Solar Now

We use cookies to improve your experience and for marketing. Read our cookie policy or manage cookies.
Search across reports, market insights, and blog stories.
Oxford PV and Fraunhofer ISE have introduced a new photovoltaic module prototype that integrates Oxford PV’s tandem perovskite-silicon cells with Fraunhofer’s matrix shingle interconnection technology, according to a joint announcement from the two organizations.
The prototype will be displayed at Intersolar Europe in Munich next week. The partners reported that two versions of the module achieved an efficiency of 25.6% across the entire module area.
Stefan Glunz, head of photovoltaics at Fraunhofer ISE, explained that in the design, Oxford PV’s tandem cells are cut into shingles, electrically connected using conductive adhesive, and encapsulated. The modules are constructed as glass-glass units with edge sealing to protect the moisture-sensitive cells. Glunz commented that the collaboration combines two high-tech European approaches in a single PV module.
Ed Crossland, chief technology officer at Oxford PV, noted that the two technologies are complementary. He said the tandem technology and the shingle interconnection work well together technologically. Because perovskite-silicon solar cells produce lower current densities, they can be cut into wider strips, which improves productivity. Crossland added that tandem solar cells generate higher voltages and efficiencies than conventional cells, while the current is lower due to its distribution across two sub-cells. This lower current density helps reduce resistive losses within the module. He also pointed out that the adhesive interconnection of the matrix shingle technology is a low-temperature process and requires no copper connectors, which can lower operating costs and reduce stresses in module construction.
The new design has been deployed in two prototype modules: a 491-watt rooftop version with an area of 1.92 square meters, and a 546-watt bifacial model covering 2.13 square meters. Both achieved the same 25.6% efficiency.
Tandem modules combining perovskite and silicon technologies are widely regarded as the next major step in solar technology evolution. Adding a perovskite layer to a silicon cell can significantly boost conversion efficiency beyond the theoretical limits of silicon-only cells. Oxford PV has been a leading developer of tandem technology and is advancing it toward commercial deployment through its pilot production facility in Brandenburg an der Havel, Germany.
Fraunhofer’s matrix shingle technology bonds solar cell strips together with electrically conductive adhesives in an overlapping, staggered pattern similar to roofing shingles. This allows complete coverage of the module surface and high tolerance to partial shading. The matrix arrangement enables current to flow around shaded areas, potentially generating up to twice the power compared to conventionally connected PV modules, depending on shading levels, according to Fraunhofer.
The new PV modules were developed as part of the HoTSun research project, funded by Germany’s Federal Ministry for Economic Affairs and Energy. Both prototypes will be on display in Munich next week.
Interactive table based on the Store Companies dataset for this report.
This report provides a comprehensive view of the global solar cells and light-emitting diodes industry, tracking demand, supply, and trade flows across the worldwide value chain. It explains how demand across key channels and end-use segments shapes consumption patterns, while also mapping the role of input availability, production efficiency, and regulatory standards on supply.
Beyond headline metrics, the study benchmarks prices, margins, and trade routes so you can see where value is created and how it moves between exporters and importers worldwide. The analysis is designed to support strategic planning, market entry, portfolio prioritization, and risk management in the global solar cells and light-emitting diodes landscape.
The report combines market sizing with trade intelligence and price analytics. It covers both historical performance and the forward outlook to 2035, allowing you to compare cycles, structural shifts, and policy impacts across countries and regions.
For the global report, country profiles provide a consistent view of market size, trade balance, prices, and per-capita indicators. The profiles highlight the largest consuming and producing markets and allow direct benchmarking across peers.
The analysis is built on a multi-source framework that combines official statistics, trade records, company disclosures, and expert validation. Data are standardized, reconciled, and cross-checked to ensure consistency across time series.
All data are normalized to a common product definition and mapped to a consistent set of codes. This ensures that comparisons across time are aligned and actionable.
The forecast horizon extends to 2035 and is based on a structured model that links solar cells and light-emitting diodes demand and supply to macroeconomic indicators, trade patterns, and sector-specific drivers. The model captures both cyclical and structural factors and reflects known policy and technology shifts.
Each country projection is built from its own historical pattern and the regional context, allowing the report to show where growth is concentrated and where risks are elevated.
Prices are analyzed in detail, including export and import unit values, regional spreads, and changes in trade costs. The report highlights how seasonality, freight rates, exchange rates, and supply disruptions influence pricing and margins.
Key producers, exporters, and distributors are profiled with a focus on their operational scale, geographic footprint, product mix, and market positioning. This helps identify competitive pressure points, partnership opportunities, and routes to differentiation.
This report is designed for manufacturers, distributors, importers, wholesalers, investors, and advisors who need a clear, data-driven picture of global solar cells and light-emitting diodes dynamics.
The market size aggregates consumption and trade data at country and regional levels, presented in both value and volume terms.
The projections combine historical trends with macroeconomic indicators, trade dynamics, and sector-specific drivers.
Yes, it includes export and import unit values, regional spreads, and a pricing outlook to 2035.
The report provides profiles for the largest consuming and producing countries, enabling benchmarking across peers.
Yes, it highlights demand hotspots, trade routes, pricing trends, and competitive context.
Report Scope and Analytical Framing
Concise View of Market Direction
Market Size, Growth and Scenario Framing
Commercial and Technical Scope
How the Market Splits Into Decision-Relevant Buckets
Where Demand Comes From and How It Behaves
Supply Footprint, Trade and Value Capture
Trade Flows and External Dependence
Price Formation and Revenue Logic
Who Wins and Why
Where Growth and Supply Concentrate
Commercial Entry and Scaling Priorities
Where the Best Expansion Logic Sits
Leading Players and Strategic Archetypes
Detailed View of the Most Important National Markets
How the Report Was Built
Largest solar manufacturer globally
Leading monocrystalline silicon producer
Major module and cell producer
High-efficiency cell and module maker
Global manufacturer and project developer
Major player in US and EU markets
Integrated PV product manufacturer
Leading thin-film CdTe manufacturer
World's largest solar cell producer
ABC cell technology leader
Major LED component and display maker
Pioneer and major supplier of LED chips
Historically leading innovator in LED technology
Leading European optoelectronics supplier
High-power LED and automotive lighting
One of world's largest LED chip producers
Major LED packaging and component supplier
Leading Taiwanese LED chip manufacturer
Innovator in WICOP and SunLike technologies
LED components for automotive and IT
IBC cell technology leader
Solar project developer and manufacturer
Integrated PV manufacturer
Historically significant in both fields
Rapidly growing cell and module producer
Solar manufacturing arm of Chint Group
Module manufacturer with US focus
Leading Indian solar manufacturer
LED packaging and lighting solutions
Major LED packaging company
Instant access. No credit card needed.
Online access to 2M+ reports, dashboards, and tables. Trusted by Fortune 500 teams.
IndexBox, Inc.
2093 Philadelphia Pike #1441
Claymont, DE 19703, USA
Contact us
© 2026 IndexBox, Inc

source

Thunderstorms…possibly severe, especially early. Storms may contain strong gusty winds. Low around 70F. Winds SSW at 10 to 20 mph. Chance of rain 100%..
Thunderstorms…possibly severe, especially early. Storms may contain strong gusty winds. Low around 70F. Winds SSW at 10 to 20 mph. Chance of rain 100%.
Updated: June 18, 2026 @ 8:58 pm
The Columbia Metropolitan Airport on Aug. 12, 2025.
Columbia Metropolitan Airport is adding a solar panel array to the top level of the airport’s parking garage.
Columbia Metropolitan Airport is adding a solar panel array to the rooftop level of the airport’s parking garage.
Growth & Development Reporter

Caleb Bozard covers business, growth and development for The Post and Courier Columbia. He has previously written for The State and the Times and Democrat. He graduated from the University of South Carolina in 2023.
The Columbia Metropolitan Airport on Aug. 12, 2025.
WEST COLUMBIA — Columbia Metropolitan Airport is making an addition to the airport’s main parking deck to improve the parking experience and reduce the airport’s power bill.
The airport has broken ground on a project to cover the rooftop level of the airport’s parking garage with an array of solar panels, airport leadership said in a June 17 press release.
Along with generating electricity to be stored for use during peak-use airport times and at night, the project will also cover an additional 700 parking spots. The airport will now offer 1,500 covered parking spots.
“The cost savings, the increased energy independence and the enhanced system reliability will allow CAE to see positive impacts with this project for many decades to come,” CAE President and CEO Chris White said in the press release.
Columbia Metropolitan Airport is adding a solar panel array to the top level of the airport’s parking garage.
The project is expected to be completed by December 2027, according to the release. Construction is beginning at the western portion of the parking garage, and signage has been redirecting drivers from the area in recent weeks.
The solar panels are expected to cut the airport’s annual utility bills by 55 percent, Vice President of Engineering and Planning for CAE Frank Murray said in the release.
The parking garage is located directly in front of the main entrance to the airport.
Columbia Metropolitan Airport hosts over 30 flights daily, with routes to nine cities including Atlanta, Charlotte, Chicago, Dallas, New York City, Philadelphia, Washington, D.C. and Newark, New Jersey.
Columbia Metropolitan Airport is adding a solar panel array to the rooftop level of the airport’s parking garage.
The airport saw a record 1.3 million passengers last year, according to the press release. Airport leadership has been working to attract local fliers who frequently choose nearby rival airports such as Charlotte Douglas International.
Columbia has lost two airlines in the past year. Allegiant Air pulled out of the market in May, citing low traffic. Spirit Airlines ceased operations at the airport in September 2025 amid financial trouble at the company. The airline folded eight months later. American, United and Delta continue to fly from Columbia.
Columbia Metropolitan Airport is located in Lexington County, about a 20-minute drive from downtown Columbia.
You can reach Caleb at cbozard@postandcourier.com. Support his work by subscribing at https://www.postandcourier.com/columbia/subscribe/caleb-bozard/.
Growth & Development Reporter

Caleb Bozard covers business, growth and development for The Post and Courier Columbia. He has previously written for The State and the Times and Democrat. He graduated from the University of South Carolina in 2023.
The Post and Courier Columbia
1225 Lady Street, Suite 201
Columbia, SC 29201
Phone: 803-765-0707
News tips: znicholson@postandcourier.com
Subscription questions: subserve@postandcourier.com
Your browser is out of date and potentially vulnerable to security risks.
We recommend switching to one of the following browsers:
, Post and Courier, an Evening Post Publishing Newspaper Group. All rights reserved. | Terms of Sale

source

Industry experts, from all the sectors of the solar industry, gathered in Napa for the fifth edition of PV ModuleTech USA earlier this month to discuss module procurement, quality and reliability, as well as co-locating solar PV with energy storage.
Here are some key takeaways from the event, which was predominantly focused on module quality concerns over the two days of the conference, with detailed presentations and engaged participation.
Get Premium Subscription
“One of the key takeaways was verify, verify, verify. The biggest issue currently is supply chain verification, especially with the limited availability of compliant cells in the US market,” said Moustafa Ramadan, head of PV Tech Market Research.
“And this emphasis on compliance has shifted focus from other areas, including quality.”
With the first sessions of the first day focusing on the current landscape for module procurement, it seems that the main concern at the moment for the US market is supply chain compliance. PV module quality concerns, although still present, have been pushed aside as the top priority in procurement decisions.
As frequent readers of PV Tech know, the quality and reliability of PV modules have been a recurring topic in the industry over the past few years. Recent reports from Kiwa PVEL and RETC, highlight that module performance remains an issue that keeps growing year after year.
One of the key aspects raised by several speakers during the conference was the importance of continuously raising awareness of current quality concerns and what can be done to resolve them.
Module buyers play an important role in this, as partnerships with manufacturers are more important than ever. This starts with checking that the necessary factory quality assurance in module manufacturing is implemented in order to catch issues early, rather than finding out after the solar panels are installed.
When discussing global manufacturing outside of China, the US, or India, much attention has been focused on the Middle East. Many of the manufacturing announcements in this region come from Chinese manufacturers partnering with local entities, which are expected to build gigawatts of solar PV manufacturing facilities, from polysilicon through to modules. Earlier this year, United Solar began production at its Oman polysilicon facility, which, once fully ramped, is expected to have a 40GW annual nameplate production capacity.
However, the reality is that many new solar PV cell or module facilities are being built in Africa, with several countries mentioned during the conference. Egypt and Ethiopia – which is currently facing an anti-circumvention inquiry request – both have operational manufacturing capacity; other countries across the continent were also mentioned, including Kenya.
This is the result not only of manufacturers building new facilities in countries not affected by antidumping and countervailing duty (AD/CVD) measures, but also of manufacturers and module buyers diversifying their supply chains with multiple suppliers and reducing supply risk.
In terms of technology, heterojunction (HJT) was frequently discussed at PV ModuleTech USA, with many manufacturers looking to build HJT facilities in the US. This follows recent announcements from Toyo Solar and SEG Solar, which unveiled plans to build a 1.5GW HJT solar cell processing plant and a 4.6GW HJT module assembly plant, respectively. Earlier this year, Canadian Solar announced it would begin producing HJT solar cells at its Jeffersonville plant in March, with a full ramp-up by the end of June.
Up until now, the majority of operational capacity in the US uses PERC technology for one clear reason: patents. Earlier this year, the US International Trade Commission (ITC) launched an investigation into TOPCon solar components in the US, after a complaint from thin-film cadmium telluride manufacturer First Solar.
Still ongoing, the investigation will determine whether TOPCon products sold for importation or sold after importation to the US violate Section 337 of the 1930 Tariff Act and infringe on First Solar’s TOPCon intellectual property. In its investigation, the ITC named 47 respondents from 11 countries.
Manufacturers looking to build domestic capacity have chosen HJT as an alternative to jump to a technology with higher efficiency than PERC.
Moreover, this contrasts with the Chinese market, where the number of manufacturers with HJT modules has decreased, as Tristan Erion-Lorico, vice president of sales and marketing at Kiwa PVEL, recently explained during a discussion of this year’s Module Reliability Scorecard.
“We saw less HJT testing than we have in previous years. Which I think is a story of the issues that Chinese HJT manufacturers are suffering. Last year, we certainly had a handful of HJT manufacturers in China. This year, we’re not seeing any of them.”
Throughout the two days of the conference and conversations with attendees, PV Tech heard the topic of hail mentioned continually. It remains a concern for projects in the US in hail risk regions, with many manufacturers now offering hail-resistant PV modules.
A 2025 report from climate insurance provider kWh Analytics highlighted that the majority of financial losses (73%) were due to hail damage, even though hail damage accounted for only 6% of total incidents.
It is clear that the solar PV industry in the US faces many challenges; however, the overwhelming sentiment at the conference is that solar PV is essential for the US to achieve energy independence and meet rising electricity demand. The manufacturing announcements show no sign of slowing. PV ModuleTech will be returning to California’s Napa Valley in 2027, where we will hear updates on how these facilities are progressing and supplying the market.

source

source

Ryan Dillon, senior commissioning and maintenance engineer for Sol Systems, leads a tour of the company’s Prairie Creek Solar farm. The company took members of its four Morgan County partner groups on a tour Tuesday of the solar panel farm north of Jacksonville.
Representatives of a solar power company providing funding for four Morgan County groups invited members of each to tour the place generating that financial backing.
Solar power company Sol Systems led its Morgan County partner groups on a tour Tuesday of its Prairie Creek Solar installation. A handful of members from Jacksonville Promise, Faith in Place, Morgan County 4-H and Lincoln Land Community College got an up-close look at the farm just outside of Jacksonville.
Advertisement
Article continues below this ad
Members of the groups got to see some of the more than 60,000 panels that make up the farm, which began operating in 2023. The farm generates 37 megawatts of power, enough for about 7,500 homes. The tour touched on how the farm generates power and the upkeep needed for such an installation.
Brendan Conley, senior associate with Sol Systems, pets Remi, a Great Pyrenees that works on the grounds of the company’s Prairie Creek Solar farm. Remi protects the 160 St. Croix sheep who graze on the solar panel farm’s grounds from predators.
A few four-legged guests also made an appearance during the tour. Participants were able to see a few of the 160 St. Croix sheep that keep the solar farm's grass trimmed and a Great Pyrenees dog named Remi that protects the sheep from predators.
Grazer Scott Robinson said using sheep instead of a mower helps maintain the health of the grounds, which will help if the grounds are converted back to farmland at some point.
Advertisement
Article continues below this ad
"It'll regenerate the soil," Robinson said. "It'll increase the organic matter. It'll make it into better farm ground, if that's what happens at the end of the lease."
Sol Systems donates some of its profits from the farm to its partner groups; in 2024, the company gave its partner groups in the county a total of $200,000. Senior Director of Community Impact Adaora Ifebigh said Sol Systems' Morgan County program is the first of its kind among the company's Illinois solar installations.
Sol Systems previously has given back to communities in which it has built installations through volunteer work and philanthropy, Ifebigh said. The company planned to continue that trend with Prairie Creek Solar from the beginning, but development of the solar farm allowed it to test the "how" of that mission, she said.
Advertisement
Article continues below this ad
"When we now had the opportunity to have large-scale projects like that, we said, 'Wait a minute, there is a way we can build that impact into the project itself,'" she said.
Jacksonville Promise co-founder Charles Sheaff (left) pets a sheep held by Sol Systems grazer Scott Robinson on the grounds of the company’s Prairie Creek Solar farm. The solar panel farm has about 160 St. Croix sheep on its grounds to help trim the grass.
Sol Systems partners with agencies based on a community's specific needs rather than a specific agenda, Ifebigh said.
"We don't do a top-down approach," she said. "We don't tell them what to do. It comes from the grassroots. What do they want to do? What benefits the community?"
Advertisement
Article continues below this ad
Members of the four partner groups utilized their share of the $200,000 for a variety of purposes. Doug Hoy, program director for industrial maintenance and electrical at LLCC, said the school used the money to buy servo motors for study and to fund scholarships for advanced manufacturing students. Sol System's funding has been "tremendously" helpful, he said.
"You need a good, viable workforce to keep getting these manufacturers in and we're utilizing their money to help impact that," Hoy said.
Faith in Place energy building director Cindy Shepherd said the non-profit uses Sol System's funding to carry out energy audits for Morgan County churches so they can learn where they can save money on their electricity bills. Working with Sol Systems has been "great," she said, with Faith in Place being able to help 10 congregations perform what normally is a "kind of costly" audit.
Advertisement
Article continues below this ad
"I'm excited to see rural communities begin to benefit from the clean energy transition," Shepherd said.
Ben Singson became a reporter for the Journal-Courier in 2022, joining after graduating from the University of Missouri-Columbia. The Lindenhurst native previously reported for KBIA, an NPR affiliate radio station, in college.
About
Contact
Services
Account

source

We use cookies to improve your experience and for marketing. Read our cookie policy or manage cookies.
Search across reports, market insights, and blog stories.
The Arizona Court of Appeals has ruled that efforts by Arizona utilities to impose extra fees on homes with residential solar systems violated due process, and has overturned a December 2024 decision that allowed such charges. According to the court’s ruling, reported by PV Tech, the decision means residential solar customers in Arizona will no longer face charges from the Arizona Public Service (APS) utility that are 1.15 times higher than those for non-solar residential customers.
The judgment was issued this week by presiding judge Daniel J. Kiley, alongside judges D. Steven Williams and Cynthia J. Bailey. Vote Solar, a community solar advocacy group that opposed the rate increase, described the fees as discriminatory. The organization estimated the charges amounted to an additional $2 to $3 per month for households.
A representative of Vote Solar commented that the decision marks progress toward a fairer and more affordable energy system, and stated that monopoly utilities should not be allowed to impose unjustified charges on households that choose to reduce their utility bills by installing solar.
The ruling is significant in Arizona, which has one of the largest residential solar sectors in the United States. According to figures from the Solar Energy Industries Association (SEIA), which also opposed the rate increase, Arizona currently ranks fourth among U.S. states in total solar PV capacity in operation and third in residential solar capacity. More than 15% of Arizona homes have solar panels installed. The state added more residential solar capacity than utility-scale capacity in 2021 and 2022, a period that coincided with APS efforts to impose higher fees on residential solar customers.
APS had previously been ordered in 2019 to remove a so-called grid access charge (GAC) for solar projects, which had been introduced in 2013. The utility introduced a new GAC in 2022. Arizona utilities have long argued that residential solar customers should pay higher rates, with APS seeking a specific charge for solar customers as early as 2013, alleging that those customers paid less than their fair share of the utility’s costs.
In 2019, the Arizona Corporation Commission (ACC), the state’s public utilities commission, instructed APS to remove the GAC, which was a charge on solar projects that paid less than their fair share of APS fixed costs. At that time, the ACC left open the possibility of future charges if APS could provide evidence of specific costs imposed by residential solar users on its system.
APS initiated a new rate case in October 2022, seeking to address an annual budget gap of $772 million by raising the base rate for all residential customers by 22.8%, regardless of whether they used solar. Neither the ACC nor APS attempted to reintroduce the GAC at that stage, instead focusing on increasing costs for all residential customers. The idea of imposing additional costs on residential solar customers came from administrative law judge Sarah Harpring, who, in a 2023 case, recommended something no one had requested.
During the 2023 case, APS witnesses distinguished between site-load COSS and delivered-load COSS, referring to a cost-of-service study (COSS) required for rate changes. The site-load COSS includes the total electricity to be delivered, including on-site generation from residential solar systems, and was said to better reflect costs incurred by the utility. APS did not ask the ACC to include its site-load COSS estimates in new rates, only referencing it to set a precedent for future proceedings. However, Harpring recommended that the ACC use the site-load COSS in rate calculations.
In March 2024, the ACC voted to adopt the recommendations, including the 22.8% base rate increase for all residential customers and Harpring’s recommendation to impose a charge on residential solar customers, increasing their bills by 1.15 times the average increase. Vote Solar and SEIA stated they were blindsided by the recommendations and the ACC’s decision.
The rate increase faced ongoing opposition. In a 2024 hearing, judge Belinda A. Martin called on the parties to address the site-load COSS issue, suggesting uncertainty remained about the utility’s cost-of-service study and the cost of serving residential solar customers. The Court of Appeals ruled this week that the ACC was aware that APS’s work in its 2022 rate case did not constitute the analysis of costs that the ACC had requested after the 2019 decision. The ACC noted that APS does not provide additional services or use additional equipment to serve residential solar customers, meaning those customers were to be charged more without receiving extra services.
Presiding judge Kiley wrote in the final ruling that although the commission determined APS does not provide unique services to residential solar customers, it nonetheless authorized a unique charge on those customers. The court’s decision does not reject the concept of grid access charges for residential solar projects in general, but rather finds that the legal process between 2019 and 2024 was flawed due to the absence of a cost-of-service study demonstrating higher costs for serving residential solar customers.
Vote Solar noted that the ACC is currently considering a separate APS proposal to increase fees for residential solar customers, which would raise costs to around $6 per month. The ongoing legal battles in Arizona highlight the uncertain policy environment facing the U.S. solar sector. Policy will be a topic of discussion at Solar Media’s PV CellTech USA Conference in San Francisco on October 13-14, 2026, particularly regarding government incentives for building more upstream manufacturing capacity in the United States.
Making Data-Driven Decisions to Grow Your Business
A Quick Overview of Market Performance
Understanding the Current State of The Market and its Prospects
Finding New Products to Diversify Your Business
Choosing the Best Countries to Establish Your Sustainable Supply Chain
Choosing the Best Countries to Boost Your Export
The Latest Trends and Insights into The Industry
The Largest Import Supplying Countries
The Largest Destinations for Exports
The Largest Producers on The Market and Their Profiles
The Largest Markets And Their Profiles
Instant access. No credit card needed.
Online access to 2M+ reports, dashboards, and tables. Trusted by Fortune 500 teams.
IndexBox, Inc.
2093 Philadelphia Pike #1441
Claymont, DE 19703, USA
Contact us
© 2026 IndexBox, Inc

source

Gautam Solar Upgrades Reliability Laboratory to Enhance Solar Module Quality and Performance Testing  SolarQuarter
source

Renewables Now is a leading business news source for renewable energy professionals globally. Trust us for comprehensive coverage of major deals, projects and industry trends. We’ve done this since 2009.
Stay on top of sector news with with Renewables Now. Get access to extra articles and insights with our subscription plans and set up your own focused newsletters and alerts.

source

Kosol Energie has commissioned a 31 MW (DC) ground-mounted solar photovoltaic project at Junachay village in Gujarat’s Kutch district for Gujarat State Electricity Corporation Limited (GSECL), further strengthening its presence in India’s rapidly expanding renewable energy sector. Developed under the state’s Renewable Energy Policy and Green Energy Mission, the project represents another milestone in Kosol Energie’s utility-scale solar portfolio and reinforces its capability to execute large renewable energy infrastructure projects for public sector utilities.
The solar plant was developed over a phased implementation period between October 2022 and March 2026, involving engineering, procurement and construction activities designed to deliver reliable clean energy generation while meeting stringent quality and performance standards. The facility incorporates advanced solar technologies to maximize energy production and operational efficiency. It is equipped with high-efficiency 550 Wp bifacial photovoltaic modules, which generate electricity from both sides of the panel to improve energy output. The installation also features a single-axis tracker system that enables the solar modules to follow the movement of the sun throughout the day, increasing solar energy capture and enhancing overall plant productivity.
Commenting on the commissioning, Kalpesh Kalthia, Chairman and Managing Director of Kosol Energie, said the project reflects the company’s continued commitment to delivering technologically advanced renewable energy solutions that support India’s transition toward a cleaner and more sustainable energy future. He noted that the Junachay project strengthens Gujarat’s renewable energy capacity while demonstrating the company’s ability to execute complex utility-scale assignments with a focus on quality, efficiency and long-term reliability.
The newly commissioned plant is expected to contribute significantly to the state’s clean electricity generation capacity by supplying renewable power while reducing dependence on conventional fossil fuel-based energy sources. It will also help lower carbon emissions and support India’s broader objective of increasing the share of renewable energy in the national power mix.
Kosol Energie has emerged as one of India’s leading solar engineering, procurement and construction companies, serving utility-scale, commercial, industrial, residential and agricultural markets. The company has executed more than 2.5 GW of utility and commercial solar projects and installed over 40,000 grid-connected and off-grid solar systems across the country. Beyond project execution, the company maintains integrated manufacturing capabilities for photovoltaic modules and solar components while offering solutions across floating solar systems, battery energy storage systems (BESS), rooftop installations and large solar parks.
Kosol Energie is a leading provider of solar energy solutions in India, committed to promoting sustainability through innovative and reliable solar technologies. The company has grown into a trusted name in the renewable energy sector under the leadership of Kalpesh Kalthia, Chairman and Managing Director (CMD).
With over 15 years of experience, Kosol Energie specializes in products such as solar water heaters, rooftop solar systems, solar pumps, and turnkey EPC solutions. Known for its dedication to quality, performance, and customer satisfaction, Kosol has successfully implemented numerous projects across India, bringing clean and reliable solar energy to homes, businesses, and farms. Kalpesh Kalthia’s visionary approach continues to drive the company’s mission to make solar power a more accessible and impactful solution for a sustainable future.
The story of ‘MAKE IN INDIA’ has reached far and wide. But who are makers of ‘MAKE IN INDIA’? What is their story? ‘Machine Maker’ is a dedicated magazine that seeks to bring the incredible stories… Read more
B-201, SPIREA, Wakad, Pune – 411057
[email protected]
+91-703-093-2700
Contact Us
Subscribe
India’s Top

source

Home > News > $95.4 million boost to keep Australia at the forefront of next-generation solar
The Australian Renewable Energy Agency (ARENA) has committed an additional $95.4 million in funding to the Australian Centre for Advanced Photovoltaics (ACAP), securing Australia’s global leadership in solar PV research and innovation.  
Led by the University of New South Wales (UNSW), ACAP is a world-leading centre bringing together a national consortium of research institutions, including the Australian National University, the Commonwealth Scientific and Industrial Research Organisation (CSIRO Energy and CSIRO Manufacturing), the University of Melbourne, Monash University, the University of Queensland, and the University of Sydney. 
The funding will extend ACAP’s existing research program out to 2033, building on more than a decade of collaboration between Australia’s leading solar researchers and industry partners to accelerate breakthroughs in high efficiency solar cells and modules. 
Minister for Climate Change and Energy, the Hon Chris Bowen MP said, “Australia helped lead the world in solar and we want to keep leading the world in the next wave of solar innovation. 
“This funding backs our best researchers and helps turn Australian ideas into real-world technologies that can strengthen our clean energy system and create economic opportunity. 
“Building more of this expertise here at home makes Australia stronger, more secure and better placed for the future.” 
Through ACAP, Australia has delivered a series of globally-recognised advances in solar technology, including major improvements in the efficiency, durability and cost of solar, and the development of next‑generation tandem solar cells. 
ARENA CEO, Darren Miller said the further investment would ensure Australia remains at the forefront of global solar innovation. 
“Australia has some of the best solar researchers in the world and ACAP has been instrumental in turning that expertise into globally recognised breakthroughs,” Mr Miller said. 
“If Australia is to achieve ultra low-cost solar, we need to keep pushing the limits of cell efficiency. ACAP’s work is doing exactly that, helping deliver high-performance solar cell and module technologies that will reduce costs at scale.  
“This work underpins ARENA’s strategy to make solar the backbone of Australia’s net zero energy system and is a critical enabler for decarbonising industries like green metals, transport, fuel production and data centres.” 
The program also plays a critical role in building Australia’s clean energy workforce, supporting researchers, engineers and PhD students while strengthening collaboration across the solar innovation ecosystem. 
ACAP Executive Director, Professor Renate Egan described how improvements in solar technology over the last decade builds on foundational research, industry development and collaboration. 
“Australia is uniquely placed, globally, in its research leadership and its connection to industry,” Professor Egan said.  
“This significant investment provides a long-term research horizon and positions Australia to build on its success in developing the technologies and talent needed to deliver on next-generation solar technologies that will power a low-carbon future Australia.” 
Read more about ARENA’s Ultra Low-Cost Solar priorities. 
We present knowledge sharing from experts in energy technology, innovation and business.
Stay up to date with this valuable knowledge by following us on social, or by signing up to ARENAWIRE or the ARENA Insights newsletter.

Get the latest in energy innovation straight to your inbox

source

SABIC has introduced NORYL V0150TW and V0150IR2, two thermoplastics designed to replace metal and traditional polymers in photovoltaic components like microinverters, solar tracker boxes, and junction boxes. 
The NORYL V0150TW (absorptive) and V0150IR2 (transmissive) resins are formulated to allow manufacturers to shift from ultrasonic welding or adhesive bonding to laser welding. This shift eliminates curing-dependent bonding materials to reduce production cycle times and lower overall assembly costs.
Compared to metal, these materials reduce component part size by up to 40%, cut weight by up to 35%, and lower overall material usage by up to 30%. The resins feature a thin-wall flame retardancy rating of UL94 V0 5VA at 1.5 mm, maintain mechanical integrity in operating temperatures up to 150°C, and provide up to 15 years of outdoor service life. 
The new NORYL grades are backed by the company’s global supply network, which relies on key polyphenylene ether (PPE) resin manufacturing and compounding hubs in Selkirk, New York, and Bergen op Zoom, Netherlands. 
Now globally available, these materials recently earned a Silver 2026 Edison Award for innovation. 

Comments
Please login to comment
The June issue of pv magazine Global is out now!
Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Thursday, June 18, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
April 01 – August 31, 2026
pv magazine USA hosts its third multi-day virtual event on advancing U.S. solar and energy storage markets, covering financing, supply chains, and distributed energy’s role in grid resilience.

You have no items in your basket.

source

Iran is working towards deploying 15 GW of small-scale solar power plants.
According to reports published by the Islamic Republic News Agency (IRNA), Iran’s Renewable Energy and Energy Efficiency Organization (SATBA) has submitted a proposal to the government for a program that would aim to deploy 15 GW of small-scale plants at speed across the domestic, commercial and agricultural solar sectors.
CEO of SATBA, Mohsen Tarztalab, told the news agency that both domestic manufacturing and importing of equipment must be pursued to enable the rapid installation of small solar systems. He also said that a proposal to increase the financial resources available for home solar installations has been submitted to the government.
Tarztalab indicated the move towards prioritizing smaller-scale solar systems will include offering packages with hybrid inverters and batteries.
“Part of our focus will be shifted from large-scale and small-scale power plants to branch, rooftop, home, commercial and agricultural power plants. In this model, small packages including solar panels, hybrid inverters and batteries are designed so that every citizen can easily use these systems,” Tarztalb said. “Achieving this goal requires general and specialized training for the installation, operation and maintenance of this equipment.”
Tarztalab recently unveiled 17 specialized solar energy training centres designed to help accelerate the development of solar power plants in Iran. SATBA has set a target of training 200,000 renewable energy specialists in the next five years.
IRNA’s latest report also reveals that Iran’s first solar project fund is likely to be launched in the coming weeks. It says the fund will have an initial capacity of 500 MW and will allow residential and commercial customers to participate in solar projects by purchasing shares. The proposed fund is geared towards increasing public participation in the development of solar projects while attracting public capital.
Iran has set a target of achieving 12 GW of renewables capacity by the end of the year. In IRNA’s report, Tarztalb says that the goal is achievable despite some delays in equipment arriving to the country.
“In the past months, due to transportation restrictions and problems, some of the equipment did not enter the country on time, but now the equipment is being imported via land, rail, sea, and air routes, and we hope that this delay will be compensated in the coming months,” he said.
Imports already include hybrid inverters and batteries alongside solar panels, Tarztalab shared, before adding that the development of hybrid packages is on SATBA’s agenda are should be offered soon.
IRNA reported earlier in June that Iran’s cumulative renewables capacity has now surpassed 5 GW.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
finally some good news from iran. as solar and batts continue to decrease in cost as lowest vcost option they should obviate need for any nuclear program civil or military. a win win for the world
The June issue of pv magazine Global is out now!
Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Thursday, June 18, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
April 01 – August 31, 2026
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy.
Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way!
We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team.
June 23-25, 2026 | MUNICH, GERMANY

You have no items in your basket.

source

Montgomery County Fire and Rescue Services (MCFRS) responded around 3:30 p.m. Thursday to the 17700 block of Chipping Court in Olney for a fire involving rooftop solar panels on a two-story end-unit townhouse.
According to Chief Spokesperson for MCFRS Pete Piringer, firefighters found several solar panels burning on the roof, but there was no apparent extension into the structure, which was unoccupied at the time of the fire. Crews deactivated the solar photovoltaic system by applying PVStop, a liquid coating that blocks sunlight, stops electricity generation, and de-energizes the entire system. The fire was contained to the roof area.
Olney, MD – Aerial of the scene of a reported house fire, several solar panels on fire in the area of 17700 Blk of Chipping Court. @mcfrsPIO @HHFireProds @ArmisteadIsaac @mcfrs #mcfrs @MoCoFireWire @Lawzfirephotos @command11b @MCFRSNews @DavidPazos15 pic.twitter.com/SClP7MZ7lD
— MyDrone.Pro (@MyDronePro) June 18, 2026

Join this free virtual Lunch & Learn to learn key financial concepts, avoid common business pitfalls, and build a stronger financial foundation for your business.
Read More
Connecting with the community and learn more about Lafayette Federal
Read More
Summer Musical with Optional Dinner Theatre Experience
Read More
Read More
Montgomery County, MD
©2026 Copyright The MoCo Show. All Rights Reserved.

source

Will ‘balcony solar’ catch on in the US?  ConsumerAffairs
source

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Scientific Reports volume 15, Article number: 2213 (2025) Cite this article
7651 Accesses
13 Citations
3 Altmetric
Metrics details
Effective energy management is crucial in greenhouse farming to ensure efficient operations and optimal crop growth. This study investigates the energy autonomy—defined as the ratio of on-site energy generation to the total energy demand—of greenhouses equipped with semi-transparent photovoltaic (STPV) systems under two scenarios: with and without a Battery Energy Storage System (BESS). STPV systems are beneficial because they generate energy while still allowing enough light to pass through for healthy plant development. Seasonal variations in energy autonomy during summer and winter were analyzed. Results show that incorporating BESS significantly reduces reliance on grid electricity, with energy autonomy improving from 43.43% to 24.17% in summer and 81.36% to 69.45% in winter. The system’s performance was highly sensitive to the transmittance rate of STPV panels and the minimum Daily Light Integral (DLI) required for crops. These findings highlight the potential of BESS to enhance energy independence and promote sustainable agricultural practices. The study provides insights into optimizing renewable energy systems in greenhouses, emphasizing practical implications for scalability and economic feasibility.
Greenhouse technology plays an essential role in modern agriculture by enabling the controlled environment cultivation of a wide variety of crops. This controlled environment ensures optimal growing conditions, independent of external weather patterns, which leads to higher yields and improved crop quality. The importance of greenhouses is underscored by their significant contribution to food security, particularly in regions with less favorable growing conditions. The Food and Agriculture Organization emphasizes the critical role of greenhouse technology in meeting the projected 70% increase in global food production demand¹. Currently, greenhouse agriculture spans over millions of hectares of lands worldwide, with a substantial presence in eastern Asia and the Mediterranean regions². While the adoption of greenhouse technology significantly enhances food production capabilities, it also leads to increased energy consumption³. As a result, this contributes to higher greenhouse gas emissions and a greater carbon footprint⁴. In this context, integrating renewable energy sources (RES) into greenhouse operations is not only going to be beneficial but necessary. Photovoltaic (PV) technology, with its decreasing costs, stands out as a promising solution. European union, for instance, is planning a significant increase in PV capacity, aiming to exceed 200 GW over the next decade⁵.
Agricultural photovoltaic, which combine PV power generation with traditional farming practices, presents a synergistic approach⁶. This approach addresses the challenges of energy demand in agriculture. Additionally, it contributes to sustainable farming practices by reducing dependence on non-renewable energy sources⁷. By installing PV systems on croplands, which are rich in solar resources, greenhouses are able to lower their dependency on fossil fuels. Integrating Semi-transparent photovoltaic (STPV) systems into greenhouses further enhances this synergy by allowing sufficient light for plant growth while simultaneously generating electricity (Fig. 1). STPV systems represent an innovative approach for integrating solar energy generation with light transmission, making them particularly suitable for applications such as greenhouses⁸. This dual functionality not only helps in maximizing land use efficiency but also aligns with sustainable agricultural practices. Unlike traditional opaque PV panels, STPV systems are designed to allow a portion of sunlight to pass through while converting the rest into electricity⁹. This selective light transmission is crucial for maintaining optimal photosynthesis conditions. Additionally, this dual functionality is achieved through various technologies¹⁰. Furthermore, STPV technology can contribute to enhanced climate resilience in agricultural practices. The ability to produce energy on-site reduces the carbon footprint associated with energy transportation and infrastructure. Compared to traditional PV systems, STPV offers the unique advantage of simultaneous energy generation and light transmittance, which is crucial for maintaining the Daily Light Integral (DLI) required for crops. Wind energy, while effective in some regions, is less reliable and difficult to integrate within the structural design of greenhouses. The dual functionality of STPV systems, combined with the flexibility of BESS, positions this approach as a superior solution for achieving energy autonomy in greenhouse farming.
Dual application of STPV in a greenhouse.
Table 1, presents information on various types of semi-transparent photovoltaic technologies, their materials, efficiencies, transparency levels, and additional relevant notes. STPV systems can employ different methods to balance light transmission and energy conversion, such as using different types of materials or designing varying layers within the panel.
While photovoltaic STPV systems offer significant advantages in renewable energy generation, they are not without their shortcomings. A notable issue is the trade-off between transparency and efficiency, where increasing transparency often results in decreased energy conversion efficiency. STPV systems typically exhibit lower efficiency compared to traditional PV panels, which can lead to intermittent energy production¹¹. Since solar panels generate electricity only during daylight hours, their output varies based on weather conditions, time of day, and seasonal changes. This intermittency can pose particular challenges for energy-intensive operations like greenhouses, where a stable energy supply is crucial for maintaining consistent environmental conditions. Periods of low or no power generation, especially during cloudy days or at night, can lead to reliability issues. Additionally, while the efficiency of STPV systems is improving, it remains lower compared to traditional opaque PV panels, meaning they generate less electricity overall⁸. This reduced efficiency can affect the viability of STPV systems as a sole energy source for high-demand applications. Despite these challenges, the lower energy yield of STPV systems might necessitate the use of supplementary energy sources or storage solutions to ensure a reliable power supply. To address these issues, ongoing research and development are focused on enhancing the efficiency and durability of STPV technologies. Future advancements could make STPV systems more viable for widespread use in agricultural settings. As STPV technologies evolve, their integration into greenhouse systems could lead to significant improvements in sustainable agriculture and energy management.
Battery Energy Storage Systems (BESS) offer a practical solution to the mentioned shortcomings by storing excess power produced at peak sunlight hours and use it during hours when solar power generation is insufficient¹². By providing a buffer against the variability of solar power, BESS ensures a reliable and continuous energy supply, which is crucial for greenhouse operations that depend on stable environmental conditions for crop production. In greenhouses, maintaining optimal temperature, humidity, and lighting conditions is vital for plant growth, and any disruptions in power supply can jeopardize these conditions. BESS also helps in load balancing, smoothing out the fluctuations in energy availability and demand¹¹. This reduces the greenhouse’s dependency on the grid and can significantly decrease energy costs¹³. By mitigating demand spikes and supplying energy during off-peak periods, BESS can also play a role in reducing the need for expensive grid upgrades, which would otherwise be necessary to handle increased loads. Moreover, by enabling the use of stored renewable energy instead of fossil fuel-based backup generators, BESS contributes to reducing the carbon footprint of greenhouse operations, promoting more sustainable agricultural practices¹⁴.
The integration of BESS into microgrids energy systems not only supports sustainability goals but also enhances energy security¹⁵. In the context of agricultural operations, especially those in remote or off-grid locations, BESS provides a critical backup power source, ensuring that vital systems remain operational during power outages or in the absence of sufficient sunlight. This capability is particularly important as climate change leads to more frequent and severe weather events, which can disrupt both solar power generation and grid reliability. In addition, the use of BESS can improve the economic viability of greenhouses by providing a more predictable energy cost structure and reducing the financial risks associated with energy price volatility. Properly sizing BESS is crucial for maximizing their effectiveness in supporting renewable energy systems like STPV in greenhouse operations. The size of BESS determines its capacity to store and discharge energy, directly influencing the system’s ability to meet energy demands during periods of low solar input or high consumption¹⁶. An undersized BESS may not provide sufficient backup power during extended periods of low solar energy generation, while an oversized system can lead to unnecessary capital expenditure and underutilization. Various methods, including mathematical modeling and optimization techniques, are employed to determine the optimal BESS size and configuration¹⁷. In¹⁸, six different optimization algorithms are employed to find the optimum performance of a hybrid battery-supercapacitor energy system. These methods consider factors such as the greenhouse’s energy demand profile, the solar generation potential, weather patterns, and the cost of energy storage technologies¹⁹. Metaheuristic algorithms have proven effective in handling the complex, nonlinear nature of optimization problems associated with BESS sizing^20,21. Different heuristic and evolutionary algorithms are investigated in handling the microgrids optimization based problems in^22,23.
Harmony Search (HS), a metaheuristic algorithm inspired by musicians’ improvisation process, has gained attention for its ability to find near-optimal solutions by iteratively adjusting solutions based on harmony memory and pitch adjustment²⁴. The HS algorithm is particularly suited for optimizing energy storage systems because it can efficiently navigate large, multidimensional solution spaces to identify configurations that balance cost, performance, and reliability. In our study, we utilized the Harmony Search algorithm to optimize the size and the spatial distribution of BESS within the greenhouse system²⁵. This optimization strategy ensures that energy storage is strategically located to minimize transmission losses and enhance overall system efficiency. By placing storage units closer to high-demand areas, we can reduce energy transmission distances and improve the overall responsiveness of the energy supply system.
The results from our optimization model demonstrate that strategic placement and sizing of BESS can lead to significant improvements in energy efficiency and cost savings. This approach not only enhances the sustainability of greenhouse operations by minimizing energy waste but also contributes to better economic outcomes through reduced operational costs and improved crop yields due to stable environmental conditions. Future research could explore integrating advanced predictive models that incorporate real-time weather data and machine learning algorithms to further enhance the accuracy and effectiveness of BESS optimization in greenhouse environments. As the cost of BESS technology continues to decline and its performance improves, its application in agriculture and other energy-intensive sectors is likely to expand, driving further advancements in renewable energy integration and sustainability.
The Daily Light Integral (DLI) serves as a critical parameter in greenhouse agriculture, representing the total amount of photosynthetically active radiation (PAR) received by crops over a 24-h period. DLI directly influences plant growth, development, and overall productivity, making it essential to maintain optimal light levels within greenhouses²⁶. The amount of PAR a plant receives affects various physiological processes, such as photosynthesis, transpiration, and nutrient uptake, which are all crucial for healthy plant growth. Managing DLI constraints effectively is pivotal in achieving a balanced integration of renewable energy technologies in greenhouse environments, aligning energy efficiency goals with agricultural productivity²⁷. Ensuring that the DLI requirements are met allows for maximum plant health and yield, even as renewable energy systems like STPV panels and BESS are integrated into the greenhouse design. Different crops have varying DLI requirements depending on their growth stages and light sensitivity. For instance, high-light crops like tomatoes and peppers require a significantly higher DLI compared to shade-tolerant crops such as leafy greens. Understanding these specific light requirements is essential for tailoring greenhouse conditions to optimize plant health and yield. In our study, we integrated DLI as a primary constraint in our optimization framework for energy management. By considering DLI requirements specific to different crop types, we ensured that energy solutions, including the deployment and operation of STPV systems and BESS, complemented rather than compromised plant growth. This approach enables greenhouse operators to balance energy savings with the provision of adequate light for photosynthesis, thereby supporting sustainable agricultural practices.
In this study, we assume that the crop-specific DLI thresholds are fixed based on existing agricultural guidelines. The DLI values used in the optimization model are based on average lighting needs for high, medium, and low light-demanding crops. While actual DLI requirements may vary due to factors like plant age and health, using established thresholds ensures the results are applicable to a wide range of greenhouse operations.
Moreover, integrating DLI into the energy management strategy helps in reducing the overall energy footprint of greenhouse operations. When DLI is properly managed, it allows for strategic use of supplemental lighting only when natural sunlight is insufficient, thereby minimizing energy consumption. This targeted approach not only optimizes energy use but also enhances crop yield and quality by providing consistent, optimal light conditions. By aligning the DLI management with renewable energy generation patterns, greenhouses can achieve a more sustainable balance between energy consumption and production. Table 2 provides the minimum DLI values for different types of crops. These values are integral to our analysis as they ensure that the integration of STPV panels does not compromise the necessary light conditions for crop cultivation. Incorporating these values into our optimization model allows for a more precise and effective deployment of renewable energy technologies. For example, STPV panels can be strategically placed or designed to ensure they provide sufficient light transmission while also generating energy. This consideration is crucial for high-light crops, where any reduction in PAR due to PV shading could adversely affect growth and yield.
While the DLI concept plays a critical role in optimizing STPV-BESS systems, several challenges need to be considered. Firstly, accurately determining the required DLI for different crops across varying environmental conditions can be complex. Additionally, ensuring that DLI constraints are effectively integrated into the optimization model requires careful balancing of energy production with crop-specific light needs. Seasonal variations further exacerbate this challenge, as fluctuating weather conditions can impact the accuracy of DLI predictions and necessitate dynamic adjustments to the system. This study addresses these challenges by incorporating a flexible optimization approach that adapts to seasonal changes and ensures optimal crop growth alongside energy management.
While several renewable energy technologies have been proposed for greenhouses, including wind turbines and traditional PV systems, these solutions often lack the dual functionality required for greenhouse environments. Traditional PV systems, for instance, block a substantial portion of sunlight, which can adversely affect crop growth. Additionally, these technologies do not account for the critical agricultural parameters, such as the DLI and specific crop requirements, which directly influence both energy demand and plant productivity. This study aims to investigate how STPV systems, which enable simultaneous energy generation and optimal light transmittance, can be effectively integrated with BESS to improve energy autonomy in greenhouses. By considering the interplay between energy management and agricultural needs—including DLI thresholds and crop types—this research offers a comprehensive approach to optimizing renewable energy systems for sustainable and efficient greenhouse operations.
Previous researches focused on utilizing STPV in a greenhouse. Ref²⁸ explores the potential of STPV cladding on greenhouse roofs to generate solar electricity while supporting crop production. The study uses energy and life cycle cost analysis, considering current and future efficiency projections for PV and horticultural lighting technologies. Results indicate that while STPV cladding currently increases lighting electricity use, it could potentially supply the greenhouse’s demand. The internal shading caused by STPV necessitates increased supplemental lighting but reduces heating energy use. Despite its current economic unattractiveness, STPV is expected to become a viable and promising cladding alternative, enhancing energy efficiency and economic performance as technology advances. However, they did not consider the different aspect of using STPV such as decreasing the energy autonomy or increasing the resiliency. Also, they did not consider the effect of energy storage system in their analysis. Ref²⁹ investigates the implementation of a new STPV module prototype in a real greenhouse setting. Their proposed technology mitigates shadow effects on crops by ensuring that the cells’ shadows do not completely eclipse the sunlight, promoting a better distribution of solar radiation within the greenhouse. Despite a slight increase in yield ratio due to ground-reflected radiation utilization, the energy produced is still insufficient to meet the greenhouse’s electrical needs. Ref²⁶ highlights the significant energy production potential of PV and STPV systems, demonstrating their capability to meet up to 30% of the annual electricity demand. The findings emphasize the importance of the proportion of the projected PV area to the total greenhouse area. In their study, they formulated the average daily radiation inside and outside of the greenhouse considering both PV and STPV panels. Also, they provided comprehensive information about DLI and transparency of STPV.
Above studies did not sufficiently consider the integration of energy storage systems, which is crucial for optimizing energy management in greenhouse environments. Without incorporating energy storage systems such as BESS, the ability to efficiently manage and balance energy supply and demand in real-time is limited. These systems play a vital role in mitigating the intermittent nature of renewable energy sources, particularly in a dynamic environment like greenhouses where energy demand fluctuates with crop growth cycles and seasonal changes. Additionally, while many studies explore the STPV systems to meet greenhouse energy demands, they often fail to provide a clear framework or specific factors that quantify the percentage of total energy demand that can be met with and without energy storage systems. This is essential for assessing the true value of energy autonomy and for understanding how much of the energy demand can be sustainably covered by renewable sources in conjunction with storage solutions. Moreover, seasonal energy dependency is largely overlooked in most existing studies. Greenhouses experience significant variations in energy demand throughout different seasons—during summer with higher sunlight and crop growth rates, and in winter when energy demands rise due to factors like lighting and heating needs. Understanding and optimizing these seasonal variations is critical for the design and management of energy systems to ensure long-term sustainability and efficiency. This study makes significant contributions to the field of sustainable agriculture and renewable energy integration.
1. This research focuses on enhancing energy autonomy in greenhouses equipped with STPV systems operating as microgrids, especially when integrated with BESS. By examining seasonal variations in energy autonomy and quantifying the impact of BESS on reducing reliance on the main grid, our study offers valuable insights into improving both energy efficiency and sustainability in greenhouse operations. Unlike prior works, which often lack a detailed exploration of seasonal dependency, this study provides a nuanced understanding of how BESS can be optimized to handle varying energy demands throughout the year.
2. The study introduces the use of the Harmony Search (HS) metaheuristic algorithm for optimizing the capacity and spatial distribution of BESS-STPV systems within the greenhouse environment. This novel approach enhances the ability to efficiently manage energy resources by considering dynamic system configurations that adapt to changing demand and seasonal factors. While existing research explores STPV and BESS independently, this study bridges the gap by integrating advanced optimization techniques that improve system performance and scalability.
3. Maintaining optimal conditions for crop growth is essential in greenhouse environments. By incorporating DLI constraints into the optimization model, the study ensures that energy solutions not only meet the greenhouse’s energy demands but also support healthy plant development. Unlike previous studies that prioritize energy efficiency alone, this approach highlights the importance of balancing energy production with agronomic needs, providing a holistic solution that addresses both energy and agricultural requirements.
The rest of the paper is organized as follows: section “Methodology” describes the study’s methodology in depth, providing a comprehensive overview of the approach used to investigate energy management strategies in greenhouse operations. Section “Mathematical formulation” outlines the objective functions, constraints, and the application of the HS algorithm for optimizing the integration of BESS with STPV systems. Section “Results” presents the findings, illustrating the outcomes of applying the optimized BESS-STPV configurations to a case study scenario during both winter and summer seasons, with comparisons made between scenarios with and without BESS. The Discussion in section “Results” interprets these findings. Section “Discussion: achievements and limitations” concludes the discussion and offers potential directions for further study in the area.
In this section, we detail the approach used to investigate and optimize the integration of BESS with STPV systems in greenhouse operations. The proposed method’s flowchart is given in Fig. 2. First, input data pertinent to the study were collected and analyzed. This included greenhouse specifications, such as dimensions and structural characteristics, as well as load demand profiles for both summer and winter seasons. Additionally, data on solar irradiance and PV output specific to the location and orientation of the greenhouse were gathered to simulate energy generation scenarios under varying seasonal conditions. The characteristics of BESS, including storage capacity, efficiency, and associated costs, were also considered in the input data. To establish a baseline, the first stage is to compute the greenhouse’s energy autonomy without the integration of BESS. The ratio of imported electricity to the overall load demand is described as energy autonomy. Subsequently, HS is employed to find the optimum spatial distribution and capacity of BESS within the greenhouse. The Harmony Search (HS) algorithm was selected due to its unique advantages in optimizing the BESS-STPV system. Compared to other metaheuristic algorithms such as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO), HS offers simplicity in implementation with fewer control parameters, making it easier to apply in practical scenarios. Additionally, HS excels in handling complex and nonlinear problems, which is essential for managing the interactions between renewable energy generation, energy storage, and crop growth. Unlike algorithms like GA, which may require extensive fine-tuning, HS effectively balances fast convergence with a lower likelihood of falling into local optima, ensuring robust and reliable results. This flexibility allows HS to adapt to the dynamic changes in seasonal energy availability and system requirements in greenhouse environments, providing a more efficient and tailored solution.
Flowchart of the proposed methodology.
The objective was to minimize energy autonomy while ensuring that the DLI requirements for crop growth were met. DLI, a critical parameter influencing plant photosynthesis and growth, was integrated into the optimization framework as a constraint to maintain optimal growing conditions. Following the optimization process, energy autonomy calculations were repeated for scenarios incorporating the optimized BESS configurations during both summer and winter seasons. These calculations allowed for a comparative analysis, evaluating the effectiveness of BESS in reducing energy autonomy and enhancing energy autonomy in greenhouse operations under varying seasonal conditions.
The following is an explanation of the steps in the approach:
Step1: Input Data Collection:
Gathered greenhouse specifications including dimensions, orientation, and structural details.
Collected historical load demand data for both summer and winter seasons to characterize energy consumption patterns.
Obtained solar irradiance data specific to the location and orientation of the greenhouse to simulate PV system output under varying seasonal conditions.
Compiled characteristics of the BESS, including storage capacity, efficiency, and cost parameters.
Step2: Calculation of Initial Energy Autonomy:
Defined and calculated the baseline energy autonomy of the greenhouse without the integration of BESS.
ED is quantified, providing a benchmark for comparison.
Step3: Harmony Search (HS) Optimization:
Applied the Harmony Search algorithm.
Formulated objective functions to minimize energy autonomy while adhering to constraints, particularly the DLI requirements critical for crop growth.
Iteratively adjusted BESS configurations based on harmony memory and pitch adjustment mechanisms to converge on near-optimal solutions.
Integration of DLI Constraint: Incorporated DLI constraints into the optimization model to ensure that energy solutions maintained adequate light levels necessary for optimal plant photosynthesis and growth.
Adjusted BESS operation schedules and configurations to balance energy storage and discharge with fluctuating solar availability and crop lighting requirements.
Step4: Calculation of Optimized Energy Autonomy:
Reassessed energy autonomy calculations for scenarios incorporating the optimized BESS configurations during both summer and winter seasons.
Compared and analyzed the reduction in energy autonomy achieved through BESS integration, evaluating its effectiveness in enhancing energy autonomy and sustainability in greenhouse operations. Furthermore, Fig. 3 provides the problem’s pseudo code.
The pseudo code of the problem.
This section details the mathematical formulation and optimization framework employed in the study. The formulation begins with defining an objective function aimed at minimizing the total cost associated with BESS, encompassing initial investment, operational expenses, and penalties for inadequate energy storage. Subsequently, the behavior of BESS is mathematically modeled, incorporating equations governing energy efficiency, charge–discharge cycles, and capacity constraints. To ensure optimal plant growth conditions, constraints based on the DLI are formulated, quantifying the minimum light intensity required by crops throughout the day. Energy autonomy, quantifying reliance on external power sources, is then calculated as a baseline metric. Finally, the HS algorithm is introduced to minimize energy autonomy by optimizing BESS operation, while meeting DLI constraints.
The aim of the study is to minimize the total expenditure linked with the BESS. The study focuses on a greenhouse integrated with STPV panels and a BESS, operating as a microgrid for sustainable energy management. To simulate and optimize this system, a detailed modeling approach was implemented in MATLAB. This simulation environment enables a comprehensive analysis of energy generation, storage, and utilization within the greenhouse. In the simulation, key input parameters are considered, including STPV area, crop type, minimum DLI requirements, STPV system transmittance rates, and BESS capacity. Also, related cost parameters are fed into the problem. These factors are essential for accurately simulating the energy flow, allowing the assessment of STPV’s performance and the BESS’s role in optimizing energy autonomy and cost-effectiveness. The output of the simulation provides critical insights into the system’s energy performance, including energy autonomy, BESS utilization, and the effects of seasonal variations on energy management. By considering these factors, the simulation ensures that both energy efficiency and crop growth are maximized, offering a practical approach for managing renewable energy resources in greenhouse environments.
The objective function generally encompasses factors such as the initial capital outlay for BESS components, ongoing operational expenses (including maintenance and replacements), the cost of installation of the STPV, and expenses incurred from importing energy from the main grid (Eq. 1)³⁰:
Here, TC represents the total cost, (C_STPV) denotes the STPV installation cost, (C_BESS) refers to the cost associated with BESS, (C_OM) stands for the STPV-BESS system operational costs, and (C_GRID) represents the price of importing energy.
The price of BESS is determined using Eq. 2, represented by (Capacity_{BESS}) and (Energy_{BESS}):
Here, (P_rated) and (E_rated) denote the rated values of the BESS power and energy, respectively.
The operation ((OC_{STPV – BESS})) and maintenance costs ((MC_{STPV – BESS})) of STPV-BESS system is formulated by Eqs. 3 and 4:
where (CC(t)) represents the charging cost of the system and (RC_{STPV – BESS}) and (LT_{STPV – BESS}) denote the substitute price and lifetime of the STPV-BESS, respectively. Additionally, (k_{cm}) represents the maintenance cost coefficient per energy of the system. Also, NT, Is the total hours of the period.
The study assumes a three-tier time-of-use (ToU) pricing structure, with distinct rates for peak, intermediate, and off-peak hours. This pricing is determined based on the rates (rho_{i,t}) for peak, intermediate, and off-peak hours and related imported energy (IE_{t}) (Eq. 5). This approach is consistent with existing energy policies and reflects realistic scenarios for agricultural energy consumers. ToU pricing captures the dynamic nature of electricity costs, making it a practical and widely recognized parameter for evaluating energy management strategies in microgrid systems.
Moreover, the energy autonomy factor (EAF) is a vital indicator that’s utilized to assess the reliance of the greenhouse on external power sources. This factor serves as an indicator of how much the greenhouse depends on external energy supplies, which has implications for both cost and sustainability.
The BESS itself is modeled using Eqs. 7 to 11. The energy which can be stored in the BESS (E_{ESS,T}), calculated based on the BESS power in charging/discharging process ((P_{ESS}^{c}) and (P_{ESS}^{d})), given in Eq. 7^16,31.
Here, respective efficiencies of charge and discharge are presented by (eta_{c}) and (eta_{d}).
In the modeling of the BESS, we assume that the battery does not experience degradation during the analysis period. Battery degradation can be highly variable depending on factors like usage patterns, temperature, and charge–discharge cycles, making it challenging to generalize in initial analyses. Instead, only the cost of replacement is considered in the event of a battery’s operational lifespan ending. This simplification aligns with the common approach in preliminary feasibility studies, where degradation modeling is often excluded to focus on broader system performance metrics. By focusing solely on replacement costs, the model simplifies the calculation while still reflecting the financial impact of battery lifespan limitations.
Another important factor influencing the BESS’s performance is its state of charge, (SoC(t),) which shows the amount of stored charge. The SoC factor is modeled by Eqs. 8 and 9, where stands for the maximum charge rate and for the minimum charge rate, respectively.
here, (DC_{b}) and (CC_{b}) are the discharge and charge consumed by battery respectively.
In addition, Eqs. 10 and 11 constrain the power (P_{ESS,t}) and energy (E_{ESS,t}) of the BESS within their rated values, determined by the BESS type³².
The load balance in the greenhouse is the primary constraint on the issue as a microgrid, minimum DLI based on the crop type, and the area of the STPV installed at the roof of the greenhouse, which are described below. First, as given in Eq. 12, the load demand at each hour should be met by STPV and BESS and imported power by the main grid.
It should be noticed that (P_{BESS}) is considered a negative value when charging (considered as a load) in this formula.
Regarding the DLI, Photosynthetically Active Radiation (PAR) is an important factor necessary for the process of photosynthesis²⁶. DLI is calculated based on the average sum of PAR for each crop in a day. Crops are categorized based on their light needs into high light-demanding (e.g., tomato, cucumber, sweet pepper), medium light-demanding (e.g., asparagus), and low light-demanding (e.g., certain floricultural crops) groups, requiring optimal DLIs of over 30, 10–20, and 5–10 mol/m² d, respectively²⁷.
The DLI is calculated using Eqs. 13 and 14 based on the average sum of outside and inside PAR received by the crop²⁶;
In these equations, SP and SP_c represent the outside and inside PAR, respectively, both measured in the same units as DLI (mol/m² d). The average daily irradiation is presented by (I_{O}) in unit of (Wh/m²/d), and f, which is fixed at 0.48, is the ratio of PAR radiation to total solar radiation. A conversion factor of 0.0036 is used to change units from Wh/m² to MJ/m², and (alpha) is a coefficient set at 4.57 to change the unit of (MJ/m²) to the unit (mol/m²). Inside a greenhouse, the DLI, or daily light integral, is affected by the material used for the greenhouse roof. Typically, greenhouse roofs have high transmittance values,(tau_{G}), averaging around 0.9, though this can vary with different materials. In our study, we propose using STPV. The transmittance of these panels is lower than that of traditional greenhouse materials, which impacts our optimization analysis. The transmittance of panels is considered uniform across the panels, and their installation is limited to the greenhouse roof area. Uniform transmittance simplifies the model while focusing on system-level energy generation and crop lighting impacts. Area constraints reflect practical limitations in greenhouse design. By considering this factor, our study aims to optimize the use of STPV panels while ensuring that crops receive adequate light for growth. In this study, the minimum required DLI is another important constraint applied to make sure that the (SP_{C}) inside the greenhouse should be more than the minimum lighting threshold required for the crop (LDI_{min ,crop}) (Eq. 15). This ensures that crops receive adequate light for photosynthesis and growth.
Finally, the total area used to install the STPV should not be more than the roof of the greenhouse (Eq. 16):
Moreover, Temperature plays a pivotal role in the efficiency of STPV panels, particularly in climates with extreme heat, such as Qatar. High temperatures can reduce the electrical efficiency of photovoltaic systems, thereby impacting energy autonomy. The efficiency of the STPV system at a given operating temperature can be expressed as:
where (eta_{t}) is the efficiency of the STPV system at temperature t. (eta_{ref}) is the efficiency at the reference temperature (T_{ref}) (typically 25 °C). Also, (beta) (typically −0.35%) represents the temperature coefficient of efficiency, which quantifies the efficiency reduction per degree increase in temperature, and the operating temperature is shown by T. The values for (T_{ref}) and (beta) are sourced from manufacturer specifications to ensure practical relevance. In the case of high-temperature regions like Qatar, the operating temperature often exceeds the reference, leading to reduced efficiency. By incorporating this formulation, we can more accurately assess the system’s performance and devise strategies, such as improved cooling or hybrid energy systems, to mitigate temperature-related losses and ensure stable energy outputs.
Determining the optimize value of BESS and its distribution during a day to minimize the total cost of a greenhouse; is an NP-Hard type problem. One prominent method shown in previous researches is utilizing the metaheuristic algorithms. In this study we employed the HS algorithm. The improvisation process in music served as the inspiration for the HS algorithm²⁴. Just as musicians seek a harmonious state in which all musical instruments are in tune, the HS algorithm seeks an optimal solution by iteratively improving a population of potential solutions²⁵. HS algorithm offers significant advantages over other optimization techniques, making it particularly suitable for optimizing the size and distribution of BESS in greenhouses. One of the primary benefits is its simplicity and ease of implementation. Unlike traditional optimization methods that often require complex mathematical formulations or gradient information, HS is straightforward to set up and execute³³. This characteristic is especially valuable in practical applications where the problem at hand involves multiple variables and constraints, such as energy management in greenhouses. Additionally, the HS algorithm excels in flexibility and global search capability³⁴. This flexibility and its global search mechanism reduces the likelihood of falling in regional optima, a common challenge faced by algorithms like hill climbing. These features make HS particularly adept at addressing the complexities of optimizing BESS configurations, where balancing multiple factors is essential. Finally, the HS algorithm’s convergence efficiency and versatility are noteworthy. It often converges faster to an optimal solution compared to other metaheuristic methods. This efficiency is due to HS’s effective exploration and exploitation mechanisms.
The HS algorithm works by generateing new harmonies using two primary control parameters: the pitch adjustment rate (PAR) and the harmony memory size (HMS). The process is described here:
– Initialization:
Initialize a population of N harmonies at random, where N > HMS.
Apply an objective function to each harmony’s fitness evaluation.
– Improvisation:
Continue until the predetermined end point is reached: a. Choose one of the three randomly selected processes below to create a new harmony:
Randomly select a harmony from the existing population.
Pitch-correct one or more components of an existing harmony.
Pitch should be adjusted with a probability of 1—PAR while taking into account memory of the best harmonies discovered.
where, (r_{i}) is selected randomly in the interval [−1, 1] and BW represents the bandwidth of the pitch.
– Evaluation and Update:
As it mentioned before, the fitness function employed here is to optimize the TC (Eq. 18).
– Completion:
Repeat the procedure until the maximum number of iterations is achieved or the result converges to the ideal value.
This section presents the outcomes of our study on optimizing the energy autonomy of a greenhouse equipped with STPV systems, with and without the integration of a BESS. We first detail the input data used for our simulations, including greenhouse specifications, load demands for both summer and winter, PV output for different seasons, BESS characteristics, and relevant cost parameters. Following this, we provide a comprehensive analysis of the results, considering the effect of BESS on energy autonomy during summer and winter. Additionally, the effectiveness of the HS algorithm in finding the optimum of BESS operation, while considering the DLI as a critical constraint, is evaluated and discussed. The greenhouse under study, as described in reference³⁵, is a 24-acres building situated in the frigid environment of Essex County, Canada. Bell peppers are grown in this greenhouse, which has a 25-degree roof slope and a gutter height of 5.5 m. Figure 4a and b, respectively, depict the load demand profile and STPV output for a typical summer and winter day. In the referenced study, normal PV panels were utilized with a careful consideration of spacing , resulting in a ground surface to solar collector area ratio of 3³⁵. In our study, we replace these normal PV panels with STPV panels. STPV panels, while having a lower efficiency compared to traditional PV panels, eliminate the need for spacing as they are integrated directly into the greenhouse structure. This allows us to use the entire greenhouse roof surface area for solar energy collection. To accurately adjust the PV output data from the reference study to reflect the characteristics of STPV panels, we applied an efficiency adjustment factor. This factor is based on the ratio of the efficiencies of STPV panels ((eta_{STPV})) to that of normal PV panels ((eta_{PV})). Additionally, we accounted for the full utilization of the greenhouse roof area, enhancing the total effective area available for solar energy harvesting. This adjustment ensures that the energy generation data used in our optimization process accurately represents the performance of STPV panels, considering both their lower efficiency and the increased area utilization afforded by their integration into the greenhouse structure.
Load demand profile of the greenhouse and STPV output.
The input data for our optimization problem includes various parameters related to electricity prices, BESS characteristics, and the HS algorithm. Table 3 summarizes these key data points. The electricity price is considered under a Time-of-Use policy. BESS characteristics include capacity, efficiency, and cost factors. Additionally, details about the HS algorithm, including its control parameters and settings, are provided.
Also, the transparency factor of the STPV panels significantly influences the amount of light transmitted into the greenhouse. We considered the transparency of the STPV panels, (tau_{G},) to be 0.671 and 0.597 as referenced from^26,28. This value ensures that a substantial portion of sunlight can penetrate the panels, providing sufficient light for crop growth while simultaneously generating electricity. This transparency factor is incorporated into our optimization analysis to accurately reflect the dual functionality of the STPV panels. Using the HS algorithm, Figs. 5 and 6 depict the allocation of power within the greenhouse (LDI = 30, (tau_{G}) = 0.671), encompassing optimized distribution of the BESS, STPV, imported power from the grid, and load demand for summer and winter, respectively. As shown in the figures, the imported power during summer is less than in winter, and the utilization and impact of the BESS are greater in summer. In summer, the BESS charges between 10:00 and 14:00 when the STPV output is high, and discharges throughout the day to minimize energy imports. In winter, the BESS charges from 10:00 to 15:00 and discharges afterwards. However, during the morning and evening (after 20:00), the load must be supplied by the main grid due to the lower output of the STPV and insufficient excess power to charge the BESS.
Energy distribution in the greenhouse in summer (LDI = 30, (tau_{G}) = 0.671).
Energy distribution in the greenhouse in winter (LDI = 30, (tau_{G}) = 0.671).
Additionally, Table 4 illustrates the energy autonomy with and without BESS for both summer and winter.
The results of Table 4 indicate a significant reduction in energy autonomy when BESS is utilized. During summer, the energy autonomy decreases from 43.43% to 24.17%, showcasing the substantial impact of the BESS in managing energy needs efficiently. In winter, although the reduction is less pronounced, the energy autonomy still decreases from 81.36% to 69.45%. The higher reduction in energy autonomy during the summer can be attributed to the greater availability of STPV power, allowing the BESS to charge more effectively and thereby supply more power during periods of high demand. In contrast, the lower solar output during winter limits the effectiveness of the BESS, resulting in higher reliance on imported power from the grid. Nonetheless, the integration of BESS still provides a notable reduction in energy autonomy, demonstrating its importance in enhancing the energy resilience of greenhouses throughout the year.
Figures 5 and 6 illustrate the hourly energy distribution in the greenhouse during summer and winter, respectively, under the conditions of a minimum DLI requirement of 30 mol/m²/day and a transmittance value of 0.671. These figures show the contributions of STPV generation, BESS utilization, imported grid energy, and load demand over a 24-h period. In summer (Fig. 5), the optimization strategy suggests a more active role for BESS. During the early morning hours, stored energy from BESS is utilized to meet the load demand, eliminating the need for grid imports. This behavior highlights the importance of leveraging BESS to enhance energy autonomy during times of low solar generation. Additionally, during peak load hours later in the day, BESS is employed to reduce dependency on expensive imported energy, aligning with the time-of-use pricing policy. The increased utilization of BESS in summer is attributed to higher STPV generation during this season, which allows for greater energy storage and strategic discharge to minimize costs and reliance on external sources.
In contrast, Fig. 6 demonstrates a different energy distribution pattern in winter. Due to lower solar generation during this season, the optimization model often suggests that BESS remain inactive, with no significant charging or discharging occurring in the morning. Instead, energy is directly imported from the grid during this time to meet load demands. However, similar to summer, BESS is strategically utilized during peak load hours to reduce reliance on high-cost grid energy. This seasonal difference in BESS utilization reflects the impact of reduced solar availability in winter and the priority of minimizing operational costs through efficient energy management. Overall, the results highlight the seasonal dynamics of energy distribution in greenhouses. The higher STPV generation in summer allows for greater reliance on BESS, while the reduced generation in winter necessitates more direct grid imports. In both seasons, the optimization strategy prioritizes the use of BESS during peak load hours, aligning with economic considerations under ToU pricing. These findings underscore the importance of seasonal adjustments in energy management strategies to maximize efficiency and sustainability in greenhouse operations.
Furthermore, as previously noted, the minimum required DLI influences the selection of STPV and BESS, thereby affecting the energy autonomy in this study. Tables 5 and 6 illustrates the optimized BESS capacity and energy dependencies for various crop types and transmittance values for summer and winter respectively.
Tables 5 and 6 present the EAF for greenhouses during summer and winter, respectively, considering variations in the DLI, transmittance values, and BESS configurations. These tables highlight the impact of these factors on achieving energy autonomy and demonstrate the interplay between greenhouse energy demands, STPV areas, and BESS capacities under seasonal variations. In Table 5, the results for summer show that the DLI requirement significantly influences the allowed STPV area. For high DLI requirements, such as 30 mol/m²/day, the allowed STPV area decreases, especially at lower transmittance levels (e.g., 0.597). This reduction constrains energy generation and leads to higher EAF values both with and without BESS. Conversely, when DLI requirements are reduced to 20 or 10 mol/m²/day, the STPV area is maximized, enabling greater energy capture while adhering to the DLI constraints. Additionally, the role of BESS is pronounced in summer, where its integration significantly lowers the EAF. For instance, at a DLI of 30 mol/m²/day and a transmittance of 0.671, the EAF decreases from 43.43% to 24.17% with BESS, illustrating its critical role in reducing reliance on external power sources. Furthermore, higher transmittance values (0.671) enable larger STPV areas at high DLI requirements, thus improving energy generation and autonomy, whereas lower transmittance values (0.597) lead to stricter constraints and reduced autonomy.
Table 6 focuses on the winter scenario, where energy dependencies are generally higher due to reduced solar radiation. This seasonality is reflected in the higher EAF values compared to summer, even with the inclusion of BESS. For example, with a DLI of 20 mol/m²/day and a transmittance of 0.671, the EAF with BESS is 64.51%, indicating the increased challenge of achieving energy autonomy during winter. While BESS still contributes to lowering EAF in winter, the reduction is less significant compared to summer, emphasizing the need for robust storage and energy management solutions during low-irradiance seasons. Similar to summer, higher DLI requirements in winter constrain the STPV area and reduce energy autonomy. However, the effect of transmittance is more pronounced in winter, where lower transmittance values further exacerbate energy dependency. These findings underscore the importance of seasonal planning in greenhouse energy management. Achieving year-round energy autonomy requires dynamic adjustments to BESS and STPV configurations to accommodate varying DLI constraints and seasonal energy availability. Furthermore, the results highlight the need for tailored greenhouse designs that account for crop-specific DLI requirements, the transmittance properties of STPV panels, and the local energy dynamics across seasons.
Finally, Table 7 presents a detailed financial analysis of integrating a STPV system with a focus on cost components during summer and winter. The findings illustrate the economic benefits of such integration and its implications for energy management in greenhouse operations.
In summer, the total operational cost of the greenhouse without a BESS is $241,277.7, with contributions from the STPV system ($90,000), fixed costs for the BESS infrastructure ($75,000), and energy imported from the grid ($76,277.7). However, when a BESS is integrated, the total cost reduces to $208,740, resulting in a cost saving of approximately $32,537.7 (13.5%). This reduction stems from the optimized utilization of the BESS. During off-peak hours, surplus energy from the STPV system is stored in the BESS, which is later discharged during peak demand hours. This strategic energy usage minimizes reliance on grid imports during high-cost periods. The lower BESS-related fixed cost ($43,740) further contributes to the overall cost reduction.
In winter, the total cost of operating the greenhouse without a BESS is $290,251.92, with the cost components being the STPV system ($90,000), BESS fixed costs ($75,000), and energy imported from the grid ($125,251.92). Integrating the BESS reduces the total cost to $272,460, yielding a cost saving of approximately $17,791.92 (6.1%). While the savings in winter are less pronounced compared to summer, the integration of the BESS remains beneficial. With lower solar energy generation during winter, the BESS is less utilized for storing surplus energy but is still employed effectively to reduce grid imports during high-cost periods. The ability to optimize energy consumption based on grid pricing demonstrates the versatility of the BESS in managing energy costs across varying seasonal conditions.
To evaluate the long-term economic feasibility of the proposed system, we calculated the Net Present Value (NPV) over 20 years, as presented in Table 7. The NPV calculations assume a 5% annual discount rate to account for the time value of money and a 3% annual increase in grid energy costs to reflect rising energy prices. A 20-year lifespan is assumed for the STPV system, with no significant replacement costs during this period. For the BESS, a 10-year lifespan is considered, requiring a single replacement cost at the 10-year mark. The analysis reveals that integrating BESS significantly enhances NPV in both summer and winter scenarios, with the greatest benefit observed in summer due to higher solar energy availability. Specifically, the NPV with BESS integration in summer is $946,730 compared to $712,392 without BESS, demonstrating its cost-effectiveness. In winter, the results show a modest improvement, with NPV increasing from $373,980 without BESS to $415,813 with BESS. This highlights the need for complementary renewable energy solutions, such as wind or biomass systems, to further reduce grid dependency during periods of lower solar output. Additionally, to further enhance system performance and economic feasibility, hybrid energy storage solutions such as hydrogen energy storage could be integrated. Hydrogen storage systems have the advantage of long-term energy retention and can address the seasonal variability of solar energy availability, particularly during winter months. By converting surplus solar energy into hydrogen through electrolysis and storing it for later use, greenhouses could significantly reduce grid dependency and improve the overall sustainability of the project.
Furthermore, Table 8 illustrates the EAF of the STPV-BESS system with and without considering the impact of temperature, highlighting the efficiency adjustments under various DLI levels and seasonal conditions.
When accounting for temperature effects ((T_{ref}) = 25C, (beta) =  − 0.35%, and (tau_{G}) = 0.671), the results show a consistent improvement in EAF across all scenarios. For summer conditions with a DLI of 30 (mol/m² d), EAF increased by 5.8%, from 24.17% to 25.57%. Similarly, winter conditions at the same DLI saw a 1.7% increment in EAF, rising from 69.45% to 70.63%. As the DLI requirement decreased to 20 and 10 mol/m² d, the improvements were even more pronounced in summer, with increments of 6.1% and 6.7%, respectively. In winter, EAF rose by 2.5% and 2.6% for the same scenarios. These findings underscore the significance of incorporating temperature effects into energy management models, particularly in climates with high ambient temperatures.
Finally, the performance of the HS algorithm was benchmarked against Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the STPV-BESS system under specific conditions (DLI 30 mol/m² d, transmittance 0.671). The results, summarized in Table 9, highlight the key differences in EAF, convergence speed, and ease of implementation among these methods.
In terms of EAF, HS outperformed GA and PSO slightly. However, the differences in EAF were relatively minor, indicating that all three methods provide comparable performance in optimizing energy autonomy. Regarding convergence speed, HS exhibited a significant advantage over GA, with a runtime of 1200 s compared to 1500 s for GA, demonstrating its efficiency in reaching optimal solutions. While PSO showed the fastest convergence (1100 s), it presented higher variability in EAF across multiple runs, indicating potential instability in certain scenarios. Lastly, the ease of implementation was rated highest for HS, owing to its straightforward structure and fewer parameters to tune, making it a practical choice for this study. Both GA and PSO were rated as moderate due to their more complex configurations and parameter dependencies. These findings underscore the balance HS offers better computational efficiency and practical implementation, justifying its selection as the primary optimization method for this research.
In this section, the key findings of the study are explained. We examine the effectiveness of integrating STPV panels and BESS in greenhouses, specifically focusing on the reduction of energy autonomy and carbon footprint. Additionally, the impact of various factors such as the minimum DLI and STPV transmittance on system performance is explored. Finally, the limitations encountered during the study to improve the integration of renewable energy solutions in agricultural practices are discussed. The findings of this study contribute to broader goals such as achieving carbon neutrality and enhancing energy security within the agricultural sector. By optimizing BESS and STPV systems, the research supports the transition towards more sustainable and self-sufficient agricultural practices. Carbon neutrality is increasingly emphasized in agricultural policies, where reducing dependence on fossil fuels and integrating renewable energy sources are key strategies. Through the efficient use of BESS-STPV systems, this study demonstrates how agricultural operations can move towards achieving net-zero carbon emissions, aligning with global efforts to combat climate change. Additionally, the study addresses energy security by ensuring that agricultural operations have a resilient and reliable energy supply, minimizing vulnerability to disruptions in grid electricity. These advancements contribute directly to fostering a more sustainable, secure, and environmentally responsible agricultural sector.
To enhance the practical relevance of the proposed STPV-BESS system, we compared our findings with two real-world studies that utilized similar approaches. The first study conducted in Greece (latitude 39.07°N) evaluated the energy generation capacity of greenhouses with STPV panels covering 50% and 100% of the roof area³⁹. Case 1, with 500 m² of coverage, achieved 63,750 kWh annually, meeting 80% of the greenhouse’s energy needs. Case 2, with 1,000 m² coverage, generated 234,000 kWh annually, covering 100% of energy needs and enabling surplus energy to be exported to the grid. The second study conducted in Arizona, USA (latitude 32.25°N) reported that 49% coverage of the greenhouse roof with STPV was sufficient to meet the energy demands, highlighting the system’s viability even in different climatic conditions⁴⁰. These comparisons illustrate the versatility and scalability of STPV systems for greenhouse energy autonomy, aligning well with our findings. Our study complements these findings by presenting a detailed analysis of energy autonomy improvements achieved through the integration of BESS with STPV systems. As shown in Table 4, in summer, the energy dependency without BESS was 43.43%. Similarly, in winter, the ED reduced from 81.36% without BESS to 69.45% with BESS. These results align with the aforementioned studies and highlight the ability of STPV-BESS systems to adapt to seasonal variations in energy demand while reducing reliance on grid electricity.
The findings underscore several achievements in optimizing the energy autonomy of greenhouses using STPV systems combined with BESS. These achievements are illustrated with numerical examples drawn from our data tables:
Reduction in energy autonomy using BESS:
In summer, the implementation of BESS reduced energy autonomy from 43.43% to 24.17%, a substantial decrease of approximately 44%. Also, in winter, although the reduction was less pronounced, energy autonomy still decreased from 81.36% to 69.45%, indicating a 15% improvement.
Effectiveness of BESS in different seasons:
The results highlight the variability in BESS effectiveness across seasons. In summer, the high output of STPV systems allowed for more effective BESS usage, resulting in a more significant reduction in energy autonomy. In winter, despite the lower STPV output and reduced charging opportunities for BESS, the system still contributed to a notable reduction in energy autonomy.
Impact of STPV transmittance and minimum DLI on system performance:
The transmittance rate of STPV panels and the minimum required DLI for crops significantly influenced the system’s performance. For instance, with a minimum DLI of 30 mol/m² d and a transmittance rate of 0.671, the energy autonomy in summer with BESS was 24.17%. However, with a reduced transmittance rate of 0.597, the energy autonomy increased to 27.12%. Similarly, in winter, with a DLI of 30 mol/m² d and a transmittance rate of 0.671, the energy autonomy with BESS was 69.45%. This increased slightly to 70.13% with a transmittance rate of 0.597.
DLI’s Role in optimizing STPV and BESS:
Lowering the DLI requirement had a noteworthy effect on system optimization. For the DLI 20 mol/m² d, the energy autonomy in summer with BESS was 21.03%, irrespective of the transmittance rate, indicating that reducing DLI can facilitate better optimization of STPV and BESS capacity. In winter, the same DLI reduction led to a dependency of 64.51% with BESS, showing a consistent pattern of reduced energy autonomy with lower DLI requirements. These achievements demonstrate the potential of combining STPV systems with BESS to significantly reduce energy autonomy in greenhouses. However, they also highlight the critical roles that seasonal variations, transmittance rates, and minimum DLI requirements play in optimizing these systems. Despite the notable improvements, the energy autonomy in winter remains relatively high, indicating areas where further technologies and solutions are needed.
The discussion in the manuscript touches upon various aspects of integrating a Semi-Transparent Photovoltaic (STPV) system and Battery Energy Storage System (BESS), but there is a need to explore the practical limitations more comprehensively.
High Initial Costs: One of the significant limitations is the high initial investment required for the implementation of STPV and BESS systems. While the cost-saving benefits are evident in the long term, the upfront expenses associated with the installation, maintenance, and infrastructure development can deter some greenhouse operators. These costs include the procurement of STPV panels, BESS infrastructure, and the necessary technological components for integration. Addressing this challenge requires a robust financial model and potential subsidies or incentives for sustainable energy solutions.
Weather Condition Effects on Panel Efficiency and Output: Another critical limitation is the impact of weather conditions on STPV panel efficiency and energy output. Solar panels, including STPV systems, are highly sensitive to changes in weather, particularly cloud cover, temperature variations, and seasonal shifts. In regions with fluctuating weather patterns, the efficiency of STPV systems may decline, affecting energy generation and consequently, the effectiveness of the integrated BESS. This necessitates the development of adaptive solutions to optimize performance, such as dynamic control strategies or hybrid energy sources to complement solar power during low-yield periods.
Technical Challenges in Implementing HS Optimization: The implementation of the Harmony Search (HS) algorithm for optimizing BESS performance also presents technical challenges. Despite its efficiency, HS may face difficulties in handling complex optimization problems with a high number of variables and constraints. Moreover, ensuring convergence to optimal solutions in a timely manner can be challenging, especially in dynamic operational environments like greenhouses where energy demand and environmental factors continuously change. Further research into refining HS or integrating it with other optimization methods can help mitigate these limitations.
Furthermore, future advancements in STPV and BESS have the potential to significantly address the observed challenges. Emerging technologies, such as more efficient solar cell designs for STPV systems, could improve energy conversion rates and increase the area’s power generation capacity. Advances in energy storage, such as the development of solid-state batteries or flow batteries, could enhance BESS performance by providing higher energy density, faster charge/discharge cycles, and longer lifespans. Additionally, the integration of artificial intelligence (AI) and machine learning into optimization frameworks could optimize BESS operations more dynamically, allowing for real-time adjustments based on weather patterns, energy demand fluctuations, and grid interactions. These technological advancements would further improve the efficiency, sustainability, and reliability of the overall system, addressing key challenges related to energy management, cost-effectiveness, and resilience in agricultural microgrids.
This paper underscores the critical importance of integrating renewable energy solutions into greenhouse operations to enhance sustainability and reduce energy autonomy. The integration of STPV systems and BESS presented a promising approach to achieving these goals. The methodology involved a detailed examination of a greenhouse in Essex County, Ontario, Canada, producing bell peppers. By using the HS algorithm, we optimized the size and distribution of the BESS while considering the DLI requirements for different crops as a primary constraint. The study aimed to minimize the total cost associated with the BESS and STPV system while ensuring adequate light levels for crop growth.
To implement the proposed system in real-world settings, it is essential to consider practical challenges such as cost, technical feasibility, and seasonal variability. Future research directions could explore alternative STPV materials that enhance efficiency in low-light conditions and develop dynamic DLI management strategies that adapt to changing environmental factors. Additionally, integration of hybrid systems combining multiple renewable energy sources, such as wind, biomass, or geothermal energy, could further optimize energy storage and usage in greenhouses.
The following highlights this study’s major outcomes: Firstly, the implementation of BESS significantly reduced EAF. For instance, in summer, the EAF decreased from 43.43% without BESS to 24.17% with BESS. Similarly, in winter, the EAF decreased from 81.36% without BESS to 69.45% with BESS, showcasing the effectiveness of BESS in lowering reliance on grid power. Secondly, the study demonstrated that the transmittance rate of STPV panels and the minimum required DLI are crucial factors in optimizing the energy system. By optimizing these factors, the system effectively balances energy generation and crop lighting needs, ensuring more efficient use of renewable energy resources. For instance, in summer, reducing the transmittance rate from 0.671 to 0.597 improved energy autonomy from 24.17% to 27.12%, while in winter, lowering DLI from 30 mol/m² d to 20 mol/m² d maintained energy autonomy at 64.51% with BESS. These findings emphasize the importance of tailoring STPV and BESS systems to crop-specific lighting demands, enhancing both sustainability and energy resilience. Furthermore, incorporating strategies for dynamic DLI management and the integration of complementary renewable energy sources, such as wind or biomass, will further optimize system performance across varying seasonal conditions. Thirdly, seasonal variations significantly impacted energy autonomy. In summer, the STPV output was sufficient to charge the BESS effectively, resulting in lower energy autonomy. However, in winter, due to lower solar radiation, the effectiveness of BESS was diminished, highlighting the need for additional optimization or supplemental renewable sources during the winter months.
To enhance the applicability of these findings, actionable insights are provided for researchers, policymakers, and practitioners. Researchers are encouraged to further explore the integration of STPV and BESS systems in diverse agricultural contexts, focusing on optimizing the balance between energy generation and crop lighting needs. Policymakers can support this integration by creating supportive policies and financial incentives to promote sustainable agricultural practices. Practitioners, such as greenhouse operators, can utilize these insights to enhance energy management, reduce operational costs, and improve the sustainability of their operations. Also, to address seasonal challenges, future work should focus on exploring advanced energy storage solutions and integration of supplementary renewable energy sources such as wind or biogas systems to improve BESS performance in winter months. Additionally, dynamic DLI management strategies can be developed to adapt to seasonal variations, ensuring that crops receive optimal light for growth throughout the year. By addressing these achievements, this research provides insightful information about the optimization of renewable energy systems in greenhouses. The integration of STPV and BESS not only enhances energy sustainability but also supports the operational needs of greenhouses, ensuring reliable and efficient energy usage throughout the year. Future research should further explore the variability of STPV technologies, regional differences, and detailed economic analyses to strengthen the applicability and robustness of these renewable energy solutions.
All required data are included in the manuscript. Additional data can be made available upon request by contacting Dr. S M Muyeen at (sm.muyeen@qu.edu.qa) or Mohammadreza Gholami at (mohammadreza.gholami@final.edu.tr).
Hollingsworth, J. A., Ravishankar, E., O’Connor, B., Johnson, J. X. & DeCarolis, J. F. Environmental and economic impacts of solar-powered integrated greenhouses. J. Ind. Ecol. 24(1), 234–247. https://doi.org/10.1111/jiec.12934 (2020).
Article  CAS  Google Scholar 
Chang, J. et al. Does growing vegetables in plastic greenhouses enhance regional ecosystem services beyond the food supply?. Front. Ecol. Environ. 11(1), 43–49. https://doi.org/10.1890/100223 (2013).
Article  MathSciNet  MATH  Google Scholar 
Barbosa, G. L., Daiane, F., Gadelha, A. & Kublik, N. Comparison of land, water, and energy requirements of lettuce grown using hydroponic vs. conventional agricultural methods. Int. J. Environ. Res. Public Health 12, 6879–6891. https://doi.org/10.3390/ijerph120606879 (2015).
Article  PubMed  MATH  Google Scholar 
Ntinas, G. K., Neumair, M., Tsadilas, C. D. & Meyer, J. Carbon footprint and cumulative energy demand of greenhouse and open-field tomato cultivation systems under Southern and Central European climatic conditions. J. Clean. Prod. 142, 3617–3626. https://doi.org/10.1016/j.jclepro.2016.10.106 (2017).
Article  CAS  Google Scholar 
Hassanien, R. H. E., Li, M. & Lin, W. D. Advanced applications of solar energy in agricultural greenhouses. Renew. Sustain. Energy Rev. 54, 989–1001. https://doi.org/10.1016/j.rser.2015.10.095 (2016).
Article  MATH  Google Scholar 
Russo, G., Anifantis, A. S., Verdiani, G. & Mugnozza, G. S. Environmental analysis of geothermal heat pump and LPG greenhouse heating systems. Biosyst. Eng. 127, 11–23. https://doi.org/10.1016/j.biosystemseng.2014.08.002 (2014).
Article  MATH  Google Scholar 
Xu, J., Li, Y., Wang, R. Z. & Liu, W. Performance investigation of a solar heating system with underground seasonal energy storage for greenhouse application. Energy 67, 63–73. https://doi.org/10.1016/j.energy.2014.01.049 (2014).
Article  MATH  Google Scholar 
Husain, A. A. F., Hasan, W. Z. W., Shafie, S., Hamidon, M. N. & Pandey, S. S. A review of transparent solar photovoltaic technologies. Renew. Sustain. Energy Rev. 94, 779–791. https://doi.org/10.1016/j.rser.2018.06.031 (2018).
Article  CAS  Google Scholar 
Zweibel, K. Thin film PV manufacturing: Materials costs and their optimization. Sol. Energy Mater. Sol. Cells 63(4), 375–386. https://doi.org/10.1016/S0927-0248(00)00057-X (2000).
Article  CAS  MATH  Google Scholar 
Betancur, R. et al. Transparent polymer solar cells employing a layered light-trapping architecture. Nat. Photonics 7(12), 995–1000. https://doi.org/10.1038/nphoton.2013.276 (2013).
Article  ADS  CAS  Google Scholar 
Gholami, M., Mousavi, S. A. & Muyeen, S. M. Enhanced microgrid reliability through optimal battery energy storage system type and sizing. IEEE Access 11(June), 62733–62743. https://doi.org/10.1109/ACCESS.2023.3288427 (2023).
Article  Google Scholar 
Bahramirad, S., Reder, W. & Khodaei, A. Reliability-constrained optimal sizing of energy storage system in a microgrid. IEEE Trans. Smart Grid 3(4), 2056–2062. https://doi.org/10.1109/TSG.2012.2217991 (2012).
Article  MATH  Google Scholar 
Buchana, P. & Ustun, T. S. The role of microgrids & renewable energy in addressing Sub-Saharan Africa’s current and future energy needs. In 2015 6th Int. Renew. Energy Congr. IREC 2015, 2015, https://doi.org/10.1109/IREC.2015.7110977.
Güven, A. F. & Yücel, E. Sustainable energy integration and optimization in microgrids: Enhancing efficiency with electric vehicle charging solutions. Electr. Eng. https://doi.org/10.1007/s00202-024-02619-x (2024).
Article  MATH  Google Scholar 
Güven, A. F. Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management. Energy 303, 131968. https://doi.org/10.1016/j.energy.2024.131968 (2024).
Article  MATH  Google Scholar 
Chen, S. X., Gooi, H. B. & Wang, M. Q. Sizing of energy storage for microgrids. IEEE Trans. Smart Grid 3(1), 142–151. https://doi.org/10.1109/TSG.2011.2160745 (2012).
Article  MATH  Google Scholar 
Gupta, P. P., Jain, P., Sharma, S. & Bhakar, R. Reliability-security constrained unit commitment based on benders decomposition and mixed integer non-linear programming. 2017 Int. Conf. Comput. Commun. Electron. COMPTELIX 2017, 328–333. https://doi.org/10.1109/COMPTELIX.2017.8003988 (2017).
Article  MATH  Google Scholar 
Fatih Guven, A., Abdelaziz, A. Y., Mahmoud Samy, M. & Barakat, S. Optimizing energy dynamics: A comprehensive analysis of hybrid energy storage systems integrating battery banks and supercapacitors. Energy Convers. Manag. 312, 118560. https://doi.org/10.1016/j.enconman.2024.118560 (2024).
Article  Google Scholar 
Nguyen, T. A., Crow, M. L. & Elmore, A. C. Optimal sizing of a vanadium redox battery system for microgrid systems. IEEE Trans. Sustain. Energy 6(3), 729–737. https://doi.org/10.1109/TSTE.2015.2404780 (2015).
Article  ADS  MATH  Google Scholar 
Mbungu, N. T., Naidoo, R., Bansal, R. C. & Bipath, M. Optimisation of grid connected hybrid photovoltaic-wind-battery system using model predictive control design. IET Renew. Power Gener. 11(14), 1760–1768. https://doi.org/10.1049/iet-rpg.2017.0381 (2017).
Article  MATH  Google Scholar 
Güven, A. F., Yörükeren, N. & Samy, M. M. Design optimization of a stand-alone green energy system of university campus based on Jaya-Harmony Search and Ant Colony Optimization algorithms approaches. Energy 253, 124089. https://doi.org/10.1016/j.energy.2022.124089 (2022).
Article  Google Scholar 
Güven, A. Heuristic techniques and evolutionary algorithms in microgrid optimization problems, 260–301. https://doi.org/10.1201/9781003481836-16 (2024)
Güven, A. F., Yörükeren, N. & Mengi, O. Ö. Multi-objective optimization and sustainable design: A performance comparison of metaheuristic algorithms used for on-grid and off-grid hybrid energy systems. Neural Comput. Appl. 36(13), 7559–7594. https://doi.org/10.1007/s00521-024-09585-2 (2024).
Article  MATH  Google Scholar 
Geem, Z. W., Kim, J. H. & Loganathan, G. V. A new heuristic optimization algorithm: Harmony search. Simulation 76(2), 60–68. https://doi.org/10.1177/003754970107600201 (2001).
Article  MATH  Google Scholar 
Assad, A. State-of-the-art review on applications of harmony search meta heuristic algorithm. Int. J. Comput. Eng. Technol. 10(1), 166–173. https://doi.org/10.34218/ijcet.10.1.2019.018 (2019).
Article  MATH  Google Scholar 
Schallenberg-Rodriguez, J., Rodrigo-Bello, J. J. & Del Río-Gamero, B. Agrivoltaic: How much electricity could photovoltaic greenhouses supply?. Energy Reports 9, 5420–5431. https://doi.org/10.1016/j.egyr.2023.04.374 (2023).
Article  Google Scholar 
Cossu, M. et al. Agricultural sustainability estimation of the European photovoltaic greenhouses. Eur. J. Agron. 118, 126074. https://doi.org/10.1016/j.eja.2020.126074 (2020).
Article  MATH  Google Scholar 
Bambara, J. & Athienitis, A. K. Energy and economic analysis for the design of greenhouses with semi-transparent photovoltaic cladding. Renew. Energy 131, 1274–1287. https://doi.org/10.1016/j.renene.2018.08.020 (2019).
Article  MATH  Google Scholar 
Cossu, M. et al. Advances on the semi-transparent modules based on micro solar cells: First integration in a greenhouse system. Appl. Energy 162, 1042–1051. https://doi.org/10.1016/j.apenergy.2015.11.002 (2016).
Article  ADS  CAS  MATH  Google Scholar 
Güven, A. F. & Mengi, O. Ö. Assessing metaheuristic algorithms in determining dimensions of hybrid energy systems for isolated rural environments: Exploring renewable energy systems with hydrogen storage features. J. Clean. Prod. 428, 139339. https://doi.org/10.1016/j.jclepro.2023.139339 (2023).
Article  CAS  MATH  Google Scholar 
Wongdet, P. et al. Optimal capacity and cost analysis of battery energy storage system in standalone microgrid considering battery lifetime. Batteries 9(2), 1–16. https://doi.org/10.3390/batteries9020076 (2023).
Article  Google Scholar 
Guven, A. F., Yorukeren, N., Tag-Eldin, E. & Samy, M. M. Multi-objective optimization of an islanded green energy system utilizing sophisticated hybrid metaheuristic approach. IEEE Access 11, 103044–103068. https://doi.org/10.1109/ACCESS.2023.3296589 (2023).
Article  Google Scholar 
Kaur, S., Kumbhar, G. B. & Sharma, J. Harmony search and OPF based hybrid approach for optimal placement of multiple DG units. In 2014 18th National Power Systems Conference NPSC 2014 (2015). https://doi.org/10.1109/NPSC.2014.7103842.
Nazeer, K. A., Sebastian, M. & Kumar, S. M. A novel harmony search-K means hybrid algorithm for clustering gene expression data. Bioinformation 9(2), 84–88. https://doi.org/10.6026/97320630009084 (2013).
Article  PubMed  PubMed Central  MATH  Google Scholar 
Naghibi, Z., Ekhtiari, S., Carriveau, R. & Ting, D. S. Hybrid solar thermal/photovoltaic-battery energy storage system in a commercial greenhouse: Performance and economic analysis. Energy Storage 3(1), 215. https://doi.org/10.1002/est2.215 (2021).
Article  MATH  Google Scholar 
Gholami, M., Muyeen, S. M. & Lin, S. Optimizing microgrid efficiency: Coordinating commercial and residential demand patterns with shared battery energy storage. J. Energy Storage 88, 111485. https://doi.org/10.1016/j.est.2024.111485 (2024).
Article  Google Scholar 
Güven, A. F. & Poyraz, E. Feasibility study and techno-economic analysis of stand-alone hybrid energy system for Muğla Province Köyceğiz. Karadeniz Fen Bilim. Derg. 11(1), 70–85. https://doi.org/10.31466/kfbd.880437 (2021).
Article  MATH  Google Scholar 
Güven, A. F., Türkmen, Ş, Aşikli, E. & Örnek, G. Investigating the effects of different types of battery impacts in energy storage systems on standalone hybrid renewable energy systems. Karadeniz Fen Bilim. Derg. 13(3), 943–964. https://doi.org/10.31466/kfbd.1275823 (2023).
Article  Google Scholar 
Vourdoubas, J. Possibilities of using semi-transparent photovoltaic modules on rooftops of greenhouses for covering their energy needs. J. Agric. Stud. 4(1), 90. https://doi.org/10.5296/jas.v4i1.8694 (2016).
Article  Google Scholar 
Okada, K., Yehia, I., Teitel, M. & Kacira, M. Crop production and energy generation in a greenhouse integrated with semi-transparent organic photovoltaic film. In Proceedings of the International Symposium on New Technologies for Environment Control, Energy-Saving and Crop Production in Greenhouse and Plant Factory – GreenSys 2017, 231–240 (2018) https://doi.org/10.17660/ActaHortic.2018.1227.28.
Download references
This publication was made possible by the 4th cycle of MME Grant No. MME04-0607-230060, from the Qatar Research, Development and Innovation (QRDI) Council, in collaboration with the Ministry of Municipality, Qatar. The findings herein reflect the work, and are solely the responsibility, of the authors. The authors also gratefully acknowledge support from Qatar University. Open Access funding provided by the Qatar National Library.
Department of Electrical and Electronic Engineering, Final International University, Kyrenia, 99320, Turkey
Mohammadreza Gholami
School of Engineering and Energy, Murdoch University, Perth, Australia
Ali Arefi
Mechanical and Industrial Engineering, Qatar University, 2713, Doha, Qatar
Anwarul Hasan
Western Crop Genetics Alliance, Food Futures Institute, School of Agriculture, Murdoch University, Perth, WA, Australia
Chengdao Li
Department of Electrical Engineering, Qatar University, 2713, Doha, Qatar
S. M. Muyeen
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
M.G. and S.M.M. conceptualized and designed the study. M.G. conducted the primary analysis, developed the energy autonomy models, and performed the optimization using the harmony search (HS) algorithm. M.G., S.M.M., and A.A. contributed to the methodology and integration of the Battery Energy Storage System (BESS) and Semi-Transparent Photovoltaic (STPV) system into the greenhouse model. A.H. analyzed the seasonal variations and their impact on energy autonomy and grid dependency. C.L. handled the crop growth models and ensured that Daily Light Integral (DLI) requirements were integrated into the optimization process. M.G. and S.M.M. wrote the initial draft of the manuscript and prepared the figures. A.A., A.H., and C.L. reviewed and revised the manuscript. S.M.M. supervised the project. All authors contributed to the discussion and interpretation of the results and reviewed and approved the final manuscript.
Correspondence to S. M. Muyeen.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissions
Gholami, M., Arefi, A., Hasan, A. et al. Enhancing energy autonomy of greenhouses with semi-transparent photovoltaic systems through a comparative study of battery storage systems. Sci Rep 15, 2213 (2025). https://doi.org/10.1038/s41598-025-85418-z
Download citation
Received: 12 September 2024
Accepted: 02 January 2025
Published: 17 January 2025
Version of record: 17 January 2025
DOI: https://doi.org/10.1038/s41598-025-85418-z
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.

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

source

Now
70°
Fri
75°
Sat
75°
by Nick Oudhoff, WSBT 22 Reporter
ST. JOSEPH COUNTY, Ind. (WSBT) — There is a potential setback for a proposal to expand a solar farm near Lakeville.
An appeal has been filed to keep it from happening.
This is an update to a story WSBT 22 first told you about in April, when we first reported that multiple agreements were filed between property owners and Dumont Solar LLC.

This appeal refutes those claims.
An administrative appeal has been filed aiming to prevent Dumont Solar Farm from expanding its boundaries.
The citizen group "No Solar" filed the appeal with the St. Joseph County Board of Zoning.
"If the planning director makes a written determination, you can appeal that determination within 15 days," said Laureen White, Anderson Indiana Attorney.
Attorney Laureen White says Hexagon’s proposal to expand the Dumont Solar Farm near Lakeville should not have been accepted as complete by the St. Joseph County Board of Zoning because, she argues, the application was missing required information.
WSBT 22 confirmed in April that multiple memoranda of understanding were filed between property owners and Dumont Solar LLC but was not disclosed in the original proposal submitted in 2024.
And now, White wants the county to take action.
"We want the BZA [St. Joseph County Board of Zoning Appeals] to say that the application is not complete. We want them to say that those lease agreements for the easements were not filed with the initial application, because we have proof that they were not," said White.
WSBT 22 previously asked St. Joseph County Area Plan Director Shawn Klein about a possible expansion back in April.
At that time, Klein said the county had not received an application to expand the Dumont Solar Project.
On Thursday, WSBT 22 reached back out to the Area Plan Commission for a response to White’s claims.
We were told Klein is out of the office until next Monday.
Lakeville resident Jason Gean says he wants more communication about what residents should expect next.
White says if they're not satisfied with the results made by the St. Joseph County Board of Zoning appeals, they plan to take this appeal to the state courts.
2026 Sinclair, Inc.

source

UK solar panel sales surge boosted by plug-in rule change  The National
source

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Scientific Reports volume 15, Article number: 3337 (2025) Cite this article
5533 Accesses
14 Citations
10 Altmetric
Metrics details
Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction. This methodology dynamically adjusts the neural network parameters during training, including the number of neurons, transfer functions, weights, and biases, to minimize the root mean square error. Evaluation was performed on twelve representative days using annual, monthly, and seasonal data, and a comparison was made with multiple linear regression and nonlinear autoregressive neural network models, demonstrating the approach’s effectiveness. Evaluation metrics such as mean square error, R-value, and mean percentage error reveal promising prediction accuracy. MATLAB is used for modeling, training, and testing, and a real 4.2 kW PV plant is used for validation. The results indicate significant improvements, with mean square errors as low as 20 W on cloudy days and 175 W on sunny days. The proposed methodology achieves prediction versus target regressions consistency, with R values ranging from 0.95824 to 0.99980, highlighting its efficiency in providing reliable predictions of PV power generation.
The global agenda for sustainable development, exemplified by initiatives such as the ‘Energy and the Green Deal’ strategy, underscores the need for secure, affordable, and environmentally friendly energy systems. At the core of this vision is integrating renewable energy sources into the grid, facilitating the transition to decarbonization, and improving energy efficiency^1,2. To this end, power generation systems based on renewables are shown as a clear alternative for the energy transition towards decarbonization of cities³ and electrification of electrically isolated systems⁴. Solar photovoltaic (PV) stands out among these sources⁵, offering considerable potential for decentralized power generation and urban electrification⁶.
Despite the benefits of PV systems, accurately predicting their power generation remains a challenge. Reliable predictions are key to optimizing the management of energy systems, in particular, given the inherent variability of renewable sources^7,8,9. This challenge has led to the search for advanced optimization and forecasting methodologies to improve prediction accuracy and support effective energy planning and management¹⁰.
Artificial neural networks (ANNs) have emerged as a powerful tool for addressing complex prediction tasks in areas of control¹¹, pattern recognition¹², or prediction¹³ due to their self-learning and adaptive capabilities. Due to their ability to capture nonlinear relationships within data, ANNs have been widely used in various fields, including power generation forecasting¹⁴. Previous studies have demonstrated the effectiveness of ANNs in prediction tasks, often outperforming traditional statistical models, such as¹⁵ through a data-driven performance analysis of a residential building¹⁶, using a multi-stage neural network approach to improve accuracy in daily insolation prediction, resulting in a reduction of the average error from 30 to 20%, and¹⁷ exploring the improvement of accuracy in hourly solar radiation predictions.
In addition, recent research suggests the integration of ANN with bio-inspired algorithms. For instance, the study of¹⁸ provides an analysis of how the weights of ANNs can be automatically updated by applying bio-inspired algorithms, mainly using the Particle Swarm Optimization (PSO) optimization algorithm, grasshopper optimization algorithm, and Grey Wolf Optimization (GWO). This bio-inspired approach has been used to evolve the weights of ANNs and find a particular architecture of ANNs in this field of work. A similar approach is followed by¹⁹ employing different evolutionary techniques to improve the voltage profile generated by the electrical network. An additional case that shows the advantages of these hybridizations is²⁰, which presents a hybrid model that combines a neural network with the PSO algorithm to predict the biomass required in a biomass gasification plant. This bio-inspired approach uses PSO to improve the efficiency of the neural model, enabling better estimation of energy demand in AC microgrids. Similarly²¹, presents a hybrid model that combines different neural architectures and Markov chain analysis to improve the accuracy of electric load prediction in smart cities.
Genetic Algorithms (GAs) are particularly among bio-inspired algorithms, recognized for improving prediction accuracy in various contexts. GAs excel in multi-objective optimization and have been widely and successfully applied in multiple domains, including optimizing and controlling renewable energy systems. This effectiveness is evidenced by a comparative analysis conducted in²² on PID controllers for power converters, where GAs and other techniques, such as PSO and the GWO algorithm, stand out for their ability to tune these controllers efficiently. Moreover, GAs have proven highly effective in the energy management of multi-microgrid systems²³. A noteworthy example of their applicability is in the structural configuration optimization of heat exchangers for commercial electric vehicles, where a multi-objective genetic algorithm has been applied to improve thermal and hydraulic performance²⁴. In the mechanical engineering field, GAs are used to enhance the performance of pump units such as turbines in storage mode²⁵. In addition, GAs are a valuable tool in remaining useful life prediction, as demonstrated by the study of²⁶, where a GA-based method was developed to select optimal sets of useful shapes with supervised learning. Also, in the context of power supply system optimization, an improved adaptive genetic algorithm has been proposed to design efficient and reliable systems²⁷.
Furthermore, various methods have been employed in the PV power generation prediction field, each with distinct advantages and challenges. On the one hand, physical models have been widely explored, as evidenced in the study entitled²⁸, which evaluates various physical and thermal models for forecasting PV power production. However, research such as²⁹ indicates that statistical approaches based on historical data analysis can outperform the predictive accuracy of physical models. Similarly, the study³⁰ examines the performance of linear regression and ANN methods, finding that the latter can provide better results. In addition to ANNs, other machine-learning methods have been investigated for predicting solar power generation, as discussed in³¹. However, interpreting the resulting models can be more complex than physical models, affecting their accuracy, generalization capability, and computational efficiency. Despite their advantages, physical models and statistical and machine learning methods may require detailed calibration and may not fully capture the complexity of system behavior³². There are still works that develop methodologies to guide the selection of ANNs that can best perform prediction in PV systems. However, these studies do not consider the hybridization and integration of such methods with intelligent optimization algorithms in this field³³. Therefore, this study proposes a hybrid approach integrating machine learning techniques with intelligent algorithms.
The combination of machine learning and intelligent algorithms can go beyond these applications, bringing together two areas that are currently undergoing a research boost and which can improve the systems above. The combined use of these two fields can enhance the accuracy of predictions compared to simple model performance³⁴, enhance the search capability, convergence speed, and accuracy of algorithms³⁵, increase efficiency and system performance³⁶, and adaptability and scalability of the obtained results³⁷. Remarkably, the synergy of the combined use of GA and ANN is proving promising solutions through studies for economic predictions, such as carbon trade forecasting³⁸, in the field of the automatic prediction modeling of data degradation in nuclear energy³⁹ or the management simulation of isolated microgrids⁴⁰.
Nevertheless, while integrating machine learning and intelligent algorithms offers significant potential, there are still challenges in optimizing modeling techniques for complex nonlinear systems³³. The study, therefore, focuses on addressing the challenges in accurately predicting solar PV power generation, a complicated task due to the inherent variability of renewable energy sources and the complexity of nonlinear power systems. This study aims to provide a new methodology for estimating the behavior of renewable generation systems, particularly PV systems. The innovation lies in developing a hyperparameter optimization model for feedforward artificial neural networks (FF-ANN) using GA techniques. The use of FF-ANN is due to the successful results in different areas of prediction, with better results than with other standard methods⁴¹, its capability to work with complex nonlinear systems⁴², particularly in the neural domain, offering very high computational power at low costs⁴³.
Emphasis is also placed on prediction time horizons in the PV forecasting domain. On the one hand, short-term forecasting⁴⁴, which ranges from minutes to hours, plays a significant role in the operational management of power grids. This type of forecasting is fundamental for power dispatch scheduling and immediate response to generation fluctuations caused by sudden weather changes. Its ability to provide an agile and efficient response significantly improves grid stability and reliability. In contrast, medium-term forecasting⁴⁵, spanning days to weeks, is mainly used in the production and maintenance planning of PV installations and in the strategic management of the purchase and sale of energy in the market. This time perspective enables resource optimization and informed business decisions. On the other hand, long-term forecasting⁴⁶, extending from months to years, is important in designing and sizing new PV infrastructures and guiding long-term strategic planning in investment, economic, and environmental impact assessment.
Although previous studies use neural networks for solar energy prediction, this study introduces an innovative methodology that dynamically optimizes the neural network structure in the field under study, significantly improving prediction accuracy. In addition, an adaptive optimization approach is incorporated that allows the neural network to adjust to changes in weather and power generation conditions dynamically, thus improving prediction accuracy. Furthermore, the forecasts proposed in this study are necessary for energy management, mitigating the effects of the inherent variability of renewable sources on the power grid. In addition to the significant technical challenges, including the need to achieve high accuracy and fast response, advanced forecasting and optimization methods, such as those developed in this study, are required. Improving the accuracy of these forecasts contributes directly to strengthening the stability and operational efficiency of the power grid, which are key aspects in the growing integration of renewable energies.
To evaluate the effectiveness of the proposed methodology, the study analyzes the performance in a wide range of weather conditions and training data scenarios, testing different times of the year with various meteorological characteristics. Additionally, a sensitivity analysis is performed to identify the most influential parameters on the accuracy of the predictions, allowing further optimization of the model performance and a better understanding of the factors affecting solar PV power generation. Finally, cross-validation of the model is carried out using multiple data sets and training techniques, ensuring the results’ robustness, replicability, and applicability to a wide range of scenarios. Moreover, performance will be compared using different training input data sets, starting with annual data and moving to seasonal and monthly data. These elements highlight the study’s innovation and significant contribution to the solar PV power generation prediction field.
Besides, the objective function of the proposed model is defined by the Root Mean Square Error (RMSE) due to its outstanding performance in optimization algorithms^47,48. The RMSE is a robust metric that allows the accuracy of the predictions made by a model to be evaluated, penalizing more significant errors more severely. This feature is especially useful in contexts where considerable errors are sought to be minimized to improve model efficiency. In addition, RMSE is widely used in the scientific literature and in practical applications, which facilitates the comparison and validation of results with other existing studies and models⁴⁹.
Furthermore, this evaluation uses the base ANN forecast, the multiple linear regression (MLR), and nonlinear autoregressive neural network (NAR) models as benchmarks. The application of such models is due to their acknowledged recognition in the field of prediction, allowing the new methodology to be compared with established approaches. The statistical MLR model represents a classic and widely used method for linear prediction, supported by its proven effectiveness in predicting solar energy production, specifically in contexts where a limited data set is available^50,51,52. While machine learning NAR offers an additional capability to capture nonlinear relationships in complex data and changing environments, it is an established strategy in the given context^53,54,55. This approach thoroughly evaluates the proposed methodology’s consistency and effectiveness in different situations and contexts.
The organization of the described work consists of the following sections: Section “Methodology” explains the facility under study, the factors influencing the power generation prediction in PV plants, the data processing, the methodology followed for the combination of GA and ANN, and the evaluation methodology. Section “Results” shows the predictions obtained, the results concerning their evaluations, and the performance of the GA and ANN. Section “Discussion” assesses the results obtained. Finally, Section “Conclusions” outlines the conclusions of the research.
The methodology section provides a detailed overview of several study aspects; see Fig. 1. It begins by examining the factors that influence power generation in PV plants. This includes an analysis of critical variables in understanding the power generation process: solar hour, temperature, solar irradiance, PV energy, dew point, humidity, wind speed, pressure, precipitation rate, and accumulated precipitation. The following subsection focuses on data processing techniques employed in the study, including data normality, correlation, and normalization analysis. Following that, the section delves into the structure of the genetic algorithm utilized for optimization purposes and outlines the process of fitness value calculation. Moreover, the design of the network topology, specifically the FF-ANN, is presented, including the number of neurons, weight matrices bias, and transfer functions (transferFcn) optimized. The subsequent subsection discusses the model approach, which integrates the abovementioned tools, depicted through a graphical representation. Next, an explanation of the statistical MLR and machine learning NAR models employed is provided. Then, the evaluation approach is outlined, encompassing metrics such as RMSE, coefficient of determination (R), mean percentage error, and computational time, providing a comprehensive assessment of the model’s performance. Lastly, a comparison of the different forecasting methodologies existing in the state of the art is made.
Methodology flowchart.
The training and objective prediction data come from the measurements made at the facility in Fig. 2, located in Valencia, Spain. The system consists of a grid-connected PV rooftop household installation, possibly feeding surplus power into the grid and receiving power when PV generation cannot meet domestic demand. The installation was commissioned in 2020, but the weather recording began in April 2021.
PV installation.
The PV installation consists of 12 monocrystalline 350Wp panels (Table 1), equivalent to 4.2 kWp, divided into two strings of six panels each, connected to each MPPT input of the inverter (Table 2).
The electronic data precision equipment (weather station) is the Datasol MET. Table 3 shows the technical specifications of the sensors associated with the variables that significantly impact the prediction: the temperature sensor and the irradiance sensor.
A weather station that measured various parameters, including temperature, dew point, humidity, wind speed, pressure, precipitation rate, accumulated precipitation, and solar irradiance, was used for meteorological data collection. This weather station sends the information to the Wunderground portal, which operates as a database through the Personal Weather software. Data related to PV power generation was collected from the PV inverter. These data are transmitted and stored in the FusionSolar software database.
Energy generation in PV plants is predominantly influenced by meteorological factors, thereby introducing uncertainty to PV energy generation. It is widely acknowledged that the energy generation of PV plants is closely tied to local weather conditions⁵⁶. While there is a certain degree of regularity between power generation and meteorological data, the overall relationship is rather complex. Thus, the strength of the relationships between the measured variables will be studied using correlation analysis, explained in the following subsection.
The data selection for the training of an ANN has a significant influence on its performance. This is why the data used has been measured from May 1st, 2021, to April 30th, 2022, i.e., 105,120 historical data of temperature, dew point, humidity, wind speed, wind direction, pressure, precipitation rate, accumulated precipitation, solar irradiance, and PV energy generation. Several analyses of the collected data have been conducted to make accurate predictions: filtering, normality test, correlation analysis, and data normalization.
To ensure the precise operation of the tools (GA and ANN), the measured data was filtered, discarding those when there was no PV energy generation. It is important to note that several studies have been carried out in PV power forecasting to improve model performance. Many of these studies have chosen not to use nighttime prediction data due to their insignificance in power generation. This approach has been observed in several studies, such as^28,57,58. Following this same methodology, the present study has also decided to exclude nighttime data from the predictions. Filtering the night-time values in the input data is due to several reasons:
Improves model accuracy: Eliminating nighttime values allows for the exclusive focus on power generation patterns during the active hours of the PV system. This facilitates the identification of specific trends and patterns related to PV power generation.
Reduction of noise and redundancy: By removing nighttime values from the input data, the introduction of noise into the model is avoided, and information redundancy is reduced. At night, PV power generation is zero, which means that the data corresponding to this period does not provide relevant information to predict power generation during the day. Introducing this data could mislead the model and affect its ability to identify meaningful patterns during the active hours of the day.
Improvement of computational efficiency: By reducing the size of the data set, eliminating nighttime values improves the computational efficiency of the model. This enhances the model’s ability to predict daytime energy production accurately.
This allows the model to focus exclusively on generation patterns during the active hours of the PV system, which can enhance its ability to predict power production during the day accurately.
Before simulating and evaluating the FF-ANN, performing a normality analysis of the input data is necessary. The Anderson–Darling normality test was conducted to determine if the data followed a normal distribution, considering a significant p-value of 0.05. If the p-value is less than 0.05, it is considered that the data do not follow a normal distribution.⁵⁹. To carry out the test, the following equations must be followed: (1) and (2) from Table 4⁶⁰.
where (n) is the number of samples, (i) is the ith order observation, and (F(x)) is the cumulative distribution function.
Since the aim is to know if the data follow a normal distribution, the following equation is used:
where (n) is the number of samples.
The p-value is calculated depending on the result obtained in A, following the guidelines below:
A correlation assessment measures the strength and direction of the association between two variables. The result obtained in the Anderson–Darling normality test affects the type of correlation to be performed. If the data follow a normal distribution, Pearson’s method should be applied; on the other hand, Spearman’s method should be applied if the data do not follow a normal distribution. In this case, Spearman’s method is used, in which the correlation coefficient is a measure that varies in the range from − 1.0 to + 1.0, and its interpretation is as follows⁶¹:
Scores close to + 1 indicate a strong and positive correlation between the variables analyzed.
Scores close to − 1 indicate a strong and negative correlation between the variables analyzed.
Scores close to 0 indicate the absence of a linear correlation between the variables. Alternatively, there may be another type of correlation, but not a linear one.
Spearman’s correlation method is evaluated from Eq. (3).
where (n) is the number of samples, and ({d}_{i}) is the difference in ranks of the ith element.
To further improve the prediction accuracy, preliminary filtering of the data samples and elimination of singular data are necessary to avoid prediction errors. This is followed by normalization processing of the data in the range [0–1]. Hence, learning is accelerated, and the ANN prediction is improved. The formula used is as follows (4):
where ({x}_{i}) is the sampling data, ({x}_{min}) represents the lowest value observed within the data sequences. In contrast, ({x}_{max}) represents the highest value observed within the data sequences.
GAs are a search heuristic method inspired by the natural selection process that is viable for solving both constrained and unconstrained optimization problems. It mimics the mechanism of natural selection via biological evolution. GAs iteratively modify a population consisting of individual solutions. At each iteration, the algorithm selects certain individuals from the current population to serve as parents, and these parents are utilized to generate offspring for the subsequent generation. The population gradually “evolves” through successive generations toward an optimal solution. This algorithm emulates the natural selection process, whereby the most adaptive individuals are chosen for reproduction. By leveraging the genetic algorithm, it becomes possible to tackle mixed integer programming problems where certain components are subject to integer constraints⁶².
According to the study, the population of the GA is the different parameterizations that the ANN may assume; that is, each population is defined by the following individuals: number of neurons, transfer functions, weights, and biases, representing the architecture of the ANN within the scope. At the start of the algorithm, the initial population comprises a random set of parameter settings for the ANN. The evolutionary algorithm proceeds to iterate through successive generations, gradually improving the quality of the solutions. In each generation, the quality of each individual in the population is evaluated using a fitness function, which, in the proposed case, is the RMSE. This is a widely accepted and used practice in the literature; this choice is based on its ability to provide a clear and objective measure of model performance, enabling comparison and evaluation in different scenarios^63,64,65,66.
The fittest individuals are more likely to reproduce and produce offspring for the next generation. During this optimization process, the crossover and mutation genetic operators are applied when creating a new generation of individuals (the former to combine the characteristics of two individuals in the population and the latter to introduce genetic variability into the population from random genetic modifications), thus allowing the search operators to explore the search space in search of optimal solutions effectively. Furthermore, it is challenging to predefine specific ratios for each parameter, especially for mutation and crossover operators. According to the state of the art⁶⁷, and after testing various mutation and crossover operators, it was observed that the training results of the ANN were more favorable for the values defined in Table 4, with a much smaller mutation operator compared to the crossover operator. The population size was determined according to the number of variables to be optimized, which depends on the number of neurons involved. The maximum number of neurons was limited to 100 since the simulations showed that the best results were achieved with fewer neurons. As for the maximum number of generations, it was set to 100 times the population size. The maximum stagnation generation criterion of the algorithm was limited so that the algorithm stops if the average relative change in the value of the best-fit function is less than or equal to 1e-6. The algorithm continues to iterate through generations until a predefined stopping criterion is reached. This criterion can be a maximum number of generations, set to 100 times the number of individuals squared, thus ensuring a reliable search in the global minimum search space. Alternatively, the algorithm can stop if a minimum improvement in the quality of the solution is reached, that is, a minimum improvement in the mean relative change in the value of the best fitness function equal to or less than 1e-6. This threshold is lowered to ensure convergence to the global minimum (Table 5).
FF-ANNs are ANNs that process information in one direction, from the input to the output layer. They consist of an input, hidden, and output layer. The input layer is the first layer of the network; it is formed by input neurons that receive the initial data to the system for processing in the following layers of the ANN. The hidden layers are responsible for processing the information and transforming it into a form the output layer can use. The hidden layer is the final layer of the ANN that generates the ANN output/prediction based on the input data and the computations performed by the hidden layer⁶⁸.
The hidden layer of an FF-ANN can comprise one or multiple neurons. The optimal number of neurons is task-dependent, and its prudent selection significantly impacts performance, the ability to learn intricate patterns and training time. Furthermore, Input Weights (IW), Layer Weights (LW), and biases play a significant role in the performance of FF-ANNs. IW corresponds to the weights connecting the input layer to the first hidden layer, while LW represents the weights connecting neurons within a layer to neurons in the subsequent layer. Biases denote values added to the weighted sum of inputs for each neuron in a layer before undergoing an activation function. These parameters have been optimized using a GA to enhance the ANN’s performance. Equations (5) and (6) show the output layer during the forward pass⁶⁹.
where (j) is the (j) th node in the hidden layer, (k) is the (k) th node in the output layer, (y) is the output, (b) is the bias, (w) is the connection (weight) strength between nodes, and (g) is the activation function.
Conversely, transfer functions are another crucial aspect to consider. The transferFcn property defines the activation function employed by the network’s neurons. It takes the weighted sum of inputs for a neuron and applies a nonlinear transformation to generate the neuron’s output. The transferFcn plays an essential role in the ANN by facilitating learning and enabling predictions based on input data. MATLAB provides built-in activation functions that can serve as transferFcn in an ANN. The available options are presented in Table 6. The choice of transferFcn relies on the specific task and the ANN’s structural configuration. It is possible to assign a transferFcn for each layer within the network, enabling different activation functions to be used in various network parts.
It is important to emphasize that an analysis of the influence of different input data configurations on the neural network has been carried out. This analysis has included the comparison of the results obtained by training the network using aggregated data sets at the annual, seasonal, and monthly levels; the reason for this analysis is given by the following:
The annual training approach may allow for capturing long-term trends and patterns in solar power generation throughout the year, providing an overview of system behavior over time.
Seasonal training may allow for analyzing seasonal variations in solar power generation, considering changes in weather and environmental conditions at different times of the year.
Monthly training may allow for individually examining short-term variations in solar power generation within each month, which enables capturing more specific and detailed patterns in system behavior.
By using these different training approaches, a more complete and detailed understanding of the performance of the proposed model on various time scales is sought. Moreover, to ensure consistent training and reliable evaluation of ANN performance, a data distribution of 70% for training, 15% for testing, and 15% for validation has been used to provide a balanced representation of the data sets and to assess the generalizability of the model comprehensively.
This study employs a combined approach, through GA and ANN, to enhance the performance of an ANN for PV energy generation forecasting by optimizing its parametrization. Figure 3 shows the methodology employed in the proposed study.
GA-FFANN model structure.
Firstly, the measurements made on the actual PV installation are normalized, filtered, and divided into data sets; an FF-ANN is then created, and the GA initializes the population, consisting of several neurons, transferFcn, LW, IW, and biases. The computed information is used to calculate the fitness value. Consequently, the population defined by the GA is used to parameterize the FF-ANN, while the normalized data are used to train the ANN. The RMSE corresponding to the training phase is then calculated from the obtained values.
Secondly, if the RMSE is not the lowest or not the last generation, the GA procedure continues by applying the selection operator, the crossover operation, and the mutation operator to create a new generation and update the GA population. This iterative procedure continues until the GA obtains a minimum value for the fitness function or reaches the last generation.
On the contrary, if it is the last generation or the RMSE is lower than the previous generation, the GA results are extracted to configure an optimal FF-ANN architecture, the weather characteristics of the target day are introduced to the neural network, to predict the PV power generation. Finally, the estimates versus target evaluation metrics are calculated: RMSE, R-value, and mean percentage error.
Moreover, the measurements of twelve days have been employed for the PV generation prediction. These days have been chosen because each of the twelve days selected represents a different month of the year, which allows for analyzing how the model behaves concerning the seasonal variability of the input data. In addition, several criteria were considered when selecting the evaluation days. First, days with very stable PV generation conditions were sought to be included, which allowed for evaluation of how the model handled predictable and consistent situations (from May to September). Second, days with low power generation were included (January, February, and December), which allowed the model to be assessed for its ability to predict generation under low solar irradiance conditions. Finally, days with abrupt fluctuations in power generation were also selected (March, April, October, and November), which allowed evaluation of the model’s ability to adapt to rapid changes in weather conditions.
Regarding the data employed, after identifying solar irradiance and temperature as the most influential variables for the prediction of solar PV generation, the data set for the optimization comprised 6,402 records for solar irradiance, 6,402 records for temperature, and 6,402 records corresponding to solar PV generation measurements obtained using the power meter. Subsequently, 288 input data for the solar irradiance variable and 288 for temperature were used in the prediction stage. Moreover, according to the data partitioning scheme described in the current state of the art⁷⁰, 75% of the available data was used for the network training, while 15% was allocated for testing and another 15% was selected for validation purposes.
MLR is a commonly used statistical method for analyzing the relationship between multiple predictor variables and a response variable. In the context of solar power forecasting, the MLR model estimates PV power production as a function of various predictors, such as solar radiation and ambient temperature. Thus, historical PV power generation data and relevant meteorological variables are collected to implement the MLR model. These data are used to train the MLR model, where the regression coefficients are adjusted to minimize the prediction error. Once trained, the model can forecast future PV power production as a function of weather conditions.
The methodological approach based on the MLR model employs a series of coefficients. These are adjusted using the ordinary least squares method, which finds the model coefficients that minimize the sum of the squares of the differences between the observed and predicted values. Equation (7) expresses the multiple linear regression model.
where (y) is the response variable, ({X}_{1}), ({X}_{2}),…, ({X}_{k})​ are the predictor variables, and ({beta }_{0}), ({beta }_{1}),…, ({beta }_{k})​ are the coefficients of the model⁷¹. In the study context, ({X}_{1}) and ({X}_{2}), will be used, the former representing solar radiation and the latter representing ambient temperature.
The methodological approach applied in predicting PV power generation using the NAR model is based on the ability of neural networks to model nonlinear relationships in time series. This strategy focuses on time series forecasting, using a recurrent dynamic network based on a linear autoregressive model with feedback connections. It is assumed that the present behavior of the variable of interest will explain its future behavior, which is reflected in the nonlinear function used to calculate the next value based on the previous steps of the output signal, as illustrated in Eq. (8).
where (y) represents the PV data series over time (t), (d) is the input delay of the data series, and (f) denotes a transfer function⁵⁴.
While training the NAR model, the historical time series of PV power generation is used as input, which allows the model to learn specific patterns and behaviors of the PV plant under study. The model’s ability to capture the dynamic and nonlinear relationships in the data is optimized by adjusting the neural network parameters. The typical architecture of a NAR model includes feedback connections that enable the use of predictor variables to predict future values of the response variable, which gives the model the ability to capture feedback effects and time dependencies in the time series of PV power generation.
Different metrics have been used to evaluate the ANN optimization and prediction thoroughly. For this purpose, the RMSE, R-value, relative mean percentage error, and computation time.
It is a measure that evaluates the difference between the prediction versus target of the model. Low RMSE indicates better model performance⁷². Formula (9) shows its calculation procedure.
where the output obtained from the ANN is represented by the symbol ({o}_{predicted}), whereas ({o}_{target}) refers to the target value obtained from the experimental data. The symbol (N) represents the total number of samples used.
A linear regression analysis between forecast and target PV power, calculating the R-value and plotting its results, has been conducted to evaluate the model’s performance. The R = 1 factor reflects the quantity and quality of available data for training the ANN and the strength between the selected input and output variables during the training process⁷³.
It is used as a metric to quantify the variation between the predicted and actual values of the model, presented as a percentage relative to the actual value. It is employed to assess the precision of the model; a low mean percentage error value means superior model performance⁷⁴.
At night, when the PV power generation is zero and any of the implemented models has a different value, the relative percentage error is considered maximum, i.e., 100% or − 100%, as appropriate, as shown in the maximum and minimum values in Fig. 5.
This parameter is measured with the other metrics to determine whether GA suits the proposed purpose. The lower the computational time with high-accuracy results, the better.
The code associated with the approach followed can be accessed via the Harvard Dataverse repository (link in the data availability statement).
This section shows the main findings of the research paper. The section is divided into four subsections: variable correlation analysis, weather conditions, comparison between forecasted versus target values, and model performance.
The variables to be analyzed in this study are solar hour, temperature, solar irradiance, dew point, humidity, wind speed, pressure, precipitation rate, precipitation accumulated, and PV power. The results of this analysis will help to determine the strength of the relationship between the different variables.
Before the correlation analysis, an Anderson–Darling Normality test was performed to ensure the normality of the data measured. For this purpose, the p-value has been calculated for each group of measurements, obtaining results in the order of 0.0005. According to the test criteria, p-values less than 0.05 do not follow a normal distribution. Hence, Spearman’s correlation method has been used.
The results of the correlation analysis are shown in Table 7. The degree of strength of the relationships of the meteorological variables with the PV power measurement reflects the parameters that condition the PV power generation. Consequently, they serve to define the meteorological variables that enter the FF-ANN.
From the results of the Spearman correlation coefficient matrix, it can be deduced that the most influential variables in the generation of solar PV energy are, from highest to lowest contribution, solar irradiance, temperature, wind speed, humidity, dew point, solar hour, precipitation rate, precipitation accumulated and pressure, some of them having an inverse relationship. However, only two variables significantly impact PV power output: solar irradiance and temperature.
The prediction performance of twelve days, one from each month of an entire year, has been compared to evaluate the methodology’s effectiveness. Table 8 shows the weather summary for each simulated day.
Considering the variables that most affect PV power generation, it is concluded that the days between October and April are cloudy, while the rest are sunny days.
Figure 4 compares predicted versus target PV generation for the base ANN prediction, the three types of GA-FFANN training data: annual, seasonal, and monthly, versus the MLR and NAR models. This comparison is performed twelve times, once each month, to visualize different types of PV generation curves with varying weather conditions.
Comparison forecast versus Target PV power. (a) 01/09/2023, (b) 02/10/2023, (c) 03/06/2023, (d) 04/10/2023, (e) 05/09/2022, (f) 06/06/2022, (g) 07/11/2022, (h) 08/15/2022, (i) 09/05/2022, (j) 10/10/2022, (k) 11/14/2022, and (l) 12/10/2022.
In the following graphs, the best-performing methodologies are highlighted in bright colors, clearly and easily identifying the most effective approaches. Conversely, the methods that have shown inferior performance are represented with lighter colors and thicker lines, thus facilitating their visual differentiation and avoiding confusion between the curves. In addition, the scale used in the graph corresponds to the maximum scale of all simulated scenarios (2.5 kW), ensuring that the evaluations of the predictions are performed under the same conditions. This scale unification is necessary to provide a balanced and accurate comparison of each methodology’s performance in the scenarios analyzed.
Different days with different weather conditions have been chosen, one day each month, as shown in Fig. 4, to evaluate the adaptability of the ANN and its proper optimization through GA in different scenarios and with varying forecasting models. In this way, different PV power generation profiles can be studied.
Examining Fig. 4 individually, it is observed that the prediction models that best fit the actual data are the GA-FFANN annual train, GA-FFANN seasonal train, and NAR. These models show outstanding ability to predict PV power generation. However, evaluating all the results is essential, considering individual predictions and aggregated results.
Figure 5 shows the relative mean percentage error between the prediction and measurement curves for the twelve days simulated and for each training dataset of the base ANN forecast, GA-FFANN: annual, seasonal, and monthly, and for commonly used MLR and NAR models. In such figures, the best-performing methodologies have been highlighted in bright colors, whereas the poorest-performing methodologies have been depicted in less bright colors. It is important to note that negative error values indicate that the prediction is lower than the actual measurement. In contrast, positive values reflect that the prediction is higher than the observed measurement. The desired goal is for the error values to be as close to zero as possible, as this indicates a prediction that is very close to reality. This approximation is necessary to assess the accuracy and reliability of the analyzed methodologies.
Target versus Output relative percentage error. (a) 01/09/2023, (b) 02/10/2023, (c) 03/06/2023, (d) 04/10/2023, (e) 05/09/2022, (f) 06/06/2022, (g) 07/11/2022, (h) 08/15/2022, (i) 09/05/2022, (j) 10/10/2022, (k) 11/14/2022, and (l) 12/10/2022.
Analyzing the results in Fig. 5, it is highlighted that the base ANN and NAR model shows a higher prediction difficulty in simulations with abrupt fluctuations and low power generation characteristics than the GA-FFANN annual train model. The percentage errors for the base ANN, NAR, GA-FFANN monthly train, and MLR models can exceed 50%, indicating a lower accuracy in predicting these scenarios. On the other hand, the rest of the models trained with annual and seasonal data show a greater capacity to anticipate these challenging scenarios with higher accuracy.
Regarding estimating the most favorable training data type for PV power generation prediction in the proposed model versus the base ANN, MLR, and NAR models, Table 9 shows its performance by assessing the prediction RMSEs.
Furthermore, when analyzing the average RMSE of all the scenarios evaluated in Table 9, it is observed that the GA-FFANN model with annual data presents the lowest value (24.17 W), followed by the MLR model (53.50 W), GA-FFANN with seasonal data (58.58 W), then the NAR model (68.83 W), followed by the GA-FFANN model with seasonal data (71.50 W). The least favorable result corresponds to the base ANN (218.92 W). These results indicate that the GA-FFANN model with annual data is the most accurate overall, with the lowest RMSE.
The R coefficient values for the different models and scenarios represented in Fig. 6 are detailed in Fig. 6 and Table 10, where the coefficient of determination (R) values are presented; it is highlighted that the GA-FFANN model shows the best match between the real and simulated values. The models are ordered from least to best R: base ANN, NAR, GA-FFANN monthly data, GA-FFANN seasonal data, MLR, and GA-FFANN annual data. This indicates that the GA-FFANN model, especially when trained on annual data, has the most outstanding ability to predict PV power generation accurately.
Comparison of scatter plots of each model for each forecasted day. (a) 01/09/2023, (b) 02/10/2023, (c) 03/06/2023, (d) 04/10/2023, (e) 05/09/2022, (f) 06/06/2022, (g) 07/11/2022, (h) 08/15/2022, (i) 09/05/2022, (j) 10/10/2022, (k) 11/14/2022, and (l) 12/10/2022.
The wide range of scenarios evaluated, covering various environmental conditions and influencing factors, enables a thorough evaluation of the predictive performance of each model in different contexts.
Moreover, to facilitate visual interpretation of Fig. 6, those better-performing methodologies have been highlighted in bright colors, whereas less prominent methodologies have been represented in less striking colors. It must be noted that the closer the points on the scatter plot are to a straight line with a slope equal to 1, the higher the prediction accuracy. This is the main objective, as an alignment close to this line indicates that the predictions are consistent with the real measurements, thus reflecting the accuracy and reliability of the methodology employed.
In Fig. 6 and Table 10, where the coefficient of determination (R) values are presented, it is highlighted that the GA-FFANN model shows the best match between the real and simulated values. The models are ordered from least to best R: base ANN, NAR, GA-FFANN monthly data, GA-FFANN seasonal data, MLR, and GA-FFANN annual data. This indicates that the GA-FFANN model, especially when trained on annual data, has the most outstanding ability to predict PV power generation accurately.
Finally, the base ANN train computing time is 4 min and 47 s. Meanwhile, the forecasting time is 1 s on average for all cases. The computing times of GA training optimizations in the ANN models have been obtained for each training data set, as well as the GA (annual, seasonal, and monthly) (Table 10) and the forecasting times of the optimized ANN for each simulated day (Table 11).
This section compares various state-of-the-art PV energy prediction methodologies, evaluated in terms of the RMSE and R-ratio, between measured and predicted values. Table 12 summarizes the results of these benchmarks, highlighting the performance of different approaches in various test cases with different PV system capacities.
Specific cases indicate that RNN-LSTM and QT-MARF consistently achieve low RMSE and high R coefficients, outperforming other methods such as IAMFN, CNN-LSTM, and CNN-GRU. It is also observed that, although methods such as ELM and SVR offer competitive results, neural networks are the most effective for accurately predicting PV power generation.
This benchmark provides a comprehensive overview of the effectiveness of various PV prediction methodologies and highlights the importance of selecting the right approach based on system capacity and accuracy requirements. The findings indicate that advanced techniques based on recurrent neural networks offer significant advantages for accurate prediction of PV power generation.
This research focuses on developing a methodology to optimize PV power generation prediction by integrating ANN and GA. PV power generation forecasting is performed through an FF-ANN. On the one hand, the best training dataset for PV generation curve prediction from annual, seasonal, and monthly data is evaluated. On the other hand, the correct parameterization of ANNs is essential for achieving good learning capability and producing accurate results. However, this task is complex due to several factors, such as the large number of parameters to be adjusted, the interdependence of the parameters, and overfitting. Therefore, this work proposes using GA to optimally configure a predictive ANN tuning the number of neurons in the hidden layer, transferFcn, LW, IW, and biases.
Regarding the correlation analysis, it is deduced that solar irradiance intensity is the most crucial factor affecting the output power of PV plants (cc = 0.99), followed by the ambient temperature (cc = 49); the remaining variables have not been considered to have a considerable impact on the PV power output. For this reason, a 2-input ANN has been modeled. Moreover, the results of optimizing the ANNs based on GAs for each training methodology show a low optimal number of neurons, consistently equal to or less than 8. Also, the optimization results in the most suitable transfer function for most cases being ‘elliotsig’, which is named after an Elliot symmetric sigmoid transfer function.
Concerning the forecasted days, since one day has been chosen for each month of the year, scenarios that present a wide variety of weather conditions are explored, for example, days with high solar irradiance (06/06/2022), cloudy days (02/10/2023), or rainy days (10/10/2022).
The development of this work compares the measured PV energy generation with that predicted by ANN, GA-FFANN optimized in training for each month of the year, besides the MLR and NAR models. Observations show that in all months, the expected curve follows a steady increase similar to the measured curve at the beginning, and after reaching a peak, a gradual reduction of the energy. The energy generation in all curves is between 7:00 h and 20:00 h. Regarding the GA-FFANN, for January, February, March, May, and June, the predicted curves are very close to the actual generation; however, for the rest of the months, the training with monthly data is quite different from the simulated scenarios, having a worse optimization and prediction performance. The methodology for GA-FFANN optimization and energy prediction shows a good performance for the training with annual and seasonal data. Furthermore, concerning the literature methods, it is observed that the MLR always performs a lower prediction value than the rest of the simulated models.
The comparison between measurement and prediction can be extended through mean percentage errors at each instant. Optimization through training with annual data proved to have a lower relative daily mean percentage error than the other options. In contrast, using monthly data has resulted in worse performance in any of the predictions performed, as it can barely achieve 0% relative errors, as opposed to the annual and seasonal data optimizations. Concerning the NAR method, in all scenarios, there are very significant mean percentage errors in hours with no solar radiation; this is caused because the PV generation prediction should be zero while the model predicts low values. Concerning the MLR, this also occurs, although to a lesser extent, in addition to observing that in both methods of the prediction literature, the mean relative percentage errors are generally higher than the optimization performed with the proposed methodology. The base ANN model has one of the worst performances in the evaluation metrics presented. This model shows the importance of data filtering since it is observed that during nighttime hours, the model continues to erroneously predict PV generation, which distorts the performance of a PV forecasting ANN.
Additionally, the prediction capacity of the ANN optimized by GA is close to actual measurements, with minimum RMSEs of 13.4 W for the prediction with monthly data for March, 31.8 W for the forecast with seasonal data for February, and 15.6 W for the prediction with annual data for August. To evaluate which of the five methodologies has had a better performance, the average RMSEs obtained are 24 W, 59 W, 72 W, 53 W, 69 W, and 219 W for the annual, seasonal, monthly GA-FANN methodologies, MLR, NAR, and base ANN respectively, being the most favorable the first one. This may be due to the data provided during training, which allows the ANN greater adaptability than in the other two training cases, and the remarkable adaptability of the GA-FFANN performance in this proposed application type.
On the other hand, the regression analysis of prediction versus target has shown varying results. The aim is to obtain R-values as close to 1 as possible. Considering them, the lowest regressions are for October and March, when the days are cloudy and rainy when weather conditions are more unstable and fluctuating. Therefore, the optimization and prediction are more complex for cloudy days than sunny days with all simulated models, but favorable results with the proposed methodology are still obtained.
Regarding the overall performance of each model, the GA-ANN is trained annually, seasonally, and monthly; although these models require a considerable amount of computational resources due to optimization with GA and face challenges in adapting to abrupt and unanticipated changes in weather conditions, they have demonstrated greater adaptability and accuracy compared to other models. In contrast, the MLR model shows limited predictive capability, especially in capturing complex nonlinear relationships, and exhibits errors at low radiation by predicting low rather than zero values during hours without solar radiation. The NAR also suffers from significant errors at low radiation and has difficulty adapting to changing conditions. Lastly, the ANN without optimization shows suboptimal performance due to the lack of optimization and mispredicts power generation during nighttime hours. Despite the aforementioned limitations, the GA-ANN model has proven superior in most evaluations, standing out for its ability to provide more accurate predictions and improved reliability against input data variations.
Comparing the benchmark results with the GA-FFANN model, it is observed that the proposed model shows superior performance in terms of RMSE and R coefficient. For example, for the day 01/09/2023, the GA-FFANN achieves an RMSE of 20W and an R of 0.99851, while the best benchmark method, QT-MARF in case 1 (1600W), has an RMSE of 43W and an R of 0.99599. This indicates that the proposed model reduces the error by less than half and improves R. Moreover, for day 03/06/2023, the GA-FFANN presents an RMSE of 33W and an R of 0.99945, compared to the RNN-LSTM in case 3 (2000W), which has an RMSE of 30W and an R of 0.99750. Although the RNN-LSTM shows a slightly lower RMSE, the correlation coefficient of the proposed model is significantly better. Furthermore, on days such as 08/15/2022, the proposed model achieves an RMSE of 16W and an R of 0.99976, compared to the best benchmark performance in case 4 (1500W) with RNN-LSTM, which has an RMSE of 20W and an R of 0.99715. This again shows that the proposed model not only has a lower prediction error but also a better R. Summing up, in the remaining cases, the comparison indicates that the proposed GA-FFANN model not only remains competitive against the best methods reported in the literature but mostly, especially with annual data, outperforms the RNN-LSTM and QT-MARF based methods in accuracy. For example, the proposed model obtains RMSE from 16 to 38W and R from 0.99850 to 0.99976, while the best benchmark methods have RMSE from 20 to 90W and R from 0.93140 to 0.99800. This performance highlights the GA-FFANN’s effectiveness for accurate PV power generation prediction, offering a reliable solution that provides advantages for renewable energy systems applications.
Finally, the GA computation time varies according to several factors: population size (number of neurons, transfer functions, weights, and biases), the complexity of the transfer function, and computational capacity of the computer (in this case, an Intel® Core™ i5 processor has been used). The training computation time varies between 1 and 5 h. The forecasting computation time is much lower since it only takes a few seconds. These computational times are scalable; if a computer with a higher computational capacity were available, they would be decreased.
This study introduces a novel approach employing a GA-based ANN to enhance the accuracy of PV power plant forecasting. The algorithm dynamically modifies the ANN’s architecture during training to minimize the RMSE. Various parameters, including the number of neurons, transfer functions, weights, and biases, are incorporated into the optimization function. Twelve representative days were selected for analysis to assess the ANN’s efficacy, utilizing annual, seasonal, and monthly input training data. The proposed methodology’s performance has been evaluated using a data acquisition system implemented in an actual PV generation facility, considering different weather conditions (sunny and cloudy, rainy). Moreover, a comparison with three training methodologies (annual, seasonal, and monthly) has been carried out, showing that the PV prediction performance of ANNs can be improved by using GA to optimize their parametrization.
Additionally, a correlation analysis of the meteorological variables with the most decisive influence on PV power generation was necessary to carry out the study. As a result, it was obtained that the factors with the most significant are solar irradiance (cc = 0.99) and temperature (cc = 0.47). Furthermore, the forecasting RMSE calculation has shown that the training methodology with better performance has been for annual data sets, most likely due to the large amount of input data provided during training enabling a better adaptation of the ANN to meteorological changes, as well as indicating that the model exhibits a more remarkable ability to capture the complex relationships present in annual temporal data compared to those of a seasonal or monthly nature. Also, the prediction versus target regression analysis helps to understand that optimization and prediction are more complex in cloudy scenarios (between October and March), with minimum regressions of 0.958241 on 12/10/2022 and instead, maximum regressions of 0.99931 on 05/09/2022.
Upon further comparison of results, the GA-FFANN model reveals a clear superiority in the PV power generation prediction context over other well-known models used in the forecasting literature, such as ANN, the MLR, and NAR models. The RMSE analysis showed that the GA-FFANN annual training obtained significantly lower errors in the prediction of solar power generation. The R values also reflected these differences, where GA-FFANN achieved R coefficients closer to 1 than the base ANN, MLR, and NAR models. These findings support the superiority of GA-FFANN in accurately and efficiently predicting solar PV power generation compared to the traditional ANN, MLR, and NAR models.
Finally, the outstanding results demonstrate the favorable performance of GAs in optimizing ANNs to predict PV power generation. The ability of GAs to dynamically adapt the neural network architecture during training, thus minimizing the RMSE, highlights their effectiveness in this context. These promising results suggest that the proposed integration can be a powerful and effective tool for improving the prediction accuracy of PV power generation, which has significant implications for the efficiency and management of PV plants under varying conditions. Future works must explore a more detailed study of the system’s response to other potential weather disturbances and further validation.
Sequence data that support this study have been deposited and can be accessible through the following link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:https://doi.org/10.7910/DVN/IIV7PI. The model approach followed in this study has been deposited and can be accessible through the following link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:https://doi.org/10.7910/DVN/RRGMCZ
Anderson test
Anderson–Darling normality test
Artificial neural network
Convolutional neural networks-gated recurrent unit
Convolutional neural networks-long short-term memory
Extreme learning machine
Feed forward-artificial neural networks
Genetic algorithm
Grey wolf optimization
Attention-based memory fully-connected network
Input weights
Layer weights
Multiple linear regression
Multimicrogrid
Nonlinear autoregressive neural network
Particle swarm optimization
Photovoltaic
Quantile-transformed multi-attention residual framework
Coefficient of determination
Root mean square error
Recurrent neural network
Recurrent neural network-long short-term memory
Support vector regression
Number of samples
Cumulative distribution function
Spearman’s correlation
Difference in ranks of the ith element
Normalized sample
Sampling data
Lowest value observed within the data sequences
The highest value observed within the data sequences
Network nomenclature
Bias of network
Connection (weight) strength between nodes of a network
Network activation function
Predictor variables of multiple linear regression
Coefficients of multiple linear regression
Input delay of the data series in nonlinear autoregressive neural network
Transfer function of nonlinear autoregressive neural network
Time of the time series in autoregressive neural network
Output obtained from the artificial neural network
Target value obtained from the experimental data
Mean percentage error
Energy and the Green Deal. Accessed 1 Jun 2023; https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal/energy-and-green-deal_en.
Díaz-Bello, D., Vargas-Salgado, C., Águila-León, J. & Lara-Vargas, F. Methodology to estimate the impact of the DC to AC power ratio, azimuth, and slope on clipping losses of solar photovoltaic inverters: Application to a PV system located in valencia Spain. Sustainability (Switzerland) 15, 2797. https://doi.org/10.3390/su15032797 (2023).
Article  Google Scholar 
Vargas-Salgado, C., Berna-Escriche, C., Escrivá-Castells, A. & Díaz-Bello, D. Optimization of all-renewable generation mix according to different demand response scenarios to cover all the electricity demand forecast by 2040: The case of the grand canary Island. Sustainability (Switzerland) 14, 1738. https://doi.org/10.3390/su14031738 (2022).
Article  Google Scholar 
Elkadeem, M. R., Wang, S., Sharshir, S. W. & Atia, E. G. Feasibility analysis and techno-economic design of grid-isolated hybrid renewable energy system for electrification of agriculture and irrigation area: A case study in Dongola, Sudan. Energy Convers. Manag. 196, 1453–1478. https://doi.org/10.1016/j.enconman.2019.06.085 (2019).
Article  ADS  Google Scholar 
Watson, S. et al. Advantages of operation flexibility and load sizing for PV-powered system design. Sol. Energy 162, 132–139. https://doi.org/10.1016/j.solener.2018.01.022 (2018).
Article  ADS  MATH  Google Scholar 
Gómez-Navarro, T., Brazzini, T., Alfonso-Solar, D. & Vargas-Salgado, C. Analysis of the potential for PV rooftop prosumer production: Technical, economic and environmental assessment for the city of Valencia (Spain). Renew. Energy 174, 372–381. https://doi.org/10.1016/j.renene.2021.04.049 (2021).
Article  Google Scholar 
Bhattacharya, S., Sadhu, P. K. & Sarkar, D. Performance evaluation of building integrated photovoltaic system arrays (SP, TT, QT, and TCT) to improve maximum power with low mismatch loss under partial shading. Microsyst. Technol. https://doi.org/10.1007/s00542-023-05564-0 (2023).
Article  PubMed  PubMed Central  MATH  Google Scholar 
Sarkar, D. & Sadhu. P. K. Power Enhancement by hybrid BIPV arrays with fewer peaks and reduced mismatch losses under partial shading. In 2023 3rd International Conference on Intelligent Technologies, CONIT 2023. (Institute of Electrical and Electronics Engineers Inc., 2023).
Sarkar, D. & Sadhu. P. K. GMPP Improvement with fewer power peaks and lower mismatch losses using a new hybrid BIPV array configuration. In 5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023. (Institute of Electrical and Electronics Engineers Inc., 2023).
Aguila-Leon, J., Vargas-Salgado, C., Chiñas-Palacios, C. & Díaz-Bello, D. Solar photovoltaic maximum power point tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms. Expert. Syst. Appl. 211, 118700. https://doi.org/10.1016/j.eswa.2022.118700 (2023).
Article  Google Scholar 
Aguila-Leon, J., Vargas-Salgado, C., Chiñas-Palacios, C. & Díaz-Bello, D. Energy management model for a standalone hybrid microgrid through a particle Swarm optimization and artificial neural networks approach. Energy Convers. Manag. 267, 115920. https://doi.org/10.1016/j.enconman.2022.115920 (2022).
Article  Google Scholar 
Alogdianakis, F., Dimitriou, L. & Charmpis, D. C. Data-driven recognition and modelling of deterioration patterns in the US National Bridge Inventory: A genetic algorithm-artificial neural network framework. Adv. Eng. Softw. 171, 103148. https://doi.org/10.1016/j.advengsoft.2022.103148 (2022).
Article  MATH  Google Scholar 
Roldán-Blay, C. et al. Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model. Energy Build. 60, 38–46. https://doi.org/10.1016/j.enbuild.2012.12.009 (2013).
Article  MATH  Google Scholar 
Zeynali, S., Rostami, N., Ahmadian, A. & Elkamel, A. Two-stage stochastic home energy management strategy considering electric vehicle and battery energy storage system: An ANN-based scenario generation methodology. Sustain. Energy Technol. Assess. 39, 100722. https://doi.org/10.1016/j.seta.2020.100722 (2020).
Article  MATH  Google Scholar 
Saryazdi, S., Mohammad, E., Etemad, A., Shafaat, A. & Bahman, A. M. Data-driven performance analysis of a residential building applying artificial neural network (ANN) and multi-objective genetic algorithm (GA). Build. Environ. 225, 109633. https://doi.org/10.1016/j.buildenv.2022.109633 (2022).
Article  Google Scholar 
Kemmoku, Y., Orita, S., Nakagawa, S. & Sakakibara. T. Daily insolation forecasting using a multi-stage neural network (1999)
Sfetsos, A. & Coonick, A. H. Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Sol. Energy 68(2), 169–178. https://doi.org/10.1016/S0038-092X(99)00064-X (2000).
Article  ADS  MATH  Google Scholar 
Braik, M., Al-Zoubi, H. & Al-Hiary, H. Artificial neural networks training via bio-inspired optimisation algorithms: Modelling industrial winding process, case study. Soft comput. 25, 4545–4569. https://doi.org/10.1007/s00500-020-05464-9 (2021).
Article  MATH  Google Scholar 
Chandrasekaran, K., Selvaraj, J., Xavier, F. J. & Kandasamy, P. Artificial neural network integrated with bio-inspired approach for optimal VAr management and voltage profile enhancement in grid system. Energy Sour. Part A Recov. Util. Environ. Effects 43, 2838–2859. https://doi.org/10.1080/15567036.2021.1919790 (2021).
Article  Google Scholar 
Chiñas-Palacios, C., Vargas-Salgado, C., Aguila-Leon, J. & Hurtado-Pérez, E. A cascade hybrid PSO feed-forward neural network model of a biomass gasification plant for covering the energy demand in an AC microgrid. Energy Convers. Manag. 232, 113896. https://doi.org/10.1016/j.enconman.2021.113896 (2021).
Article  Google Scholar 
Safari, A., Kharrati, H. & Rahimi, A. Multi-term electrical load forecasting of smart cities using a new hybrid highly accurate neural network-based predictive model. Smart Grids Sustain. Energy 9, 8. https://doi.org/10.1007/s40866-023-00188-9 (2024).
Article  MATH  Google Scholar 
Aguila-Leon, J. et al. Particle swarm optimization, genetic Algorithm and grey Wolf optimizer algorithms performance comparative for a DC-DC boost converter PID controller. Adv. Sci. Technol. Eng. Syst. 6, 619–625. https://doi.org/10.25046/aj060167 (2021).
Article  Google Scholar 
Aguila-Leon, J., Chiñas-Palacios, C., Garcia, E. X. M. & Vargas-Salgado, C. A multimicrogrid energy management model implementing an evolutionary game-theoretic approach. Int. Trans. Electr. Energy Syst. 30, e12617. https://doi.org/10.1002/2050-7038.12617 (2020).
Article  Google Scholar 
Meng, L. et al. Multi-objective optimization of plate heat exchanger for commercial electric vehicle based on genetic algorithm. Case Stud. Therm. Eng. 41, 102629. https://doi.org/10.1016/j.csite.2022.102629 (2023).
Article  MATH  Google Scholar 
Zhang, F. et al. Performance improvement of a pump as turbine in storage mode by optimization design based on genetic algorithm and fuzzy logic. J. Energy Storage 62, 106875. https://doi.org/10.1016/j.est.2023.106875 (2023).
Article  MATH  Google Scholar 
Ahn, G., Jin, M. K., Hwang, S. B. & Hur, S. Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning. Heliyon https://doi.org/10.1016/j.heliyon.2022.e12111 (2022).
Article  PubMed  PubMed Central  MATH  Google Scholar 
Chen, Q. & Hu, X. Design of intelligent control system for agricultural greenhouses based on adaptive improved genetic algorithm for multi-energy supply system. Energy Rep. 8, 12126–12138 (2022).
Article  MathSciNet  MATH  Google Scholar 
Dolara, A., Leva, S. & Manzolini, G. Comparison of different physical models for PV power output prediction. Sol. Energy 119, 83–99. https://doi.org/10.1016/j.solener.2015.06.017 (2015).
Article  ADS  MATH  Google Scholar 
Massidda, L., Bettio, F. & Marrocu, M. Probabilistic day-ahead prediction of PV generation. A comparative analysis of forecasting methodologies and of the factors influencing accuracy. Sol. Energy 271, 112422. https://doi.org/10.1016/j.solener.2024.112422 (2024).
Article  MATH  Google Scholar 
Sahin, G., Isik, G. & van Sark, W. G. J. H. M. Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression. Energy Rep. 10, 2837–2849. https://doi.org/10.1016/j.egyr.2023.09.097 (2023).
Article  MATH  Google Scholar 
Scott, C., Ahsan, M. & Albarbar, A. Machine learning for forecasting a photovoltaic (PV) generation system. Energy 278, 127807. https://doi.org/10.1016/j.energy.2023.127807 (2023).
Article  MATH  Google Scholar 
Wu, Y. K., Huang, C. L., Phan, Q. T. & Li, Y. Y. Completed review of various solar power forecasting techniques considering different viewpoints. Energies (Basel) 15, 3320 (2022).
Article  MATH  Google Scholar 
Moreira, M. O. et al. Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting. Renew. Sustain. Energy Rev. 135, 110450. https://doi.org/10.1016/j.rser.2020.110450 (2021).
Article  MATH  Google Scholar 
Aslam Khan, M. N. et al. Prediction of thermal diffusivity of volcanic rocks using machine learning and genetic algorithm hybrid strategy. Int. J. Therm. Sci. 192, 108403. https://doi.org/10.1016/j.ijthermalsci.2023.108403 (2023).
Article  MATH  Google Scholar 
Xiong, J., Liang, W., Liang, X. & Yao, J. Intelligent quantification of natural gas pipeline defects using improved sparrow search algorithm and deep extreme learning machine. Chem. Eng. Res. Des. 183, 567–579. https://doi.org/10.1016/j.cherd.2022.06.001 (2022).
Article  CAS  MATH  Google Scholar 
Ji, B. et al. Research on optimal intelligent routing algorithm for IoV with machine learning and smart contract. Digit. Commun. Netw. 9, 47–55. https://doi.org/10.1016/j.dcan.2022.06.012 (2023).
Article  MATH  Google Scholar 
Wu, L. et al. Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: Application in contrasting climates of China. J. Hydrol. (Amst) 577, 123960. https://doi.org/10.1016/j.jhydrol.2019.123960 (2019).
Article  Google Scholar 
Nadirgil, O. Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm. J. Environ. Manage 342, 118061. https://doi.org/10.1016/j.jenvman.2023.118061 (2023).
Article  PubMed  Google Scholar 
Zheng, S., Xiao, Y. & Liu, J. Automatic prediction modeling for Time-Series degradation data via Genetic algorithm with applications in nuclear energy. Ann. Nucl. Energy 186, 109781. https://doi.org/10.1016/j.anucene.2023.109781 (2023).
Article  CAS  MATH  Google Scholar 
Cheng, T., Zhu, X., Yang, F. & Wang, W. Machine learning enabled learning based optimization algorithm in digital twin simulator for management of smart islanded solar-based microgrids. Solar Energy 250, 241–247. https://doi.org/10.1016/j.solener.2022.12.040 (2023).
Article  ADS  Google Scholar 
Muruganandam, S. et al. A deep learning based feed forward artificial neural network to predict the K-barriers for intrusion detection using a wireless sensor network. Meas. Sens. 25, 100613. https://doi.org/10.1016/j.measen.2022.100613 (2023).
Article  MATH  Google Scholar 
Tian, S. et al. Using perceptron feed-forward artificial neural network (ANN) for predicting the thermal conductivity of graphene oxide-Al₂O₃/water-ethylene glycol hybrid nanofluid. Case Stud. Therm. Eng. 26, 101055. https://doi.org/10.1016/j.csite.2021.101055 (2021).
Article  MATH  Google Scholar 
Hajduk, Z. Reconfigurable FPGA implementation of neural networks. Neurocomputing 308, 227–234. https://doi.org/10.1016/j.neucom.2018.04.077 (2018).
Article  MATH  Google Scholar 
Davis, D. & Brear, M. J. Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets. Energy Econ. 130, 107304. https://doi.org/10.1016/j.eneco.2024.107304 (2024).
Article  Google Scholar 
Matrenin, P. et al. Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change. Energy Rep. 8, 439–447. https://doi.org/10.1016/j.egyr.2021.11.112 (2022).
Article  MATH  Google Scholar 
Kim, J. H., Lee, B. S. & Kim, C. H. A Study on the development of long-term hybrid electrical load forecasting model based on MLP and statistics using massive actual data considering field applications. Electric Power Syst. Res. 221, 109415. https://doi.org/10.1016/j.epsr.2023.109415 (2023).
Article  MATH  Google Scholar 
Uyeh, D. D. et al. Grid search for lowest root mean squared error in predicting optimal sensor location in protected cultivation systems. Front. Plant Sci. 13, 920284. https://doi.org/10.3389/fpls.2022.920284 (2022).
Article  PubMed  PubMed Central  MATH  Google Scholar 
Wang, J. et al. Photovoltaic cell parameter estimation based on improved equilibrium optimizer algorithm. Energy Convers. Manag. 236, 114051. https://doi.org/10.1016/j.enconman.2021.114051 (2021).
Article  ADS  MATH  Google Scholar 
Wu, X. et al. Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm. Renew. Sustain. Energy Rev. 167, 112703. https://doi.org/10.1016/j.rser.2022.112703 (2022).
Article  MATH  Google Scholar 
AlShafeey, M. & Csáki, C. Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods. Energy Rep. 7, 7601–7614. https://doi.org/10.1016/j.egyr.2021.10.125 (2021).
Article  MATH  Google Scholar 
Babatunde, A. A. & Abbasoglu, S. Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool. Environ. Prog. Sustain. Energy 38, 13098. https://doi.org/10.1002/ep.13098 (2019).
Article  CAS  MATH  Google Scholar 
De Giorgi, M. G., Congedo, P. M. & Malvoni, M. Photovoltaic power forecasting using statistical methods: Impact of weather data. IET Sci. Meas. Technol. 8, 90–97. https://doi.org/10.1049/iet-smt.2013.0135 (2014).
Article  MATH  Google Scholar 
Khojasteh, D. N. et al. Long-term effects of outdoor air pollution on mortality and morbidity–prediction using nonlinear autoregressive and artificial neural networks models. Atmos. Pollut. Res. 12, 46–56. https://doi.org/10.1016/j.apr.2020.10.007 (2021).
Article  CAS  MATH  Google Scholar 
Nogay, H. S. Estimating the aggregated available capacity for vehicle to grid services using deep learning and nonlinear autoregressive neural network. Sustain. Energy Grids Netw. 29, 100590. https://doi.org/10.1016/j.segan.2021.100590 (2022).
Article  Google Scholar 
Sunayana, K. S. & Kumar, R. Forecasting of municipal solid waste generation using non-linear autoregressive (NAR) neural models. Waste Manag. 121, 206–214. https://doi.org/10.1016/j.wasman.2020.12.011 (2021).
Article  CAS  PubMed  MATH  Google Scholar 
Sobri, S., Koohi-Kamali, S. & Rahim, N. A. Solar photovoltaic generation forecasting methods: A review. Energy Convers. Manag. 156, 459–497 (2018).
Article  ADS  Google Scholar 
Jakoplić, A., Franković, D., Kirinčić, V. & Plavšić, T. Benefits of short-term photovoltaic power production forecasting to the power system. Optim. Eng. 22, 9–27. https://doi.org/10.1007/s11081-020-09583-y (2021).
Article  Google Scholar 
Behera, M. K. & Nayak, N. A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm. Eng. Sci. Technol. Int. J. 23, 156–167. https://doi.org/10.1016/j.jestch.2019.03.006 (2020).
Article  MATH  Google Scholar 
El estadístico de Anderson-Darling – Minitab. Accessed 13 Mar 2023; https://support.minitab.com/es-mx/minitab/21/help-and-how-to/statistics/basic-statistics/supporting-topics/normality/the-anderson-darling-statistic/.
Puig, P. & Stephens, M. A. Tests of fit for the laplace distribution, with applications. Technometrics 42, 417–424. https://doi.org/10.1080/00401706.2000.10485715 (2000).
Article  MathSciNet  MATH  Google Scholar 
Extenso Juliana
Kramer O Studies in Computational Intelligence 679 Genetic Algorithm Essentials
Abdel-Basset, M. et al. Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques: A comparative study. Expert Syst. Appl. 255, 124777. https://doi.org/10.1016/j.eswa.2024.124777 (2024).
Article  MATH  Google Scholar 
Gao, X. et al. Special trans function based exact expressions for the double and triple diode models of solar cells: Superior fitness, accuracy and convergence. Energy Rep. 11, 5252–5270. https://doi.org/10.1016/j.egyr.2024.05.016 (2024).
Article  MATH  Google Scholar 
Liu, Q. et al. Multi-strategy adaptive guidance differential evolution algorithm using fitness-distance balance and opposition-based learning for constrained global optimization of photovoltaic cells and modules. Appl. Energy 353, 122032. https://doi.org/10.1016/j.apenergy.2023.122032 (2024).
Article  Google Scholar 
Chen, X. et al. A two-stage method for model parameter identification based on the maximum power matching and improved flow direction algorithm. Energy Convers. Manag. 278, 116712. https://doi.org/10.1016/j.enconman.2023.116712 (2023).
Article  MATH  Google Scholar 
Hassanat, A. et al. Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Information (Switzerland) 10, 390. https://doi.org/10.3390/info10120390 (2019).
Article  Google Scholar 
Ketkar N, Moolayil J (2021) Deep learning with python: Learn Best Practices of Deep Learning Models with PyTorch. Apress Media LLC
Adedeji, B. P. & Kabir, G. A feedforward deep neural network for predicting the state-of-charge of lithium-ion battery in electric vehicles. Decis. Anal. J. https://doi.org/10.1016/j.dajour.2023.100255 (2023).
Article  MATH  Google Scholar 
Genç, B. & Tunç, H. Optimal training and test sets design for machine learning. Turk. J. Electr. Eng. Comput. Sci. 27, 1534–1545. https://doi.org/10.3906/elk-1807-212 (2019).
Article  MATH  Google Scholar 
Granados RM Modelos de regresión lineal múltiple
Ćalasan, M., Abdel Aleem, S. H. E. & Zobaa, A. F. On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function. Energy Convers. Manag. https://doi.org/10.1016/j.enconman.2020.112716 (2020).
Article  Google Scholar 
Regresión lineal – MATLAB & Simulink – MathWorks España. Accessed 12 Mar 2024; https://es.mathworks.com/help/matlab/data_analysis/linear-regression.html
Baraldo M, Furlanut M (1995) ERROR: A USER’S NOTE
Mirza, A. F. et al. Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction. Renew. Energy https://doi.org/10.1016/j.renene.2023.119604 (2024).
Article  MATH  Google Scholar 
Akhter, M. N. et al. An hour-ahead PV power forecasting method based on an RNN-LSTM model for three different PV plants. Energies (Basel) https://doi.org/10.3390/en15062243 (2022).
Article  PubMed Central  Google Scholar 
Hossain, M. et al. Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. J. Clean Prod. 167, 395–405. https://doi.org/10.1016/j.jclepro.2017.08.081 (2017).
Article  MATH  Google Scholar 
Download references
This research has been funded by “Modelado, experimentación y desarrollo de sistemas de gestión óptima para microrredes híbridas renovables” (CIGE/2021/172). (01/01/22–31/12/23). Investigación competitiva proyectos. Conselleria de Educación, Universidades y Empleo, GENERALITAT VALENCIANA. Additionally, one of the authors (D.D.B) was supported by the Ministry of Universities of Spain under the grant FPU21/00677.
Instituto de Ingeniería Energética, Universitat Politècnica de València, Valencia, Spain
Dácil Díaz-Bello, Carlos Vargas-Salgado, Manuel Alcazar-Ortega & David Alfonso-Solar
Departamento de Ingeniería Eléctrica, Universitat Politècnica de València, Valencia, Spain
Carlos Vargas-Salgado & Manuel Alcazar-Ortega
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
D.D.-B. Conceptualization, Methodology, Data curation, Writing—original draft, Visualization, Investigation, Validation. C.V.-S.: Conceptualization, Methodology, Visualization, Investigation, Supervision, Validation, Writing—review & editing. M.A.-O.: Conceptualization, Methodology, Data curation, Writing—original draft. All authors reviewed the manuscript. D.A.-S.: Data curation, Writing—original draft, Supervision, Writing—review & editing.
Correspondence to Carlos Vargas-Salgado.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The parameterization resulting from ANN optimization across GA for PV power generation prediction is shown in Tables 13 and 14. The optimal setting is specified for each training methodology: annual, seasonal, and monthly, by showing the number of neurons of the hidden layer, the transfer functions to be implemented, and the values of IW, LW, and biases.
The parameterization resulting from the base ANN for PV power generation prediction is shown in Tables 15 and 16.
The parameterization resulting from MLR for PV power generation prediction is shown in Table 17.
The parameterization resulting from NAR for PV power generation prediction is shown in Table 18.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
Díaz-Bello, D., Vargas-Salgado, C., Alcazar-Ortega, M. et al. Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement. Sci Rep 15, 3337 (2025). https://doi.org/10.1038/s41598-024-80424-z
Download citation
Received: 16 January 2024
Accepted: 19 November 2024
Published: 27 January 2025
Version of record: 27 January 2025
DOI: https://doi.org/10.1038/s41598-024-80424-z
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative
Scientific Reports (2025)
Advertisement
Scientific Reports (Sci Rep)
ISSN 2045-2322 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source

Solupup Expands Portable Solar Line to Meet Growing Demand for Off-Grid and Emergency Power Solutions  Barchart
source

New Yorkers in apartments could soon plug solar panels into their wall outlets to cut bills  Yahoo
source

Infinity Power inks 3 renewable energy agreements at AEF  Enlit World
source

From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox.
Discover the engineering revolution transforming modern defense with Strength, Stealth, Speed: The Very Fast Future of Advanced Defense
Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience.
All Rights Reserved, IE Media, Inc.
Follow Us On
Future of Defense
Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience.
All Rights Reserved, IE Media, Inc.
Beyond higher efficiency, perovskite technology offers the potential for lower manufacturing costs.
Chinese solar manufacturer Trina Solar has unveiled a commercial-scale perovskite-silicon tandem solar panel that has set a new global benchmark for efficiency and power output, according to a report.

The newly developed module achieved a conversion efficiency of 29.2 percent and a power output of 907 watts, making it one of the most powerful solar panels ever produced for commercial applications. The achievement highlights the rapid progress being made in perovskite-based photovoltaic technology, which many experts consider the next major evolution in solar energy.
Traditional solar panels rely primarily on silicon cells to convert sunlight into electricity. While silicon technology has improved steadily over the years, it is approaching its practical efficiency limits. Perovskite materials offer a promising alternative because they can absorb different parts of the solar spectrum more effectively. By combining a perovskite layer with a silicon cell in a tandem structure, manufacturers can capture more sunlight and generate significantly more electricity from the same panel area.

The record-setting module uses an advanced tandem design that integrates a perovskite top cell with a silicon bottom cell. This configuration enables higher energy conversion rates than conventional silicon-only panels. Independent testing confirmed the panel’s performance, demonstrating that the technology is moving beyond laboratory experiments and toward large-scale commercial deployment.
China’s latest achievement also represents a significant milestone in the global race for solar innovation. The breakthrough allows the country to reclaim a leading position in solar efficiency after strong competition from manufacturers in other regions. It further strengthens China’s role as the world’s largest producer of solar equipment and a major driver of renewable energy development.

Beyond higher efficiency, perovskite technology offers the potential for lower manufacturing costs. The materials can be produced using simpler processes and require less energy-intensive manufacturing compared to traditional silicon cells. If commercial production can be scaled successfully, the technology could reduce the cost of solar electricity while increasing energy output.

However, challenges remain before perovskite solar panels become mainstream. One of the industry’s biggest concerns is long-term durability. While silicon panels can operate efficiently for decades, perovskite materials are more sensitive to environmental factors such as heat, moisture, and ultraviolet radiation. Researchers and manufacturers are therefore focused on improving stability and ensuring that the new generation of panels can withstand real-world operating conditions over extended periods.

Despite these hurdles, the latest efficiency record demonstrates how quickly the technology is advancing. Industry analysts believe that tandem solar cells combining silicon and perovskite materials could become a key component of future renewable energy systems, helping countries generate more power from limited space while accelerating the transition to clean energy.
Trina’s solar cell isn’t just any solar cell. Its record was achieved using a perovskite-on-silicon tandem design, which stacks two different solar materials on top of each other to capture a broader range of sunlight. The perovskite layer absorbs higher-energy wavelengths while the silicon layer captures light that would otherwise pass through, allowing the cell to convert more of the sun’s energy into electricity, reported Oil Price.

Prabhat, an alumnus of the Indian Institute of Mass Communication, is a tech and defense journalist. While he enjoys writing on modern weapons and emerging tech, he has also reported on global politics and business. He has been previously associated with well-known media houses, including the International Business Times (Singapore Edition) and ANI.
Premium
Follow

source

Vyhanek: The economics of utility-scale solar have always depended on execution – getting the right equipment to the right place at the right time, then keeping systems running at full capacity. But as utility-scale solar projects grow larger and more complex, managing multiple suppliers for trackers, transformers, eBOS, and inspection programs creates more handoffs, greater risk, and more time spent on logistics rather than performance.
Vyhanek: GameChange Energy, with over 58 GW of trackers deployed across six continents since 2012, has spent the past two years building an integrated platform to reduce the supply chain burden on developers and EPCs.
Last year, GameChange’s expansion into transformer manufacturing addressed one of the industry’s most persistent procurement bottlenecks. With lead times stretching to 18 months or more, developers have faced delays that threaten project economics before construction even begins. GameChange’s 180,000 square foot Navi Mumbai facility, with 5,400 MVA of annual capacity, delivers in weeks. It’s already surpassed 1,400 MVA in transformer orders since opening in 2025. The facility is ISO 9001, 14001, and 45001 certified and recently earned CPRI short-circuit certification, providing the third-party validation that bankability assessments require.
Vyhanek: TerraSmart’s eBOS division, with its Michigan-based manufacturing and 14 GW of deployed experience, has brought a different kind of value to GameChange. eBOS has traditionally been specified late in the design process, after tracker and racking layouts are fixed – limiting the opportunity to optimize how power flows from panel to inverter to transformers.
With tracker and eBOS engineering now under one roof, developers benefit from layouts designed together from the start. For instance, optimized wire topology can reduce string wire usage by up to 25% on a given site, lowering both material costs and resistive losses.
Vyhanek: This partnership extends integration into long-term operations. By connecting GeniusVision (GameChange’s tracker monitoring solution) with Raptor Maps’ Sentry robotic inspection platform, O&M teams gain a system that responds to anomalies autonomously – deploying inspections without crew dispatch, returning data to improve tracker performance over time, and providing systematic site coverage after severe weather events that would otherwise take days to assess manually.
Vyhanek: These three expansions, now brought under the single brand GameChange Energy, model client-centered integration that results in fewer handoffs, less overhead coordination, and more accountability concentrated in a single partner throughout a project’s lifetime.
The questions and responses in this sponsored interview article were provided by GameChange Solar. 
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
The June issue of pv magazine Global is out now!
Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Thursday, June 18, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
April 01 – August 31, 2026
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy.
Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way!
We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team.
June 23-25, 2026 | MUNICH, GERMANY

You have no items in your basket.

source

SolarEdge Technologies SEDG has been taking significant steps to expand its manufacturing capacity in the United States. The company continues to benefit from its optimized inverter solutions, which address a broad range of solar market segments, from residential solar installations to commercial and small utility-scale solar installations.
This Zacks Rank #3 (Hold) company faces risks related to policy-driven demand swings.
Factors Acting in Favor of SEDG
SolarEdge Technologies’ optimized inverter architecture spans residential, commercial and small utility PV and pairs naturally with batteries and software services across markets. In the first quarter of 2026, the company recognized revenues on roughly 50.5 thousand inverters, 2.4 million optimizers and 331 MWh of batteries for PV applications, supporting revenue growth from the prior year.
SEDG continues to optimize its U.S. manufacturing footprint to align with incentives that favor domestic content, including residential inverters in Texas, optimizers and commercial inverters in Florida, and batteries in Utah. Management views its ability to offer products that are designed to comply with domestic content requirements and FEOC regulations as a structural advantage in the U.S. commercial and industrial (C&I) rooftop market, supporting deeper penetration into enterprise accounts that typically provide more stable demand and better project visibility.
These initiatives can also open opportunities for incremental sales when installers add a battery or EV charger to a safe harbor project, while helping the company optimize production over time. To support this strategy, management expects capital expenditures of $60-$80 million in 2026, primarily for expanding U.S. solar PV and battery manufacturing capacity and investing in new platforms, while targeting near-breakeven operating results in the second quarter of 2026.
Challenges Faced by SEDG
The U.S. residential market started 2026 slowly as customers faced changes in tax credit policies and uncertainty related to FEOC, which management said has decelerated tax equity funding for third-party ownership players and strained installer businesses and cash flows. The recent pull-in ahead of the end-2025 Section 25D timeline and ongoing debate around commercial credit terms can also shift demand between quarters, complicating channel inventory planning for customers.
Even if longer-term policy evolution shifts demand toward the 48E credit and higher battery attach rates, the transition period can create uneven ordering patterns and higher sensitivity to installer financing conditions and project pipelines.
SEDG’s Share Price Performance
In the past three months, shares of the company have risen 19.8% compared with the industry’s 14.4% growth.
 
Stocks to Consider
Some better-ranked stocks from the same sector are T1 Energy Inc TE, FuelCell Energy FCEL and Occidental Petroleum OXY, each carrying a Zacks Rank #2 (Buy) at present. You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here.
The Zacks Consensus Estimate for TE’s 2026 EPS implies an increase of 85.3% from that recorded in 2025. The Zacks Consensus Estimate for TE’s 2026 sales implies year-over-year growth of 19.1%.
The Zacks Consensus Estimate for FCEL’s fiscal 2026 EPS implies an increase of 59.4% from that recorded in fiscal 2025. The company delivered an average earnings surprise of 14.4% in the last four quarters.
The Zacks Consensus Estimate for OXY’s 2026 EPS implies an increase of 162% from that recorded in 2025. The company delivered an average earnings surprise of 49.7% in the last four quarters.
Radical New Technology Could Hand Investors Huge Gains
Quantum Computing is the next technological revolution, and it could be even more advanced than AI.
While some believed the technology was years away, it is already present and moving fast. Large hyperscalers, such as Microsoft, Google, Amazon, Oracle, and even Meta and Tesla, are scrambling to integrate quantum computing into their infrastructure.
Senior Stock Strategist Kevin Cook reveals 7 carefully selected stocks poised to dominate the quantum computing landscape in his report, Beyond AI: The Quantum Leap in Computing Power.
Kevin was among the early experts who recognized NVIDIA's enormous potential back in 2016. Now, he has keyed in on what could be "the next big thing" in quantum computing supremacy. Today, you have a rare chance to position your portfolio at the forefront of this opportunity.See Top Quantum Stocks Now >>
Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report
T1 Energy Inc (TE): Free Stock Analysis Report
Occidental Petroleum Corporation (OXY): Free Stock Analysis Report
FuelCell Energy, Inc. (FCEL): Free Stock Analysis Report
SolarEdge Technologies, Inc. (SEDG): Free Stock Analysis Report
This article originally published on Zacks Investment Research (zacks.com).
Zacks Investment Research
Select market data provided by ICE Data Services. Select reference data provided by FactSet. Copyright © 2026 FactSet Research Systems Inc.Copyright © 2026, American Bankers Association. CUSIP Database provided by FactSet Research Systems Inc. All rights reserved. SEC fillings and other documents provided by Quartr.© 2026 TradingView, Inc.

source

AboutContactMedia kit

Loading…
Food and drink firms seek greater control over volatile energy costs
Food and drink manufacturers are accelerating investment in rooftop solar as they look to gain greater control over volatile energy prices and reduce reliance on the grid.
According to installer Fusion 360, enquiries from food and drink businesses have risen sharply since early 2026, driven by global instability and the sector’s heavy dependence on refrigeration and chilled storage.
Across UK industrial estates, factories, warehouses and distribution centres are increasingly covered in solar panels — a shift that reflects both cost pressure and the need for more resilient energy strategies. For a sector where refrigeration, chillers and refrigerated trailers consume significant power, solar is becoming a practical way to stabilise costs and strengthen supply‑chain security.
Joanne Skinner, commercial director at Fusion 360, said reduced reliance on mains electricity is now a major priority for producers.
“With energy‑hungry refrigeration and chillers, it’s clear why we’re seeing growing interest from businesses helping to feed Britain.”
The company, which specialises in solar PV design and installation, recently replaced more than 4,000 panels at Aldi’s regional distribution centre in Bathgate, Scotland, upgrading the site to the latest generation of high‑efficiency technology. Continuous advances in PV performance, Skinner said, are delivering greater power output, longer life and faster returns on investment.
For many producers, the motivation is twofold: cutting emissions and insulating operations from unpredictable energy markets. The pandemic, conflicts in Europe and Asia, and the recent US‑Iran tensions have all heightened the need for more secure, self‑generated power.
Fusion 360 reports that food and drink companies are increasingly seeking systems that allow them to use solar power on site, with the option to restrict or export surplus energy depending on grid arrangements.
“Businesses tell us they want greater control and more predictable costs,” Skinner added. “With inflationary pressure across the food sector, onsite solar is one practical way to reduce exposure to volatile electricity prices.”
As energy uncertainty persists, rooftop solar is becoming a core part of how food and drink manufacturers manage risk, protect margins and future‑proof their operations.
Fusion 360
Bell Publishing Group Limited
The Maltings
57 Bath Street
Gravesend
Kent DA11 0DF
United Kingdom
Tel: +44 1474 532202
Email: [email protected]
You can unsubscribe at any time.
© Bell Publishing Group Limited, 2026. All rights reserved. Website by e-Motive Media Limited.

source

Early voting is underway.
The Calcasieu Parish Planning and Zoning Board has approved a proposal for a solar farm supported by nearby residents, a major utility, an economic development alliance and planning staff.
Locally-owned Sweet Lake Land and Oil Company has proposed using 1,100 acres near Lionel Derouen and Fruge roads in Bell City for Southern Prairie Solar, a subsidiary of Canada-based Westbridge Renewable Energy Corporation.
The board passed the proposal in an 8-1 vote on June 16, with at-large board member Genelle Hyatt the sole vote against the project.
The solar farm will generate 200 megawatts of alternating current (MWac) with 55 megawatts of battery storage, according to a letter to the board from Southern Prairie Solar Vice President Margaret McKenna. Planning staff advised the board they expect minimal site impacts.
The Southwest Louisiana Economic Development Alliance wrote the project “reinforces the Highway 27 corridor as a priority growth area for industrial and energy development, consistent with the region’s long-term economic strategy” in a letter of support.
After losing a third of its tree canopy to extreme weather, Lake Charles has fallen short of restoration goals. One tree’s fate may change the city’s course.
Jeff Davis Electric Co-op plans to integrate the solar project into its own transmission loop. CEO Michael Heinen emphasized in a letter of support that the project would enhance existing electricity infrastructure and lower costs for its customers.
Four neighbors with properties adjacent to the project also provided letters of support. Environmental advocates also spoke in favor of the project at the meeting.
McKenna told the board she expects construction to begin late 2027, with 12-18 months until completion. The project would provide fewer than ten permanent jobs.
Calcasieu Parish resident Steven Vance, who lives a mile and a half from the project site, voiced concerns about how the solar farm could impact traffic and drainage, and exacerbate damage from hurricanes.
“You cannot scrape 1,100 acres of natural grass and top soil, hack down the dirt, cover it in heavy equipment and expect the water to just disappear. That runoff is going to rush straight into our local channels, overflow our ditches and flood our homes,” Vance said.
Chris Guidry, representative of engineering and environmental and survey firm Fenstermaker, said there “will likely be a stormwater pollution prevention plan” to reduce sediment erosion from the site during construction, and that they did not expect much land leveling to occur for the project.
“I see the benefits, obviously, if the Jeff Davis Power Company sees the benefits for the infrastructure support, even economically. I hope that is passed along to everyone in their electricity bills each month,” said board member Jake Porche.
In 2024, the parish board rejected a proposal for a $400 million solar farm after many residents worried about the consequences of the project, including the possibility that it would lower their property values.
A 2024 literature review by Gregory Upton and Sarang Talpur of LSU Center for Energy Studies estimated that homes within half a mile of a solar project in other select states experienced a reduction of 1.5% to 6.9% in value.
More specifically, “studies that analyze housing values in rural areas specifically find that utility-scale solar is associated with a 2.5% – 5.8% percent reduction in housing values,” according to the review.
Natalie McLendon is an investigative reporter covering issues across Southwest Louisiana. She has a bachelor’s degree in sociology from McNeese State University. Her work has appeared in The New York Times, The Guardian, Louisiana Illuminator, and other regional outlets. Contact her at [email protected] or (337) 485-9229.
Commenting is reserved for donors. Make a Donation »
Already donated? Log In »
Legislators dedicated $30 million for the current lottery round. Another 5,000 grants are to be distributed later this year.
With some previously supportive business owners now calling for changes to the design, the Boulet administration is considering a phased approach to the project.
Entergy Louisiana has proposed a new power station near its existing Westlake site. Some neighbors say the area is already overburdened by industry.
Get it first. Sign up for our free newsletters. Learn more »

source

Electricity generated by utility-scale solar installations in the region overseen by the California Independent System Operator (CAISO) surpassed natural gas generation in the first five months of 2026, as reported by the U.S. Energy Information Administration (EIA)
The region, which encompasses nearly all of the state of California save for parts in the far north and far southeast of the state, has seen a 60% decrease in energy produced by natural gas generators — coupled with a 21% increase in solar generation — compared to 2024. 
When considering total daily generation by source in CAISO, utility-scale solar generated more electricity than natural gas on 82% of days in the first five months of 2026.
Gas generation capacity (i.e. nameplate rated power output) in the region has remained steady at 29 GW in recent years, but the utilization of that capacity has decreased as utility-scale solar and battery storage capacity has grown. 
Between April 2024 and April 2026, CAISO solar capacity increased by 19% to 25 gigawatts (GW), and battery storage capacity increased by 79%, or 16 GW.
On an average day in the first 5 months of 2026, batteries charge up with a massive amount of solar energy between the hours of 8 am and 5 pm, with the average daily peak charging rate reaching approximately 8 GW, a significant increase over just two years ago.
The state has also become a net importer of electricity during all hours of the average day, with the vast majority of that imported energy coming from wind, nuclear, and solar projects in nearby states.
100% renewables a near-daily occurrence
The combined renewable sources of wind, hydroelectric, and solar are regularly meeting 100% of the needs of the California grid for part of each day. 
According to Mark Jacobson, a Stanford University professor who tracks the amount of energy delivered to the CAISO grid by renewable sources, as of June 17 (the 168th day of 2026), these sources had served 100% of total demand on the grid during at least part of the day on 143 days. The sources also set a record of 29.5 GW of combined peak output on that day.
According to the latest edition of the EIA’s Electric Power Monthly, large-scale renewable project developers in the state plan to add nearly 10.5 GW of new battery energy storage capacity and 8.3 GW of new utility-scale solar capacity in the coming years.
Comments
Please login to comment
The June issue of pv magazine Global is out now!
Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Thursday, June 18, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
April 01 – August 31, 2026
pv magazine USA hosts its third multi-day virtual event on advancing U.S. solar and energy storage markets, covering financing, supply chains, and distributed energy’s role in grid resilience.

You have no items in your basket.

source

This $17 solar panel effectively gave my doorbell camera infinite battery life – how I set it up  ZDNET
source

PENDER COUNTY, N.C. (WECT) – The North Carolina Court of Appeals has upheld Pender County’s decision to deny a special use permit for a 2,360-acre solar farm.
The court ruled Tuesday, June 17, that Coastal Pine Solar, LLC failed to provide sufficient evidence that adequate utilities were in place or being provided for the project.
The company applied for the permit in 2022 to build the solar farm in a rural agricultural zoning district. The Pender County Board of Commissioners unanimously denied the application in September 2022.
The appeals court found the company did not prove existing transmission lines could handle the electricity the solar farm would produce. The company presented evidence that two Duke Energy 230-kilovolt transmission lines cross the site, but did not provide evidence that those lines could currently serve the property.
Coastal Pine Solar said Duke Energy would construct a substation and switching station to connect the project to the grid. The court ruled this testimony was too speculative to meet permit requirements.
The court also noted concerns about drainage. The Pender Soil and Water Conservation Director testified at a 2022 hearing that the land had been removed from agricultural use due to wetland designation. No evidence was presented about measures to offset water runoff from clearing 2,300 acres of timberland, according to the ruling.
Coastal Pine Solar appealed the county’s decision to superior court, which initially sent the case back to Pender County commissioners to review additional permit standards. After the board again denied the permit in July 2023, the superior court affirmed that decision in September 2023.
The company then appealed to the state Court of Appeals, arguing the county improperly denied the application and violated its due process rights.
The appeals court rejected those arguments. The three-judge panel found the county board’s decision was supported by the evidence and did not violate the company’s constitutional rights.
The company had presented expert testimony from six consultants at the 2022 hearing, including engineers and planners. Eight landowners and farmers from the community testified in opposition, raising concerns about land use, aesthetics and water runoff.
Copyright 2026 WECT. All rights reserved.

source

People Behind the Transition: An Update on Workforce and Gender in India’s Renewable Energy Story  NRDC
source

TIME magazine this week published an article by Alexis Abramson, the dean of the Columbia Climate School, who said the failed assault on Iran has altered the way people think about solar energy. “Across the U.S. and globally, interest in clean energy is accelerating faster than at any point in history, and not necessarily because of anything the clean energy movement achieved on its own. Understanding why is critical,” she said.
The move toward clean energy began with the first Earth Day in 1970, during which 20 million turned out to support curbing human caused pollution because it was simply the right thing to do. “The environmental conviction was real, and it moved a committed minority. By 2010, after four decades of moral-based advocacy, solar still represented less than 0.1% of U.S. electricity generation,” Abramson noted.
Then came the economic revolution, which focused on making solar affordable enough that it could compete head to head with fossil fuels. The solar investment tax credit — supported by both political parties — debuted in 2006. Over the next 15 years, solar grew by more than 10,000%. Then the Inflation Reduction Act turbocharged the solar industry to the point that in 2024, solar accounted for more than 80 percent of new grid-scale generating capacity. Abramson wrote:
“Now we are entering a third era, one defined not by values or economics, but by a drive for control. Psychologists have long documented that when people feel external forces are governing their lives, they seek out whatever domains they can control. Energy has now become one of those domains. Gas prices set by an unpredictable war. Blackouts from an aging grid. Energy bills that keep climbing.
“Rooftop solar, a home battery, an electric vehicle offer something the grid, the gas station, and the utility bill cannot — certainty. They are no longer just products. They are acts of self-determination. These solutions meet people where they already are — anxious, exhausted, and done feeling exposed — and offer them what they have been missing — stability.
“The data confirms the shift is already underway. Even before the war began, nearly 78 percent of US homeowners expressed concern about power grid reliability. Sixty-four percent say recurring blackouts would make them more likely to go solar within five years. Since the war began, nearly half say they are extremely or very concerned about affording fuel in the coming months.
“The conversation has shifted from ‘How much will I save?’ to ‘How do I protect my family from the next crisis?’ — whether that crisis arrives as a blackout, a gas price spike, or an economic shock. Americans want control. A growing number want to generate their own power, store it, and insulate themselves from volatile energy prices and an unreliable grid. And solar delivers exactly that.”
“The long-term economics still hold,” Abramson wrote. “Once panels are up, the energy is free. Every dollar invested in clean energy infrastructure today is a hedge against tomorrow’s uncertainty. For 50 years, the clean energy movement tried to change how Americans think about power. In the end, it may be global turbulence that ultimately moves what decades of advocacy could not. While the motivation may seem misaligned with the original mission, the outcome is what matters.”
How odd that an administration in thrall to fossil fuel interests has lit the fuse on demand destruction for those fuels. A weak, powerless bully and his failed secretary of war — Pomade Pete — have engineered a stunning loss for the US.
Shortly after the war on Iran began, David Wallace-Wells, who has written extensively on climate change and the politics of energy, wrote an opinion piece for the New York Times in which he said the conflict is a harbinger of something bigger — a recognition by all concerned that fossil fuels are the past, while renewables are the future.
He described what is happening in Iran as “a new age of resource conflict arising just as the old energy order was being upended but before the new one has really taken hold.” He called it a “mid-transition war, one that spans the old paradigm of fossil energy and the new paradigm of renewable energy.”
There aren’t any wars being started over solar panels, wind turbines, electric motors, or batteries, he noted, and then asked this rather pointed question: “Why continue to rely so heavily on imports from erratic authoritarians overseas when you can instead harvest the bountiful sun, wind, hydropower and geothermal found nearly everywhere on earth?”
Writing for The Climate Brink, Andrew Dessler, a professor of atmospheric sciences at Texas A&M, said: “When people debate the cost of fossil fuels versus renewables, the conversation almost always centers on the price at the pump or the cost per kilowatt-hour of your electricity bill. That’s understandable — those are the costs you can see — but they’re not the whole story.”
He said the discussion usually focuses on subsidies for renewable energy, but fossil fuels get enormous subsidies as well. They are deeply hidden, however, as they are spread across government budgets, healthcare systems, and military spending in ways most people can’t connect back to their energy choices. To the extent that they do get attention, most of it goes to the implicit subsidy for fossil fuels from climate change and air pollution, which economists have valued at trillions of dollars per year.
“There’s another hidden subsidy that few talk about — national security. [The DOJ was just played the national security card this week. It was part of an attempt to convince a judge to dismiss a lawsuit filed by the NAACP on behalf of residents in Mississippi who are being inundated by pollution from the portable methane generators being used by xAI to power its massive data centers.]
“According to Securing America’s Future Energy, a nonpartisan national security organization led by retired senior military officers, about one fifth of the entire Department of Defense base budget exists, at least in part, to keep oil flowing through vulnerable choke points like the Strait of Hormuz, the Suez Canal, and shipping lanes in the South China Sea.
“The US each year spends more than $81 billion to protect the global supply of oil, but that cost does not appear at the gas pump. That makes it a subsidy, paid for by taxpayers, which makes oil look cheaper than it actually is. Spread across all US oil consumption, it is equivalent to about $11 per barrel — or about 28 cents per gallon — money that is hidden in the defense budget,” he wrote.
For a typical fill-up, that subsidy amounts to about $5.00, and that is just to be ready for war, Dessler says. Once the shooting starts, the costs go up exponentially. The 2003 Iraq War’s cost was estimated to be $3 trillion, which translates to nearly $10,000 per US citizen. Dessler wrote:
“When you add it all up, fossil fuels are not cheap. They’ve never been cheap. We’ve just been brilliant at hiding the costs — in the defense budget, in emergency rooms, in FEMA disaster relief, and so forth. And it’s not just the cost of fighting the war. Despite trillions spent protecting global oil routes, we will always be economically vulnerable to disruption in oil supplies.
“Why? Because oil is a globally priced commodity, meaning everyone pays the same price. When something disrupts supply anywhere in the world, prices go up everywhere, including the US. This happens despite the United States being the largest oil producer in the world.
“We saw this play out in real time [recently]. Following US and Israeli strikes on Iran, oil prices surged. Gas prices are following. And this was before the conflict escalated to directly threaten the Strait of Hormuz, through which roughly 20 percent of the world’s petroleum and LNG flows every day.
“Fossil fuel pushers don’t want you to understand this. And they particularly don’t want you to recognize that the price of solar energy and wind energy is not affected by events in the Middle East [emphasis added]. A missile strike on Iranian oil infrastructure has zero effect on the cost of generating electricity from a solar panel in Texas or a wind turbine in Iowa. The ‘fuel’ — sunlight and wind — is free, domestic, and geopolitically inert.”
And so, what we are left with is a group of incompetents who started a war of choice and lost bigly, while pulling the rug out from under those who paid handsomely to get this cabal of grifters elected. Now the chickens have come home to roost and the takeaway from all this chest thumping and bloviating is this — payback is a certified bitch. In America, the stupid are leading the gullible over a cliff. The country may not survive this carefully coordinated assault on sanity.
CleanTechnica’s Comment Policy
Steve writes about the interface between technology and sustainability from his home in Florida or anywhere else The Force may lead him. He is proud to be “woke” and believes weak leaders push others down while strong leaders lift others up. You can follow him on Substack at https://stevehanley.substack.com/ but not on Fakebook or any social media platforms controlled by narcissistic yahoos.
Steve Hanley has 6700 posts and counting. See all posts by Steve Hanley

source

e-Edition
Get the latest news delivered daily!

Get the latest news delivered daily!
e-Edition
Trending:
By Emily Forgash | Bloomberg
Solar power is surpassing natural gas generation in California, demonstrating the sustained and growing impact of renewable energy in the biggest US market for photovoltaic panels.
Utility-scale solar generation in California exceeded power from gas during 82% of the days this year through May, according to a Tuesday report from the US Energy Information Association. While solar has supplied more power to the state’s grid for short periods in the past, this marks the first year when average generation in the first five months has outpaced the fossil fuel that’s the biggest source of US electricity.
The solar industry is growing despite efforts from the Trump administration to thwart the use of renewable energy. US policies favor traditional electric sources like coal and nuclear, which can produce power around the clock, unlike solar and wind. In May, solar overtook coal in US power generation for the first time.
California is aggressively adding renewable energy to meet a 2045 goal of reaching carbon neutrality, and has installed more panels and energy storage than any US state. Utility-scale solar capacity climbed 19% in the two years through April, while gas capacity was flat, according to the report.
Copyright 2026 Orange County Register. All rights reserved. The use of any content on this website for the purpose of training artificial intelligence systems, algorithms, machine learning models, text and data mining, or similar use is strictly prohibited without explicit written consent.

source

Sign up for email newsletters

Sign up for email newsletters
Don't miss:
Copyright 2026 Baltimore Sun. All rights reserved. The use of any content on this website for the purpose of training artificial intelligence systems, algorithms, machine learning models, text and data mining, or similar use is strictly prohibited without explicit written consent.

source

T1 Energy, a startup solar panel maker that says it is “bringing solar technology and know-how back to America,” is making a push to retain eligibility for some of the most generous U.S. tax benefits even as the Trump administration prepares to crack down on “green energy” businesses’ ties to China. T1 is certainly vulnerable to the looming crackdown: The company maintains business ties to a massive Chinese solar company that is led by a member of the Chinese Communist Party and subsidized by the Chinese government, according to financial disclosures reviewed by the Washington Free Beacon, something experts say could disqualify T1 from the tax credits it is seeking.
T1 was formed after Changzhou, China-based Trina Solar, one of the five largest solar panel manufacturers in the world, sold its newly constructed Texas solar panel factory to the firm in December 2024. Trina had built the plant anticipating that doing so would make it eligible to receive U.S. tax credits designed to encourage more green energy production, but sold it amid heightened bipartisan scrutiny of Chinese companies taking advantage of such tax credits.
Last week, the Pentagon placed Trina on its list of companies affiliated with the Chinese military. In addition, the company’s founder and CEO, Gao Jifan, serves as a deputy to the 14th National People’s Congress, which is China’s national legislature and has been described as the country’s “highest organ of state power.”
Under President Donald Trump’s 2025 One Big Beautiful Bill Act, projects that are owned or controlled by Chinese companies are denied eligibility for the tax credits in question. The Trump Treasury Department is soon expected to finalize rules for how it will enforce those restrictions, something that could be the difference between a company receiving hundreds of millions of dollars in tax benefits or receiving nothing. T1 sold its first batch of credits for $160 million late last year (its gross profit in 2025 was $55.6 million, for comparison).
While T1 has sought to distance itself from Trina and has repeatedly said it is building a domestic supply chain as the Trump administration develops those rules, Trina remains the company’s second-largest shareholder. According to financial disclosures filed late last month, Trina owns 30.7 million shares in T1, equivalent to an 11 percent stake in the company and worth hundreds of millions of dollars. That makes Trina a principal shareholder in T1, a formal classification that means it has significant influence over the company.
T1 separately reported in its annual filing with the SEC in late March that it maintains a business relationship with Trina: The Chinese solar behemoth remains under contract with T1 to provide advisory, manufacturing, training, and logistics support at T1’s Texas plant. Trina also handles the marketing and sales for solar panels made at the plant. In exchange for its services, T1 pays Trina a share of its earnings and a commission on its sales. Those contracts don’t expire until late 2029.
In addition, T1 reported that Trina was by far its largest customer in 2025, selling it a total of $632.2 million worth of solar panels that year, according to its SEC filing. At the same time, T1 manufactured those solar panels using Trina’s components and services—according to Trina’s own disclosures, in 2025, it sold T1 $95.5 million in goods and $45.8 million in labor.
During the first three months of 2026, T1 reported sales of $177.4 million to Trina and reported purchases of $119 million from Trina while paying the Chinese company $8.5 million in commissions and royalty fees, according to T1’s latest quarterly filing submitted in May. The three figures each represent significant year-over-year increases. T1’s sales to Trina have been 99.9 percent of its total net sales in 2026 so far.
The Free Beacon reported that T1 reached an agreement with Trina in late 2025 to license Trina’s solar technology not from Trina directly, but from a company in Singapore. T1 made the deal to distance itself from Trina while ensuring it could use its technology. Company spokesman Russell Gold said at the time that Trina “has no control over T1 Energy, period.”
And T1 announced this month that it had purchased the battery storage company Kore Power. The Free Beacon reported in February 2024 that the company is co-owned by Chinese battery company DFD New Energy, which is run by a Chinese Communist Party official. In response, KORE Power said that “reducing the equity stake of Chinese shareholders has been a priority of KORE.”
T1 declined to comment.
T1’s continued business ties to Trina appear to undercut the American-made image it has presented and could threaten its effort to retain eligibility for green energy production tax credits. According to advocates for the American solar energy sector, it presents an important test case for the Trump Treasury Department as it decides how aggressively it will enforce the restrictions laid out by the One Big Beautiful Bill Act.
“T1 is the one that’s just been out there so much that if we don’t do something there to signal that there’s going to be enforcement and that we’re going to be looking at these things very closely, I think that it’s going to happen over and over and over,” Thomas Beline, a partner with the trade law firm Cassidy Levy Kent with experience representing U.S. solar companies in China-related litigation, told the Free Beacon, adding that the T1 case is the “tip of the spear.”
Nathan Picarsic, the cofounder of the Washington, D.C., supply chain research firm Horizon Advisory, argued that the Treasury Department should implement a multi-pronged test that accounts for various ways Chinese companies can exert control over an American company. Doing so would ensure that companies that appear American, but are effectively controlled by Chinese entities, do not receive U.S. tax benefits.

“It looks like T1 is playing this game as smartly as they can, but there’s still a lot of uncertainty,” he said in an interview.
“The risk of dependence and the supply chain risk isn’t as simple as just equity ownership,” Picarsic continued. “The ties that can come from supply dependency or from depending overly on a few different customers induce some of the same risks. That places this challenge on the Treasury Department and IRS to be thoughtful about the complexity of the supply chains and the way that Chinese state-backed actors are looking to sow dependence.”
Trina, meanwhile, is itself closely linked to the Chinese government. In addition to its ties to the Chinese military and Chinese Communist Party, Trina has also received substantial subsidies from the Chinese government while evading U.S. tariffs.
China’s five-year plans, which serve as a blueprint for its economic and industrial priorities, have emphasized the importance of growing the nation’s solar industry for two decades. As such, China continues to dominate the global solar supply chain, according to the International Energy Agency.
“China seeks to dominate the solar industry as a matter of national strategy, and they even want America to pay for Chinese dominance with U.S. tax credits,” said Michael Lucci, the founder, chairman, and CEO of the China watchdog group State Armor. “Federal authorities should closely scrutinize any American companies that are deeply tied to the CCP’s solar national champions, and deny them tax credits if the ultimate beneficiary is a CCP-controlled company.”
Published under: CCP , China , Chinese Communist Party , Green Energy , Solar Energy , Texas , Trump Administration
2026 All Rights Reserved
Subscribe to the Morning Beacon, where Washington Free Beacon editor in chief Eliana Johnson breaks down the news the way the mainstream media won’t—right in your inbox, every day.
By subscribing you agree to Substack’s Terms of Use, our Privacy Policy and our Information collection notice

source

Strengthening India’s Solar Manufacturing Ecosystem: Joint Solar Advocates Quality, Technology Adoption, and Reliable Renewable Energy Solutions  SolarQuarter
source

Sign up for email newsletters

Sign up for email newsletters
Don't miss:
Copyright 2026 Baltimore Sun. All rights reserved. The use of any content on this website for the purpose of training artificial intelligence systems, algorithms, machine learning models, text and data mining, or similar use is strictly prohibited without explicit written consent.

source

By Aman Malik
ACEN Corp, the listed renewable energy arm of the Philippines' Ayala Group, has agreed to sell up to a 49% stake in Tejorupa solar-energy project in Rajasthan to Diamond India Renewables One B.V., a Netherlands-based affiliate of Japan's Mitsubishi Corporation, as the company looks to recycle capital from existing assets to fund fresh growth.
Two ACEN subsidiaries have signed agreements with Diamond India Renewables paving way for its entry into the special purpose vehicle that is developing the 250 MW solar-power project, according to a filing made by ACEN at the Metro Manila-based stock exchange this week.
The deal will be completed in stages. 
Diamond India Renewables will first acquire an initial 10% voting interest in the project company, with subsequent tranches taking its total stake up to 49%, subject to customary closing conditions and regulatory approvals. ACEN did not disclose the financial terms of the transaction.
Rajasthan is one of India's most active markets for utility-scale solar development, drawing sustained investor interest because of the strong resource potential and supportive state policy. 
The stake sale comes a few months after ACEN moved to take full control of its India business. 
The company had built its India presence through a joint venture with US-based UPC Renewables, under which the two partners held equal stakes in the platform housing their Indian projects. In February, an ACEN subsidiary bought out UPC India's remaining 50% voting interest in the venture, giving ACEN sole ownership of a portfolio comprising more than 1,000 MW of renewable energy projects under construction and in development across Rajasthan and Karnataka. That buyout also brought into ACEN's fold the three operating solar assets the partners had built together in India.
India ranks among ACEN's largest international markets. As of the end of last year, the country accounted for roughly a quarter of the company's net attributable capacity outside the Philippines. 
ACEN currently operates three solar projects in India with a combined capacity of more than 1,300 MW, in addition to assets under construction and in the pipeline.
The latest transaction underscores a broader trend in India's renewable energy sector, where developers are increasingly looking to bring in strategic investors at the project level to free up capital for new development, while retaining a foothold in a market that continues to attract significant international investment in utility-scale solar and wind.
ACEN's group chief investments officer, who also serves as president and chief executive of ACEN International, has said previously that India is a core market for the company's overseas expansion, and that the deal reflects long-term confidence in the country's renewable energy sector. 
Beyond India and the Philippines, ACEN holds renewable energy assets in Australia, Vietnam, Indonesia and Laos, as well as the United States. Across these markets, the company's attributable renewable energy capacity stands at roughly 7 GW, spanning operating, under-construction and committed projects.
Share article on
Follow VCCircle on Google News for the latest updates on Business and Startup News
Insights Focus is a marketing initiative for posts. No VCCircle / TechCircle journalist was involved in the creation of this content.
Copyright @ 2026 VCCircle.com. Property of Mosaic Media Ventures Pvt. Ltd., a part of Mosaic Digital, a 100% subsidiary of HT Media Limited.

source

The UK government has launched a public consultation to enable the use and sale of plug-in solar systems, a technology that would allow consumers to generate electricity using small photovoltaic panels connected directly to a standard household socket.
The consultation, launched by the Department for Energy Security and Net Zero (DESNZ), proposes amending existing regulations to allow these systems to be connected without the need for a conventional rooftop solar installation, provided they meet a set of safety and performance requirements.
The initiative aims to expand access to self-generation for groups that currently face greater barriers to installing traditional solar systems, including renters, apartment residents and households without suitable roof space for photovoltaic installations.
The proposal has received backing from some of the UK’s largest retailers. Amazon, Currys, B&Q, Lidl, Asda, Wickes and Screwfix took part in a government roundtable to discuss the potential of the technology and its future commercialization in the British market.
According to the government, plug-in solar systems could provide a more affordable way for households to reduce their reliance on grid electricity and lower part of their energy consumption costs.
The interim technical specification proposed by the government establishes a maximum output of 800 VA and a maximum current of 3.5 amps.
The systems must include a grid-connected microinverter, use standard UK household plugs and comply with a range of electrical safety, fire protection, electromagnetic compatibility and automatic disconnection requirements in the event of grid disturbances.
The proposal initially limits installations to one system per household and excludes products with integrated battery storage.
According to the government’s analysis, plug-in solar kits currently available in Europe cost between £400 and £600 for an 800 W system.
This is significantly lower than the average cost of a conventional residential solar installation in the UK, which stands at around £1,595 per kilowatt installed.
The government believes that this lower entry cost could help expand access to distributed solar generation for households that are unable to afford a full rooftop solar installation.
The consultation is supported by a technical study commissioned by the UK government to assess how these systems perform within domestic electrical installations.
The report concluded that plug-in solar systems can operate safely in UK homes without requiring modifications to existing wiring, consumer units or protection devices, provided they operate within the defined technical limits and comply with the proposed certification requirements.
The assessment examined thermal performance, overload protection, response to grid faults, electromagnetic compatibility and interactions with typical household electrical systems.
Solar Energy UK welcomed the regulatory progress and said the consultation will help establish a dedicated regulatory framework for a technology that previously lacked a clear legal pathway in the British market.
The association noted that the development of technical standards and safety requirements will provide greater certainty for manufacturers, retailers and consumers, while also supporting the integration of this new self-consumption category into the country’s electricity system.
The initiative forms part of the UK’s broader strategy to accelerate renewable energy deployment and achieve its clean power targets by 2030.
According to government figures, 269,000 solar installations were completed across the country in 2025, setting a new annual record. Around 255,000 of those were rooftop installations, accounting for approximately 95% of all new solar capacity deployed during the year.
The consultation will remain open until 30 June 2026 and will help shape the final regulatory framework that could allow plug-in solar products to enter the UK market in the coming months.
Sé el primero en comentar…

source

If you don't take a stand, others will
The global news and digital marketing platform for Renewable Energies. We create spaces designed to position companies in the sector.
Copyright © Energía Estratégica 2026.

source

NTPC Renewable Energy Ltd (NTPC REL) has launched a tender inviting bids for the engineering, procurement and construction (EPC) packages for 1,090 MW/4,360 MWh of interstate transmission system (ISTS)-connected battery energy storage systems (BESS) across its three solar project sites in Rajasthan.
The tenders cover a 240 MW/960 MWh BESS at the Devikot solar plant, a 550 MW/2,200 MWh BESS at the Shimboo Ka Burj solar plant, and a 300 MW/1,200 MWh BESS at the Nokhra solar plant. All three projects are based on four-hour storage duration.
The selected EPC contractor will be responsible for design, engineering, supply, installation, testing, and commissioning of the grid-connected battery storage system on a turnkey basis.
The scope of work also includes comprehensive operation and maintenance (O&M), including performance insurance, warranty coverage, and an annual maintenance contract under a service-level agreement for a period of 15 years.
According to the tender specifications, the BESS must have a design life of 25 years from the date of commissioning, with capacity degradation as per the bidder’s proposal considering daily single-cycle operation.
Batteries must be rated for minimum 10,000 cycles of operation.
The bidder must guarantee minimum 98% of dispatchable capacity at POI across all 15 years starting with 100% of rated dispatchable capacity for the first year.
Comments
Please login to comment
The June issue of pv magazine Global is out now!
Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Thursday, June 18, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
April 01 – August 31, 2026
pv magazine Insight will be held on October 30, at The Battery Show India Expo 2025 and moderated by pv magazine’s Uma Gupta and Mark Hutchins.

You have no items in your basket.

source

Xinyi Solar’s ultra-clear photovoltaic glass may not sit on your roof itself, but it shapes how efficient and affordable modern solar modules become. The industrial sheets target panel makers that need consistent quality, durability, and tight cost control at scale.
Reviewed: ad hoc news Software & Services desk. Edited and checked on 2026-06-18, 15:49. Details in the imprint.
With its ultra-clear photovoltaic glass for solar modules, Xinyi Solar supplies the silent workhorse that decides how much sunlight really reaches the cells. The glass looks almost ordinary, yet its iron-poor clarity and toughened surface aim directly at panel makers’ yield and warranty costs.
Xinyi Solar links its glass business with its Hong Kong listing, giving investors direct exposure to demand from global module makers.
At first glance it is just a sheet of glass, but Xinyi Solar’s ultra-clear photovoltaic glass uses low-iron formulations to improve light transmittance compared with standard float glass. Panel makers squeeze extra module efficiency out of every cell with this higher clarity.
The company emphasizes controlled thickness, flatness, and surface quality so that fully automated module lines can laminate at high speed without frequent breakage or optical defects. This quiet reliability matters more than any marketing slogan on a finished rooftop panel.
Once installed, the glass must survive decades of hail, sand, and sudden temperature swings on open roofs and solar farms. Xinyi Solar tempers and chemically strengthens its glass, targeting high mechanical load ratings and resistance to micro-cracks during handling.
Anti-reflective surface treatments further reduce light lost at the air-glass interface, especially under low-angle morning and evening sun. That small gain, multiplied across millions of modules, adds up to meaningful extra kilowatt-hours for project owners over a 25-year lifetime.
Xinyi Solar produces its photovoltaic glass in large integrated bases in mainland China to keep unit costs down for module manufacturers at home and abroad. Its customer list spans Chinese heavyweights and export-focused panel assemblers shipping to Europe, India, and emerging markets.
To keep up with demand for high-performance solar modules, the company is investing in additional ultra-clear solar glass production lines in Ordos, Inner Mongolia, each with a substantial daily melting capacity according to recent company disclosures. This expansion underlines how glass supply has become a strategic bottleneck in the solar chain.
Module makers ultimately care about three things when choosing glass: power output, breakage rate, and cost over the full run. With ultra-clear formulations and stable coating quality, Xinyi Solar tries to squeeze a few extra watts from each panel while keeping defect rates low.
For installers and project developers, the glass choice shows up in real life as modules that degrade more slowly and keep their rated output closer to the original data sheet. Fewer cracked modules during transport and mounting also helps large solar parks stay on schedule and on budget.
There is no retail price tag here, because Xinyi Solar’s photovoltaic glass is sold in bulk contracts directly to module manufacturers, often indexed to raw glass and energy costs. Negotiations typically weigh planned production volumes against guaranteed delivery slots.
European rooftop owners will never order this glass by name. Instead they encounter it indirectly inside panels from their preferred brands, which in turn depend on Xinyi Solar to keep quality and deliveries stable even when energy and logistics markets turn volatile.
Xinyi Solar ties this glass business into a broader footprint in solar materials and power assets, positioning itself as a key supplier in China’s photovoltaic ecosystem. Shares of Xinyi Solar Holdings Ltd (HK0968003713) traded on the Hong Kong Stock Exchange at 3.43 HKD on 2026-06-17, based on recent quote data.
This article was AI-assisted and editorially reviewed. Product information without guarantee; prices and availability may change at short notice. No investment advice, no buy or sell recommendation. Stock-market transactions involve risks up to total loss.

source

Please enable JavaScript to proceed.

source

News
News
News
News
News

source

By: Luis Reyes
Published: Jun 18, at 9:00am ET
Solar farms go where the land is cheap and the sun is reliable. Most of the time that means a desert, a stretch of scrubland nobody is farming, or the roof of a distribution center. China has spent the last decade pushing that idea about as far as it will go on dry land, from a 250-mile “great wall” of panels strung across the Kubuqi desert to a Tibetan-plateau array so productive that the operator had to bring in thousands of sheep to keep the grass from shading the modules. The newest one skips the land question entirely. It sits about five miles off the coast of Dongying, in Shandong province, bolted to the seabed on nearly 3,000 steel platforms, with fish farming going on in the water underneath.
That project is called HG14, and as of late December 2025 it is fully wired into the grid. Built by Guohua Energy Investment, a subsidiary of state-owned China Energy Investment Corporation (CHN Energy), it is a 1-gigawatt photovoltaic plant that the company bills as the world’s first gigawatt-scale offshore solar farm, and the largest sitting in open sea anywhere. The “offshore” part is doing real work, because almost every big floating solar project you have read about lives on a calm reservoir or an inland lake. This one is out in the actual ocean, in a bay that ices over in winter.
Worth getting the engineering straight first, because “floating solar farm” is the label that keeps getting stuck on HG14 and it is not quite right. The plant does not float. It is a fixed-pile system: steel piles are driven into the seabed, and the platforms holding the panels sit rigidly on top of them. That approach works here because the water is shockingly shallow, between roughly 3 and 13 feet (one to four meters) deep across the entire 1,223-hectare site, which works out to about 4.7 square miles, or close to a fifth of Manhattan. At that depth you can anchor straight to the bottom instead of building expensive floating pontoons, and you end up with a far sturdier structure.
Sturdiness is the whole game out there, because the waters off Kenli district turn genuinely hostile in winter. Air temperatures drop below 14°F (-10°C), Siberian winds push saline spray that can freeze on contact, and the shallow bay forms sheets of sea ice. CHN Energy says the fixed-pile design was engineered specifically to take waves, tides, strong winds and seasonal ice without buckling. There are 2,934 of those platforms by the developer’s count, each one about 197 by 115 feet (60 by 35 meters), held down by a combined 11,736 steel piles. To put them in fast enough, the construction crews used a setup that drives four piles into the seabed at once with automated leveling, which is the kind of unglamorous detail that decides whether a project like this finishes this decade or next.
Building solar at sea sounds like an answer to a problem nobody has, given how much empty land is lying around. But eastern coastal China is not empty. It is where the people and the factories are, and flat, cheap, sun-soaked land near those load centers is genuinely scarce. Parking a gigawatt of panels just offshore puts the electricity right next to where it gets consumed, without bulldozing farmland or picking a fight over a desert.
The catch is that this exact approach only works in a narrow set of places. Fixed-pile offshore solar needs a shallow coastal shelf with a stable seabed that can hold the piles and ride out the local wave climate, and most coastlines simply do not offer that. So while the topline makes HG14 sound like a blueprint the rest of the world can photocopy, the conditions behind it (a broad, shallow, ice-prone bay sitting right next to a major demand center) are fairly specific. It is less a universal template than a very good use of one unusual stretch of sea.
The record here is not just about size. HG14 is the first time China has paired a 66-kilovolt offshore cable with an onshore cable to move solar power that far at high capacity, with the current stepped up to 220 kilovolts once it reaches land. It is also, according to pv magazine, the first offshore solar facility approved under China’s national three-dimensional sea-use rights framework, which is the bureaucratic machinery for letting more than one industry legally share the same patch of water. Pairing the panels with on-site storage and that transmission setup lifts the plant’s effective capacity by around 20% and trims unit costs by roughly 15%, per CHN Energy’s figures.
“The project provides valuable experience for future offshore solar farm construction,” Zhang Bo, deputy manager of the Kenli project at Guohua Energy Investment, told state broadcaster CGTN, as Electrek relayed. Which is corporate-speak for “we are going to build more of these.”
No spam. Unsubscribe anytime. Privacy policy (opens in new window)
The detail that tends to stop people is the second business running in the same water. HG14 uses what CHN Energy calls an integrated fishing-and-PV model: power generation up on the platforms, aquaculture in the sea below. The company expects the fish farming alone to bring in more than 27 million yuan a year, roughly $3.8 million, on top of whatever the electricity earns. The panels throw shade and the structure offers shelter, which in theory makes the water beneath friendlier to farmed fish than open sea.
On the power side, the developer’s own numbers are the ones to weigh, since this is a state operator reporting on its own project. CHN Energy says HG14 will generate around 1.78 terawatt-hours a year at full output, enough by its math to cover about 2.67 million urban residents and roughly 60% of Kenli district’s total electricity demand. It puts the annual savings at 503,800 tons of coal and 1.34 million tons of CO2 avoided. A 100-megawatt, 200-megawatt-hour battery sits alongside the array to soak up midday generation and push it back onto the grid at full power for about two hours when it is needed most.
It is tempting to read a gigawatt of offshore panels as China running away with the energy transition, and the national numbers do look like that from a distance. By the end of 2025 the country’s installed solar capacity reached about 1,200 gigawatts, up roughly 35% in a single year, according to its National Energy Administration. Wind and solar combined first passed China’s thermal (mostly coal) capacity back in early 2025, and Carbon Brief reports that the China Electricity Council expects solar capacity alone to overtake coal for the first time during 2026.
Capacity is not the same as electricity, though, and that is where the tidy version falls apart. Solar plants in China run at an average capacity factor of around 14%, against roughly 50% for coal, so a coal station still puts out several times more actual power per gigawatt installed. China also kept building coal hard last year: developers put forward about 161 gigawatts of new coal-fired capacity in 2025, per the Centre for Research on Energy and Clean Air and Global Energy Monitor, even as coal-fired generation itself slipped around 2%, its first drop in six years. Coal is not being switched off. It is being nudged toward backup and grid-balancing while the renewables get built out around it.
So HG14 is a real, working gigawatt sitting in the open ocean, which is a new thing and a hard one to pull off in a bay that freezes solid every winter. It is also a single project, in shallow water that happens to suit it, owned by a utility that controls exactly how the numbers get reported. Both of those are true at once. The real test is the next one: whether a fixed-pile plant like this turns up somewhere the water is deeper and the permitting is messier, or whether five miles out to sea stays a Shandong specialty.
Image credits: cscec.com
Did we nail it or blow it?
Luis Reyes · Jun 10, 2026
Olivia Richman · Jun 11, 2026
Dave McQuilling · May 30, 2026
Olivia Richman · May 30, 2026
Dave McQuilling · May 23, 2026
Luis Reyes · May 25, 2026
Luis Reyes · Jun 18, 2026
Luis Reyes · Jun 18, 2026
Luis Reyes · Jun 18, 2026
Olivia Richman · Jun 17, 2026
Luis Reyes · Jun 17, 2026
Autonotion is the English-language automotive editorial by Autonocion.com — car news, reviews, and industry analysis for American readers.
Other links
Company
Subscribe
Get the latest car news in your inbox:
By submitting your email you allow autonocion.com to send you news or promotions. More info

source

Norwegian independent power producer (IPP) Scatec has reached financial close for the 120MW Sidi Bouzid II solar PV project in Tunisia.
The project is being developed in partnership with Aeolus SAS, part of the Toyota Tsusho Group, with Scatec and Aeolus each holding a 50% ownership stake. The plant is under construction and is expected to enter commercial operation in the second half of 2027.
Get Premium Subscription
Total capital expenditure for the project is estimated at €96 million (US$110 million) and will be financed through a combination of non-recourse debt and equity, with leverage of approximately 70%.
“Sidi Bouzid II is our third project starting construction in Tunisia and reinforces our partnership with Aeolus and our position in Tunisia, with strong fundamentals for renewables and strong growth potential. The project demonstrates our ability to scale our business through repeatable tender-based opportunities, backed by a strong partnership with Aeolus, and a capital-light execution model,” says Terje Pilskog, CEO of Scatec. 
The project has secured financing from the European Bank for Reconstruction and Development (EBRD) and the European Investment Bank (EIB), while additional support comes from grant funding provided through the EU Neighbourhood Investment Platform (NIP) and guarantees from the European Fund for Sustainable Development Plus (EFSD+).
Scatec will deliver engineering, procurement and construction (EPC), asset management and operations and maintenance (O&M) services for the project. The company’s EPC contract scope represents approximately 75% of the project’s total capex.
The project is backed by a 25-year power purchase agreement (PPA) with Tunisian state utility Société Tunisienne de l’Electricité et du Gaz (STEG).
Tunisia currently relies heavily on natural gas for electricity generation, with 95% of its power production sourced from the fuel and more than 60% of gas supplies imported. The country has set a target of sourcing 35% of its electricity generation from renewable energy by 2030 as it seeks to reduce generation costs and improve energy independence.
The Sidi Bouzid I solar plant, with a capacity of 60MW, reached commercial operation in March 2026.

source

Perovskite solar module manufacturer Oxford PV announced it achieved a power conversion efficiency of 25.6% for a perovskite-silicon tandem solar module relying on a shingled architecture developed by Germany’s Fraunhofer Institute for Solar Energy Systems (Fraunhofer ISE).
“For the first time, the two organizations have successfully combined Oxford PV’s perovskite-silicon tandem solar cells with Fraunhofer ISE’s Matrix Shingle module technology,” Ed Crossland, CTO of Oxford PV, told pv magazine. “Beyond the efficiency gains, the combination also reduces resistive losses, removes the need for copper interconnects, and improves resilience under partial shading – all key considerations as the industry looks to reduce costs while increasing energy yield.”
“The module presented is a prototype, but it is built using standard production cells and in a way that is fully compatible with mass production. Our current tandem modules are already delivering efficiencies of 25% with 10-year lifetime today, and this result builds directly on that. We continue to make progress along our roadmap, with a 26% product planned for release this year and a path to 27% with extended lifetimes by 2027,” Crossland added.
The Matrix Shingle approach improves conventional solar module interconnection by replacing traditional busbar-and-ribbon architectures with a dense, overlapping cell layout. In this method, photovoltaic cells are precision-cut into narrow strips and reconfigured into a shingled pattern, similar to roof tiles. Adjacent strips overlap slightly and are bonded using electrically conductive adhesive (ECA), which provides both mechanical adhesion and electrical interconnection between neighboring cell segments.
By eliminating soldered interconnect ribbons and busbars, the architecture removes inactive spacing that would otherwise block incoming light. As a result, optical shading losses are significantly reduced and a larger fraction of the module surface becomes active photovoltaic area, improving packing density. The reduction in metallization shading also enhances current collection efficiency, as more of the cell surface is exposed to sunlight.
In addition, the shingle configuration shortens current pathways and distributes current more uniformly across the module, which can reduce resistive losses and localized heating. The use of ECA instead of high-temperature soldering also reduces thermal stress during assembly, helping to preserve cell integrity and potentially improve long-term reliability. Overall, the Matrix shingle approach increases module power density by combining higher active-area utilization with improved electrical and optical performance.
“We are delighted to be able to combine two high-tech approaches from Europe in this PV module,” said Stefan Glunz, head of photovoltaics at Fraunhofer ISE. “To achieve this, we have cut the solar cells from Oxford PV into shingles, arranged them in a matrix structure, electrically connected them using conductive adhesive, and then encapsulated them.”
Two tandem glass-glass modules were built with this configuration and edge sealing to protect the moisture-sensitive solar cells: a 491 W rooftop module with an area of 1.92 m², and a 546 W bifacial module with an area of 2.13 m². “Both achieved an efficiency of 25.6% across the entire module area,” Oxford PV’s spokesperson said.
“Our tandem technology and the shingle interconnection work well together technologically,” said Ed Crossland, chief technology officer at Oxford PV. “Due to the lower current densities of the perovskite–silicon solar cells, they can be cut into wider strips, which increases productivity. Tandem solar cells achieve significantly higher voltages and efficiencies than conventional cells, while the current is lower due to distribution across two sub-cells. This lower current density is beneficial, as it helps reduce resistive losses within the PV module. At the same time, the adhesive interconnection of the Matrix shingle technology is a low-temperature process and requires no copper connectors.”
Oxford PV unveiled its first perovskite-silicon tandem solar module with 26.9% efficiency in June 2024. A few months later, the company announced the commercial launch of perovksite-silicon tandem modules in the United States.
It began working on its perovskite tandem solar modules in 2014 and claims to have a “clear roadmap” to bring the technology to over 30% efficiency.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
The June issue of pv magazine Global is out now!
Available in print and digital – get your copy today!
Thursday, July 9, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
Thursday, June 18, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors
Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects.
April 01 – August 31, 2026
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy.
Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way!
We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team.
June 23-25, 2026 | MUNICH, GERMANY

You have no items in your basket.

source

IKEA, Ingka & the Rush to Capitalise on Spain’s Solar Boom  Energy Digital
source

© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.
That is enough to keep basics such as a fridge and laptop running during sunny hours.
Photo Credit: iStock
A bill moving through Albany could give New Yorkers who rent or own condos a new way to use solar at home.
As Canary Media reports, the fate of the bill now lies in the hands of Gov. Kathy Hochul, who hasn’t been the most climate-friendly so far in her tenure.
New York legislators approved the SUNNY Act, short for Solar Up Now New York, late last month. The bill would permit small solar units, often described as DIY or balcony solar.
If Gov. Kathy Hochul signs it, people would be allowed to connect these compact panels to a standard outlet and produce part of their own power.
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers in your area.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Rather than full-scale rooftop arrays, these systems would be limited to 1,200 watts, Canary Media noted. That is enough to keep basics such as a fridge and laptop running during sunny hours.
According to Canary Media, seven states already allow this kind of installation and more than two dozen more are weighing similar proposals. If New York follows through, it would be the most populous state yet.
Gov. Kathy Hochul now must decide whether to approve or reject the bill, and she has until the end of the year to do so. Supporters speaking with Canary Media say the measure’s broad appeal could help push it over the finish line.
“We’re hopeful that [Hochul] will see this as a slam dunk — something that she can really point to for her affordability agenda,” Priya Mulgaonkar, campaign director of the co-op shareholders group Green Co-op Council, told the outlet.
FROM OUR PARTNER
Want to go solar but not sure who to trust? EnergySage has your back with free and transparent quotes from fully vetted providers that can help you save as much as $10k on installation.
To get started, just answer a few questions about your home — no phone number required. Within a day or two, EnergySage will email you the best local options for your needs, and their expert advisers can help you compare quotes and pick a winner.
Nearly half of households in the United States are unable to use rooftop solar, Canary Media reported, often because they are renters, live in apartments, or have roofs that are too shaded or too small.
That challenge is especially pronounced in New York City, where, as Canary Media reported, about 75% of homes are in buildings with at least three units.
The proposal would open a solar option to people who have long been shut out of the market. 
That’s important because going solar is one of the best ways for consumers to save money on home energy. For homeowners who can install a conventional solar system, EnergySage’s free tools are a great starting point to compile quick installation estimates and compare bids.
💡Go deep on the latest news and trends shaping the residential solar landscape
Even on a smaller scale, plug-in solar could still trim household energy costs. Canary Media reported that clean energy advocates say an 800-watt system costing $1,099 could lower the average New York household’s electric bill by nearly $300 annually.
Generating more electricity from the sun can also reduce dependence on polluting energy sources that drive climate-warming pollution.
For homeowners who can’t install solar, they’ll have to wait for Hochul’s decision. 
If you can install a rooftop system, EnergySage’s free services can help you save as much money as possible in the process. With their help, the average homeowner could save up to $10,000 on solar purchases and installations.
There are also tools that will help you make sure you are getting the best deal in your area. EnergySage’s mapping tool reflects the average costs of a home solar panel system on a state-by-state level alongside specific incentives for each state.
Lastly, adding battery storage to a solar setup is one of the best ways to protect your home during outages, save money on energy, and go off grid. Batteries can store extra solar power for later use, helping households keep essentials running when the grid is under strain. 
EnergySage’s free tools can provide information and competitive installation estimates to empower homeowners to land the best deal.
Get TCD’s free newsletters for easy tips, smart advice, and a chance to earn $5,000 toward home upgrades. To see more stories like this one, change your Google preferences here.
© 2025 THE COOL DOWN COMPANY. All Rights Reserved. Do not sell or share my personal information. Reach us at hello@thecooldown.com.

source

Our website uses cookies to enhance your browsing experience. By using our website you agree to the use of cookies as per our privacy policy
Explore why rooftop solar for MSMEs in India remains largely untapped, and what is needed to make solar adoption easier and accessible for smaller businesses.
Jun 18, 2026
Sections
The MSME sector contributes around 30% to India’s GDP, over 35% of manufacturing output, and nearly 45% of exports (Ministry of Micro, Small & Medium Enterprises), while supporting livelihoods across the country. Yet despite its economic importance, MSME solar adoption in India remains limited even as solar deployment accelerates elsewhere.
This raises an important question. If solar has already scaled across India, why has that momentum not reached smaller businesses at the same pace? The answer may lie not in whether solar works, but in how easily it fits into the realities of MSME operations.
Why MSMEs think differently about energy
The gap is structural, not just awareness-led. Large corporates can evaluate solar through long-term savings, dedicated teams, and easier access to capital. MSMEs operate in a different reality, where decisions are owner-led, cash-flow driven, and risk-sensitive.
Even when SME solar solutions in India offer attractive payback, upfront costs, trust gaps and operational concerns often delay solar adoption despite the sector’s clear need and potential.
High energy dependence, low optimization
Despite their economic weight, many MSMEs operate in energy-intensive sectors such as textiles, metals and ceramics, together accounting for a sizeable share of India’s industrial energy use.
Unlike large industries, they often depend on grid supply at higher tariffs and with limited flexibility. This makes solar for small businesses in India financially compelling, but energy is still treated as an operating expense, not a strategic investment, leaving rooftop solar for MSMEs underused despite India’s rapid solar growth.
MSMEs are naturally aligned with how solar power is generated and consumed. Most units operate during daytime hours, allowing rooftop solar for MSMEs to directly offset grid electricity use without requiring storage or load shifting. This improves utilization and makes decentralized solar operationally relevant for the sector.
MSMEs also account for a significant share of industrial electricity demand, making them an important segment for India’s clean energy transition.
Electricity is a major operating cost for MSMEs –
This makes even small reductions in electricity costs financially meaningful. For many businesses, solar becomes less of a sustainability decision and more of a cost optimization strategy.
The economics of solar remain compelling –
This creates potential savings of ₹2–₹4 per unit, making SME solar solutions in India an attractive option for businesses operating on tight margins. Over time, these savings compound into meaningful cost reductions and improved competitiveness.
Despite strong economics and operational alignment, adoption remains low.
MSMEs consume roughly half of industrial electricity while offering significant rooftop potential, yet deployment remains disproportionately low. This highlights the gap between the business case for rooftop solar for MSMEs and actual implementation.
Limited access to financing
For many businesses, the biggest challenge is not intent but capital.
As a result, viable projects frequently stall at the financing stage despite offering strong long-term returns.
Awareness and information gaps
Many businesses understand the concept of solar but lack clarity on:
Without trusted advisory support, solar for small businesses in India often remains a consideration rather than a decision. (Source: Climate Investment Funds, Deloitte)
Policy and regulatory complexity
Even when financing is available, businesses may face challenges related to –
For smaller enterprises with limited internal resources, these complexities can delay adoption.
Operational constraints on the ground
Many MSMEs operate under practical limitations –
These challenges can make deployment difficult despite strong financial fundamentals.
Perceived risk vs immediate business priorities
At its core, the hesitation is behavioral as much as structural. MSMEs prioritize survival and liquidity –
This is where SME solar solutions in India often fail to connect. The value proposition is long-term, while MSME decision-making is immediate.
For MSMEs, the impact of solar begins with the balance sheet.
For businesses operating on thin margins, even modest savings can create a meaningful advantage. This is where solar for small businesses in India moves beyond efficiency and becomes a competitive lever.
The environmental benefits are equally significant.
As supply chains increasingly prioritize sustainability, SME solar solutions in India can also strengthen long-term business relevance.
India is targeting 500 GW of non-fossil fuel capacity by 2030, with solar playing a central role.
This makes MSME solar adoption in India important not only for businesses, but for the country’s energy transition.
The greatest impact may emerge at the cluster level. Industrial hubs such as textile parks, foundries, and food-processing zones can benefit through –
India added more than 5 GW of rooftop solar capacity in FY2025, highlighting growing momentum in decentralized energy adoption. If MSMEs participate at scale, they could become one of the strongest drivers of India’s clean energy transition. (Source: JMK Research, Annual India Solar Report Card FY2025).
New financing models, digital platforms, and policy support are making solar adoption more accessible, helping convert strong solar economics into practical business outcomes for MSMEs.
OPEX and RESCO models reducing upfront burden
Demand aggregation and cluster-based deployment
Digital platforms simplifying adoption
Government schemes and subsidy evolution
 
One of the biggest shifts in MSME solar adoption in India has been the emergence of OPEX and RESCO models.
Individually, MSMEs may be small, but collectively they represent a significant market opportunity.
These models directly address one of the biggest barriers to MSME solar in India: fragmented demand.
Digital tools are helping simplify the solar adoption journey by enabling –
By reducing process complexity, these platforms make solar for small businesses in India easier to evaluate and implement.
Policy support continues to improve project viability through:
While implementation still varies across states, continued policy evolution is helping create a more supportive environment for SME solar solutions in India. As these solutions scale, the gap between solar potential and actual adoption is expected to narrow significantly.
Helping MSMEs turn solar economics into business advantage
Tata Power's strength lies in its ability to operate across the entire solar value chain, from manufacturing and financing support to installation and maintenance.
Today, Tata Power has built one of India's largest rooftop solar portfolios –
This positions Tata Power as a provider that understands the operational realities of businesses across industries and scales.
Tata Power has focused on addressing one of the biggest barriers to MSME solar adoption in India: financing.
As Dr. Praveer Sinha, CEO & MD, Tata Power, noted – "MSMEs are the backbone of India's economy. They operate across industrial segments and are major consumers of electricity. Our strategic collaboration with SIDBI will facilitate the ease of opting for renewable energy in the MSME sector and power its quest to become more efficient and globally competitive."
For many MSMEs, complexity can be a bigger challenge than technology itself.
Tata Power helps reduce that complexity through –
Trust and execution capability are often decisive factors for businesses evaluating rooftop solar for MSMEs, and end-to-end delivery helps reduce both risk and effort.
Solar should be evaluated on lifetime value rather than installation cost alone.
With rooftop solar tariffs ranging from ₹3.8–₹6.5 per kWh compared to ₹5.6–₹9.9 per kWh for grid electricity, the focus should be on long-term savings, payback periods, and energy cost reduction over 20–25 years.
Choosing the right ownership model is critical.
The right approach depends on how the business balances cash flow, risk, and investment priorities.
Before installation, MSMEs should assess –
Since MSMEs account for a significant share of industrial energy demand, correct load matching is essential to maximize value from rooftop solar for MSMEs.
Execution quality ultimately determines project success.
Businesses should prioritize partners with –
The right partner not only installs a system but helps ensure that projected savings become measurable for business outcomes.
If you’re looking to understand the solar journey end-to-end before choosing the right partner, this guide to solar energy breaks it down step by step.
 
India’s solar journey is entering its most meaningful phase yet – not just scaling capacity but expanding impact. MSMEs sit at the heart of this shift, where every rooftop has the potential to become a growth engine. The economics already work. The solutions are getting simpler. And with trusted players like Tata Power enabling access, the path is clearer than ever. What was once seen as a long-term investment is quickly becoming a smart business move. And as that shift takes hold, MSMEs will no longer sit on the edge of India’s energy transition; they will be one of the forces driving it forward.
 
Yes, MSMEs need DISCOM approval. Every rooftop solar for MSMEs project must get approval from the local DISCOM before installation. The process is now largely online through national or state portals, which has made things smoother than before. For MSME solar India projects, delays usually happen at this stage. Once approval is cleared, installation itself is relatively quick and straightforward with the right partner
 
For most MSMEs, the challenge is not “why solar” but “how to actually do it.” This is where players like Tata Power become relevant. By offering end-to-end support, from assessment to maintenance, they reduce the number of decisions a business needs to make. That shift turns solar from a complicated project into a manageable, almost plug-and-play business upgrade
 
If your system produces more electricity than you consume, the excess can be exported back to the grid through net metering, depending on state policies. This helps offset future electricity bills and improves overall savings. For MSMEs in India, this feature makes solar not just a cost-saving tool but also a way to optimize energy usage more efficiently.
 
System sizing is important for MSMEs when it comes to monetary returns. Many DISCOMs link system size to sanctioned load and consumption to prevent oversizing. For rooftop solar for MSMEs, oversizing can reduce efficiency and delay payback. The best-performing systems are not the biggest ones. They are the ones that match actual usage and maximize self-consumption
 
MSMEs typically operate during daytime hours, aligning closely with solar generation cycles. For solar for small businesses in India, this improves self-consumption and reduces dependence on storage. Combined with high electricity cost sensitivity, MSMEs are structurally one of the best-fit segments for solar, even though adoption has not yet caught up with this advantage
 
MSMEs are critical to India’s clean energy transition because they contribute nearly 45% of manufacturing output and consume over 25% of industrial energy in India. Even small efficiency gains or partial solar adoption across this segment can create large-scale impacts. Without MSMEs, India’s clean energy transition grows in capacity, but not in real economic penetration
1.  MSME sector accounts for 30.1% of India’s GDP, 35.4% of manufacturing and 45.73% of exports in the country: Union Minister for MSME
2.  As India's MSME sector matures, IDBI Bank's Sumit Phakka says the lending framework must keep pace
3.  The Engine’s Primed: Energy efficiency awaits MSME leadership
4.   Roadmap for Green Transition of MSMEs
5.   How MSMEs can lead India’s Green Transition: Practical Sustainability Roadmap
6.   rooftop-solar-energy-efficiency-vital-to-msme-decarbonization-niti-aayog
7.    Solar Energy Solutions for Indian MSMEs: Benefits and Success Stories
8.    IEEFA India: Scaling up rooftop solar finance
9.    Drivers and Challenges for Rooftop Solar Loans to Small and Medium Enterprises in India
10.  Identifying Barriers for Rooftop Solar Uptake in MSMEs and Development of a Mitigating Financial Framework, 2020
11.  Scaling up of rooftop solar in the SME sector in India
12.  India rooftop solar market outlook to 2030
13.  Identifying barriers for rooftop solar uptake in MSMEs and development of a mitigating financial framework
14.  ROOFTOP SOLAR DEVELOPMENT IN INDIA: MEASURING POLICIES AND MAPPING BUSINESS MODELS
15.  Tata Power Renewables to Accelerate Rooftop Solar Adoption Among C&I Customers in Chhattisgarh
16.  Tata Power Solar and SIDBI sign MoU to offer easy financing to MSMEs for solar adoption
Keep reading…
BLOG
The real reason MSMEs aren’t going big on rooftop solar in India
June 18, 2026
BLOG
How Tata Power creates value: From strategy to real outcomes
June 12, 2026
BLOG
Can solar solve the biggest challenge for textile MSMEs?
June 10, 2026
BLOG
7 benefits of workplace EV charging companies should know
June 05, 2026
Who we are
What we do
INVESTORS
People
Other Essential Links
Related Websites
© Copyright 2026 Tata Power. All Rights Reserved
Related Websites

source

By: Luis Reyes
Published: Jun 18, at 11:42am ET
Everyone knows what solar panels do. You bolt enough of them to a roof or a field, the sun hits them, and you get electricity instead of a power bill. That part is settled.
But a team of German climate scientists working in the Gulf has spent the last few years chasing a stranger possibility: that if you build a solar farm big enough, in exactly the right spot, the thing might start making its own rain.
Not as a metaphor. Actual clouds, actual water falling out of the sky over one of the driest places on Earth.
And as of this spring, the idea has officially graduated from “interesting model on a computer” to “we are now hauling laser equipment into the desert to see if it holds up.”
The whole thing rides on convection, which is the same boring process that makes a summer thunderstorm. The sun heats the ground, the hot ground heats the air above it, that warm air rises, and if it climbs high enough into cooler altitudes carrying moisture, the water vapor condenses into clouds and eventually falls back down as rain.
Cities do an accidental version of this all the time. Asphalt and concrete soak up more heat than grass and dirt, which is why a downtown core runs hotter than the suburbs around it, and that extra heat can nudge rainfall downwind.
Dark solar panels do the same trick, just more so. They are built to absorb sunlight rather than bounce it back, so a big enough array becomes an artificial hot patch sitting in the middle of a cool, reflective desert.
The bigger the temperature gap between the panels and the sand around them, the harder the air gets shoved upward. Get the updraft strong enough, hand it a supply of moist air, and in theory you have the front end of a rain cloud.
No spam. Unsubscribe anytime. Privacy policy (opens in new window)
That last ingredient is the catch, and it is also why the United Arab Emirates is the test case rather than, say, the middle of the Sahara. The UAE has a hyper-arid interior but a humid sea breeze rolling in off the Persian Gulf every day.
The breeze brings the water. The panels bring the heat. The hope is that the two meet over the array and go up together.
The numbers come from a modeling study led by Oliver Branch, a climate scientist at the University of Hohenheim in Germany, published in the journal Earth System Dynamics and covered widely in the science press.
Rather than simulate real solar panels, the team modeled an “artificial black surface” cranked up to absorb 95% of incoming sunlight, which is darker than most panels actually are, and ran it in a weather prediction system at five sizes: 10, 20, 30, 40, and 50 square kilometers.
The 10-square-kilometer version did nothing. Too small to move the atmosphere.
But once the surface hit roughly 20 square kilometers, the model started spitting out measurable rain within a 90-kilometer radius. As Freethink laid out, the paper estimated that a single pair of 20- or 50-kilometer surfaces, firing off about ten rainfall events in a summer, could supply enough water for somewhere between 3,000 and 15,000 people depending on the size.
Branch put it about as plainly as a scientist is willing to: “Maybe it’s not science fiction that we can produce this effect.”
For context, the UAE currently chases rain the old-fashioned modern way, with cloud seeding, flying roughly 300 missions a year to dump particles into existing clouds and coax water out of them.
The problem with seeding is that you need clouds there in the first place, and you need pilots willing to fly into them. A solar farm that brews its own weather on the ground, while also generating gigawatts of electricity, would be a fundamentally different kind of tool.
Here is where the story stops being a two-year-old paper and becomes current. In May 2026, pv magazine reported that the concept has moved into a funded, multi-year field project.
The money comes from the UAE Research Program for Rain Enhancement Science, which puts $5 million a year into precipitation research, and Branch’s proposal was picked from around 120 international submissions for three years of funding.
He’s running it alongside Volker Wulfmeyer, his colleague at Hohenheim, and the two have spent more than a decade studying how deserts move heat and moisture around.
The plan is to stop guessing and start measuring. The team is deploying high-resolution LiDAR systems near real solar installations in the UAE, including the Mohammed bin Rashid Al Maktoum Solar Park outside Dubai, to capture three-dimensional profiles of temperature, humidity, and wind all the way up to the altitude where clouds form.
That field data then feeds ultra-high-resolution weather simulations run on a pair of supercomputers, Hunter and HoreKa, operated by the University of Stuttgart and the Karlsruhe Institute of Technology. The goal is to nail down the optimal size, placement, and panel design to actually trigger rain instead of just modeling that it might.
They are also looking at something genuinely odd: building artificial dunes several hundred meters tall to act as man-made mountains.
Real mountains force incoming air to rise and dump its moisture on the windward side, which is why one slope of a range is lush and the other is desert. The idea is to fake that effect with engineered terrain and stack it on top of the heat-island effect from the panels.
The Dubai site is a useful yardstick for whether any of this is buildable at the right scale. The Mohammed bin Rashid Al Maktoum Solar Park reached 3,860 megawatts of installed capacity by the end of 2025, and DEWA has revised its 2030 target sharply upward to exceed 7,260 megawatts, well past the original 5,000-megawatt goal.
Branch has said elsewhere that some solar farms are already getting close to the footprint his model needs. The world is, almost by accident, building installations in the right size range. Nobody designed them to make rain, but the raw scale is arriving anyway.
The Gulf is not the only candidate, either. Branch’s team has pointed to a couple of other coastlines where the same recipe might work, naming Namibia and Mexico’s Baja Peninsula.
This is real science from a credible team, not a viral aggregator headline, and it deserves to be taken seriously.
It also comes with a stack of caveats big enough that anyone telling you solar panels “make it rain” is getting ahead of the evidence by a wide margin.
Start with the obvious one: this is a model graduating into a measurement campaign, not a working rain machine. Nobody has built a solar farm and watched it conjure a storm.
The simulation also used a surface darker than commercial panels actually are, which means real-world arrays would need special coatings or dark ground cover between the rows to hit that 95% absorption figure.
And the original case studies didn’t run on random summer days. The team picked days with partially unstable weather to give the effect the best possible shot, so the regularity of those conditions in any given location is its own open question.
Then there’s the scary version. A separate line of research on covering the Sahara with solar found that doing it at continental scale could disrupt atmospheric teleconnections and shift cloud cover thousands of miles away, with knock-on effects reaching North Africa, southern Europe, the southern Arabian Peninsula, India, North Asia, and even eastern Australia.
Local rain in one desert is not the same as quietly rewiring the planet’s weather, and the line between “useful regional tool” and “global side effects” is exactly the kind of thing the new field data is meant to pin down before anyone gets ambitious.
The honest framing is that the UAE itself isn’t betting the farm on this either.
The country remains committed to its cloud seeding program while it studies the convection idea on the side, which is roughly the posture you’d expect from a government that needs water now and is happy to fund a long shot in parallel.
The interesting thread here isn’t really about rain. It’s that we keep discovering that giant solar installations do things their designers never planned for.
China’s largest array turned a high-altitude sand desert into enough grassland that operators had to bring in sheep to keep the vegetation from shading the panels.
France spent five million euros on a solar road that cracked apart and got torn up after eight years. And Tesla’s own Solar Roof has quietly faded from the product it was hyped to be.
Solar at scale is full of surprises, and not all of them are the good kind.
What makes the rain project worth watching is that it treats one of those accidents as a feature instead of a footnote.
If the field data holds up, a desert nation could end up with a single piece of infrastructure that generates clean power and freshwater at the same time, which would be a genuinely big deal in a part of the world where water is worth more than oil.
If it doesn’t hold up, it joins the long list of solar’s weird unintended consequences. Either way, the next few years of LiDAR data out of the Gulf will settle whether the clouds were ever really there, or just hiding in the model.
Don’t bite your tongue. Speak up.
Luis Reyes · Jun 4, 2026
Luis Reyes · Jun 16, 2026
Olivia Richman · May 28, 2026
Luis Reyes · Jun 17, 2026
Luis Reyes · May 29, 2026
Luis Reyes · May 28, 2026
Dave McQuilling · Jun 18, 2026
Luis Reyes · Jun 18, 2026
Luis Reyes · Jun 18, 2026
Luis Reyes · Jun 18, 2026
Luis Reyes · Jun 18, 2026
Autonotion is the English-language automotive editorial by Autonocion.com — car news, reviews, and industry analysis for American readers.
Other links
Company
Subscribe
Get the latest car news in your inbox:
By submitting your email you allow autonocion.com to send you news or promotions. More info

source