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The Sustainable and Holistic Integration of Energy Storage and Solar PV (SHINES) program develops and demonstrates integrated photovoltaic (PV) and energy storage solutions that are scalable, secure, reliable, and cost-effective.
The projects will work to dramatically increase solar-generated electricity that can be dispatched at any time – day or night – to meet consumer electricity needs while ensuring the reliability of the nation’s electricity grid. Achieving the SHINES goals is a critical step in the pathway toward enabling hundreds of gigawatts of solar to be integrated reliably and cost-effectively onto the electric grid. SHINES is part of the Energy Department’s Grid Modernization Initiative, which aims to accelerate the strategic modernization of the U.S. electric power grid and solve the challenges of integrating conventional and renewable sources, while ensuring a resilient energy system combining energy storage with central and distributed generation.
These awards were announced on January 19, 2016. Read the press release and Assistant Secretary David Danielson’s blog post. 
This is the first funding program within the Department of Energy focusing exclusively on connecting renewable power to storage. The solutions developed under this program incorporate dynamic load management, advanced forecasting techniques, utility communication and control systems, and smart buildings and smart appliances to work seamlessly to meet both consumer needs and the demands of the electricity grid. These solutions will enable widespread sustainable deployment of low-cost, flexible, and reliable PV generation, and provide for successful integration of PV power plants with the electric grid.
The widespread adoption of storage solutions will be a transformative influence on the current state-of-the-art of solar grid integration and will significantly contribute to an economically viable pathway toward energy efficient and sustainable integration of solar generation at much higher penetration levels than currently possible today. These solutions will enable widespread sustainable deployment of reliable PV generation and provide for successful integration of PV power plants with the electric grid at the system levelized cost of energy (LCOE) of less than 14 cent per KWh.
Location: Austin, Texas
SunShot Award Amount: $4,300,000
Awardee Cost Share: $4,337,683
Project Description: The goal of the Austin SHINES project is to demonstrate a solution adaptable to any region and market structure that offers a credible pathway to a LCOE of 14¢/kWh for solar energy when augmented by storage and other distributed energy resource management options. The solution aims to establish a template for other regions to follow to maximize the penetration of distributed solar PV. In addition, the proposed solution will enable distribution utilities to mitigate potential negative impacts of high penetration levels of PV caused by the intermittency and variability of solar production.
Location: Pittsburgh, Pennsylvania
SunShot Award Amount: $1,036,963
Awardee Cost Share: $1,038,083
Project Description: This project will develop and demonstrate a distributed, agent based control system to integrate smart inverters, energy storage, and commercial off-the-shelf home automation controllers and smart thermostats. The system will optimize PV generation, storage, and load consumption behaviors using high-performance, distributed algorithms.
Location: Oakbrook Terrace, Illinois
SunShot Award Amount: $4,000,000
Awardee Cost Share: $4,000,000
Project Description: This project will address availability and variability issues inherent in the solar PV technology by utilizing smart inverters for solar PV/battery storage and working synergistically with other components within a microgrid community. This project leverages on the DOE-funded microgrid cluster controller and is connected to the existing DOE-funded 12 megawatt IIT microgrid.
Location: Knoxville, Tennessee
SunShot Award Amount: $3,124,685
Awardee Cost Share: $3,240,262
Project Description: In this project, EPRI will work with five utilities to design, develop and demonstrate technology for end-to-end grid integration of energy storage and load management with photovoltaic  generation. The technology is a simple, two-level, and optimized control architecture. This technology will be demonstrated and its effectiveness verified at three field locations. 
Location: Boston, Massachusetts
SunShot Award Amount: $3,493,921
Awardee Cost Share: $3,560,744
Project Description: This project will develop and demonstrate a highly scalable, integrated PV, storage, and facility load management solution. Through the SunDial Global Scheduler, the system tightly integrates PV, energy storage, and aggregated facility load management to actively manage net system power flows to and from the feeder, regardless of whether these individual components are co-located at the same site, or distributed at different sites.
Location: Honolulu, Hawaii
SunShot Award Amount: $2,437,500
Awardee Cost Share: $2,437,500
Project Description: This project will demonstrate successful SHINES deployments and will show the system-level benefits of enhanced utility visibility and control of distribution system/edge-of-network electricity resources. This project will enable proliferation of a reliable base of PV and storage distributed technologies that offer more plug-and-play customer options for grid participation, and provide cost-effective “grid response” capabilities to system operators.
Learn more about SunShot’s other systems integration funding programs.
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Baseus’ official Amazon storefront is offering its S1 Pro Wireless Outdoor Solar Security 2-Cam Kit with a 16GB hub down at $99.98 shipped right now. Normally, this kit goes for $320 at full price, which we saw keep at a Prime-exclusive $150 to $120 range until March. By the time last month’s Big Spring Sale rolled around, it had dropped further to $100 for the first time and has held on to that discount since, giving you a massive 69% markdown with $220 savings at its newest all-time low price we have tracked.
If you’re looking for a more affordable means to upgrade your security, you’re getting quite the deal on this Baseus S1 Pro 2-cam kit while keeping at this low price – not to mention, there is an included 16GB local storage hub here (which you can expand up to 16TB) that you’d expect to keep the price much higher (as we see from other brands).
The two cameras in this package not only provide 3K HD video of anything that passed in front of them, but they also come sporting integrated solar panels to maintain power levels so you don’t have to wire it into your home’s grid, saving you on added electricity costs. What’s more, those panels even have sun-tracking, letting them move 40-degrees to the right or left to follow the sun and get the most amount of light to top off internal batteries. They boast an IP67 protection rating against the weather, intelligent notifications for people and vehicles entering the zones set in the in-app smart controls, and even a 2-year warranty. One thing to note is that these are more tailored for event capturing (when anything moves to trigger them) over 24/7 continuous recording.
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Residential solar has long been sold as a win-win for consumers and the environment. It was marketed as an affordable way for homeowners to reduce energy costs and support clean energy goals. What’s not to like? Yet the latest Solar Market Insight Report shows US residential solar installations slowed by 2 percent in 2025, which reveals that the market is not immune to economic and policy pressures.
At the same time, some Republican lawmakers are now pushing to reinstate federal clean energy tax credits. This sign of political uncertainty underscores how reliant the residential solar market has been on government incentives.
In a previous piece, I covered how the Residential Clean Energy Credit (RCEC) and related financing structures spurred rapid market growth alongside unintended consequences. Introduced under the Inflation Reduction Act, the RCEC was intended to jumpstart the residential solar market with a substantial federal tax credit for installing panels. By lowering upfront costs for homeowners, it created a strong financial incentive for consumers and developers to invest in residential solar at an unprecedented pace.
While the credit expanded solar panel adoption, it also accelerated bankruptcies, contributed to at least one alleged fraud case, cost taxpayers millions, distorted energy markets, and funneled investment into subsidy-driven projects rather than economically efficient ones. Because residential solar economics have been tied more to federal incentives than to market fundamentals, these vulnerabilities are now impossible for policymakers and investors to ignore.
Why residential solar is vulnerable without subsidies
Incentives to maximize the fair market value and favor certain financial instruments over others shape how residential solar companies operate, as recent solar industry bankruptcies illustrate.
Sunnova and Mosaic, for instance, grew rapidly using heavily leveraged financing structures. Sunnova carried over $10 billion in debt at the time of its bankruptcy, while Mosaic built its business on long-term loans for residential solar installations. Similarly, SunPower was structured on a loan-based business model, whereas PosiGen focused on no‑upfront-cost leases or loan‑based financing.
These strategies reveal a pattern of overvaluation and aggressive expansion. By structuring operations to maximize RCEC benefits, companies were incentivized to overvalue systems, take on excessive debt, and chase growth divorced from economic reality. Such models leave residential solar particularly vulnerable when interest rates rise, consumer credit tightens, or the RCEC expires and the easy money disappears.
The RCEC’s influence on upfront costs and financing structures means the residential market likely would not have reached its current size without this federal incentive. Its expiration is therefore expected to have significantly adverse effects on the US residential solar sector.
Residential solar is less economically efficient than advertised
Financial advisory firm Lazard’s 2024 report shows that rooftop residential solar has a higher levelized cost of electricity (LCOE) than utility-scale solar and many conventional generation options. LCOE averages total costs over a system’s lifetime electricity output. While a higher LCOE does not automatically mean higher consumer prices, it signals that rooftop solar is less economically efficient per unit of electricity produced.
As my colleague Paige Lambermont pointed out, the RCEC rewards upfront capital investment over efficient or economically sound energy production. Lazard’s findings illustrate how subsidies can distort investment incentives and encourage deployment that may not follow the lowest-cost or most efficient path to meeting US electricity demand.
How the RCEC reshapes capital markets
The RCEC affects more than electricity costs; it also distorts energy finance. As a large, upfront, non-refundable tax credit, it incentivizes solar developers to prioritize projects that maximize tax benefits.
Because developers and homeowners cannot use the RCEC directly, projects rely on tax equity investors, which are large corporations or banks with substantial tax liabilities, to provide the upfront capital in exchange for credit. These investors use financing structures such as partnership-flips, sale-leasebacks, and inverted leases to convert future tax benefits into immediate funding.
Without the RCEC, tax-equity investors would likely have directed capital to other tax-advantaged opportunities or conventional energy projects. The RCEC therefore does more than subsidize residential solar. It channels investment toward projects that maximize subsidy capture, illustrating how federal incentives can reshape financial markets and capital allocation in ways that do not necessarily produce economic efficiency.
Time to test residential solar’s viability
In summation, the RCEC has highlighted the financial vulnerabilities and structural challenges within the residential solar sector. From overleveraged companies to misaligned investment incentives, the program illustrates how federal subsidies can reshape markets in ways that do not always promote economic efficiency.
Whether it is residential solar or the Trump administration recently giving a $625 million subsidy to the struggling coal industry, the RCEC is a fine reminder that the government should not pick winners and losers.
If solar power can succeed in the residential sector, it should be able to do so on its own merits and without government assistance. If subsidies are the only thing keeping residential solar market solvent, then the RCEC is less a bridge for a clean energy revolution than a taxpayer-funded crutch. It is time to see if residential solar can survive without a government handout.
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Egypt adds over 1.5GW renewable capacity in 2025, driven by solar and wind  ZAWYA
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Sangam Solar One, a subsidiary of Indian solar PV manufacturer Waaree Energies, has commissioned a 3GW PV module manufacturing facility in Samakhiali, Kutch, Gujarat. 
The facility features four production lines, each with a capacity of 750MW per year, bringing the site’s total nameplate capacity to 3GW. The latest commissioning expands on earlier developments at the site, where two 750MW module lines had already been installed, establishing an initial 1.5GW of manufacturing capacity. 
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The development came against the backdrop of Waaree Energies outlining its “Waaree 2.0” strategy, focused on building a fully integrated solar value chain spanning polysilicon, ingots and wafers through to cells and modules. 
The Mumbai-headquartered company has been steadily scaling its manufacturing footprint and currently operates 22.3GW of global solar module capacity, alongside up to 5.4GW of solar cell capacity. 
Recently, Waaree broke ground on its 10GW solar ingot and wafer manufacturing facility in Butibori, Nagpur, Maharashtra. The plant, designed with a production capacity of 10GW each for solar ingots and wafers, had been planned across a 300-acre site.  
The company had committed an investment of approximately INR62 billion (US$671 million) towards the development. Once operational, the facility is expected to generate more than 8,000 local jobs. 
In October 2025, Waaree Energies secured four solar module supply contracts totalling 692MW. 
Three orders in India, totalling 570MW, were awarded by undisclosed developers – 220MW, 210MW and 140MW – while its US subsidiary, Waaree Solar Americas, secured a 122MW contract from an anonymous utility-scale solar and storage developer. All four one-time supply contracts are scheduled for delivery over the next two years. 

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South Australia has opened up more than 11,000 square km of land for the potential development of renewable energy projects as it continues the march to its target of 100% net renewables by 2027.
Image: South Australian Department of Energy and Mining
From pv magazine Australia
The South Australia government is calling for investors from around the globe to propose large-scale solar, wind, and storage projects across more than 11,000 square km of land released under the state’s renewable energy framework.
Applications are now open for renewable energy feasibility licenses covering the Whyalla West and Gawler Ranges East areas released under South Australia’s Hydrogen and Renewable Energy Act.
The Gawler Ranges East release area comprises approximately 5,200 square km on the Upper Eyre Peninsula, while the Whyalla West release area spans about 6,500 square km in the Upper Spencer Gulf region.
South Australia’s Department of Energy and Mining (DEM) said the two areas include some of the highest co-incident wind and solar resources in the state, with estimates suggesting they could host projects capable of powering more than 500,000 homes.
The DEM said the tender does not limit applicants to specific technology types, with investors invited to propose how they would optimize land use and renewable energy resources in the release areas.
“Tenders must address the prescribed criteria in their application, including how they plan to deliver the content within a timeframe, their experience, environmental management credentials, and how the project will benefit the state and the traditional custodians of the land,” it said.
The call for tenders in both areas is open until June 28, 2026, with the DEM saying the extended period allows applicants time to prepare bids and engage with native title holders on agreements.
South Australia is at the forefront of Australia’s clean energy transition, with the state currently averaging 75% net variable renewable energy annually and regularly achieving 100% instantaneous variable renewable energy generation, driven by large-scale wind and solar and rooftop PV. The state is targeting 100% net renewables by the end of 2027.
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By Ian M. Stevenson | 04/07/2026 01:27 PM EDT
Wind and solar companies say they can’t use the mapping tool, which can quickly determine issues with endangered species or habitat.
A wind turbine and solar panels are seen in Atlantic City, New Jersey. Mel Evans/AP
The bureaucratic hurdles erected by the Interior Department for wind and solar projects get down into the nitty-gritty.
Since last summer, developers are no longer allowed to use a government website that helps with a basic preliminary task for projects: identifying what endangered species are found in a particular area.
Unlike any other companies that must clear their permitting through federal agencies, wind and solar companies can’t rely on a Fish and Wildlife Service online map tool that can quickly determine if there is an imperiled species, migratory bird or sensitive habitat that could be harmed by a project.
Wind and solar advocates say the loss of a basic online tool is just one example in the permitting gauntlet the Trump administration set up for their projects, whether on public or private land.
Interior’s policies last year froze nearly all clean energy projects proposed for federal land that had been in the pipeline at the end of the Biden administration — although in recent months around 20 solar projects have begun to move through the process again.
In a lawsuit filed in January against the Interior Department and Army Corps of Engineers, clean energy groups say the loss of the online tool “functionally prevents” many developers from getting water-related permits and has “stalled” endangered species reviews.
A dozen projects were stopped in their tracks because of the lost access to the database, according to the lawsuit brought by groups including Renew Northeast, a nonprofit that advocates for renewable power.
“The practical implication of not having access to [the database] is delay,” said Cynthia Stroman, an attorney at the law firm King & Spalding who represents clean energy developers but is not involved in the lawsuit. While in some instances projects could hire consultants to examine endangered species that could be harmed by a project, those findings provide less liability protection than those provided by the government database, Stroman added.
An Interior spokesperson did not respond to questions about the online tool. The Fish and Wildlife Service did not respond to a request for comment.
In court filings, Trump administration lawyers have said that developers “remain fully capable of obtaining relevant species information for ESA consultation purposes from the appropriate” FWS field office, calling the database “merely a facilitative tool.”
But employees in those offices have determined they can’t work with companies unless they first obtain the approval of Interior Secretary Doug Burgum or the deputy secretary, again throwing up a roadblock, according to attorneys who work with renewable energy developers.
The Information for Planning and Consultation database maintained by FWS indexes locations across the U.S. with information about endangered species, offering developers the means to quickly decipher whether a proposed project could infringe on a protected species in its area.
Projects that aren’t likely to harm a threatened or endangered species can generate automatic records showing they are in compliance with the Endangered Species Act, allowing them to proceed with a variety of required government reviews.
But the chain of orders by Interior issued last year has imposed heightened scrutiny on renewable energy, torpedoing several new projects and otherwise freezing scores of others in various stages of the permitting process. Last July, the department ordered agencies like FWS to hold off on processing all kinds of approvals related to wind and solar project until they can obtain reviews from senior Interior officials.
Since then, a banner on the service’s website states that solar and wind projects are “currently not eligible” to use the database without those approvals. Instead, the banner specifically directs wind and solar developers to contact FWS field offices.
The prohibition has also prevented some projects from getting state-level approvals, because some state offices rely on it as well, according to the lawsuit. In a few cases, developers have responded by redesigning their projects to avoid protected areas, while others may be prevented “from proceeding at all,” the lawsuit said.
The same catch-22 applies in cases where other agencies must get in touch with FWS about a project, according to the attorneys.
Among other applications, regulators use the database to determine whether a project requires elevated levels of review with other agencies. Projects that encroach on wetlands, for instance, often require authorization from the Army Corps, which is required to first consult with FWS to ensure the project’s activities will not harm endangered species.
Construction of wind farms and solar arrays can include moving dirt into waterways during excavations, drainage into creeks or the creation of roads, culverts and electrical lines that cross streams.
For projects that aren’t likely to harm endangered species, the Army Corps would previously rely on the database before issuing a general use permit. Now, this becomes yet another step where the officials with the Army Corps must consult with FWS — and first get approval from Interior higher-ups, the lawsuit said.
The Army Corps did not respond to a request for comment.
In Illinois, a 140-megawatt project on private land called the Austin Creek Solar Project could affect wetlands and was unable to access the FWS database to determine its potential effects on wetlands, according to the lawsuit. The developers, who have already invested $2 million in their project, have as a result been unable to apply to the Army Corps for a water permit.
In Minnesota, a 151-MW wind project had similarly been unable to use the tool, preventing the developers of the Black Spruce Wind Project from finalizing any Army Corps permit application, according to the lawsuit.
Benjamin Cowan, an attorney at Troutman Pepper Locke who is representing plaintiffs in the Renew Northeast lawsuit, said the database allowed developers of many projects to essentially “self-certify” their compliance with the Endangered Species Act before being eligible for a general Army Corps permit.
“It alleviated the need to speak to the Fish and Wildlife Service as frequently,” Cowan said. “That was something that developers used on a regular basis in their planning process.”
Sean Gallagher, senior vice president of policy at the Solar Energy Industries Association, said that despite some recent movement on Interior reviewing solar farms the “vast majority” of developers are “still in the dark about whether or how their projects and permits will be considered by the Department of the Interior.”
“Developers and investors need confidence that their projects will be able to move through the permitting process in good faith and without unfair treatment based on energy source,” Gallagher said in a statement. “The reality is that  Interior could provide that clarity today by revoking their July memo.” 
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Nonprofit group Solar United Neighbors announces the launch of the Fields and Forests Solar Co-op to offer north central Minnesota residents a solar option. The solar co-op is an opportunity for local homeowners and small businesses to learn about solar energy and if it is right for them.
Free public “Solar 101” information sessions to explain the ins and outs of going solar will be offered:
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6:30 p.m. Thursday, April 9, online webinar. RSVP at mobilize.us/solarunitedneighbors/event/907277/
6:30 p.m. Tuesday, May 5, Initiative Foundation, 405 First St. SE, Little Falls. RSVP at mobilize.us/solarunitedneighbors/event/907303/
6:30 p.m. Tuesday, June 9, Crow Wing County Land Services building, 322 Laurel St., Brainerd. RSVP at mobilize.us/solarunitedneighbors/event/907314/
6:30 p.m. Thursday, June 25, online. RSVP at mobilize.us/solarunitedneighbors/event/907293/
Sign up for the co-op or an information session at the solar co-op web page .
“If you’ve ever thought about going solar, now’s your chance,” John Anderson, Minnesota program director for Solar United Neighbors, said in a news release. “As energy costs continue to climb, going solar is a way to get a handle on your electric bill by taking control of where your energy comes from.”
The solar co-op is free to join and open to homeowners and business owners in Cass, Crow Wing, Morrison, Todd and Wadena counties. Solar co-op members will learn about solar energy and leverage their numbers to purchase individual solar systems.
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After a competitive bidding process facilitated by SUN, which remains vendor neutral, solar co-op members will select a single solar company to complete the installations.
Joining the solar co-op does not obligate members to purchase solar. Instead, members will have the option to individually purchase panels and electric vehicle chargers based on the installer’s group rate.
The Initiative Foundation and Rural Renewable Energy Alliance are partnering with Solar United Neighbors on the solar co-op.
“Solar energy helps communities thrive by generating power locally and reducing strain on the grid,” Elizabeth Mboutchom, clean energy and community resiliency program officer for the Initiative Foundation, said in the release. “A solar co-op makes it easier for community members to learn about going solar and how it can save them money on their utility bills.”
SUN has hosted 29 solar co-ops in Minnesota since 2018. According to the group’s estimates, the nearly 500 homes and businesses that now have solar panels because of co-ops represent: 4 MW of solar power, $12 million in local solar spending, and more than 98 million pounds of lifetime carbon offsets.
“Solar energy helps people control their energy costs and creates good, local jobs,” said Marc Morrison of the Rural Renewable Energy Alliance, which works on renewable energy projects in the northland and is partnering with SUN on the co-op. “This solar co-op is a great opportunity to bring solar’s benefits to north central Minnesota.”
Like what you’re reading? Check out our other PineandLakes news articles , updated daily. Remember to pick up a copy of The PineandLakes Echo Journal — on newsstands Wednesdays and online daily in our e-edition and website.

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Scientific Reports volume 15, Article number: 43417 (2025) Cite this article
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In this study, a novel optoelectronic system for fault detection in photovoltaic (PV) cells has been developed. Three sensors, each with a photodiode, were manufactured and mathematical models developed to interpret the fault results from the sensors. The photodiodes sweep across the PV panel to identify areas of high light intensity. The goal is to produce diagnostic images of PV panels that are comparable to standard electroluminescence (EL) imaging. Each sensor was tested under two conditions: darkness and sunlight exposure. For all the sensors, the results obtained in darkness closely match the EL images. However, PV panel exposure to sunlight produces mixed results due to differences in light intensity across the PV cells. To address this issue, two enhancement techniques were developed. First, a collector was used to improve sunlight directionality, with an improved result shown in Sensor 3. Second, a voltage step was added to the PV panel, showing an improved result in all three sensors. Among the tested combinations, the combination of Sensor 3 with an alternative collector and a step-type voltage source produced the best performance. These results clearly indicate that the sensor-based approach can effectively diagnose the PV panel health condition.
Currently there has been a rise in the installation of non-conventional or renewable energy sources^{1,2,3,4,5,6,7}. This is due to an increase in both global population as well as energy usage. Energy consumption and environmental problems can be considerably decreased by substituting these fossil fuels with these sources. Solar energy has garnered significant attention due to its vast potential, especially in countries with extensive clear sky regions, such as deserts^5,7. Solar photovoltaic (PV) technology is advancing rapidly, with many countries integrating solar energy into their future energy strategies. However, to maintain optimal performance and meet life cycle operating requirements, PV facilities require regular maintenance. This includes not only cleaning the solar panels but also assessing their current operational conditions to ensure they produce the expected amount of electricity over their lifespan^8,9.
To evaluate the current state of PV panels, different diagnostic tools are being used: such as thermography^{8,10,11,12,13,14}, studies of current-voltage (I-V) curves^{8,15,16,17,18}, visual inspection^19,20, signal transmission technique⁸, ultraviolet (UV) fluorescence^21,22,23, and electroluminescence (EL)^{24,25,26,27,28,29}. Thermography detects hot spots in solar panels, which can be identified using specialized cameras. Failures, such as corroded circuits or defective solder joints, often cause localized temperature increases, making them easier to spot with this technology. Currently, thermography has expanded to include the use of drones, allowing for more efficient and rapid identification of such defects¹¹. The limitation of thermography is that it does not provide a quantitative assessment of how the detected anomaly affects the performance of the PV module. In contrast, I-V curves offer a detailed representation of the operational characteristics of each solar panel, establishing a baseline for comparison with future measurements. A key aspect of this system is the system curve, obtained by placing the solar panel on a test bench equipped with an artificial light source, controlled temperature, and a data acquisition system, while adjusting the module’s voltage or current using external systems. In field conditions, applying this approach according to the IEC 60904-3 standard is not feasible. However, if a pyranometer is used to measure irradiance, the collected data must be corrected according to the standard¹⁷. Nonetheless, it should be noted that using I-V characteristics requires turning off the PV generator in order to take measurements of the I-V characteristic, limiting the applicability of this approach to online monitoring. The visual inspection approach, specified in the IEC61215-1 and IEC61730-2 standards for new modules, cannot be applied to modules that have been degraded by usage. Strategies have been developed to standardize the inspection of modules that have been damaged, with the inspection being divided into components and types of PV panels. In addition, the primary defects are established for each type of panel, creating a baseline for the search of problems and improving the process. This is complemented by documentation guidelines that should be managed to comply with the notification of damage. One of the major drawbacks of the visual inspection method is that it does not work well with weathered modules. The UV fluorescence (UVF) is another technique used for PV fault detection. While UVF imaging with a camera provides information on the luminescence intensity and thus the fluorophore density, UVF spectroscopy determines the type of fluorophores present by analyzing the emitted spectrum at a specific location on the module. As a result, it allows inferences about the cell temperature history, for example, since greater temperatures result in additional peaks in the observed spectrum. This technique also has its drawbacks including that the recorded UVF signal can be modified by a variety of factors, such as module position, time of operation, actual temperature as well as temperature history, experienced doses of heat, humidity, and UV radiation.
In the EL diagnostic technique for PV panels, a voltage is applied to the solar panels, inducing a current that generates light in the infrared or near-infrared region of the spectrum. This process results in EL emission within the mid-infrared spectrum, typically around 1150 nm. A cooled charge-coupled device (CCD) camera is employed to capture the emitted light from the energized PV cell. Photodetectors sensitive to this specific wavelength range then convert the captured light into data that can be interpreted by the operator^24,25,30. The International Electrotechnical Commission (IEC) currently has only draft versions of international standards for the quantitative interpretation of PV cells using the EL technique. The IEC technical specification outlines procedures for capturing EL images of PV cells, processing these images to derive quantitative descriptors, and performing qualitative evaluations of the results²⁵. This standard applies to PV modules tested in a forward bias condition, i.e., by forcing current flow with a power supply whose leads are connected to that of the PV module of the same polarity. EL photography can also be done using specialized tripods or drones. However, the standard approach is to utilize cameras that are sensitive to the spectrum, as well as light filters to reduce the bandwidth of the light spectrum being captured²⁵. In the EL photography, defects are displayed as dark regions. Since EL imaging is not affected by blurring caused by lateral heat propagation, it can be used at high resolutions. Nevertheless, the analysis of EL images is typically a manual, costly, and time-consuming process that requires expert knowledge. CCD or complementary metal oxide semiconductors (CMOS) are frequently used as camera detectors. They can be cooled, usually through thermoelectric cooling, to lower device current originating from thermally generated charges and improve the signal to noise ratio. The number of pixels, noise, quantum efficiency at the desired wavelength, and dynamic range are important factors when selecting detectors. 
However, the EL technique faces challenges when applied in industrial settings. For accurate measurements, it is essential that the camera captures only the emission from the panel, minimizing interference from external light sources. Solar radiation and other ambient light can disrupt the measurements, making it crucial to isolate the panel’s emission from these extraneous sources³¹. A number of strategies are adopted to attain this goal, such as operating the PV module at night or constructing a shadow around the solar panel in question²⁵. All of the aforementioned techniques can negatively impact the solar panel’s continuous operation, as they necessitate halting the panel’s function during the assessment. To mitigate this issue, one approach is to conduct inspections and diagnostics during non-productive hours when the solar panel is not generating power²⁵. These circumstances provide an opportunity to explore alternative methods that complement existing approaches or serve as viable options when conventional techniques fall short.
This study introduces a novel EL analysis technique for PV modules using a photo-sensor. The photodiode performs a comprehensive sweep of the solar panel to collect data on regions of high light intensity, enabling the identification of active and inactive areas within the PV module. This method also facilitates the creation of a graphical representation of the emitted luminous intensity, closely resembling the images produced by traditional EL techniques. One advantage of this new technique is its ability to take measurements under conditions where traditional cameras may not yield reliable results. To assess its effectiveness, several photodiodes were characterized and evaluated for their suitability in this application. Additionally, various light collectors are employed to control the directionality of incident light on the sensor. The impact of applying a voltage step to the solar panel is examined using data from the photodiode. The primary aim of this work is to develop an optoelectronic monitoring system to assess the condition of PV cells according to the “IEC TS 60904-13:2018” standard. A secondary objective is to measure EL both under direct sunlight and in darkness. This is crucial for determining the acceptable ranges of luminosity variation as specified by the standard, based on the measurements conducted in this study.
This paper is organized as follows: Sect. 1 presents the introduction. Section 2 describes the methodology, and Sect. 3 presents the results and discussion.
In this study, four distinct tests were conducted: one in darkness, one under sunlight exposure, one with an alternative collector, and one with an intermittent light source. Table 1 outlines the conditions for each of these tests.
Six interchangeable LED boards were fabricated, each carrying nine identical through-hole LEDs (Young Sun LED Technology, 5 mm epoxy package). The boards were strictly monochromatic (red (≈ 625 nm), blue (≈ 470 nm), yellow (≈ 590 nm), white (broad-spectrum phosphor, CCT ≈ 6000 K), ultraviolet (≈ 395 nm) and infrared (≈ 850 nm)) so that no mixed-color illumination was introduced. During a given experiment only one board (i.e., one color) was operated, and the optical intensity was modulated solely by adjusting the BAKU BK-1502D + supply voltage.
Figure 1a illustrates the test bench setup used to characterize photodiodes and assess their suitability for detecting PV defects. The test bench features LEDs on one side and a measurement circuit, which employs photodiodes as the sensing element, on the other. The primary objective is to construct a sealed system to prevent light ingress and thereby minimize measurement interference. The system includes a light emitter, depicted on the left side of Fig. 1a and the gray section of Fig. 1b, consisting of nine LEDs of the same color. This LED lighting system is powered by a BAKU brand model BK-1502D + variable DC voltage source, which can supply a DC voltage of up to 15 V. The intensity of the light emitted by the system is proportional to the voltage of the source.
The sensor consists of an amplifier and a measurement circuit, featuring a socket and a mounting plate designed to hold the photodiodes. The circuit plate is shown in Fig. 1c. The sensor is powered by two DC voltage sources, namely 12 V and 9 V. The 12 V supply powers the amplification circuit, while the 9 V powers the measuring circuit, aiming to decrease measurement noise. A UNI-T brand oscilloscope model UT81B was utilized to measure voltage.
(a) Schematic of the sensor test bench (LED light source on the left, photodiode measurement circuit on the right); (b) Photograph of the test bench setup; (c) Detail of the sensor’s amplification circuit.
In this study, three sensors were developed. Sensor 1 is a photodiode (SD003-151-001) with a sensitivity spectrum extending into the near-infrared (NIR) region. This characteristic makes it particularly suitable for use with InGaAs PV panels. Although the test panels used in this study are not made from InGaAs, a key objective is to assess the sensor’s performance with various PV panels and evaluate the impact of sunlight interference. Sensor 2 is a photodiode (VTP9812FH-ND) that, while not optimal for this application, offers a broader wavelength sensitivity and low power consumption. Sensor 3 is a photodiode (BP-104FS) with high sensitivity to the infrared spectrum, making it well-suited for the sensor development needs. However, due to its limited sensitivity to the blue spectrum, careful attention must be paid to avoid component saturation.
The characterization of the photodiodes was conducted under the following conditions and procedures. All measurements were performed at night to eliminate interference from sunlight. The test began at 0 V, with voltage increments of 0.5 V up to a maximum of 15 V. The voltage applied to the sensor was recorded, and measurements were repeated every 5 min for each photodiode. Additionally, each LED color was tested three times, and the average values were calculated. The averaged results for each LED color were then graphed separately for each photodiode.
To minimize stray light, the LED array and the photodiode under test were placed inside a light-tight black enclosure. The sensor PCB was fixed at a distance of 270 mm from the centre of the 3 × 3 LED array to ensure uniform illumination. The photodiode/amplifier stage was powered from two isolated DC rails (12 V for the trans-impedance amplifier and 9 V for the photodiode), while the LED array was driven by the BK-1502D + supply used for the voltage sweep. Output waveforms were monitored with a UNI-T UT81B digital oscilloscope and logged to a PC for off-line averaging.
The sensor incorporated an amplifier to integrate the signal with the measuring instrument, specifically a UNI-T UT81B oscilloscope. The enclosure was designed for ease of handling during measurements. Initial tests revealed that the sensors were not fully opaque to all light spectra. To prevent interference from external light sources, the sensors were covered with aluminum foil and Kapton tape, as illustrated in the Appendix (Figure A.1). Additionally, the collector is mounted on the front opening, as shown in Figure A.1.
The opening that permits light entry into the enclosure was fitted with a collector made from flexible filament. To enhance opacity and control the directionality of incoming sunlight, two designs were developed to mitigate the effects of direct sunlight during measurements. The first design is the original collector, depicted in the Appendix (Figures A.2a and A.2b), while the second is an alternative collector shown in the Appendix (Figures A.2c and A.2d). The alternative collector features a slotted design to provide improved directionality.
To effectively use the sensors in this investigation, a measurement strategy was developed to gather sufficient data for a comprehensive diagnostic of the PV panel. The experimental procedure involves dividing the solar panel into 36 distinct sections. Each cell within these sections is scanned using the sensors. Following the scanning process, the solar panel is disconnected from the power supply for 30 min to ensure accurate measurements. This process is repeated until the desired number of measurements is obtained.
In darkness, the sensor scans the PV panel and records the light intensity for each cell. This scanning process is repeated three times to determine the minimum intensity for each cell, as calculated using Eq. 1. The resulting data forms the initial matrix.
Where:
Where i is the row index and j is the column index of the PV cells. Then, using Eq. 2, a second matrix is produced in which the luminescence value of each cell is subtracted from the minimum luminance value derived from the first matrix.
In order to reduce the error, the mean light intensity for each cell was obtained, as described in the Eq. 3.
A gradient with three distinct points was created to visually match the electroluminescence (EL) photograph. The center point represents the average light intensity across all sections of the panel. The other two points correspond to the minimum and maximum light intensity values. Colors were selected as follows: purple for the lowest intensity, mauve for the average intensity, and magenta for the highest intensity. This color scheme was chosen to produce results visually comparable to the EL photograph. To ensure accurate measurements in darkness, the light sources were covered with aluminum foil to prevent interference. Measurements under sunlight were taken between 12:00 and 16:00 during the summer months to minimize interference and obtain the most reliable results. In the Appendix, Figure A.3 shows a graphical representation of an EL test conducted in darkness using Sensor 1 on a specific panel. The EL values displayed in the matrix indicate the light intensity measured by the sensor and reflect the difference between the panel’s active (on) and inactive (off) states. These values are influenced by the light intensity at the time of measurement.
To minimize errors caused by the shifting position of the sun, data were first collected using the original collector and subsequently with an alternative collector. This time-dependent error is a constant factor affecting the measurements. Because the collector’s effectiveness depends on the geometry of incoming sunlight, the measurement system must be robust enough to mitigate this issue. To ensure results comparable to the EL photograph, measurements were repeated both in darkness and under sunlight.
Two distinct datasets for light intensity were collected from the cells of the PV panel: one with the power supply turned on and another with it turned off. The matrices for these two datasets were then subtracted using the following Eq. (4).
Where:
(:{varvec{X}}_{on}=) matrix obtained from the light intensity values of the solar panel energized.
(:{varvec{X}}_{off}=) matrix obtained from the light intensity values of the solar panel not energized.
To obtain a result for the measurement, the previous three-point gradient system was adopted.
This section presents the results of all measurements taken with the three sensors under the four specified conditions. To ensure robustness, five PV panels were tested; however, only the results from the first PV panel are included here. The results from the remaining panels are provided in the appendix. Figure 2 shows the functional block diagram of the optoelectronic system, illustrating the main subsystems: (i) BK-1502D + programmable DC source, (ii) photovoltaic panel, (iii) DC power source (iv) sensor housing with collector, photodiode, and TIA amplifier stage, (v) data acquisition unit/UNI-T UT81B oscilloscope, and (vi) computer for data processing.
Functional block diagram of the optoelectronic system.
There are two protocols for measurements, the first method is applied when measuring without the step in the power source.
The first diagnostic procedure follows the five-step sequence below:
Segmentation: The PV module is divided into 36 cells arranged in a 4 × 9 matrix.
Powering: The DC voltage is applied to the solar cells.
Scanning: The sensor housing is placed sequentially over each cell (i, j); the photodiode output is sampled for 5 s and averaged.
Data logging: The averaged value is recorded at the corresponding position M(i, j) in a 4 × 9 data matrix.
Condition loop: Steps 1–3 are repeated under the four test conditions listed in Table 1, yielding one matrix per condition.
The second diagnostic procedure is applied when using the step power source, with the protocol as follows:
Segmentation: The PV module is divided into 36 cells.
Scanning: The sensor housing is placed sequentially over each cell (i, j); the photodiode output is sampled for 5 s.
Step up source: The DC voltage is applied to the solar cells.
Scanning: The sensor housing is placed sequentially over each cell (i, j); the photodiode output is sampled for 5 s.
Data logging: The difference between the first and second value is recorded at the corresponding position M(i, j) in a 4 × 9 data matrix.
Condition loop: Steps 1–3 are repeated under the four test conditions listed in Table 1, yielding one matrix per condition.
These matrices are subsequently compared with the EL photograph to assess the spatial correlation between sensor data and standard EL imaging.
Figure 3 presents the light intensity measurement results for PV panel 1 under darkness. Sensor 1 (Fig. 3a) demonstrates the lowest responsiveness to the light spectrum among the sensors, showing minimal variation between maximum and minimum light intensity values. When compared to the EL photographs in Fig. 3d, the results for Sensor 1 are notably similar, indicating a successful test. The small offset observed in the sensor’s output does not affect the results, indicating that the amplification circuit’s stability is adequate.
(a–c) graphical representation of the measurements obtained from the EL test on PV panel 1, in darkness, (d) results obtained from the EL photograph for comparison, each area is a size of 35 × 55[mm].
The results indicate that Sensor 2 (Fig. 3b) outperforms Sensor 1 (Fig. 3a), as evidenced by its greater variability in light intensity measurements. This improved performance is attributed to the higher quality of the photodiode used, which is more commonly employed in practice. Sensor 3 (Fig. 3c) demonstrates the broadest range of light intensity values and exhibits the closest correlation with the EL photographs. Due to its high contrast, Sensor 3 allows for the implementation of complementary techniques, which could potentially reduce light intensity measurements. Additional results from testing the three sensors on four different PV panels are provided in Appendix A. More technical specifications and selection criteria for the sensors is given below:
Sensor 1 (SD003-151-001): It is a surface-mount InGaAs photodiode with an approximate spectral range from 800 nm to 1700 nm. The wide infrared range that particularly includes the NIR spectrum makes it especially suitable for capturing electroluminescence emissions from InGaAs photovoltaic cells. It has been indicated previously that this device was specifically selected for its high sensitivity in the NIR, considering potential future diagnostic applications for InGaAs panels.
Sensor 2 (VTP9812FH-nd): It is an “ambient light” silicon photodiode with an IR-blocking filter. Its peak sensitivity is in the visible region (~ 580 nm), covering an approximate spectral range from 400 nm to 700 nm. This sensor features a very high shunt resistance, low capacitance, and minimal dark current, resulting in low noise and low power consumption. It has been explained previously that this photodiode was chosen to represent a broader-spectrum sensor in the visible range and to serve as a comparative reference, although it is not optimal for detecting the infrared electroluminescence of solar cells. Its inclusion allowed us to verify the system’s performance with a wide-spectrum visible sensor and to confirm the importance of infrared sensitivity for this type of diagnostic.
Sensor 3 (BP-104FS): This sensor is a silicon PIN photodiode equipped with a daylight filter, highly sensitive in the near-infrared range (approximately 780 nm to 1100 nm). We have added that this sensor has a peak response around 950 nm with a typical high sensitivity (~ 0.7 A/W at 950 nm) and an acceptance angle of approximately 60°. Its integrated daylight filter effectively rejects most visible light (particularly blue wavelengths, to which the sensor is especially sensitive), enhancing infrared detection. This sensor was chosen due to its high infrared sensitivity, making it ideal for capturing electroluminescence from silicon panels while minimizing interference from ambient visible light. Due to its limited blue sensitivity, precautions were taken to avoid saturating the photodiode with luminous intensities outside its range (although, in practice, its filter prevents most visible light from affecting it).
Overall, all sensors provided satisfactory results across various sections of the PV panel, closely resembling the images obtained using the standard EL approach. However, high-intensity areas do not necessarily indicate faults in the PV panel. When panel conduction is disrupted, the remaining material may experience increased current density, leading to bright spots that could be mistakenly identified as hot spots during defect analysis. Similarly, dark regions resulting from complete cell disconnections can cause adjacent areas to exhibit increased intensity. Therefore, examining both low-intensity areas and their surroundings is crucial, as these observations can help in making preliminary diagnoses and identifying potential issues within the panel.
Figure 4a–c shows the light intensity measurements on PV panel 1 under sunlight exposure. Compared to the results in Fig. 3 there is a noticeable loss of similarity to the EL photographs (Fig. 4d). Additional results from testing the three sensors on four different PV panels are presented in Appendix B. Significant color discrepancies are observed, particularly in rows 1 and 9 of panels 2, 3, and 5, with complete mismatches in panel 4.
The results from Sensor 1 (Fig. 4a) and Sensor 2 (Fig. 4b) exhibit a stark contrast with the EL photograph, particularly in panels 3 and 5 (Appendix B). This discrepancy is especially evident in rows 1 and 9, as well as column A. Sensor 3 (Fig. 4c) shows similar results to Sensors 1 and 2, with notable discrepancies in rows 1 and 9 and column A of PV panel 1. These findings indicate that sunlight exposure significantly distorts the light intensity values across different areas of the PV panel, making it challenging to compare with EL photographs. Additionally, the errors are concentrated mainly along the edge sections of the solar panel, likely due to the angle of sunlight incidence.
Each sensor exhibits distinct properties in capturing light intensity. Sensor 1 does not fully align with the EL photographs due to its narrower sensitivity range to the various spectra of natural sunlight. Sensor 2, while more sensitive to the different spectrum of sunlight, produces lower-quality results compared to Sensor 1. Sensor 3, with its adequate sensitivity to the infrared spectrum, is not narrow enough to filter out sunlight effectively. As a result, Sensor 3 shows the greatest variation in light intensity, which negatively impacts the results. The study anticipated the influence of sunlight on the sensors and proposes two mitigation measures, the effects of which are discussed in the following results.
Sensor 1, being an InGaAs photodiode, is mainly sensitive to the longer-wavelength infrared range (approximately > 800 nm) and shows virtually no response to the visible spectrum. Under sunlight conditions, this means it captures part of the panel’s infrared electroluminescence emission (the useful signal) but also a significant portion of the incident solar infrared radiation (background IR). This dual contribution (useful signal + solar IR background) raises the baseline intensity, reducing similarity with the reference EL image when the panel is under sunlight. However, its lack of visible sensitivity prevents it from detecting shorter-wavelength solar reflections or scattered visible light, meaning that the discrepancy stems primarily from the reduced intensity response. This reduction in contrast between the maximum and minimum values makes the measurements more susceptible to the limitations of the instrumentation.
Sensor 2, by contrast, is more sensitive to the solar visible spectrum (approximately 400–700 nm, with peak sensitivity around 580 nm) due to its IR-blocking filter. Therefore, under solar illumination, this sensor primarily registers solar visible light reflected from the panel cells, capturing almost none of the infrared electroluminescence emission (which falls outside its spectral range). Consequently, the measurements obtained in sunlight conditions deviate significantly from the EL image, as the sensor essentially measures solar illumination variations across the panel rather than the cells’ infrared emission. In practice, Sensor 2 exhibited the greatest loss of similarity to the EL photograph under sunlight due to this spectral misalignment with the electroluminescence emission.
Sensor 3 exhibits an intermediate behavior: its daylight filter allows it to ignore most of the visible (particularly blue) light, focusing instead on the near-infrared spectrum (780–1100 nm). Thus, under sunlight, Sensor 3 is not significantly affected by the visible component of solar irradiance but still captures a considerable portion of solar infrared radiation (extending up to approximately 1100 nm). Moreover, we note that the spectral range of Sensor 3 does not fully encompass the electroluminescence wavelength of silicon cells (1050 ~ 1150 nm), falling slightly short of this peak. Consequently, under solar conditions, Sensor 3 captures variable IR intensities from sunlight through its slots and may lose part of the effective EL signal, thus increasing the variability of its measurements and explaining why it initially showed the greatest dispersion and uncertainty in daytime results.
In summary, it has been clarified that each sensor responds differently to sunlight due to its spectral band: infrared-filtered sensors (Sensors 1 and 3) avoid visible-light noise but still suffer from solar IR interference, whereas the visible-spectrum-oriented sensor (Sensor 2) essentially measures reflected solar irradiance rather than electroluminescence emission.
(a–c) Graphical representation of the measurements obtained from the EL test on PV panel 1 conducted under the exposure to sunlight, (d) results obtained from the EL photograph for comparison, each area is a size of 35 × 55[mm].
Two separate measurements were conducted: one at 11:00 and another at 17:00. To facilitate a clearer interpretation of the results, measurements using an alternative collector were compared with those obtained using the original collector. Figure 5 presents the light intensity on PV panel 1 using the alternative collector at 11:00 AM, while Fig. 6 shows the intensity with the original collector at the same time. Figures 7 and 8 display similar measurements taken at 17:00 for both collectors.
To assess the effectiveness of the new collector in reducing interference from direct sunlight, we utilize two main tools: the intensity measurements depicted in the figures and the color variations of each cell. Comparing these measurements with the positions of the cells in relation to the EL photographs provides a comprehensive interpretation. By examining both the positioning and changes in intensity of adjacent cells, we can effectively evaluate the impact of the new collector.
The results indicate that for Sensor 1, the new collector did not improve performance compared to the original collector. In most cases, there was a noticeable reduction in light intensity with the new collector, particularly around cells C-5, C-6, and C-7, where high-intensity areas were visible in the measurements but not in the original photograph. The data obtained with the original collector did not exhibit this issue. Sensor 2 showed similar results to Sensor 1, with some improvement observed particularly in row 9. However, the alternative collector resulted in a loss of definition in regions of medium intensity, thus failing to reduce errors effectively and proving less suitable for the proposed EL test. Sensor 3 demonstrated overall improvement at 11:00 with the new collector. Although some discrepancies were noted at 17:00 likely due to reduced sunlight intensity, the alternative collector still provided better results compared to the original collector.
A practical way to judge the collector’s effectiveness is to inspect how the measured intensity of each cell compares with that of its immediate neighbors. Stray sunlight typically produces abrupt jumps at the panel edges (rows 1 and 9, column A) and local hot spots in the central region (e.g., cells C-6 and C-7). With the slotted collector, these artefacts were largely suppressed. For instance, on PV panel 1, the high-intensity patches seen at 11:00 h with the original collector (Fig. 6c) disappeared when the slotted collector was used (Fig. 5c), yielding a color distribution that matches the EL photograph much more closely. The improvement remained evident in the 17:00 h test, where the overall color scale became smoother and no out-of-range spikes persisted (compare Fig. 8c with Fig. 7c). Although Sensors 1 and 2 showed only marginal gains (attributable to their lower amplitude response), the qualitative analysis of neighboring-cell intensity confirms that the slotted collector significantly enhances the spatial fidelity of Sensor 3 under direct sunlight.
Overall, the sensor results vary primarily due to the collector’s impact on the EL intensity reaching the photodiodes. Sensor 1 experienced a significant reduction in EL intensity because its photodiode has a lower amplitude response to the light emitted by the solar cells. Sensor 2 showed mixed results, with some improvements but still inconsistencies. Sensor 3, however, demonstrated substantial improvement. These observations suggest that while the new collector helps, it alone is insufficient to fully mitigate sunlight interference issues. The effectiveness of the collector is largely dependent on the photodiode’s amplitude response to the light intensity from the solar cells.
(a–c) Graphical representation obtained after processing the measurements from the EL test, taken under condition of direct sunlight, with the alternative collector at 11:00 in the panel 1, (d) results obtained from the EL photograph for comparison, each area is a size of 35 × 55[mm].
(a–c): Graphical representation obtained after processing the measurements from the EL test, taken under condition of direct sunlight, with the original collector at 11:00 in the panel 1, (d) results obtained from the EL photograph for comparison, each area is a size of 35 × 55 [mm].
(a–c): Graphical representation obtained after processing the measurements from the EL test, taken under condition of direct sunlight, with the alternative collector at 17:00 in the PV panel 1, (d) results obtained from the EL photograph for comparison, each area is a size of 35 × 55 [mm].
(a–c) Graphic representation obtained after processing the measurements from the EL test, taken under condition of direct sunlight, with the original collector at 17:00 in the panel 1, (d) results obtained from the EL photograph for comparison, each area is a size of 35 × 55[mm].
Figure 9 presents the results of the EL test conducted using an intermittent power source with the original collector on Panel 1. Results for PV panels 2, 3, 4, and 5 are detailed in Appendix C.
The data in Fig. 9 indicate that the intermittent power source has enhanced the performance of Sensor 1 (Fig. 9a) compared to the results obtained with the original collector. The improved results with Sensor 1 are now more consistent with those observed under darkness (Sect. 3.1), preserving the distinction between high and low intensity points. However, variations in medium intensity points remain, indicating that while the overall quality has improved, there are still fluctuations in the medium intensity readings. This is regarded as having less resolution than other sensors, especially Sensor 3. The results from sensor 2 (Fig. 9b) are also comparable to those obtained in darkness, although in comparison with sensor 1 there is more variation of intensity, which permits a higher resolution in the color scale, making the results better when compared with the EL photography. Sensor 3 (Fig. 9c) shows an improvement in the results that is quite clear in all observed cases, with the exception of some points brighter than expected. This might be due to human error during the measurement process.
The intermittent-mode test consisted of two consecutive acquisitions under identical ambient light: (i) PV module forward-biased for 5 s and (ii) module open-circuited for 5 s. Subtracting the ‘OFF’ matrix from the ‘ON’ matrix isolates the electroluminescence signal and rejects the constant solar background.
Overall, employing an intermittent power source enhances the performance of the sensors, though the degree of improvement varies based on the sensitivity and spectral response of the photodiodes. Sensor 1 shows the least benefit from this technique compared to the other sensors.
(a–c) Graphical representation of the measurements from the EL test, taken under condition of intermittent power source for the PV panel 1, with the original collector, (d) results obtained from the EL photograph for comparison, each area is a size of 35 × 55[mm].
Based on the results, it can be concluded that the developed sensor is a viable alternative to conventional methods for EL testing of PV panels, especially under the conditions outlined in the study objectives. This is particularly evident from the tests conducted in darkness.
The EL tests conducted in darkness demonstrated that the quality of results from the various sensors was comparable to that of conventional techniques. Despite significant variations in light intensity among the sensors, mathematical techniques were employed to process their data effectively. However, under direct sunlight, the sensors exhibited mixed performance. Sunlight introduced areas of high light intensity in some parts of the PV cells, while causing others to appear opaque. To improve performance under sunlight, two strategies were employed. The first involved using a new collector designed to enhance sunlight directionality, which yielded better results only for Sensor 3. The second approach involved applying a voltage step to the PV panel, which improved results across all sensors.
It can be concluded that the sensor-based approach to PV fault detection proposed in this study represents a valuable method for assessing the operational condition of PV panels. While it may not diagnose specific issues, it effectively provides insights into the overall health and functionality of the panels. Among the various strategies and sensors evaluated, the optimal configuration involves using Sensor 3 in conjunction with an alternative collector and a step-type power source. Notably, tests conducted with the step-type source revealed bright areas not observed during darkness tests or in EL photographs. These anomalies may indicate faults in the PV cells resulting from sudden changes in the power supply.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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This work was supported by the National Agency for Research and Development (ANID) through the projects Fondecyt Regular 1230135 and Fondef TA24I10002, and in part by the ANID Project CTI250019 Innovation Center for Sustainable Energy Transition (SET).
Department of Electrical Engineering, Jubail Industrial College, Royal commission for Jubail, Jubail, Saudi Arabia
Abdullahi Abubakar Mas’ud & Hassan Z. AlGarni
Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago, Chile
Ignacio Cuadra, Jorge Ardila-Rey & Antonio Sánchez-Squella
Center for Energy Transition, Faculty of Engineering and Sciences, Universidad Adolfo Ibañez, Peñalolén, Chile
Rodrigo Barraza
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Abdullahi Abubakar Mas’ud, Ignacio Cuadra, Jorge Ardila-Rey: Conceptualization, Methodology, Software, Visualization, Investigation, Writing- Original draft preparation. Abdullahi Abubakar Mas’ud, Rodrigo Barraza, Antonio Sanchez, Oscar Danilo Montoya. Data curation, Validation, Supervision, Resources, Writing – Review & Editing. Abdullahi Abubakar Mas’ud, Jorge Ardila-Rey, Ignacio Cuadra, Hassan Z. AlGarni: Project administration, Supervision, Resources, Writing – Review & Editing.
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Solar Panel Recycling Initiatives: Leading Technologies and Programs 2026  discoveryalert.com.au
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Solar Power World
By Kelly Pickerel | April 7, 2026
In an April filing with the U.S. Securities and Exchange Commission (SEC), solar panel manufacturer Maxeon revealed it has applied to be placed under “judicial management” in its headquartered country of Singapore. This is a method of debt restructuring when a company is under financial distress, but is not a bankruptcy filing.
Maxeon stated that the company has been under economic stress ever since U.S. Customs and Border Protection (CBP) began seizing solar panels in 2024 for reviews under the Uyghur Forced Labor Prevention Act (UFLPA). Despite proving UFLPA compliance, Maxeon said CBP is not allowing its solar panels into the country.
Maxeon said CBP’s continued denial of entry has “negatively impacted the company’s ability to generate cash flow” and fulfill contractual commitments that have led to customers filing lawsuits against Maxeon, seeking damages of over $70 million.
Maxeon, the manufacturing spinoff from the once-dominant SunPower brand, decided in 2024 to only focus on supplying the U.S. market. The efficient brand was being contracted for large projects, including the 1-GW Gemini Solar project in Nevada. Maxeon was assembling solar panels in Mexico using Malaysian solar cells and had plans to start U.S. manufacturing in Albuquerque, New Mexico.
In the SEC filing, the company said it had a purchasing agreement with a third-party for modules assembled in the United States to bypass the CBP holding.
Maxeon should have some money coming in soon. The company entered a patent license agreement with Aiko Solar earlier this year, wherein Aiko will have access to Maxeon’s back contact solar cell and module patents outside of the United States for the next five years. Maxeon says this will result in an installment payment of $14 million by the end of this month.
Maxeon will have a hearing on the judicial management in Singapore’s Supreme Court later this week.
Kelly Pickerel has more than 15 years of experience reporting on the U.S. solar industry and is currently editor in chief of Solar Power World. Email Kelly.

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Recurrent Energy Sells 42.5 MWp Higher Witheven Solar Project In Cornwall After Securing UK CfD Contract  solarquarter.com
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Easee has put its foot to the floor in 2026, completing a pilot project with Subaru that demonstrates the viability of solar-powered electric-vehicle charging in the harsh winter conditions of Canada’s Northwest Territories. It follows a collaboration between Easee and the Japanese car manufacturer in 2025 that saw the two companies deploy an electric vehicle charger on the South Atlantic island of Saint Helena, billed at the time as the world’s most remote electric-vehicle charging infrastructure. Adam Dunwoodie, technical manager at Easee UK, told pv magazine that locating the latest project in Canada’s Northwest Territories has enabled a pilot project to push the technology another step further.
Solar can effectively charge electric vehicles, even in the coldest climates.
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Federal permitting delays have held up 11GW of new renewable energy deployment in the US in the last year alone, and the presence of permitting rules could increase project development costs by up to 10%.
These are some of the takeaways from the latest report published by clean energy platform Crux, which surveyed 50 renewable energy developers across the US to assess the impacts of federal permitting regulations on project development.
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Published today, ‘The Impact of Federal Permitting on Clean Energy Development’ finds that the deployment of new solar and wind capacity has been significantly delayed, and made more expensive, by the current policy landscape in the US.
For instance, 94% of survey respondents said that permitting regulations had resulted in a delay of at least one month in the last year, with almost half—46%—reporting a delay of three to six months due to these rules. A total of 8% of respondents said they had experienced “multi-year delays”, more than the 6% that reported “no real change” in project timelines in the last year; these trends are shown in the graph below.
There is a similar trend for project costs, with the majority of respondents reporting a sizable increase in project costs over the last year, and a significant majority reporting an increase in project costs of more than 25%, the largest figure in the Crux survey. Of the 50 respondents, 4% said that they had experienced project cost increases of more than 25%, alongside 4% of respondents who said they had experienced increases of 16-25%.
More strikingly, not a single respondent reported no cost increase. The 6-10% cost increase answer was the most popular, with 58% of respondents reporting this scale of price increase; these trends are shown in the graph below.
This is particularly notable for the solar PV sector, as Crux notes that for a 100MW solar project, these increased costs translate to US$10-18 million in additional development costs, which in turn translate to 5% higher energy bills for customers. Just this week, independent power producer (IPP) Geronimo Power started commercial operations at an Ohio PV project of this scale, and with several developers seeing slimmer margins than they anticipated as tax credit deadlines loom, more expensive permitting costs would not be welcomed by project developers.
The majority of the report’s respondents also said that they sited projects differently from their original plans, explicitly to avoid having to secure permits from the federal government. In total, 82% of respondents said they had done this, suggesting that the federal permitting process is so flawed that it is actively impacting project design and siting.
This echoes the situation that was seen in the UK under the previous Conservative government; central government approval is required for renewable energy projects of 50MW and above, but the Conservative party’s opposition to renewable energy meant that this was a de facto ban on renewable energy projects of this size. As a result, the UK saw a raft of projects developed, of a capacity of exactly 49.9MW, in the years leading up to the Conservatives’ loss in the 2024 general election, following which 1.3GW of larger-scale solar projects were immediately awarded permits.
What is perhaps most striking, however, is not that the need for greater federal approval is delaying project development or making the deployment of new renewable energy capacity more expensive, but that these obstacles are often a surprise to developers. Of the developers surveyed in the report, the majority—72%—said that they would want “more predictable outcomes” from the federal approval process.
This compares with 12% of respondents who called for shorter permitting timelines, suggesting that delays and expenses may be unwelcome, but are disruptions that can ultimately be planned for and overcome; unexpected permitting obstacles, however, have proven much harder to prepare for.
“Investors are ready to act, but capital markets need confidence that these projects will reach commercial operation on a predictable timeline,” explained Hasan Nazar, Crux head of policy. “Developers have made it clear that responsible reform of the permitting system is a powerful lever to deploy new energy to keep pace with demand.”    
The Crux report points to several types of federal regulations that, once triggered, could significantly increase the time and cost required to secure federal approval. These include regulations pertaining to local animal habitats, protection of local water and wetlands and the fact that renewable energy projects—even those sited on private land—can require approval from the National Environmental Policy Act (NEPA) if it receives federal funding or encroaches on federal land.

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Scientists are always pushing the boundaries of solar cell efficiency – how much of the available sunshine gets turned into electricity – and a new approach to the technology has resulted in an astonishingly high 130 percent ‘quantum yield‘.
It’s important to note that this is a quantum-level energy return, so we’re not talking about a solar panel converting sunlight into electricity at a 130 percent rate. However, the breakthrough is an efficiency improvement in terms of how often a specific event occurs per photon absorbed by the system.
To break through the 100 percent barrier, the new approach splits the energy harvested from a single incoming light photon into two, which then powers two excited states (known as excitons) in the receiving material.
It’s a process known as singlet fission, and as the international team behind the research explains, it prevents excess energy from being lost as heat.
That loss is part of the reason that solar cells typically max out at around the 33 percent mark in terms of overall efficiency, a restriction known as the Shockley-Queisser limit.
“We have two main strategies to break through this limit,” says chemist Yoichi Sasaki, from Kyushu University in Japan.
“One is to convert lower-energy infrared photons into higher-energy visible photons. The other, what we explore here, is to use singlet fission to generate two excitons from a single exciton photon.”
The researchers used an organic molecule called tetracene to act as the splitting material here, through which singlet fission can work. Its properties make it suitable for splitting one high-energy packet into two lower-energy packets through electron excitation.

Singlet fission isn’t a completely new concept, though, and is only half of the story here. A major stumbling block in previous experiments had been giving singlet fission enough time to work before the energy was lost or transferred elsewhere.
This is where the metallic element molybdenum comes in, again chosen for its particular properties. By mixing it with tetracene, the team was able to catch the split excitons in the molybdenum compound.
At the tiniest quantum level, the molybdenum acts as what’s called a spin-flip emitter. First, it locks in energy, and then it uses a quantum spin-flip to turn the invisible states into light. That gave the team the breakthrough result: 1.3 molybdenum-based metal complexes excited per photon absorbed.
“The energy can be easily ‘stolen’ by a mechanism called Förster resonance energy transfer (FRET) before multiplication occurs,” says Sasaki.
“We therefore needed an energy acceptor that selectively captures the multiplied triplet excitons after fission.”
It’s worth emphasizing again that these are early lab tests. The next steps are to convert the liquid solution used here into a solid form that can be fitted to a solar panel, reliably and effectively – which the researchers themselves admit will be quite a challenge.
There’s also the issue of the molybdenum complexes hanging onto the energy long enough for it to be useful, as well as capturing it in the first place. This “decay process” is something else the study addresses.
Related: New Solar Panels Can Heal Themselves From Damage in Space
However, those future practical concerns shouldn’t take away from the excitement of the research: It clearly sets out a path towards solar panels that can go above and beyond the efficiency limits of today, and there are multiple ways that this proof-of-concept can be tweaked and experimented with going forward.
With solar energy a vital part of reducing our reliance on fossil fuels and slowing down climate change, being able to substantially improve conversion rates on solar panels would potentially be transformative for the energy industry – especially when paired with new energy storage mechanisms.
“This work represents a significant step toward developing exciton/photon amplification materials by combining singlet fission materials with transition-metal complexes, advancing the application of singlet fission beyond conventional limitations,” write the researchers in their paper.
The research has been published in the Journal of the American Chemical Society.

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Communications Earth & Environment volume 6, Article number: 1006 (2025) Cite this article
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Growth in solar photovoltaic capacity supports grid decarbonization but can result in land transformation. Quantifying land–solar interactions is hampered by inconsistent methods and data. We develop a consistent, replicable framework to quantify land-solar interactions and apply it to annotated aerial imagery covering 719 solar photovoltaic projects (13,272 megawatts of installed capacity) connected to the Western Interconnection in the United States. We train a deep-learning convolutional neural network to characterize solar photovoltaic land footprints, post-process outputs with geospatial land-cover overlays, and compute land-use efficiency and energy-normalized land transformation per project. Across the sample, mean capacity-based land-use efficiency is 24.7 ± 15.2 watts per square meter and mean lifetime land transformation is 0.846 ± 0.722 square meters per megawatt-hour; regional differences and engineering choices explain project-level variability. Our open-source inventory and method enable more consistent large-scale assessments of planning, life cycle impacts, and ecological trade-offs of solar expansion.
Renewable energy sources are an important conduit through which energy-related greenhouse gas emissions can be reduced, particularly photovoltaics (PV) due to the abundant and ubiquitous nature of solar energy^1,2,3. Despite having only generated 3.2% (856 TWh) of global electricity generation in 2020, the PV industry has maintained high growth rates for the past decade and its cumulative capacity may reach ten terawatts by 2030^4,5. The growth of ground-mounted PV may experience land constraints, yet the degree to which it may be constrained has yet been confirmed due to inconsistent methodologies and a lack of comprehensive datasets. Further, ambitious solar energy development could potentially create unanticipated knock-on effects on land use on a global scale if existing land uses are displaced, highlighting the importance of optimizing co-use of land. As the world transforms its energy system toward lower carbon and renewable sources, understanding not only the benefits but also how to overcome potential drawbacks of solar PV has important implications for sustainability. Our analysis contributes a transparent and systematic method to quantify land-solar interactions and comprehensive dataset of to be scientifically accurate 719 solar power PV projects in the U.S. portion of the Western Interconnection.
The installation of solar PV projects captures free and abundant solar irradiance over large parcels of land which are neither free nor unlimited. The issue of land use has been perceived as a limitation to the large-scale growth of ground-mounted solar power, with early studies suggesting that it can compromise the overall sustainability and return on investment of solar energy systems^6,7,8. A total of 32 environmental impacts have been linked to the utilization of solar energy, indicating that high growth in solar power may result in unintended consequences to various environmental impact categories⁹. At the same time, solar energy has been noted as having both lower and higher land use in comparison to wind and fossil fuel powered electricity using life cycle assessment (LCA), a cradle-to-grave analysis of the impacts of products and processes^10,11. Land transformation (square meters per megawatt hour ((frac{{m}^{2}}{{MWh}}))) serves as an important baseline metric for LCA in terms of area per unit of electricity generated. The quantification of land-solar interactions can enable an understanding of how to ameliorate impacts while supporting techno-ecological synergies^{6,12,13,14,15,16,17}; this framework to engineer solutions that benefit both solar technologies and ecological systems by design has become a major thrust of research in recent years. Transforming our perspective on systems design can ensure that solar energy is developed to align with both climate and sustainable development goals (SDGs)¹⁸.
Beyond LCA, inconsistent metrics, data, study boundaries, and methods have produced conflicting results for land-energy interactions^6,19,20,21. Over short timescales (e.g., <10 years), renewable energy systems have been noted as much more land-intensive than fossil-fuel-based systems^10,22,23,24. Solar power has also been noted as having lower scores in capacity-based land use efficiency (watts of installed capacity per square meter ((frac{{W}_{c}}{{m}^{2}}))) and generation-based land use efficiency (watts of operational capacity per square meter ((frac{{W}_{o}}{{m}^{2}})))¹¹ in comparison to non-renewable alternatives with strong dependence on geographical locations²⁵. Such results overlook the fact that renewable power can increase its cumulative electricity produced without expanding its land footprint whereas non-renewable power requires the continual development of new land for fuel extraction²⁶. Furthermore, solar PV facilities can increase their land use efficiency by having higher packing factor—the ratio of PV array area to total site area for a facility²⁷. Considering the implicit time factor in land-use metrics, renewable energy sources can have comparable or even lower land impacts when compared to fossil counterparts when examined over longer timescales, such as the average lifetime of solar projects (30 years)^10,24,28. Solar power can reach land equivalency (i.e., the amount of time it takes for fossil-fuel-based systems and renewable power systems to have the equivalent land footprint and cumulative energy production) with natural gas and coal alternatives in less than a decade^10,24.
Systematic research that quantifies the land-solar interactions  covering large populations of PV projects across extensive tracts of land remains relatively limited. Existing analyses rely on relatively small datasets (e.g., <200 power plants in Hernandez et al.⁶) or present results focused on the direct panel area rather than the total project site^29,30,31 (i.e., including the panels and all land use up to the project fence line), limiting the ability to directly use findings to confirm the land-use impacts associated with current and future solar energy generation²⁴. Without readily available data describing the land use of solar PV facilities, the potential higher-order, indirect effects of solar land use on the global scale become even more difficult to measure. Furthermore, the variable use of metrics in existing literature prevents broad consensus about land-solar interactions, which can be circumvented by adopting a transparent methodology and consistent set of metrics²¹. Our analysis of the 719 U.S. solar PV projects (Fig. 1) contributes a systematic and replicable methodology that produced a dataset quantifying  the resulting land transformation (the land area altered by the solar facility per unit of energy produced during its 30-year lifetime) and land-use efficiency (the land area altered per unit of installed nameplate capacity or electricity generation). The power plants in our sample are all connected to the U.S. Western Interconnection, which occupies an area rich in solar energy in terms of global horizontal irradiance (GHI in ({kWh}/frac{{m}^{2}}{{year}}))—a close proxy for the total endowment of solar resources available at each given location on the earth’s surface.
These figures show the locations of solar PV arrays documented for this study (Energy Technology and Policy Assessment Research Group, ETAPA) and USGS studies. The 2016 U.S. Geological Survey (USGS) data identifies the locations and footprint of 740 solar PV facilities (1207 clusters of PV arrays) for the conterminous U.S. by 2015 (green). We document 719 solar PV facilities (8089 clusters of PV arrays) in the U.S. Western Interconnection (separated by the blue stroke) by 2019 (blue). The base layer shows the annual average GHI produced by the National Renewable Energy Laboratory (NREL). a This map shows the locations of solar PV arrays documented in the USGS data that are connected to the Western Interconnection. b This map shows the locations of solar PV arrays examined in our study that are connected to the Western Interconnection.
By combining deep learning and energy systems analysis, we present a workflow that can be broadly applied to solve inconsistencies in the quantification of land-solar interactions while elucidating the use of the built environment. While previous studies have documented efforts to localize and create inventories of solar PV installations in the U.S.^{29,31,32,33,34,35,36,37}, access to the resulting data has been limited^33,34,36 and the annotation of solar PV installations has been focused on the panel arrays²⁹). We introduce a replicable and efficient machine-learning-based approach for analyzing land-use associated with solar PV that differentiates between the panel array area and the full project area of solar PV facilities. Our approach is applicable across diverse regions and installation types, not only generating robust datasets but also providing consistent metrics and transparent, replicable methods that offer critical insights for resolving inconsistencies in LCA and other macro-scale energy analyses.
We examined 719 utility-scale solar PV facilities from U.S. Energy Information Administration (EIA) records that are connected to the Western Interconnection (Fig. 1a). Each of our utility-scale facilities has a nameplate capacity of at least 1 megawatt as defined by the International Energy Agency (IEA)³⁸, with the median, mean, and total nameplate capacity being 3.5 megawatts, 19.7 megawatts, and 13,272 megawatts respectively. Our dataset also includes 110 utility-scale rooftop PV installations, predominantly situated on substantial structures such as university campuses, government laboratories, and corporate facilities, averaging 2.1 megawatts in nameplate capacity and comprising 1.8% (236.3 megawatts) of the total analyzed capacity. Our results suggest that solar projects examined in the study area cover 538 ({{{mathrm{km}}}}^{2}) of land, 416 ({{{mathrm{km}}}}^{2}) of which is covered by solar panels. Our PV panel footprint results show strong alignment with prior works completed by the U.S. Geological Survey (USGS) and Lawrence Berkeley National Laboratory (USPVDB)^30,31 (Fig. 2). Out of the 719 solar PV facilities examined in our study area, 252 and 609 were once examined by the aforementioned studies (Fig. 1b). When comparing across the same PV projects, our estimation for the area of PV panel arrays is close to that of the existing data. The minor differences can be attributed to three factors: 1) the later expansion in capacity for some projects after 2015; 2) inconsistent geopositioning of specific projects; and 3) different methods in accounting for the gap separating PV panel arrays. Specifically, solar arrays separated by more than 30 meters are annotated as separate polygons in the 2016 USGS data while arrays are examined on a case-by-case basis using our approach (see methods). Differences in methods point to the need for judicious use of results from different studies as they may not have been produced with commensurable methods. Our approach provides the land-use footprint of not only the panel area but also that of the entire solar PV facility by evidence of fencing, which is largely missing from existing data^29,30,31,32. This additional information allows us to evaluate PV array spacing using the packing factor metric—the ratio of panel array footprint to that of the entire facility²⁷. An evaluation of the 719 solar PV facilities shows a median and capacity-weighted average packing factor of 74% and 75%, respectively.
The chart shows the distribution of land-use footprint estimates for the same clusters of PV panels in our findings (green), the 2016 USGS study (blue), and 2023 USPVDB (blue). The results closely mirror each other, with minor differences stemming from the different approaches toward annotation. We also contribute by providing statistics on the land-use of entire solar PV facilities. Mean values are shown as black-centered lines. Vertical error bars denote 95% confidence intervals of the mean. Y-axis is restricted to 0–2 km² for visual legibility; full distributions of project footprint reach an upper limit of 11.2 ({{{mathrm{km}}}}^{2}).
The land transformation for solar PV projects in U.S. states is, of course, influenced by the available solar resources (Fig. 3a, b). Specifically, the average land transformation of PV facilities is lower by 16% in the U.S. Southwest (Arizona, California, Colorado, Nevada, New Mexico, Texas, and Utah) than in the Northwest (Idaho, Montana, Oregon, Washington, and Wyoming). When evaluated in terms of capacity- and generation-based land use efficiency, Southwestern PV facilities outperform those located in the Northwest by 22% and 36%, respectively (Fig. 3c–f). The PV projects in Arizona and El Paso, Texas on average are the best performers among all projects in our sample. For both land transformation and land use efficiency metrics, evaluating PV projects solely on the footprint of solar arrays yields better performance results than estimates based on the total project footprint, which has important implications for future studies on PV power considering the balance of system for PV projects is increasingly likely to include potentially land-consuming subjects such as utility-scale battery packs. On average, the 609 ground-based solar PV facilities have a capacity- and generation-based land use efficiency of 24.7 (frac{W}{{m}^{2}}) and 5.8 (frac{W}{{m}^{2}}). Accounting for the varying installed capacity of PV projects, our results show a capacity- and generation-weighted average land use efficiency of 25.4 (frac{W}{{m}^{2}}) and 6.9 (frac{W}{{m}^{2}}), updating prior estimates²⁷. The average lifetime land transformation for the examined facilities is 0.85 (frac{{m}^{2}}{{MWh}}). Rooftop installations are excluded from these land-use calculations derived from project footprints, due to the absence of clearly and exclusively defined project site areas for these installations but are available in the published dataset.
These graphs show the average land transformation and land use efficiency of PV projects in each state at the panel- and project-level. The land transformation of each PV plant is calculated using the function specified in the Method & Data section. Annual generation data for each plant in 2019 is retrieved from form EIA-923. All states are connected to the Western Interconnection (WECC). The states of Arizona (AZ), California (CA), Colorado (CO), Nevada (NV), New Mexico (NM), Texas (TX), and Utah (UT) are categorized as ‘southwestern states’ (SW). The remaining states of Idaho (ID), Montana (MT), Oregon (OR), Washington (WA), and Wyoming (WY) are categorized as ‘northwestern states’ (NW). a This graph shows the average land transformation of PV projects in each state. b This graph shows the average land transformation of PV projects in each region. c This graph shows the average capacity-based land use efficiency of PV projects in each state. d This graph shows the average generation-based land use efficiency of PV projects in each state. e This graph shows the average capacity-based land use efficiency of PV projects in each region. f This graph shows the average generation-based land use efficiency of PV projects in each region.
Results differ between PV facilities using different mounting systems. On average, PV panel arrays on dual-axis tracking systems outperform their fixed-rack and single-axis neighbors within the same state by 25% and 39%, respectively in terms of land transformation (Fig. 4a). However, in terms of total land transformation for the entire project site, PV facilities built with dual-axis tracking systems are greater on average than their single-axis and fixed-rack counterparts in the same state by 58% and 13%, respectively (Fig. 4a), meaning the former has a larger land footprint than the latter. The same trend persists when evaluated using land use efficiency (Fig. 4b, c). This dichotomy arises because fixed-rack/single-axis PV panel arrays are built in a much more compact formation, taking up less total land than dual-axis panel arrays which require each individual panel to freely and fully track the movement of the sun. On the other hand, individual PV panels on dual-axis mounting systems exhibit greater efficiency in capturing sunlight during the day than their counterparts do.
These box and whisker plots show the difference in the land transformation and land use efficiency between PV projects using dual-, fixed-, and single-axis panel mounting systems. Out of the 719 PV projects examined in our study area, three are built with a mixture of dual-axis and fixed/single-axis mounting systems, 13 are built with dual-axis mounting systems, 110 are built with fixed-rack/single-axis mounting systems on rooftop, and 593 are built with fixed-rack/single-axis mounting systems on open ground. Among the 13 projects with dual-axis mounting systems, 11 are in California, two are in New Mexico and the remaining one is in Colorado. For consistent comparisons of all three mounting systems, only these three states are shown. a This graph shows the average land transformation of PV projects with different mounting systems. b This graph shows the average capacity-based land use efficiency of PV projects with different mounting systems. c This graph shows the average generation-based land use efficiency of PV projects with different mounting systems. The box represents the interquartile range, the line inside the box is the median, and the whiskers extend to the most extreme data point that is within 1.5 times the interquartile range.
Land sparing—including siting generation on already-developed surfaces such as rooftops or redeveloped brownfields—can support the potential synergy between technology and ecological health for sustainability energy development^29,39,40. Using National Land Cover Data (NLCD) created by the Multi-Resolution Land Characteristics (MRLC) consortium, we classify the surface cover within each project footprint (Fig. 5a, b) and report both contemporaneous and historical results. In 2019, 59% of projects in our sample are located on land classified as developed (including low, medium, and high intensity), accounting for 65% of total installed capacity. Using NLCD from 2001, when U.S. solar development remained in its infancy, only 1.6% of total installed capacity in our dataset was located on land classified as developed; however, 38% of projects were developed on land that was classified as cultivated crops. Our results confirm previous findings that rural anthromes (e.g., cropland) and biomes (e.g., grassland) that are close to human population are the most likely to have been previously sited for PV projects^29,41. This outcome points to the potential for optimizing the dual use of land (e.g, agrivoltaics) to mitigate impacts on local ecological systems while sustaining growth of solar PV. Results indicate that PV projects are being built on various types of land, indicating the need to carefully design dual use systems and consider the sustainability of siting decisions across a variety of local environments. Approximately 14% of existing PV projects in our study area now are located on cultivated cropland, which could be retrofitted into dual use systems to obtain techno-ecological synergies²⁹. An additional 15% of the PV projects utilize rooftop modules, which occupy surfaces within the built environment for solar energy. It can be argued that they do not directly transform additional land, highlighting their potential advantage as a land sparing opportunity. Land sparing opportunities exist but are underrepresented in our sample—PV deployment has in many places driven measurable changes in land cover.
These figures show the number and total installed capacity of existing solar PV projects delineated in our dataset across various land cover types (woody wetlands and grassland/herbaceous are new classification types that are not present in the 2001 data). The number atop each bar shows the aggregate installed capacity of all solar PV projects constructed on each specific land cover type. The dark bars indicate developed land cover types while the medium and light bars indicate agricultural and undeveloped land cover types, respectively. a This graph shows the land covers for all PV plant location in 2019. b This graph shows the land covers for the same PV plant location in 2001.
Our systematic methodology enabled the first digital dataset containing project-level land information of solar PV connected to the U.S. Western Interconnection using deep learning. Using this dataset, we contributed a detailed demonstration of land-solar interactions in the region, resulting in novel records of the land use efficiency and land transformation of existing PV facilities with varying geographic and structural characteristics. Our findings confirm that land-solar interactions are highly dependent on local solar output potential and designs of panel mounting systems^42,43. We build upon prior studies that apply deep learning to detect and characterize the area of solar PV as the first to characterize the land required by solar PV projects in their entirety as opposed to the solar arrays alone^29,33,34,36. In doing so, we provide information on solar land use that is rarely seen in existing data—the entire project site of ground-mounted solar PV projects—and present a more complete picture of land-solar interactions.
A novel observation differentiating our findings from prior research is that facilities with higher performance, dual axis tracking mounting systems, are less efficient in terms of project-level land-use when compared to their fixed/single-axis counterparts. We attribute this finding to the much larger clearing space needed to install individual dual-axis solar arrays (e.g., to avoid inter-panel shading), which thereby reduce the panel density⁴⁴. These results point to the need for analyses of land-solar interactions to go beyond the array footprint alone to better understand impacts related to the full project area. Additionally, our data enabled an important classification of the land cover occupied by PV installations, presenting an important approach that highlights the benefits of using the built environment to reduce ecosystem impacts⁴⁵. The majority of the land used for PV projects utilizes land that previously belongs to the rural anthromes and biomes and is now developed, suggesting that solar energy projects are most frequently sited near human population and that land sparing outcomes can be achieved by siting projects to make use of both dual use agrivoltaics and the built environment. Definitive conclusions about the aggregate land-use impacts of solar PV facilities would necessitate ex-ante land cover classification for the same plots of land and potential knock-on effects of local solar energy developments on land use in other areas.
Importantly, our study represents the first attempt to quantify land use metrics using deep learning results, making it the first large-scale analysis of this kind that examines land transformation and land use efficiency. The results demonstrate the potential of using machine learning techniques to perform tasks that are prohibitively difficult and labor intensive (e.g., annotation of PV project site). The combination of GIS and machine learning tools is contributing valuable instruments for consistent collection and analysis of the land-use implications of energy developments. A nascent stream of research has shown the prospects of utilizing open-access map such as the OpenStreetMap to detect and create land-use inventories^46,47. Such approaches have the benefit of coverage of large areas but have so far relied on relatively coarse imagery and remain challenged in achieving the high-precision boundary delineation required for intra-facility solar land-use analysis. Crowdsourced data, such as OpenStreetMap, also may be limited in completeness, geographic coverage, and consistency, and has resulted in incomplete solar inventories⁴⁷. High-resolution aerial imagery such as that we use here more closely represents the ground truth of energy system development, ensuring a replicable approach to producing up-to-date analyses of the rapid development of solar PV. While semantic segmentation for PV detection is increasingly well established, we extend the field by integrating land use efficiency and land transformation metrics^21,48.
While this study primarily focuses on quantifying land-solar interactions for PV projects, results have broad implications for data-driven decision-support in fields such as LCA and energy systems planning. LCAs of electricity generation, including solar PV, are increasingly including spatiotemporal inventories⁴⁹. Site-level data, such as the inventory we produce here, can enable analyses that account for improved geographic representativeness and variability of impacts across projects. While spatial variability is important for solar PV, public inventories have been limited to date^11,33,34,36. Future work could further enhance geospatial information by better characterizing transmission lines, access roads and distribution infrastructure^50,51.
With rapid growth in solar energy, it is vitally important to improve land management and preserve ecological health while mitigating climate change⁴⁵. A large-scale map of existing PV projects can inform a better understanding of land-solar interactions, including other social and geographical aspects of existing land-based economies and ecosystems³⁰. Our methods and results contribute important decision-support for planning, policymaking, and analyzing the deployment of PV across states in the Western Interconnection. By publishing an open-access, georeferenced inventory with a carefully documented methodology, we establish a replicable approach to quantifying land-solar interactions, serving as an important benchmark for understanding the land implications of energy transitions with high growth in solar power.
The study area for this research project is the U.S. part of the Western Interconnection which includes the states of Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming, and the El Paso County of Texas. We identified potential solar facility locations in our study area and collect annual generation data using the EIA facility data from 2019 (e.g., EIA-860 and EIA-923), which include electricity generating units with installed capacity of at least 1 megawatt. The land area of each solar PV project is determined using a combination of deep learning technology and aerial imagery analysis, the latter of which involved manual annotation to inform the deep learning model. The aerial imagery used in this study comes from the National Agriculture Imagery Program (NAIP) database administered by the U.S. Department of Agriculture. The NAIP images are acquired at a resolution of <1-meter ground sample distance across the entire US during agricultural growing seasons, representing the more accurate depiction of land-use compared to crowdsourced map databases used in some existing studies⁴⁷. The image database is updated with a 3-year cycle—the one used in this study is published in 2019. The NLCD is a detailed mapping of the US with consistent and relevant land cover information from 2001 to 2019 with a two-to-three-year interval, created by a group of U.S. agencies named the MRLC. Data for the year 2001 and 2019 are used to better elucidate the change in land cover before and after almost all solar PV installations in our study area are built. The pixel resolution for NLCD is 30 m—suitable for solar PV projects which typically occupy around 200,000 square meters of land and much finer than what has been used in previous findings²⁹.
We compare our estimates against two datasets on the land use of solar PV facilities for the conterminous U.S. published by the USGS in 2016 and jointly by the USGS and the Lawrence Berkeley National Laboratory in 2023^30,31. The 2016 USGS dataset identifies locations of solar PV panels in the conterminous US in EIA utility-scale facilities data from 2015 and digitizes the footprint area of each solar array using aerial imagery from NAIP from the same year. In total, they documented that there were 740 U.S. solar PV facilities (1207 arrays) by 2015. Since the scope of our study is limited to the U.S. part of the Western Interconnection, we have a different study area than the two existing datasets.
The solar PV projects examined in our study are distributed across different climate regions with vastly different landscapes. Before deploying a deep learning model for image annotation, which has been shown to be a useful tool for land-use inventory-making and analysis, we need to ensure the performance of the model^{29,33,34,36,37,47}. Our deep learning model needs to be able to capture the distinct features of solar PV systems against the varying backdrops of land cover types. To that end, we begin by training the model using a diverse set of manual annotations. Each solar PV power plant is located using coordinates obtained from the U.S. Energy Information Administration on NAIP. The image of the plant and its immediate surrounding area is then cropped from the aerial imagery and saved for annotation. Specifically, a randomly selected group of 100 projects were manually annotated at first for the development of a deep learning model. The entire portfolio was then automatically annotated by the deep learning model. The product generated by the model then goes through manual post-processing before being compiled into a master shapefile.
Table 1 shows a summary of the six annotation classes we use for solar PV projects. In this solar PV annotation framework, PV panels, inverters, and other infrastructures (e.g., battery storage stations or buildings) are all within the project site. In addition, there will be no overlaps between PV panels and inverters, nor between external access roads and project sites.
In this annotation framework, we only consider (visually distinguishable) external access roads as a separate class, and consider internal roads for operation and maintenance as part of the project site without further classification. In addition, we consider the overall project site as a permanent site clearing and do not distinguish other temporary land use, which usually recovers or is cleared once the project construction is finished, and the project is in operation.
The PV panels class can be annotated in multiple ways based on different decision rules on polygon delineation. Here we adopt a “visually distinguishable segments” rule to delineate solar PV panels—separating clusters of PV panels by evidence of relatively larger empty space that can be potentially utilized for other purposes (e.g., dirt road for maintenance vehicles). We decide on this rule in order to separate PV panels from adjacent support infrastructure such as inverters and acknowledge spaces that are reserved for maintenance access, while also categorizing small intervals between PV panels as part of the PV panels class since these parcels of land would not be used for other purposes. For instance, Fig. 6 shows an example of the annotation based on this “visually distinguishable” rule. Here solar PV panels are separated from on-site inverters.
This is part of the AV Solar Ranch One (240 megawatts) in CA. The color codes can be found in Table 1.
To understand the impacts on PV panel areas using different polygon delineation rules, we compared the land area impacts with four different decision rules. The four rules being tested are based on three delineation methods: 1) the PV panel-only rule, which only accounts for physical PV panels and separates small intervals between panels; 2) the visually distinguishable segments rule, which is the decision rule we adopt as described above; and 3) the quantitative distance rule, which uses a quantitative measure to decide on segmentation, i.e., if panels have a distance larger than the threshold, they are categorized as different polygons, otherwise categorized as a single polygon. For the 3^rd method, we use both 10 m and 30 m as decision rules to test, where the 30-m rule is adopted in the previous USGS study. An example of the annotation for the PV panels class based on different decision rules can be found in Fig. 7.
Sample project: Pine Tree Solar Project (8.5 megawatts), CA.
We compare the PV panel land areas for four solar PV projects using these four different decision rules. Potential differences between the decision rules that can be used in this methodology are illustrated in Table 2. Only accounting for PV panels results in about a 50 percent reduction in land footprint compared to using a 30-m rule in these sample projects. However, other than the PV panel-only method, the land area impacts using the other three decision rules are very similar, suggesting minor deviations when switching between these three rules. According to the example shown here, the difference in land area impacts of using different methods would be greater with larger size of projects.
While developing annotated images for solar PV, we also measure two distances in order to justify decision rules for the paper: minimum distance segmented and maximum distance not segmented. An example of the two distances being measured is shown in Fig. 8. The minimum distance segmented represents the minimum distance where two segments of PV panels are separated (due to inverters, roads, or other visually distinguishable features), whereas the maximum distance not segmented represents the maximum distance between PV panels that are aggregated as a single segment.
Example of minimum distance segmented and maximum distance not segmented.
A deep learning-based network was trained to perform automatic segmentation of the PV panels based on a set of 100 solar PV facilities which is manually annotated according to rules detailed in the section above. We specifically chose U-Net as the deep network for segmentation as it has been seen to work well for segmentation-based applications in aerial imaging where precise boundaries are needed⁵². U-Net is an encoder-decoder architecture where the encoder takes in the image as input and converts it to a lower dimensional latent space. These features are then forwarded to a decoder which converts the low-dimensional features to the segmentation map. The encoder path resembles a traditional convolutional neural network (CNN) architecture, commonly seen in more recent end‑to‑end deep‑learning frameworks for mapping solar arrays^33,34,36. It consists of a series of convolutional and pooling layers that progressively reduce the spatial dimensions of the input image while extracting high-level features. Each stage in the encoder path typically consists of two convolutional layers followed by a max pooling operation. The decoder uses a combination of upsampling (transposed convolution) and concatenation operations to recover the spatial information lost during the encoding process. Each stage in the decoder path consists of an upsampling layer followed by two convolutional layers. U-Net incorporates skip connections between corresponding stages of the encoder and decoder paths. These skip connections allow the network to retain low-level details during the upsampling process, enabling precise localization of objects in the segmented output. Repeating such computation process using our training set of 100 annotations enables the model to take in any image and outline different objects within the image (e.g., PV panels). We develop the model by splitting the data into a 70-15-15 split for the train-validation-test. The model is developed using Python for coding, the Pytorch library for training, and a NVIDIA A100 GPU cluster for computation. We use an Adam optimizer with a learning rate of 0.001 with a batch size of 16. The model is trained for 300 epochs until convergence.
Having developed our deep learning model, we start examining the land-use profile of our entire portfolio of solar PV projects. First, we geo-locate each solar plant in WECC using publicly available information from the EIA and download the corresponding NAIP images using USGS EarthExplorer. In rare cases where the coordinates in EIA surveys do not point to the actual location of the solar PV projects, we try to visually locate said projects using NAIP images or Google Maps. Areas of interest are then determined by creating a buffer area around the perimeter of each plant, with the buffer distance ranging from over 50 meters to include possible land use near the perimeter. The NAIP images within the buffer boundary are split into multiple images, each with a resolution of 1024 pixels by 1024 pixels. Each bundle of images for one plant is then fed into our deep learning model for examination, the outcome of which is merged back into a single shape file delineating the captured land use profile for each plant. Finally, we manually post-process each shape file, correcting any imperfections and collecting data for further land use analysis (Fig. 9).
Example of the workflow of our methodology at the plant-level process.
In the end, our model performs well in terms of identifying image features that resemble empty spacing between PV panels as well as PV panels themselves. However, we do not provide land-use information on ancillary infrastructure in the final results because our model recognizes the various types and shapes of ancillary infrastructure across different landscapes with relatively poorer accuracy than it does panels. To identify any systemic bias in the annotations of panels and project sites, we benchmark our results pre and post manual corrections utilizing the Intersection over Union (IoU) score as our performance metric. Our analysis shows a mean IoU score of 0.822 with high statistical significance (p = 0.0062) and low variance (0.035). The results indicate little difference between the images pre and post manual correction, meaning that there was not much manual correction needed during the post-processing process.
The typical nomenclature used in existing literature quantifies land-use efficiency in terms of land area “transformation” and “occupation” metrics. The former is used to evaluate the installation impact of energy systems, while the latter is used to consider the aggregate impact of system installation and operation^9,11,26. The inherent difficulty in reporting land-use results under the current nomenclature lies in the lack of standardization in calculations of land area transformation and occupation. Currently, there are dozens of different terms used in existing studies to describe essentially the same two metrics, the majority of which are used both in capacity-based calculations and generation-based calculations with varying timescales^19,48. The variation in methods and reporting metrics makes land-use estimations largely incomparable and, in turn, obscures the accurate translation of scientific findings into policy decisions²¹.
For this study, we proposed the usage of “land transformation” which has been one of the most common metrics used in life cycle assessment literature and other fields^{11,53,54,55,56}. Each project was analyzed by calculating the land area altered per unit of energy produced in the assumed 30-year lifetime using the following equation:
The land area of each project was estimated in terms of the total project footprint and the PV panel footprint. The former refers to all the land within the project site boundaries while the latter refers to the land occupied by the solar arrays. We also differentiate land-use efficiency by calculating capacity-based and generation-based land use efficiency metrics, which are needed to provide a holistic review of the theoretical and real-world land-use efficiency of solar PV facilities¹⁴. Importantly, our published data enable users to transparently calculate metrics as appropriate to their analyses⁴⁸. Each project was analyzed by calculating the land area altered per unit of installed nameplate capacity or annual electricity production in 2019 using the following equations:
The post-processed inventories (panel and project footprints) that are used produce the findings of this study are available on Zenodo (https://doi.org/10.5281/zenodo.17058755). High-resolution aerial imagery used for annotation and validation were obtained from the U.S. Department of Agriculture National Agricultural Imagery Program (https://earthexplorer.usgs.gov/). National Land Cover Database land-cover layers were obtained from the Multi-Resolution Land Characteristics consortium (https://www.mrlc.gov/). Project metadata (installed capacity, generation, etc) were compiled from the Form-860 (https://www.eia.gov/electricity/data/eia860/) and Form-923 (https://www.eia.gov/electricity/data/eia923/) by the U.S. Energy Information Administration. The 2016 solar photovoltaic panel annotations used for comparison were obtained from the U.S. Geological Survey (https://doi.org/10.5066/F79S1P57). The 2023 solar photovoltaic footprint data produced by the U.S. Geological Survey and the Lawrence Berkeley National Laboratory were obtained from https://doi.org/10.5066/P9IA3TUS).
The deep-learning model and code to reproduce the figures are archived alongside the data on Zenodo (https://doi.org/10.5281/zenodo.17058756).
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This research was supported by the Alfred P. Sloan Foundation (grant number: G-2023-19646). We acknowledge the feedback on our research and generosity of time provided by an external expert review panel, comprised of government, nonprofit, industry, and academics. Experts included Timothy Skone, Jim Kuiper, Garvin Heath, Matthew Bailey, Barry Woertz, Benjamin Riggan, Rama Chappella, James O’Sullivan, Christopher Newman, Tim Hayes, Jane Long, Armond Cohen, and Anders Johnson. We also acknowledge Johns Hopkins support from IDIES (Gerard Lemson and Alexander Szalay) and in GIS training (Bonni Wittstadt). Tao Dai was a postdoctoral scholar working on this project and provided helpful assistance to the lead author based on his concurrent work. Views and opinions presented in this article are those of the authors.
Department of Agricultural and Applied Economics, University of Wisconsin-Madison, Madison, WI, USA
Siyuan Hu
Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, USA
Yinong Sun
Department of Land, Air and Water Resources, University of California Davis, Davis, CA, USA
Rebecca R. Hernandez
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
Jeya Maria Jose Valanarasu & Vishal M. Patel
Department of Civil Engineering and the Trottier Institute for Sustainability in Engineering and Design, McGill University, Montreal, QC, Canada
Sarah M. Jordaan
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S.H.: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Resources, Writing—original draft, Writing—review & editing, Visualization. Y.S.: Methodology, Writing—original draft. R.R. H.: Methodology, Visualization, Writing—review and editing. J.M.J.V.: Methodology, Software, Validation, Formal analysis, Writing—original draft. V. M. P.: Methodology, Software, Supervision. S. M. J.: Conceptualization, Methodology, Formal analysis, Data curation, Resources, Writing—original draft, Writing—review & editing, Visualization, Supervision, Project administration, Funding acquisition. All authors contributed to the work and have approved the final version of the manuscript.
Correspondence to Sarah M. Jordaan.
The authors declare no competing interests.
Communications Earth & Environment thanks and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Martina Grecequet. A peer review file is available.
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Hu, S., Sun, Y., Hernandez, R.R. et al. Quantifying land-use metrics for solar photovoltaic projects in the western United States. Commun Earth Environ 6, 1006 (2025). https://doi.org/10.1038/s43247-025-02862-5
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The Solar Stewardship Initiative (SSI) and the Initiative for Responsible Mining Assurance (IRMA) have signed an agreement to strengthen traceability and sustainability standards across solar supply chains.
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From pv magazine France
SSI and IRMA have signed a memorandum of understanding to strengthen responsible sourcing of minerals used in solar supply chains. The partnership aims to cover the full value chain, from mining through to PV module manufacturing, as renewable energy deployment accelerates.
The collaboration is intended to improve oversight of the social and environmental impacts associated with solar technologies. As PV deployment expands, challenges around raw material traceability, human rights, and working conditions across supply chains are becoming more prominent.
The two organizations plan to focus on several areas, including improving traceability of critical minerals used in solar technologies and supporting industry stakeholders through training and capacity-building initiatives. They will also explore the gradual integration of mining assurance standards into solar supply chains, linking extractive sector requirements more closely with those of the PV industry.
Beyond technical measures, the agreement also reflects increasing regulatory pressure and rising expectations from investors and consumers. The growing number of certification and reporting frameworks has highlighted the need for greater coordination to reduce duplication and clarify standards for industry participants.
SSI CEO Rachel Owens described the agreement as a major step toward improving transparency and sustainability standards in the solar sector, and emphasized the importance of broader engagement with stakeholders, including civil society and industry actors.
IRMA Executive Director Aimee Boulanger said the partnership offers an opportunity to better align energy transition goals with mining practices, noting that while solar supports a low-carbon future, it must also rely on more responsible extraction practices.
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While Lake County is likely to see a drop in solar projects with the sunsetting of federal tax credits, the county remains “committed” to the technology, County Board member Marah Altenberg said.
The end of federal tax credits for solar projects is part of the federal tax and spending plan, the “Big Beautiful Bill” passed last year. Deadlines for residential solar projects have already come and gone, but Dave Wilms, a senior project developer with solar company SunPeak, said there’s an important upcoming deadline.
SunPeak has previously worked on the solar array that powers Lake County’s new Regional Operations and Communications (ROC) Facility in Libertyville. Wilms was also a local AP environmental science school teacher for decades.
During a County Board meeting, Wilms noted the upcoming June 4 deadline specifically for commercial solar projects. If, by then, a project has a signed contract and businesses have either begun physical work or made a minimum 5% financial investment, it has until 2030 to be completed.
Although Illinois has strong financial incentives of its own for solar projects, the loss of federal rebates is still a “significant change,” Wilms said in an interview, increasing the cost of a project “anywhere from 40% to 50%.”
“It’s going to hit everybody that’s trying to do solar,” Wilms warned. “It’s going to cost more to do the same projects.”
Wilms said there are  “ulterior motives” for the federal rebate cuts, saying it is an attempt to boost the demand for fossil fuel electricity production. Such sources of energy have a greater impact on the environment than renewable power generation, such as wind and solar, he argued.
Lake County residents are likely familiar with some of those drawbacks: Waukegan’s decommissioned coal power plant is a regular source of anxiety for local residents and officials with the site’s coal ash contamination.
Marah Altenberg, chair of the county’s Planning, Building, Zoning and Environment Committee, said federal money for solar projects is unlikely to return under the current presidential administration. She anticipates a rise in solar projects as people try to take advantage of the last of the tax rebates, followed by a likely drop.
But regardless, Lake County remains “committed to solar,” she said, pointing to the county’s five-year strategic plan.
“Sustainability is an important philosophy throughout our strategic plan, and we are going to continue to support that philosophy,” Altenberg said.
The most notable recent solar project in Lake County was at the ROC, the county’s first net-zero building. Solar is a better and more fiscally responsible way of generating electricity, she said, and a source of power that isn’t impacted by depleted oil reserves. Rising oil prices have dominated the national news as the war with Iran continues.
Locally, Altenberg said she is aware of two solar projects underway in Zion; the first is about 20 acres, generating roughly five megawatts of power, and the second is 35 acres, producing about seven megawatts.
According to a Lake County representative, the county itself currently has a single solar project in development, installing a 165kW system on multiple roofs at the county building in Waukegan.
The county has already made “strong progress” in advancing solar energy at its government facilities, including solar installations at the Central Permit Facility and ROC in Libertyville.
No additional solar projects are underway, the Lake County representative said, and while they are “monitoring” the loss of federal funding, it would not impact any current county government projects.
“Solar energy remains an important component of Lake County’s net-zero policy goals. As we look ahead to future opportunities, we will continue to evaluate project feasibility and actively pursue a range of funding sources — federal, state and private — to support further development when we are ready to move forward,” the representative said.
With the approaching deadline for commercial solar projects, Lake County has seen a rush of businesses trying to get in before the door closes, according to Michael Lazzaretto, president and business manager of the Laborers Local 152 based in Highland Park.
That’s meant higher demand, causing some strain on the labor force but providing good jobs for resident workers, he said. “It’s keeping our local workers in the communities here.”
Although Lazzaretto assumes there will be a drop in such projects after the tax credits are gone, he does not see that as a guarantee.
“If there’s a huge need for solar, they’re going to keep building,” Lazzaretto said. “If the market is there for it, it’s going to keep going.”
Beyond the projects in Zion and at Lake County facilities, he also noted another upcoming project in Antioch, although it is non-union, he said.
It isn’t just solar-installation jobs that could be keeping workers employed locally. Lazzaretto pointed to news from earlier this year that Solarge, a Netherlands-based manufacturer, is considering Waukegan for its first U.S. facility that would create lightweight solar panels.
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Solar Power World
By Kelly Pickerel | April 7, 2026
In an April filing with the U.S. Securities and Exchange Commission (SEC), solar panel manufacturer Maxeon revealed it has applied to be placed under “judicial management” in its headquartered country of Singapore. This is a method of debt restructuring when a company is under financial distress, but is not a bankruptcy filing.
Maxeon stated that the company has been under economic stress ever since U.S. Customs and Border Protection (CBP) began seizing solar panels in 2024 for reviews under the Uyghur Forced Labor Prevention Act (UFLPA). Despite proving UFLPA compliance, Maxeon said CBP is not allowing its solar panels into the country.
Maxeon said CBP’s continued denial of entry has “negatively impacted the company’s ability to generate cash flow” and fulfill contractual commitments that have led to customers filing lawsuits against Maxeon, seeking damages of over $70 million.
Maxeon, the manufacturing spinoff from the once-dominant SunPower brand, decided in 2024 to only focus on supplying the U.S. market. The efficient brand was being contracted for large projects, including the 1-GW Gemini Solar project in Nevada. Maxeon was assembling solar panels in Mexico using Malaysian solar cells and had plans to start U.S. manufacturing in Albuquerque, New Mexico.
In the SEC filing, the company said it had a purchasing agreement with a third-party for modules assembled in the United States to bypass the CBP holding.
Maxeon should have some money coming in soon. The company entered a patent license agreement with Aiko Solar earlier this year, wherein Aiko will have access to Maxeon’s back contact solar cell and module patents outside of the United States for the next five years. Maxeon says this will result in an installment payment of $14 million by the end of this month.
Maxeon will have a hearing on the judicial management in Singapore’s Supreme Court later this week.
Kelly Pickerel has more than 15 years of experience reporting on the U.S. solar industry and is currently editor in chief of Solar Power World. Email Kelly.

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Obtaining data about rooftop photovoltaic installations presents a challenge for energy researchers. Some research efforts have attempted to utilize computer vision approaches to identify photovoltaic installations from aerial imagery. This dataset consists of manually labeled locations of photovoltaic installations for publicly available aerial imagery of Queens, New York, USA in 2018. The labels comprise 14,000 polygons corresponding to roughly 5,500 separate installations. The median polygon size is 13 m², with a total area of close to 380,000 m². Researchers may be interested in applying this dataset for the training of deep learning models for computer vision or to investigate deployment of photovoltaics in urban areas. While other similar datasets exist, there are several unique aspects of this location that make it attractive for further study: it encompasses a densely populated, urban environment; imagery contains four channels (three colors, plus infrared); and the source dataset is re-acquired periodically by the state of New York, offering the opportunity for these labels to form the basis of a time resolved study of photovoltaic deployment.
Data about rooftop photovoltaic (PV) installations is important for energy researchers, electric utility operators, public policy makers and a variety of other stakeholders with an interest in distributed energy development. Reliable sources of data about these installations may be difficult to obtain¹, and data availability, quality and format varies significantly across jurisdictional boundaries. Accurate representations of these data remain impactful though, with up to 40% of global PV capacity estimated to be made up of rooftop-scale systems².
Identifying photovoltaic installations from aerial imagery data has been a promising area of research in recent years. Several investigators have demonstrated the ability of neural network-based computer vision approaches to develop PV datasets in literature^{3,4,5,6,7,8,9} and published software packages, e.g. Satlas (https://satlas.allen.ai/). A problem of scale exists, where data with large coverage area (e.g. satellite imagery) usually lack sufficient resolution to completely detail rooftop scale systems¹⁰. Thus, identifying PV on the rooftop scale typically requires the use of aerial data, with pixel resolution sufficiently fine (< 0.3 m/pixel¹⁰) to fully resolve rooftop scale systems.
Efforts have been made to develop foundation models that could serve as the basis for other deep learning approaches to object identification in aerial imagery¹¹. However, investigators have observed that generalizability of PV identification across datasets is difficult, meaning that it is typically necessary to label some local data to train models that reach their maximum performance on a given data set^12,13,14. Deep Active Learning strategies have been proposed to reduce the labeling effort while maintaining high levels of performance¹⁵. In addition to simply segmenting PV images, investigators have also utilized overhead data to extract system metadata (e.g. capacity and orientation) from imagery^16,17,18.
As mentioned, the generalizability of trained models for PV identification has proved difficult, and efforts to improve or validate generalizability will necessarily require access to diverse reference data. Global level segmentation datasets for PV exist including those created by Kruitwagen et al.³ and Li et al.⁷ as well as the original DeepSolar⁴ and the updated DeepSolar-3M⁹ (https://github.com/rajanieprabha/DeepSolar-3M). Public datasets also exist with a higher resolution for supporting segmentation of PV at a rooftop scale, including those covering multiple cities in California (USA)¹⁹, two datasets (from different sources) covering areas of France²⁰, two areas of Italy²¹, areas of the Netherlands²², China²³, Southern Germany²⁴ and Denmark²⁵. The dataset presented in this paper adds to these existing datasets with labels for 2018 aerial imagery of Queens, New York, in the United States. Partial versions of this dataset have been used for training in previous studies^14,15,26,27. Compared to existing datasets, the data from Queens has a more urban character, featuring comparably fewer open green spaces and many large flat-roof systems that may be useful for studies of generalizability¹⁴. A summary of information comparing the high-resolution, human-labeled datasets is presented in Table 1.
The dataset described in this study²⁸ consists of manually generated polygons outlining PV installations in Queens, New York. The dataset was constructed using publicly available orthographic projection aerial imagery (orthoimagery) from the New York State GIS Clearinghouse (https://gis.ny.gov/new-york-city-orthoimagery-downloads). The New York State GIS Clearinghouse collected aerial imagery data on a two year cycle going back to 2004 as described in their metadata (https://github.com/CityOfNewYork/nyc-geo-metadata/blob/2543257399a62fb51255c349161ad95d679a558a/Metadata/Metadata_AerialImagery.md). Data were acquired with a resolution of 6 inches (around 0.15 m) per pixel. Since 2008, imagery consists of four data channels (three color plus infrared). A map indicating the region and the distribution of labeled polygons is shown in Fig. 1. Note that some labels fall outside the municipal borough boundary, because the labeling effort was based on the footprint of the Queens-designated imagery tiles rather than the formal civil boundary of Queens.
Map showing the region of interest with all PV labels.
To create the labeled polygons, the orthoimagery data were downloaded for the borough of Queens, New York, for the year 2018 from the New York State GIS Clearinghouse (https://gisdata.ny.gov/ortho/nysdop9/new_york_city/spcs/zips/boro_queens_sp18.zip). The year 2018 was chosen as it was the most recent data available when labeling began. These images comprised 667 tiles, each with a size of 5000 x 5000 pixels. The tiles were four-channel color infrared images in a JPEG2000 format. The four channel images were first converted to three channel visible images in PNG format, so that they could be opened and interpreted by users operating common computer software. However, as no changes to the image locations occured in this transition, the labels remained valid for the original imagery. These visible image files were accessed using the LabelMe computer software package (https://github.com/labelmeai/labelme). Labels were first created for each image by a team of individual annotators, who manually inspected the images one at a time and outlined visible PV instances with polygons. After all images were initially labeled, a consensus-seeking process was employed for final quality control. A team of three trained annotators formed a review committee and re-inspected each image for missed or mislabeled areas, logging any proposed changes prior to making adjustments. Confusing or ambiguous areas were discussed collectively and subjected to year-over-year imagery comparisons.
Two label classes were used: pv and notpv. The former was used to represent the outer boundary of photovoltaic arrays observed in the images. As the LabelMe package did not allow for polygons with inner rings, the use of a notpv class was necessary to mark the inner gaps within a larger area of PV. An example of an image showing both classes is shown in Fig. 2.
Example of pv (red) and notpv (green) polygons.
The dataset is available in a Zenodo repository named Segmentation Dataset of Labeled Photovoltaic Installations for Orthographic Aerial Imagery in Queens, New York²⁸. The data that make up the labels are presented in three formats listed below. All annotation and label data are licensed under a Creative Commons Attribution International 4.0 license. A copy of the source imagery data used to create this dataset is provided in a zip format in the repository for reference only. For full details of the files in the source imagery dataset, please refer to the original New York Data repository https://gis.ny.gov/new-york-city-orthoimagery-downloads and its metadata (https://github.com/CityOfNewYork/nyc-geo-metadata/blob/2543257399a62fb51255c349161ad95d679a558a/Metadata/Metadata_AerialImagery.md)).
The data repository consists of two zip files. The following data organization is used by the repository:
NYQ18-labels.zip – a zip file containing the the PV labels making up the new dataset. All items in this archive were created by the authors in this study.
json directory contains the polygon labels for each frame in a format readable by the LabelMe package that was used in their creation. The filenames for each JSON file correspond to the tile name from the source NYS GIS Clearinghouse dataset.
mask directory contains binary mask PNG images derived from the JSON, each with a size of 5000 x 5000 pixels, corresponding to the initial imagery size. File names correspond to the tile name from the source NYS GIS Clearinghouse dataset.
shapefile directory – contains a zipped ESRI shapefile that contains all polygons for the entire dataset. It was generated by georeferencing the JSON files back to the coordinate reference system used by the source data set. Each polygon is additionally tagged with the name of the image tile to which it corresponds. The shapefile uses the New York State Plane Coordinates, Long Island East Zone, NAD83, US foot coordinate system corresponding to EPSG:2263.
boro_queens_sp18.zip a time-of-publication copy of the source imagery dataset downloaded from the New York State GIS Clearinghouse at (https://gisdata.ny.gov/ortho/nysdop9/new_york_city/spcs/zips/boro_queens_sp18.zip). This data is reproduced here for the convenience of users and reshared under the terms of CC-BY 4.0 as specified in the metadata.
Some summary statistics related to the polygon data may be found in Table 2. Note that the number of individual installations was not calculated directly, by virtue of lacking a formal definition for what constitutes a single installation. Rather, polygons representing a common installation were instead inferred by grouping nearby polygons based on a 9.1 meter spatial buffer. Some sample labeled images from the dataset are shown in Fig. 3.
Sample images from the dataset.
As this dataset consists of human labeled images that are the result of a manual process, the primary source of uncertainty in the dataset creation falls within the realm of human error. To minimize errors and maintain consistency, each image was viewed by the labeling team four times (once on initial labeling and once more by each member of the three-member quality review team), as described in the Methods section. However, despite these best efforts, the possibility exists that the dataset contains PV arrays that were not identified (false negatives), PV arrays that were incorrectly labeled (false positives) and incorrectly drawn boundaries.
We considered comparison with other datasets covering this region as a potential source of validation. Global data⁷ covering this area uses a 20 m resolution that does not provide sufficient resolution to indicate rooftop systems, and consequently was not suitable for comparison. We conducted a quantitative comparison with data from the DeepSolar-3M analysis⁹, which performed a computer vision based survey of PV at the level of counties and census block groups in the United States. We computed the number of systems within each census block in our dataset, as found by merging nearby polygons in the dataset using a 9.1 meter buffer. The county level counts do not agree directly between the two sources, which may be attributable to the difference in the year of the data. As shown in Table 2, our dataset identified 5,523 systems from 2018, compared to 13,680 identified in DeepSolar-3M in 2022. We conducted a census block group comparison of the number of systems identified by our dataset and DeepSolar-3M and found a correlation coefficient of around 0.8, as shown in Fig. 4. We note that while DeepSolar-3M does not represent a source of ground truth due to being machine labeled, this comparison shows a good degree of agreement between the data, demonstrating that these two datasets identify PV in similar locations.
Census Block Group correlation between counted systems in the dataset (aggregated with 9.1 m buffer) and from DeepSolar-3M⁹.
Some known edge cases that were encountered during dataset creation for which labeling proved difficult or uncertain for human labelers are described here. PV array and module boundaries were drawn as faithfully to the outer edge of the PV as possible, but wide variation in the amount of spacing between rows and modules were observed throughout the data. Where it was reasonable to do so, we opted to select panels in individual rows to produce polygons that selected only actual PV modules, but there remains some ambiguity and labeler judgment in the distinction, because it was not possible to objectively quantify row spacing using the tools available. Example cases where module rows were and were not separated are shown in Fig. 5.
Contrasting examples of rows being identified in whole vs. being identified individually due to wider spacing.
Some images contained cases that were difficult to categorize, or may remain uncertain or ambiguous, even to a trained human labeler. Examples included the presence of tar paper rooftops, highly reflective panels, greenhouses or similar patio roofs, awnings and modules with visually indistinct frames. On the other hand, parking spaces and crosswalks were typically distinguishable by human labelers though these sometimes present challenges for machine trained systems. Communication between the labelers took place throughout the entire labeling process to share experience and discuss ambiguous cases, with special attention paid to reaching agreement during the quality review process. To quantify the converging interpretation, a log was maintained of editing done on each of the quality review passes, indicating any edits that were proposed. Referring back to the 667 original images, a total of 353 images were edited in some way during the quality review process. At the conclusion of the process, only 10 images had received notes from all three reviewers, indicating that the final dataset reflects a state of strong consensus that had been reached by the process.
Due to the availability of multiple years of data in the source data archive, when ambiguity in the presence of PV panels existed, images for alternate years were consulted to improve judgment where possible. Consulting alternate years was particularly useful for discerning cases where solar reflections from the modules were observed for the 2018 data, such as in Fig. 6, where alternate years typically did not experience an identical specular reflection. Despite this, some ambiguous cases remained even when considering multiple years of data. For example, the rooftops shown in Fig. 7 have dark rectilinear shapes that appear en masse between 2008 and 2010 images and remain essentially unchanged in all images since. On the other hand, they lack visible module edges that would unambiguously identify them as solar arrays and represent a judgment call on the part of the labelers. In the case of the example shown here, the regions are labeled as PV in the dataset. The number of these completely indeterminate cases encountered in the final quality review were few, but we do note that due to the absence of an available cross-check of rooftop solar installation data for this location, this practical limit to human discernment of the imagery is a limitation of manually labeled data validation.
Example of bright specular reflection from modules.
Example of changes from year to year that are at the limit of labeler capability to distinguish with certainty. Left and right show the same location for different years in the archive.
The dataset is available at the repository DOI (https://doi.org/10.5281/zenodo.15084216)²⁸.
Auxiliary python code for the project is available in a GitHub repository named pvlabels_queens (https://github.com/jranalli/pvlabels_queens). The provided codes can be used to convert the LabelMe JSON files into binary mask images, to aggregate the polygons from the JSON files into the overall shape file, and to convert the dataset’s raw JPEG2000 images into PNG files for more convenient use with many common software tools. Since the native tile size of 5000 × 5000 pixels is quite large for most machine learning approaches, users may wish to consider the split-image python package (https://pypi.org/project/split-image/) that can be used to automate generating tiles from large images as described in the pvlabels_queens repository’s README.
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We acknowledge the following additional students who participated in preliminary labeling during the project: Springsky Chee, Raymond Krezel, Mark Lee, Richard Ray, Adam Torres, Brian Tylutke. The authors acknowledge financial support of the project by Penn State Hazleton Department of Academic Affairs.
Penn State Hazleton, Hazleton, PA, 18202, USA
Tyler Furedi, Edwin Kimsal, Samara Cornejo, Nicholas Liero & Joseph Ranalli
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T. Furedi: Data Curation, Validation, Writing – Review & Editing, E. Kimsal: Data Curation, Validation, Writing – Review & Editing, S. Cornejo: Data Curation, N. Liero: Data Curation, J. Ranalli: Supervision, Conceptualization, Data Curation, Validation, Writing – Original Draft and Review & Editing.
Correspondence to Joseph Ranalli.
The authors declare no competing interests.
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Furedi, T., Kimsal, E., Cornejo, S. et al. Labeled photovoltaic installations for orthographic aerial imagery in Queens, New York. Sci Data 13, 207 (2026). https://doi.org/10.1038/s41597-025-06523-2
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Josh Craft, the Director of Government Relations and Public Affairs for Utah Clean Energy, shows the outdoor plug that connects his solar panels to his home in Salt Lake City on Friday, March 20, 2026.
Portable solar panels and an invertor system, seen here installed on an apartment balcony, are now possible for Utah residents, and states across the country are following suit.
Plug-in solar panels are seen on top of a residential home in Salt Lake City on Friday, March 20, 2026.

Josh Craft, the Director of Government Relations and Public Affairs for Utah Clean Energy, shows the outdoor plug that connects his solar panels to his home in Salt Lake City on Friday, March 20, 2026.
Portable solar panels and an invertor system, seen here installed on an apartment balcony, are now possible for Utah residents, and states across the country are following suit.
Plug-in solar panels are seen on top of a residential home in Salt Lake City on Friday, March 20, 2026.
Note to readers• This story is made possible through a partnership between The Salt Lake Tribune and Grist, a nonprofit environmental media organization.
Affordable, portable clean energy is on the brink of becoming widespread in the U.S., thanks to Utah leading the way in making the stars — and solar panels — align.
Plug-in solar panels — sometimes called “balcony solar” — allow people to generate electricity by plugging panels directly into a standard outlet and help cut down on utility bills, without the need for expensive rooftop installations. The relatively cheap technology has taken off in parts of Europe, and recent Utah law has sparked interest across the U.S.
Utah lawmakers passed HB 340 in 2025 with bipartisan and unanimous support, becoming the first state to allow residents to plug solar systems directly into residential outlets.
“It’s great for anyone who wants a little solar power but does not want to pay $30,000 for a roof install,” said Rep. Raymond Ward, R-Bountiful, who sponsored the bill.
Ward learned about plug-in solar panels after reading an article about their popularity in Germany. Balcony panels there added 10 percent more solar capacity to the grid in just a few months, The New York Times reports, just as Russia’s war with Ukraine was draining energy supplies.
A year after HB 340 passed, 30 more states plus the District of Columbia have drafted similar bills, according to information tracked by the plug-in solar lobbying group Bright Saver.
“Thank you, Utah,” said Cora Stryker, a co-founder of the California-based nonprofit. “It’s a common-sense, no-brainer thing that should keep sweeping the country.”
Virginia’s plug-in solar bill currently sits on the governor’s desk awaiting a signature. Bills in Hawaii, Massachusetts, New Hampshire, Oklahoma and Vermont have passed one chamber in statehouses so far.
Despite that momentum, U.S. residents still can’t buy plug-in panels from the same big box stores that sell other consumer electronic appliances, like hair dryers, washing machines or toasters.
That’s partly because Utah and other states also need rules and regulations for the panels, because while they sound simple, they flip the way the electrical utility system works on its head.
Residential households are only designed to pull power off the grid, through wires to outlets, and into plugged-in devices. Balcony solar does the opposite by creating power and pushing it backward into the outlet and “upstream” through a home’s wires, Ward explained.
“Utilities tend, in general, not to want anybody else to make power,” Ward said.
Power providers also have concerns about safety, the lawmaker said. If line workers are trying to repair an electrical line they think is switched off, for example, but a condo’s panels are still pushing electricity through that line, it could put those employees in danger.
But to Ward, those problems were solvable.
“The electricity is the same over [in Europe] as it is over here,” Ward said. “All the same rules of physics work and have proved to be safe.”
But U.S. residents can’t smuggle balcony solar systems over in a suitcase from Europe, because North America uses different plugs and voltages.
Ward collaborated with Utah’s largest electricity provider, Rocky Mountain Power, to craft language for his bill so the plug-in movement here can be home grown.
A spokesperson for Rocky Mountain Power noted the utility took no position on HB 340.
“We remain concerned that some products entering the market may not meet the requirements of the bill,” the spokesperson wrote in an email, “potentially creating electrical hazards for utility workers.”
The legislation removes liability for the utility, and panel owners can’t ask for payments like those with rooftop arrays who participate in net metering. It also requires a company called Underwriters Laboratories, often shortened to UL Systems, to develop safety certification for plug-in panels.
“We were a little surprised,” said Kenneth Boyce, vice president of engineering for UL, “but we take it very seriously.”
UL develops all kinds of safety standard for consumer products, building materials and other goods. But Utah’s legislation marked the first time they were asked to test plug-in panels, and the company got to work over the summer.
It issued a white paper in November outlining potential hazards with the panel systems themselves as well as how they might interact with a typical home’s wiring. From there, they developed product-level requirements that will allow the UL mark to appear on certified products.
“We’re … making sure we keep (consumers) safe while they get the benefits of participating in the energy transition,” Boyce said. “We can do both.”
Testing explored ways to ensure plug-in panels don’t make circuit breakers explode, or that GFCI plugs that are supposed to trip and switch off — commonly found in bathrooms, kitchens and outdoors — don’t fry and malfunction without the residents’ knowledge.
No plug-in systems have been certified by UL to date, Boyce said.
“We expect that will change soon,” he said, noting he’s heard from multiple manufacturers. He expects the UL stamp to appear on U.S. panels in “months, maybe even weeks.”
Some inventive individuals, including the popular Utah YouTuber JerryRigEverything, have cobbled together their own plug-in systems in the meantime. They use components that are individually UL certified, like panels, cords and inverters. But all the components combined into a balcony system haven’t been tested and green-lit for safety, Boyce cautioned.
For those willing to take the risk, a global company called EcoFlow is currently one of the most popular online retailers for plug-in panels in the U.S. They’re currently in conversations with UL about how to certify their product, according to company spokesperson Ryan Oliver.
They’ve sold portable solar systems for about four years in Europe “where they’re very popular,” Oliver said.
“In fact,” Oliver said, “we actually had this product ready to go when Utah legalized plug-in solar.”
An inverter, which brings electricity from the solar panels into the home and shuts down generation to ensure safety, currently costs about $300 and is only available in Utah for now. A system that includes a battery to store solar energy costs $1,200. And compatible solar panels run between $250 to $1,000, depending on the size of the array.
“It’s consistent with Utah’s values of wanting to supply your own energy, and letting people make their own decisions around meeting their needs,” said Josh Craft, director of government relations and public affairs for Utah Clean Energy.
He’s currently experimenting with his own plug-in system donated by EcoFlow.
“It works. It’s fun,” Craft said. “I have foldable panels set up on my patio roof.”
The panels could also amp up an entirely new market for clean energy. The balcony solar movement comes at a time when the Trump administration is slashing subsidies for wind and solar projects, even as energy bills are expected to spike due to demands from data centers and artificial intelligence, Craft noted.
Utah code resulting from Ward’s bill caps power output from plug-in systems at 1,200 watts, which means they won’t offset all the electrical needs from a typical household.
JerryRigEverything reported that his array saves about a dollar a day on his electricity bill. Craft figures his system, which is combined with a battery, cuts down his power bill by about 10%, but he hasn’t tested it while running an air conditioner.
In just the last few weeks, Ward said he’s had conversations with lawmakers in Hawaii, Washington, Minnesota and Colorado about how to facilitate plug-in solar in their states.
So, is the lawmaker proud that Utah happened to launch a national plug-in solar trend?
“Heck yeah,” Ward said.
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Interesting facts
The heat of the day has become fuel for the cold of the night. In Florida, a homemade system has started using solar energy to freeze water and store this thermal reserve to cool small environments after sunset.
The proposal stands out for combining low cost, simplicity, and reduced dependence on the electrical grid. In practice, the equipment serves compact rooms, cabins, vans, and technical spaces with a solution that takes advantage of peak sunlight to deliver comfort hours later.
The heart of the project lies in a block of ice that functions as a thermal battery. During the day, the energy captured by the solar panels powers the process of freezing water, creating a cold reserve that can last for hours and, in some scenarios, even days.
Couple finds hidden basement during home renovation and property value skyrockets by up to R$ 476 thousand.
Map reveals how the Earth may look in 250 million years: continents will reunite in the Last Pangaea, 92% of the planet could become uninhabitable for mammals, and Spain appears among the rare possible exceptions.
After two years of internal discussions, Paraguay has finally regulated the law that opens the country to foreign technology and service companies with tax incentives that promise to create thousands of jobs and change the economy.
The Brazilian city founded by a former Austrian minister that preserves Tyrolean dialect, alpine architecture, and a centennial festival in Santa Catarina; with fewer than 10,000 inhabitants, Treze Tílias has become a rare symbol of European heritage.
This storage occurs because water can retain a lot of energy when it changes from a liquid to a solid state. With efficient insulation, cold loss is kept low, which helps maintain performance even after solar generation decreases.
The structure was assembled with 2 solar panels of 100 W, a 35 Ah battery, and a small compressor with refrigerant R600. The system removes heat from a reservoir containing about 7.5 liters of water installed in an insulated box with foam and fiberglass.
Once the ice forms, a pump circulates a mixture of water and glycol inside the frozen block. This cold is directed to a radiator with a fan, which pushes the cooled air into the environment without requiring significant additional consumption.
Tests conducted in vehicles during the Florida summer indicated that the equipment can significantly cool the cabin for several hours. The performance was close to that of a small window air conditioner, but without direct electrical consumption at the point of use.
According to Interesting Engineering, a portal specialized in innovation and engineering, the method demonstrated the ability to maintain cooling even under severe heat conditions, reinforcing the potential of the solution for compact and off-grid uses.
In terms of capacity, thermal storage is even more impressive. A 1 cubic meter ice can store about 93 kWh of cooling energy, a volume comparable to that of large batteries, but at a lower cost and without wear from charge and discharge cycles.
This is one of the strongest advantages of the system. Water does not lose its phase change potential, allowing the process to be repeated many times without the typical degradation of chemical technologies.
The prototype was designed for small rooms, cabins, and vans, but the logic can be scaled up. By increasing the volume of water and the number of panels, the same idea can serve larger spaces, provided the property has adequate thermal insulation.
The expectation is that a more robust version can help cool a small residence. This opens up possibilities to reduce peak consumption, relieve the grid, and expand the use of renewable energy in very hot regions.
Despite its appeal, the solution does not eliminate challenges. The total weight of the system reduces portability, the power still falls short of conventional split systems, and the circuit with refrigerant requires secure assembly and technical attention.
Another important point is the humidity in the air. Part of the energy needs to be used first to condense this vapor before the temperature drops more noticeably, which can delay the initial cooling of the environment.
The advancement of this proposal shows how solar energy and thermal storage can combine simply to meet the rising demand for cooling. In hot locations, this represents more autonomy and less pressure on electrical consumption.
Even without solely replacing traditional systems, the project reinforces an important perspective. When cold is stored and released at the right time, climate control reaches a new level, and the energy bill enters a different logic.
Sou jornalista argentino baseado no Rio de Janeiro, com foco em energia e geopolítica, além de tecnologia e assuntos militares. Produzo análises e reportagens com linguagem acessível, dados, contexto e visão estratégica sobre os movimentos que impactam o Brasil e o mundo. 📩 Contato: noelbudeguer@gmail.com
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By Canary Media
Canary Media
7 April 2026
This article was originally published on April 6, 2026, and last updated on April 7, 2026.
Balcony solar is one of the hottest ideas in renewable energy right now. Boosters say the systems — DIY kits that can be plugged right into a standard outlet — save users money without any need for subsidies, government incentives, or utility permission.
As Americans continue to struggle with soaring power prices, about half the states in the U.S. are considering legislation to pave the way for residents to adopt plug-in solar and start generating some of their own electricity from their own backyard or porch.
“It’s about energy affordability,” said Cora Stryker, co-founder of Bright Saver, a nonprofit that promotes plug-in solar. ​“Every legislator wants their constituency to have less trouble meeting their energy demands.”
As these efforts work their way through the legislative process, we will be monitoring the action here, using information from Bright Saver and bill-tracking databases.

Latest action: Maine Gov. Janet Mills (D) signed the state’s plug-in solar bill into law on April 6.

Know of a balcony solar update that’s not on the map yet? Fill us in. Reach out to [email protected].

We’re glad you read this story. If you rely on independent, paywall-free reporting like ours, please consider making a tax-deductible contribution in celebration of Canary Media turning 5!
Sarah Shemkus is a reporter at Canary Media who is based in Gloucester, Massachusetts, and covers New England.
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Bifacial photovoltaic (PV) modules have been receiving increasing attention because of the harvesting light from both front and back sides. End-of-Life (EoL) PV modules output grow annually, which are rich in recyclable resources such as silicon and metals. A critical prerequisite for recovery is the separation of the laminate. This study presented a novel and rapid separation strategy by laser (1200 W power, 2000 Hz frequency, 5% duty cycle), achieving complete separation of the silicon cells from the Ethylene Vinyl Acetate (EVA) interlayer. Characterizations analysis showed that the surface morphology of anti-reflection coatings (ARCs) on the silicon cells was damaged. In addition, the content of silicon nitride, main component of ARCs, was decreased. Furthermore, the analysis of EVA verified the cleavages C-O bond, while the cross-linked structure of EVA was not disrupted. Thus, complete separation was driven by ARCs disappearance and the bond cleavage. Life cycle assessment demonstrated that this approach was more environmentally friendly than thermal and chemical methods. It also overcame the drawbacks of product mixing and difficult sorting associated with mechanical methods, enabling simple, rapid separation and easy large-scale automation. This strategy dramatically reduces the energy consumption and provides sustainable pathway for recycling EoL bifacial PV modules.
The global photovoltaic (PV) industry has experienced rapid growth in recent years. The International Energy Agency predicts that by 2030, the volume of waste PV modules will reach 1.7-8 million tons, and surge to 60–78 million tons by 2050¹. Given the presence of the heavy metal lead in End-of-Life (EoL) PV modules, the European Union has classified them as hazardous waste, which mandates proper recycling and disposal^2,3,4. Meanwhile, PV modules fall under the category of electronic waste, often referred to as “urban mines”. They contain not only high-value metals such as tin, lead, copper, silver, and aluminum but also valuable materials including tempered glass and silicon (Si)^5,6,7. In turn, the recycling of EoL PV modules can alleviate shortages of primary mineral resources while mitigating the high energy consumption and pollution associated with the traditional metallurgical industry⁸.
EoL PV modules are categorized into monofacial and bifacial types. Unlike monofacial PV modules, bifacial modules replace the backsheet with glass. Additionally, their silicon cells are equipped with silver wires and anti-reflection coatings (ARCs) on both sides (Fig. 1). By harvesting light from both front and back sides, bifacial PV modules achieve higher PV output^9,10,11. From the backside to the frontside, they comprise an external junction box, aluminum frame, and an internal laminated structure—sequentially including glass, upper EVA (Ethylene Vinyl Acetate) layer, silicon cells layer, lower EVA layer, and glass. While bifacial PV modules manufacturing are more costly than monofacial modules as a result of additional materials (e.g., double glass) and processes (e.g., post-screen printing contacts), their levelized cost of electricity still holds a competitive advantage^10,12,13. The International Technology Roadmap for photovoltaic predicts that the market share of bifacial PV modules will hit 85% by 2032¹⁴. However, the research community has conducted relatively few studies on the recycling of bifacial PV modules. To date, the focus of most PV module recycling studies remains on backsheet-based modules.
For the recycling of PV modules, junction boxes and backsheets are typically removed via mechanical methods initially^15,16,17. Subsequently, the remaining PV laminate is separated, and the resulting materials are recovered. In the current research, three primary methods are widely employed for laminate dissociation: thermal treatment, chemical processes, and physical methods^16,18,19,20. Thermal treatment typically entails high-temperature heating to incinerate or pyrolyze EVA, thereby achieving dissociation. However, direct thermal treatment induces release of toxic gases, thereby requiring additional gas treatment²¹. Chemical processes typically involve immersing EVA in chemical solvents (e.g., toluene, xylene, trichloroethylene, etc.) to either dissolve or swell it, or utilizing supercritical fluid technology. EVA consists of non-polar ethylene segments and polar vinyl acetate segments. This unique structure enables EVA to be dissolved by certain non-polar and polar solvents^{22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37}. Physical methods involve first crushing the laminate using equipment such as crushers or high-pressure pulse systems. Material recovery subsequently occurs either by utilizing compositional differences among particle sizes or by separation techniques such as electrostatic separation and flotation^{38,39,40,41,42,43,44,45,46}. Each method has inherent limitations: thermal treatment requires large-scale equipment, and suffers from high energy consumption plus the need for waste gas treatment; chemical processes are plagued by low treatment efficiency, costly solvents, and large volumes of waste liquid generation; physical methods often produce mixed products and low separation efficiency, creating challenges for subsequent resource purification and recovery^{15,18,47,48,49,50}.
As an emerging separation technology proposed in recent years, laser-based separation offers distinct advantages for interlayer separation of PV laminates, including low pollution, high efficiency, and strong selectivity. Li et al. irradiated the silver-aluminum coating of silicon cells using a pulsed laser, successfully stripping and recovering EVA without altering its properties⁵¹. Their study noted that the silver-aluminum coating on the silicon cells backside absorb laser energy, causing localized temperature increases at the interface and weakening the bonding strength of EVA. By contrast, Anwar et al. used a pulsed laser to directly transmit through glass and EVA to irradiate silicon cells⁵². They attributed the complete separation of the EVA-silicon cells interface to chemical changes or vaporization of the ultra-thin EVA layer at the interface under instantaneous high temperatures. This prevented re-adhesion to the silicon cells and thus separated the bonding surface from the inside. Notably, both studies only achieved separation of specific layers within the PV laminates and lacked a clear elaboration of the separation mechanism.
This study proposed a continuous laser-based method for separation of bifacial PV laminates. This method enabled separate recovery of silicon cells from bifacial PV laminates, with selective separation at the silicon cells-EVA and no residual EVA on the silicon cells surface. The study focused on three key objectives: (i) Determining the optimal laser parameters for separation. (ii) Investigating changes in EVA during laser processing via Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and Thermogravimetric analysis (TGA). (iii) Characterizing the surface morphology of silicon cells using scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS), and X-ray photoelectron spectroscopy (XPS). This work highlights three core scientific innovations: selective removal of the ARCs, establishment of the mechanistic connection between ARCs degradation behavior and EVA debonding efficiency, and demonstration of continuous laser’s superiority in PV recycling.
Since PV modules are designed to convert light energy into electricity, their design inherently prioritizes high light transmittance for glass and encapsulant materials. Specifically, PV glass requires a light transmittance of over 90%, while EVA encapsulants exhibit minimal absorption of light within the 380–2200 nm wavelength range This provides a robust foundation for laser-based separation^51,53. The laser penetrates the glass and EVA to irradiate the silicon cells, with nearly all its energy absorbed by the cells. Given the absence of external electrical devices or energy storage systems, the generated electrical energy cannot be consumed or stored. Thus, almost all laser energy is converted into heat. To prevent EVA pyrolysis (and subsequent waste gas generation) due to temperature increases during laser processing, it is critical to determine EVA’s pyrolysis temperature. TGA shows that EVA in the bifacial modules studied exhibited mass loss starting at 243 ℃ (Fig. S11). Consequently, during laser processing, the temperature of PV modules must be maintained below this threshold. Moreover, as a thermoplastic material, EVA is typically integrated into PV modules via hot-pressing during manufacturing. It begins to soften with reduced viscosity at 60 ℃, loses viscosity entirely around 150 ℃, and regains viscosity upon cooling to room temperature^54,55.
To investigate the effect of temperature on PV module separation, a PV strip was placed on a heating stage with the temperature set to 190 ℃. The PV strips took 14 s to reach 190 ℃ on the heating stage, whereas laser treatment required 38 s to achieve the same temperature. Both heating methods were conducted under continuous temperature monitoring, with heating terminated immediately upon reaching 190 ℃. However, the temperature of the PV strips continued to rise within a few seconds after the laser irradiation ceases. Therefore, laser scanning was stopped when the monitored temperature reached 176 ℃, resulting in a maximum temperature peak of 189 ℃. Such stringent control over temporal and temperature parameters effectively excluded the influence of the total thermal budget on the experimental results (Fig. 2).
The separation behavior at this temperature was shown in Fig. 3a: both the glass-EVA and EVA-silicon cells interfaces separated, but EVA remained adhered to the silicon cells surface. Silicon cells were irregularly distributed on the upper and lower EVA layers. This was because the temperature exerted the same effect on the upper and lower contact surfaces between silicon cells and EVA. These observations indicated that temperature can reduce EVA viscosity, thereby facilitating separation, but separation driven solely by temperature lacks selectivity.
A 600 W laser was then used to scan the PV modules with a 5% duty cycle. This parameter set was selected due to its moderate temperature rise rate. Scanning was halted once the temperature reached 190 ℃, after which the glass, together with EVA, was peeled away (Fig. 3a). Notably, no EVA residue was observed on the silicon cells surface, and most of the cells separated from the upper EVA layer. The selective separation of the silicon cells-upper EVA interface was achieved .
The experiment was repeated using the same parameters: after the PV strips cooled, scanning was performed again, and this process was repeated ten times. During scanning, the deep blue color of the silicon cells surface gradually faded to gray (Fig. 3b). This phenomenon indicated that the ARCs were damaged under laser irradiation. So we reached a preliminary conclusion. Once the ARCs was damaged, EVA loses its attachment sites, leading to weakened adhesion. However, since ARCs damage was confined to the laser-scanned side, the ARCs on the opposite side of the silicon cells remained intact. This leaves EVA on the non-scanned side still tightly bonded to the cells, resulting in most of the silicon cells remaining adhered to the lower EVA layer.
To verify this preliminary conclusion, five PV strips with varying degrees of ARCs damage were prepared, and peeling force tests were performed on them (Fig. S10). The relationship between their gray discoloration area (corresponding to ARCs damage) and the peeling force is presented in Table 1. It can be clearly observed that as the area of the damaged ARC region increases, the required pulling force decreases.
This observation presented a critical opportunity for laser-based selective separation. The subsequent section will optimize laser parameters to identify conditions yielding the optimal selective separation effect.
First, PV strips were scanned at a maximum power of 1200 W with a 100% duty cycle. At a 100% duty cycle, the laser operates continuously, so no frequency parameter applies. Thick smoke and open flames emerged during scanning, forcing the process to stop within 2 s. The scanned PV strip was shown in Fig. 4a: the silicon cells surface turned completely gray, attributed to ARCs damage. After lifting the glass, most silicon cells were found attached to the lower EVA layer. The upper EVA-silicon cells interface had separated. A yellow oily substance was observed on the silicon cells surface, identified as EVA pyrolysis products^56,57. Black deposits on EVA were presumed to result from EVA carbonization at high temperatures. Excessive laser power caused the PV strip to absorb large amounts of energy, leading to uncontrolled, rapid temperature rise.
Bifacial PV module structure and silicon cells structure diagram.
Schematic diagram of separating photovoltaic laminate with laser.
(a) Thermal treatment vs. laser treatment effect diagram. (b) The anti-reflective coatings gradually disappeared as the number of scans increases.
(a) Photovoltaic strips after laser irradiation at 1200 W with a duty cycle of 100%. (b) Photovoltaic strips after laser irradiation under conditions of 500 W and a duty cycle of 100%. (c) Photovoltaic strips after laser irradiation under conditions of 200 W and a duty cycle of 100%. (d) Photovoltaic strips after laser irradiation under conditions of 500 W, 40% duty cycle and 200 Hz. (e) The photovoltaic strips after laser irradiation under the conditions of 1200 W, duty cycle of 5%, and frequency of 2000 Hz.
To control temperature and eliminate smoke/flames, the power was reduced to 500 W. Scanning at 500 W and 100% duty cycle avoided open flames. While the heating rate was controllable, a faint odor was still detected during laser irradiation. The scanned PV strip (Fig. 4b) showed most of the silicon cells surface had turned gray. After peeling, black deposits were still present, but no yellow oily substance formed. The vast majority of silicon cells remained attached to the lower EVA layer, with good selective separation.
To further prevent exhaust gas pollution from EVA denaturation, power was reduced to 200 W. PV strips were scanned until the temperature reached 190 ℃, and no exhaust gas was detected by VOC detection equipment. The PV strip (Fig. 4c) showed no color change on the silicon cells surface. After peeling, silicon cells adhered to both upper and lower EVA layers. This separation was consistent with the heating stage effect described above. These results confirmed that ARCs damage was power-dependent: higher power leads to more effective ARCs degradation.
Next, the effect of frequency on separation was investigated. PV strips were scanned at 500 W, 40% duty cycle, and 200 Hz. The effective power output per unit time at 40% duty cycle was equivalent to that of a 200 W laser operating at 100% duty cycle. After scanning to 190 ℃, the PV strip (Fig. 4d) exhibited blue-white stripes. This occurred because the low frequency prevented the laser’s effective irradiation coverage from exceeding its displacement per unit time (at a 30 cm/s scanning speed). Specifically, under the conditions of 2000 Hz and a 5% duty cycle, the laser-off time per cycle was calculated as 475 µs. During this period, the PV strips traveled a distance of 0.1425 mm at a scanning speed of 30 cm/s. Since the beam spot width (0.4 mm) was larger than this displacement, enhanced uniformity of anti-reflective coatings (ARCs) degradation was achieved. In contrast, at 200 Hz (with the same 5% duty cycle), the traveled distance (1.425 mm) exceeded the beam spot width, leading to the formation of blue-white ablation stripes. After peeling, some silicon cells remained attached to the upper EVA layer, failing to maximize the laser’s separation benefits.
Two solutions exist for this issue: reducing scanning speed or increasing frequency. However, reducing scanning speed would decrease processing efficiency, it was an undesirable outcome, so the frequency was set to its maximum value of 2000 Hz. Reducing the duty cycle effectively regulated power output per unit time, preventing rapid temperature rose while avoiding exhaust gas generation and unsymmetrical ARCs damage. At 1200 W and 2000 Hz, the duty cycle was gradually reduced to 5%, at which point no exhaust gas was detected by VOC equipment.
Scanning the PV strip at 1200 W, 2000 Hz and 5% duty cycle to 190 ℃ yielded the result shown in Fig. 4e: the ARCs was uniformly damaged. Blue streaks were caused by laser scattering from glass cracks, which led to less ARCs damage. The upper EVA-silicon cells interface was completely separated, with nearly all silicon cells attached to the lower EVA layer. This was attributed to that the interfacial adhesion of the upper EVA layer on the laser-irradiated surface was significantly weaker than that on the non-irradiated side. This difference in interfacial adhesion may originate from the surface modifications of the silicon cells. Irradiating the opposite glass side with the laser allowed easy scraping of the silicon cells and PV ribbons (Fig. 4e).
To investigate the separation mechanism, characterization tests were conducted on EVA and silicon cells, starting with silicon cells analysis. Figure 5a presented the surface morphology and elemental composition of the pristine silicon cells. At 50 μm magnification, the silicon nitride on the surface exhibited a distinct rhombic structure. Figure 5b showed the surface morphology and elemental composition of Sample 2. Its central region retained the same morphology as the pristine silicon cells (Sample 1), while its peripheral regions displayed morphological changes induced by laser treatment. Partial destruction of silicon nitride resulted in a significant decrease in nitrogen content and a corresponding increase in silicon content. Figure 5c illustrated the surface morphology and elemental composition of Sample 3. Thorough laser treatment led to the complete disappearance of silicon nitride, with no rhombic structure observable. Notably, the same laser-induced morphological features were observed in Fig. 5c as in Fig. 5b. Additionally, Fig. 5c also exhibited porous structures induced by the high temperature generated by the laser, which was consistent with the findings of Coyne et al⁵⁸. The spherical agglomerated structure, meanwhile, aligned with results reported in Ulmeanu et al. and Kumar et al⁵⁹. The significant increase in oxygen content was attributed to the formation of silicon oxide, while the continuous decrease in nitrogen content further confirmed the progressive destruction of silicon nitride.
XPS analysis was performed on the three samples, with their full survey spectra shown in Fig. 6a-c. In the peak fitting analysis, the background type was set to Tougaard, and the FWHM constraints were specified as (0.2, 5). The full spectra of Sample 1 and Sample 2 were nearly identical, whereas Sample 3 exhibited a strong peak at 532.7 eV—assigned to silicon oxide—consistent with the EDS results. Following laser treatment (Sample 2), the nitrogen content (defined as the atomic percentage of nitrogen relative to the total atomic content of nitrogen, silicon, and oxygen) was lower than that of the pristine silicon cells (Sample 1). After complete laser-induced removal of the ARCs (Sample 3), the nitrogen proportion relative to nitrogen, oxygen and silicon further decreased. This aligned with the elemental proportion trends observed via EDS.
The high-resolution Si2p spectrum of Sample 1 (Fig. 6d) showed two distinct peaks: one at 101.9 eV (assigned to N-Si-N) and another at 102.6 eV (assigned to Oₓ-Si-N_γ), which aligned with the findings of Chen et al. and the Handbook of X-ray Photoelectron Spectroscopy^61,62. For Sample 2 (Fig. 6e), the high-resolution Si2p spectrum revealed a significant decrease in the intensity of the N-Si-N peak (101.9 eV), accompanied by the emergence of a new peak at 103.1 eV (assigned to silicon oxide). This indicated partial destruction of silicon nitride. In the case of Sample 3 (Fig. 6f), the silicon nitride peaks were weakened to near-undetectable levels, while the silicon oxide peak was highly prominent. Additionally, a peak at 99.3 eV (assigned to elemental Si) appears, which was attributed to unoxidized silicon exposed following the destruction of the silicon nitride layer. The progressive attenuation and near-disappearance of ARCs-specific silicon nitride peaks (N-Si-N and Ox-Si-Ny) with increasing laser treatment confirmed structural breakdown of the ARCs. The emergence of an elemental silicon peak (99.3 eV) was observed in the fully treated sample, which arose from unoxidized silicon exposed only after the ARC layer was removed.
Collectively, the aforementioned SEM/EDS and XPS analyses confirmed that the ARCs on the laser-irradiated surface of the silicon cells was damaged. In the manufacturing process of PV modules, glass and silicon cells are bonded using EVA as the adhesive via vacuum hot pressing⁶³. More specifically, the EVA is adhered directly to the ARCs of the silicon cells. Consequently, the damage to the ARCs on the irradiated side resulted in the loss of EVA’s original adhesive sites, ultimately leading to complete adhesion of all silicon cells to the lower EVA layer. Notably, Samples 1 and 2 exhibit significantly higher carbon content, indicating the potential presence of organic components on the silicon cells surfaces. For Sample 3, which underwent additional laser processing, these organic components were pyrolyzed, leading to a reduction in carbon content. These organic species are inferred to derive from EVA pyrolysis, highlighting the need for further analysis of EVA.
To investigate whether EVA undergoes property changed during laser processing and to determine their role in facilitating separation. FTIR spectroscopy, Raman spectroscopy, and TGA were performed on pristine EVA and laser processed EVA. Laser energy is selectively absorbed by the silicon cells and confined to the EVA-silicon cells interface (irradiated area). This process induces only structural and chemical modifications within a limited interfacial EVA layer (≈ 10–50 μm), including the breaking of C-O bond and mild melting of the EVA matrix. Thus, all the aforementioned characterizations were targeted specifically at this thin interfacial EVA layer.
FTIR results were shown in Fig. 7a, the laser processed EVA exhibited the disappearance of the peak at 1029 cm^{− 1} (corresponding to the C-O bond) indicating a deacetylation reaction, which aligned with the first step of EVA pyrolysis^57,64. This phenomenon occurred as follows: during laser irradiation, the silicon cells surface absorbs energy, causing a rapid temperature rise. This led to the temperature at the silicon cells-EVA interface exceeding EVA’s thermal stability threshold. Notably, given that the mass of silicon cells was much smaller than that of glass and EVA, glass and EVA heated up slowly during heat transfer. Thus, while the bulk temperature measured during the experiment did not exceed 190 ℃, the local temperature at the silicon cells-EVA interface had already reached the threshold required for EVA’s initial pyrolysis. During the laser processing, it was observed that the surface temperature of the silicon cells rose to 289 ℃ and stabilized, indicating that heat absorption and heat dissipation were in equilibrium. This temperature satisfies the conditions for the deacetylation reaction but does not meet the criteria required for the main-chain pyrolysis of EVA. This effectively accounts for the high carbon content observed in XPS analysis, which derives from acetic acid generated during the initial pyrolysis of EVA. Furthermore, the elimination of acetic acid contributed to the reduction in interfacial adhesion. Retention of EVA’s other characteristic peaks confirms the integrity of its main chain.
Raman spectroscopy results (Fig. 7b) indicated that the characteristic peaks of the two EVA samples (pristine EVA and laser processed EVA) were generally consistent. The main difference resided in peak intensity: both 1065 cm^{− 1} and 1447 cm^{− 1} correspond to the characteristic peaks of the C-O bond, with an intensity of 1610⁶⁵. In contrast, for laser processed EVA, the intensities of these two C-O bond peaks decreased to 1256. This observation further confirmed a reduction in C-O bonds, supporting the occurrence of the deacetylation reaction (consistent with FTIR findings). Notably, the peaks in the 2800–3000 cm^{− 1} range (corresponding to C-H stretching vibrations in EVA’s main chain) were perfectly matched across the two samples. This indicated that EVA’s cross-linked structure remained intact⁶⁷.
TGA showed that in the first stage (below 400 ℃), the weight loss curves of the two EVAs overlapped initially. At 360.15 ℃, the weight loss rate of pristine EVA exceeded that of laser processed EVA. This was attributed to the partial thermal decomposition of the laser processed EVA. Specifically, the EVA in contact with the silicon cells had already undergone preliminary deacetylation during laser processing^64,68. Since this contact-area EVA accounted for only a small fraction of the total EVA mass, the weight loss rates of the two samples remained consistent before 360.15 ℃. Beyond this temperature, pristine EVA still required extensive acetic acid removal, while the laser processed EVA was nearly finished with acetic acid release. This led to the temperature-dependent weight loss percentage of pristine EVA being exceeded by the laser processed sample—a trend reflected in the first intersection of the two curves in Fig. 7c.
In the second stage (main chain pyrolysis, above 400 ℃^64,68), the curve trend mirrored that of the first stage: the weight loss rates overlapped in the early phase but diverged in the later phase. This followed the same mechanism: for samples of equal mass, the laser processed EVA had a higher proportion of intact main chain (since partial low-molecular-weight components had already been removed during laser processing). During main chain pyrolysis, this higher main chain proportion caused the mass percentage of the laser processed EVA to change more rapidly with temperature compared to pristine EVA. This difference was visualized in Fig. 7d.
The above analyses collectively confirm that the selective separation of EVA and silicon cells is attributed to the combined effects of ARCs ablation and alterations in EVA properties. Specifically, ARCs ablation eliminated EVA’s adhesion sites on the silicon cells surface. Concurrently, EVA property modification led to the loss of its viscosity, while C–O bond cleavage in the thin interfacial EVA layer weakened its interfacial adhesion.
The SimaPro 9.4.0.1 software (https://simapro.com) was used to perform an LCA, comparing the environmental impacts of the method developed in this study with those proposed by two other researchers for PV laminate separation. Pang et al. employed a chemical method using trichloroethylene (scenario 3)⁶⁹. Gao et al. adopted a combined physical-thermal approach (scenario 2)⁷⁰. This study used a laser layering strategy (scenario 1). The LCA was conducted on all three processes at the laboratory scale, assessing the separation of silicon cells from 100 g PV laminations. The system boundary was defined to include the process from obtaining photovoltaic lamination to separating silicon cells. Upstream processes, such as the transportation of PV modules and the initial stripping of Al frame and junction box from the modules, were excluded from the analysis. The materials and energy consumption involved in the production of the equipment themselves, such as laser devices and milling machines, were also excluded from the analysis. In contrast, the electricity and chemicals consumed for the treatment of PV strips, as well as the products obtained post-treatment, were included in the analysis.
Based on the Ecoinvent 3 database, the input and output flows of the processes involved in this study as well as detailed energy flow data were calculated. The analysis was calculated according to their energy flow. Detailed energy flow data are shown in Table S1-3. LCA evaluated 18 environmental impact categories, of which 12 were selected for presentation, including but not limited to climate change, ozone depletion, terrestrial acidification, freshwater eutrophication, human toxicity, photochemical oxidant formation, particulate matter formation, terrestrial ecotoxicity, ionising radiation, urban land occupation and fossil depletion (Table S4). These twelve impact categories were selected due to their strong correlation with the electricity consumption and chemical usage involved in the three scenarios examined in this study.
This study’s method was more environmentally friendly than the other two. By avoiding chemical reagents and thermal steps, CO₂ emissions and fossil fuel consumption were significantly reduced. In addition, the laser-based method in this study offered higher efficiency than the other two: if the laser width was increased to half the width of the PV laminate, only 40 s would be required to process a 160 × 100 cm PV laminate. It also outperformed the other two methods in terms of other environmental impacts, as was illustrated in Fig. 8. However, the LCA process was solely based on small-scale treatment processes at the laboratory scale. For industrial-scale production, the reduction in environmental costs resulting from the scale effect should also be taken into account.
SEM/EDS and elemental contents of (a) Sample 1: pristine Si cells. (b) Sample 2: laser processed Si cells. (c) Sample 3: Si cells with the anti-reflection coatings completely removed.
(a) The full XPS spectra of (a) pristine Si cells, (b) laser processed Si cells and (c) Si cells with the anti-reflection coatings completely removed. The Si2p spectra of (d) pristine Si cells, (e) laser processed Si cells and  (f) Si cells with the anti-reflection coatings completely removed.
(a) FTIR and (b) Raman analysis of the pristine EVA and laser processed EVA. (c) TGA and (d) Deriv. Weight of the pristine EVA and laser processed EVA.
Environmental impacts of the three scenarios.
The laser penetrates the glass and EVA layers to irradiate the silicon cells. This achieved separation of the silicon cells-EVA interface by destroying the ARCs and inducing the denaturation of thin interfacial EVA directly contacting with the silicon cells. Temperature elevation effectively reduces EVA viscosity, aiding separation, but cannot alone enable selective separation.
Collectively, characterization results support the separation mechanism: XPS and electron microscopy analyses confirm ARCs degradation and silicon oxide formation on the silicon cells surface. Higher laser power enhancing ARCs destruction and higher frequencies promoting uniform ARCs damage. FTIR and Raman spectroscopy reveal that EVA undergoes deacetylation at the instantaneous high temperature of the silicon cells-EVA interface, causing viscosity loss, it is an additional key driver of separation. This is corroborated by thermogravimetric analysis. Laser-based separation enables efficient silicon cells recovery from bifacial PV modules, with the equipment easily adaptable to industrialization and automation. LCA studies have confirmed that this method is more environmentally friendly than thermal and chemical approaches.
This study offers novel insights into laser-assisted PV laminate separation and validates a practical approach for silicon cells recovery from PV modules. Future research should address industrialized challenges in processing full-size, large-area PV modules, such as, integration with existing recycling infrastructure, and real-time thermal monitoring for process optimization.
The scheme proposed in this study incurs a certain degree of increase in processing costs compared with the traditional mechanical crushing scheme, owing to the introduction of laser equipment. This cost arises from the large area of PV modules to be processed in actual production, which may involve the simultaneous operation of multiple laser devices, leading to additional expenses related to equipment procurement and energy consumption. Furthermore, the laser-based scheme is not applicable to certain types of PV modules that contain regions without silicon cells (Fig. S12).
The laser automated scanning system is equipped with a Hongben HB-C1500 continuous laser, with the laser’s central wavelength being 1080 ± 10 nm. The laser output head is mounted on a servo motor-controlled guide rail and programmed via a computer to operate in the horizontal plane along a predefined trajectory at a set speed. The overall setup is shown in Fig. S1. Commercially retired bifacial PV modules used in the experiments are illustrated in Fig. S2. The heating stage is a self-constructed pure resistance heating platform, regulated via a transformer to enable precise temperature control (Fig. S3). The temperature of the entire PV strips is measured using an infrared (IR) sensor thermometer, while the temperature of the silicon cells is monitored via thermocouples (Fig. S4). For volatile organic compound (VOC) detection, the MiniRAE 3000 + portable handheld monitor (RAE Systems/Honeywell) with a sensor resolution of 0.1 ppm is employed (Fig. S5). Peeling force measurements are conducted using a DK-F200S tensile tester manufactured by Deka (Fig. S6).
The adjustable parameters of the laser include output power, frequency, and duty cycle. The output frequency (f) refers to the number of laser pulses emitted per unit time, as denoted by count n in Fig. S7a. The interval between two consecutive laser emissions is defined as the period (T), with each period lasting 1/n seconds. The duty cycle is defined as the proportion of time within one cycle during which the laser emits energy. For instance, a 50% duty cycle means the laser emits energy for half the duration of one cycle; a 70% duty cycle indicates energy emission for 70% of the cycle duration, as illustrated in Fig. S7b.
First, the junction box and frame were removed. Bifacial PV laminate was cut into 8 × 5 cm strips (length × width) for subsequent use, as shown in Fig. S8. These strip-shaped bifacial PV strips were heated to 190 ℃ using a heating stage; silicon cells were extracted and labeled as Sample (1) New PV strips were then subjected to laser treatment, with parameters set as follows: power ranging from 100 to 1200 W, duty cycle from 100% to 5%, and frequency from 200 to 2000 Hz. The laser scanning speed was set to 30 cm/s, while the rectangular beam spot had a length of 5 cm and a width of 0.4 mm. During scanning, an IR sensor electronic thermometer continuously monitored the temperature. Scanning was halted once the temperature of surface glass reached 190 ℃, followed by cooling. During scanning, thermocouple probe was inserted into the side of the PV strips and tightly attached to the surface of the silicon cells to monitor their temperature. After multiple scans, the ARCs partially degraded, allowing the light-exposed side of the silicon cells to be easily manually separated from EVA. Through repeated trials, the optimal laser parameters were determined as 1200 W power, 2000 Hz frequency, and 5% duty cycle. The opposite side of the silicon cells were re-irradiated under these parameters, enabling easy scraping of the cells. These cells was collected for analysis and labeled as Sample (2) The complete experimental process is illustrated in Fig. 2. Separated silicon cells were further processed by direct surface irradiation using the optimal laser parameters until the ARCs was completely removed. These cells were collected for characterization and labeled as Sample (3) All three samples were shown in Fig. S9. Experiments under each parameter set were performed in triplicate (n = 3), and the results obtained from the three PV strips under the same parameter set were consistent with each other.
TGA was conducted on both pristine EVA from PV modules and laser processed EVA using a thermal analyzer (TA Instruments, Q500, US). The temperature was ramped from room temperature to 550 °C at a heating rate of 10 °C/min, with the entire experiment performed under an air atmosphere at a flow rate of 50 mL/min; sample mass was 5 mg. The objectives were twofold: first, to determine the pyrolysis temperature of EVA, thereby preventing waste gas generation due to excessive temperatures during laser treatment; second, to compare the thermogravimetric curves of pristine and laser processed EVA to investigate changes in their properties.
FTIR spectroscopy (Thermo Fisher, Scientific Nicolet iS20, US) was performed on pristine EVA and laser processed EVA. Measurements were conducted in attenuated total reflection (ATR) mode over a wavenumber range of 600–4000 cm^− 1. The aims were to investigate the effects of laser treatment on EVA.
A Raman spectrometer (HORIBA Scientific, LabRAM HR Evolution, Japan) was used to analyze pristine EVA and laser processed EVA. The excitation laser wavelength for the measurement was 785 nm, and the measurement wavenumber range was 50–4000 cm^− 1. The aims were to investigate the effects of laser treatment on EVA.
Cold Field Emission Scanning Electron Microscope (S-4800 SEM, Hitachi, Japan). Electron Gun – Cold cathode field emission type. Accelerating voltage 15 kV, working distance 4 mm − 1.0 nm and accelerating voltage 1 kV, working distance 1.5–2.0 nm. EDS analysis at a point or over user-defined regions (Point&ID), between any two points (LineScan, TruLine, and QuantLine), EDS element mapping (LayerMap, AutoLayer and TruMap), and EDS phase mapping (AutoPhaseMap). The objective was to analyze the surface morphological changes and variations in elemental content of silicon cells following different levels of laser treatment.
X-ray Photoelectron Spectroscopy (Thermo ESCALAB 250XI XPS, Thermo Kalpha, US). The monochromated X-ray beam can be focused to spot sizes ranging from 900 μm to 200 μm. XPS is based on the photoelectric effect that can identify the elements that exist within a material (elemental composition) or are covering its surface, as well as their chemical state, and the overall electronic structure and density of the electronic states in the material. The elements analyzed are carbon and Si, aiming to examine the material changes on the surface of silicon cells.
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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This work was financially support from the National Natural Science Foundation of China (no.52270130) and the Science and Technology Committee Foundation of Shanghai (23DZ1201503).
School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai, 201209, China
Chenglong Zhang, Zhengzhong Zhao, Ruixue Wang & Xiaonuan Wang
School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
Youcai Zhao
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Xiaonuan Wang: Writing – review & editing, visualization; Zhengzhong Zhao: Methodology, investigation, writing – Original Draft; Ruixue Wang: Project administration, formal analysis; Youcai Zhao: resources; Chenglong Zhang: Conceptualization, supervision, funding acquisition;
Correspondence to Xiaonuan Wang.
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In Australia’s state of New South Wales, the state’s Independent Planning Commission (IPC) has granted approval for the Dinawan Solar Farm and battery project being developed by Spark Renewables in the state’s southwest, some 658 kilometers from Sydney, in a particularly sunny and windy region of Australia, well-known for farming.
Spark, owned by Malaysian electricity giant Tenaga Nasional Bhd (TNB), said the Dinawan project combines an 800 MW solar installation comprising about two million solar panels with a 356 MW / 1,574 MWh battery energy storage system (BESS).
The developer said the hybrid project, which sits within the South West Renewable Energy Zone (a designated high-capacity corridor for green energy), will deliver large-scale dispatchable renewable power to Australia’s grid, contributing to “improving grid stability and energy security, while reducing reliance on fossil fuel-based generation.”
The AUD $1.35 billion (USD 930 million) solar farm and battery project was recommended for approval by the Department of Planning, Housing and Infrastructure in December but referred to the IPC for determination after more than 50 public objections were made during its assessment period.
The IPC has now approved the project after considering concerns raised relating to cumulative impacts, traffic and roads, noise, contamination, social impacts, emergency planning, local infrastructure and insurances.
In its statement of reasons, the Commission said the project would assist in “improving grid stability and energy security” and aligns with the New South Wales (NSW) government commitments to transition to renewable energy.
The project is also expected to create approximately 400 full-time jobs during construction and once operational will generate enough renewable energy to power approximately 142,000 homes.
The IPC has imposed some conditions of consent to minimise the potential adverse impacts from the project, including requiring Spark to implement a traffic management plan, noise management protocols and fire safety study and emergency plan.
Spark CEO Anthony Marriner said the approval of the solar and battery is a major step forward for the planned Dinawan Energy Hub, a complex that is to also include a 1.2 GW wind farm.
“With the solar farm now approved, we look forward to the upcoming determination of the Dinawan Wind Farm and progressing the full Dinawan Energy Hub toward delivery.”
The approval of the solar and battery project comes as new research suggests Spark is set to become an increasingly important lever for TNB’s renewable energy expansion outside Malaysia, while also serving as a critical learning platform to support that country’s net-zero 2050 ambitions.
Malaysia-based Hong Leong Investment Bank Research (HLIB Research) said Spark’s current contribution to TNB’s overall operation is minimal, as its only operational asset is the 100 MW Bomen Solar Farm, but noted that the growth pipeline is substantial.
Spark, acquired by TNB in 2023, is currently developing more than 3 GW of solar, wind, and battery storage projects across Australia’s National Electricity Market, including the Mallee solar, wind and battery energy hub, and the 615 MW Wattle Creek solar and battery project, both in the same state.
HLIB Research said beyond asset expansion, Spark also offers TNB exposure to more advanced electricity market structures, adding that insights gained in Australia could be applied to Malaysia’s own energy transition.
“The platform allows TNB to understand renewable energy implementation and power sector structures in more advanced countries,” it said.
The research house said TNB is also leveraging Spark for talent development and knowledge transfer, with staff secondments supporting capability building in renewable energy technologies, financing structures and regulatory frameworks.
TNB, the largest listed energy utility company in Southeast Asia with a market capitalisation of about $28 billion, is targeting the installation of 14.3 GW of renewable energy capacity globally by 2050.
From pv magazine Australia.
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Published 1:30 am Monday, April 6, 2026
By William Hurley
Hurley is a retired professional engineer in the field of commercial Naval Architecture and Marine Engineering and served as president of a major engineering firm in Seattle. He spent the last 20 years of his 45-year career working in the offshore wind industry, engineering deep-water floating wind turbine foundations and conducting techno-economic studies for the U.S. Department of Energy, large European energy companies and international developers. Hurley is a part-time resident of Decatur Island.
Utility-scale solar energy installations are being proposed for the San Juan Islands in the northwest corner of Washington state. While renewable energy is generally desired among the islanders, of which I am one, it is important to recognize the economics of solar energy.
We face challenges to our energy security. Electricity demand on the San Juan Islands is predicted to grow. The increasing statewide demand for fixed-capacity mainland hydropower could impose a greater percentage of higher Tier 2 electricity prices on us, and there is a concern that our electricity allocations could be limited.
Immediate reaction to these energy security challenges is to add local solar generation capacity, and our energy co-op, OPALCO, is aggressively promoting utility solar energy in the County. Our County’s new 2025-2045 Comprehensive Plan includes extensive utility solar energy installations, yet there has been little discussion of the costs. While seemingly a good idea, the promise of utility solar needs to be tempered with pragmatic realism.
I have prepared an in-depth economic analysis comparing the economics of rooftop solar with utility solar. The study demonstrates that rooftop solar makes good economic sense, but utility solar in the San Juan Islands does not. Here is a link to the full report, https://docs.google.com/document/d/1Nf3u0EBTtMHgki8UJQKseDHr_0XDLxIl/edit.
Rooftop solar
The basic economic measure is Levelized Cost of Energy (LCoE): the lifetime discounted cost in $/kW-hours of a renewable energy installation. LCoE considers all the cost elements and the annual energy production from the specific site. A San Juan Island homeowner who installs a large 20kW rooftop solar installation, utilizing the residential 30% tax credit (good until 2032), and expects a 25-year life from the panels and components, can expect an LCoE of 9.6₵/kW-hr. The homeowner realizes a 3.9₵/kW-hr discount from the Tier 1 retail rate! Unfortunately, the initial installation cost is beyond the financial means of many islanders, and this creates an inequitable situation.
Utility solar
The proposed 8-acre 2,500 kW DC Solar Array Expansion project on Decatur Island consists of two new arrays, and the generated power goes directly into the OPALCO grid with no battery storage.
Capital expenses are high because the installation is on a remote island without ferry access. Costs are estimated to be $4.6M after deducting the $1M WA state grant received to support the project.
Annual Energy Production is low in the San Juan Islands. To make matters worse, the site for the Decatur project is right up against a forest to the south of the arrays, and the forest trees shade an estimated 20% of the array area. The shading reduces the output from the unshaded 13% Capacity Factor to 10%. Capacity Factor, in DC/AC, is the measure of the energy production realized. California, in comparison, has a 23% Capacity Factor.
Combining the high CAPEX with the low energy production, the LCoE of this utility array is 16.5₵/kW-hr, more than twice the current Tier 2 cost OPALCO pays BPA. The high cost of remote island installation, coupled with the very low solar irradiance level, makes solar energy production in the San Juans very expensive.
Conclusion
As San Juan Islanders face our energy future, let’s be realistic in our pursuit of renewable energy. Strategies should include increased energy conservation, growth management and a focus on rooftop behind-the-meter solar.
If we pursue utility-scale solar energy, we need to carefully site the projects. Deforestation and otherwise harming the environment must be avoided in favor of siting panels on impervious surfaces and in dual-use agri-solar locations. The Decatur project, which involves extensive deforestation, needs to be resited.
We also need to take stronger steps to provide equitable access to renewable energy. Significant strategic thinking is still needed before we make a blanket endorsement of utility solar in the San Juan Islands.

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Japan’s government has adopted a bill requiring mega solar operators to submit panel disposal plans. The regulation focuses on managing end-of-life solar panels and reducing landfill volumes. Operators must specify panel volumes, disposal timelines, and treatment methods. The bill also encourages recycling and material recovery from used panels. Japan expects solar panel waste to reach 500,000 tons by 2040. This projection represents nearly six times the current disposal volume. Authorities aim to strengthen waste management as solar installations expand nationwide.

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Baltimore Fishbowl
YOUR WORLD BENEATH THE SURFACE.
By CASEY GLICKMAN
Capital News Service
When Janete Gonzalez went to the Druid Hill Park farmers market in the fall of 2022, she was a new Baltimore City resident, having just moved after a house fire destroyed everything she owned. That day, she expected to leave the northern Baltimore market with food and maybe some health care products.
Instead, she left with solar panels. 
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“I originally assumed that solar panels were for people who had bigger land or lived in a better neighborhood,” Gonzalez said. “I just didn’t think it was for us.”
But Civic Works, a nonprofit working to improve energy accessibility in Maryland, is changing that.
After visiting the organization’s booth at the farmers market, Gonzalez joined its solar accessibility program. Now, she is one of more than four dozen Baltimore City residents who have received free solar panel installations as part of the Baltimore Shines program. 
The program emerged as a partnership between the Baltimore City Department of Housing and Community Development and Civic Works as an affordable solution for low-income residents to lower their electricity bills and make a positive impact on the environment.

Baltimore Shines started this round of solar installs in 2024 and as of December had completed 50 solar installations for income-qualifying homeowners. By the end of 2026, the program hopes to bring that number to 170 installations.
“Our goal is to really make it as easy and worry-free a process as possible for the resident,” said Eli Allen, the senior program director at Civic Works.

Cost savings

After Gonzalez’s first introduction to Baltimore Shines, she went through an almost yearlong process of information sessions, online applications, a roof assessment and several house visits. Her solar panels were installed in June 2023, and by December, they were generating power. 
Now Gonzalez saves about 50% on her Baltimore Gas and Electric Company bill. Bills that came in around $400 now average $176-$230 a month, she said.
“It gives that safeguard to really embrace the house that you have and lets you focus on family life,” Gonzalez said.
Those savings are nothing unusual. According to 2024 fiscal year data from the Maryland Energy Administration, Baltimore Shines has cut residents’ electricity bills by an average of $1,500 annually. 
“That’s quite a significant amount,” said Angel Saules, Maryland Energy Administration program manager. “That’s over $100 a month that people are able to save by having these systems installed.” 
On average, however, these savings are not consistent throughout the year due to seasonal changes in solar production.
Solar panels convert sunlight into electrical energy through photovoltaic panels. During the winter months, with fewer hours of sunlight, solar systems produce less energy. Coupled with an increase in heating needs, hot water usage and electricity for lighting, that means residents typically don’t save as much in their energy bills during the colder months. 
“It’s great for the summer, not too much for the winter,” said Baltimore Shines participant Tyresa German.  
In the winter, German said she saves about $50 per month; BGE bills that used to come in around $250 now average $200 per month. But once summer rolls around, German’s bills drop to $10-$30 a month.
“My friends hate me,” German joked. “Prior to getting the solar panels, I was doing a lot of overtime just so I could not feel drowned in the BGE bill.” 

How it works

Baltimore Shines also ensures city residents aren’t drowned by the cost of solar panels.
In Baltimore City, the average row home can safely handle an 11-kilowatt solar system, which costs residents between $15,000 and $18,000, said Victor Walters, associate director of outreach and intake at Civic Works.
That price tag makes solar energy a luxury that is out of reach for some.
With Baltimore Shines, residents pay zero out-of-pocket costs — but only low-income homeowners qualify for the program. Income limits range from $26,338 for a single-person household to $54,600 for a family of four to $92,260 for a family of eight.
Under the program, Civic Works owns and operates the solar panels it installs on homes for a 20-year lease term, covering any maintenance issues or replacements residents may need.
To finance the program, Civic Works receives grants from a variety of sources, which previously included funding from a program called Solar for All. 
However, after the U.S. Environmental Protection Agency terminated $7 billion in grants for Solar for All programs in August 2025, Baltimore Shines was forced to restructure to adjust for the lack of funding.
“We have had to cap the size of the solar system we are installing to be able to offer solar to more community residents,” Walters said.
Now residents’ solar systems are limited to 5.7 kilowatts — roughly half the size of previous systems installed under the program. If residents want to expand their system size, those costs come out of pocket, Walters said.
The Maryland Energy Administration Residential Energy Equity Program now serves as one of the program’s main funding sources — and it expects demand for the program to grow.
“The way we expect to see that unfold is that we’ll have more applicants for solar than we have in the past because there isn’t going to be access in other ways,” Saules said.

Why it matters

The chance to switch to solar matters for Baltimore City residents as BGE utility rates continue to climb. Since January 2025, BGE customers have seen multiple increases in their energy bills, with residents expecting to pay an average of $26.06 more per month for combined gas and electric bills, according to 2025 energy bill information for BGE customers.
Low-income residents bear the brunt of the energy burden. In Baltimore, the median energy burden of low-income households was four times higher than non-low-income households, according to a 2020 report by the American Council for an Energy-Efficient Economy. 
The median household in Baltimore spent 3% of its income on its energy bill, yet median low-income Baltimore households spent 10.5%, according to the report.
Addressing the energy bills of low-income households simultaneously addresses climate change, Saules said.
“Our goals as a state are to reduce greenhouse gas emissions by a certain amount by a certain time,” Saules said. “A good way to achieve that goal is to address the highest energy burden, which is typically in lower-income households.”
Energy efficiency education is a crucial part of this conversation, she added. Being energy efficient can be as simple as knowing how your everyday behaviors affect your energy usage, like turning off the water while brushing your teeth and not constantly adjusting your thermostat. 
At Baltimore Shines, solar panels are the first step in making a home more energy efficient. Then comes homeowner support and education to help residents understand how usage affects their electricity bills each month.
“When we install a new efficiency model in someone’s home, people sometimes think they can overuse any system,” Walters said. “People start to use more energy because they are assuming that this newer product is going to save them so much.” 
Walters said staffers help residents feel confident in their decision to go solar. However, given the program’s limited staffing size, this support is not always as timely as residents want it to be, he said.
“The biggest feedback that we have gotten from program participants is not knowing step by step what’s going on,” he said.
In some cases, after residents have gotten their solar panels installed, they think their system will be turned on immediately. However, solar panels can sit on the roof of someone’s home for two to three months, awaiting city inspection and for BGE to connect the system. 
To get ahead of such issues, Civic Works is working on new ways to improve communication with residents, Walters said.
But Gonzalez said the support she’s gotten from Civic Works has been a key part of her Baltimore Shines experience. The program goes beyond just covering finances; it’s about having access to resources to better understand the energy options available and how different systems will affect your finances and carbon footprint. 
“I had access to learn about these things as a new homeowner — understanding the importance of energy savings and going green and all of these things we can do differently to contribute to the environment,” Gonzalez said.

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Which reign supreme: chocolate or marshmallow treats? Maryland — and the majority of states — have a favorite when it comes to Easter candy.
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Source: Xinhua
Editor: huaxia
2026-04-06 20:26:00

A drone photo taken on April 6, 2026 shows a view of a solar thermal and photovoltaic (PV) integrated project by China General Nuclear Power Group in Damxung County of Lhasa, southwest China’s Xizang Autonomous Region. China General Nuclear Power Group announced on Monday that construction of a 50 MW trough-based concentrated solar power plant at an altitude of 4,550 meters started in southwest China’s Xizang Autonomous Region. The solar thermal plant is part of a solar thermal and photovoltaic (PV) integrated project, which can absorb curtailed PV power and effectively compensate for the intermittency and fluctuation of PV generation. (Xinhua/Tenzin Nyida)
SHENZHEN, April 6 (Xinhua) — China General Nuclear Power Group (CGN) announced on Monday that construction of a 50 MW trough-based concentrated solar power plant at an altitude of 4,550 meters started in southwest China’s Xizang Autonomous Region.
According to the press release issued by the company based in Shenzhen, south China’s Guangdong Province, the facility is located in Damxung County in the regional capital Lhasa.
Using parabolic trough technology with thermal oil as the heat transfer fluid, it features a mirror field of 242,000 square meters and a 6-hour molten salt energy storage system, enabling nighttime power generation.
The solar thermal plant is part of a solar thermal and photovoltaic (PV) integrated project, which can absorb curtailed PV power and effectively compensate for the intermittency and fluctuation of PV generation.
Construction of the PV section, designed with 400 MW of power generation capacity, began in September last year.
Located in a cold, high-altitude zone with low oxygen, the site allows construction only from April to October. The team has installed heating, oxygen supply and a hyperbaric chamber to ensure workers’ health and safety.
Invested and developed by CGN New Energy (Damxung) Co., Ltd., the integrated project is scheduled for full commissioning by 2027.
Once operational, it is expected to generate about 719 million kWh annually, saving roughly 216,900 tonnes of coal equivalent and cutting carbon dioxide emissions by 652,300 tonnes.
The project has already created over 2,000 local jobs and generated more than 5.2 million yuan (approximately 753,600 U.S. dollars) in local economic income through labor and equipment use.
Despite being burdened by its high altitude and harsh environment, Xizang is in a strong position to develop the clean energy sector, thanks to its abundant solar, wind and water resources.
According to its government work report, the region aims to increase its installed power generation capacity from 13 million kW in 2025 to 20 million kW in 2026, with integrated power bases combining wind, solar and hydropower to be built at different locations. ■

A drone photo taken on April 6, 2026 shows the scene of groundbreaking of a 50 MW trough-based concentrated solar power plant by China General Nuclear Power Group in Damxung County of Lhasa, southwest China’s Xizang Autonomous Region. China General Nuclear Power Group announced on Monday that construction of a 50 MW trough-based concentrated solar power plant at an altitude of 4,550 meters started in southwest China’s Xizang Autonomous Region.
The solar thermal plant is part of a solar thermal and photovoltaic (PV) integrated project, which can absorb curtailed PV power and effectively compensate for the intermittency and fluctuation of PV generation. (Xinhua/Tenzin Nyida)

A drone photo taken on April 6, 2026 shows a view of a solar thermal and photovoltaic (PV) integrated project by China General Nuclear Power Group in Damxung County of Lhasa, southwest China’s Xizang Autonomous Region. China General Nuclear Power Group announced on Monday that construction of a 50 MW trough-based concentrated solar power plant at an altitude of 4,550 meters started in southwest China’s Xizang Autonomous Region.
The solar thermal plant is part of a solar thermal and photovoltaic (PV) integrated project, which can absorb curtailed PV power and effectively compensate for the intermittency and fluctuation of PV generation. (Xinhua/Tenzin Nyida)

A drone photo taken on April 6, 2026 shows the scene of groundbreaking of a 50 MW trough-based concentrated solar power plant by China General Nuclear Power Group in Damxung County of Lhasa, southwest China’s Xizang Autonomous Region. China General Nuclear Power Group announced on Monday that construction of a 50 MW trough-based concentrated solar power plant at an altitude of 4,550 meters started in southwest China’s Xizang Autonomous Region.
The solar thermal plant is part of a solar thermal and photovoltaic (PV) integrated project, which can absorb curtailed PV power and effectively compensate for the intermittency and fluctuation of PV generation. (Xinhua/Tenzin Nyida)

This photo taken on April 6, 2026 shows the scene of groundbreaking of a 50 MW trough-based concentrated solar power plant by China General Nuclear Power Group in Damxung County of Lhasa, southwest China’s Xizang Autonomous Region. China General Nuclear Power Group announced on Monday that construction of a 50 MW trough-based concentrated solar power plant at an altitude of 4,550 meters started in southwest China’s Xizang Autonomous Region.
The solar thermal plant is part of a solar thermal and photovoltaic (PV) integrated project, which can absorb curtailed PV power and effectively compensate for the intermittency and fluctuation of PV generation. (Xinhua/Tenzin Nyida)

A drone photo taken on April 6, 2026 shows the scene of groundbreaking of a 50 MW trough-based concentrated solar power plant by China General Nuclear Power Group in Damxung County of Lhasa, southwest China’s Xizang Autonomous Region. China General Nuclear Power Group announced on Monday that construction of a 50 MW trough-based concentrated solar power plant at an altitude of 4,550 meters started in southwest China’s Xizang Autonomous Region.
The solar thermal plant is part of a solar thermal and photovoltaic (PV) integrated project, which can absorb curtailed PV power and effectively compensate for the intermittency and fluctuation of PV generation. (Xinhua/Tenzin Nyida)

A drone photo taken on April 6, 2026 shows the scene of groundbreaking of a 50 MW trough-based concentrated solar power plant by China General Nuclear Power Group in Damxung County of Lhasa, southwest China’s Xizang Autonomous Region. China General Nuclear Power Group announced on Monday that construction of a 50 MW trough-based concentrated solar power plant at an altitude of 4,550 meters started in southwest China’s Xizang Autonomous Region.
The solar thermal plant is part of a solar thermal and photovoltaic (PV) integrated project, which can absorb curtailed PV power and effectively compensate for the intermittency and fluctuation of PV generation. (Xinhua/Tenzin Nyida)

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US independent power producer (IPP) Geronimo Power has begun operations at a 117MW solar PV project in Ohio.
The Dodson Creek project in Highland County, Ohio, began operations on Friday, 3April, when it was connected to the PJM transmission network.
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The site brings Geronimo Power’s Ohio solar capacity to a cumulative 675MW, according to Andy Cukurs, Geronimo’s COO. The company was previously known as National Grid Renewables, the US development arm of the UK grid operator, but rebranded when it was acquired by Canadian asset owner Brookfield.  
The site deploys Series 7 modules from cadmium telluride (CdTe) thin-film manufacturer First Solar, which were produced at its facility in Perrysburg, Ohio. First Solar’s head of strategic accounts, Mounir El Asmar, said the deployment of locally made solar modules “underscores how genuinely American solar technology can drive economic growth while supporting the nation’s need for affordable energy.”
The project was built by engineering, procurement and construction (EPC) firm, Kiewit Power Constructors.
Geronimo Power has PV development operations across a number of Midwest states. In October, it began construction on two projects in Illinois and Michigan; the 150MW Bee Hollow and the 125MW Jackson County projects, respectively. It is also currently building a 250MW project in Wisconsin, and began operations at a 167.5MW Ohio project last June.
Last month, local politicians in Ohio rejected the construction of a 94MW solar PV project following complaints by locals. The site’s developer, Open Road Renewables, told PV Tech Premium that the rejection was potentially undermined by “anti-solar activists” and undermines Supreme Court precedents.

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Snow this morning will change to rain showers this afternoon. High 42F. ESE winds shifting to W at 10 to 15 mph. Chance of rain 80%..
Partly cloudy skies. Low 23F. Winds NW at 10 to 20 mph.
Updated: April 7, 2026 @ 9:57 am

In this 2021 photo, Dan Weeks, vice president for business development for ReVision Energy, shows the array of solar panels on the roof of the Associated Grocers’ distribution facility in Pembroke.

Senior Political Reporter
Major private employers and renewable energy advocates urged a House committee to increase by five times how much of their own electricity industrial customers could generate and sell any excess back to the power grid.
In this 2021 photo, Dan Weeks, vice president for business development for ReVision Energy, shows the array of solar panels on the roof of the Associated Grocers’ distribution facility in Pembroke.
But officials with the state Department of Energy said a Senate-approved bill was “unworkable.” They also said it’s unknown how much the enhanced net metering for some customers would shift higher costs onto all other ratepayers.
Net metering allows owners who produce their own renewable energy to send excess back to the grid and get credits to offset the cost of any future energy they have to buy.
Right now, industrial users can net meter up to 1 megawatt (MW) of power.
When the project produces less energy than the owner needs, such as at night for a solar panel project, they use their credits to pay for any power they need from the grid.
The Senate-passed bill at issue (SB 449) would increase from one to five MWs of power how big a net metering project could be for an industrial customer.
“I look at this not as an energy bill but a jobs bill. We need to help our manufacturers,” said Senate Ways and Means Committee Chairman Tim Lang, R-Sanbornton and the bill’s prime sponsor.
Kyle King, sustainability manager for Coca Cola Beverages Northeast, said if this bill passed, the firm would seriously consider building a 5 MW solar array on top of its bottling plant in Londonderry as the corporate goal is to become a net zero, carbon-based energy using company by 2050.
Kristopher Tiernan is with the facilities planning and sustainability offices at Lonza, a pharmaceutical firm that employs 2,000 in Portsmouth.
“This gives us the option to incorporate more renewable energy on a larger scale,” Tiernan said.
D.J. Burke with the Business and Industry Association of New Hampshire said the manufacturing sector is twice as big a factor in this economy as it is in Massachusetts.
“This is not a magic bullet, but it does provide an option for our large energy users,” Burke said.
Josh Elliot, director of policy and programs, said industrial customers consume 17% of all electricity in the state and as written he said this bill could permit a user to build a multiple of 5 MW plants.
“This could shift risks from (business) developers to ratepayers” Elliot said.
“I don’t think the bill is workable as it is.”
Sam Evans Brown, executive director of Clean Energy NH, said past studies suggest the cost benefit to ratepayers were greater than what net metering customers received from these arrangements.
“We might be saving our ratepayers money by building more of these,” Evans Brown said. “This is sort of a win-win as far as we can tell based on the best evidence we have.”
But Rep. Michael Harrington, R-Strafford, and a former Public Utilities Commission member, questioned if these deals didn’t produce a bigger windfall at the expense of other ratepayers.
Legally, companies currently can produce 5 MW of power on site as long as they consume all for their own use, Harrington said.
“If they aren’t doing it right now, how is this then not a subsidy?” Harrington asked rhetorically.
What’s Next: The House Science, Technology and Energy Committee has until the end of the month to make a recommendation.
Outlook: Unlikely. Under both parties’ control, the Senate, going on five years, has been unable to convince the House to support a change in net metering for the private sector.
klandrigan@unionleader.com
The New Hampshire Public Utilities Commission is considering how much power companies should pay solar customers who send the excess electrici…
CONCORD — A Senate-passed bill would create a dangerous precedent by letting a renewable energy company sell excess hydropower back to the pow…
Senior Political Reporter
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SolarEdge Tumbles 7%, Enphase Energy Sinks 4% Amid Cash Burn Concerns, Fierce Competition  AOL.com
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On Monday, Gov. Janet Mills signed a proposal into law to allow Mainers to install small, portable solar energy systems in their homes.
The measure from Sen. Nicole Grohoski, D-Ellsworth, allows electricity customers to use certain small solar generation and battery systems, which plug directly into wall sockets, similar to gas generators. The panels are portable, unlike traditional solar panels.
The Democratic governor signed the bill Monday after the Legislature gave it final approval last week. The House passed it mostly along party lines, while the Senate gave it broader approval apart from a few Republican opponents.
The governor’s signature on the bill, which will take effect 90 days after lawmakers adjourn, comes as Mainers face high electricity prices driven largely by natural gas prices spiking in New England and as the conflict with Iran drives up energy prices generally.
Proponents argue the small, plug-in generators could offset a household’s electricity usage and lower monthly utility bills, all for a significantly lower upfront cost than larger-scale, more traditional solar outfits. One estimate from Rep. Gerry Runte, D-York, found an 800-watt system could save an average Central Maine Power Co. customer more than $250 annually.
Twenty-eight states — including nearly every state in New England — are considering similar “balcony solar” proposals, according to an analysis by Canary Media.
Officials in the United Kingdom announced in March that plug-in solar panels would “be in shops within months,” noting they have already been adopted elsewhere in Europe.
Billy covers politics for the Press Herald. He joined the newsroom in 2026 after also covering politics for the Bangor Daily News for about two and a half years. Before moving to Maine in 2023, the Wisconsin… More by Billy Kobin
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Author: PPD Team Date: April 7, 2026
Waaree Energies Limited has commenced operations at a new solar module manufacturing facility in Samakhiali, Kutch, Gujarat, with a total annual capacity of 3,000 MW. The plant began production at 10:00 am on April 6, 2026, according to a regulatory filing of the same date.
The facility is operated by Sangam Solar One Private Limited, a wholly owned subsidiary of Waaree Energies Limited. It includes four production lines, each with an annual capacity of 750 MW, taking the total installed capacity at the site to 3,000 MW. The plant is located in the industrial zone of Samakhiali in Kutch district. 
Incorporated in December 1990 and headquartered in Mumbai, Waaree Energies Limited is India’s largest manufacturer and exporter of solar photovoltaic (PV) modules. The company has a global solar module manufacturing capacity of 22.77 GW, including 20.17 GW in India and 2.6 GW in the United States, along with 5.4 GW of solar cell capacity. Its operations cover module and inverter manufacturing, engineering, procurement and construction (EPC) services, battery energy storage systems (BESS), green hydrogen, and broader energy infrastructure.
Author: PPD Team Date: October 15, 2024 Saatvik Green Energy has secured a contract with Maharashtra State Power Generation Co. Ltd (MAHAGENCO) to supply 200 MW of n-type TOPCon 580 Wp modules to various locations across Maharashtra. The total value of the order is estimated at INR 30.2 billion. With a module manufacturing capacity of 3.8 GW as of FY24-25, Saatvik is strategically expanding and developing PV modules with advanced zero-busbar and 24-busbar technology. The…
Read More Saatvik Green wins 200 MW solar module contract from MAHAGENCO
Author: PPD Team Date: June 20, 2025 Spain’s grid operator Redeia is at the centre of a major controversy after a government report blamed its mismanagement for the country’s worst electricity blackout in recent history. The outage, which began shortly after 12:30 PM on 28 April 2025, plunged much of Spain—and parts of Portugal—into darkness. The blackout lasted into the night, disrupting transport, mobile networks, internet services, and critical infrastructure. In just five seconds, Spain…
Read More Spain’s April blackout: Grid failure or planning flaw?
Author: PPD Team Date: October 21, 2024 The 450 MW Neart na Gaoithe (NnG) offshore wind farm, located 15.5km off the coast of Fife, Scotland, has delivered its first power to the UK national grid.  Once fully operational by summer 2025, NnG will provide clean energy to 375,000 homes, offsetting over 400,000 tons of CO₂ emissions annually. The project, owned by EDF Renewables UK and Irish electricity firm ESB, will feature 54 turbines and create…
Read More Neart na Gaoithe offshore wind farm generates first power for UK grid
Author: PPD Team Date: December 10, 2024 The African Development Bank (AfDB) has been named the mandated lead arranger for the Moyi Power metro-grids project, a $340 million private sector-led electrification initiative in the Democratic Republic of Congo (DRC). The project is now entering the financing stage. Spearheaded by the Moyi Power consortium, which includes Gridworks, Eranove, and AEE Power, the initiative aims to provide clean, reliable, and affordable electricity to one million people across…
Read More AfDB appointed lead arranger for Moyi Power metro-grids project in DRC
Author: PPD Team Date: October 17, 2025 The Defence Research and Development Organisation (DRDO) has signed a Memorandum of Understanding with the Solar Energy Corporation of India (SECI) to jointly develop 300 MW of solar-based renewable energy projects. The projects will be set up across DRDO facilities nationwide. The collaboration aims to make all DRDO campuses in strategic locations net zero by 2027. SECI operates under the Ministry of New and Renewable Energy, while DRDO…
Read More DRDO and SECI to develop 300 MW solar projects across India
Author: PPD Team Date: January 14, 2026 Vikram Solar Limited has announced the appointment of Mr. Biresh Ranjan Das as Senior Vice President, Human Resources.  The company said Mr. Das has over two decades of cross-industry experience. Vikram Solar stated that his appointment aligns with its focus on strengthening talent development, expanding the employability pool, and building a sustainable pipeline of professionals for the clean energy ecosystem. Additionally, Vikram Solar informed that Mr. Arindam Chakraborty,…
Read More Vikram Solar appoints Biresh Ranjan Das as SVP HR
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POWERGRID Floats Tender For 800 kV HVDC Line To Evacuate 6 GW Rajasthan Solar Power Project  solarquarter.com
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Nature Communications volume 17, Article number: 1928 (2026) Cite this article
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Rapid global expansion of photovoltaics is driving degraded module flows to emerging markets. This flow occurs amid limited regulatory oversight and recycling capacity, posing substantial environmental risks to importing regions. Mitigating these risks necessitates cross-border governance and traceable end-of-life systems.
The rapid growth of the photovoltaic (PV) industry has made it an important force in driving the global energy transition and carbon neutrality goals. The global installed PV capacity is expected to further increase to about 4500 Gigawatt (GW) by 2050¹. China, the United States. India, Japan, and Germany are leading countries of PV module manufacturing² (see Fig. 1b, c). As PV deployment expands worldwide, rapid technological progress in major manufacturing countries is also accelerating the replacement of older modules, generating substantial volumes of decommissioned or degraded PV modules. With limited resale value in mature PV markets, a large share of these degraded modules is redirected to secondary markets in emerging and developing regions³. Although the low-price importation of degraded PV modules has alleviated energy access challenges, their inherent performance limitations transfer operational and environmental burdens to importing nations. This increasing exposure to degraded PV modules creates emerging cross-border risks due to weak quality assurance, limited recycling capacity, and the potential accumulation of unmanaged PV waste.
a Photovoltaic power potential²³; b Global Photovoltaic capacity, and Panel waste¹; and c Cumulative waste volumes of five countries (China, the United States, Japan, India, and Germany) of end-of-life Photovoltaic panels in 2050¹.
PV modules exhibiting cosmetic defects, unstable electrical performance, or operational degradation, thereby failing to meet established quality standards, are classified as degraded PV modules⁴. Degraded PV modules predominantly result from manufacturing defects, technological obsolescence, premature decommissioning, and operational failures⁵. Rapid advancements in PV technology and escalating efficiency standards in technologically advanced markets, such as China, the United States, Japan, and Germany, have rendered earlier-generation, lower-efficiency modules commercially obsolete. In these mature PV markets, earlier-generation low-power modules, typically below 400–500 watt-peak (Wp), have virtually no remaining market presence and now constitute a major source of degraded PV components⁶. Operational failures constitute a critical factor in the growing number of degraded PV modules. Additionally, approximately 70% of PV modules are decommissioned before reaching the end of their design lifespan (around 25-30 years)⁷. A considerable share of these prematurely retired modules is refurbished, typically through basic cleaning and electrical testing, and reintroduced into secondary markets. Many of these refurbished units are subsequently exported, often exhibiting 10–20% power degradation and inconsistent quality⁸.
Degraded PV modules are disseminated through three primary channels: direct trade, international aid programs, and bulk low-cost sales. These modules predominantly flow to Africa (including Nigeria, Kenya, and South Africa) and the Middle East (notably Saudi Arabia and the United Arab Emirates)⁹. However, most of these emerging markets lack mature quality assurance systems for imported PV equipment, including mandatory testing or performance certification. As a result, recycling regulations are fragmented, and mandatory certification protocols are inconsistently applied. Moreover, exporting nations have not yet established a comprehensive regulatory framework that covers the entire life-cycle of PV products. For instance, in Nigeria, the absence of formal e-waste legislation and a dedicated PV waste recycling framework has led to the uncontrolled accumulation of imported second-hand solar panels, with informal recyclers handling disposal using unsafe practices that release hazardous substances into the environment^10,11. Similarly, South Africa, one of the world’s largest and fastest-growing PV markets with abundant solar resources (Fig. 1a), has yet to develop a comprehensive regulatory framework for the end-of-life management of solar PV products¹². Collection and recycling are mainly conducted by private or pilot programs with limited traceability and no standardized reporting requirements. These examples highlight the fragmented and uneven regulatory landscape across PV markets, underscoring the urgent need for harmonized policy frameworks and infrastructure development to ensure environmentally sound PV waste management. Consequently, the cross-border movement of degraded PV modules operates under weak regulatory control and limited information transparency. Although exporters may label products as degraded, importers often lack sufficient information on their quality and associated environmental risks. In regions with high energy access costs, users tend to prioritize low initial investment over long-term environmental and maintenance costs.
The export of degraded PV modules has precipitated multiple environmental challenges. Their elevated power degradation rates and reduced lifespans exacerbate environmental management burdens and complicate e-waste disposal. Many emerging and developing regions lack robust regulatory oversight, traceability of environmental liabilities, and policy support for second-hand modules, resulting in inadequate PV waste management. As export volumes increase, these issues become increasingly pronounced. The lack of recycling infrastructure and technical capacities (e.g., high-temperature pyrolysis and chemical etching) hinders the development of even basic systems for PV waste collection, sorting, and transportation of the increasing volume of discarded modules¹¹. The PV waste is usually treated through land-filling and incineration, further exacerbating environmental degradation.
By 2050, Africa and the Middle East are expected to generate 1.6 million tonnes (Mt) and 1.7 Mt of PV waste modules, respectively, with over 90% originating from imports and accounting for failures at different stages, including infant, mid-life, and wear-out, before reaching the 30-year lifespan¹. Under the assumption that end-of-life PV modules in these regions will be disposed of in landfills, a life-cycle impact assessment was conducted using the ReCiPe 2016 midpoint method. The results indicate that the primary environmental impacts are fossil energy consumption, climate change, and human toxicity (Fig. 2c). Land-filling PV waste is projected to consume metal resources equivalent to 10.3 Mt Fe and fossil resources equivalent to 17.2 Mt oil, increasing pressure on raw material extraction and exacerbating the ongoing energy and resource crisis¹³. In addition, land-filling PV waste is estimated to emit approximately 63 Mt of CO₂, thereby intensifying global climate change and undermining progress toward Sustainable Development Goal (SDG) 13 (Climate Action) (Fig. 2a).
a, b refers to the key environmental impacts: climate change (Mt CO₂ eq), ozone depletion (tonnes of trichlorofluoromethane equivalents, t CFC-11 eq), freshwater eutrophication (kilograms of phosphorus equivalent, kg P eq), human toxicity (kilograms of 1,4-dichlorobenzene equivalents, 1,4-DB eq), agricultural land occupation (Billion m²·a), urban land occupation (Billion m²·a), metal depletion (Fe eq), and fossil resource depletion (kg oil eq); c shows the normalized key environmental impacts assessment results, calculated using the ReCiPe 2016 midpoint (H) method. These standardized indicators facilitate cross-category comparison of environmental burdens.
Moreover, heavy metals and hazardous chemicals such as cadmium, selenium, and ethylene-vinyl acetate (EVA) can leach into soil and groundwater during land-filling, resulting in human toxicity equivalent to 4.64 Mt 1,4-dichlorobenzene (1,4-DB) and freshwater ecotoxicity equivalent to 1.1 Mt 1,4-DB¹⁴ (Fig. 2a). Such leaching of hazardous substances not only endangers groundwater safety and undermines access to clean drinking water and sanitation (SDG 6), but can also introduce selenium-related contaminants into aquatic and terrestrial food webs, where they may bioaccumulate and exert severe toxic effects, thereby increasing disease burden, shortening life expectancy, and contributing to premature mortality. Such impacts may trigger a severe public health crisis, contravening the objectives of ensuring healthy lives and promoting well-being for all (SDG 3)¹⁵. The land-filling of PV waste modules is projected to yield pollutants equivalent to 29.8 kilotonnes (Kt) of phosphorus-equivalent freshwater eutrophication agents, which may destabilize aquatic ecosystems, exacerbate algal blooms, and cause widespread mortality among aquatic organisms, potentially culminating in complete ecosystem collapse (Fig. 2a). Such pollutant-induced ecological disturbances pose a significant threat to marine and coastal ecosystems and biodiversity, thereby hindering the sustainable management of marine resources (SDG 14).
Meanwhile, the non-biodegradable nature of these materials results in the long-term occupation of urban and agricultural lands, estimated at 0.6 billion square metres per year (m²a) and 4.3 billion m²a, respectively, thereby reducing land-use efficiency and constraining space for ecosystems and food production, with ecological restoration potentially requiring decades to centuries (Fig. 2b). Additionally, the release of 14.6 t trichlorofluoromethane equivalents (CFC-11) of ozone-depleting substances undermines the ozone layer, increasing harmful ultraviolet radiation and compromising global ecological safety (Fig. 2a). Given that PV waste also contains substantial quantities of recyclable resources and critical metals such as molybdenum, magnesium, gallium, and indium, improper disposal poses a long-term threat to environmental management and sustainable development, underscoring the urgent need for accelerated recycling and resource recovery.
Effective management of degraded PV modules requires a coordinated governance framework that differentiates the responsibilities of exporting countries, importing countries, and international institutions. Exporting countries should provide transparent documentation and ensure compliance with applicable international standards, such as International Electrotechnical Commission (IEC) 61215 series (Terrestrial photovoltaic modules – Design qualification and type approval) and IEC 61730 series (Photovoltaic module safety qualification), which certify module performance, durability, and environmental safety before shipment. These documents should disclose essential upstream information, including manufacturing specifications, material composition, warranty and testing records, and any refurbishment or repair history, thereby enabling importing countries to verify product quality and assess potential environmental risks. Such information helps prevent the transfer of low-quality or environmentally hazardous products into markets with limited regulatory capacity. Importing countries should establish pre-import testing, independent certification systems, and regulatory mechanisms that verify module integrity, energy efficiency, and expected lifespan. Governments can further introduce fiscal incentives and deposit—refund mechanisms to support licensed recyclers and encourage the development of domestic recycling industries. At the international level, establishing coordinated governance and clear responsibility mechanisms is essential to prevent unmanaged end-of-life flows of degraded PV modules from becoming major sources of environmental leakage. Drawing on life-cycle assessment findings, frameworks such as Germany’s business-to-consumer (B2C) and business-to-business (B2B) frameworks can clarify stakeholder responsibilities and ensure equitable cost-sharing for recycling. The implementation of extended producer responsibility (EPR) can further require manufacturers to retain post-export collection and disposal obligations¹⁶, thereby improving material efficiency and promoting responsible waste management in alignment with SDG 12. In many emerging and developing regions, recycling industries remain nascent, constrained by limited financial resources, inadequate facilities, and technological gaps. Hence, multilateral cooperation is essential to provide technical assistance, concessional financing, and targeted recycling incentives. Lessons from the European Union’s Waste Electrical and Electronic Equipment (WEEE) Directive -based EPR system, including mandatory producer registration, centralized collection schemes, and standardized reporting requirements, offer practical policy models for importing regions such as the Middle East and Africa to establish traceable, regulated end-of-life pathways for their own PV waste management frameworks. Extending existing e-waste recycling operations to incorporate PV modules would enhance processing efficiency, lower costs, and promote a globally coordinated PV recycling network¹⁷. A standardized global trading system for recycled PV modules and materials, integrated within a unified life-cycle management framework, can bridge policy gaps, improve resource efficiency, and advance recycling industry professionalization.
A transparent, reliable global database for PV modules is essential for tracking degraded PV modules throughout their life cycle and ensuring accountability in transboundary movements. The immediate priority is to establish standardized, internationally accessible databases that record module origin, type, composition, and estimated service life. Exporting countries should be mandated to disclose these data through harmonized reporting platforms verified by third-party agencies, while importing countries should use the information to improve customs screening, verify product authenticity, and align national recycling capacity with actual inflows. In the long term, international organizations such as the International Renewable Energy Agency (IRENA) and the International Organization for Standardization (ISO) are encouraged to coordinate the development of unified classification systems, data formats, and verification protocols. The main challenge lies not in the availability of digital technologies but in the absence of consistent reporting standards, transparent data sharing, and reliable verification mechanisms. Therefore, near-term efforts should focus on building open-access databases, unified reporting formats, and cross-border information exchange protocols to eliminate data asymmetry between exporting and importing countries. Advanced digital tools, such as Internet of Things tagging and blockchain, can later complement these systems once a solid data infrastructure is established¹⁸. Pilot initiatives such as the SolarCoin^19,20 renewable energy certification program and the UNDP-supported Blockchain for Sustainable Supply Chains project²¹ have demonstrated the potential of blockchain-based systems²² to enhance transparency and traceability in renewable energy and e-waste management. However, large-scale deployment remains unrealistic for most developing countries due to limited digital capacity, high system costs, and fragmented governance frameworks. Prioritizing fundamental data standardization and transparent reporting will thus provide the institutional foundation for future digitalization of PV waste management. While these governance measures primarily aim to reduce environmental risks, they also involve economic and social trade-offs. Stricter export controls and higher recycling standards may increase compliance costs, potentially influencing energy affordability and access in importing countries, particularly in regions with limited financial capacity. Balancing environmental integrity with economic feasibility and social equity is therefore essential for the effective implementation of sustainable PV module management.
Promoting Modular and Recyclable PV Designs through Manufacturer, Regulator, and Standards Body Collaboration. At the industry level, PV manufacturers should prioritize reduced resource consumption and lower material intensity to maximize product durability and optimize existing assets. Standardizing module materials with an emphasis on environmentally benign alternatives and easily disassembled structures will facilitate efficient end-of-life recycling. Reducing toxic material use, adopting recyclable encapsulants and adhesives, and developing novel lead-free solders can substantially lower waste management costs. At the policy level, exporting and importing governments should adopt clear eco-design standards and provide fiscal or regulatory incentives to encourage the adoption of recyclable and low-toxicity materials. International organizations such as the ISO and the IRENA could facilitate the development of harmonized technical standards and promote cross-border technology transfer. Beyond design and regulatory standards, effective end-of-life management will depend on scaling up advanced dismantling and recovery technologies through coordinated action among public authorities, industry, and research institutions. Collectively, these measures can close material loops, increase the reintegration of secondary raw materials into production, and accelerate the transition toward a circular and low-carbon PV industry.
Despite notable international endeavors to address PV waste management, existing governance frameworks remain insufficient to mitigate the rising transboundary risks associated with degraded PV modules. Addressing this challenge requires coordinated action among exporting and importing countries, as well as international organizations, to promote circular design, effective recycling, and environmentally sound waste management. We advocate for establishing a recycling framework for degraded PV modules, enhancing resource recovery efficiency, and promoting innovative, environmentally sound recycling technologies as core elements of a circular PV economy. Leading PV producing and consuming nations should adopt equitable, transparent, and sustainable waste management practices. These initiatives can strengthen the sustainability of the global PV industry while supporting Affordable and Clean Energy (SDG 7) and Sustainable Cities and Communities (SDG 11), particularly in regions with limited power supply. Future research should focus on quantifying the environmental and economic benefits associated with the reuse of degraded PV modules, evaluating their transboundary environmental impacts, and formulating region-specific strategies to optimize resource-efficient material recovery.
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This work was financially supported by the National Natural Science Foundation of China (52370197 and 72574146).
Department of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai, China
Beijia Huang & Yuqiong Long
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B.J.H. was responsible for the study’s conception and initiation, provided supervision throughout the revision, and acted as the corresponding author in communication with the editor. Y.Q.L. was responsible for conceptualizing the research framework and conducting the data analysis, as well as for drafting and subsequently revising the manuscript.
Correspondence to Beijia Huang.
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DOI: https://doi.org/10.1038/s41467-026-69171-z
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Civic Works’ Baltimore Shines program brings affordable solar installations to low-income homeowners
Apr. 6, 2026 at 5:00pm
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The Baltimore Shines program, a partnership between the Baltimore City Department of Housing and Community Development and Civic Works, is making solar power more accessible for low-income residents. By providing free solar panel installations, the program has helped over 50 Baltimore City homeowners reduce their electricity bills by an average of $1,500 annually. Despite some challenges with communication and seasonal fluctuations in savings, the program is a crucial step in addressing the energy burden faced by the city’s most vulnerable residents while also reducing greenhouse gas emissions.
As utility rates continue to rise, low-income Baltimore residents are disproportionately impacted by high energy costs. The Baltimore Shines program aims to alleviate this ‘energy burden’ by making solar power more accessible, helping families save hundreds of dollars per year on their electricity bills. This not only provides direct financial relief, but also contributes to the city’s broader goals of reducing greenhouse gas emissions and promoting sustainability.
The Baltimore Shines program covers the full cost of solar panel installation for income-qualifying homeowners, with no out-of-pocket expenses. Civic Works owns and operates the solar systems for a 20-year lease term, handling any maintenance or replacements. However, due to the loss of federal funding for the Solar for All program in 2025, the program has had to scale back the size of the solar systems it can install, limiting them to 5.7 kilowatts instead of the previous 11 kilowatts. This has reduced the potential savings for participants, though the program still aims to bring solar power to as many low-income residents as possible.
A nonprofit organization working to improve energy accessibility in Maryland and the primary partner in the Baltimore Shines program.
The city agency that partnered with Civic Works to launch the Baltimore Shines program.
A Baltimore City resident who joined the Baltimore Shines program and has seen her electricity bills reduced by about 50%.
A Baltimore Shines participant who saves around $50 per month in the winter and $10-$30 per month in the summer on her electricity bills.
The Maryland Energy Administration program manager, who notes the significant savings the Baltimore Shines program has provided to participants.
“I originally assumed that solar panels were for people who had bigger land or lived in a better neighborhood. I just didn’t think it was for us.”
— Janete Gonzalez, Baltimore Shines participant
“Our goal is to really make it as easy and worry-free a process as possible for the resident.”
— Eli Allen, Senior program director at Civic Works
“It’s great for the summer, not too much for the winter.”
— Tyresa German, Baltimore Shines participant
The Maryland Energy Administration Residential Energy Equity Program is expected to serve as a key funding source for the Baltimore Shines program going forward, as demand for the program is anticipated to grow due to the termination of the federal Solar for All grants.
The Baltimore Shines program demonstrates how targeted solar initiatives can provide significant financial relief and environmental benefits to low-income communities. Despite recent funding challenges, the program’s commitment to making solar power accessible to underserved residents is a model for other cities looking to address energy inequity and promote sustainability.
Apr. 9, 2026
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Batteries are now cheap enough to enable solar to meet 90% of India’s electricity demand economically, says Ember analysis  Down To Earth
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Kosol Energie Executes 100-Ton Solar Cell Airlift, Ensuring On-Time Project Delivery  solarquarter.com
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AB Energia has announced the launch of Arkannect. Sagar Saxena Appointed CEO to drive distributed solar growth in residential and MSME segments
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This strategic airlift was undertaken to ensure uninterrupted PV module manufacturing and supply for customers across utility-scale and commercial & industrial (C&I) segments, supporting critical project timelines.
Kosol Energie
Kosol Energie, a solar module manufacturer and EPC provider, has airlifted around 100 tons of high-efficiency solar cells via a specially chartered cargo aircraft to Sardar Vallabhbhai Patel International Airport, Ahmedabad, Gujarat. This strategic airlift was undertaken to ensure uninterrupted PV module manufacturing and supply for customers across utility-scale and commercial & industrial (C&I) segments, supporting critical project timelines.
“In an environment characterized by global supply fluctuations, extended transit timelines, and logistical uncertainties, Kosol Energie adopted a proactive approach by leveraging expedited logistics solutions,” stated the company. “By choosing air freight over conventional shipping methods, Kosol Energie prioritized speed, reliability, and customer commitments despite significantly higher logistics costs. This ensured that project execution timelines remained intact, enabling timely commissioning and minimizing potential delays.”
Kosol Energie is recognized among the Grade-A Top Tier-1 Global Solar Manufacturers 2025 by Wood Mackenzie and awarded India’s Best PV Module Manufacturer 2025 by BARC Asia. It has executed 2.5 GW in the utility and C&I sectors and installed 40,000+ on-grid and off-grid solar systems.
The company is currently executing several major assignments, including a 250 MW solar project for NLC India Ltd in Tamil Nadu, a 145 MW project for Coal India Ltd, and 109 MW of installations under the PM-KUSUM Yojana. It has supplied over 3 GW of PV modules to the Indian market as well as international markets.
 
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China stands to benefit most from the war-driven energy crisis  The Washington Post
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Revolve Renewable Power Moves Bright Meadows Solar Facility Toward Grid Integration in Alberta  Energies Media
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A new global study by engineers at UNSW Sydney has highlighted potential challenges for the manufacturing of next-generation solar panels, showing that ultraviolet (UV) radiation could reduce panel lifespans by up to 10 years in some regions.
Researchers developed a high-precision global model to calculate UV exposure for solar panels based on climate, atmospheric conditions, and mounting configuration, as revealed in a news release. 
It noted that the work provides the first comprehensive comparison of UV radiation for fixed-tilt versus sun-tracking solar systems, offering manufacturers a clearer picture of long-term durability.
“Modules with similar technology and orientation can still exhibit region-specific degradation,” the study notes. “This underscores the need for climate-specific indoor testing and accelerated tests for reliability and better lifetime predictions.” 
The findings suggest UV photodegradation alone may account for nearly a quarter of annual degradation in monocrystalline silicon modules in high-UV regions.
The study also highlights that panels on tracking systems, which follow the sun, receive significantly higher UV exposure than fixed-tilt installations. 
“They’re always trying to track the sun to catch the maximum amount of sunlight. That means they’re also getting the maximum UV on top of them, which makes those panels more susceptible and vulnerable,” said Dr?Shukla Poddar, lead author of the research.
According to the university, current international testing standards require solar modules to endure UV exposure equivalent to 15 kilowatt-hours per square metre. 
The UNSW study shows that in high-irradiance locations such as Alice Springs, Australia, panels can reach this threshold in just over a month, suggesting that existing standards may underestimate real-world exposure.
Dr?Poddar added, “With new high-efficiency PV technologies being rolled out so quickly, we need to ensure the standards reflect real-world conditions. Our modelling tool helps manufacturers and developers make better-informed decisions before installation.”
The research, supervised by Prof.?Bram Hoex and A.?Prof.?Merlinde Kay, with contributions from Dr?Phillip Hamer and Mr?Shuo Liu, was published in the IEEE Journal of Photovoltaics and is intended to support more resilient solar panel manufacturing and deployment worldwide.
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Solar-Plus-Battery Systems Could Meet 90% of India’s Power Demand at Competitive Costs: Ember Analysis  solarquarter.com
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