident helped lead the yearslong charge against wind energy’s looming expansion into the county.
“It’s been a long road,” he told The Voice News after the vote.
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Now six years later, another historically controversial energy source — nuclear power — could be coming. Last month, the Nebraska Public Power District, or NPPD, announced a list of four potential sites for a new nuclear power plant. Gage County, south of Lincoln on the border with Kansas, is on it. This time, though, Allder has no plans to mount an opposition.
“I think that’s a great idea. I like nuclear energy,” Allder said. “I think it’s the way of the future.”
Despite a legacy that often invokes fear, there are signs nuclear development won’t face the backlash that other energy sources, especially renewables, have generated for Nebraskans in recent years. “They were just trying to stick the wind turbines really close to my property, and I do not like wind energy,” Allder said. He considers the turbines to be “ugly.” More substantively, Allder thinks that wind and solar projects produce “very inefficient and very costly and very intermittent power.” Nuclear, however, he said, is “clean and it doesn’t take up much land space.”
Grist spoke with leaders in the four communities identified by NPPD — Beatrice, Sutherland, Norfolk, and Brownville— and most said their communities are open to a new nuclear project.
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“I think the general consensus is still that we’re supportive of nuclear energy,” Madison County Commissioner Troy Uhlir said. “There’s definitely more people speaking up and saying, ‘No, not here,’ (but) it’s not overwhelming.”
Beatrice Mayor Bob Morgan said his community is excited to be in the top four site options.
In Sutherland, a few residents have voiced questions on safety, said Scott Meyer, chairman of the village board. Both Uhlir and Meyer believe those concerns can be calmed by education.
“What I find pleasing and reinforcing is that there is a lot of support out there,” NPPD CEO Tom Kent told Grist. “Those communities are really interested in hosting and being a location for this kind of development, and Nebraska has always been a state that’s been very supportive of nuclear power.”
Nationally, lawmakers in both parties have begun embracing nuclear power, as have everyday people like Allder. It also is being eyed by utilities, lured — amid growing demand for electricity — by its ability to generate large amounts of power without spewing climate-warming greenhouse gases.
Technological advancements offer another selling point. The next generation of nuclear power plants aims to solve problems the industry has historically grappled with, including their high costs, lengthy constructions, and safety concerns.
Proponents of nuclear say that advanced reactor plants like small modular reactors, or SMRs, could solve those problems that have long beset the industry. These reactors are also expected to be flexible, generating more or less power as needed, which can work well with renewables, said Joseph Giitter, a former senior executive at the Nuclear Regulatory Commission. And the latest innovation wave has generated a massive amount of support from private tech companies and investors who are betting on nuclear as a solution for the spike in electricity demand from data centers.
While projects involving new nuclear designs have started in Tennessee, Wyoming, and Washington, Nebraska is probably a decade away from seeing a new nuclear plant, which is why it’s important to start research now, Kent said.
“When nuclear takes off, it’s going to take off quick. So we want to be ready to be in that first set of fast follower orders, right? Or we’ll miss the middle of the next decade,” he said.
NPPD was recently awarded over $27 million in cost-shared funding by the Department of Energy to apply for a federal permit needed to site a new nuclear plant. According to Kent, the funding will cover less than half of the application costs. In terms of designs, Kent says NPPD is considering designs similar to the small reactors being tested in Wyoming and Tennessee. But it remains to be seen whether this next generation of nuclear reactors can deliver what its proponents promise.
The utility is also open to large-scale reactors, like the ones installed at Plant Vogtle in Georgia — a cautionary tale for Nebraska.
Georgia’s two new nuclear reactors started producing power in 2023 and 2024, 15 years after the utility applied for a license, according to the Associated Press. These reactors are more advanced than most operating in the U.S.. The project wrapped up years behind schedule and, at more than $30 billion, was over budget. In the end, the new reactors led to rate hikes for power customers, which fueled public backlash.
Southern Company’s CEO, Chris Womack noted its subsidiary Georgia Power faced unique obstacles, including a nearly nonexistent workforce and supply chain, complications posed by the Fukushima nuclear accident in Japan in 2011 and the COVID-19 pandemic in 2020, and the bankruptcy of the design contractor.
But nuclear projects have historically run into significant delays and gone way over budget, said Edward Kee, CEO of Nuclear Economics Consulting Group. Large or small, these projects in the U.S. can be a gamble for utilities and their rate payers.
For context, NPPD’s Cooper Nuclear Station, which opened in 1974 and is the state’s only commercial nuclear plant in operation, cost about $313 million to build. Adjusted for inflation, that price tag translates to roughly $2.1 billion in today’s dollars. Omaha Public Power District’s now-retired Fort Calhoun Nuclear Station, which started operating in 1973, cost about $165 million to build. That would be roughly $1.2 billion today.
Sometimes, that gamble pays off, as happened in south Texas where, 20 years later, customers are experiencing lower power rates, Kee said. But in other cases, the projects never made it to completion. Since 2010, there have been at least 11 canceled commercial nuclear power reactor plans, according to the NRC.
While new advanced reactors may minimize issues seen in Georgia, they too carry financial risks because they haven’t been tested, Giitter said.
“The promise of the technology is there, but it hasn’t been proven yet,” Giitter said.
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As trade barriers mount and FEOC provisions bite, only the big may survive.
In the wake of the GOP’s One Big Beautiful Bill’s passing, U.S. crystalline-silicon manufacturers face a new challenge.
Sourcing solar cells that will comply with new “foreign entity of concern” (or FEOC) provisions is likely to become both more difficult and more expensive. Meanwhile, a new antidumping and countervailing duty (AD/CVD) investigation launched on July 17 could choke the already narrow pipeline of compliant solar cells. Together, these new restrictions threaten the viability of all but the most well-capitalized players in the industry.
After weeks of debate on Capitol Hill about the budget bill, the 45X manufacturing tax credit was maintained, offering a fleeting glimmer of optimism amid dramatic cuts to other Inflation Reduction Act provisions. But that hope is dimmed by new uncertainties.
The FEOC provisions embedded in the OBBB require solar module manufacturers to source an increasing share of components from non-FEOC countries in order to qualify for federal tax incentives. (In solar, “non-FEOC” essentially means non-Chinese and non-Chinese-owned, though there is already confusion about the specifics of the definitions)
To claim benefits under sections 45Y and 48E — and the 10% “adder” under 45X — manufacturers must begin sourcing at least 50% of their module content (by cost) from non-FEOC countries by 2026. That threshold climbs steadily to 85% by 2029. The rules also prohibit extensive licensing arrangements with Chinese entities. For crystalline-silicon manufacturers, this means sourcing non-FEOC cells starting next year, and possibly wafers by 2027.
But the U.S. has just 4 to 5 gigawatts of domestic cell production — and 50 GW of module manufacturing. So even before OBBB, sourcing cells was already a hurdle. Now, the new AD/CVD investigation threatens to push compliant cell prices even higher, perhaps beyond reach.
Last Friday, a group supported by major U.S.-based manufacturers including First Solar and Qcells filed a new AD/CVD petition. If successful, it would impose duties on cell and module imports from Indonesia, India, and Laos — countries that were previously alternatives to China. This adds to an already lengthy list of affected nations, including China, Cambodia, Malaysia, Thailand, and Vietnam.
The timing is critical. A decision is expected by mid-2026, just as the FEOC sourcing mandates take hold. Alex Barrows, head of solar at commodity intelligence firm CRU, warns that compliant supply could become severely constrained. India, once seen as a likely beneficiary of the FEOC requirement, may now be cut off just as demand peaks.
“If you’re a U.S. module maker and you need to buy cells, there’s really not that much out there,” he told Latitude Media. “I mean, maybe you can buy some from one of the integrated players — maybe VSun or Qcells will sell you some of theirs.” (VSun is a Japanese-owned module maker, which is executing plans for 4 GW of wafer-to-module production in Vietnam.)
Barrows said that while CRU tallies approximately 30 GW of non-FEOC PV cell capacity also not impacted by AD/CVD provisions currently available, the vast majority of that is from integrated manufacturers, which use the cells in their own module production. Excluding those integrated sources, as well as Indonesia, Laos, and India, Barrows said, leaves the market with “only about 5 GW” at the end of this year.
These emerging constraints give a significant advantage to large, integrated manufacturers with upstream capabilities and deeper financial resources. These companies are better positioned to weather a downturn in demand, particularly as solar tax credits begin to expire earlier than originally expected. The result may be a wave of consolidation across the U.S. solar manufacturing base.
That shift is already underway. Chinese companies are exiting the U.S. market, opening the door for domestic players to seize more demand. On Monday, materials science company Corning announced that it has acquired JA Solar’s 2 GW module factory in Arizona. Corning also owns Hemlock Semiconductor and is planning to establish a $1.5 billion crystalline-silicon wafer plant in Michigan, which Barrows reports could add 6 GW of capacity (and more than double domestic capacity).
Meanwhile, Qcells is investing in 3.3 GW of ingot and wafer capacity in Georgia, although industry insiders caution that the company has previously faced delays in ramping its upstream U.S. manufacturing on schedule.
Michael Parr, executive director of the Ultra Low-Carbon Solar Alliance, is cautiously optimistic about the potential of domestic crystalline-silicon production. He believes that a compliant, non-FEOC supply chain is “reasonably manageable” — but also warns that the rushed nature of current policy paired with rising costs could chill demand for U.S.-made modules.
“If you have U.S. wafer, U.S. poly, and U.S. cell [production] you’re in pretty good shape. But with very limited wafer and cell capacity at the moment and little incentive for any further investment, things have pretty well been undermined,” said Parr, adding that other materials like glass, frames, junction boxes, and encapsulants will pose a further challenge. “It feels like the FEOC rules were intentionally written to make them difficult to comply with.”
Parr is skeptical that new U.S. cell factories will be built in the near term, given the cost, complexity, and slow ramp-up time involved.
“The big driver for the cell guys was the domestic content bonus, 45X, and with tax incentives going away [earlier], that domestic content goes away as well,” he said. “If I was financing a new cell plant, I’d be putting that back in my pocket and seeing how things play out.”
Jonathan is a journalist specialized in solar PV and energy storage. He has worked in broadcast and print media since 2006 and previously held the position of pv magazine’s global Editor in Chief for close to a decade.
*Subscribe to "Kim Ki-hyuk's Tesla World" to easily grasp the future of the electric vehicle, robotics, AI, autonomous driving, and energy industries being pursued by Tesla and Elon Musk. We also directly explain fresh news that is hard to find in foreign and domestic media. We look forward to your subscription.
SpaceX, which listed on the U.S. Nasdaq on Wednesday, has identified space artificial intelligence data centers (AIDC) as the technology to maximize its corporate value. Based on the massive funds raised through this initial public offering (IPO), the company is expected to accelerate building space infrastructure while also speeding up its own procurement of solar cells to supply unlimited energy to these data centers.
According to Park Jun-kyu, a researcher at Samsung Securities, SpaceX executives including the chief financial officer (CFO) and chief operating officer (COO) held a public webinar last Monday. The event featured a variety of questions about future business plans. In the course of answering, securing solar cells was presented as a major challenge for commercializing space data centers.
The specific answer was as follows: "In terms of securing components related to orbital computing satellites, more focus is needed on solar cell procurement capabilities. We are currently building a 10 million-square-foot solar cell production facility on the outskirts of Austin, Texas."
Prior to this, SpaceX CEO Elon Musk posted a video explaining future plans on his X account on Saturday. Musk referred to the experience SpaceX gained in building the Starlink satellite internet system, saying, "Making data center satellites will be simpler than making Starlink satellites for internet service." He added, "AI satellites essentially only need many solar cells, heat sinks, and some laser links," noting that "the very complex antennas that go into Starlink satellites are not needed. The AI satellite is the one that is easier to design."
Combining the webinar and Musk's answers, it can be seen that they self-assessed that there are no technical hurdles to building space AI data centers. The point is that once financing and the component supply chain are properly in place, the establishment of space AIDCs, which had felt distant, could become reality faster than expected.
In this regard, SpaceX has applied to the U.S. Federal Communications Commission (FCC) for permission, saying it will launch up to 1 million satellites carrying semiconductors for AI computing to build a data center in orbit. The plan is for the satellites to be constantly powered by solar energy, adding 100 gigawatts (GW) of AI computing capacity annually.
Currently, tech companies around the world are facing an energy bottleneck in operating AI on the ground. Musk has also pointed to power issues at venues such as the World Economic Forum (WEF) as the biggest obstacle blocking the expansion of AI infrastructure.
Musk's strategy is to transform satellites from mere communication relays into "floating computing nodes" equipped with AI processors. Depending on altitude and orbit, in space, strong sunlight can be directly absorbed continuously for 95 to 99 percent or more of the year, without interference from Earth's day-night cycles, climate, or atmospheric resistance. It is also free from cooling costs and carbon emission regulations, which are chronic problems for ground-based data centers.
However, some point out that cooling will be difficult given the space environment, which differs from the ground. Space is commonly known as an extremely cold space reaching minus 270 degrees Celsius, so it is easy to think, "Won't it cool down on its own if left alone?" But thermal radiation has extremely lower heat transfer efficiency compared to conduction or convection. Analysts say that to release the high heat emitted by servers through radiation alone would require radiators that are unimaginably large and heavy.
In response, SpaceX executives said at the public webinar last Monday: "Basically, radiators are components that do not require high technical difficulty, mostly aluminum and heat pipes, so they do not need special technological innovation, and cooling-related issues are not something we are concerned about internally." They added, "We have not yet fully secured the mass production capability for radiators for orbital computing satellites, but we judge it is not a matter of concern." It can be said that SpaceX's assessment is that there is no technical hurdle, only that supply chain procurement remains insufficient.
Original reporting by Kim Ki-hyuk for Seoul Economic Daily.
AI-translated from Korean. Quotes from foreign sources are based on Korean-language reports and may not reflect exact original wording.
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13 June 2026
According to Ukrinform, Oleh Kiper, head of the Odesa Regional Military Administration, reported this on Facebook.
“Another missile strike damaged solar panels on the plant’s premises. Fortunately, there were no casualties,” Kiper noted.
Work is underway at the scene to address the damage.
As reported by Ukrinform, on June 12, a Russian missile struck a residential area in the Odesa region. Three houses were destroyed, and two people sustained shrapnel wounds.
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Behind Iberdrola’s Community Approach to Solar in Portugal Energy Digital Magazine
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Open source local news · Lexington, Kentucky ·
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LEXINGTON, Ky. — The City of Lexington is bringing back its Solarize Lexington program, a community initiative designed to make solar panel installation more affordable and accessible to homeowners and other property owners.
The program helps households reduce electricity use by connecting residents with a vetted solar installer and offering a 20 percent discount on installation costs. Mayor Linda Gorton said the program “helps Lexington homeowners lower their energy costs, reduce their dependence on the electric grid, and champions a more sustainable future for our community.”
Solarize Lexington is open to homeowners as well as nonprofits, small businesses, and places of worship. Interested participants must complete an online interest form by Oct. 2. The program includes a free solar assessment to determine whether a property’s roof is suitable for panels. If the assessment is positive, the resident’s information is shared with Solar Energy Solutions, the program’s selected solar installer. If a roof is not a good fit for solar, residents will be informed of the reasons why.
Contracts for solar purchases and installation must be in place by Oct. 16, with all installations required to be completed by the end of 2026. Unlike previous years, there is no grant funding available this year, meaning participants are responsible for paying the discounted cost of panels and installation.
The program has a track record of success. In 2023 and 2024, Solarize Lexington was supported by federal funding, and the program is now expanding geographically. According to the City, Solarize Lexington is being restructured to serve all six counties in the Lexington-Fayette County Kentucky Metropolitan Statistical Area.
Officials emphasize that completing the interest form does not obligate residents to purchase solar panels. No one from the Solarize Lexington team will contact participants unless they submit the form, and the team will never demand immediate payment or pressure quick decisions. All solar installations are individual contracts between property owners and the installer, and the City assumes no liability related to installation.
This article was generated by AI (claude-haiku-4-5-20251001) based on source material from LFUCG General News, enriched with 2 web searches. The original source is available at https://www.lexingtonky.gov/news/solarize-lexington-can-help-households-go-solar.
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Copyright © 2025–2026 · The Lexington Times · Lexington, KY
Wednesday, June 10, 2026 at 4:21 p.m.
Solarize Lexington is back! This City program helps households reduce electricity use by making it easier and less expensive to install solar panels.
“Solarize Lexington is an outstanding resource for residents to learn about solar energy and have solar panels safely installed on their home at a discounted rate,” Mayor Linda Gorton said. “This program helps Lexington homeowners lower their energy costs, reduce their dependence on the electric grid, and champions a more sustainable future for our community.”
Solarize Lexington provides residents with a step-by-step walk-through of the solar installation process. Households are connected to a vetted solar installer and are eligible for a discounted rate of 20%. There is no grant funding available this year.
The program is open to all homeowners. Other property owners, such as non-profits, small businesses and places of worship, may also participate. If you are interested in Solarize Lexington, fill out the online interest form. The Solarize Lexington team will use this information for a free solar assessment of your property to see if your roof is a good fit for solar. If it is, your information will be shared with our only solar installer, Solar Energy Solutions. If your roof is not a good fit for solar panels, the reason(s) will be shared with you. Filling out the form does not mean you must participate in the program or buy anything.
No one from the Solarize Lexington team will contact you unless you’ve filled out the online interest form. The team will never demand immediate payment or pressure you to make decisions quickly.
Solarize Lexington interest forms must be completed by Oct. 2. Contracts must be in place by Oct. 16. Installation must be completed by the end of 2026. Participants are responsible for the (discounted) cost of solar panels and installation. All solar installations are individual contracts between the property owner and the solar installer. The City will not assume any liability related to the installation.
Solarize Lexington has now expanded to the surrounding counties of Clark, Scott, Bourbon, Jessamine and Woodford. These counties are part of the Lexington-Fayette County Metropolitan Statistical Area. Solarize Lexington was included in the Regional Priority Climate Action Plan for the Lexington-Fayette County MSA as an implementation-ready program. Lexington has restructured its program to include all six counties in the MSA.
Find out more about Solarize Lexington and fill out the interest form at lexingtonky.gov/Solarize.
Jada Griggs
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jgriggs@lexingtonky.gov
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Is First Solar Stock Outperforming the S&P 500? Yahoo Finance
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Sunny to partly cloudy. High 87F. Winds light and variable..
Partly cloudy skies this evening will become overcast overnight. Low 67F. Winds light and variable.
Updated: June 13, 2026 @ 11:29 am
Cattle graze under solar panels Tuesday, April 28, 2026, at a farm in Christiana, Tenn.
Cattle graze under solar panels Tuesday, April 28, 2026, at a farm in Christiana, Tenn.
Along W.Va. 2 south of Ravenswood, you can’t help but notice acre after acre of solar panels. Those panels are part of a solar farm called BHER Ravenswood Solar 1. It’s a joint project of Precision Castparts Corp.’s TIMET titanium facility near Ravenswood and BHE Renewables to provide power for TIMET. According to Energy Information Agency data, the solar farm started producing power in February.
Drive a few more miles into Ravenswood, West Virginia, and cross over the bridge into Meigs County, Ohio. Turn left onto County Road 338A and you will find another solar power installation. This one is called the Great Bend Solar Project. It sits on a 370-acre site and delivers enough power to the regional grid to supply 9,000 homes. It was developed by Doral Renewables of Philadelphia.
Great Bend Solar is on or near land America Electric Power acquired a long time back in hopes another coal-burning power plant would be needed. A decade or two or three ago, a power plant using new coal-burning or gasification technology was proposed for that part of Meigs County, but it never got beyond the proposal stage.
If you prefer looking for solar power in Kentucky, there’s an operation called Cooperative Solar One a few miles east of Lexington. It’s been in operation since 2017.
Solar power in this region is more than rooftop panels on homes and businesses, and it’s more than small operations supplying supplemental power to factories such as Toyota West Virginia in Putnam County. In recent months, solar power has made its presence more noticeable, and given the secrecy of large-scale industrial developments nowadays, it’s difficult to say what projects are in the planning stages at this moment.
It so happens the Associated Press ran a story this week that solar has surpassed coal as a source of electricity in the United States.
“Data released Wednesday by global energy think tank Ember, along with a report by the Solar Energy Industries Association and analytics firm Wood Mackenzie, show the continued growth of solar and decline of coal in the United States despite federal policy,” the AP reported.
“In May, for the first time, solar supplied more of the nation’s electricity than coal, or 12.8%, Ember said. Coal supplied 12.2%, its fourth-lowest monthly share ever.
The solar farms in this region have one thing in common. They are all in flat areas where hills and their shadows are far away. They are in some of the prime space that can be used for industry or farmland. Speaking of farmland, there has been controversy in Kentucky this year in at least two places where people have spoken against proposed solar farms that would take up land currently or potentially used for agriculture. Solar has its societal costs, too, apparently.
Solar is here, although not without critics. But no source of energy is critic-free. It’s a set of trade-offs, and – like it or not — for now, solar is on the rise and coal is on the decline. Even this close to coal country.
If you’re interested in submitting a Letter to the Editor, click here.
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Cattle graze under solar panels on April 28 at a farm in Christiana, Tenn. (AP Photo/Joshua A. Bickel)
Cattle graze under solar panels on April 28 at a farm in Christiana, Tenn. (AP Photo/Joshua A. Bickel)
Even as President Donald Trump boosts coal over clean energy, solar power is hitting new milestones in the U.S. and remains the leading source of new power.
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This story was originally published by Grist and is reproduced here as part of the Climate Desk collaboration.
Solar energy just provided more electricity in the United States than coal for the first time on record—marking a milestone for the rise of renewables in America.
While gas and nuclear plants still lead the country’s energy mix, solar contributed 12.8 percent of the nation’s electrons in May, according to an analysis of government data by Ember, an energy think tank. Coal, meanwhile, provided just 12.2 percent. Just five years ago, solar was less than half of its current levels and coal was at 20 percent.
“Overtaking coal for the first month on record shows just how far solar has come, from a niche contributor to the third-largest and fastest-growing source of power in the US electricity system,” said Nicolas Fulghum, senior data analyst at Ember, in a press release. “From Texas to California, markets across the US are betting on solar to meet rising power needs.”
The turnaround comes even as political headwinds have shifted against renewable energy.
“Spending $700 million to bail out the coal industry is like throwing a lifeline to a ship that has already sunk.”
Last summer, Congress passed the One Big Beautiful Bill Act, which rolled back enormous swaths of former President Joe Biden’s landmark climate change legislation, the 2022 Inflation Reduction Act. And President Donald Trump has actively sought to hinder renewable energy development, even offering to pay at least one oil company $1 billion to stop building its offshore wind projects.
The latest electricity data comes the same month that the Trump administration announced $700 million in funding for investments in the coal industry. It included money for what would be the country’s first new coal-fired power plants in 13 years—sourced from funds previously dedicated to reducing the country’s dependence on fossil fuels, not deepening it.
“Today we’re taking historic action to bring down the price of energy and the cost of living for all Americans with the power of clean, beautiful coal,” said Trump, who campaigned on the coal-friendly slogan ‘dig, baby, dig.”
Ember’s analysis found that coal generation in May was actually up slightly from April, when it hit an all-time low. Its share of the grid will also likely tick up in the summer, as cooling needs peak. But the steady downward trend over the last several years suggests that even all the president’s men might not be able to put the coal industry back together again.
“Spending $700 million to bail out the coal industry is like throwing a lifeline to a ship that has already sunk,” Lena Moffitt, executive director of the environmental group Evergreen Action, told the Associated Press. Rich Nolan, president and CEO of the National Mining Association disagreed, telling the AP that coal generation helps shield consumers from the impacts of volatile energy prices and supply challenges exacerbated by AI.
Regardless of what coal does, experts believe the solar market will continue its upward march. While installations dropped in 2025 compared to 2024, according to the Solar Energy Industry Association, it still accounted for more than half of all newly installed electricity capacity. Even MAGA influencers are promoting it.
“We’re going to just keep seeing more and more renewables brought onto the grid,” said Patrick Drupp, director of climate policy at the Sierra Club. “That’s good for people’s wallets, it’s good for their health, it’s good for the planet.”
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The sun now generates more electricity for U.S. consumers than coal, despite President Donald Trump’s penchant for carbon-heavy power.
Solar power rose to become the third largest electricity source in the U.S. last month, according to a Wednesday report from energy think tank Ember. Ember’s updated statistics come days after Trump invoked the Defense Production Act and other tools to provide $700 million to support the coal industry, all in an effort to reduce energy prices spiked by the war in Iran.
“Overtaking coal for the first month on record shows just how far solar has come, from a niche contributor to the third-largest and fastest-growing source of power in the U.S. electricity system,” Nicolas Fulghum, Senior Data Analyst at Ember, said. “From Texas to California, markets across the U.S. are betting on solar to meet rising power needs.”
The share of solar in the U.S. nearly doubled since 2021, rising from 5.4% of energy generation capacity in May of that year to 12.8% last month. Coal, meanwhile, plummeted from 19.7% to 12.2% of American energy last month.
Coal, which fueled the U.S.’s industrial rise, generated its smallest share of national power on record in April.
Solar’s growth in the American market is partially due to government incentives. While Trump’s One Big Beautiful Bill eliminated Biden-era subsidies for green energy, which he called a “giant SCAM” in a June 2025 post on Truth Social, various state governments continue to offer tax credits for businesses and homeowners who install solar panels.
The Biden administration pumped more than $7 billion into solar generation capacity through the “Solar for All” initiative alone. That initiative, announced in April 2024, sought to bring solar to more than 900,000 low-income households nationwide. Trump’s Environmental Protection Agency moved to cancel those grants in August 2025, with only $53 million of the $7 billion budget having been spent.
Trump has long admired what he refers to as “beautiful clean coal.”
THE MAN LEADING TRUMP’S NUCLEAR RENAISSANCE
His energy secretary, Chris Wright, is pushing ahead on an ambitious project to build a coal export terminal on a former military site in Oakland, California. The West Gateway Terminal would provide the capacity to ship more than 12 million tons of American coal to the Asian market, though the project is expected to be mired by legal challenges.
Solar now trails gas and nuclear as the third-largest source of electricity in the U.S.
Northwest Suburban High School District 214 school board members Thursday inked a deal to place solar panels atop Rolling Meadows High School, amid looming expiration of federal incentives and rising energy costs from the standard grid.
The school could offset 65% of its annual electricity usage, officials estimate, which is higher than an earlier 37% projection when they began exploring the possibility last fall.
Superintendent Scott Rowe — who oversaw installation of solar panels at his last district, Huntley Community School District 158, in 2019 and 2020 — suggested District 214’s five other high schools and district headquarters could get panels in the future, too.
“If we might be able to get more from our coverage than we originally thought, there is an opportunity for us to continue exploring this,” Rowe said. “This is an area that we should continue exploring and pushing the boundaries on. … We can just lock in and reduce our bill, so that way our dollars can go toward the actual learning.”
Under a so-called power purchase agreement approved Thursday night, Chicago-based Verde Solutions will install the solar arrays at no upfront cost, while offering the district a fixed rate for solar power over 25 years.
The district is currently paying a little under 8 cents per kilowatt-hour for electricity, but will pay about half that — just under 4 cents per kWh — with solar.
Technically, that’s $0.078 per kWh on the grid, versus $0.0379 per kWh for solar.
It translates to a $3.4 million savings over the 25-year contract, based on current rates and assuming future adjustments for inflation, according to consultant Nania Energy Advisors.
Becky Thompson, a senior energy adviser at Nania, which also advises the school district on its electricity and natural gas purchases, said solar presents a stabilized cost that will allow officials to budget year over year, while grid power continues to go up considerably.
“I’m in the market every single day watching energy prices,” Thompson said. “With data center integration, electrification of vehicles, manufacturing moving to automated systems, currently our demand is outpacing our supply, which is really the simplest way to explain why energy prices continue to go up.”
While the district and its consultant have been exploring solar for about a year, officials are now staring down a deadline to capture federal incentives before they expire.
Solar installations need to be done by the end of 2027 to take advantage of the incentives, officials said.
After putting a request for proposals out on the street in October, five solar vendors submitted plans and were ranked by a district evaluation team according to price, technical qualifications and experience. The top three were invited for interviews in December.
Verde, the recommended installer, was founded in 2012 by Christopher Gersch, who is a 2000 Rolling Meadows High School graduate.
Aaron Raftery, a solar expert with Nania, said that connection didn’t factor into the selection team’s scoring criteria. Verde has done 2,800 projects across 48 states, including a few schools in Illinois, some municipal governments, and many commercial projects, Raftery said.
The solar arrays, which are owned by Verde, will be installed on a racking system atop a slip sheet roof membrane. The panels won’t be anchored to the school roof, so there is no impact on the roof warranty, Raftery said.
It’s a 59-week installation process, from engineering to when the system can be turned on. That’s expected before school starts in fall 2027.
As part of the contract, Verde will offer six annual student internships over five years, a 10-year scholarship program, and other learning experiences at the school.
HVR Solar Ltd is establishing a 1.2 GW TOPCon solar cell manufacturing line in Uttar Pradesh through strategic MoUs signed at SNEC PV Power Expo 2026. Partnering with global technology providers, the facility aims to boost India’s domestic renewable energy supply chain, reduce import reliance, and create over 500 local jobs.
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Carbon removal is barely denting emissions, and scaling it now looks like a solar-sized feat, experts claim Yahoo
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Experts would likely conclude that Power Ray's rebranding is a strategic response to the solar sector's execution challenges, emphasizing reliability and problem-solving in a high-pressure market.
MIDLAND, TX – June 12, 2026 – In the sprawling, sun-drenched landscapes of Texas, the utility-scale solar industry is a study in contrasts. On one hand, demand is explosive, driven by power-hungry data centers and ambitious corporate climate goals. Solar, paired with storage, accounted for an astounding 91% of new U.S. electricity-generating capacity in the first quarter of this year. On the other hand, the sector is straining under immense operational pressure. It is within this crucible of high stakes and high stress that Midland-based Power Ray LLC, a solar construction company, has just unveiled a new brand and website. The move, however, isn't a pivot or a reinvention. It's a deliberate clarification, a strategic bet that in a market defined by turbulence, the most valuable commodity is predictability.
"We didn't change who we are. We clarified it," said Jarel Ray, CEO of Power Ray. "The work has always been about helping projects move forward when things get hard. The new brand just says that more directly." This statement cuts to the heart of a systemic challenge facing the entire renewable energy ecosystem. While headlines celebrate gigawatts of new capacity, the project managers and EPC (Engineering, Procurement, and Construction) contractors on the ground are navigating a minefield of constraints. Power Ray's rebranding is a signal that it understands this reality intimately and is positioning itself not just as a vendor, but as a vital execution partner.
To understand Power Ray's strategy, one must first appreciate the immense pressure bearing down on the utility-scale solar sector. The industry is in a race against the clock, with developers rushing to meet critical deadlines for federal tax incentives. This creates accelerated, often unforgiving, project schedules. Compounding this challenge is a severe labor shortage. Recent industry reports estimate a deficit of over 53,000 skilled workers nationwide, with nearly 90% of solar employers struggling to fill positions. This deficit of electricians, technicians, and construction managers directly threatens project timelines and inflates costs.
Beyond labor, the global supply chain remains a source of persistent friction. EPC contractors must contend with volatile material availability, logistical hurdles, and a complex web of trade policies. New sourcing requirements, such as the Foreign Entity of Concern (FEOC) rules taking effect in 2026, are forcing a strategic re-evaluation of entire supply chains, adding another layer of risk to multi-million-dollar projects. "The success of a massive solar farm today depends less on a single grand vision and more on the flawless execution of a thousand small, interconnected tasks," noted one senior project manager at a large EPC firm. "A single unreliable subcontractor can create a domino effect of delays that costs millions. We need partners who absorb complexity, not create it."
It is this environment that has fundamentally shifted the value proposition of subcontractors. Capacity is still important, but consistency, communication, and on-the-ground problem-solving have become paramount. Large-scale solar projects, sprawling across hundreds or thousands of acres, are dynamic and unpredictable. The ability to manage unforeseen challenges—from difficult soil conditions to weather delays—is what separates successful projects from stalled ones.
Power Ray's rebranding is a direct response to this market need. The company, which provides essential mechanical and electrical construction services, is doubling down on its identity as a problem-solver. Its new messaging focuses on its role in keeping projects organized, productive, and on schedule, even when conditions are difficult. This is more than marketing; it's a statement of operational philosophy.
"Most projects don't fail because of one big issue," Ray explained. "They stall because small problems aren't handled well. Our job is to deal with those problems early and keep the job moving." This ethos is reflected in the company's suite of services, which form the backbone of solar farm construction: solar pile driving, racking and structural assembly, module installation, and electrical work like trenching and conduit installation. By offering an integrated package of these critical-path services, the firm aims to provide a seamless and reliable experience for its EPC partners.
The emphasis on being an "execution partner" is a key differentiator. It positions the company as an extension of the EPC's own team, one that is empowered to make decisions and adapt in the field. This is a departure from a more traditional, transactional vendor relationship and speaks directly to the need for greater collaboration and trust on complex job sites.
Power Ray's strategy is further sharpened by its deliberate regional focus. The company primarily supports projects within a twelve-hour drive of Dallas, an operational footprint that encompasses the heart of the American solar boom. Texas remains the nation's fastest-growing solar market, with immense contracting activity driven by the energy demands of its technology and data center industries.
This regional model provides a distinct competitive advantage. While national construction firms may boast larger fleets, Power Ray's proximity allows for rapid mobilization of crews and equipment. More importantly, it enables a continuous on-site presence, allowing its teams to stay close to a project from start to finish. In an industry where field conditions can change daily, this hands-on approach is invaluable. Testimonials on the company's new website underscore this point, with partners praising its ability to navigate weather delays and maintain project momentum.
This regional expertise is supported by an organizational structure built for agility. The company highlights its "Field-First Leadership" and reliance on its own "Self-Performing Crews." This model empowers on-the-ground leaders to make critical decisions without layers of corporate bureaucracy, leading to faster problem resolution, fewer costly change orders, and better safety compliance. In essence, the firm is built to mirror the dynamic nature of the very projects it helps construct.
The final piece of Power Ray's strategic clarification is its new digital presence. The revamped website is not merely a cosmetic update; it is a tool meticulously designed for its target audience: busy EPC project managers and procurement teams. The site offers a clear, concise overview of the company's capabilities, regional reach, and project history. The goal is to give potential partners a quick and accurate assessment of the firm's value, streamlining the process of requesting a bid.
This focus on clear communication and accessibility reflects the core values the company says have guided it for years: integrity, accountability, and results. By translating its on-the-ground reliability into a clear and efficient digital experience, Power Ray is building a bridge of trust before the first pile is ever driven. As Jarel Ray puts it, the objective is simple and directly addresses the core anxiety of his clients. "If you're running a solar project, you don't need another vendor creating noise," he said. "You need a team that shows up, communicates clearly, and gets the work done. That's what we've always focused on."
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At a time when the Centre is aggressively promoting rooftop solar power as a tool of energy se curity, economic resilience and climate action, the Goa government has allowed its own solar subsidy scheme to lapse, leaving hundreds of applicants stranded while a promised extension awaits official approval. The State subsidy for roof top solar installations has remained in abeyance since March 31, 2026, despite Chief Minister Pramod Sawant as suring the Legislative Assem bly during his Budget speech on March 6 that the scheme would be extended for an other three years. More than four months later, however, the promised notification has yet to be issued. The delay has plunged Goa’s rooftop solar pro gramme into uncertainty, with several applications submitted after March 31 caught in administrative limbo.
Goa Energy Devel opment Agency (GEDA) of ficials have acknowledged that benefits under the revised scheme cannot be processed until the govern ment formally approves and notifies the extension. GEDA Sources said the Department of New and Re newable Energy has already written to the government seeking continuation of the scheme, while the GEDA has framed a revised policy. Yet the file continues to await approval, stalling installa tions and creating uncer tainty among consumers and vendors alike. “It is absurd that the State government is not pro moting the shift towards self-sufficiency, especially in the clean energy sector, at a time when the country is grappling with the West Asia crisis,” said John Dias from Mapusa.
The policy paralysis threatens to undermine years of progress in rooftop solar adoption. According to official figures, 3,928 consumers are al ready registered for rooftop solar systems in Goa, while solar photovoltaic power plants have been installed on 48 government buildings across the State. Until the end of the last financial year, Goa offered one of the most attractive rooftop solar incentive structures in the country because consumers could avail themselves of sub sidies from both the Central and State governments. (See graphic) For many households, the economics were compelling. A typical 10 kW rooftop solar system costs about Rs 6 lakh. Under the earlier subsidy regime, a consumer could receive approximately Rs 78,000 from the Centre and around Rs 2.25 lakh from the State, reducing the effective investment to nearly half the original cost. Given Goa’s electricity tariff structure, a household consuming around 1,000 units of electricity every month would typically pay power bills of about Rs 6,500 a month, or nearly Rs 78,000 annually.
With both subsidies availa ble, the investment could generally be recovered within four years. Without the State subsidy, however, the payback period almost doubles, significantly weakening the financial in centive for prospective adopters and threatening the rapid growth witnessed in recent years. Vendors however are hopeful that the government will extend the State subsidy. “The consumers who have installed panels during this financial year have not yet realised that the subsidy is not coming because it usually takes about four to six months to be disbursed. In a couple of months, the news will spread, and that will completely kill the momentum,” said a stake holder from the solar industry.
Industry players say the government risks squandering a rare success story in public policy. Founding Director of Anmax Energy Anant Kochhar said, “Solar power is economical and clean. We generally recom mend that consumers install a 10 kW rooftop solar system at their residences.” According to empanelled vendors, rooftop solar systems start becoming financially attractive when household con sumption exceeds 200 units per month. For consumers us ing more than 300 units, the economic case becomes even stronger. Sanket Angle of J N Solar Power LLP said awareness about rooftop solar systems has grown substantially in re cent years. “The State and Central subsidies have helped drive the market penetration of solar panels.
The subsidies are usu ally released within six months,” said Angle. “Even during adverse weather conditions, solar panels are capable of generating nearly 50 per cent of their usual power output,” he added. Sun360 founder and CEO Anish Sousa said consumers continue to derive significant long-term value from invest ing in rooftop solar systems. “The State subsidy has undoubtedly accelerated adop tion, but rooftop solar remains a financially attractive investment for many households. With electricity costs expected to rise over time and solar systems lasting more than 25 years, consumers continue to benefit from sub stantial long-term savings,” he said. “We have installed more than 1,000 solar systems across the State, and many consumers have already recovered their investment through savings on electricity bills,” he added. “The way the net metering system works is that, at the end of the financial year, the units consumed from the grid are adjusted against the units generated by the solar pan els on your rooftop. If the units generated exceed the units consumed, the government purchases the surplus power at the rate of Rs 3.8 per unit,” explained Sousa. “If the consumer opts to go partly off-grid, they would need to invest in batteries and an inverter, which for a 10 kW system would typically cost an additional Rs 3 lakh. This makes particular sense when there are different tariffs for different times of the day.
Consumers can then use their battery reserve during peak tariff periods and switch back to grid power when rates are lower,” Sousa added. On the lifespan of each component, Sousa said, “General ly, solar panels last for about 30 years, while batteries and inverters have a lifespan of around 10 years each.” Ayrito George from Ambaji-Fatorda, who has installed a rooftop solar system, said, “I have installed rooftop solar systems at both my residence and my shop, and the elec tricity bill is minimal. I receive monthly bills of around Rs 250 for my house and Rs 290 for my shop. I invested about Rs 6.45 lakh, of which nearly 50 per cent was recovered through government subsidies.” Atmaram Desai from Sankhali said, “If I produce excess power, the government purchases the surplus electricity generated by me at a lower rate under the net metering system.” But not everything is about economic viability. Many ear ly adopters made the switch for environmental reasons. Savio Figueiredo from Saligao said, “I installed a 4.4 kW system in January 2020. I paid about Rs 3.2 lakh at the time and received a State subsidy of Rs 1 lakh. Back then, I did it purely for environmental reasons, and I still have not got ten around to checking whether I have broken even.”
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Scientific Reports volume 16, Article number: 17416 (2026)
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Accumulation of dust on solar panels lowers performance and limits energy production, particularly in dry locations. Dust accumulation on photovoltaic panels diminishes performance and reduces energy output, especially in arid regions. This study uses four identical modules in Roorkee, India, from October to December to examine the impact of cleaning frequency on photovoltaic (PV) performance. The reference panel is cleaned daily, while the remaining panels are cleaned weekly, biweekly, and monthly. Alongside short-circuit current measurements, environmental parameters including global horizontal irradiance, ambient temperature, wind speed, and relative humidity are continuously recorded. In this study, soiling loss (%) is examined as the primary performance indicator under various cleaning intervals to observe dust accumulation progression and its impact on the performance of the solar photovoltaic module. Experimental data are utilized to develop an empirical regression model that describes the trend of dust accumulation. The daily average soiling loss ranges between 0.17 and 0.21%. Furthermore, machine learning models, including Decision Tree, K-Nearest Neighbour, support vector regression, artificial neural network, and a stacking ensemble method, are developed for accurate prediction of soiling loss from environmental variables and cleaning frequency. The stacking model consistently achieves the best performance across all months, with root mean square error as low as 0.03–0.045, mean absolute error below 0.03, and R² = 0.999 compared to other models. Moreover, statistical analyses such as Bland–Altman plots and the Wilcoxon signed-rank test are employed to validate the significance and agreement of the predicted outcomes. The study highlights the benefits of data-driven solutions for predictive operation and maintenance of solar photovoltaic systems and provides valuable insights into the impact of cleaning frequency on reducing soiling losses.
The solar energy extensively uses for heating purpose, desalination of water, cooling process and production of electrical energy, serving a diverse array of applications from home to industrial as well in agricultural sectors1,2. About half of the world’s electricity will come from wind and solar power alone by 20503. Until then, around two-thirds of India’s electricity will come from solar and wind. The price of solar energy has dropped by around 85% since 20104. The primary factors for India’s solar PV to develop exponentially are the sharp decline in solar energy prices and Indian government policies subsidies schemes to use solar PV technology to produce power. India has abundant solar insolation due its location inside the tropical belt, enabling it to fulfil its daily electrical requirements. It gets an average solar insolation of 4 to 7 kWh/m² and around 2300 to 3200 h of sunlight annually5. Numerous components affect the efficiency of solar photovoltaic power generating systems as shown in Fig. 1 such as type of material, spacing of solar cell, module area, tilt angle and orientation, environmental condition, surface dust of solar photovoltaic (PV) panels. The most often occurring element influencing the solar photovoltaic panel performance is surface dust6,7,8. The accumulation of dust on the surface of solar panels can result in changes in the electrical charecteristics of the panel array. These changes can cause the panels to have a reverse bias, which in turn can result in a loss of power generated by the panels9. The synthesized bio-derived TiO₂ nanoparticles using plant extract and demonstrated improved photovoltaic performance through enhanced light absorption and charge transport, highlighting the importance of material-level enhancements for solar cell efficiency10.The soiling rates vary between 0.05% and 0.55% per day in India11,12, while in Dhaka, Bangladesh, it is 0.78%13.
Factors contributing dust accumulation impacts on PV modules.
Large-scale solar farms in remote locations are particularly affected by the soiling issue. This is due to the fact that regular cleaning and inspection may be challenging and costly, given the expenses of labor and long-distance travel14,15. In order to determine the amount of power that is lost by dirty photovoltaic modules, it is desirable to have automated soiling detection. Inadequate maintenance of the cleanliness of solar photovoltaic panel surfaces will lead to significant economic losses. Consequently, utilising precise and effective techniques to identify dust buildup on the surfaces of solar photovoltaic panels is crucial. This facilitates prompt cleaning of the panels, thereby ensuring their safe and efficient functioning. Dust and ambient temperature energy losses were quantified using artificial neural network (ANN) and extreme learning machine (ELM) algorithms16. Both the ELM and ANN models predict 91.42 and 90.69% accurately. Multivariate Linear Regression (MLR) and ANN models were used to estimate dust-related energy and economic losses in solar panels17. ANN and MLR models estimated dust-related cost and energy losses at 89.97% and 86.78%, respectively. In another study Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to predict the dust-exposed solar module performance18. The ANFIS model achieves root mean square error (RMSE) of 0.18719 and coefficient of determination (R²) of 0.99803 for monocrystalline silicon PV modules. In comparison, polycrystalline PV modules have an RMSE of 0.87098 and a R² of 0.99714. The study in19 predicted dust-induced PV panel performance deterioration in Qatar using ANN and MLR models. The ANN model has a R² of 0.537 and mean square error (MSE) of 0.0038, while the MLR model has a R² of 0.167 and an MSE of 0.0082. The ANN model outperformed the MLR model. Study in20 estimated dust losses using artificial neural networks. Modern technology allows ANN to estimate losses with normalized root mean square errors (NRMSE) of 6.79 and correlation coefficients (R) of 0.91. Another of21 utilised AMM, MLR, Interactive Multivariate Linear Regression Model (MLRWI) and Response Surface Methodology (RSM), to predict the loss caused by dust on solar PV module surface. The artificial neural network produced better predictive results than the other machine learning models. The results for R² and RMSE are 0.813 and 0.026, respectively. The separate studied carried out focusing on machine learning (ML) methods implemented and their performance as shown in Table 1.
The studies summarised in Fig. 2 show that the reported soiling losses vary widely with location, exposure duration and local climatic conditions —observed rates in the literature span roughly 0.1% to 1.1% perday, with the highest values typically found in arid, dusty environments (e.g. Bahrain, Qatar) and lower values in regions with occasional rainfall or wind cleaning. Differences in measurement period, panel tilt, dust type, and cleaning practice (and the diversity of experimental protocols) make direct comparison difficult. Overall, the literature indicates a clear need for (a) longer-term, standardized measurements across diverse climates, (b) studies that relate soiling loss to measured environmental drivers (global horizontal irradiance (GHI), wind speed (WS), relative humidity (RH), dust deposition rate (DDR)), and (c) predictive models ( ML approaches) validated against controlled experiments36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55. These gaps motivate the present work, which combines experimental measurements with ML models to produce robust soiling-loss predictions.
Real-world natural dust build up on PV panels under four cleaning frequencies (daily, weekly, biweekly, monthly) is studied instead of controlled dust deposition research, offering practical insights.
This work established two empirical models: one for PV short circuit current (Isc) prediction and one for soiling loss (SL), encompassing both electrical performance and soiling effects.
Novel stacking model for SL prediction and comparison with other ML models (ANN, SVM, KNN, DT) provide strong empirical and data-driven comparisons.
Bland–Altman analysis and the Wilcoxon signed-rank test for model validation provide a unique level of statistical validity, assuring model dependability.
Soiling loss and study duration across locations.
The methodology of the present work is shown in Fig. 3 with an experimental setup consisting of four PV panels subjected to different cleaning frequencies (P1-daily, P2-weekly, P3-biweekly, and P4-monthly). Data including GHI, RH, WS, ambient temperature (AT), and the short-circuit current (Isc) of each panel were recorded using a data logger. Based on the collected data, two empirical models were developed: An Isc model as a function of environmental parameters such as global horizontal irradiance (GHI), ambient temperature (AT), wind speed (WS), relative humidity (RH) are considered in present study, and a soiling loss (SL) model as a function of RH, WS, AT, and cleaning frequency (CF). To improve predictive capability, machine learning models (ANN, SVM, KNN, DT, and stacking ensemble) were implemented for SL prediction. The performance of empirical and ML models was compared using evaluation metrics RMSE, mean absolute percentage error (MAPE), MAE, MSE, and R². The statistical validation using Bland–Altman analysis and the Wilcoxon signed-rank test was carried out to assess the significance and agreement of the models.
Methodology of the work.
The experimental work was conducted in Roorkee, India (29.86°N, 77.89°E), located in the Indo-Gangetic plain and characterised by a subtropical climate with distinct winter, summer, and monsoon seasons. The study period, October to December, represents the dry-winter season with frequent dust-laden winds, moderate humidity, and occasional foggy conditions, making it suitable for investigating soiling effects. Rainfall-induced natural cleaning was avoided to maintain experimental consistency and isolate the effects of environmental variables and manual cleaning intervals.
The experimental setup is shown in Fig. 4, consisted four identical crystalline silicon PV modules, each rated at the same electrical capacity (:{P}_{max}) 20 W, installed outdoors with a fixed tilt angle of 300 and south-facing orientation to maximize solar exposure. All the PV modules are mounted on a common frame to ensure that they experienced identical environmental conditions such as GHI, AT, RH, and WS.
The present study isconducted during the dry season because the primary objectiveis to evaluate the impact of manual cleaning at fixed and predefined intervals (daily, weekly, biweekly, monthly) under controlled accumulation conditions. During the monsoon season, frequent rainfall events act as natural cleaning mechanisms. Such stochastic and uncontrolled cleaning would interfere with the predefined manual cleaning schedules and compromise the controlled comparison between different cleaning frequencies.
Experimental setup: (1–4) PV modules with different cleaning frequencies, (5) weather station, (6) data logger, and (7) PV analyser.
The data acquisition architecture shown in Fig. 4 as follows:
The ATMEGA2560-based data logger is shown measuring:
Global horizontal irradiance (GHI).
Ambient temperature (AT).
Relative humidity (RH).
PV module current and voltage.
Timestamp via real-time clock (RTC).
The Weather Station is separately indicated as the source of:
Wind Speed (WS).
The experiment is conducted under natural outdoor exposure, allowing dust to accumulate under real environmental conditions. Although dust composition may vary geographically, the measured electrical performance and environmental parameters inherently capture the net effect of dust deposition and adhesion. This approach ensures that the dataset reflects realistic soiling behaviour and provides a reproducible foundation for predictive modelling.
To evaluate the impact of cleaning frequency on soiling losses, four panels are exposed to several cleaning regimens. Panel P1 served as the clean reference and underwent daily cleaning, whilst P2, P3, and P4 were cleaned weekly, biweekly, and every four weeks, respectively. Cleaning occurred at 06:00 AM utilizing distilled water and a gentle, lint-free cloth to avert scratches or more surface contaminants. Electrical measurements encompassed the short-circuit current of each module, recorded at consistent intervals as the principal performance metric for dust build-up. Meteorological parameters were continually recorded, including global horizontal irradiance, ambient temperature, relative humidity, and wind speed. The data were obtained using calibrated sensors incorporated with a data logging system, ensuring synchronous environmental and electrical recordings. The experimental configuration included several sensors to enable precise data collection and real-time observation of solar PV system metrics presented in Table 2.
Electrical and environmental parameters (GHI, AT, RH, WS, (:{I}_{SC}), and voltage) were recorded at fixed and uniform intervals using the ATMEGA2560-based data acquisition system. Measurements were sampled at regular intervals (hourly), ensuring temporal consistency throughout the experimental period. For modeling analysis, the recorded data were aggregated into daily average values to represent the cumulative effect of dust deposition under each cleaning frequency. The Fig. 5 illustrates the cleaning schedule followed during the experimental period from October to December, clearly highlighting the systematic maintenance intervals adopted for each panel.
Cleaning schedule timeline (Oct-Dec 2024).
To account for the influence of external climatic variables, meteorological parameters were continuously recorded during the experimental study. The Fig. 6 illustrates the daily average variation of global horizontal irradiance and wind speed, while Fig. 7 presents the daily average variation of relative humidity and ambient temperature from October to December 2024. Statistical description of the collected data is shown in Table 3. These measurements highlight the dynamic environmental conditions under which the PV panels operated, providing essential context for analysing the soiling effect and validating the empirical and machine learning models.
Daily average global horizontal irradiance and wind speed during the experimental period (October–December 2024).
Figure 6 shows that the wind speeds stayed in the moderate range (~ 1–3 m/s) during the experiment. Under such conditions, particle transport and deposition mechanisms are likely to dominate over aerodynamic removal, explaining the observed positive correlation between wind speeds and soiling loss.
Daily average relative humidity and ambient temperature during the experimental study (October–December 2024).
The abrupt increase in relative humidity as shown in Fig. 7 accompanied by a drop in ambient temperature corresponds to short-duration high-humidity events commonly observed during the winter season in the Indo-Gangetic Plain. These events are typically associated with fog formation, condensation, or transient cloud cover rather than measurable rainfall.
The monthly data analysis from October to December 2024 shows how the performance of PV is affected by both changes in the environment and how often it is cleaned. The daily-cleaned panel (P1) consistently exhibited higher short-circuit current values, while progressive reductions were observed in P2, P3, and most significantly in P4, reflecting the cumulative effect of soiling at longer cleaning intervals. The variability of irradiance, indicated by higher standard deviation values, further underscores the dynamic operating environment of the panels. These findings confirm that both environmental conditions and soiling accumulation substantially impact PV output, thereby establishing the need for predictive models. Accordingly, the subsequent section develops empirical models to express.
(:{I}_{SC:})as a function of GHI, AT, RH, and WS, and to quantify soiling loss as a function of RH, WS, AT, and CF, thus providing a foundational framework for later comparison with machine learning models.
The experimental dataset was utilised to derive empirical regression models for short-circuit current and soiling loss in percentage, using global horizontal irradiance, ambient temperature, wind speed, relative humidity, and cleaning frequency as predictors. Multiple linear regression is adopted to establish these relationships.
The regression Eq. (1) obtained for (:{I}_{SC:}) is:
Table 4 presents the estimated regression coefficients for the empirical model of short-circuit current56. The results clearly highlight GHI as the most dominant predictor (Estimate = 0.001265, p < 0.001), consistent with the physical dependence of Isc on solar irradiance. Cleaning frequency also shows a highly significant negative effect (p < 0.001), indicating the reduction in current with increasing days between cleaning. Ambient temperature has a small but significant positive effect, while relative humidity shows a minor negative influence. In contrast, wind speed was statistically insignificant (p = 0.238), confirming its limited role in determining (:{I}_{SC:}).
To further ensure model robustness, adjusted R² and residual diagnostics were evaluated before and after removing GHI. The change in adjusted R² was negligible (< 0.001), confirming that irradiance does not contribute to predictive power in the SL formulation. The removal therefore improves model parsimony without compromising explanatory strength, consistent with regression theory principles. The regression Eq. (2) for soiling loss (SL) expressed as,
Table 5 presents the estimated regression coefficients for the empirical model of of soiling loss show that cleaning frequency is the most significant predictor (Estimate = 0.18487, p < 0.001), highlighting the strong impact of longer cleaning intervals on increased soiling losses. Relative humidity also exhibits a significant positive influence (p = 0.007), which may be attributed to dust adhesion and cementing effects under humid conditions. Ambient temperature has a significant negative effect (p < 0.001), suggesting that higher temperatures may reduce relative deposition or increase self-cleaning effects. Wind speed shows a near-significant positive influence (p = 0.048), reflecting its dual role in either removing or redistributing dust. In contrast, GHI is statistically insignificant (p = 0.989), indicating that irradiance itself does not directly drive soiling loss but instead affects PV output through (:{I}_{SC}).
To further ensure model robustness, adjusted R² and residual diagnostics were evaluated before and after removing GHI. The change in adjusted R² was negligible (< 0.001), confirming that irradiance does not contribute to predictive power in the SL formulation. The removal therefore improves model parsimony without compromising explanatory strength, consistent with regression theory principles.
The performance of the four PV modules was evaluated in terms of short-circuit current (Isc), soiling ratio (SR) and soiling loss (SL%). The SR and SL can be computed using ISC as mention below Eqs. (3) and (4) as,
Where Isc soiled is the short-circuit current of the soiled panel and Isc clean is that of the clean reference panel (P1). The soiling loss percentage (SL%) was calculated as,
To further improve predictive accuracy beyond the empirical formulations, machine learning algorithms were employed using the experimental dataset described in Sect. 3. The empirical SL model achieved a high determination coefficient (R2 = 0.978) with low RMSE and MAE; however, the mean absolute percentage error (MAPE = 28%) remained relatively high, reflecting systematic nonlinearities and residual bias. To address these limitations, an ML-based predictive framework was developed.
The experimental dataset consist of both environmental and operational parameters: global horizontal irradiance (GHI), ambient temperature (AT), wind speed (WS), relative humidity (RH), reference short-circuit current of the clean panel ((:{I}_{SC:left(Cleanright)})) and cleaning frequency (CF). Categorical variables such as cleaning interval were encoded as hot encoding method, while all continuous features were standardized to zero mean and unit variance. To assess the relative contribution of selected input variables, a sensitivity analysis using the CAM approach was carried out under the Sect. 2.4. The Fig. 8 presents the scatter matrix of the selected features (CF, GHI, AT, WS, RH, and SL), excluding the month variable. In contrast to the off-diagonal scatter plots, which represent the pairwise correlations between the parameters, the diagonal histograms illustrate the distribution of each parameter.
Pairwise matrix of experimental features and relationship with soiling losses.
Global horizontal irradiance, ambient temperature, relative humidity, and wind speed have substantial temporal autocorrelation, making subsequent measurements not statistically independent. Randomly mixing time-dependent samples destroys temporal structure and leaks temporal data, enabling the model to indirectly learn patterns from subsequent observations. This may result in inappropriately optimistic assessment outcomes and exaggerated performance measures (e.g., R²). To evade this, the dataset was divided into 80% training and 20% testing observations using a time-ordered split. This method retains temporal causality and provides realistic model generalization in practice.
Time-ordered data splitting for temporally correlated environmental data.
Figure 9 compares random and time-ordered (blocked) data splitting for temporally auto-correlated environmental variables (GHI, RH, AT, WS). Random splitting leaks temporal data and inflates performance measures by include samples from comparable time periods in practice and testing. Time-ordered splitting conserves chronology and enables realistic generalization.
In this work, the cosine amplitude method (CAM) was used to assess the correlation between the input and output data. The mathematical formulation of CAM given in Eq. (5).
There is a connection between the cosine function and the dot product, as shown by Eq. (3). Whereas the inner product of two vectors where Xi input vector while Y is the output vector equal to zero when they are at right angles to one another, the product of two vectors that are collinear is equal to one. A greater directional similarity (and hence sensitivity) to the output is seen by features that have higher CAM scores (closer to 1) than those with lower scores as shown in Fig. 10.
Cosine amplitude scores (CAM) for feature importance.
CAM is the cosine similarity between the features (GHI, AT, WS, RH and CF) and target vectors soiling loss (%) is scale-invariant. The Fig. 9 indicate that the CF is the strongest driver of soiling loss in comparison with meteorological variables which shows moderate CAM score.
To evaluate potential multicollinearity among environmental predictors, the Variance Inflation Factor (VIF) was computed for GHI, RH, and AT. The obtained VIF values were 1.023 (GHI), 1.5987 (RH), and 1.5809 (AT), all of which are substantially below the threshold value of 5. These results confirm the absence of significant multicollinearity and demonstrate that the regression coefficients are stable and not adversely affected by linear dependency among predictors.
To further validate feature sensitivity, permutation importance analysis57 was performed, as shown in Fig. 11 Cleaning frequency was identified as the dominant predictor of soiling loss, consistent with physical dust accumulation mechanisms. Environmental variables such as ambient temperature, relative humidity, irradiance, and wind speed exhibited smaller but meaningful contributions. The inset plot provides a detailed view of environmental feature importance. These findings confirm the robustness and physical consistency of the predictive model.
Permutation-based feature importance analysis.
After pre-processing data, soiling loss are estimated using various method of ML such as DT, KNN, SVM, ANN and Stacking. The stacking ensemble combined ANN, SVM, and DT as base learners, with a gradient boosting regressor (GBR) as the meta-learner. GBR effectively refined the base predictions by capturing residual nonlinear patterns, resulting in improved accuracy and reduced bias. MATLAB R2024 a is used to run simulations on a Dell laptop, featuring a Core i9-11900 H processor and 32 GB of RAM.
ANN is intended to replicate the neuronal organisation of the human brain by employing layers that are interconnected in order to capture complicated interactions, as seen in (Fig. 12)58,59. Backpropagation is used to train the model in this study in order to minimize the errors. In given Eq. (6) wn are representing weights corresponding to each inputs xn and b is the bias, while final predicted output represented by Y.
ANN model.
Figure 13 shows how support vector machine (SVM) uses kernel functions to divide data in high-dimensional regions and capture complicated connections for classification and regression60. It estimates SL in this work and expressed in Eq. (7) as,
where, Z is the input vector, W is the weight and B is the bias term.
Support vector machine (regressor).
RT, as seen in Fig. 14, are decision trees used to forecast continuous variables SL by segmenting data according to defined criteria and computing the mean target value for each subgroup. They are interpretable, resilient to outliers, and adept at managing non-linear connections successfully61. The proposed approach employs regression trees by segmenting the feature space and predicting the target variables SL inside each segment as mentioned in Eq. (8).
Decision tree (regressor).
In a regression tree, N is the total number of nodes (leaves), each region Zn represents a partition of the feature space, Cn is the mean target value within that region, and I (X∈Zm) is an indicator function that equals 1 if X belongs to Zn otherwise 0.
The K-nearest neighbours (KNN) algorithm is a simple, non-parametric method that predicts outputs based on the average of the k closest data points in the feature space as shown in Fig. 15. In the context of this work, KNN estimates soiling loss by finding similar conditions of GHI, AT, WS, CF and RH from experimental data. It is intuitive and effective for capturing local patterns without requiring an explicit training phase.
K-nearest neighbours.
In KNN regression equation where K is the number of nearest neighbours, NK(X) is the set of those neighbours, and Yi are their target values, making the prediction the average of the K closest points.
The stacking model is an ensemble learning approach that combines multiple base learners to improve predictive performance shown in Fig. 16. In this work, ANN, SVM, and DT were used as base learners to capture diverse data patterns, and their outputs were blended by a Gradient Boosting Regressor (GBR) as the meta-learner. This framework leverages the strengths of each individual model while compensating for their weaknesses. As a result, the stacking model achieved higher accuracy and robustness compared to single-model approaches.
For the L base learner the stacking prediction given in Eq. (10) as,
where mL (x) are the base learner outputs (ANN, SVM, DT in this work) and g(⋅) is the meta-learner (GBR) that combines them to produce the final output.
Stacking ensemble model.
In this study, conventional random k-fold cross-validation was not employed because the dataset represents a physically time-ordered environmental process. Environmental variables such as irradiance, temperature, humidity, and wind speed exhibit strong temporal autocorrelation and causal continuity. Randomized cross-validation would mix past and future observations, introducing information leakage and leading to overly optimistic performance estimates, particularly for stacking models where the meta-learner learns second-order correlations. To ensure physically realistic and leakage-free validation, a time-ordered training–testing strategy was adopted. As illustrated in the Fig. 17, the dataset is divided chronologically into a training window (earlier observations) and a testing window (later unseen observations). The base learners (ANN, SVM, and DT) were trained exclusively on the training window, and their predictions were used to train the meta-learner (Gradient Boosting Regressor). The trained stacking model was then evaluated only on the testing window, which contained future unseen samples.
Time-aware training of stacking ensemble without cross-validation leakage.
The hyperparameters of all machine learning models, including ANN, SVM, DT, KNN, and the GBR used in the stacking ensemble, were selected using a systematic tuning procedure based on grid search combined with validation on the training dataset62. The hyperparameter combinations presented in Table 6 which is used to all machine learning model in order to minimized prediction error while avoiding overfitting. For each model, a range of candidate hyperparameters was evaluated, and the optimal configuration was selected based on minimum RMSE and stable generalization performance. The same training dataset and evaluation criteria were applied consistently across all models to ensure fair comparison.
It is important to evaluate the precision of the prediction model. A variety of measures have been used to evaluate the precision of predicting PV output power production12, which include:
(a) Mean Absolute Error (MAE): Computes the average of absolute differences between actual and predicted values, giving equal weight to all errors as expressed in Eq. (11)
(b) Mean Square Error (MSE): Measures the average of squared differences between actual and predicted values, penalizing larger errors more, its mathematical expression mention in Eq. (12)
(c) Root Mean Square Error (RMSE): Square root of MSE, expressing as Eq. (13) prediction error in the same units as the target variable.
(d) Coefficient of Determination (R2): Indicates how much variance in the actual data is explained by the model, with values closer to 1 showing better fit.The expression shown below in Eq. (14)
(e) Mean Absolute Percentage Error (MAPE): Represents as shown in Eq. (15) the average absolute error as a percentage of actual values, useful for relative accuracy.
This section discusses the performance and findings of the suggested models. The testing findings under actual environmental condition from the Roorkee area, India, are also given according to month and cleaning frequency. The empirical model produced from the experimental data is constructed and compared with machine learning models.
The performance of the four PV modules was evaluated in terms of short-circuit current (Isc), soiling ratio (SR), soiling loss (SL%), and current–voltage (I–V) and power–voltage (P–V) characteristics. The daily-cleaned panel (P1) was taken as the clean reference, while P2, P3, and P4 represent panels cleaned at weekly, biweekly, and monthly intervals, respectively.
Short-circuit current ((:{I}_{SC:})) was adopted as the primary soiling indicator because dust accumulation predominantly reduces optical transmission, directly affecting photocurrent generation. Since Isc is approximately proportional to irradiance, it provides a linear and direct measure of optical attenuation. In contrast, power output (Pmax) incorporates nonlinear temperature and fill factor effects, which may obscure pure dust-related losses.
In addition to (:{I}_{SC:})based metrics, I–V and P–V curves were generated using a calibrated PV analyzer for each panel at different cleaning intervals. These curves provide detailed insight into the effect of dust accumulation not only on the short-circuit current but also on the maximum power point (.
(:{P}_{MPP})), open-circuit voltage ((:{V}_{oc})), and fill factor (FF). Figures 18 and 19 shows daily average.
(:{I}_{SC:}) and daily soiling ratio (SR) trend over the time period of the experiment while Fig. 20 illustrate about the monthly soiling loss in percentage.
Daily average variation of short-circuit current for PV panels with different cleaning frequencies.
Daily variation of soiling ratio for PV panels cleaned at different intervals.
Monthly average soiling loss (%) for PV panels with different cleaning intervals: P2 (clean weekly), P3 (clean biweekly), and P4 (clean monthly).
The Figs. 18, 19 and 20 collectively illustrate the impact of cleaning frequency on PV performance. The daily-cleaned panel (P1) maintained the highest (:{I}_{SC:}), while P2–P4 showed progressive reductions with longer cleaning intervals. The soiling ratio (SR) exhibited a stepwise decline within each cleaning cycle, steepest for the monthly-cleaned panel (P4). Monthly average soiling losses confirmed this trend, increasing from 1 to 1.5% (P2) to 2.5% (P3) and5% (P4). These results clearly demonstrate that extended cleaning intervals accelerate dust-induced performance degradation.
PV analyser measurement of I-V and P-V characteristics at GHI 415 w/m2 (a) P1:-clean daily (b) P2:- clean weekly (c) P3:- clean biweekly (d) P4:- clean monthly.
Figure 21 shows the I–V and P–V characteristics of the four PV panels under different cleaning frequencies. The clean reference panel (P1, cleaned daily) achieved the highest maximum power point ((:{P}_{MPP}) = 6.00 W) and current at MPP ((:{I}_{mpp}) = 0.370 A). Panels with reduced cleaning frequency demonstrated progressive reductions in both (:{I}_{mpp}) and (:{P}_{MPP}): P2 (weekly) produced 5.80 W, P3 (biweekly) dropped to 5.59 W, and P4 (monthly) showed the lowest performance at 5.31 W. The open-circuit voltage ( (:{V}_{oc})) remained relatively stable across all panels, indicating that dust accumulation primarily impacts the short-circuit current and the maximum power output.
Validation performance of empirical (:{I}_{SC:}) models for four PV panels under different cleaning frequencies during October–December are shown in Fig. 22 as, (a) RMSE, (b) MAE, and (c) MAPE. Panels P1–P3 (daily, weekly, and biweekly cleaning) maintained low errors (RMSE ≤ 0.009 A, MAE ≤ 0.007 A, MAPE = 1–1.5%), whereas P4 (monthly cleaning) exhibited significantly higher deviations (RMSE up to 0.020 A, MAE = 0.017 A, MAPE = 3.7% in December), highlighting the negative impact of extended cleaning intervals on model accuracy.
Monthly cleaning frequency wise performance evaluation of empirical model (a) RMSE (b) MAE (c) MAPE.
Figure 23 shows that the four PV panels have a R² value of 0.99 or above, proving that the empirical modelling framework is reliable for describing the changes in I_(SC) under various cleaning conditions. The gradual decline in R² with reduced cleaning frequency highlights the sensitivity of empirical models to dust accumulation patterns.
Experimental vs. Empirical model R-Squared plot (Oct-Dec 2024) of (a) P1:-clean daily (b) P2:- clean weekly (c) P3:- clean biweekly (d) P4:- clean monthly.
The three PV panels are compared from October to–December to analyse the measured vs. projected soiling loss (SL, %) as shown in Fig. 24. The estimated regression line explains most of the variation (monthly R² =0.97–0.98) with minor absolute errors (RMSE = 0.35, MAE = 0.27). The moderate relative error (MAPE = 26–31%) suggests systemic bias or nonlinear effects that the basic empirical fit cannot capture. The following part uses machine-learning to lessen this relative inaccuracy.
Experimental vs. empirical SL model R-squared plot for month (a) October (b) November (c) December.
The empirical SL model’s performance is shown in Fig. 25. PV Panel (a) displays the regression plot between actual and expected soiling loss values. The data points closely correspond with the fitted regression line, indicating a high coefficient of determination (R2 = 0.978). The model’s low RMSE (0.364) and MAE (0.278) validate its trend capture. However, the mean absolute percentage error (MAPE) remains greater (28%), showing relative variances, especially for lower SL values. Panel (b) shows the residual distribution, where errors are centred around zero but include outliers. This residual spread shows systematic deviations not completely represented by the empirical formulation, motivating the upcoming section to use sophisticated machine-learning algorithms to minimize relative error while maintaining high R2.
Overall empirical SL model plot of (a) R-squared (b) residual.
Although the empirical SL model achieved a high coefficient of determination (R² ≈ 0.978), the MAPE value (~ 28%) appears relatively high. This is primarily due to the sensitivity of MAPE to small denominator values. Since several SL observations fall within low ranges (below 2%), even small absolute deviations lead to inflated percentage errors. Furthermore, the linear regression framework may not fully capture nonlinear dust accumulation patterns, contributing to structural bias at low SL levels. This constraint led to the introduction of machine learning algorithms to make percentage-based predictions more accurate. .
Experimental data was used to develop the machine learning model. The model inputs are AT, GHI, RH, WS, and CF, while the target variable is solar PV module SL. The prediction data size was 5 × 336 and divided 80:20 for training and testing, as shown in Table 7. SL is predicted using stacking, ANN, SVM, DT, and KNN models.
Statistical metrics are needed to assess machine learning models’ prediction performance for reliability and robustness. This research evaluated solar panel soiling loss models using MAE, RMSE, MAPE, and R2. These measures show the models’ capacity to reduce prediction errors, capture data variability, and generalize across environmental conditions.
The stacking ensemble model’s tight alignment of projected and observed responses in training and testing datasets showed high predictive performance in Fig. 26. The Fig. 27 shows residual plots with random residuals around zero, confirming the model’s dependability and lack of systematic bias. Performance metrics as shown in Table 8, which demonstrated the model’s resilience, with R² values of 0.9995 (training) and 0.9997 (testing) and low error values (RMSE: 0.0566 and 0.0456; MAE: 0.0404 and 0.0333). The stacking model generalizes effectively across datasets and outperforms individual models, making it a very accurate soiling loss prediction framework.
R2 plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
To examine whether cleaning frequency (CF) dominates the learning process, a feature ablation study was conducted by evaluating models trained using (i) the full feature set, (ii) CF alone, and (iii) environmental variables alone.
The full model consistently achieved the lowest prediction error. Although CF-only models exhibit strong correlation with soiling loss due to their causal relationship, they produce significantly higher absolute errors compared to the full model. Conversely, models trained exclusively on environmental variables perform poorly. Table 9 shows that environmental characteristics give important extra information and that cleaning frequency does not hide environmental learning.
The scatter plots reveal that the ANN model accurately predicted soiling loss as shown in Fig. 28. The stacking model had somewhat less departures from the ideal prediction line than the ANN, especially at higher response levels. The Fig. 29 residual plots reflect this tendency, with residuals spreading more broadly and displaying patterns at extreme values, indicating small bias in specific ranges. Performance measurements is shown in Table 10, which supports this result, with R² values of 0.9923 (training) and 0.9806 (testing) and greater error levels (RMSE: 0.2138 and 0.2822; MAE: 0.1277 and 0.1358). The ANN model is highly predictive, but its error distribution and somewhat lower accuracy than the stacking model suggest it cannot completely capture nonlinear data variability.
R2 plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
Compared to the ANN and stacking models, the DT model predicted well but had lesser accuracy. The scatter plot (Fig. 30) demonstrates that although projected responses track the actual values, deviations from the ideal prediction line are greater at higher response levels. The Fig. 31 shows residual plots with larger dispersion and predictable patterns, showing overfitting in specific areas. This is supported by performance measurements (Table 11), including R² values of 0.9767 (training) and 0.9777 (testing), and higher error levels (RMSE: 0.3766 and 0.4020; MAE: 0.1989 and 0.2090). The DT model captures the input-soiling loss connection, but its restricted generalization and higher residual spread make it less suitable than sophisticated ensemble approaches.
R2 plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
The scatter plots are shown in Fig. 32 to show that the Support Vector Machine (SVM) model predicted values that matched observed responses. Significant departures from the ideal prediction line, especially at higher response levels, imply limits in catching extreme instances. The Fig. 33 residual plots show hetero-scedasticity in predictions, with errors spreading further at higher response levels. Performance measures (Table 12) indicate lower R² values (0.9527) and greater error values (RMSE: 0.5365 and 0.4418; MAE: 0.3124 and 0.2917) compared to ANN and stacking. SVM has superior generalization and testing performance than DT, but its lower accuracy and higher residual spread restrict it compared to the stacking ensemble.
R2 plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
As seen in the scatter plots (Fig. 34), the k-Nearest Neighbor (kNN) model had mixed predictive performance, with projected values following the genuine responses but deviating at higher response levels. The Fig. 35 residual plots show higher error dispersion and systematic bias in extreme ranges, indicating model resilience is lowered. Performance measures (Table 13) demonstrate high generalization on test set but poor fit during training, with R² values of 0.7662 (training) and 0.9701 (testing). Our error measurements were greater, with RMSE values of 1.1926 (training) and 0.4657 (testing) and MAE values of 0.6948 and 0.3904. Despite good testing accuracy, the kNN model’s large training error and residual spread overfit local patterns and impair dependability compared to stacking.
R2 plot of (a) Training (b) Testing data set.
Residual plot of (a) Training (b) Testing data set.
The slightly higher testing R² compared to training R² for the KNN model is attributed to the local interpolation nature of KNN and the distribution of samples in feature space, rather than data leakage. Since testing samples fall within well-represented regions of the training feature space, stable prediction performance is achieved.
To assess potential overfitting and validate the generalization capability of the proposed stacking ensemble model, learning curve analysis is performed in accordance with statistical learning theory. The training and validation errors were evaluated as a function of increasing training data size. The learning curves demonstrate that although the training error decreases with increasing sample size, the validation error converges to a stable and closely aligned value without divergence. The narrow gap between training and validation errors confirms that the stacking model does not suffer from overfitting and generalizes well to unseen data.
The ensemble structure successfully balances the bias-variance trade-offs, so the validation error does not increase as the model capacity increases. These results provide theoretical and empirical evidence that the high R² values achieved by the stacking model are due to robust learning rather than memorization of the experimental dataset.
Learning curve for training and testing.
Also, the fact that the validation error doesn’t go up when the model capacity goes up shows that the ensemble structure does a good job of balancing bias and variation. Learning curves showing in Fig. 36 convergence of training and testing RMSE for the stacking ensemble, indicating strong generalization and absence of overfitting.
To assess the robustness of the stacking model against potential measurement noise, controlled Gaussian perturbations (± 3%) were introduced to environmental input variables. The model was retrained using the same train–test partition to ensure consistency. Figure 37 illustrates the residual distribution comparison between the original inputs and perturbed inputs.
Residual distribution: original vs. noisy inputs (± 3%).
Model accuracy diminishes with longer cleaning intervals, with weekly cleaning reaching R² = 0.995 and low RMSE (between 0.05 and 0.1), whereas monthly cleaning drops R² to 0.964 and raises RMSE over 0.4, as shown in Fig. 38. In all intervals, the stacking model had the lowest error (e.g., MAPE < 10%, RMSE ≈ 0.05) and greatest R² (> 0.99), demonstrating its durability over individual models.
Model performance comparison (MAPE vs. RMSE, bubble ∝ R²) across cleaning frequency intervals (a) clean weekly (b) clean biweekly (c) clean monthly.
Figure 39 demonstrates that stacking had the lowest MSE at all cleaning intervals: 0.003 (weekly), 0.001 (biweekly), and 0.001 (monthly). Empirical and KNN models had the largest errors, 0.022–0.294 and 0.144–0.158, respectively, especially during longer cleaning intervals. These findings confirm that stacking provides the most accurate and consistent forecasts regardless of cleaning frequency.
Model MSE comparison across cleaning-frequency intervals (a) clean weekly (b) clean biweekly (c) clean monthly.
Figure 40 shows MAE fluctuation by cleaning interval. The stacking model had the lowest MAE values (0.030 (weekly), 0.022 (biweekly), and 0.018 (monthly), whereas empirical and KNN models had the largest errors (0.468 and 0.337, respectively). This proves stacking’s prediction error-reducing ability under protracted soiling.
Model MAE Heatmap across cleaning intervals P2:- clean weekly P3:-clean biweekly P4:- clean monthly.
These findings show that stacking is the best accurate method for soiling loss estimate across cleaning frequencies and is resilient to increasing soiling buildup.
Table 14 indicates that stacking consistently outperformed other models with RMSE ranging from 0.03 to 0.045, MAE ≤ 0.03, and R² = 0.999 throughout all months. Empirical and KNN models had the largest errors (e.g., MAPE up to 35.38% and MAE > 0.4), especially in December, proving the stacking ensemble’s better resilience and dependability.
Shewhart control charts of RMSE for October–December 2024 forecasting models are shown in Fig. 41. Control charts, or Shewhart charts, provide performance data over time to determine control limits. The upper and lower control limits (UCL and LCL) set the permitted range of variation. Values over these limits indicate instability or unexpected swings. Central line (CL) shows process mean. The stacking model showed the most consistent projected accuracy throughout all months, with an average RMSE of 0.04 and tight control limits (UCL = 0.06, LCL = 0.01). ANN showed RMSE variation between 0.13 and 0.30 (mean 0.20), DT between 0.27 and 0.33 (mean 0.29), and KNN peaked at 0.49 (mean 0.38), suggesting greater variability. The stacking ensemble predicts well because of its low errors (Figs. 42 and 43) and process stability.
Shewhart control charts of RMSE for different predictive models (Oct–Dec 2024).
The Shewhart control chart of RMSE (Fig. 41 shows that prediction errors remain well within the statistical control limits across all months, confirming stable model performance and absence of instability due to non-stationarity. The empirical regression model also maintained consistent performance, with R² values between 0.97 and 0.98 across different months, further supporting the temporal robustness of the predictive framework. The environmental variables recorded during the experimental period exhibited natural variability while remaining within the same physical operating regime, enabling the model to learn stable relationships between environmental drivers and soiling loss. Since the models rely on physically meaningful predictors such as cleaning frequency, humidity, and irradiance, the learned relationships remain consistent over time. These results confirm that the predictive models demonstrate stable performance across different months without evidence of significant parameter drift or non-stationary.
Month-wise comparison of MAPE (%) across predictive models.
Month-wise comparison of MAE across predictive models.
The Shewhart control chart analysis shows that the stacking model predicts soiling loss with the lowest prediction errors and retains stability within restricted limits, making it the most dependable strategy63.
To ensure that the superior performance of the stacking model was not merely a consequence of increased model flexibility, several safeguards were employed:
Independent testing evaluation (80:20 split) demonstrated that training and testing R² values were nearly identical (0.9995 vs. 0.9997), indicating strong generalization without overfitting.
Residual analysis showed random dispersion without systematic patterns.
Month-wise and cleaning-frequency-wise evaluations confirmed consistent performance across operational conditions.
Control chart stability analysis demonstrated low variance and stable error distribution across months.
These results collectively indicate that the improved performance of the stacking model arises from its ability to capture nonlinear environmental interactions rather than merely from increased complexity.
To evaluate the effectiveness of the proposed machine learning framework, its performance was compared with the semi-empirical regression model developed in Sect. 3.2 under identical validation conditions. The empirical model represents a physics-based baseline using environmental predictors such as irradiance, temperature, humidity, wind speed, and cleaning frequency. As shown in Table 12, the stacking ensemble achieved significantly lower prediction error (RMSE = 0.03–0.045, MAE ≤ 0.03) compared to the empirical model (RMSE = 0.348–0.386, MAE = 0.266–0.294). This represents an approximately 85–90% reduction in prediction error. These results demonstrate the superior predictive capability of the proposed machine learning framework in capturing nonlinear soiling dynamics compared to conventional semi-empirical models.
After performance assessment, statistical analysis verified machine learning model dependability and robustness. Although error measurements like RMSE, MAE, MAPE, and R² assess accuracy, they do not adequately resolve bias between estimated and actual soiling loss levels. A Bland–Altman (BA) plot was utilized to visually evaluate agreement, highlight recurring deviations, and indicate prevalent prediction error boundaries. A non-parametric Wilcoxon signed-rank test was employed to see whether predicted and actual values differed significantly. These graphical and inferential methods analyse model performance comprehensively.
To evaluate the statistical independence of the experimental observations, the autocorrelation function (ACF) of the soiling loss time series was analyzed, as shown in Fig. 44 Since environmental and PV performance data are collected sequentially, temporal autocorrelation may reduce the effective sample size and affect model validity64.
The ACF results show that autocorrelation values decrease rapidly and remain within the 95% confidence bounds for most lags. Only short-term correlations are observed at very small lags, while longer lags exhibit negligible autocorrelation. This indicates weak temporal dependence and confirms that the observations are sufficiently independent for predictive modelling.
Furthermore, the natural variability in environmental parameters, including irradiance, temperature, humidity, and wind speed, along with different cleaning frequencies, ensured diverse operating conditions across samples. This variability further supports the effective independence of observations and validates the robustness of the machine learning models.
Autocorrelation function of daily-averaged soiling loss residuals.
The Bland–Altman (BA) study assessed the agreement between anticipated and actual soiling loss values. The BA plot shows bias and limitations of agreement, indicating systematic and random model prediction deviations, unlike traditional error measures. A lower bias value and narrower ranges of agreement imply that model predictions match data. This approach helps validate if machine learning models can reproduce experimental observations across operational circumstances.
The arrangement of dots around zero illustrates the degree of concordance between predictions and actual values, with tighter clustering near the red bias line signifying enhanced consistency. The dispersion within the limits of agreement (LoA) indicates the model’s variability, while outliers situated far beyond the LoA denote instances of inaccurate predictions, as depicted in Figs. 45 and 46, respectively.
.
Bland–Altman plot for (a) Empirical model (b) Stacking ML model (c) ANN (d) DT (e) SVM (f) KNN.
Model biases with limits of agreement (vertical lines).
The Fig. 47 shows the Wilcoxon signed-rank test comparing real and forecasted soiling loss values to assess the models’ predictive ability. A statistically insignificant result (p > 0.05) suggests that model predictions match experimental results, indicating model resilience. However, a significant finding (p < 0.05) indicates consistent disparities between projected and actual values. All models’ actual and expected soiling loss values were compared using the Wilcoxon signed-rank test. Despite having the lowest median difference (0.0016), the stacking model has a substantial p-value (p = 0.0083), demonstrating its capacity to capture tiny deviations with high consistency. The empirical (p = 0.593), decision tree (p = 0.276), and KNN (p = 0.428) models had non-significant p-values, indicating no statistically significant difference between their predictions and actual values.
Wilcoxon signed-rank test plot for (a) Empirical model (b) Stacking ML model (c) ANN (d) DT (e) SVM (f) KNN.
Even with strong numerical performance, ANN (p = 2.71e-42) and SVM (p = 7.4e-24) showed substantial discrepancies, suggesting systematic prediction errors. Stacking is the most reliable technique since it has minimum bias and statistically significant consistency, whereas empirical and tree-based approaches are equivalent but less robust. Although the Wilcoxon signed-rank test yielded a statistically significant p-value (p < 0.01) for the stacking model, indicating that the median difference between predicted and actual values is not exactly zero, the magnitude of this deviation was extremely small (≈ 0.001–0.002). Given the relatively large sample size, even minor deviations can become statistically significant. However, absolute error metrics (RMSE and MAE) remained very low, suggesting that the detected bias is negligible in practical terms. Therefore, the stacking model demonstrates high predictive accuracy with minimal practical bias rather than perfect agreement.
Bland–Altman analysis and Wilcoxon signed-rank test p-values vary because they employ different statistical methods. The Bland–Altman approach estimates the p-value using a paired t-test to see whether the mean difference (bias) between actual and predicted values is substantially different from zero. The Wilcoxon signed-rank test, on the other hand, tests if the median of the paired differences deviates considerably from zero without assuming normality. BA focuses on systematic bias in the mean, whereas Wilcoxon confirms median differences, therefore p-values may vary. Two methods give a more complete statistical assessment of model performance.
Although the dataset originates from a single geographical site and season, meaningful domain shifts exist within the data due to temporal variation in environmental conditions and operational variation in cleaning frequency. Figure 48 illustrates covariate distribution shifts of global horizontal irradiance across months, confirming changes in the input feature space.
Figures 49 and 50 further demonstrate that the stacking model maintains stable RMSE across temporally distinct months and across different cleaning frequencies. The consistency of predictive performance under these distributional and operational shifts indicates robust within-domain generalization rather than simple interpolation of identical conditions. While the present study does not claim cross-climate transferability, the proposed framework demonstrates strong robustness within the studied domain.
Covariate distribution shift of GHI across months.
Temporal domain shift evaluation of stacking model.
Operational domain shift evaluation of stacking model.
To statistically validate the observed performance dominance of the stacking ensemble, paired Diebold–Mariano tests were conducted on squared prediction error sequences. The results indicate as shown in Table 15 that the stacking model significantly outperforms ANN, DT, SVM, KNN, and empirical models, with DM statistics ranging from − 3.97 to − 12.08 and corresponding p-values well below 0.05.
The negative DM statistics confirm that the stacking model consistently yields lower prediction errors than competing models. These findings demonstrate that the superior performance of the stacking ensemble is statistically significant and not attributable to random variation.
This study investigated natural soiling on solar panels subjected to different cleaning protocols and developed empirical and machine learning models for predicting soiling loss. We employ experimental analysis and data-driven methods to test, evaluate, and predict how well PV systems will work when they are dirty in the real world. Ensemble learning outperforms empirical approaches in accuracy and robustness. The experimental study on four PV panels with different cleaning frequencies (daily, weekly, biweekly, monthly) confirmed that natural soiling significantly impacts PV performance, with higher losses under longer intervals.
This study contributes to the field by establishing a validated experimental–empirical–machine learning framework for real-world soiling prediction under controlled cleaning intervals.
The proposed stacking model significantly outperforms the semi-empirical baseline, demonstrating improved predictive accuracy and robustness under identical validation conditions.
Unlike purely simulation-based studies, the proposed approach is grounded in field measurements and incorporates temporal causality-aware validation, making it both scientifically rigorous and practically deployable.
The findings provide a reproducible methodology for future PV degradation studies and open avenues for intelligent, data-driven operation and maintenance optimization in solar energy systems.
Two empirical models were created one for (:{I}_{SC:})prediction (dominated by GHI) and another for Soiling Loss (SL) (mainly impacted by RH and cleaning frequency). The SL model has R² = 0.978, but a high MAPE = 28%, suggesting insignificant nonlinear effects.
Machine learning showed considerable increases, with the stacking ensemble obtaining the highest accuracy (R² = 0.9997, RMSE = 0.0456, MAE = 0.0333) and surpassing individual models (ANN, DT, SVM, KNN), among other models.
Model accuracy falls according to decreased cleaning frequency, but stacking remains strong (MSE ≤ 0.003, MAE < 0.03) even with monthly cleaning.
Statistical validation using Bland–Altman and Wilcoxon signed-rank tests confirmed stacking’s superiority, with minimal bias and narrowest limits of agreement, although minor statistically detectable differences were observed in some cases.
Overall, the integrated experimental–empirical–ML framework demonstrates that ensemble-based data-driven models can reliably predict soiling loss, enabling optimized maintenance scheduling and predictive O&M strategies for PV systems.
While prediction error differences may appear numerically small, their operational significance becomes substantial when translated into cumulative energy losses over extended periods. As shown in Table 3, soiling losses exceeding 5% were observed under extended cleaning intervals. Accurate prediction of soiling progression enables optimized maintenance scheduling, improving energy yield and reducing operational costs. The proposed model is developed based on short-term dry-season data, during which module surface properties are assumed constant. Long-term surface degradation, coating wear, and micro-roughness evolution may influence dust adhesion behaviour and soiling accumulation rates. Such effects represent gradual structural drift and would require multi-season or multi-year datasets for comprehensive modelling. Therefore, the current framework is primarily applicable to short- and medium-term predictive maintenance planning.
The present model was developed using data collected during dry environmental conditions to ensure controlled soiling accumulation. Extreme events such as rainfall-induced natural cleaning or dust storms introduce regime shifts that require representative training data for accurate prediction. Future work will incorporate multi-season datasets including rainfall and extreme environmental conditions to enhance model robustness and generalization capability.
Data is available based on request.
AdaBoost
Autoencoder
Artificial neural network
Ambient temperature
Backpropagation neural network
Cleaning frequency
Convolutional neural network
Cell temperature
Diffuse horizontal irradiance
Atmospheric pressure
Particulate matter (PM10 / PM2.5)
Relative humidity
Direct normal irradiance
Wind speed
Global horizontal irradiance
K-nearest neighbor
Linear regression
Long short-term memory
Mean absolute error
Mean absolute percentage error
Machine learning
Multilayer perceptron
Mean square error
Root mean square error
Recurrent neural network
Seasonal auto regressive integrated moving average with exogenous variables
Sunshine hour
Soiling loss
Soiling ratio
Support vector machine
Support vector regression
Random forest
RGB images of solar panels
Decision tree
Extreme learning machine
Gated recurrent unit
Extreme gradient boosting
Coefficient of determination
Current at maximum power point
Short-circuit current
Short-circuit current of clean reference panel
Short-circuit current of soiled panel
Power output of clean panel
Maximum power output
Power output of soiled panel
Temperature of dusty panel
PV module temperature
Voltage at maximum power point
Open-circuit voltage
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This Research was conducted with the financial support provided by UPES, Dehradun, India. The authors express gratitude to the Research & Development Department at UPES, Dehradun, Uttarakhand, India for their support under Grant Number UPES/R&D-SoAE/25062025/27.
Open access funding provided by Manipal University Jaipur.
Electrical Cluster, School of Advanced Engineering, UPES, Dehradun, 248007, India
Ashutosh Shukla & Rupendra Kumar Pachauri
Miyan Research Institute, International University of Business, Agriculture and Technology, Dhaka, 1230, Bangladesh
Rupendra Kumar Pachauri
UCRD & CSE-APEX, Chandigarh University, Mohali, Punjab, India
Ranjan Walia
Department of Electrical Engineering, Manipal University Jaipur, Jaipur, India
Vinay Gupta
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Ashutosh Shukla (AS): Conceptualization, Methodology, Writing – original draft, Software, Visualization. Rupendra Kumar Pachauri (RKP): Methodology, Data curation, Writing – review and editing, Supervision. Ranjan Walia (RW): Investigation, Writing – review and editing, Supervision. Vinay Gupta (VG): Investigation, software, Visualization, data analysis, Writing – review and editing.
Correspondence to Vinay Gupta.
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In May, for the first time, solar supplied more of the nation’s electricity than coal, according to a global energy think tank.
Even as President Donald Trump boosts coal over clean energy, solar power is hitting new milestones in the U.S. and remains the leading source of new power.
Data released Wednesday by global energy think tank Ember, along with a report by the Solar Energy Industries Association and analytics firm Wood Mackenzie, show the continued growth of solar and decline of coal in the United States despite federal policy. In May, for the first time, solar supplied more of the nation’s electricity than coal, or 12.8%, Ember said. Coal supplied 12.2%, its fourth-lowest monthly share ever.
“For years solar power has risen in the U.S. electricity mix,” said Nicolas Fulghum, senior energy and data analyst at Ember. ”At the same time, coal power has lost its status, first as the largest source in the U.S. mix, and then gradually over the years has fallen even further.”
Solar also became the third-largest source of electricity in the U.S. in May, behind natural gas and nuclear, Fulghum said. Coal generation hit an all-time monthly low in April and rebounded only modestly in May, allowing increasing solar generation to overtake coal, he added.
Electricity is produced by converting sources of energy — fossil fuels, renewable resources and nuclear — into electrical power. Burning coal, oil, and natural gas for electricity emits carbon dioxide, trapping heat in the atmosphere and warming the planet. By contrast, solar, wind, geothermal, hydropower, and nuclear are carbon-free.
After about two decades of essentially flat electricity consumption in the U.S., electricity demand is increasing to power artificial intelligence, grow domestic manufacturing, and electrify transportation and heating. Fulghum said he expects to see more months when solar exceeds coal generation, before overtaking it on an annual basis in a few years.
These milestones signify that solar “has staying power” at a time when there’s less support for renewable energy at the federal level, he added.
Wind and solar combined have overtaken coal in the past, and wind power alone has outpaced coal during spring months when wind speeds pick up. Ember gets its hourly and monthly data from the U.S. Energy Information Administration.
Globally, electricity generation from renewables is growing rapidly. Renewables will become the largest global energy source, used for almost 45% of electricity generation by 2030, according to the International Energy Agency.
Last week, Trump, a Republican, announced a plan to boost the struggling U.S. coal industry by spending nearly $700 million to support coal-fired power plants and coal exports. Trump said at a White House event that “coal’s a great business” and that “in terms of power, there’s really nothing like it.”
Martin Pochtaruk, CEO and founder of Canadian-based solar panel manufacturer Heliene, said Trump can say that coal is coming back but investors will invest their money in whatever brings the best return. And for power generation that is solar, making it the fastest-growing fuel, he added.
A White House spokesperson defended the Trump administration’s overall energy policies, saying they were geared toward strengthening the country’s security.
“The President has reversed the Left’s devastating policies, saved the American coal industry, prevented the retirement of more than 17 gigawatts of power, and saved lives during heightened demand periods,” Taylor Rogers said in a statement.
While Trump is trying to reverse the coal industry’s decline, solar has been the top source for new power for five years, SEIA said. SEIA and Wood Mackenzie said solar and battery storage were practically the only energy resources being built in the first quarter, making up 91% of all new generating capacity.
The Trump administration has canceled solar and wind projects, implemented policies that slowed clean energy permitting and development, and terminated $7 billion in funding intended for affordable solar energy projects across the U.S.
“As power demand skyrockets, political and regulatory attacks are slowing down the exact resources we rely on,” Darren Van’t Hof, interim president and CEO of SEIA, said in a statement. “Impeding the only sector that is actively building new power is a reckless gamble that will only drive electricity bills higher.”
Several groups sued the Environmental Protection Agency over canceling the Solar for All program. A district court dismissed the case last week citing lack of jurisdiction. The plaintiffs have another filing pending in the Court of Federal Claims.
In a ruling Saturday, a federal judge struck down guidance from the Internal Revenue Service restricting tax credits for wind and solar projects.
Trump has blamed renewable energy sources such as wind and solar power for skyrocketing energy costs. But energy analysts say recent price hikes are based on growing demand, aging infrastructure, and increasingly extreme weather events that are exacerbated by climate change. Most recently, the war in Iran that Trump launched has also led to a spike in energy costs.
Blaming clean energy is “nonsensical,” said U.S. Rep. Jared Huffman. The California Democrat said that “not even lighting $700 million of taxpayer money on fire” can save the dying coal industry.
“The rest of the world will move ahead toward a clean energy future with countries other than the United States leading the charge, unfortunately,” he said Wednesday. “Trump will fail in this agenda. But, he will do enormous damage to our global leadership on clean energy and to the cost of living for struggling Americans.”
States won by Trump in the 2024 election accounted for 74% of all solar capacity installed in the first quarter of 2026, with Texas, Florida, Ohio, Indiana, Michigan, Arizona, and Mississippi ranking among the top 10 states for new solar additions, SEIA said. The U.S. now exceeds a total of 6 million installations nationwide across all solar sectors, which includes large-scale solar arrays, commercial, community solar, and residential or rooftop solar.
Johanna Neumann, at the Environment America Research and Policy Center, said it’s “good news for our health and our planet that solar continues to grow,” and also, not surprising.
“Today we can harness solar more affordably than any other energy source. It’s scalable. And it’s also our most abundant renewable energy source,” said Neumann, senior director of the center’s campaign for 100% renewable energy. “So I think it’s hard to keep the lid on a good idea, especially if the economics are tilting in your favor as well, which they are in the case of solar.”
Environment America’s renewable energy dashboard shows that 32 U.S. states generated at least 10% of their retail electricity sales from solar, wind and geothermal energy last year, compared to 18 states in 2016. Clean energy in the South is booming, particularly in Florida, Arkansas, and Mississippi, Neumann said.
“I think there is a misconception in the United States that clean energy is something for the coasts and liberal cities,” she said. “The true story of renewable energy is a 50-state story.”
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Perovskite photovoltaics (PV) have delivered rapid efficiency gains; however, commercial deployment remains constrained by issues related to scale-up, reliability and system-level uncertainties. The field is now limited less by material discovery than by the complex choreography of commercialization. In this Perspective, we reframe the commercialization of perovskite PV as a multidimensional, product-centric evolution spanning materials, manufacturing, standards, policy and market design. In this Perspective, we examine perovskite and perovskite–silicon tandem photovoltaic technologies, focusing on their manufacturing maturity and commercial readiness. We highlight a plateau effect, in which additional laboratory-scale efficiency gains provide limited benefit unless accompanied by improvements in production yield, operational stability and overall factory economics. Drawing on lessons from early pilot lines, regional industrial strategies and analogue technologies such as OLEDs, we highlight the roles of supply chains, adaptive standards and risk capital in creating bankable products. Future research must treat manufacturability, stability, resource constraints and recyclability as primary design variables, and coordinated, application-driven roadmaps are essential to translate perovskite PV from record-setting devices into a credible product.
Perovskite photovoltaics (PV) are no longer constrained primarily by efficiency but by the ability to integrate materials, manufacturing, standards and finance into a coherent product and value chain.
Conventional performance metrics (efficiency and stability of small-area cells) are insufficient for investment and deployment decisions; technology, manufacturing and commercial readiness levels, together with bankability and warranty-compatible reliability data, must guide policy and funding.
Industrially relevant manufacturing routes, robust supply chains and gigawatt-scale factory economics, such as capital expenditure, yield, throughput and learning curves, need to be treated as upfront design constraints rather than downstream optimization problems.
Regional differences in policy, industrial capacity and risk appetite, such as China’s vertically integrated model versus more fragmented ecosystems in Europe and the United States, will shape where and how perovskite PV first reaches meaningful scale.
Perovskite single-junction modules are expected to succeed in differentiated applications such as building-integrated PV, aerospace and agrivoltaics, whereas high-efficiency tandem architectures remain the most promising pathway for mass-market adoption.
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This work has received funding as part of the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101147311 of the LAPERITIVO project, grant agreement no. 101079488 of the TESTARE project, grant agreement no. 101291137 of the TRANSPIRE project and grant agreement no. 101120397 of the Approach project. A.K.H. acknowledges funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101153019. Z.-F.H. acknowledges funding from the National Science and Technology Council (114-2917-I-564-018). H.T. thanks the National Key R&D Program of China (2022YFB4200304) and the National Science Fund for Distinguished Young Scholars (T2325016); K.X. thanks the National Natural Science Foundation of China (62504100).
IMEC, IUMAT, Thin Film PV Technology, Genk, Belgium
Amit Kumar Harit, Zi-Fan He, Yinghuan Kuang, Tamara Merckx, Aranzazu Aguirre, Tom Aernouts & Anurag Krishna
UHASSELT, Institute for Materials Research (IUMAT), Hasselt, Belgium
Amit Kumar Harit, Zi-Fan He, Yinghuan Kuang, Tamara Merckx, Aranzazu Aguirre, Tom Aernouts & Anurag Krishna
EnergyVille, IUMAT, Genk, Belgium
Amit Kumar Harit, Zi-Fan He, Yinghuan Kuang, Tamara Merckx, Aranzazu Aguirre, Tom Aernouts & Anurag Krishna
National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Frontiers Science Center for Critical Earth Material Cycling, Jiangsu Physical Science Research Center, Nanjing University, Nanjing, China
Manya Li, Ke Xiao & Hairen Tan
Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore
Xi Wang & Yi Hou
Solar Energy Research Institute of Singapore (SERIS), National University of Singapore, Singapore, Singapore
Xi Wang & Yi Hou
Hangzhou Microquanta Semiconductor, Hangzhou, China
Buyi Yan
Research and Development Center, Renshine Solar (Suzhou), Changshu, Jiangsu, China
Hairen Tan
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A.K.H. and A.K. conceived the article, collected the data and wrote the first draft of the manuscript. A.K., A.K.H. and Z.-F.H. prepared the figures. All authors contributed substantially to the discussion of the content, reviewed and/or edited the manuscript before submission.
Correspondence to Anurag Krishna.
Y.H. is the founder of Singfilm Solar, a company commercializing perovskite PV. H.T. is the founder, chief scientific officer and chairman of Renshine Solar (Suzhou), a company that is commercializing perovskite PV. B.Y. has ownership interests of Hangzhou Microquanta Semiconductor, a company that is commercializing perovskite PV. The other authors declare no competing interests.
Nature Reviews Clean Technology thanks Yuanyuan Zhou, Teresa Gatti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Harit, A.K., He, ZF., Kuang, Y. et al. Taking perovskite photovoltaics from promise to product. Nat. Rev. Clean Technol. 2, 453–466 (2026). https://doi.org/10.1038/s44359-026-00173-2
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Malta-headquartered solar developer and operator GoldenPeaks Poland Holding has filed for Chapter 11 bankruptcy protection in the US after a severe liquidity crunch left the company struggling under nearly US$1 billion of debt.
The company and 39 affiliated entities filed voluntary petitions in the US Bankruptcy Court for the Southern District of Texas on 29 May. Court filings show GoldenPeaks had less than €1.1 million (US$1.27 million) in available cash against approximately US$952 million in funded debt obligations.
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The filing comes despite GoldenPeaks operating one of Poland’s largest solar portfolios, with 664MW of installed PV capacity and a development pipeline of more than 500MW.
According to court documents, the company’s financial difficulties were driven by a combination of persistent grid curtailments imposed by Poland’s transmission system operator, project delays, rising supply chain costs and the collapse of Spectris Energy, its primary engineering, procurement, construction (EPC) and operations subcontractor.
GoldenPeaks said the failure of Spectris Energy forced the company to separate from the wider GoldenPeaks Capital (GPC) group and establish itself as a standalone business focused on its Polish solar assets.
The company’s business model had previously relied on internal support from the wider GPC group, which provided development, EPC, financing, power sales and operations services. Following Spectris Energy’s collapse, GoldenPeaks was left without an in-house operations and maintenance platform.
Court filings state that the debtor entities own the solar assets but have no direct employees. The company is currently managing its entire 664MW operating portfolio through a single asset management agreement signed just 16 days before the Chapter 11 filing.
GoldenPeaks’ Polish portfolio consists of 548 solar projects spread across approximately 136 special purpose vehicles (SPVs). The operational fleet is organised into nine portfolio groups with 664MWp of installed capacity, while a further five portfolio groups representing more than 500MWp remain under development.
To support operations during the restructuring process, controlling shareholder Brookfield has proposed a US$162.8 million debtor-in-possession (DIP) financing package.
The financing is intended to fund a four-month court-supervised process aimed at either selling the business or implementing a reorganisation plan.
In the bankruptcy petition, GoldenPeaks reported assets of between US$1 billion and US$10 billion and liabilities ranging from US$500 million to US$1 billion.
The Chapter 11 process will allow the company to continue operating its solar portfolio while seeking a longer-term solution for its capital structure and ownership.
Brookfield also recently formed a joint venture (JV) with Japanese financial services company Mitsubishi HC Capital to own and operate renewable generation projects in Europe.
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The details of his post made clear that the installation involved more than the panels on the roof.
Photo Credit: iStock
One Cape Cod homeowner says the appeal of rooftop solar had little to do with maximizing payback. Three weeks after installing a 9-kilowatt system, he said the main benefits were having power during outages and reducing costly summer electric bills.
In a post on Reddit, a Cape Cod, Massachusetts, resident described the first few weeks with his new setup. The homeowner said his system uses 20 Talesun panels, each rated at 450 watts, and he has been constantly checking the Enphase app since it was turned on.
He captured his view of the purchase in a single sentence: “Best 27k cash I ever spent,” adding
“I don’t care about ROI.”
The details of his post made clear that the installation involved more than the panels on the roof. He said the system also includes about 50 kilowatt-hours of Jackery backup batteries, a smart switch, and a transfer switch.
Additionally, he benefits from 1-to-1 net metering, meaning he can get paid for the energy he produces and feeds back into the grid.
His focus on backup power appears to be central to his decision to buy the system, especially given the area’s severe weather. In his words: “I don’t care at all about roi – I care about powering my whole house through a 80 hour blizzard and not getting a 400 dollar August bill.”
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The original poster’s experience highlights several reasons people go solar, and that not all buyers are singularly focused on a break-even timeline. Rather, many care more about adding backup power, lower monthly bills, and more control over energy use at home.
In places that regularly deal with major storms, the ability to keep lights on, save refrigerated food and medication, and run most of the house for days can matter more than a strict return-on-investment calculation.
If you want to make the switch to solar power, EnergySage can help you save up to $10,000 on your installation and connect you with vetted local installers. If buying panels isn’t in your budget, Palmetto’s $0 down LightReach program can save you up to 20 percent on your monthly energy bills.
Meanwhile, 1-to-1 net metering can make daytime solar production more valuable by offsetting electricity used later, helping reduce expensive summer usage.
The post also points to a broader truth about solar in states like Massachusetts, where upfront costs can be higher even as policy support improves the economics. Programs and incentives such as SMART and renewable energy credits can improve the overall financial picture over time.
Get TCD’s free newsletters for easy tips, smart advice, and a chance to earn $5,000 toward home upgrades. To see more stories like this one, change your Google preferences here.
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SHANGHAI, June 12, 2026 /PRNewswire/ — GCL System Integration Technology Co., Ltd. ("GCL SI" or "the Company") has showcased its scenario-based PV solutions at the SNEC 2026 which was held in Shanghai, China from June 3 to 5. GCL SI showcased a comprehensive multi-matrix product portfolio, including high-efficiency TOPCon and BC modules, scenario-based component solutions, and mobile green energy systems. The exhibition highlighted the Company's latest achievements across four strategic dimensions: high-efficiency cells, scenario-based solutions, mobile energy, and ecosystem development.
The GCL SI booth at SNEC 2026 in Shanghai showcases scenario-based PV solutions
Advancing Application-Specific Solar Deployment
The photovoltaic industry is evolving from scale-driven growth toward value creation through more precise and differentiated applications. To address this shift, GCL SI showcased five scenario-specific module solutions: anti-dust modules for commercial and industrial applications, anti-glare modules for airports and highways, high-efficiency modules for utility-scale ground-mounted projects, steel-frame modules for 2,000V desert PV applications, and composite-frame modules for marine environments. Covering land, sea, high altitude, typhoon zones, and corrosive environments, this scenario-based approach is designed to address the varying performance, reliability and economic requirements of different deployment environments.
For extreme wind and high‑altitude environments, GCL SI's steel-frame modules offer twice the tear resistance of traditional aluminum frames, withstanding extreme wind conditions of up to 60 m/s in typhoon-prone regions while improving overall project economics compared with conventional aluminum-frame designs. For harsh high-salt, high-humidity and high-heat offshore conditions, the marine-specific modules feature double-coated glass, high-water-resistance encapsulants, and full-structure protection, passing top-level salt spray and rigorous reliability tests to solve corrosion, hot spot, and aging challenges. In the commercial and industrial distributed PV market, the S-series anti-dust modules combine high-density encapsulation, full-screen anti-dust design, and advanced sealing for enhanced reliability.
GCL SI's focus on product quality and reliability was further validated by its designation as a 2026 Kiwa-PVEL TOP PERFORMER, one of the photovoltaic industry's most widely recognized benchmarks for module performance and reliability.
Digitalization Supporting Low-Carbon Value Creation
GCL SI, together with Ant Digital Technologies, launched "GCL Carbon Chain 3.0" at SNEC 2026 and initiated the Carbon Chain 4.0 roadmap.
Leveraging GCL SI's deep industry expertise in PV manufacturing, supply chain management, and low-carbon product innovation, and supported by Ant Digital's blockchain, AI and digital technologies, Carbon Chain 3.0 establishes a product carbon footprint monitoring and verification mechanism. The platform integrates carbon footprint management across procurement, manufacturing and delivery processes, enhancing transparency and supporting low-carbon value creation throughout the supply chain. Additionally, GCL SI has enhanced its integrated "module + green certificate" service capability, offering green asset solutions for both domestic and international markets.
GCL SI is also translating its application-driven strategy into international markets through targeted partnerships. In Africa, the company is collaborating with PowerChina Guiyang Engineering Corporation to develop off-grid integrated solar-storage solutions. In Latin America, GCL SI is deepening its strategic partnership with Coexito to strengthen channel presence and brand positioning.
On the ecosystem front, GCL SI is working with Ant Digital Technologies and Digital China to advance digital energy ecosystem development, integrating blockchain, AI, and data governance to build a smarter, more connected clean energy future.
Looking ahead, GCL SI believes the next phase of solar industry growth will be driven by solutions that deliver superior performance, sustainability, and application-specific value. The company will continue to advance technologies that support the evolving needs of global energy markets.
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The World Fused Disconnects for Photovoltaics market addresses a critical safety function within solar electrical systems: combined isolation and overcurrent protection for photovoltaic circuits. These devices serve as both a disconnecting means for maintenance and emergency shutdown and as a fuse-holder for overcurrent protection, operating on the DC side of solar arrays between panels and inverters. The product category spans discrete fuse-holder-disconnect switches, integrated units within combiner boxes, and replacement fuse carriers and fuse bases used in ongoing maintenance.
Demand is fundamentally derived from global solar photovoltaic capacity additions, which have averaged annual growth of 20-30% over the past decade and continue to accelerate as nations pursue decarbonization targets and energy security objectives. The World market for fused disconnects is therefore shaped by the same macro drivers as the broader solar industry: declining levelized cost of electricity from solar, policy support mechanisms including feed-in tariffs and renewable portfolio standards, corporate renewable energy procurement, and grid parity in an increasing number of markets. Unlike solar modules, which have experienced dramatic price compression and commoditization, fused disconnects retain a stronger technical-specification-driven purchasing dynamic, with safety certification, voltage rating, current capacity, and enclosure rating (ingress protection) serving as primary selection criteria.
The World Fused Disconnects for Photovoltaics market is expanding at a pace that closely tracks global solar PV installation volumes, though with important structural nuances. Annual solar PV additions globally surpassed 400 GW in 2024 and continue to climb, with most major forecasts projecting 500-700 GW of annual installations by 2030. Fused disconnect demand grows in correlation with these capacity additions but is also influenced by the number of separate array strings per installation, the voltage architecture chosen, and replacement demand from the existing installed base, which globally exceeds 1.5 TW of cumulative solar capacity.
Growth in the World market over the 2026-2035 forecast period is projected in the high single digits to low double digits annually in volume terms, with value growth tracking slightly higher due to ongoing specification migration toward higher-voltage and more feature-rich products. The replacement segment, while smaller than new-installation demand through most of the forecast period, is expected to gain share steadily after 2030 as the first wave of large-scale solar farms commissioned in the 2010-2020 period enters its major component replacement cycle. Regionally, the fastest demand growth is occurring in markets with aggressive solar deployment targets, including China, India, the Middle East, and Southeast Asia, while the replacement-heavy markets of Europe and North America provide more stable, less cyclical demand.
By type, discrete fused disconnect components and modules represent the largest volume segment, accounting for an estimated 55-70% of unit demand in the World market. Integrated systems, which combine fused disconnects with combiner boxes, monitoring electronics, and surge protection in a factory-assembled enclosure, are the fastest-growing segment, driven by the preferences of large-scale project developers for reduced field labor and simplified procurement. Consumables and replacement parts, including fuse carriers, fuse bases, and replacement fuse links, constitute a smaller but highly recurring and margin-stable segment, typically serving the installed base of solar plants beyond their fifth year of operation.
By end-use sector, utility-scale solar farms generate the largest absolute demand, representing an estimated 50-60% of World fused disconnect procurement. Commercial and industrial rooftop solar installations account for roughly 25-30%, with residential solar comprising the remainder. The utility segment is notable for its preference for 1500V DC components, its tendency to procure through engineering, procurement, and construction contractors and system integrators, and its sensitivity to total installed cost rather than component price alone.
Commercial and residential segments, by contrast, more frequently procure through electrical wholesale distribution channels and are more price-sensitive at the component level, often using 600V or 1000V rated products. OEM integration, including sales to inverter manufacturers and electrical enclosure fabricators, represents a distinct procurement channel with longer contract cycles and greater emphasis on qualification testing and supply reliability.
Pricing in the World Fused Disconnects for Photovoltaics market is structured across several layers, with wide variation by voltage rating, current capacity, enclosure material, and certification level. Standard 600V DC fused disconnects for residential and light commercial applications are priced in the range of USD 15-45 per unit at typical distribution pricing, while 1000V DC industrial-grade units range from USD 35-80. Premium 1500V DC fused disconnects, which require more robust arc extinguishing capability, higher creepage distances, and more stringent thermal management, command prices of USD 60-150 or more, particularly when supplied with stainless steel enclosures, NEMA 4X ratings, or auxiliary contact packages.
Cost drivers for the World market are dominated by raw material inputs: copper for current-carrying components, silver-alloy or silver-tungsten for contact surfaces, engineering thermoplastics (polyamide, polycarbonate) for housings and fuse carriers, and steel or aluminum for enclosures. Material costs have shown significant volatility, with copper prices fluctuating 25-40% over recent multi-year periods and polymer resin prices influenced by petrochemical feedstock cycles. Labor costs are a secondary but non-trivial factor, particularly for products requiring manual assembly and testing.
Certification costs, including UL 98, UL 4248, IEC 60947-3, and regional marks such as CE, CCC, and TÜV, add USD 20,000-80,000 per product family and are amortized over production volumes, meaning that suppliers with broader product portfolios and higher unit volumes enjoy a structural cost advantage. Volume contract pricing for large project orders typically carries a 15-25% discount relative to list pricing, while service and validation add-ons, including factory witness testing and documentation packages, may add 5-12% to the transaction value.
The World Fused Disconnects for Photovoltaics supply base is moderately concentrated, with the top 8-10 manufacturers accounting for an estimated 60-75% of global revenue. The competitive landscape includes specialized electrical protection companies, diversified industrial automation and electrical equipment conglomerates, and regional manufacturers serving domestic or neighboring markets. Prominent participants recognized in the World market include Mersen, Littelfuse, Eaton Corporation, Siemens, Schneider Electric, ABB, Socomec, and Weidmüller, alongside China-based suppliers such as CHINT and Shanghai Liangxin. The competitive dynamic is shaped by product certification breadth, voltage rating coverage, distribution network reach, and the ability to supply integrated solutions alongside discrete components.
Competition in the World market is stratified by voltage rating and application tier. In the 600V segment, price competition is intense, with multiple suppliers offering functionally equivalent products and brand differentiation limited. In the 1000V and particularly 1500V segments, competition shifts toward technical capability, safety certification depth, and field reliability track record, with fewer suppliers qualified and wider margins available. Supplier qualification cycles for large project specifications can extend 12-24 months, creating meaningful switching costs and long-term supply relationships.
Regional manufacturers in India, the Middle East, and Southeast Asia are increasingly gaining share in their home markets through competitive pricing and responsive local service, though they face certification barriers when targeting European or North American projects.
Production of Fused Disconnects for Photovoltaics occurs primarily in manufacturing facilities located in China, Germany, the United States, France, India, and Mexico, reflecting the historical locations of the major electrical component manufacturers. China is the largest production base by volume, hosting both international manufacturers with local factories and domestic suppliers serving the world’s largest solar installation market. Production involves metal stamping and forming for current-carrying parts, injection molding for thermoplastic housings, contact welding or riveting using silver-alloy materials, manual or automated assembly, and electrical testing for continuity, insulation resistance, and arc performance.
The supply chain for the World market faces several structural bottlenecks. Silver and copper prices are subject to global commodity market dynamics, with silver in particular experiencing supply constraints that affect contact material costs. High-temperature engineering polymers suitable for DC arc resistance are sourced from a limited number of global chemical suppliers, with lead times and pricing subject to petrochemical feedstock availability.
Capacity for 1500V DC product testing, including short-circuit and arc-fault testing, is concentrated in a relatively small number of accredited laboratories globally, creating a qualification bottleneck for new products and suppliers. Quality documentation requirements, including production lot traceability, test reports, and material certifications, add administrative overhead that favors established suppliers with mature quality management systems.
International trade in Fused Disconnects for Photovoltaics is substantial and reflects the geographic separation between production centers and end-use markets. China is the world’s largest exporter of these components, supplying fused disconnects to project sites across Asia, the Middle East, Africa, Europe, and the Americas through both branded international manufacturers operating in China and Chinese domestic suppliers. Europe functions as both a significant production region and a net importer, with Germany, France, and Italy hosting major manufacturing operations while also importing products from China and Eastern Europe for price-sensitive project segments.
Trade patterns for the World market show several notable characteristics. The United States is a major importer, drawing supply from Mexico, China, and Germany, with import patterns influenced by tariff classifications that vary by product voltage rating and enclosure type. India has emerged as a growing manufacturing base, supported by policy incentives for domestic solar component production, though its export volumes remain modest relative to China.
The Middle East, particularly the Gulf Cooperation Council states, represents a high-growth import market driven by large-scale solar park developments, with procurement often specifying European or North American certification to meet project insurer and developer requirements. Tariff treatment for fused disconnects across World markets depends on product code classification, country of origin, and applicable trade agreements, with typical most-favored-nation duties in the range of 2-8% for most developed markets and higher duties in certain emerging economies where domestic industry protection is a policy objective.
China is both the largest demand center and the largest production base for Fused Disconnects for Photovoltaics in the World market, driven by its dominant position in solar PV manufacturing and installation. The country’s annual solar additions exceed 200 GW and continue to grow, creating massive demand for both discrete components and integrated combiner box solutions. India is the second-largest demand center in Asia and one of the fastest-growing markets globally, with solar capacity targets of 500 GW by 2030 driving substantial procurement of fused disconnects, a notable share of which is sourced through imports from China and domestic manufacturers.
Europe collectively represents the most mature regional market for fused disconnects, with Germany, Spain, the Netherlands, France, and Italy as leading demand hubs. The European market is characterized by a high proportion of replacement and upgrade procurement, strong preference for products carrying IEC and TÜV certification, and a growing shift toward 1500V DC systems in utility-scale projects.
North America, led by the United States, is a large and high-value market where safety certification requirements (UL listing) and preferences for North American manufacturing create a relatively insulated competitive dynamic, with import penetration primarily from Mexico and, to a lesser extent, from European and Asian suppliers with UL-recognized products. The Middle East, particularly Saudi Arabia and the United Arab Emirates, has emerged as a high-growth demand center for large-scale solar projects, with procurement specifications that often require IEC-certified products and favor established international brands.
The World Fused Disconnects for Photovoltaics market is governed by a layered framework of product safety standards, installation codes, and certification requirements that vary significantly by region and application. In North America, products must comply with UL 98 (enclosed and dead-front switches) and UL 4248 (fuseholders), with listing by a Nationally Recognized Testing Laboratory such as UL, CSA, or Intertek required for code compliance under the National Electrical Code. In Europe and many other markets, the applicable standard is IEC 60947-3 for switches, disconnectors, and switch-disconnectors, with CE marking and compliance with the Low Voltage Directive as minimum requirements, and TÜV or similar third-party certification often specified by project developers and insurers.
Beyond product safety standards, installation codes such as the US National Electrical Code and IEC 60364 influence demand by specifying the locations, ratings, and types of disconnecting means required in photovoltaic systems. The NEC, for example, has specific requirements for rapid shutdown, arc-fault protection, and disconnecting means for PV systems that directly shape product specifications. For the World market, compliance with both the destination market’s standards and the project developer’s internal specifications is essential, and suppliers must maintain certification portfolios that can span multiple jurisdictions.
Certification costs and timelines act as a meaningful barrier to entry, particularly for suppliers from emerging economies seeking to access European or North American markets, and they create a structural advantage for established manufacturers with existing certified product families.
Over the 2026-2035 forecast period, the World Fused Disconnects for Photovoltaics market is expected to see sustained expansion, with demand approximately doubling from 2026 levels by the end of the forecast horizon. This growth is anchored by the fundamental trajectory of global solar PV deployment, which most independent forecasts project to reach 600-900 GW of annual additions by 2035, up from approximately 400-500 GW in the 2024-2026 period. Replacement demand from the existing global installed base will become an increasingly important component of total demand, particularly after 2030, as the first generation of utility-scale solar farms reaches the 12-18 year mark when fused disconnect replacement becomes common practice.
In value terms, growth is projected to run slightly ahead of volume growth due to the ongoing shift toward higher-voltage 1500V DC products, which carry a significant price premium, and the increasing adoption of integrated systems that bundle fused disconnects with monitoring and protection functions. Price erosion in standard 600V segments will continue, potentially at 1-3% annually, but this will be offset by the mix shift toward premium products. The competitive landscape is expected to see gradual fragmentation as regional manufacturers in high-growth markets gain certification and scale, though the core group of international suppliers with broad certification coverage and established distribution networks is likely to maintain its collective market share through the forecast period.
The most significant opportunity in the World Fused Disconnects for Photovoltaics market lies in the transition to 1500V DC system architectures in utility-scale solar. Products rated for this voltage class command substantially higher prices and margins than standard 600V or 1000V equivalents, and demand is growing faster than the overall market. Suppliers that can offer comprehensive 1500V DC portfolios with UL and IEC certification, field reliability data, and integrated solutions including combiner boxes and monitoring will be well positioned to capture above-market growth.
The replacement and lifecycle support segment represents a second major opportunity: as the global installed base of solar PV systems ages, demand for fuse carriers, replacement fuse bases, and module-level fused disconnects will grow steadily, and suppliers that establish service and spare parts programs with system operators and operations-and-maintenance contractors can build recurring revenue streams with higher margins than new-installation sales.
Emerging markets in Africa, Central Asia, and Latin America, where solar PV deployment is accelerating from a low base, offer long-term growth potential for fused disconnect suppliers. These markets are typically import-dependent, price-sensitive, and less rigid in their certification requirements, creating opportunities for competitive suppliers, particularly those from China and India, to establish early positions and build brand recognition.
Digitalization and connectivity features also represent a differentiation opportunity: fused disconnects with integrated arc-fault detection, temperature monitoring, and communication interfaces can command premium pricing and align with the broader trend toward digital energy management and predictive maintenance in solar plants. Finally, the growing focus on energy storage systems, which require DC-side disconnects with similar technical characteristics to photovoltaic systems, represents an adjacent market opportunity that many fused disconnect suppliers are beginning to address through product extension and certification efforts.
This report provides an in-depth analysis of the Fused Disconnects for Photovoltaics market in the world, covering market size, growth trajectory, demand structure, supply capability, trade flows, pricing, competitive landscape, and forecast to 2035.
The study is designed for manufacturers, distributors, importers, exporters, investors, procurement teams, advisors, and strategy teams that need a consistent, data-driven view of market dynamics and a transparent analytical definition of the product scope.
This report covers the market for fused disconnects specifically designed for photovoltaic (PV) systems, including devices that integrate overcurrent protection and disconnection functionality for solar arrays, inverters, and combiner boxes. The scope encompasses both standalone fused disconnect switches and modular units used in residential, commercial, and utility-scale solar installations.
The report combines the standard market-statistics backbone with strategic chapters that are useful for commercial planning, sourcing decisions, market entry, competitor monitoring, and portfolio prioritization.
The market is segmented into decision-relevant buckets so that demand drivers, pricing logic, supply constraints, and competitive positions can be compared across the same analytical frame.
The classification coverage encompasses products categorized by product type (fused disconnects for photovoltaics, components and modules, integrated systems, consumables and replacement parts), by application (industrial automation and instrumentation, electronics and optical systems, semiconductor and precision manufacturing, OEM integration and maintenance), and by value chain segment (upstream inputs and critical components, manufacturing assembly and quality control, distribution integration and channel partners, after-sales service replacement and lifecycle support).
Coverage includes global totals, major demand markets, production and sourcing hubs, leading exporters and importers, and country profiles for the top national markets.
The report combines official statistics, trade records, company disclosures, product-level evidence, and analyst validation. Data are standardized, reconciled, and cross-checked to keep market sizing, trade flows, pricing, and forecasts comparable across countries and time periods.
All indicators are mapped to a consistent product definition and reviewed against the segmentation framework used in the Table of Contents.
Report Scope and Analytical Framing
Concise View of Market Direction
Market Size, Growth and Scenario Framing
Commercial and Technical Scope
How the Market Splits Into Decision-Relevant Buckets
Where Demand Comes From and How It Behaves
Supply Footprint, Trade and Value Capture
Trade Flows and External Dependence
Price Formation and Revenue Logic
Who Wins and Why
Where Growth and Supply Concentrate
Commercial Entry and Scaling Priorities
Where the Best Expansion Logic Sits
Leading Players and Strategic Archetypes
Detailed View of the Most Important National Markets
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Key player in fused disconnect switches for PV systems
Offers fused disconnects for solar applications
Provides PV-specific fused disconnect solutions
Manufactures fused disconnects for photovoltaic systems
Specializes in fuse-based disconnect switches for solar
Offers fused disconnect switches for PV applications
Brand under Eaton, key in PV fused disconnects
Provides fused disconnect solutions for solar
Manufactures fused disconnects for PV systems
Offers fused disconnect switches for solar installations
Provides fused disconnects for photovoltaic applications
Supplies fused disconnect components for solar
Manufactures fused disconnects for PV systems
Key distributor of fused disconnects for solar in APAC
Offers fused disconnect switches for photovoltaic
Specializes in PV fused disconnects for European market
Produces fused disconnects for solar applications
Offers fused disconnect solutions for PV systems
Manufactures fused disconnects for photovoltaic
Key Chinese producer of fused disconnects for PV
Supplies fused disconnect switches for solar market
Specializes in PV fused disconnects
Offers fused disconnects for photovoltaic systems
Provides fused disconnect solutions for solar
Manufactures fused disconnects for PV applications
Offers fused disconnect switches for solar
Supplies fused disconnects for photovoltaic systems
Distributes fused disconnects for solar in Oceania
Offers fused disconnect switches for PV
Manufactures fused disconnects for solar applications
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