Unlike traditional solar panels, which can be costly and complex to install, plug-in panels are plugged directly into a mains socket like any other device(Image: Krisztian Bocsi/Bloomberg via Getty Images)
Plug-in solar panels will be sold in shops "within months" the Government has announced alongside confirming new homes will be built with both solar and heat pumps installed. These plug-in solar panels use an ordinary plug so can be connected to locations at home such as balconies or fences.
They are already common in Germany and the Government is working with Amazon and Lidl, alongside manufacturers including EcoFlow, to bring them to the UK – so shoppers could soon see solar panels in the Lidl middle aisle and other outlets.
The panels cut the amount of electricity being drawn from the grid, lowering bills and helping reduce the UK’s dependence on volatile fossil fuel markets for its electricity supplies, the Department for Energy Security and Net Zero (DESNZ) said.
The Government estimates that a typical UK home could save £70 to £110 a year on their energy bills from plug-in solar(Image: John Keeble/Getty Images)
The move to speed up the delivery of plug-in solar is happening as new rules come into force to implement the “future homes standard”, building regulations that will make solar panels and clean heating standard in new homes.
Under the new standards, homes will be built with heat pumps or linked to heating networks, rather than gas boilers, and the majority of homes – with some exceptions – will be built with onsite renewable electricity generation, which is likely to be mostly solar.
The long-awaited implementation of the future homes standard comes a decade after measures to ensure homes were built to net-zero carbon standards were scrapped.
Energy Secretary Ed Miliband said: “The Iran war has once again shown our drive for clean power is essential for our energy security so we can escape the grip of fossil fuel markets we don’t control. Whether through solar panels fitted as standard on new homes or making it possible for people to purchase plug-in solar in shops, we are determined to roll out clean power so we can give our country energy sovereignty.”
Under the new standards, homes will be built with heat pumps or linked to heating networks, rather than gas boilers(Image: Emily Beament/PA Wire)
Housing Secretary Steve Reed said: “Building 1.5 million new homes also means building high-quality homes that are cheaper to run and warmer to live in. As we make the switch to clean, homegrown energy, today’s standard is what the future of housing can and should look like.
“Not only will these changes protect hardworking families from shocks abroad but will also slash hundreds of pounds off their energy bills every year.”
The moves have been welcomed by the energy sector, with Dhara Vyas, industry body Energy UK’s chief executive, describing the future homes standard as a “landmark moment” for clean energy in Britain. “New homes built under this standard will benefit from clean heating solutions and solar, protecting households from volatile gas prices and putting energy security within the home itself.
“Combined with higher fabric efficiency standards, these homes will be warmer and cheaper to run – offering real and tangible change in people’s homes.”
She added that the new standards would give businesses the long-term certainty they needed to invest in manufacturing, scale up supply chains and build a skilled workforce.

Plug-in solar panels are generally low-cost panels households can install themselves on their balconies or outdoor spaces. Unlike traditional solar panels, which can be costly and complex to install, these panels are plugged directly into a mains socket like any other device.
The appliances reduce the amount of electricity a household draws from the grid, thereby cutting a family’s energy bills. Plug-in panels do not require installation, so the only upfront cost is the purchase. Panels are currently on the market from about £400.
The Government estimates that a typical UK home could save £70 to £110 a year on their energy bills from plug-in solar, meaning a family could make their money back in between four to six years. As it is common for solar panels to have a 15-year lifespan it would means households should expect around nine to 10 years of profit after they've paid for the initial cost.
The Government has said it is already working with Lidl and Amazon, alongside manufacturers such as EcoFlow, to bring plug-in solar to the UK market. The Department for Energy Security and Net Zero has promised that the solar panels will be available in shops “within months”, while EcoFlow has said it hopes people will be able to use them this summer.
An EcoFlow solar panel is already being sold on Amazon for £449. The EcoFlow STREAM Balcony Solar System, 800W Micro Inverter, 2 × 450W PV Solar Panels, Smart Grid-Feed Inverter with Wi-Fi & App Control for Balcony, Garden, Roof & Vertical Walls currently has an average rating of 4.3 out of 5 based on 21 reviews.
Georgina Hall, corporate affairs director at Lidl GB said: “At Lidl GB, we are committed to making sustainable living affordable for everyone and we welcome the Government’s move to modernise regulations in the UK. Updating the regulatory landscape for this ‘plug-and-play’ technology is a positive step towards empowering British households to manage their energy costs and support the nation’s net-zero ambitions.”
Lorna Wallace-Smith, head of UK Communications for EcoFlow, said: “Allowing plug-and-play solar is a very positive step for expanding access to renewable energy in the UK. Seeing these systems available in stores by summer would be a major win for households, enabling people to take advantage of the longer, brighter days and start generating their own clean electricity straight away.
“For many households – particularly those living in flats or rented homes – solar has not always been straightforward. Plug-and-play systems remove that barrier, making it far easier to get started.”
Looking for more from MyLondon? Subscribe to our daily newsletters here for the latest and greatest updates from across London.
At Reach and across our entities we and our partners use information collected through cookies and other identifiers from your device to improve experience on our site, analyse how it is used and to show personalised advertising. You can opt out of the sale or sharing of your data, at any time clicking the "Do Not Sell or Share my Data" button at the bottom of the webpage. Please note that your preferences are browser specific. Use of our website and any of our services represents your acceptance of the use of cookies and consent to the practices described in our Privacy Notice and Terms and Conditions.

source

JinkoSolar has announced the launch of a new solar module tailored specifically to meet the power density and extreme reliability needs of data centres and AI-driven computing.
Image: JinkoSolar
Chinese clean energy technology manufacturer JinkoSolar said its new AIDC (artificial intelligence data centre) modules are built on its Tiger Neo 3.0 tunnel oxide passivated contact (TOPCon) platform and offer front-side efficiency of 24.8% and a power output of 670 W or more.
JinkoSolar also highlighted the modules’ high bifaciality of about 85%, saying the power generated from the rear side effectively provides “free computing capacity expansion.”
“Taking a 670 W module with an 85% bifaciality ratio as an example, under ground-reflectance conditions of 0.26, the total output power can reach 844 W,” the company said. “For every 1 W of front-side power cost paid by the user, they actually obtain 1.26 W of total power generation capacity.”
JinkoSolar said the AIDC modules are specifically designed for the high power demands of conventional and AI-driven data centres, graphics processing unit clusters, supercomputing facilities, and semiconductor manufacturing units.
The modules have been optimised for low-irradiance conditions, with JinkoSolar saying even in extreme low-light scenarios such as dense fog or building shading, their relative power output remains stable at 95%–98%.
“The power generation curve better aligns with data centres’ long-term, round-the-clock, stable, and smooth power consumption needs,” the company said, adding that the modules are designed to maximise roof and land utilisation while minimising levelised cost of electricity (LCOE) across the full lifecycle.
JinkoSolar said the new product has successfully passed the 55 mm hail impact test, far exceeding conventional 25 mm standards, with high-strength tempered glass and shock-absorbing encapsulation ensuring reliable performance under extreme weather conditions.
On safety, JinkoSolar said the AIDC modules comply with stringent fire resistance standards, including IEC 61730-2:2023 and UL 790 Class A, and incorporate flame-retardant materials and arc-resistant technologies to reduce fire risk.
The launch of the new modules follows the release last week of a new national interest framework for data centres and AI being built in Australia.
The framework sets out five expectations data centres developments must meet, including prioritising Australia’s national interest, using water sustainably and responsibly, investing in local skills and jobs, and strengthening research, innovation and local capability.
The new framework also requires data centre and AI infrastructure developers to strongly support the country’s clean energy transition.
The release of the framework comes as data centre growth continues to accelerate in Australia with the Australian Energy Market Operator (AEMO) forecasting that data-centre power demand could triple in five years.
Data centres now draw about 2% of electricity from the National Electricity Market, consuming about 3.9 TWh of electricity in 2025. AEMO expects that share to grow at an average annual rate of 25.1% to reach 12 TWh, or 6% of grid demand, by 2030, and 34.5 TWh by 2050.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
More articles from David Carroll
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.


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

source

Alabama lawmakers in race against clock to halt massive solar farm project: ‘Slow it down’  AL.com
source

50W Monocrystalline Solar Panel With 10A Waterproof Controller – For Camping, Travel, Outdoor Charging  ruhrkanal.news
source

Shared Video
Shared Video
Shared Video
Shared Video
Shared Video
Shared Video

source

News
15+ First Alert Weather
News
News
Forecast

source

Melbourne-based renewables developer Enervest has begun construction of a 500 kW floating solar array on a water storage reservoir in Warrnambool, Victoria, which it says will be Australia’s largest floating solar installation on completion.
Image: Enervest
From pv magazine Australia
Enervest said that works have officially commenced on a 500 kW floating solar array being installed for Victoria government-owned authority Wannon Water on the Brierly Basin water storage reservoir in the regional city of Warrnambool.
“We’re excited to see construction underway and look forward to sharing progress as the project takes shape,” Enervest said.
The AUD 2 million ($1.4 million) project, first announced in 2022, is to feature about 1,200 bifacial panels mounted on pontoons floated on Brierly Basin. The use of bifacial modules is expected to achieve approximately 20% better energy production due to the reflection of light from the water’s surface.
The pontoons will include integrated walkways while cables will secure the floating array to anchors placed at the bottom of the reservoir. The anchoring system has been designed to allow the pontoons to rise and fall with the changing level of the reservoir.
Wannon Water said once complete, the power plant will generate more than 600,000 kWh of renewable energy annually, significantly cutting its electricity costs at the Brierly Basin site.
“We use a lot of electricity to pump the water stored in Brierly Basin up to the Warrnambool Water Treatment Plant, making on-site solar a very attractive alternative,” the water authority said, adding that the business case shows the project will have a net-positive value of more than AUD 500,000.
The floating solar array is also expected to reduce the water authority’s greenhouse gas emissions by more than 600 tonnes per year while other potential benefits include a reduction in sunlight entering the water at Brierly Basin, helping to minimize algae growth, and reduced evaporation rates.
Launched by the state government as part of its push for all Victorian water companies to reach net zero emissions by 2045, the Brierly Basin system will upon completion be the largest floating solar plant.
While the technology is gaining acceptance worldwide, its adoption in Australia has been comparatively slow but there are signs of increased interest.
Operational systems include the 350 kW floating array deployed by Gippsland Water at the Drouin wastewater treatment plant, in Victoria’s east, and a 50 kW system installed by community group Gippsland Climate Change Network on a lake at exhibition venue Lardner Park in the state’s southeast.
Other systems include a 100 kW floating solar array installed at the East Lismore Sewage Treatment Plant in northern New South Wales and a 157 kW array at the Jamestown wastewater facility in South Australia.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
More articles from David Carroll
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.


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

source

CPRE has welcomed the government’s latest announcement on solar energy, including new rules requiring solar panels on new homes.
The new measures signal a stronger commitment to integrating renewable energy into new homes, including making solar panels standard and helping renters and homeowners alike benefit from solar power with plug-in solar. We have long called for a greater focus on rooftop solar as a way to transition to cleaner energy without increasing the pressures on rural landscapes.
Elli Moody, director of policy and advocacy at CPRE, said:
‘This announcement shows that the government is serious about harnessing the enormous potential of the UK’s rooftops to generate clean, sustainable energy. Mandating solar panels on new homes and making solar more accessible to renters as well as homeowners will deliver meaningful climate action without placing further pressure on iconic landscapes and productive farmland.’
By prioritising solar on buildings, the government can help reduce the need for large-scale solar developments on our landscapes. We will continue to advocate for solutions that tackle climate change while safeguarding the countryside.
Dive deeper into the topics we care about with our handy explainer guides.
CPRE welcomes government’s Warm Homes Plan
Two-thirds of mega solar farms built on productive farmland
CPRE responds to Solar Roadmap 
We respond to House of Commons debate on mega solar farms
We’ll send you the latest news and information on our work, campaigns, fundraising, events and other ways you can support our vision for a thriving countryside.

* indicates a required field
Your first nameYour last nameYour email
We hold and manage your details in accordance with the Data Protection Act 2018. We won’t share, sell or swap your information with other organisations for their own marketing purposes. You can read our privacy policy for more information.

source

Coal India Approves ₹3,160 Crore Guarantee For 875 MW Solar Project In Rajasthan  SolarQuarter
source

US independent power producer (IPP) Sunraycer has signed long-term power purchase agreements (PPAs) with Google for its Lupinus and Lupinus 2 solar projects in Texas. 
The PPAs will support the development and operation of the company’s 400MWac Lupinus solar facility, which is expected to reach commercial operation by late 2027. 
Get Premium Subscription
According to Sunraycer, the Lupinus projects will add affordable renewable capacity to the ERCOT market, bolstering grid reliability amid rising electricity demand in Texas.  
The deal was facilitated through the renewable transaction infrastructure platform LevelTen Energy’s Accelerated Process (LEAPO), which standardises PPA transactions to improve certainty for developers and buyers. 
“Through our proprietary process, we were able to go from RFP launch to contract execution in under ten weeks,” said Rob Collier, senior vice president of marketplaces at LevelTen Energy. “We’re excited to see these projects come online, and the long-term impact that they’ll deliver to local communities in Texas and to the grid.” 
Earlier this month, Sunraycer broke ground on three solar-plus-storage projects in Texas, including the two-phase Lupinus project in Hagansport and the 100MW Eagle Springs project in Lake Creek. Lupinus combines 520MW of solar PV with 411MWh of battery storage, while Eagle Springs will feature 100MW of solar and 66MWh of storage. 
Sunraycer Renewables, a portfolio company of Crayhill Capital Management based in Annapolis, Maryland, has a renewable pipeline of around 3GW of solar and battery utility-scale projects across development, construction and operational stages.
After five editions of Large Scale Solar USA, the event becomes SolarPLUS USA to mirror where the market is heading. The 2026 edition, held this week in Dallas, Texas, will bring together developers, investors and utilities to discuss managing hybrid assets, multi-state pipelines, power demand increase from data centres and AI as well as the co-location of solar PV with energy storage in a complex grid. For more details and how to attend the event, visit the website here.

source

chatter matter

Efforts to expand renewable energy and technical education have increasingly focused on equipping young people with practical skills that prepare them for emerging industries. Across Pakistan, initiatives aimed at building capacity in clean energy are also encouraging greater participation of women in technical fields that have traditionally remained male-dominated.
Within this context, forty young women engineers from rural Sindh received hands-on training in solar roof installation, leadership and team-building under the Ladiesfund Solar Roof Installation Training Programme at Mehran University of Engineering and Technology (MUET), Jamshoro. The initiative was implemented by Dawood Global Foundation (DGF) and sponsored by Oil and Gas Development Company Limited (OGDC) along with DGF’s Educate a Girl programme. It aimed to equip participants with technical skills and industry-relevant exposure for the country’s growing renewable energy sector.
The participants represented several districts of Sindh, including Sanghar, Badin, Ghotki, Hyderabad, Sukkur, Khairpur, Tando Allah Yar, Jamshoro, Sujawal, Tando Muhammad Khan, Mirpurkhas, Umerkot, Larkana, Dadu, Matiari and Shaheed Benazirabad. During the training, they received practical instruction in solar roof installation, system maintenance and safety standards. The programme also included sessions on leadership, teamwork and basic business skills to help participants navigate professional environments.
Twenty-five students were sponsored by OGDC, while fifteen were supported through DGF’s Educate a Girl initiative. As part of the applied component of the training, the participants will also contribute to solarisation work at Darul Aman Women’s Shelter in Hyderabad. Internship opportunities were also offered, providing a pathway from training to professional engagement.
The programme was inaugurated by Hyderabad Commissioner Fayyaz Hussain Abbasi, while Deputy Commissioner Hyderabad Zain Ul Abedin Qasmani attended as guest of honour.
“This initiative is about empowering our youth with skills that shape their future. Real leadership lies in creating opportunities for others and giving back to society,” said Abbasi.
Qasmani added, “It is inspiring to see so many female engineers stepping confidently into the field of renewable energy. Programs like this break barriers and build leadership among young women.”
Aaisha Zaidi, a final-year Electrical Engineering student at MUET from Sanghar, said the experience strengthened her career goals. “Being part of this programme has been transformative. I have gained hands-on experience installing solar PV panels and testing systems, strengthening my goal of pursuing a Master’s in renewable energy and building a meaningful career in this field.”
Officials noted that initiatives linking technical education with community projects and industry exposure could play an important role in expanding women’s participation in the renewable energy workforce.
– You! desk
Date:

source

(Southern Alliance for Clean Energy via Minnesota News Connection)
 
 
(By Mike Moen. Minnesota News Connection) – The idea of making solar part of your home’s power source mix is to keep your rising electricity bill in check but analysts said up-front costs complicate planning, and Minnesota wants to be part of an emerging market featuring less expensive plug-in models.
The units, also known as “balcony solar,” typically cost several hundred dollars. Traditional rooftop solar panels have long enjoyed tax credits but still come with price tags well into the thousands.
Will Mulhern, electricity program director for the advocacy group Fresh Energy, said balcony solar setups are much smaller and can be easily transported, allowing more renters to adopt the technology.
“When you think about rooftop solar, a lot of folks will generate electricity and then they’ll sell that back to the power grid,” Mulhern observed. “But with balcony solar, it’s really just going to serve the devices that are in your home.”
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

source

Finland Switches On Its First EU-Backed Solar Park Under The EU Renewable Energy Financing Mechanism (RENEWFM)  SolarQuarter
source

In the canton of Ticino, the Serfontana shopping center has become a regional reference not only for its commercial history but also for its technological commitment to energy sustainability. By means of a photovoltaic system 93.1 kWp photovoltaic system installed on its roof, the site has been able to cover its entire electricity consumption with solar energy.
The installation uses Neostar 2P modules of 470 W of ABC Type N technology technology developed by AIKO. This solution allows greater efficiency and energy density per square meter, a crucial aspect in roofs with limited space. Thanks to this choice, Serfontana has avoided increasing the available surface area, achieving more production with fewer resources.
The planning includes three solar systems: one already in operation, one recently completed and a third one on the parking lot canopy, which shows a comprehensive vision of energy self-consumption.
paCompared to systems based on TOPCon technology, AIKO’s ABC panels allow a 9.2ne increase in annual production, with an additional cumulative generation of 327 MWh over 30 years. This yield represents an estimated savings of 1.14 million euros, with a 900% return on investment and an IRR of over 47%.
In addition, the installation reduces the amortization period by 6.7% compared to conventional alternatives and maintains costs 8% lower, thus consolidating its economic attractiveness.
Serfontana’s consumption pattern coincides with the hours of highest solar radiation, which allows for an ideal integration between demand and production. The photovoltaic modules modules demonstrate thermal resistance, low annual degradation and tolerance to partial shading and micro-cracks, ensuring long-term stable generation.
The choice of Ecosinergie Tienergy SA as the installation company and Krannich as technical distributor reinforces the quality of the project. According to Gionata Mongodi, project manager, the choice was based on the performance per square meter and the extended warranties offered by AIKO: 25 years for the product and 30 years for the performance.
Source and photo: AIKO
Ammonia serves as an excellent hydrogen carrier, facilitating much more economical transport than pure hydrogen.
Shell technology increases gas capacity in Nigeria without new facilities by optimizing existing processes.
BASF begins industrial production of 3D-printed catalysts to optimize efficiency and performance in chemical reactors.
LOTO ensures a Zero energy state and protects workers from hazardous energy.
A culture of excellence in industry goes beyond compliance: it integrates continuous improvement, operational leadership, and quality management into daily work to achieve sustainable results.
Explore how 3D Laser Scanning revolutionizes plant shutdowns, eliminating dimensional risks and optimizing prefabrication with real data.
Stainless steel abrasives improve surface cleaning and reduce consumption in industrial blasting processes.
Cathodic protection with magnesium protects infrastructure against corrosion in demanding industrial environments.
AMPP advances its global growth with a focus on talent, innovation, and expansion in the materials industry.
INSPENET LLC
433 N Loop W, FWY
Houston, TX 77018
hola @ inspenet.com
 
About Us
Connect
Help

source

Industry Overview
Market forecast and expert KPIs for 1000+ markets in 190+ countries & territories
Insights on consumer attitudes and behavior worldwide
Detailed information for 39,000+ online stores and marketplaces
Flexible integration for any environment
AI researchers delivering human-verified insights
Trusted data, wherever you work
Directly accessible data for 170 industries from 150+ countries and over 1 million facts:
Statista+ offers additional, data-driven services, tailored to your specific needs. As your partner for data-driven success, we combine expertise in research, strategy, and marketing communications.
Full-service market research and analytics
Strategy and business building for the data-driven economy
Transforming data into content marketing and design:
Statista R identifies and awards industry leaders, top providers, and exceptional brands through exclusive rankings and top lists in collaboration with renowned media brands worldwide. For more details, visit our website.
See why Statista is the trusted choice for reliable data and insights. We provide one platform to simplify research and support your strategic decisions. Learn more
Expert resources to inform and inspire.
Detailed statistics
Global cumulative installed solar PV capacity 2000-2024
Detailed statistics
Global cumulative solar PV capacity 2024, by select country
Detailed statistics
Solar PV cumulative installed capacity in the United Kingdom (UK) 2009-2024
Renewable Energy
Solar photovoltaic energy production in the United Kingdom 2004-2022
Renewable Energy
Energy used for heat generation from active solar heating in the UK 2010-2024
Renewable Energy
Monthly solar PV installation cost in the UK 2016-2023
Global cumulative installed solar PV capacity 2000-2024
Cumulative solar PV capacity installed worldwide from 2000 to 2024 (in megawatts)
Global cumulative solar PV capacity 2024, by select country
Cumulative solar photovoltaic capacity globally as of 2024, by select country (in gigawatts)
World’s largest solar PV power plants worldwide 2024
Largest solar photovoltaic power plants worldwide as of June 2024, by capacity (in gigawatts)
Global share of solar power in electricity mix 2024, by country
Share of solar energy in electricity generation worldwide in 2024, by leading country
Solar PV cumulative installed capacity in the United Kingdom (UK) 2009-2024
Cumulative installed capacity of solar photovoltaic in the United Kingdom (UK) from 2009 to 2024 (in megawatts)
Support for solar power developments in the United Kingdom (UK) 2012-2024
Generally speaking, do you support or oppose solar renewable energy developments?
Number of solar PV energy generating sites in the United Kingdom (UK) 2007-2023
Number of sites generating electricity from solar photovoltaic in the United Kingdom (UK) from 2007 to 2023
Quarterly Feed-in Tariff installations of solar PV in Great Britain 2010-2023
Number of solar photovoltaic installations under Feed-in Tariff scheme in Great Britain from 2nd quarter 2010 to 2nd quarter 2023
Quarterly feed-in tariff capacity addition of solar PV in Great Britain 2010-2023
Solar photovoltaic capacity added under Feed-in Tariff scheme in Great Britain from 2nd quarter 2010 to 2nd quarter 2023 (in kilowatts)
Monthly solar PV installation cost in the UK 2016-2023
Average installation cost of small scale solar photovoltaic systems in the United Kingdom (UK) from April 2016 to March 2023, by size band (in British pounds per kilowatt installed)
Cumulative solar PV capacity in the United Kingdom (UK) 2010-2022, by tariff scheme
Cumulative capacity of solar photovoltaic (PV) installed by accreditation in the United Kingdom (UK) from 2010 to 2022 (in megawatts)
Installed capacity of solar PV power in England 2008-2024
Cumulative installed capacity of solar photovoltaic power in England from 2008 to 2024 (in megawatts)
Installed capacity of solar PV power in Scotland 2009-2024
Cumulative installed capacity of solar photovoltaic power in Scotland from 2009 to 2024 (in megawatts)
Installed capacity of solar PV power in Wales 2010-2024
Cumulative installed capacity of solar photovoltaic power in Wales from 2010 to 2024 (in megawatts)
Installed capacity of solar PV power in Northern Ireland 2010-2024
Cumulative installed capacity of solar photovoltaic power in Northern Ireland from 2010 to 2024 (in megawatts)
Solar PV installed capacity in the United Kingdom (UK) 2023, by site
Largest operational solar photovoltaic farms in the United Kingdom (UK) as of October 2023 (in megawatts)
Solar photovoltaic energy production in the United Kingdom 2004-2022
Generation of electricity through solar photovoltaic power in the United Kingdom from 2004 to 2022 (in gigawatt hours)
Load factor of electricity from solar PV in the United Kingdom (UK) 2010 to 2024
Load factor of electricity from solar photovoltaics in the United Kingdom (UK) from 2010 to 2024 (in percentage)
Global share of solar power in electricity mix 2024, by country
Share of solar energy in electricity generation worldwide in 2024, by leading country
Energy used for heat generation from active solar heating in the UK 2010-2024
Energy from active solar heating used to generate heat in the United Kingdom (UK) from 2010 to 2024 (in 1,000 metric tons of oil equivalent)
Solar energy use in the United Kingdom 2005-2021
Solar energy use in the United Kingdom (UK) from 2005 to 2021 (In thousand metric tons of oil equivalent)
Solar energy industry turnover in the United Kingdom (UK) 2014-2022
Estimated turnover of the solar energy industry in the United Kingdom (UK) from 2014 to 2022 (in million GBP)
Employment in the solar energy industry in the United Kingdom (UK) 2014-2022
Estimated number of employees in the solar energy industry in the United Kingdom (UK) from 2014 to 2022
Solar employment in the United Kingdom (UK) 2022, by region
Estimated number of employees in the solar energy industry in the United Kingdom (UK) 2022, by country
Detailed statistics
Global cumulative installed solar PV capacity 2000-2024
Detailed statistics
Global cumulative solar PV capacity 2024, by select country
Detailed statistics
Solar PV cumulative installed capacity in the United Kingdom (UK) 2009-2024
The most important key figures provide you with a compact summary of the topic of "Solar power in the UK" and take you straight to the corresponding statistics.
Number of solar PV energy generating sites in the United Kingdom (UK) 2007-2023
Monthly solar PV installation cost in the UK 2016-2023
Solar photovoltaic energy production in the United Kingdom 2004-2022
Load factor of electricity from solar PV in the United Kingdom (UK) 2010 to 2024
Solar PV installed capacity in the United Kingdom (UK) 2023, by site
Solar energy industry turnover in the United Kingdom (UK) 2014-2022
Employment in the solar energy industry in the United Kingdom (UK) 2014-2022
Feel free to contact us anytime. We will respond to your inquiry as quickly as possible.

source

Europe Today
Euronews' flagship morning TV show with the news and insights that drive Europe, live from Brussels every morning at 08.00. Also available as a newsletter and podcast.
The Ring
The Ring is Euronews’ weekly political showdown, where Europe’s toughest debates meet their boldest voices. In each episode, two political heavyweights from across the EU face off to propose a diversity of opinions and spark conversations around the most important issues of EU affairs and the wider European political life.
No Comment
No agenda, no argument, no bias, No Comment. Get the story without commentary.
My Wildest Prediction
Dare to imagine the future with business and tech visionaries
The Big Question
Deep dive conversations with business leaders
Euronews Tech Talks
Euronews Tech Talks goes beyond discussions to explore the impact of new technologies on our lives. With explanations, engaging Q&As, and lively conversations, the podcast provides valuable insights into the intersection of technology and society.
The Food Detectives
Europe's best food experts are joining forces to crack down on fraud. Euronews is following them in this special series: The Food Detectives
Water Matters
Europe's water is under increasing pressure. Pollution, droughts, floods are taking their toll on our drinking water, lakes, rivers and coastlines. Join us on a journey around Europe to see why protecting ecosystems matters, how our wastewater can be better managed, and to discover some of the best water solutions. Video reports, an animated explainer series and live debate – find out why Water Matters, from Euronews.
Climate Now
We give you the latest climate facts from the world’s leading source, analyse the trends and explain how our planet is changing. We meet the experts on the front line of climate change who explore new strategies to mitigate and adapt.
Europe Today
Euronews' flagship morning TV show with the news and insights that drive Europe, live from Brussels every morning at 08.00. Also available as a newsletter and podcast.
The Ring
The Ring is Euronews’ weekly political showdown, where Europe’s toughest debates meet their boldest voices. In each episode, two political heavyweights from across the EU face off to propose a diversity of opinions and spark conversations around the most important issues of EU affairs and the wider European political life.
No Comment
No agenda, no argument, no bias, No Comment. Get the story without commentary.
My Wildest Prediction
Dare to imagine the future with business and tech visionaries
The Big Question
Deep dive conversations with business leaders
Euronews Tech Talks
Euronews Tech Talks goes beyond discussions to explore the impact of new technologies on our lives. With explanations, engaging Q&As, and lively conversations, the podcast provides valuable insights into the intersection of technology and society.
The Food Detectives
Europe's best food experts are joining forces to crack down on fraud. Euronews is following them in this special series: The Food Detectives
Water Matters
Europe's water is under increasing pressure. Pollution, droughts, floods are taking their toll on our drinking water, lakes, rivers and coastlines. Join us on a journey around Europe to see why protecting ecosystems matters, how our wastewater can be better managed, and to discover some of the best water solutions. Video reports, an animated explainer series and live debate – find out why Water Matters, from Euronews.
Climate Now
We give you the latest climate facts from the world’s leading source, analyse the trends and explain how our planet is changing. We meet the experts on the front line of climate change who explore new strategies to mitigate and adapt.
Solar installations in Africa are expected to get more expensive next month as China ends discount schemes.
China’s decision to end value-added tax rebates on solar panel exports is expected to take effect on 1 April. At the beginning of next year, the country will also phase out incentives for making battery storage equipment.
It may complicate efforts to expand renewable energy to close vast electricity gaps across Africa, which relies heavily on imported Chinese technology, though experts say the impact likely will be manageable.
“We are likely to see solar panel prices increase in Africa because most of the inputs come from China,” says Wangari Muchiri, an energy analyst focused on Africa’s clean energy sector. “Removing the rebate will add to existing costs, especially when you consider shipping, logistics, and other import fees.”
Africa already pays significantly more for solar equipment than other regions because of transport costs, smaller import volumes and tariffs.
China‘s policy change reflects broader shifts after fierce competition among Chinese manufacturers pushed solar module prices to as little as €0.06 per watt in 2025, from €0.22 in 2022. That helped drive global adoption of solar energy, but left many companies with heavy losses.
Some Chinese companies built VAT rebates into their export pricing, effectively transferring those subsidies to their overseas buyers. But Beijing has cut back on those payments as it reins in overcapacity and shifts toward more advanced technologies.
Rather than a sharp price shock, the loss of such rebates will likely gradually raise prices, setting a firmer global price floor.
“The changes are significant, but not catastrophic,” says John van Zuylen, CEO of the Africa Solar Industry Association.
“The entire recent solar boom was built on artificially cheap Chinese pricing,” van Zuylen says. “That era is now ending.”
“When a structural rebate is removed, exporters typically either absorb the cost, raise prices, or reduce discounting,” van Zuylen says. “African countries will likely feel this as a gradual upward shift in pricing rather than a single dramatic spike.”
Even with modest price increases, solar is expected to remain competitive across much of the continent since it’s the cheapest source of energy in Africa, Muchiri says.
“Even with higher panel prices, it will still be significantly cheaper than alternatives like diesel,” she says.
“It will increase project costs slightly and might delay the project construction pipeline due to supply chain shortages and contractual changes, stockpiling rush, congestion in shipment for the countries heavily reliant on Chinese imports,” says Sonia Dunlop, CEO of the Global Solar Council, an industry association.
Battery storage, critical for providing electricity after sunset, may face a bigger challenge as incentives are phased out through 2027. Higher costs may affect smaller users the most, van Zuylen says.
“Batteries matter more than panels for Africa because storage is what makes solar reliable for off-grid and backup users,” he says.
Basil Abia, co-founder of the Nigerian energy research firm Truva Intelligence, says that “batteries have historically been expensive, and many solar installations in Africa were built without them.”
“Only recently have we started seeing more systems combining solar with battery storage,” Abia says.
He says that even without rebates, solar modules remain relatively affordable. Through 2024 and early 2025, module prices fell sharply from around €0.22 per watt in previous years to as low as €0.06 per watt.
Demand for solar, which now supplies 3 per cent of power generation in Africa, is expected to continue growing as storage improves reliability. Meanwhile, the heavy dependence on Chinese equipment is drawing attention to limited local manufacturing capacity.
“The VAT removal will slow, but not reverse Africa’s clean energy transition,” Abia says. “Countries that use this moment to accelerate local manufacturing will emerge stronger. Those that do not will remain exposed to Beijing’s next industrial policy adjustment.”


Browse today's tags

source

Plug-in solar will also enable more UK households to save money on their energy bills.
The current conflict highlights that the only route to energy security and sovereignty for the UK is to end dependence on fossil fuel markets and accelerate the drive for clean, homegrown power, as well as new renewables and nuclear.
The government has already taken significant steps to accelerate the transition to clean energy in response to the conflict.
Last week, the government’s annual renewables auction was brought forward to July. The most recent auction was the biggest ever, and together with the previous auction, it has confirmed enough clean energy to power 23 million homes.
Plug-in solar panels are widely used across Europe, with Germany installing around half a million every year.
The free solar power can be used via a mains socket with no installation costs, thereby reducing the amount drawn from the grid and lowering overall costs.
Retailers like Lidl and Amazon, alongside manufacturers such as EcoFlow, are working with the government to enable them to enter the UK market.
Energy Secretary Ed Miliband said: “Whether through solar panels fitted as standard on new homes or making it possible for people to purchase plug-in solar in shops, we are determined to roll out clean power so we can give our country energy sovereignty.”
The government has taken decisive action in response to the conflict in the Middle East to fight for consumers and businesses on the cost of living, and is speeding up plans for more clean, homegrown energy that the UK controls to ensure energy sovereignty and security.
This is alongside new rules coming into force today that implement the Future Homes Standard, which includes common sense measures to ensure the majority of new homes are built to be cheaper to run, with solar panels and clean heating as standard.
These measures on new homes could save families up to £830 a year on their energy bills, compared to a standard home with an EPC rating of C.
“Building 1.5 million new homes also means building high-quality homes that are cheaper to run and warmer to live in,” explained Housing Secretary Steve Reed.
“As we make the switch to clean, homegrown energy, today’s standard is what the future of housing can and should look like. Not only will these changes protect hardworking families from shocks abroad, but they will also slash hundreds of pounds off their energy bills every year.”
This will ensure they are more comfortable and affordable, and create at least 75% less carbon emissions than those built to the 2013 standards.
Additionally, an innovative approach will launch in time for winter, enabling energy companies to offer customers discounted energy bills on windy days.
Mainly benefiting Scotland and the East of England, bills will be reduced instead of continuing the previous practice of paying wind turbines to turn off.
Historic underinvestment in Britain’s electricity grid means that wind farms in these areas are being paid to switch off on windy days when the network can’t carry all the clean power they produce.
The government will now seek to introduce new legislation to ensure that clean, homegrown power can be discounted for consumers during these times.
Save my name, email, and website in this browser for the next time I comment.


Δ

The latest handpicked content, focusing on breaking developments and in-depth analysis from key research and innovation partners.
As a Crossref Sponsored Member, we are able to connect your content with a global network of online scholarly research, currently over 24,000 other organisational members from 166 countries. Crossref drive metadata exchange and support nearly 2 billion monthly API queries, facilitating global research communication.
Innovation News Network brings you the latest research and innovation news from the fields of science, environment, energy, critical raw materials, technology, and electric vehicles.
Disclaimer: This website is an independent portal and is not responsible for the content of external sites.
Please Note: Phone calls may be recorded for training and monitoring purposes.
© Pan European Networks Ltd

source

Amazon and Lidl to sell solar panels in the UK to combat rising energy bills  Cybernews
source

LGE India has signed long-term solar power purchase agreements (PPAs) totaling 20.8 MWp with Hinduja Renewables and Sunsure Energy to supply renewable electricity to its manufacturing facilities in Greater Noida and Pune.
Image: Sunsure Energy
LG Electronics (LGE) India has signed long-term solar power purchase agreements (PPAs) totaling 20.8 MWp with Hinduja Renewables and Sunsure Energy to supply renewable electricity to its manufacturing facilities in Greater Noida and Pune.
The company has contracted 9.8 MWp of solar capacity from Hinduja Renewables for its Pune facility and 11 MWp from Sunsure Energy for its Greater Noida plant. It will source approximately 3.21 crore units of renewable energy annually for both facilities, collectively offsetting around 0.61 million metric tonnes of CO2e over the project lifetime.
Hinduja Renewables will supply 1.61 crore units of clean power annually from its 27.7 MWp solar plant in Nanded, Maharashtra, helping meet 40% of the Pune facility’s energy needs and offset 0.31 million metric tonnes of CO2e over the project lifetime.
Similarly, Sunsure Energy will supply approximately 1.6 crore units of renewable electricity annually from its 82.5 MWp solar plant in Erach, Uttar Pradesh, enabling LGE India to meet around 30% of its Greater Noida facility’s energy requirement and increase total renewable energy consumption at the plant to approximately 50%, while offsetting 0.30 million metric tonnes of CO2e over the project lifetime.
Both PPAs are signed for a 25-year period and are scheduled to commence in the second quarter of 2026. The agreements also mark LGE India’s first strategic equity investment in Indian special purpose vehicles for renewable power generation.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
More articles from Uma Gupta
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website


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

source

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: 25100 (2025) Cite this article
3420 Accesses
8 Citations
Metrics details
Recently, lead-free Cs₄CuSb₂Cl₁₂ has garnered attention as an excellent material to be used as an absorber of perovskite solar cells (PSCs). In this work, Cs₄CuSb₂Cl₁₂ absorber-based PSCs were studied and the conditions to get high performance for PSCs were investigated. Here, six different materials for electron transport layers (ETLs) and 10 different materials for hole transport layers (HTLs) were studied. A numerical approach was followed by using SCAPS-1D simulator. During the work, various device parameters of PSC were investigated such as thickness variation of the absorber and ETL layers, acceptor density variation of the absorber and HTL layers, variation of the donor density of the ETL layer, and effect of total defect density of absorber. Also, other parameters such as the impact of resistance, temperature, J-V graph, Q-E graph, and carrier generation rate at different positions of the PSCs were assessed. Among the studied 10 HTL materials, MWCNTs outperformed other studied materials, hence it was selected for further investigations. Then the structures were optimized based on the device parameters outcome, and the structure having MZO and STO ETLs both showed the maximum power conversion efficiency (PCE) of 28.23%. (Al/FTO/MZO/Cs₄CuSb₂Cl₁₂/MWCNTs/Au) the structure showed an open-circuit voltage (V_oc) of 1.249 V, short-circuit current density (J_sc) of 25.11 mA/cm² and a fill factor (FF) of 90.1%. The performance was also evaluated with respect to key electrical parameters. Optimum performance was achieved at a series resistance of 1 Ω·cm² and a shunt resistance of 1000 Ω·cm², beyond which performance gains saturated. The other best-performing STO ETL-based (Al/FTO/STO/Cs₄CuSb₂Cl₁₂/MWCNTs/Au) structure had V_oc of 1.25 V, J_sc of 25.11 mA/cm², and FF of 90.01% Under the optimized condition other structure with CdS, PC₆₁BM, SnS₂ and ZnSe ETLs showed PCE of 27.68%, 27.8%, 25.67% and 28.22%. This work gives good insights into several Cs₄CuSb₂Cl₁₂ based PSC structures and shows in the future they have great potential to be developed practically for highly efficient performances.
To overcome the global challenge of heavy reliance on natural resources for energy and to ensure the smooth transition in the future to green energy the policy makers are now focusing on the development of renewable energy sources^1,2,3,4,5,6. Different renewable sources of energy such as windmills, hydroelectricity, bioenergy, and solar cells^{7,8,9,10,11,12} are the most reliable and have the quality to be used on a large scale^13,14,15,16. Among these options, solar cells have the best potential to develop, can be used everywhere from a smaller scale to a larger scale, have good durability, and need less effort to maintain making them the best energy source for usage^17,18,19. Solar cells convert the energy of photons and convert them to generate electricity. It is a clean and reliable source of energy. There are different types of solar cell technologies: DSCC^20,21,22,23crystalline solar cells²⁴polycrystalline solar cells²⁵silicon solar cells^26,27,28,29and thin-film solar cells (TFSC)^17,30,31. TFSC needs comparatively less material than other technologies and it is more environment friendly. Among the several types of TFSC perovskite solar cells^{32,33,34,35,36,37,38,39}one of the best-performing technologies shows high power conversion efficiency (PCE)^40,41,42,43. It is cheap, environment-friendly, and suitable for production in industries^44,45. It shows convenient electrical, and optical properties and a good absorption coefficient making it an excellent choice for the usage of solar cells^46,47.
Copper-Zinc-Telluride-Sulfide (CZTS)⁴⁸Copper-Zinc-Telluride selenide (CZTSe)^49,50CdTe¹¹and etc. inorganic absorber material-based PSC currently popular in the scientific studies for their potentials. Recently, several organic-inorganic absorber materials have gained a lot of attraction in PSC research. Formamidinium lead Iodide (FAPbI₃)⁵¹Formamidinium Tin Iodide (FASnI₃)⁵²Methylammonium Tin Iodide (MASnI₃)⁵³Methylammonium Lead Iodide (MAPbI₃)⁵⁴ are some of the bests performing organic-inorganic absorbers of TFSC. Many perovskites have AMX₃ general formula where A can be Cesium (Cs), Flouridinium (FA), or methyl ammonium (MA); X represents halogen such as Cl, Br, I and M denotes cations such as Pb, Sn, or Ge based organic-inorganic have been discovered. These perovskites show remarkable optoelectronic properties such as a high absorption coefficient of > 10⁵ cm^− 1, tunable band gap, and a wide range of light for absorption^9,10. Having these suitable properties perovskites with AMX₃ general formula can be used for optoelectronic devices including LEDs^50,55,56photodetectors⁵⁷and solar cells^58,59. However, the presence of Pb in perovskites makes them detrimental to the environment⁶⁰ so choosing Pb for solar cells is not ideal. One of the solutions is to replace Pb²⁺ with Sn²⁺ and Ge²⁺ which is not viable since both make perovskite centers susceptible to oxidation, thus hampering the longevity of perovskites⁶¹. So, heterovalent Bi³⁺ and Sb³⁺ have been used as lead-free alternatives in zero-dimensional and two-dimensional chalcogenides^62,63. Lately, a new three-dimensional A₂B’B” X₆ double perovskite structure has been studied^64,65. This structure can contain non-toxic cations and metal ions and shows stability. However, Cs₂AgBiBr₆, which shows the best optoelectronic properties among the structures without Pb presence that follows the AMX₃ general formula, has a wide bandgap of 2.19 eV that limits the potential to be used in solar cells^66,67. However, by applying techniques such as pressure-assisted band gap tuning⁶⁸ and dilute alloying⁶⁹ the bandgap can be reduced to make it applicable in photovoltaics.
Mostly, 3D structured double perovskites which are lead-free are bad absorbers due to the property of large indirect bandgap. After exploring suitable perovskites, Cs₄CuSb₂Cl₁₂ (CCSC) showed suitable photoelectric properties having a band gap of 1eV⁶⁹. Cs₄CuSb₂Cl₁₂ bulk has high electron effective mass which causes slow electron mobility and thus deteriorates performances of solar cells. The strategy to decrease the particle size of Cs₄CuSb₂Cl₁₂ (CCSC) to nanoscale paved the way to tune energy band structure⁶⁹. In two experimental studies, the average particle size of (CCSC) with nanocrystals (NCs) was constructed as 3 nm⁷⁰ and 3.9 nm⁷¹ using different research methods. Perovskites with nanocrystals have some salient properties including variable bandgap, high electrical conductivity, and large absorption spectrum which are conducive to the applications of photovoltaics^72,73,74.
Exploration on Cs₄CuSb₂Cl₁₂ absorber-based PSCs structure designing is in the initial stages since the material is developed in the recently made suitable for application. In one study, 16.6% PCE⁷⁵ was obtained using these Cs₄CuSb₂Cl₁₂ NCs. In a simulation study where (FTO/TiO₂/CCSCNCs/Cu₂O/Au) PSC device structure was studied and showed a PCE of 23.07%⁷⁶ when optimized using the SCAPS-1D simulator. (FTO/WS₂/Cs₄CuSb₂Cl₁₂/CuSbS₂/Ni) PSC device structure was proposed after examining several materials for ETL and HTL layers in one of the recent works on Cs₄CuSb₂Cl₁₂ absorber PSCs. 23.10% PCE⁷⁷ was achieved after the optimization of the PSC device using SCAPS-1D simulator. In another research article, after examining 244 different PSC device structures using SCAPS-1D simulator, two different device structures were proposed, one structure showed 29.71% PCE having an HTL layer, and the other device structure was designed without any HTL layer but Pt as the back contact showed 29.61% PCE⁷⁸. In both device structures, SnO₂ was used as an ETL layer.
There is a big gap in research studies on the ETLs and HTLs for Cs₄CuSb₂Cl₁₂ PSCs. Understanding the potential of this absorber and limited conducted studies on ETL and HTL materials, here six ETL materials were investigated: PC₆₁BM, ZnSe, STO, MZO, CdS, and SnS₂. Besides 10 HTLs were also studied as follows: ZnTe, Sb₂S₃, MWCNTs, MoO₃, Cu: NiO, MASnBr₃, TiO₂:N, CZGS, NiCo₂O₄ and CuAlO₂. A broad exploration of the performances of the PSC devices for different charge transport layer materials was conducted. This study examines designs of many possible device structures of the Cs₄CuSb₂Cl₁₂₋PSC technology which could give useful insights in the future for practical implementations. The objectives of this paper could be summarized as: (i) exploration of ETL and HTL materials for the Cs₄CuSb₂Cl₁₂-absorber-based PSC technology, (ii) study different PSC structures and optimize them to get highly efficient device performances, (iii) assess the performances of the devices for optimum conditions, (iv) propose the best device structures after evaluating practical challenges, and (v) provide an outline of device structure modifications for future works.
Here, the SCAPS-1D tool was used which was developed by the Electrical and Information Engineering Department of the University of Gent of Belgium^{59,79,80,81,82}. This tool is specifically designed for the simulation works of solar cells which has been proved reliable when compared to the experimental research in many works. This tool is useful in solar cell research works since close results of real performances can be replicated in simulation and it is now widely used. SCAPS-1D tool is effective in TFSC simulation works. It can be used to study performance characteristics such as J_sc, V_oc, FF, and PCE of solar cells. Besides, it can be used to study different graphs such as the J-V curve, Q-V curve, C-f curve, band diagram, etc. Other factors that impact TFSCs such as series resistance, shunt resistance, temperature, etc. can studied. The device structure of PSCs can design and parameters of materials such as band diagram, mobility of electrons and holes, electron affinity, doping density of carriers, several types of defects such as defects at the interfaces of material layers, recombination defect, etc. can be defined in the device’s structure designing. Up to seven different layers of materials can be used to construct the expected device structures. SCAPS-1D uses several equations to calculate the performances of PSCs such as Poisson’s equation, electron continuity equation, hole continuity equation, drift, and diffusion of carriers’ equations, etc. This tool uses numerical methods such as Newton-Raphson, continuity equations to solve equations.
The Poisson’s equation can be defined as Eq. 1. Here, ψ is represented as the potential of the electric field, ∈₀ is denoted as permittivity in free space whereas ∈_r is represented as permittivity in a relative medium. q is denoted as the amount of charge, ρ_n is represented as the distribution of electrons, and ρ_p is represented as the distribution of holes. N_A is the density of acceptor carriers and N_D is symbolized as the density of donor carriers.
The continuity equations can be defined as Eqs. 2 and 3. Here, Eq. 2 is the continuity equation of electrons, and Eq. 3 is the continuity equation of holes. Here, J_n is denoted as the current density for electrons and J_p is denoted as the current densities of holes. G_n is denoted as the electron recombination rate and G_p is symbolized as the hole recombination rate.
Equation 4 and Eq. 5 represent the relation of charge carrier drift-diffusions to calculate the current densities of the electrons and holes of solar cells. Here q represents the total number of charges; µ_n and µ_p are the mobility of electron and hole carriers respectively.
The performance of a photovoltaic cell is measured by the quality fill factor (FF). FF depends on the product value of the maximum voltage and maximum current representing the maximum power to the calculated theoretical power (Pt), considering the open circuit voltage (V_oc) and short circuit current (J_sc) shown in Eq. 6. The power conversion efficiency (PCE) (Eq. 7) shows the ratio of output energy that can be found from the photovoltaic solar cells to the given input energy. Its performance relies on the product of V_oc, J_sc, and FF to the given input power as shown in the Eq. 
In this work, Al/FTO/ETL/ Cs₄CuSb₂Cl₁₂/HTL/Au structure devices were studied. Six different ETLs were studied as follows: CdS, PC₆₁BM, SnS₂, MZO, STO, and ZnSe. Besides, 10 HTLs were investigated for the Cs₄CuSb₂Cl₁₂ absorber-based structure. Sb₂Se₃, Cu doped with NiO. Cu₂Te, ZnTe, MoTe₂, CuAlO₂, CZGS, MASnBr₃, MoO₃, MWCNTs, NiCo₂O₄, and TiO₂ doped with nitrogen were initially chosen for device structures to study. Here, gold (Au) was selected as the back contact⁵⁰ and Al as the front contact layer. FTO was chosen as the window layer for the structure devices. This work, extensively focused on studying the performances of ETL and HTL layers. All the configurations of the device structure were investigated using the SCAPS-1D tool (input parameters are given in Tables 1, 2 and 3) under the 1.5 AM solar radiation condition and 100 mW/cm² power density was applied. The Cs4CuSb2Cl12-based proposed PSC device structures are shown in Fig. 1.
Cs₄CuSb₂Cl₁₂ based proposed PSC device structures.
Energy band alignment of the studied PSC device structures based on various ETL materials with MWCNTs as the HTL.
Figure 2 represents the energy band diagrams for the studied PSC structures. Proper energy band alignment between layers is essential for efficient charge carrier transport^1,40. The conduction band offset (CBO) and valence band offset (VBO) describe the energy difference between the conduction bands and valence bands, respectively, of two adjacent materials. These offsets are primarily influenced by the differences in electron affinity (for CBO) and ionization energy (for VBO). The electron transport layer (ETL) does not absorb photon energy; rather, its primary role is to facilitate the extraction and transport of photogenerated electrons from the absorber layer to the electrode. For efficient electron transport, a small or near-zero CBO at the ETL/absorber interface is preferred. If the conduction band edge of the absorber lies below that of the ETL, a spike-shaped CBO is formed, which may hinder electron extraction. Conversely, if the absorber’s conduction band is above the ETL’s, a cliff-shaped CBO occurs, which can promote recombination. Therefore, optimizing the CBO is critical for enhancing device performance^40,43.
Comparison of performances of HTL materials on PSC parameters.
At the beginning of the work, the most suitable HTL layer for the Cs₄CuSb₂Cl₁₂ absorber-based PSCs was selected. Among the studied HTLs ZnTe and CuAlO₂ showed the poorest PCE of 0.77% and 4.44% (Fig. 3). Among other HTL materials Sb₂S₃, CZGS, NiCo₂O₄, MASnBr₃, MoO₃, Cu: NiO, and TiO₂:N when used as the HTL layer showed 20.95%, 22.52%, 18.57%, 22.63%, 23.74%, 23.72%, and 23.22% PCE respectively in the Cs₄CuSb₂Cl₁₂-based PSC structures. MWCNTs showed the best performance showing a 24.65% PCE in PSC structure when applied as an HTL layer. Also, V_oc of 1.14 V, J_sc of 24.82 mA/cm², and FF of 87.07% were achieved for the device structure (Fig. 3). Here, the PSC structure with MWCNTs HTL showed 0.91% higher PCE than the second-best Cu: NiO HTL material. Considering overall performances, MWCNTs were chosen as the HTL material for further investigation.
Effect of (a) thickness variation of absorber, and (b) thickness variation of ETL on device performance (PCE, FF, V_OC, and J_SC) .
To optimize the PSCs structure at first the impact of varying thickness of absorber material on six different device structures was studied. Figure 4(a) shows the effect on performances when absorber thickness was changed from 0.5 μm to 1.20 μm. As the thickness of absorber material increases PSCs can capture more photons which leads to more electron-hole pairs resulting in converting absorbed energy from photons to electricity. From Fig. 4(a), it is evident that the absorber showed the best PCE with the MZO ETL combination, reaching a PCE of 25.56% at 1 μm. Similar to PCE, Jsc increases until gets saturated when absorber thickness is enhanced. For tall the combinations J_sc increased as the thickness was increased. In all the combinations a stable FF was observed. For V_oc, except for the combination with CdS structure, all others remained unaffected by the alteration of the thickness of the absorber. In the CdS ETL combination structure, a small slump of V_oc was observed at an absorber thickness of 0.8 μm and then it became saturated. Notably, the CdS-based structure exhibits a slight decrease in efficiency when the absorber thickness reaches 1.0 μm, which may be due to increased parasitic absorption in the CdS layer and enhanced recombination losses at the CdS/Cs₄CuSb₂Cl₁₂ interface. This contrasts with other ETLs such as MZO and ZnSe, which maintain or improve performance due to their wider bandgaps and better optical transparency^64,65.
Having an optimum thickness for the ETL layer helps to have minimal effect of recombination and provide effective performance. The best PCE for the structure in obtained for the different configurations when CdS, MZO, PC₆₁BM, SnS₂, STO, and ZnTe had a thickness of 0.15 μm, 0.15 μm, 0.02 μm, 0.15 μm, 0.03 μm and 0.05 μm thickness respectively. For the ETL layer at 0.15 μm, the highest PCE of 25.56% was achieved (Fig. 4(b)). For all parameters of FF, J_sc, and V_oc all the ETL device structures showed relatively stable performances over the variation of ETL layers from 0.01 μm to 0.5 μm thickness except the PC₆₁BM ETL combination showed degrading when thickness was increased. Therefore, a minimum of 0.02 μm was chosen as the optimum thickness for PC₆₁BM ETL material. Figure 4(b) shows that performance variation with ETL thickness depends on material properties. Inorganic ETLs (e.g., MZO, STO, ZnSe) maintain stable performance due to high electron mobility and favorable energy alignment. In contrast, PC₆₁BM shows a marked decline with increasing thickness, mainly due to its lower electron mobility and higher series resistance, which increase recombination and reduce charge collection efficiency⁵⁹.
An increase in acceptor density helps the material’s ability to move holes more effectively and thus increases conductivity. However, enhancing acceptor density also leads to an increase in defects with a higher density which leads to deterioration of the overall PCE of the photovoltaic device. Figure 5(a) shows the effect on overall performances when the acceptor density of the absorber was studied between 10¹⁵ cm^− 3 to 10²⁰ cm^− 3. For all the PSC combinations 10¹⁶ cm^− 3 had the best PCE. After reaching an acceptor density of 10¹⁶ cm^− 3 of absorber for all the structures PCE performance degraded. Among all the studied ETLs, the SnS₂-based structure showed a comparatively sharper decrease in PCE beyond the optimum acceptor density of 10¹⁶ cm⁻³. This behavior is likely due to increased interfacial recombination resulting from the energy level mismatch and potential interface traps at the SnS₂/Cs₄CuSb₂Cl₁₂ interface, which becomes more pronounced at higher doping concentrations^59,81,82. Overall, the MZO ETL combination for absorbers with the highest PCE of 25.84% was achieved. Similarly to PCE, after reaching an optimum acceptor density for the absorber they had a fall in J_sc value since the value gets hampered by the presence of higher defect density due to the presence of a higher acceptor density.
Figure 5(b) represents the effect of the total defect density of absorber material. A higher defect density in PSC absorbers leads to trapping charge carriers. As a result, it can increase recombination and have a negative impact on an electric field. So, both V_oc and FF get affected directly which also leads to the deterioration of PCE performance. In Fig. 5(b), the defect density of the absorber was varied from 10¹⁰ cm^− 3 to 10¹⁶ cm^− 3. Until 10¹³ cm^− 3 PCE, V_oc, J_sc, and FF had stable results. After reaching the threshold defect density, impaired material properties started to lead to a quick fall in performance in all device structure combinations. Therefore, 10¹³ cm^− 3 can be considered the highest tolerable defect density of the absorber.
Significance of variation of absorber (a) acceptor density, and (b) defect density on device performances.
Figure 6(a) shows the effect of changing the donor density of ETL from 10¹⁴ cm^− 3 to 10²⁰ cm^− 3. When SnS₂ and CdS ETLs reached donor density at 10¹⁷, a sharp rise in PCE and FF was witnessed in their structures. PCE and FF remained steady for the change in other ETLs. J_sc was stable in PSCs when donor density was varied in MZO, SnS_2, ZnSe, and STO ETLs. However, a minor increase was found in the CdS ETL layer’s device, and a decrease was witnessed in the combination of the PC₆₁BM layer’s device. V_oc was increased when the donor density of SnS₂ and CdS ETLs were enhanced from 10¹⁹ cm^− 3 to 10²⁰ cm^− 3 in the PSCs. The change had no visible impact on other device structures⁶⁷.
Figure 6(b) represents the impact of acceptor density variation on PSCs. Having a higher acceptor density in HTLs helps to have a strong electron field presence at the interface of the absorber and HTL. It helps to minimize recombination and separate electron-hole pairs more effectively. Therefore, it affects PSC by improving performance in general. From Fig. 6(b), a similar expected performance can be seen. Acceptor density for ETLs varied from 10¹⁵ to 10²⁰ cm^− 3 where a surge in PCE was observed. Device structures having CdS, MZO, PC₆₁BM, SnS₂, STO, and ZnTe HTLs showed improved PCE from 21,48% to 27,56%, 22.28–27.99%, 22.08–27.57%, 21.28–25.75%, 22.29–27.29% and 22.18–27.98% respectively.
Effect of (a) donor density of ETL, (b) acceptor density variation of HTL on device performances.
The combined resistance of metal layers and semiconductor layers is known as series resistance (R_s). High series resistance causes hampers in carriers’ mobility in PSCs that result in the degrading performance of PCE and FF. Figure 7(a) shows series resistance was studied between 0 Ω-cm² to 6 Ω-cm². For all the device structures PCE and FF decreased linearly. Whereas J_sc and V_oc maintained an unchanged performance over the variation of series resistance. This behavior can be explained by the role of R_s in the solar cell operation. Series resistance primarily impacts the current flow in the external circuit and causes power losses during load conditions, which significantly affects FF and PCE⁵⁹. However, J_sc is determined under short-circuit conditions (zero voltage), where R_s has minimal influence, and V_oc is derived from the intrinsic properties of the absorber and junction, which are not directly altered by R_s. Therefore, R_s has a negligible effect on these two parameters within the studied range. Overall, the decline in PCE was not more than 4% in all of the PSC combinations and the device structures showed reliable performance when a higher series resistance was considered.
Effect of variation of (a) series resistance, (b) shunt resistance, and (c) temperature on V_OC (V), J_SC (mA cm^− 2), FF (%) and η (%).
Figure 7(b) represents the effect of shunt resistance (R_sh) on our PSC devices. The shunt is the resistance in the junction of PSC that provides an alternative path for the current. Lower shunt resistance causes a high loss of PCE whereas higher shunt resistance can help to bypass the intended path to the external circuit more effectively, hence showing improved overall performances. Here, shunt resistance was studied from 10 Ω-cm²⁶ to 10 Ω-cm². In all combinations, PCE and FF increased sharply from 10 Ω-cm²³ to 10 Ω-cm² and then both PCE and FF became stable. However, beyond a certain threshold (10³ Ω·cm²), the shunt resistance becomes sufficiently high that leakage currents are effectively negligible. At this stage, further increases in Rsh do not significantly impact the current pathways or performance of the device, leading to a saturation in the values of PCE, FF, J_sc, and V_oc. Hence, all PV parameters remain unchanged for R_sh values greater than 10³ Ω·cm². This plateau indicates the device has reached an optimal regime where shunt resistance no longer limits performance⁸¹. The STO and MZO ETL combination PSCs showed the highest PCE of 27.69% at 10⁶ Ω-cm². J_sc and V_oc achieved became saturated at 10¹² Ω-cm² in all PSC structures.
Higher temperature has an inverse relationship with PCE and FF. It increases resistance and leads to more recombination, which reduces the overall performance of the PSC. So, PCE and FF deteriorate when at higher temperatures. Figure 7(c) shows the effect of temperature variations from 275 K to 450 K. At 275 K temperature the highest PCE was observed in the structure with an STO ETL layer of 29.06% followed by the structure having a ZnSe ETL layer of 29.04%. Among all the PSCs, the combination with SnS₂ showed the best resilience over the enhancement of temperature. Its PCE at 275 K was 25.98% which dropped to 21.43% at 450 K, meaning a decrease of 4.55%which is smaller than the decrease observed in PSCs with MZO, STO, CdS, and PC₆₁BM ETLs, where the PCE dropped by more than 7%. In FF and V_oc output results a comparable change is observed to PCE. A small linear increment in J_sc was found when the temperature was increased in the PSC structures. An increase in temperature provides more thermal energy and enhances the electron-hole generation rate, hence increasing J_sc in the output^59,81,82.
Figure 8 represents the contour mapping of the impact of variation of the absorber layer thickness and with the change of total defect density of absorber N_t. The presence of N_t in heterojunction devices negatively impacts performances by curbing the mobility of carriers. For the studied devices the best PCE performances were observed when the thickness of the absorber layer was between 950 nm and 1100 nm. From 10¹⁰ to 10¹² cm^− 3 the absorber showed good stability in performances. At a defect density of 10¹⁰ cm^− 3, a noticeable increase in V_OC was observed due to the minimal recombination losses in the simulation, which does not account for interfacial defects. This idealized scenario can temporarily elevate V_OC values beyond typical limits like the Shockley–Queisser threshold, highlighting the impact of ultra-low defect densities in enhancing carrier lifetimes and quasi-Fermi level splitting^40,43,83.
For the studied six PSCs its stable performance in PCE was observed from 10¹⁰ to 10¹² cm^− 3. In the CdS ETL-based device, the PCE of 27.99% was observed when the studied thickness of the absorber was 1100 nm and N_t 10¹⁰ cm^− 3. In the MZO-based ETL structure, a maximum of 28.31% PCE was noted at an absorber thickness and N_t of 1100 nm and 10¹⁰ cm^− 3. For, the same optimum conditions other ETL materials-based PSCs showed their best PCE. When PC₆₁BM, SnS₂, STO, and, ZnSe were used as the ETL layers the highest PCE was noted at 27.88%, 25.84%, 28.31%, and 28.3% respectively.
Contour mapping of PCE photovoltaic parameter with respect to thickness of absorber, and total density of absorber in device structures based on various ETL materials.
Contour mapping of PCE photovoltaic parameter with respect to acceptor density and total defect density variation on various ETL materials based PSC devices.
Figure 9 shows the contour mapping of the variation of acceptor density and defect density variation of absorber where acceptor density was varied from 10¹³ to 10¹⁸ cm^− 3 and defect density was changed from 10¹⁰ to 10¹⁵ cm^− 3 for the studied PSC devices. Higher defect density in semiconductor materials of PSCs is unwanted since it leads to the degradation of performances. However, defects in materials are found so the impact of it has to be considered while designing PSCs. Materials that show high stability at comparatively higher defect density can be considered ideal for practical implementation. For the variation of acceptor density and defect density, Fig. 9 shows all the device structures maintained high PCE at 10¹³ cm^− 3. When choosing the ideal acceptor density, an optimum density is needed. Low acceptor density in absorber lacks effectiveness and a high density can increase recombination leading to an unoptimized PCE performance. From Fig. 9, the optimum condition for PCE was found at the acceptor density of the absorber of 10¹⁷ cm^− 3. Among the studied PSCs for both MZO and PTO ETL-based device structures the highest PCE of 28.23% was found when the acceptor density of the absorber was 10¹³ cm^− 3 and the defect density of the absorber was 10¹⁰ cm^− 3. It is important to note that the optimum acceptor density of 10¹³ cm^− 3 reported here corresponds to a condition of ultra-low defect density (10¹⁰ cm^− 3). This contrasts with the single-variable study in Sect. 3.2.3, where an acceptor density of 10¹⁶ cm^− 3 was found optimal under a fixed defect density. This highlights the importance of considering combined parameter interactions when optimizing device performance.
Carrier generation rate (a) before optimization (b) after optimization in the device structures. Carrier recombination rate (c) before optimization, (d) after optimization in the device structures.
Figure 10 illustrates the spatial distribution of photogenerated carriers within the studied PSCs. Carrier generation, the process of creating electron-hole pairs through photon absorption, is primarily concentrated in the absorber layer, as shown in Fig. 10(a). However, through structural optimization, the generation profile can shift toward the ETL side, as seen in Fig. 10(b), resulting in a more distributed and efficient generation of charge carriers throughout the device. This shift is achieved by optimizing the ETL properties such as thickness, doping concentration, and energy level alignment. A well-optimized ETL enhances the absorption of incident light near the ETL/absorber interface and facilitates efficient charge extraction^40,43,59,64. These modifications reduce carrier recombination, improve band alignment for smoother electron transport, and maintain a strong internal electric field to support carrier separation and collection. As a result, devices show increased carrier lifetimes and improved performance metrics, particularly in J_sc and FF. Therefore, the optimization process involves tailoring both the optical and electronic environments of the ETL and absorber layers. By controlling where carriers are generated and ensuring their efficient extraction, overall device efficiency is enhanced^59,67,81.
Figure 10(c) and 10(d) present the carrier recombination profiles across the device structure before and after optimization, respectively. Recombination, the loss mechanism where photogenerated electrons and holes annihilate without contributing to current, is a critical factor in determining the photovoltaic performance^1,40,43. In the unoptimized structure (Fig. 10(c)), elevated recombination rates are observed, particularly near the absorber interfaces, indicating inefficient charge extraction and higher losses. After optimization (Fig. 10(d)), a notable suppression of recombination is achieved throughout the active layers, especially near the ETL/absorber and absorber/HTL interfaces. This reduction is attributed to improved energy level alignment, enhanced carrier mobility, and reduced defect densities introduced by tuning the ETL parameters. Lower recombination not only leads to higher carrier lifetimes but also contributes to increased open-circuit voltage (V_oc) and fill factor (FF). Thus, minimizing recombination alongside enhancing generation ensures that more carriers contribute to the photocurrent, enabling a more realistic and efficient device performance^40,43,59,64.
Figures 11(a) and (b) illustrate the current density and open-circuit voltage (V_oc) of the PSCs before and after optimization. The current density remains largely unchanged, indicating consistent carrier collection across all configurations. In contrast, Voc shows a noticeable increase after optimization, suggesting a reduction in non-radiative recombination and better energy level alignment at interfaces. Since the FF is directly influenced by V_oc, this enhancement contributes to an overall improvement in PCE^59,67,81.
Figures 11(c) and (d) present the external quantum efficiency (EQE) spectra before and after optimization. EQE measures the efficiency with which incident photons are converted into charge carriers at each wavelength. Across all devices, a peak EQE of nearly 100% is observed around 360 nm, particularly for CdS, MZO, SnS₂, STO, and ZnSe-based ETLs. After optimization, a slight increase in EQE is noted over a broad spectral range (300–800 nm), confirming improved carrier generation and collection^40,43,59,64.
(a) J-V curve before optimization, (b) J-V curve after optimization, (c) quantum efficiency before optimization, and (d) quantum efficiency after optimization in the device structures.
Although the spectral response range does not expand significantly post-optimization, the observed enhancements in V_oc and EQE confirm better device operation due to reduced losses and improved interfacial quality. This indicates that the optimization process mainly enhanced charge transport and reduced recombination rather than altering the optical absorption characteristics of the absorber layer^40,43,59,64. Absorber thickness and optical modeling were not varied in this specific analysis, and their effect on light harvesting may be considered in future work.
Table 4 presents a comparison of the recent research works^76,77,78,99 on the Cs₄CuSb₂Cl₁₂ absorber PSC devices. Among the studies, only one work⁷⁸ shows over 25% PCE after device optimizations. To close the gap of existing knowledge on Cs₄CuSb₂Cl₁₂₋absorber-based PSCs, in this work, an extensive study was carried out on charge transport layers. Over 27% PCE was obtained under the optimized condition for the six different ETL materials.
Among the studied HTL materials, MWCNTs highlighted superior performances to other studied materials so it was chosen as the preferred HTL material here. Both MZO and STO ETLs in the device structures showed 28.23% PCE. Among these materials, MZO consists of elements that are cheap and less toxic. However, it faces adversity to the exposure of moisture therefore, water-restrained additive methods in MZO are used for better durability¹⁰⁰. STO is comparatively more expensive and detrimental to the environment. STO is not susceptible to moisture and has excellent chemical and thermal stability making it a great choice to be deemed for longevity^101,102. Overall, the (Al/FTO/MZO/Cs₄CuSb₂Cl₁₂/MWCNTs/Au) device structure is appropriate to maintain cost-effectiveness and to ensure less environmental damage. (Al/FTO/STO/Cs₄CuSb₂Cl₁₂/MWCNTs/Au) device structure can be considered if getting the best long-term durability in PSC performances is the primary priority.
In this work, among 10 studied HTL materials, MWCNTs were selected as the best-performing HTL layer in the Cs₄CuSb₂Cl₁₂ PSC device structure for further exploration. Al/FTO/STO/Cs₄CuSb₂Cl₁₂/MWCNTs/Au and Al/FTO/MZO/Cs₄CuSb₂Cl₁₂/MWCNTs/Au device structures demonstrated the best performances, showing over 28% PCE after optimization. Increasing the thickness of the absorber layer for the studied structures showed enhancement in performance and showed the best PCE when the thickness was 1 μm. PSC structure having CdS ETL showed a big drop off in performance compared to the other devices’ structure when the thickness was increased further. On the other side, the performances of PSC structures were affected slightly with the increase of ETL thickness except the PC₆₁BM affected the PSC structure significantly when the thickness was increased from 0.02 μm.
Further optimization of absorber properties revealed that The acceptor density of 10¹⁶ cm^− 3 of absorber showed the best performance on the studied devices. The defect density of the acceptor was stable until 10¹⁴ cm^− 3. After that, increasing further total defect density sharply damaged the PSC devices’ performances. Donor densities of ETLs and acceptor densities of HTL were examined. As ETL materials MZO, and STO had the best donor density in the Cs₄CuSb₂Cl₁₂ -PSC device at 10¹⁸ cm^− 3 and 10¹⁶ cm^− 3 respectively. Changing temperature exhibited linear degradation in all the studied devices. Overall, the examined device structures demonstrated excellent PCE at 300 K and over 20% PCE at an extreme 450 K temperature. A similar effect was noticed when series resistance was enhanced. Shunt resistance showed the optimum performance from 1000 Ω-cm²and showed saturated performance when the resistance was enhanced further. These findings emphasize the crucial role of device physics parameters in determining overall stability and efficiency, highlighting the value of multi-parameter optimization.
Post-optimization, J–V and quantum efficiency (QE) curves confirmed significant performance improvements in the best-performing structures. These included an increase in open-circuit voltage and enhanced quantum efficiency across the visible range. Such improvements validated the effectiveness of the chosen transport layers and structural modifications in boosting device performance. This indicates that a combination of proper energy alignment, material selection, and structural optimization can significantly improve charge extraction and light absorption, leading to high device performance under various conditions.
In future works, organic materials can be widely studied as charge transport layers for the Cs₄CuSb₂Cl₁₂ PSCs. Besides, device modifications such as the effect of the back-surface field layer, and the effect of double HTLs can be considered. Studied MZO could be considered the best performing ETL if we consider cost and toxicity issues, though STO could show better durability than MZO. The findings of these studies can be further researched by working experimentally by following the framework of this work. Overall, this study provides a comprehensive framework for developing efficient, lead-free PSC devices and serves as a valuable reference for experimental validation and future commercialization efforts.
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study and are available from the corresponding author on reasonable request.
Hossain, M. K. et al. Design and simulation of CsPb.625Zn.375IBr2-based perovskite solar cells with different charge transport layers for efficiency enhancement. Sci. Rep. 14, 30142 (2024).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Fleck, A. K. & Anatolitis, V. Achieving the objectives of renewable energy policy – Insights from renewable energy auction design in Europe. Energy Policy. 173, 113357 (2023).
Article  Google Scholar 
Zhang, H., Jing, Z., Ali, S., Asghar, M. & Kong, Y. Renewable energy and natural resource protection: unveiling the nexus in developing economies. J. Environ. Manag. 349, 119546 (2024).
Article  PubMed  Google Scholar 
Zhang, X., Yu, G., Ibrahim, R. L. & Sherzod Uralovich, K. Greening the E7 environment: how can renewable and nuclear energy moderate financial development, natural resources, and digitalization towards the target? Int. J. Sustain. Dev. World Ecol. 31, 447–465 (2024).
Article  Google Scholar 
Han, Z., Zakari, A., Youn, I. J. & Tawiah, V. The impact of natural resources on renewable energy consumption. Resour. Policy. 83, 103692 (2023).
Article  Google Scholar 
Lei, X., Yang, Y., Alharthi, M., Rasul, F. & Faraz Raza, S. M. Immense reliance on natural resources and environmental challenges in G-20 economies through the lens of COP-26 targets. Resour. Policy. 79, 103101 (2022).
Article  Google Scholar 
Hossain, M. K., Rahman, M. T., Basher, M. K., Manir, M. S. & Bashar, M. S. Influence of thickness variation of gamma-irradiated DSSC Photoanodic TiO2 film on structural, morphological and optical properties. Optik (Stuttg). 178, 449–460 (2019).
Article  CAS  Google Scholar 
Hossain, M. I. et al. Effect of back reflectors on photon absorption in thin-film amorphous silicon solar cells. Appl. Nanosci. 7, 489–497 (2017).
Article  ADS  CAS  Google Scholar 
Jung, H. S. & Park, N. Perovskite solar cells: from materials to devices. Small 11, 10–25 (2015).
Article  CAS  PubMed  Google Scholar 
Baikie, T. et al. Synthesis and crystal chemistry of the hybrid perovskite (CH3NH3)PbI3 for solid-state sensitised solar cell applications. J. Mater. Chem. A. 1, 5628 (2013).
Article  CAS  Google Scholar 
Kirk, A. P. Comment on CdTe solar cell efficiency. J. Alloy Metall. Syst. 5, 100050 (2024).
Article  Google Scholar 
Hossain, M. K. et al. Efficiency enhancement of natural dye sensitized solar cell by optimizing electrode fabrication parameters. Mater. Sci. 35, 816–823 (2017).
CAS  Google Scholar 
Chowdhury, A. F. M. K. et al. Hydropower expansion in eco-sensitive river basins under global energy-economic change. Nat. Sustain. https://doi.org/10.1038/s41893-023-01260-z (2024).
Article  Google Scholar 
Ali, B. et al. A comparative study to analyze wind potential of different wind corridors. Energy Rep. 9, 1157–1170 (2023).
Article  Google Scholar 
Pourasl, H. H., Barenji, R. V. & Khojastehnezhad, V. M. Solar energy status in the world: A comprehensive review. Energy Rep. 10, 3474–3493 (2023).
Article  Google Scholar 
Errera, M. R., Dias, T. A., d., C., Maya, D. M. Y. & Lora, E. E. Global bioenergy potentials projections for 2050. Biomass Bioenerg. 170, 106721 (2023).
Article  Google Scholar 
Aftab, S. et al. Advances in flexible perovskite solar cells: A comprehensive review. Nano Energy. 120, 109112 (2024).
Article  CAS  Google Scholar 
Wu, C. et al. Ultrahigh durability perovskite solar cells. Nano Lett. 19, 1251–1259 (2019).
Article  ADS  CAS  PubMed  Google Scholar 
Green, M. A. et al. Solar cell efficiency tables (Version 64). Prog. Photovolt. Res. Appl. 32, 425–441 (2024).
Article  Google Scholar 
Hossain, M. K. et al. Influence of natural dye adsorption on the structural, morphological and optical properties of TiO 2 based photoanode of dye-sensitized solar cell. Mater. Sci. 36, 93–101 (2017).
MathSciNet  Google Scholar 
Venkatesan, S., Chang, Y. C., Teng, H. & Lee, Y. L. Enhance the performance of dye-sensitized solar cells with effective compact layers and direct contact cell structure. J. Power Sources. 628, 235889 (2025).
Article  CAS  Google Scholar 
Mahalingam, S. et al. Recombination suppression in TiO₂/boron-doped reduced graphene oxide-based dye-sensitized solar cells. Renew. Sustain. Energy Rev. 209, 115088 (2025).
Article  CAS  Google Scholar 
Hossain, M. K., Rahman, M. T., Basher, M. K., Afzal, M. J. & Bashar, M. S. Impact of ionizing radiation doses on nanocrystalline TiO2 layer in dssc’s photoanode film. Results Phys. 11, 1172–1181 (2018).
Article  ADS  Google Scholar 
Liang, B. et al. Progress in crystalline silicon heterojunction solar cells. J. Mater. Chem. A. https://doi.org/10.1039/D4TA06224H (2025).
Article  Google Scholar 
Tachibana, T. et al. Highly passivating and blister-free electron selective Poly-Si based contact fabricated by PECVD for crystalline silicon solar cells. Sol. Energy Mater. Sol. Cells. 282, 113339 (2025).
Article  CAS  Google Scholar 
Basher, M. K., Hossain, M. K., Uddin, M. J., Akand, M. A. R. & Shorowordi, K. M. Effect of pyramidal texturization on the optical surface reflectance of monocrystalline photovoltaic silicon wafers. Optik (Stuttg). 172, 801–811 (2018).
Article  CAS  Google Scholar 
van Nijen, D. A. et al. Analyzing the PN junction impedance of crystalline silicon solar cells across varied illumination and temperature conditions. Sol. Energy Mater. Sol. Cells. 279, 113255 (2025).
Article  Google Scholar 
Adnan, M., Irshad, Z. & Lim, J. Impact of structural advancements interface engineering operational stability and commercial viability of perovskite/silicon tandem solar cells. Sol. Energy. 286, 113190 (2025).
Article  CAS  Google Scholar 
Basher, M. K. et al. Study and analysis the Cu nanoparticle assisted texturization forming low reflective silicon surface for solar cell application. AIP Adv. 9, 1–6 (2019).
Article  Google Scholar 
Sharma, S., Jain, K. K. & Sharma, A. Solar cells: in research and Applications—A review. Mater. Sci. Appl. 06, 1145–1155 (2015).
CAS  Google Scholar 
Goetzberger, A., Luther, J. & Willeke, G. Solar cells: past, present, future. Sol. Energy Mater. Sol. Cells. 74, 1–11 (2002).
Article  CAS  Google Scholar 
Kundara, R. & Baghel, S. Performance analysis of LaFeO3 perovskite solar cells: A theoretical and experimental study. Solid State Commun. 389, 115590 (2024).
Article  CAS  Google Scholar 
Kundara, R. & Baghel, S. Performance optimization of lead-free KGeCl3 based perovskite solar cells using SCAPS-1D. Sol. Energy. 287, 113253 (2025).
Article  CAS  Google Scholar 
Investigating the Efficiency. and Optimization of Germanium-based perovskite solar cell using SCAPS 1D. Indian J. Eng. Mater. Sci. 30 (2023).
Kundara, R. & Baghel, S. Performance optimization of CsSnI3-based perovskite solar cells using SCAPS-1D and machine learning analysis. J. Opt. https://doi.org/10.1007/s12596-025-02510-3 (2025).
Article  Google Scholar 
Bouri, N. et al. Comparative study of solar cells based on triple and graded absorber layers with the compound CsSn1-xGexI3: numerical study and optimization. J. Phys. Chem. Solids. 199, 112561 (2025).
Article  CAS  Google Scholar 
Bouri, N., Geleta, T. A., Guji, K. W., Behera, D. & Nouneh, K. Numerical analysis of photovoltaic performance in NaSnCl3 and KSnCl3 perovskite absorber layers for solar energy harvesting: SCAPS-1D study. Mater. Today Commun. 40, 110014 (2024).
Article  CAS  Google Scholar 
Bouri, N. et al. CH3NH3Pb1-xCuxI3-based solar cell: numerical study and optimization with different inorganic hole transport layers. Chem. Phys. Impact. 10, 100873 (2025).
Article  Google Scholar 
Guji, K. W., Geleta, T. A., Bouri, N. & Ramirez Rivera, V. J. First principles study on the structural stability, mechanical stability and optoelectronic properties of alkali-based single halide perovskite compounds XMgI 3 (X = Li/Na): DFT insight. Nanoscale Adv. 6, 4479–4491 (2024).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Hossain, M. K. et al. Enhancing efficiency and performance of Cs2TiI6-based perovskite solar cells through extensive optimization: A numerical approach. Inorg. Chem. Commun. 168, 112964 (2024).
Article  CAS  Google Scholar 
Afroz, M., Ratnesh, R. K., Srivastava, S. & Singh, J. Perovskite solar cells: progress, challenges, and future avenues to clean energy. Sol. Energy. 287, 113205 (2025).
Article  CAS  Google Scholar 
He, W., Lan, C., Zhou, Y., Li, R. & Guli, M. Effects of aromatic compounds as interfacial layer materials on the performance of perovskite solar cells. J. Power Sources. 626, 235731 (2025).
Article  CAS  Google Scholar 
Hossain, M. K. et al. An extensive study on charge transport layers to design and optimization of high-efficiency lead-free Cs2PtI6-based double-perovskite solar cells: A numerical simulation approach. Results Phys. 61, 107751 (2024).
Article  Google Scholar 
Yang, Z., Zhang, S., Li, L. & Chen, W. Research progress on large-area perovskite thin films and solar modules. J. Mater. 3, 231–244 (2017).
Google Scholar 
Röhm, H. et al. Ferroelectric properties of perovskite thin films and their implications for solar energy conversion. Adv. Mater. 31, 1806661 (2019).
Article  Google Scholar 
Zheng, X. et al. Synthesis, structural and optical properties of a novel double perovskite for LED applications. Ceram. Int. 50, 1474–1487 (2024).
Article  CAS  Google Scholar 
Chen, H., Li, M., Wang, B., Ming, S. & Su, J. Structure, electronic and optical properties of CsPbX3 halide perovskite: A first-principles study. J. Alloys Compd. 862, 158442 (2021).
Article  CAS  Google Scholar 
Chauhan, P. et al. Impact on generation and recombination rate in Cu ₂ ZnSnS ₄ (CZTS) solar cell for ag ₂ S and in ₂ se ₃ buffer layers with CuSbS ₂ back surface field layer. Prog. Photovolt. Res. Appl. 32, 156–171 (2024).
Article  CAS  Google Scholar 
Mukhamale, S. V., Kartha, M. J. & Khirade, P. P. Experimental, theoretical and numerical simulation-based investigations on the fabricated Cu2ZnSn thin-film-based Schottky diodes with enhanced electron transport for solar cell. Sci. Rep. 14, 15970 (2024).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Ahamed, T. et al. Optimization of buffer layers for CZTSSe solar cells through advanced numerical modelling. J. Phys. Chem. Solids. 204, 112744 (2025).
Article  CAS  Google Scholar 
Zhao, C. et al. Design of Bridge molecules for High-Efficiency FAPbI ₃ -Based perovskite solar cells. ACS Energy Lett. 9, 1405–1414 (2024).
Article  CAS  Google Scholar 
Saha, P., Singh, S. & Bhattacharya, S. FASnI3-based eco-friendly heterojunction perovskite solar cell with high efficiency. Micro Nanostruct. 186, 207739 (2024).
Article  CAS  Google Scholar 
Shah, M. et al. Utilizing density functional theory and SCAPS simulations for modeling High-Performance MASnI3‐Based perovskite solar cells. Energy Technol. 12 (2024).
Ritu, P., Kumar, V., Kumar, R. & Chand, F. A theoretical comparison of MAPbI3, FAPbI3 and (FAPbI3)1 – xMAPb (Br3 – yCly)x based solar cells. J. Opt. 53, 2625–2630 (2024).
Article  Google Scholar 
Jung, E. I. et al. Recent progress on chiral perovskites as chiroptical active layers for next-generation leds. Mater. Sci. Eng. Rep. 160, 100817 (2024).
Article  Google Scholar 
Rahman, M. S. et al. Insights from computational analysis on novel Lead-Free FrGeCl3 perovskite solar cell using DFT and SCAPS-1D. Inorg. Chem. Commun. 171, 113578 (2025).
Article  CAS  Google Scholar 
Yao, Z. et al. Tunable periodic nanopillar array for MAPbI ₃ perovskite photodetectors with improved light absorption. ACS Omega. 9, 2606–2614 (2024).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Kaleemullah, N. S., Hussain, M. S., Ashwin, V., Ajay, G. & Sirajuddeen, M. M. S. Novel ternary halide perovskite AMX3(A/M = Li, na, K, rb, be, mg, ca, sr, ba, x = br, cl, F, I) for optoelectronic applications. Chem. Phys. Impact. 8, 100545 (2024).
Article  Google Scholar 
Hossain, M. K. et al. Exploring the optoelectronic and photovoltaic characteristics of Lead-Free Cs 2 TiBr 6 double perovskite solar cells: A DFT and SCAPS‐1D investigations. Adv. Electron. Mater. 11, 2400348 (2025).
Article  CAS  Google Scholar 
Schileo, G. & Grancini, G. Lead or no lead? Availability, toxicity, sustainability and environmental impact of lead-free perovskite solar cells. J. Mater. Chem. C. 9, 67–76 (2021).
Article  CAS  Google Scholar 
Xiao, Z., Meng, W., Wang, J. & Yan, Y. Thermodynamic Stability and Defect Chemistry of Bismuth-Based Lead‐Free Double Perovskites. ChemSusChem 9, 2628–2633 (2016).
Pal, J. et al. Synthesis and optical properties of colloidal M ₃ Bi ₂ I ₉ (M = Cs, Rb) perovskite nanocrystals. J. Phys. Chem. C. 122, 10643–10649 (2018).
Article  CAS  Google Scholar 
Pal, J. et al. Colloidal synthesis and photophysics of M ₃ Sb ₂ I ₉ (M = Cs and Rb) nanocrystals: Lead-Free perovskites. Angew. Chem. Int. Ed. 56, 14187–14191 (2017).
Article  ADS  CAS  Google Scholar 
Hossain, M. K. et al. High-Efficiency Lead-Free La 2 NiMnO 6 -Based double perovskite solar cell by incorporating charge transport layers composed of WS 2, zno, and Cu 2 FeSnS 4. Energy Fuels. 37, 19898–19914 (2023).
Article  CAS  Google Scholar 
Islam, S. et al. Introducing a new and highly efficient Double-Absorber solar cell with combination of Sr3PBr3 and CsPbI3 perovskites. Phys. Status Solidi Appl. Mater. Sci. 2500148, 1–12 (2025).
Google Scholar 
Zhou, L., Xu, Y., Chen, B., Kuang, D. & Su, C. Synthesis and Photocatalytic Application of Stable Lead-Free Cs ₂ AgBiBr ₆ Perovskite Nanocrystals. Small 14 (2018).
Hossain, M. K. et al. An in-depth study on charge transport layers for designing and optimizing high-efficiency lead-free CsSnGeI3-based double-perovskite solar cells: A numerical approach. J. Phys. Chem. Solids. 203, 112715 (2025).
Article  CAS  Google Scholar 
Li, Q. et al. High-Pressure Band‐Gap engineering in Lead‐Free Cs ₂ AgBiBr ₆ double perovskite. Angew. Chem. Int. Ed. 56, 15969–15973 (2017).
Article  CAS  Google Scholar 
Slavney, A. H. et al. Defect-Induced Band-Edge reconstruction of a Bismuth-Halide double perovskite for Visible-Light absorption. J. Am. Chem. Soc. 139, 5015–5018 (2017).
Article  CAS  PubMed  Google Scholar 
Wang, X. D. et al. The top-down synthesis of single-layered Cs ₄ CuSb ₂ Cl ₁₂ halide perovskite nanocrystals for photoelectrochemical application. Nanoscale 11, 5180–5187 (2019).
Article  CAS  PubMed  Google Scholar 
A, PP et al. Layered Cs 4 CuSb 2 Cl 12 nanocrystals for Sunlight-Driven photocatalytic degradation of pollutants. ACS Appl. Nano Mater. 4, 1305–1313 (2021).
Article  Google Scholar 
Rathod, R. et al. Restricting anion migrations by atomic Layer-Deposited alumina on perovskite nanocrystals while preserving structural and optical properties. Chem. Mater. 36, 1719–1727 (2024).
Article  CAS  Google Scholar 
Suhail, A., Teron, G., Yadav, A. & Bag, M. Tuneable structural and optical properties of inorganic mixed halide perovskite nanocrystals. Appl. Res. 3 (2024).
Morales-Acevedo, A. Fundamentals of solar cell physics revisited: common pitfalls when reporting calculated and measured photocurrent density, open-circuit voltage, and efficiency of solar cells. Sol. Energy. 262, 111774 (2023).
Article  CAS  Google Scholar 
Burgelman, M., Nollet, P. & Degrave, S. Modelling polycrystalline semiconductor solar cells. Thin Solid Films. 361–362, 527–532 (2000).
Article  Google Scholar 
He, Y., Xu, L., Yang, C., Guo, X. & Li, S. Design and numerical investigation of a lead-free inorganic layered double perovskite cs4cusb2cl12 nanocrystal solar cell by scaps-1d. Nanomaterials 11, 1–19 (2021).
Article  Google Scholar 
Karmaker, H., Siddique, A., Das, B. K. & Islam, M. N. Modeling and performance investigation of novel inorganic Cs4CuSb2Cl12 nanocrystal perovskite solar cell using SCAPS-1D. Results Eng. 22, 102106 (2024).
Article  CAS  Google Scholar 
Yadav, S. C., Manjunath, V., Srivastava, A., Devan, R. S. & Shirage, P. M. Stable lead-free Cs4CuSb2Cl12 layered double perovskite solar cells yielding theoretical efficiency close to 30%. Opt. Mater. (Amst). 132, 112676 (2022).
Article  Google Scholar 
Saidarsan, A., Guruprasad, S., Malik, A., Basumatary, P. & Ghosh, D. S. A critical review of unrealistic results in SCAPS-1D simulations: causes, practical solutions and roadmap ahead. Sol. Energy Mater. Sol Cells. 279, 113230 (2025).
Article  CAS  Google Scholar 
Kumar, S., Allam, L., Bharadwaj, S. & Barman, B. Enhancing SrZrS3 perovskite solar cells: A comprehensive SCAPS-1D analysis of inorganic transport layers. J. Phys. Chem. Solids. 196, 112378 (2025).
Article  CAS  Google Scholar 
Uddin, M. S. et al. An In-Depth investigation of the combined optoelectronic and photovoltaic properties of Lead‐Free Cs 2 AgBiBr 6 double perovskite solar cells using DFT and SCAPS‐1D frameworks. Adv. Electron. Mater 10 (2024).
Hossain, M. K. et al. Design insights into La 2 NiMnO 6 -Based perovskite solar cells employing different charge transport layers: DFT and SCAPS-1D frameworks. Energy Fuels. 37, 13377–13396 (2023).
Article  CAS  Google Scholar 
Hossain, M. K. et al. Effect of various Electron and hole transport layers on the performance of CsPbI 3 -Based perovskite solar cells: A numerical investigation in DFT, SCAPS-1D, and WxAMPS frameworks. ACS Omega. 7, 43210–43230 (2022).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Kumar, A., Gupta, N., Jain, A., Goyal, A. K. & Massoud, Y. Numerical assessment and optimization of highly efficient lead-free hybrid double perovskite solar cell. Results Opt. 11, 100387 (2023).
Article  Google Scholar 
Nowsherwan, G. A. et al. Preparation and Numerical Optimization of TiO2:CdS Thin Films in Double Perovskite Solar Cell. Energies 16, (2023).
Alipour, H. & Ghadimi, A. Optimization of lead-free perovskite solar cells in normal-structure with WO3 and water-free PEDOT: PSS composite for hole transport layer by SCAPS-1D simulation. Opt. Mater. (Amst). 120, 111432 (2021).
Article  CAS  Google Scholar 
Singh, N., Agarwal, A. & Agarwal, M. Performance evaluation of lead–free double-perovskite solar cell. Opt. Mater. 114, 110964 (2021).
Article  CAS  Google Scholar 
Singh, N. K., Agarwal, A. & Kanumuri, T. Performance enhancement of environmental friendly Ge-Based perovskite solar cell with Zn ₃ P ₂ and SnS ₂ as charge transport layer materials. Energy Technol. 10 (2022).
Bansal, S. & Aryal, P. Evaluation of new materials for electron and hole transport layers in perovskite-based solar cells through SCAPS-1D simulations. Conf. Rec IEEE Photovolt. Spec. Conf. 2016, 747–750 (2016).
Google Scholar 
Singh, N. K. & Agarwal, A. Performance assessment of sustainable highly efficient CsSn0.5Ge0.5I3/FASnI3 based perovskite solar cell: A numerical modelling approach. Opt. Mater. 139, 113822 (2023).
Article  CAS  Google Scholar 
Singh, N. K. & Agarwal, A. Numerical investigation of electron/hole transport layer for enhancement of ecofriendly Tin-Ge based perovskite solar cell. Energy Sources Part Recov. Util. Environ. Eff. 45, 3087–3106 (2023).
CAS  Google Scholar 
Mohammed, K. A. M. et al. Improving the performance of perovskite solar cells with carbon nanotubes as a hole transport layer. Opt. Mater. 138 (2023).
Shamna, M. S. & Sudheer, K. S. Device modeling of Cs2PtI6-based perovskite solar cell with diverse transport materials and contact metal electrodes: a comprehensive simulation study using solar cell capacitance simulator. J. Photon. Energy. 12, 1–17 (2022).
Article  Google Scholar 
Saha, P., Singh, S. & Bhattacharya, S. Efficient and Lead-Free perovskite solar cells based on Defect-Ordered Methyl ammonium antimony iodide. IEEE Trans. Electron. Devices. 70, 1095–1101 (2023).
Article  ADS  CAS  Google Scholar 
Sabbah, H. Numerical simulation of 30% efficient Lead-Free perovskite CsSnGeI3-Based solar cells. Mater. (Basel). 15, 3229 (2022).
Article  ADS  CAS  Google Scholar 
Pochont, N. R. & Sekhar, Y. R. Numerical simulation of Nitrogen-Doped titanium dioxide as an inorganic hole transport layer in mixed halide perovskite structures using SCAPS 1-D. Inorganics 11, 1–17 (2023).
Google Scholar 
Jamil, M. et al. Numerical simulation of perovskite/Cu2Zn(Sn1-x Gex)S4 interface to enhance the efficiency by Valence band offset engineering. J. Alloys Compd. 821, 1–6 (2020).
Article  Google Scholar 
Mottakin, M. et al. Photoelectric performance of environmentally benign Cs2TiBr6-based perovskite solar cell using spinel NiCo2O4 as HTL. Optik (Stuttg). 272, 170232 (2023).
Article  CAS  Google Scholar 
Deepthi Jayan, K. Bandgap tuning and input parameter optimization for Lead-Free All‐Inorganic single, double, and ternary Perovskite‐Based solar cells. Sol RRL. 6, 2100971 (2022).
Article  CAS  Google Scholar 
Sarswat, P. K. & Free, M. L. Long-term stability of mixed perovskites. MRS Proc. 1771, 193–198 (2015).
Article  Google Scholar 
Rabha, S. & Dobbidi, P. Structural, electrical properties and stability in microwave dielectric properties of (1 – x) MgTiO3-x SrTiO3 composite ceramics. J. Alloys Compd. 872, 159726 (2021).
Article  CAS  Google Scholar 
Medvedeva, J. E. et al. Structure and electronic properties of amorphous strontium titanate. Phys. Rev. Mater. 6, 075605 (2022).
Article  CAS  Google Scholar 
Download references
The SCAPS-1D program was kindly provided by Dr. M. Burgelman of the University of Gent in Belgium. The authors would like to express their gratitude to him. The authors extend their appreciation to Taif University, Saudi Arabia for supporting this work through project number (TU-DSPP-2024-19).
The research was funded by Taif University Saudi Arabia project number TU-DSPP-2024-19.
Dept. of Electrical and Electronic Engineering, Mymensingh Engineering College, Mymensingh, 2200, Bangladesh
Kazi Md Sadat
Institute of Electronics, Atomic Energy Research Establishment, Bangladesh Atomic Energy Commission, Dhaka, 1349, Bangladesh
M. Khalid Hossain
Department of Advanced Energy Engineering Science, Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan
M. Khalid Hossain
Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1216, Bangladesh
M. Shihab Uddin
Research and Innovation Cell, Rayat Bahra University, Mohali, Punjab, India
P. Prabhu
Department of Mechanical Engineering, Mattu University, Mettu, 318, Ethiopia
P. Prabhu
Department of ECE, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, Mohali, Punjab, 140307, India
Ankita Aggarwal
Department of Electronics and Communication Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India
K. Gopalakrishna
Department of EEE, Raghu Engineering College, Visakhapatnam, 531162, Andhra Pradesh, India
P. Sasi Kiran
Department of Electrical & Electronics Engineering, Siksha ’O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India
Alok Kumar Mishra
Department of Electronics & Communication engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, Uttarakhand, India
Sanjeev Kumar Shah
Department of Physics & Astronomy, East Texas A&M University, Commerce, TX, 75428, USA
Sahjahan Islam
Department of Physics, College of Sciences, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
Abdullah M. S. Alhuthali
Department of Chemistry, College of Science, University College of Taraba, Taif University, P.O. Box 11099, Taif, Saudi Arabia
Magda H. Abdellattif
School of Chemical Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea
V. K. Mishra
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
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.K. Hossain: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, and Writing – review & editing; K.M. Sadat: Formal Analysis, Investigation, Software, Validation, Visualization, and Writing – original draft, M.S. Uddin, Prabhu P, S. Islam, A.M.S. Alhuthali, M.H. Abdellattif, and V. K. Mishra: Data curation, Formal Analysis, Investigation, Software, Validation, Visualization, and Writing – review & editing; A. Aggarwal, K. Gopalakrishna, P.S. Kiran, A.K. Mishra, and S.K. Shah: Formal Analysis, Investigation, Validation, Visualization, and Writing – review & editing.
Correspondence to M. Khalid Hossain, P. Prabhu or V. K. Mishra.
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
Sadat, K.M., Hossain, M., Uddin, M.S. et al. Exploration and optimization of different charge transport layers for Cs₄CuSb₂Cl₁₂ based perovskite solar cells. Sci Rep 15, 25100 (2025). https://doi.org/10.1038/s41598-025-10731-6
Download citation
Received: 30 March 2025
Accepted: 04 July 2025
Published: 11 July 2025
Version of record: 11 July 2025
DOI: https://doi.org/10.1038/s41598-025-10731-6
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

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Scientific Reports volume 16, Article number: 9596 (2026) Cite this article
424 Accesses
Metrics details
The paradigm shift toward electric transportation is a necessary step in mitigating greenhouse gas discharges in connection with the internal combustion engine emissions. Nevertheless, the Electric Vehicle (EV) charging infrastructures predominantly rely on fossil fuel-based power generation, which again aggravates climate change. Imparting renewable energy sources for powering the charging systems is therefore essential, with photovoltaic (PV) power standing out as a scalable and portable solution. In PV-based charging setups, the power available from the panels can vary widely, especially when some modules fall under shade. To keep the charging process steady during such conditions, a smarter MPPT approach becomes necessary. In this work, a single-stage full-bridge converter operating with phase-shift control is combined with an Social group optimization based MPPT method to improve how the system reacts to these fluctuations. The converter has been designed so that the switches achieve soft-switching, which helps in cutting down losses and keeping the output voltage steady over different operating points. A 3 kW prototype was built and tested along with detailed simulations. Both sets of results show that the converter, together with the MPPT strategy, is able to draw consistent power from the PV array and continue charging the battery smoothly even when the sunlight changes abruptly. The system achieves a peak efficiency of 97%, representing a notable improvement over conventional dual-stage system. Additionally, the output voltage regulation is enhanced by 2%, demonstrating the viability of the proposed converter-MPPT architecture for future PV-powered EV charging stations with improved energy conversion efficiency and resilience under environmental uncertainties.
Electric Vehicle fleet is fast emerging across the globe. In Indian sub-continent perspective, the growth of EV has its impact on the promises rendered in COP 26¹. Attaining net zero commitment by 2070 is ambitious, but with the given development in the EV infrastructure and rigorous policies for renewable source deployment, it is not a distant dream to attain net zero². Among the renewable sources, the photovoltaic (PV) and Fuel cell (FC) are very compatible with EV drive and charging infrastructure. In fact, FC will be a very good candidate for direct EV drives as a coveted source³ as the energy density factor is very high. However, the high costs and complex technologies involved in hydrogen storage and handling make it a less pragmatic candidate for many applications. PV, on the other hand will be a handy source to be deployed in charging stations for two wheelers and three wheelers so that the grid power reliance can be reduced considerably⁴. The lack of charging infrastructure in developing economies poses stiff challenges for EV proliferation. The cost involved in building charging infrastructure as well as relevant communication protocols play a crucial role^5,6. Moreover, if the charging infrastructures are levied only from grid power, the mission of net-zero will face a major setback, as the predominant share of power is generated from fossil fuels such as coal. Therefore, the charging infrastructures can be built with PV array as source which makes the EV sector greener. But the major issues here are the inherent intermittency that PV possesses in nature. The output power-voltage (P–V) characteristics of a PV array are nonlinear and exhibit a unique maximum power point (MPP) corresponding to the optimal combination of voltage and current. MPPT algorithms are employed to identify this point in real time, using power electronic converters to regulate the operating point of the PV system⁷. While the impact of temperature on power output is relatively modest due to its logarithmic influence, irradiation plays a dominant role as it has a near-linear relationship with the output power. In most PV systems, the MPPT controller adjusts the duty cycle of the converter to locate the point at which the array delivers its highest power. The commonly used P&O and INC methods work reasonably well when the sunlight is uniform across the panel surface^8,9. P&O changes the operating voltage step by step and checks whether the power moves up or down, but it tends to keep oscillating around the best operating point. INC improves this behaviour by using the slope of the P–V curve to decide the direction of movement, although the method requires more computation because it relies on derivative information.
When part of the array is shaded, the output power curves develop several small peaks, and this makes the tracking process far more complicated. Under these conditions, both the basic and enhanced versions of P&O and INC often end up locking onto one of the local peaks instead of the true global maximum. A variety of MPPT schemes have been reported in literature, but many of them still struggle when the irradiance changes rapidly or when severe shading occurs. Issues such as slow response, unnecessary oscillations, and failure to move out of local traps remain common, which underlines the need for MPPT techniques that are more flexible and capable of handling such irregular operating conditions¹⁰. To overcome the limitations of conventional MPPT methods under partial shading, research has increasingly focused on global search, bio-inspired, and Artificial Intelligence (AI) based algorithms¹¹. These intelligent techniques are broadly classified into evolutionary (e.g., Genetic Algorithm, Differential Evolution) and bionic approaches, with the former gaining prominence in the 1990s for their population-based search and adaptability. Evolutionary algorithms like Genetic Algorithm (GA) and Differential Evolution (DE) rely on initialization, crossover, and mutation based on the principle of survival of the fittest^12,13. The Particle Swarm Optimization (PSO), inspired by swarming behaviour of birds and fish, remains to be the most effective among the global search algorithms¹⁴. The reason for its relevance till date is its optimum convergence accuracy and simple implementation. But, when the irradiation pattern tends to vary rapidly, the algorithm at times stagnates at a pseudo- peak. Therefore, the exploration on proposing inventive algorithms is at steady pace. The Grey Wolf Optimization (GWO)¹⁵, inspired by the hunting adoption of leadership hierarchy and hunting strategy of grey wolves, tries to balance both exploration and exploitation. This facilitates rational evading of local maxima during the search process. But here too, large steady-state oscillations prevail under dynamic insolation pattern. Another interesting algorithm, Hippopotamus Algorithm (HOA)¹⁶ is emulated by natural behaviour of hippopotamus. The tracking efficiency is high, but it involves higher computational effort. Other algorithms like MFO (Moth Flame Optimization) and Cuckoo Search Algorithm (CSA) have also been tried and tested, but these algorithms exhibit poor tracking reliability under fast-changing irradiance¹⁷. The recent advancement in deep learning computation has also been deployed in MPPT through DLCI (Deep Learning and Cognitive Inspired) neural decision models and learning architectures. The advantage is the speed of tracking is swift under learned conditions, but on the other hand, requires large training data. Artificial Bee Colony (ABC), and Grey Wolf Optimization (GWO) offer improved global search capabilities¹⁸, but face issues like slow convergence and increased complexity. Enhanced variants like E-PSO have attempted to address these limitations using fast-response digital signal processing (DSP) controllers¹⁹. Ant Colony Optimization (ACO), inspired by the foraging behaviour of ants, is valued for its ability to explore complex solution spaces and avoid local maxima due to its collective intelligence mechanism²⁰. However, its sluggish response in highly dynamic irradiance conditions limits its suitability for real-time MPPT applications where fast convergence is critical. Despite the individual strengths observed in various global MPPT strategies, the recurring limitations such as slow convergence, local trapping, or high computational burden warrant the exploration of more adaptive solutions. In this context, Social Group Optimization (SGO) has been employed in the present work due to its proven capability to balance exploration and exploitation through socially driven interactions²¹. Social Group Optimization (SGO) is chosen as the MPPT strategy because its two-phase search mechanism (improving and acquiring phases) provides a strong balance between exploration and exploitation, which is essential for reliably locating the global maximum power point (GMPP) in multi-peaked P–V curves under partial shading. The algorithm is parameter-lean, requiring only a self-introspection factor, which reduces implementation complexity compared to other global search techniques. Its update rules are computationally efficient and easily mapped to the PSFB control framework, making it suitable for embedded real-time applications. Benchmark studies demonstrate that SGO achieves competitive or superior solutions with fewer fitness evaluations than many existing metaheuristic counterparts, which directly benefits MPPT tasks where iteration budgets are constrained. Moreover, the mechanism inherently mitigates steady-state oscillations by guiding particles based on both the global best and peer influence, yielding faster convergence with stable operation.
Resonant converters are an appropriate choice for battery charging, as zero-voltage switching (ZVS) and zero-current switching (ZCS) are achieved²². The non-isolated topologies of resonant converters are least preferred due the concerns like electromagnetic interference, increased common mode noise, and need for competent protection due to the absence of galvanic isolation²³. Among the isolated topologies, the full bridge converter is advantageous, as it can handle high-power by utilizing the entire transformer during operation. Besides that, the four switches in the topology reduces the current stress on individual components, leading to lower conduction losses²⁴. Apart from full bridge there are numerous topologies of resonant converters, but the prudent choice needs to be based on the power capacity, efficiency requirement and specific application. The half bridge circuit possess lesser number of switches, but the power handling capacity is less. The typical buck, boost converters also quite suitable for the charging circuits but the lack of galvanic isolation raises protection and common mode noise issues. The flyback topology provides galvanic isolation, but the single-switch design adds stress, and the full potential of the ferrite-core transformer cannot be utilized due to inevitable two-phase charging and discharging operations. The interleaved topology is better but the increased in components and control complexity will exist. The push-pull topology is apt for high power as transformer is centrally tapped and two switches are employed. The transformer core saturation will happen when the controller is not prudently chosen, the full bridge converter provides a robust solution for battery charging due to its efficiency, flexibility, and isolation capabilities.
The selection of an appropriate control strategy is equally crucial for optimizing the performance and efficiency. The advanced controllers like sliding mode control and model predictive control can handle multi variable problems and could excel in non-linear changes in the system, but when it comes to implementation, high expertise is in demand for handling the coding complexity. However, these controllers are reliable for real-time forecasting and dynamic optimization, and they establish good response under rapidly changing environmental and load conditions²⁵. Fuzzy logic control (FLC) provides flexibility and handles uncertainty well but the execution and the output reliability depend on the versatility of the rule set. Adaptive control scheme is very adjustable to dynamic changes in the system, but the design complexity is high. Among all control schemes the phase shift modulation stands out as a particularly effective control strategy for full-bridge converters used in battery charging²⁶. To regulate the output voltage and efficient power transfer the phase difference between the two halves of the converter have been adjusted. It provides superior efficiency, reduced component stress, and excellent performance across a wide range of operating conditions, making it a highly advantageous choice in this context. Figure 1 presents a typical charging infrastructure with PV, resonant power electronic converter, and advanced MPPT controller cuddled with modulation schemes. This hybrid control scheme facilitates competitive maximum power tracking during shading as well ensures optimized battery charging. While numerous MPPT techniques and power converter topologies have been proposed independently, an integrated strategy that robustly handles both global tracking under partial shading and high-efficiency power conversion remains underexplored. Although a wide range of MPPT algorithms (P&O, INC, PSO, GWO, HOA, MFO, CSA, ACO, ABC, DE, DL-based methods, etc.) and several DC-DC converter topologies such as buck, buck-boost, flyback, interleaved, half bridge and full-bridge have been extensively studied, these two domains are largely explored independently. Existing MPPT-focused works primarily address global peak tracking under partial shading but do not consider how the chosen converter influences switching behaviour, soft-switching windows, or power-transfer efficiency. Conversely, converter-oriented studies optimise ZVS/ZCS operation and efficiency but overlook the effect of dynamic and multi-peaked PV characteristics on control stability and energy extraction. The research gap lies in the lack of a unified and coordinated co-design approach that simultaneously integrates a global MPPT technique with a high-efficiency resonant converter for EV battery charging, especially under rapidly varying irradiance and partial shading. This missing coordination results in sub-optimal system performance when both global tracking accuracy and converter soft-switching requirements must be satisfied concurrently. Although numerous MPPT strategies and converter control methods have been explored individually, there are no studies that bring a socially inspired global MPPT algorithm and a phase-shift full-bridge (PSFB) resonant converter together within a single, unified framework. The adaptive behaviour of SGO allows it to identify the global peak even when irradiance varies rapidly, while the PSFB resonant stage ensures isolated power transfer, reduced switching losses, and improved operational safety. When combined, these two elements complement each other the MPPT algorithm consistently extracts the available PV power, and the converter maintains high-efficiency regulation over a wide range of operating conditions.
This integrated concept also builds upon the authors’ earlier work on socially inspired MPPT approaches, enabling the present system to remain lightweight, scalable, and suited for practical hardware implementation. Motivated by this gap, the proposed architecture couples an SGO-based MPPT technique with a PSFB resonant converter and investigates their coordinated operation under both uniform and partial-shading scenarios. The findings show improved tracking accuracy, enhanced conversion efficiency, and stronger reliability, thereby addressing the identified gap and offering a practical pathway for efficient PV-powered battery charging in EV applications.
PV aided EV charging station.
The primary contributions of this research are as follows:
An adaptive MPPT scheme based on Social Group Optimization (SGO) is employed to improve power extraction from the PV array, with its robustness demonstrated under various partial-shading patterns.
A high-efficiency PSFB Full bridge resonant converter is developed, incorporating soft switching to establish enhanced power delivery.
A unified phase-shift control approach is introduced to coordinate MPPT and battery charging, enabling natural ZVS through device capacitances and transformer leakage, which helps lower switching losses.
The structure of the paper is as follows: “SGO algorithm based PSFB for battery charging” section deals with mathematical modelling of the photovoltaic system. The partial shading conditions are critically analysed, highlighting the impact on output characteristics such as multiple maximum power points. To address this the SGO algorithm has been introduced. “Phase shift full bridge resonant converter” section deals with PV fed phase shift full bridge resonant converter with efficient power conversion. The simulation validation and hardware implementation which ensures the efficiency improvement, soft switching characteristics and voltage regulation. In addition, the integration of SGO algorithm with phase shift full bridge resonant converter under partial shading condition provides improved maximum power point tracking and efficient converter performance. “Conclusion” section presents the key findings of the MPPT algorithm and the converter efficiency.
A PV cell’s equivalent circuit is depicted as a current source connected in parallel with leakage elements, represented by a shunt resistance R_sh. Figure 2 Shows that s single solar cell modelling which should be expandable as a PV array. Voltage is produced by the solar panel as a result of sunlight irradiation and the panel’s temperature. The Eqs. (1–3) are derived from the equivalent circuit and formulated through the diode equation and Kirchoff’s rules.
Equivalent circuit of pv cell¹⁹.
Here the diode current is considered as equal to short circuit current.
From the equivalent circuit
Maximum power refers to the peak instantaneous power determined by the prevailing environmental conditions. It is calculated as the product of voltage and current at that moment, as expressed in the Eq. (4)
Figure 3a illustrates the string arrangement for uniform shading which is providing 3 kW power to the full bridge converter with irradiation of 1000 w/m². Figure 3b shows that the partial shading in PV systems occur due to the hindrances that obstruct the exposure of PV panels to sunlight. These obstructions may happen due to natural blockages like trees, buildings etc. or due to man-made ones like chimneys, utility poles etc. or even due to weather and environmental disturbances like clouds, dust, debris etc. Due to the shading of even fewer cells in a panel, the net output power decreases. The cells with shading acts as a resistive load and it do emit heat instead of electric power. In a standard PV panel, the cells are connected in series, and shaded cells generate less current. This reduced current becomes the overall current, leading to a decrease in the total power output. Therefore, there may be 50% of power loss even if there is 10% of shading. Bypass diodes are prudent choice for mitigating the impact of shading. These diodes facilitate the blocked current of the shaded cells to get bypassed and thereby aiding to have better efficiency levels for the entire solar array. This ensures more consistent energy production, especially in environments prone to partial shading. When bypass diodes are used, a key issue is the formation of multiple power peaks in the current-voltage (I–V) and P–V curves. Under uniform sunlight, the P–V curve has a single, well-defined maximum power point (MPP). However, if part of the PV array is shaded, the bypass diodes redirect current around the shaded panels, resulting in multiple power peaks, one corresponding to the unshaded area and another to the shaded area.
Solar Photovoltaic system under partially shading (a) string arrangement for uniform shading (b) string arrangement for non-uniform (c) shading characteristics analysis for multiple peaks.
Figure 3c Depicts the string configuration of a partially shaded PV array, where the I–V and P–V curves exhibit multiple power peaks. Traditional MPPT algorithms, which scan the P–V curve to locate the maximum power point, often get stuck at local peaks, resulting in significantly reduced power output. To overcome this limitation and ensure the delivery of maximum global power, this study employs an intelligent SGO based global search algorithm.
The SGO algorithm makes most out of the individual knowledge of participants in a group and achieve the goal. The members in a group, based on their competencies can be named as leaders, followers²¹.The leaders share their experience, and the followers and learners acquire the knowledge shared and with the experience they gain in the search process move towards the objective. The SGO consists of two phases: (i) Improving Phase (ii) Acquiring Phase. The first phase intends to diversify of the search by different regions of the solution space. This phase investigates the search space to identify potential solutions. The second phase is used to utilize the regions of the search space. Individuals share and leverage the collective knowledge within their social groups to concentrate their efforts on areas with potential optimal solutions.
In this phase, the top performing candidate of each social group, referred to as the global optimum (g_opt), shares knowledge with other members of the group. This knowledge sharing process enhances the performance of the participating members. The objective function for maximization is defined as g_opt = max {F_i | i = 1, 2, …., M}. where M represents the total number of candidates in the group, and F_i is the fitness value of the i-th candidate. Additionally, during each iteration of this phase, knowledge is exchanged and updated among the candidates, as represented by Eq. (5).
where ξ is random selection, (:{text{Y}:}_{text{n}text{e}text{w},text{j}}^{text{t}}) is the Updated new position, (:{upbeta:}) is the learning factor, (:{text{g}}_{:text{o}text{p}text{t}}^{:text{t}}) is the current best solution in the group at iteration t, (:{text{Y}}_{text{o}text{l}text{d},text{j}}^{text{t}}) previous position. After calculating (:{text{Y}:}_{text{n}text{e}text{w},text{j}}^{text{t}}) its fitness is evaluated. If the new state performs better than the old one in terms of the objective function, the update is accepted.
During this phase, each group member gains knowledge from the most knowledgeable individual and engages in random interactions with other members. Candidates acquire new insights both from one another and from the top performer, referred to as g_best if another individual surpasses g_best in knowledge, they will take the position of the best candidate, as illustrated in Fig. 4. the updated new knowledge valu can be calculated by Eqs. (6) and (7).
If the selected member (Q_r) has lower knowledge than the current candidate (Q_j)
If the selected member Q_r has greater knowledge than the current candidate Q_j
where,
Q_j = The current candidate.
Q_r = A randomly selected group member.
(:{text{Z}:}_{text{n}text{e}text{w},text{j}}^{text{k}}) = The updated new knowledge value of candidate Q_j in the k^th dimension.
(:{text{Z}}_{text{o}text{l}text{d},text{j}}^{text{k}}) = The previous value of candidate Q_j in the k^th dimension.
(:{text{g}}_{text{b}text{e}text{s}text{t}}^{:text{k}}) = Best knowledge in the group.
(:{phi:}_{1}) = Learning coefficient component.
(:{phi:}_{2}) = Global learning coefficient.
k = Dimension index.
Social group optimization with individual group.
The process begins by randomly initializing the duty cycle of the PSFB within a defined range, constrained by the open -circuit voltage (V_oc) and short-circuit current (I_sc) of the PV system. By using this initial duty cycle, the power output of the PV system is computed. The duty cycle corresponding to the highest power output is identified as the leader, while the remaining duty cycles are categorized as learners. To achieve maximum power point tracking, the search mechanism is updated iteratively, with solutions progressing toward the leader. The duty cycles represent the participating members in this optimization framework.
In the exploration phase, candidates moved based on their previous positions and the influence of the best-performing member. This phase is represented as Eq. (8)
(:{text{D}}_{text{n}text{e}text{w},text{j}}^{text{k}})—Updated duty cycle of the candidate j at iteration k.
(:{text{D}}_{text{o}text{l}text{d},text{j}}^{text{k}})—Previous duty cycle of candidate j.
(:{upgamma:})—Self adjustment factor in the range (0,1).
ρ—Random coefficient to introduce variability from (0,1).
(:{text{G}}_{text{b}text{e}text{s}text{t}}^{:text{k}})—Current best solution in the group.
The candidates further refine their solutions based on comparisons with randomly selected alternatives. This as follows in the Eqs. (9) and (10).
If (:{text{D}}_{text{n}text{e}text{w},text{j}}^{text{k}}) performs better than (:{text{D}}_{text{r}text{a}text{n},text{j}}^{text{k}}):
If (:{text{D}}_{text{r}text{a}text{n},text{j}}^{text{k}}) performs better than (:{text{D}}_{text{n}text{e}text{w},text{j}}^{text{k}})
(:{text{D}}_{:text{r}text{a}text{n},text{j}}^{:text{k}})—Duty cycle of a randomly selected candidate.
(:{{upsigma:}}_{1}:,) (:{{upsigma:}}_{2})—Random scaling influencing local and global adjustments.
(:{text{g}}_{text{b}text{e}text{s}text{t}}^{:text{k}})—Influencing of the best-performing duty cycle.
The partially shaded PV array is optimized through SGO MPPT, and it is hybridized with the PSFB converter for battery charging in EV bays. This section details the SGO MPPT, full bridge design and resonant operation and phase-shift modulation. Figure 5. presents PV aided charging system through the full bridge resonant converter and hybrid SGO phase shift control scheme. The PV system consists of 12 series connected modules of 275 W yielding a voltage of 469 V (12 × 39 V) at open circuit and 390 V (12 × 32.5 V) at maximum power.
PV aided phase shift full bridge resonant converter.
The phase shift full bridge resonant converter is used to regulate the output power while frequency is constant which implies to reduce the magnetic design. The converter achieves zero voltage switching (ZVS) using transformer leakage inductance and MOSFET capacitance, reducing switching losses and improving efficiency. The PSFB converter can be used for wide input and output voltage and provides a fast transient response which is suitable for dynamic loads. At light loads, it maintains good performance through burst mode control. The PSFB converter is having some additional characteristics such as (i) galvanic isolation is provided by the high frequency transformer which ensures the safety and ground loop interference (ii) smooth control of power flow is achieved by modulating the phase difference between the two inverter legs, eliminating the need of duty cycle variation. (iii) reduced switching stress and EMI due to zero voltage switching in the switch which cause smaller magnetic and filter components. (iv) flexible transformer ratio allows adaptation to a wide range of input PV voltages and battery charging voltages. (v) compatibility with digital control platforms, enabling seamless integration with MPPT algorithms and closed loop voltage and current regulation. The bridge converter consists of four switches S₁, S₂, S₃, and S₄ on the primary side of the high-frequency transformer, with a centre taped rectifier connected on the secondary side. The battery pack is rated at 3.3 kW with 48 V as the operating voltage. The phase shift full bridge resonant topology is employed here to ensure efficient power delivery. The phase shift controller ensures good voltage regulation, achieves ZVS, and provides better efficiency with reduced power losses. The resonant frequency (f_r) of the tank circuit is determined by the specified maximum transition time and the requirement for stored inductive energy. The components of this tank circuit consist of the resonant inductor (Lr) and capacitor (Cr) which are derived from the output capacitors of the two switches. The resonant tank parameters are calculated using the Eqs. (11–14).
The resonant capacitance is
The resonant inductance is
Phase 1 (0 to t₁)
Figure 6. illustrates modes of operation of PSFB converter. At the start, at time t = 0, the primary side current is zero. As time progresses from 0 to t₁. Switch S₁ begins conducting, as illustrated in Fig. 6a. During this initial phase, the primary current remains constant due to resonance, which is determined by the transformer leakage inductance (I_lk). When diode D₁ starts conducting, energy is transferred from primary to secondary side. Following this, switch S₄ is turned off, causing the transformer to enter a short-circuit state, and the voltage across the transformer drops to zero. The parasitic output capacitance (C_oss) of S₄ is charged, while the C_oss of switch S₃ discharges.
Based on the phase 1 equivalent circuit of the PSFB²⁷, the primary current and voltage across the circuit are calculated as per Eqs. (15) and (16)
Converter modes of operation (a) Phase 1 (0 to t₁), (b) Phase 2 (t₁ to t₂), (c) Phase 3 (t₂ to t₃), (d) Phase 4 (t₃ to t₄) (e) Key waveform of PSFB.
Phase 2 (t₁ to t₂)
At instant t₁, when switches S₁ and S₄ are turned off, the inductor current (I_L) discharges the parasitic capacitances (C_oss) of S₁ and S₄, while simultaneously charging the capacitances of S₂ and S₃ in preparation for the next switching transition as shown in Fig. 6b.
The current and voltage of the primary can be expressed in Eqs. (17) and (18)
Phase 3 (t₂ to t₃)
When switches S₁ and S₄ turn off, the diagonal switches S₂ and S₃ will begin conducting. The current path on the primary side will shift, passing through the parasitic capacitance (C_oss) of switch S₁. This current path helps raise and lower the voltage across switch S₂, enabling it to transition under ZVS conditions. The body diode of S₂ temporarily conducts to clamp voltage, maintaining control over the primary current. Once S₂ begins to turn on, switch S₃ (already conducting) will allow power transfer to proceed through the transformer as shown in Fig. 6c.
From Eqs. (19) and (20) the current through the diode rectifier D₁ and D₂ is
Phase 4 (t₃ < t < t₄)
Now, the phase-shifted cycle is now equivalent to a standard square wave conversion. After switch S₄ turns off, the cycle repeats from the initial stage. Switch S₃ will remain off, but current flows through the parasitic capacitance, increasing the input voltage from zero to the source voltage as shown in Fig. 6d. Key waveform of PSFB is shown in Fig. 6e. All these modes of operation are presented in Table 1.
To optimize voltage regulation and efficiency, it is crucial to carefully select key parameters, including the parasitic capacitance of the switches, the shim inductor, the transformer core, and its magnetising inductance. On the secondary side, critical considerations include the use of a half- wave rectifier and the design of the output filter. In a PSFB topology, the transformer plays avital role in transferring energy from the PV input to the battery charging output through magnetic coupling. It facilitates resonant operation by managing the phase shift between switching pulses and achieves voltage transformation between the primary and secondary sides based on the turn’s ratio.
The turns ratio is calculated by using Eqs. (21)–(22) and from the magnetising inductance which is mentioned in Eq. (23)
The PSFB operates in voltage mode control for low values and in peak current mode control in for high values, magnetizing inductance (L_mag) can be calculated by Eq. (24)
The transformer primary and secondary current ca. be calculated by Eqs. (25)-(26)
To maintain the continuous current the inductor has been selected and it reduces the electromagnetic interference, and it helps to improve the efficiency. The output inductor and capacitor can be calculated by Eqs. (27) and (29).
The transient voltage is selected for 10% transient voltage (V_t)
The selection of shim inductor is based on the energy required to achieve ZVS in primary side and based on the selection of parasitic capacitance of switch. The minimum value of the shim inductor can be calculated by Eq. (30). Circuit parameters and their corresponding values are given in Table 2)
Simulation results of MPPT (a) Simulation result of partial shading pattern (b) simulation result of dynamic shading.
Simulation results of PSFB (a) Primary side voltage of High frequency transformer (b) Primary side current of the PSFB (c) Gate signal of MOSFET (d) Secondary side voltage of the transformer (e) Output voltage and Output Current (f) ZVS and ZCS implementation (g) CV mode of the battery (h) CC mode of the battery (i) 30% SoC of the battery.
The PSFB validation was conducted in MATLAB/SIMULINK with an input voltage of 400 V. The overall simulation results with three different optimization methods deployed are shown in Fig. 7. Figure 7a presents the dynamic changes in the irradiation from uniform to partial and compares the competencies of the global search algorithms. For every 2-sec there is a variation in the irradiation pattern. Throughout the full simulation duration (0–8 s), SGO consistently delivers fast convergence, minimal overshoot, and smooth transitions during step changes in power demand. The zoom view of simulation result (0–0.5 s) further emphasizes SGO’s rapid start-up response, with stable tracking of the 3000 W power target in under 0.1 s, while GWO and PSO exhibit delayed and oscillatory behaviour. Voltage regulation remains close to the target of 400 V with SGO, showing the least deviation during transients. Current tracking is similarly stable and noise-free under SGO, ensuring reduced stress on power components. The performance comparison presented in Fig. 7b includes the P&O, PSO, GWO and SGO. The simulation results clearly demonstrate the superior performance of the Social Group Optimization as it quickly reaches the maximum power point with minimal fluctuation, while P&O takes longer and shows more oscillation. The voltage and current graphs also show that SGO stabilizes faster than the others. During 0–2 s, when the irradiation is uniform, the P&O actively participates and can track the peak power 3000 W but the major drawback is the power output is oscillatory in nature. The GWO and PSO perform better than P&O but are not as fast or stable as SGO. The duty cycle graph confirms that SGO adjusts more smoothly and quickly. Overall, SGO gives the best performance with fast response and stable output, while P&O performs the worst due to slow response and high fluctuations. The Table 3 compares the performance of SGO, GWO, PSO, and P&O algorithms under uniform and partial shading conditions. It is inferred that the conventional P&O will have least power tracked and for simpler understanding if the search is related with the multi peak pattern represented in Fig. 3c, the tracked power will be only 550 W as stated in Table 4. Table 5 illustrates a comparative analysis of PSFB converter efficiency under partial shading conditions using different MPPT algorithms. The SGO algorithm achieved the highest maximum PV power of 1393 W and a corresponding PSFB output of 1261 W, resulting in the highest observed efficiency of 90.6%. GWO, PSO, and P&O also maintained similar efficiencies around 90.5%, though they extracted slightly less power from the PV source Fig. 8 illustrates the MATLAB/Simulink results of the PSFB converter. In the MATLAB simulation, ideal components such as the MOSFET, diode, high-frequency transformer, and controller are used, resulting in lossless operation. The suitable parasitic capacitance and shim inductance are chosen as 100 pF and 16 µH, respectively, for operating a 3-kW battery charging station. To ensure proper functioning of the primary and secondary voltages and currents of the high-frequency transformer, the leakage inductance of the transformer is considered as the resonant inductor. Figure 8a and b illustrate the primary-side voltage and current of the transformer. The single MOSFET gate pulse is shown in Fig. 8c. In this PSFB, ZVS is attained by utilizing the energy stored in the power transformer’s leakage inductance to softly switch each of the four power MOSFETs. Figure 8d illustrates the secondary-side current of the transformer. The simulation is verified with both a resistive load as well as battery. Figure 8e shows the output voltage and current for the resistive load. Figure 8f illustrates the achievement of ZVS in the PSFB converter with respect to S₁ and S₄. When the SoC is 30%, the battery charger operates in constant current (CC) mode, during which the battery voltage increases gradually. Once the battery voltage reaches 54.6 V, the converter transitions from constant current (CC) to constant voltage (CV) mode, as shown in Fig. 8g–i.
Table 2 presents design parameter of the system. The PSFB converter operates at a frequency of 100 kHz, with maximum duty cycle of 50%. Figure 9. Presents the experimental set-up of the proposed system comprising the PV emulator, PSFB converter and battery storage system. The measuring devices current probe, differential voltage probe is also presented in the Figure. The Fig. 10. illustrates a detailed schematic of a PSFB on a printed circuit board (PCB), with key components labelled for identification. The system starts with a DC EMI filter (1), which prevents electromagnetic interference from affecting the circuit. Additionally, Voltage Regulator (2), ensuring stable voltage levels for the system’s operations. The driver unit (3) controls the power transistors, enabling efficient switching, while the PSFB controller (4) generating PWM pulses to the PSFB converter. A buffer (5) has been added to stabilize the transfer of signals components. The microcontroller unit (MCU) (6) is the core processor, coordinating the overall control of the system. Energy is transferred by using the high-frequency transformer (7), isolating different sections of the circuit and adjusting voltage levels. The battery current sensing unit (8) monitors current flow to ensure efficient charging or discharging of the battery. The PSFB (9) handles high-efficiency DC-DC conversion, and finally, the diode rectifier (10) converts AC into DC to charge a battery. The emulator-based validation demonstrates the real-time feasibility of the proposed SGO-based MPPT with PSFB charging, confirming that the algorithm can be executed efficiently on embedded hardware, adapt rapidly to dynamic irradiance changes, and maintain stable converter operation with minimal oscillations, thereby improving overall energy harvesting. These outcomes suggest strong potential for deployment in practical PV-powered charging systems and scalability to larger standalone or grid-integrated applications. While the present work has been carried out using a PV emulator rather than an outdoor array, and the performance depends on appropriate tuning of algorithmic factors, these aspects mainly indicate directions for extended field validation and refinement rather than fundamental drawbacks.
Hardware set up for measurement.
PCB layout of PSFB.
Performance characteristics of photovoltaic system (a) I–V and P–V curve for V_mp=400 V (b) Irradiance curve at 1000 W/m² (c) -V and P–V curve for V_mp=500 V (d) Irradiance curve at 1000 W/m².
The Fig. 11. presents the performance characteristics of a PV system under varying irradiance and temperature conditions. The Fig. 11a. Shows the I–V and P–V curves of the PV module. The current decreases as voltage increases, while the power initially rises, peaking at the maximum power point (MPP) before declining. The maximum current is about 5.7 A, with power peaking around 1820 W at a voltage of 430 V. The Fig. 11b. Shows constant irradiance at 1000 W/m² and temperature at 25 °C over time, indicating standard test conditions. The Fig. 11c. Shows similar I–V and P–V characteristics but at higher irradiance or temperature, with the current reaching 6.2 A and power peaking at 2400 W at a higher voltage range (500–600 V). Figure 11d. illustrates the solar irradiance remaining at 1000 W/m², while the temperature has increased to 50 °C, which provides impact the system efficiency, leading to a shift in the maximum power point.
Figure 12 illustrates how the phase shift between the primary side switching signals controls the transfer of energy from the 400 V input to the transformer’s secondary side, operating with a 50% duty cycle. Figure 13 illustrates the phase-shifted gating signals of S1 and S2. Figure 14. shows that with a constant input voltage of 366 V, the output current of 62.5 A increases as the load demand rises, corresponding to a time interval of 5µs.The phase shift adjusts accordingly, regulating the amount of energy transferred to the secondary side to meet the increased load. As shown in Fig. 15. the primary side current and gate signal are depicted. The results indicate the achievement of both ZVS and ZCS. It is observed that when the primary side current is zero, the gate signal is deactivated, allowing the switch to achieve soft switching under zero current conditions. Additionally, at full load, the primary side current is higher, making it easier to achieve ZVS for the leading leg. Figure 16 shows that the varying input voltage with constant output voltage. Figure 17 illustrates the relationship between the transformer’s primary voltage and primary current. The circulating current is sustained by the transformer’s leakage and magnetizing inductance, which maintain the current flow during the freewheeling interval, even when the primary voltage is zero. Figure 18a. shows the system output power under various levels of irradiation while maintaining a constant panel temperature, along with the PSFB converter efficiency at 97%. As a result, the power increases with the irradiance, while the converter maintains an efficiency above 80%. At full load, the converter achieves 97% efficiency with reduced losses. Figure 18b. shows the system output power versus efficiency under constant irradiance and varying temperatures, with the converter maintaining an efficiency above 80%.
Ch1 = voltage across PSFB 200 V/divand Ch2 = output voltage of 20 V/div witht = 5 μs/div.
Ch1 = V_gs2 of 200 V/div Ch2 = V_gs1 of200 V/div.
Ch1= I_o of 30 A/div and Ch2= V_in of100V/div.
Ch1 = Ipirmary and Ch2 = gate sourcevoltage (V_gs1).
Ch1 = V_in of 100 V/div and Ch2 = V_o of20 V/div.
Ch1 = V_in of 200 V/div and Ch2= IPrimaryof 3 A/div.
Efficiency curves of the PSFB converter for different solar panel parameters (a) The output power vs. efficiency of the PSFB converter for constant temperature with different irradiation (b) The output power vs. efficiency of the PSFB converter for constant irradiation with different temperature.
Efficiency with load variation.
Figure 19 shows the efficiency of the PSFB converter compared to the PWM-based conventional resonant converter under various load conditions. It is observed that the efficiency improves by 1% at full load and by 2% at light load due to the reduction in switching losses achieved through phase-shift control.
This research work advocates a phase shift modulation and social group power tracking algorithm controlled full bridge DC-DC converter for EV application. The developed controller is highly dynamic in responding to irradiation changes and partial shading among the panels in the array. Also, the phase shift full bridge resonant converter achieves ZVS ensuring minimized losses and voltage regulation. The experimental and simulation results demonstrate that the system achieves a high efficiency of 97% under variable input voltages and maintains voltage regulation within ± 2%, ensuring stable power delivery to EV loads. These outcomes validate the effectiveness of combining intelligent control algorithms with soft-switching power converter topologies to enhance the reliability and performance of EV charging systems. However, the proposed system possess some limitations and they are : The proposed system has been developed under controlled operating conditions within tested cases. However, the uneven shading conditions may still demand fine tuning to ensure complete real-time adaptability. Also, during light loads, the ZVS margin may experience a dip which results in increased switching losses. Future scope:
Future work can explore integrating adaptive control techniques with the social group optimization power tracking algorithm to further enhance real-time adaptability under highly dynamic environmental conditions such as non-uniform irradiance.
The proposed work can be further extended by integrating multiple renewable sources through a multiport converter topology, enabling coordinated energy management across diverse inputs such as solar, wind, and battery systems. Additionally, the SGO-based MPPT algorithm can be evolved into a predictive or adaptive control framework by leveraging machine learning techniques or model predictive control (MPC) strategies.
Data Availability: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Random number
Random number
Junction capacitance
Output capacitance
output capacitance between drain and source
Equivalent parallel capacitance
Resonant capacitance
Cognitive parameter
Social parameter
Maximum duty cycle
Resonant switching frequency
Switching frequency
Diode current
Diode current
Magnetising current
Maximum current
Output inductor
Primary peak current
Saturation current
Primary RMS current
Secondary RMS current
Boltzman constant (1.38 × 10–23 J/K)
Leakage inductance
Resonant inductance
Magnetising inductance
Output inductance
Shim inductance
Maximum power of the PV panel
Primary turns
Secondary turns
Transformer turns ratio
Output power
Charge of electron (1.602 × 10–19 C)
Series resistor
Shunt resistor
Time taken to L_out changes from
Absolute temperature of the panel
Output voltage
Photovoltaic voltage
Drain source resistance
Maximum inertia weight
Minimum inertia weight
Diode ideal constant
Output ripple current 90% to full load
‘(PDF). Climate Change Concern and COP26: An Indian Context of Climate Action and Support’. Accessed 21 Jun 2024. [Online]. Available: https://www.researchgate.net/publication/358266245_Climate_Change_Concern_and_COP26_An_Indian_Context_of_Climate_Action_and_Support.
Vigneshkumar,B. & Kumar, A. G. Optimal sizing and operation of solar PV powered EV battery swapping station for Indian petroleum retail outlet. in Proceedings of the 3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies: Computational Intelligence for Smart Systems, ICICICT 2022 442–447 (2022). https://doi.org/10.1109/ICICICT54557.2022.9917711.
Shukl, P. & Singh, B. Distributed energy resources based EV charging station with seamless connection to grid. IEEE Trans. Ind. Appl. 59(3), 3826–3836 (2023). https://doi.org/10.1109/TIA.2023.3239583
Article  Google Scholar 
Mazumdar, D., Biswas, P. K. & Sain, C. GAO optimized sliding mode based reconfigurable step size Pb&O MPPT controller with grid integrated EV charging station. IEEE Access 12, 10608–10620 (2024).
Article  Google Scholar 
Ashok, B. et al. Transition to electric mobility in india: barriers exploration and pathways to powertrain shift through MCDM approach. J. Institution Eng. (India): Ser. C. 103(5), 1251–1277. https://doi.org/10.1007/S40032-022-00852-6 (2022).
Article  ADS  Google Scholar 
Ustun, T. S., Hussain, S. M. S. & Kikusato, H. IEC 61850-Based communication modeling of EV Charge-Discharge management for maximum PV generation. IEEE Access. 7, 4219–4231 (2019).
Article  Google Scholar 
Roy, B., Adhikari, S., Datta, S., Devi, K. J. & Devi, A. D. Harnessing deep learning for enhanced MPPT in solar PV systems: an LSTM approach using Real-World data. Electricity 5, 843–860 (2024).
Article  Google Scholar 
Pervez, I. et al. Most valuable player algorithm based maximum power point tracking for a partially shaded PV generation system. IEEE Trans. Sustain. Energy. 12(4), 1876–1890 (2021).. https://doi.org/10.1109/TSTE.2021.3069262
Article  ADS  Google Scholar 
Lv, R. & Yang, Y. ‘Robust Design of Perturb & Observe Maximum Power Point Tracking. in ICPE 2023-ECCE Asia – 11th International Conference on Power Electronics – ECCE Asia: Green World with Power Electronics, Institute of Electrical and Electronics Engineers Inc., 66–72 (2023). https://doi.org/10.23919/ICPE2023-ECCEAsia54778.2023.10213910
Asoh, D. A., Noumsi, B. D. & Mbinkar, E. N. Maximum power point tracking using the incremental conductance algorithm for PV systems operating in rapidly changing environmental conditions. Smart Grid Renew. Energy. 13(05), 89–108. https://doi.org/10.4236/sgre.2022.135006 (2022).
Article  Google Scholar 
Abdolrasol, M. G., Ayob, A. & Mutlag, A. H. Optimal fuzzy logic controller based PSO for photovoltaic system. Energy Rep. 9, 427–434 (2023).
Article  Google Scholar 
Ahmed, J. & Salam, Z. An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions. IEEE Trans. Sustain. Energy. 9(3), 1487–1496 (2018). https://doi.org/10.1109/TSTE.2018.2791968
Article  ADS  Google Scholar 
Mazumdar, D. et al. Optimizing MPPT Control for enhanced efficiency in sustainable photovoltaic microgrids: A DSO-based approach. Int. Trans. Electr. Energy Syst. 2024, 5525066 (2024).
Article  Google Scholar 
Mazumdar, D. et al. Performance analysis of drone squadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions. Renew. Energy Focus. 49, 100577 (2024).
Article  Google Scholar 
Youssef,A. R., Hefny, M. M. & Ali, A. I. M. Investigation of single and multiple MPPT structures of solar PV-system under partial shading conditions considering direct duty-cycle controller. Sci. Rep. 13(1), 1–21 (2023). https://doi.org/10.1038/s41598-023-46165-1
Article  CAS  Google Scholar 
Mazumdar, D. et al. MPPT Solution for solar DC microgrids: leveraging the hippopotamus algorithm for greater efficiency and stability. Energy Sci. Eng., 13: 2530–2545. (2025).
Article  Google Scholar 
Ahessab, H., Gaga, A. & Hadadi, B. E. L. Revolutionizing photovoltaic power: An enhanced grey wolf optimizer for ultra-efficient MPPT under partial shading conditions. Sci. Afr. 27, e02586 (2025). https://doi.org/10.1016/j.sciaf.2025.e02586
Article  CAS  Google Scholar 
Ali, A. I. M. et al. An enhanced P&O MPPT algorithm with concise search area for grid-tied PV systems. IEEE Access 11, 79408–79421. https://doi.org/10.1109/ACCESS.2023.3298106 (2023).
Article  Google Scholar 
Altin, C. Differential evolution algorithm based very fast renewable energy system optimisation tool design. Elektronika ir. Elektrotechnika 29(4), 44–53 (2023). https://doi.org/10.5755/J02.EIE.33872
Article  Google Scholar 
Da Rocha, M. V., Sampaio, L. P. & Da Silva, S. A. O. Comparative analysis of ABC, Bat, GWO and PSO algorithms for MPPT in PV systems. in 8th International Conference on Renewable Energy Research and Applications, ICRERA 2019, 347–352, (2019). https://doi.org/10.1109/ICRERA47325.2019.8996520
Hakam, Y., Tabaa, M., Ahessab, H., Gaga, A. & ELHadadi, B. Optimizing charging battery efficiency in partially shaded PV systems with enhanced particle swarm optimization using dual-core DSP microcontroller in EV applications. Sci. Prog. 108 (2). https://doi.org/10.1177/00368504251334685 (2025).
Sangrody, R., Taheri, S., Cretu, A. M. & Pouresmaeil, E. An improved PSO-based MPPT technique using stability and steady state analyses under partial shading conditions. IEEE Trans. Sustain. Energy 15(1), 136–145. https://doi.org/10.1109/TSTE.2023.3274939 (2024).
Article  ADS  Google Scholar 
Vadivel, S. et al. Hybrid social grouping algorithm-perturb and observe power tracking scheme for partially shaded photovoltaic array. Int. J. Energy Res. 2023(1), 9905979. https://doi.org/10.1155/2023/9905979 (2023).
Article  Google Scholar 
Vadivel, S. et al. Performance enhancement of a partially shaded photovoltaic array by optimal reconfiguration and current injection schemes. Energies 14(19), 6332 . https://doi.org/10.3390/EN14196332 (2021).
Article  Google Scholar 
Ustun, T. S. & Mekhilef, S. Effects of a static synchronous series compensator (SSSC) based on a soft switching 48-Pulse PWM inverter on the power demand from the grid. J. Power Electron. 10(1), 85–90 (2010).
Article  Google Scholar 
Hakam, Y., Ahessab, H., Tabaa, M., ELHadadi, B. & Gaga, A. Hybrid ANN–GWO MPPT with MPC-based inverter control for efficient EV charging under partial shading conditions. Sci. Prog. 108(2). https://doi.org/10.1177/00368504251331835 (2025).
Zhao, L., Li, H., Hou, Y. & Yu, Y. Operation analysis of a phase-shifted full-bridge converter during the dead-time interval. IET Power Electron. 9(9), 1777–1783. https://doi.org/10.1049/IET-PEL.2015.0174 (2016).
Article  Google Scholar 
Download references
The authors gratefully acknowledge the support received under the Teachers Associateship for Research Excellence (TARE) Scheme, File No. TAR/2022/000547, funded by the ANRF – Anusandhan National Research Foundation (formerly SERB). The research work was carried out at the Renewable Energy Research Laboratory, SRM Institute of Science and Technology, Kattankulathur.
Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu , 603 203, India
Jayachitra Jayaraman & Sridhar Ramasamy
Department of Electronics and Instrumentation Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, 603 203, India
Srinivasan Vadivel
Department of Electrical and Electronics Engineering, National Institute of Technology, Puducherry, 609 609, India
S. Thangavel
Department of Electrical, Telecommunications and Computer Engineering, Kampala international university, Kampala, Uganda
Hassan Abdurrahman Shuaibu
AIST (FREA), Fukushima Renewable Energy Institute, National Institute of Advanced Industrial Science and Technology (AIST), Fukushima, Koriyama, 9630298, Japan
Taha Selim Ustun
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Conceptualization, Investigation, Writing—Initial Draft, Writing—Review and editing; J.J., S.R., S.V., T.S., H.A.S., T.S.U.
Correspondence to Sridhar Ramasamy or Hassan Abdurrahman Shuaibu.
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
Jayaraman, J., Ramasamy, S., Vadivel, S. et al. Social group algorithm-based MPPT coupled with phase shift resonant converter for battery charging through partially shaded PV systems. Sci Rep 16, 9596 (2026). https://doi.org/10.1038/s41598-025-31674-y
Download citation
Received: 19 February 2025
Accepted: 04 December 2025
Published: 18 March 2026
Version of record: 23 March 2026
DOI: https://doi.org/10.1038/s41598-025-31674-y
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

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Nature Energy volume 10, pages 1427–1438 (2025)Cite this article
7157 Accesses
10 Citations
57 Altmetric
Metrics details
Liquid-state 4-tert-butylpyridine is essential for achieving high performance in n–i–p perovskite solar cells. 4-tert– Butylpyridine effectively dissolves the lithium bis(trifluoromethanesulfonyl)imide dopant and stabilizes lithium ions. However, its high volatility and corrosive nature can degrade the perovskite layer and promote the formation of byproducts and pinholes in the hole transport layer under thermal stress, ultimately compromising device stability. Here we introduce a non-volatile, solid-state alternative—4-(N-carbazolyl)pyridine (4CP)—which stabilizes lithium ions and facilitates the formation of lithium bis(trifluoromethanesulfonyl)imide complexes. Perovskite solar cells incorporating 4CP achieve a power conversion efficiency of 26.2% (25.8% certified) and maintain 80% of their initial performance for over 3,000 h at maximum power point tracking. The unencapsulated devices retain 90% of their initial efficiency after 200 thermal shock cycles between −80 °C and 80 °C, and under continuous exposure to 65 °C and 85 °C. The adoption of 4CP could help improve the stability of n–i–p perovskite solar cells.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
USD 39.95
Prices may be subject to local taxes which are calculated during checkout
The data that support the findings of this study are available within the Article and its Supplementary Information. Source data are provided with this paper.
McMeekin, D. P. et al. A mixed-cation lead mixed-halide perovskite absorber for tandem solar cells. Science 351, 151–155 (2016).
Article  Google Scholar 
Saliba, M. et al. Cesium-containing triple cation perovskite solar cells: improved stability, reproducibility and high efficiency. Energy Environ. Sci. 9, 1989–1997 (2016).
Article  Google Scholar 
Jeon, N. J. et al. A fluorene-terminated hole-transporting material for highly efficient and stable perovskite solar cells. Nat. Energy 3, 682–689 (2018).
Article  MathSciNet  Google Scholar 
Yoo, J. J. et al. Efficient perovskite solar cells via improved carrier management. Nature 590, 587–593 (2021).
Article  Google Scholar 
Turren-Cruz, S.-H., Hagfeldt, A. & Saliba, M. Methylammonium-free, high-performance, and stable perovskite solar cells on a planar architecture. Science 362, 449–453 (2018).
Article  Google Scholar 
Jeong, J. et al. Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells. Nature 592, 381–385 (2021).
Article  Google Scholar 
Park, J. et al. Controlled growth of perovskite layers with volatile alkylammonium chlorides. Nature 616, 724–730 (2023).
Article  Google Scholar 
Kim, J. et al. Susceptible organic cations enable stable and efficient perovskite solar cells. Joule 9, 101879 (2025).
Article  Google Scholar 
Chen, H. et al. Improved charge extraction in inverted perovskite solar cells with dual-site-binding ligands. Science 384, 189–193 (2024).
Article  Google Scholar 
Peng, W. et al. Reducing nonradiative recombination in perovskite solar cells with a porous insulator contact. Science 379, 683–690 (2023).
Article  Google Scholar 
Li, G. et al. Highly efficient p–i–n perovskite solar cells that endure temperature variations. Science 379, 399–403 (2023).
Article  Google Scholar 
Li, B. et al. Highly efficient and scalable p–i–n perovskite solar cells enabled by poly-metallocene interfaces. J. Am. Chem. Soc. 146, 13391–13398 (2024).
Article  Google Scholar 
Li, Z. et al. Stabilized hole-selective layer for high-performance inverted p–i–n perovskite solar cells. Science 382, 284–289 (2023).
Article  Google Scholar 
Yang, W. S. et al. Iodide management in formamidinium-lead-halide–based perovskite layers for efficient solar cells. Science 356, 1376–1379 (2017).
Article  Google Scholar 
Jung, E. H. et al. Efficient, stable and scalable perovskite solar cells using poly(3-hexylthiophene). Nature 567, 511–515 (2019).
Article  Google Scholar 
Min, H. et al. Perovskite solar cells with atomically coherent interlayers on SnO₂ electrodes. Nature 598, 444–450 (2021).
Article  Google Scholar 
Kim, C. et al. Trimming defective perovskite layer surfaces for high-performance solar cells. Energy Environ. Sci. 17, 8582–8592 (2024).
Article  Google Scholar 
Min, H. et al. Efficient, stable solar cells by using inherent bandgap of α-phase formamidinium lead iodide. Science 366, 749–753 (2019).
Article  Google Scholar 
Rombach, F. M., Haque, S. A. & Macdonald, T. J. Lessons learned from spiro-OMeTAD and PTAA in perovskite solar cells. Energy Environ. Sci. 14, 5161–5190 (2021).
Article  Google Scholar 
Ouedraogo, N. A. N. et al. Oxidation of spiro-OMeTAD in high-efficiency perovskite solar cells. ACS Appl. Mater. Interfaces 14, 34303–34327 (2022).
Article  Google Scholar 
Hawash, Z., Ono, L. K. & Qi, Y. Moisture and oxygen enhance conductivity of LiTFSI-doped spiro-MeOTAD hole transport layer in perovskite solar cells. Adv. Mater. Interfaces 3, 1600117 (2016).
Article  Google Scholar 
Juarez-Perez, E. J. et al. Role of the dopants on the morphological and transport properties of spiro-MeOTAD hole transport layer. Chem. Mater. 28, 5702–5709 (2016).
Article  Google Scholar 
Wang, S. et al. Unveiling the role of tBP–LiTFSI complexes in perovskite solar cells. J. Am. Chem. Soc. 140, 16720–16730 (2018).
Article  Google Scholar 
Jena, A. K., Ikegami, M. & Miyasaka, T. Severe morphological deformation of spiro-OMeTAD in (CH₃NH₃)PbI₃ solar cells at high temperature. ACS Energy Lett. 2, 1760–1761 (2017).
Article  Google Scholar 
Shin, Y. S. et al. De-doping engineering for efficient and heat-stable perovskite solar cells. Joule 9, 101779 (2025).
Article  Google Scholar 
Al-Ashouri, A. et al. Conformal monolayer contacts with lossless interfaces for perovskite single junction and monolithic tandem solar cells. Energy Environ. Sci. 12, 3356–3369 (2019).
Article  Google Scholar 
Jegorovė, A. et al. Starburst carbazole derivatives as efficient hole transporting materials for perovskite solar cells. Sol. RRL 6, 2100877 (2022).
Article  Google Scholar 
Radhakrishna, K., Manjunath, S. B., Devadiga, D., Chetri, R. & Nagaraja, A. T. Review on carbazole-based hole transporting materials for perovskite solar cell. ACS Appl. Energy Mater. 6, 3635–3664 (2023).
Article  Google Scholar 
Puerto Galvis, C. E., González Ruiz, D. A., Martínez-Ferrero, E. & Palomares, E. Challenges in the design and synthesis of self-assembling molecules as selective contacts in perovskite solar cells. Chem. Sci. 15, 1534–1556 (2024).
Article  Google Scholar 
Wang, S. et al. Role of 4-tert-butylpyridine as a hole transport layer morphological controller in perovskite solar cells. Nano Lett. 16, 5594–5600 (2016).
Article  Google Scholar 
Habisreutinger, S. N., Noel, N. K., Snaith, H. J. & Nicholas, R. Investigating the role of 4-tert-butylpyridine in perovskite solar cells. Adv. Energy Mater. 7, 1601079 (2017).
Article  Google Scholar 
Kim, G. et al. Impact of strain relaxation on performance of α-formamidinium lead iodide perovskite solar cells. Science 370, 108–112 (2020).
Article  Google Scholar 
Kim, M. et al. Methylammonium chloride induces intermediate phase stabilization for efficient perovskite solar cells. Joule 3, 2179–2192 (2019).
Article  Google Scholar 
Duijnstee, E. A. et al. Understanding the degradation of methylenediammonium and its role in phase-stabilizing formamidinium lead triiodide. J. Am. Chem. Soc. 145, 10275–10284 (2023).
Article  Google Scholar 
Tian, L. et al. Divalent cation replacement strategy stabilizes wide-bandgap perovskite for Cu(In,Ga)Se₂ tandem solar cells. Nat. Photon. 19, 479–485 (2025).
Shibayama, N. et al. Control of molecular orientation of spiro-OMeTAD on substrates. ACS Appl. Mater. Interfaces 12, 50187–50191 (2020).
Article  Google Scholar 
Coropceanu, V. et al. Charge transport in organic semiconductors. Chem. Rev. 107, 926–952 (2007).
Article  Google Scholar 
Malinauskas, T. et al. Enhancing thermal stability and lifetime of solid-state dye-sensitized solar cells via molecular engineering of the hole-transporting material spiro-OMeTAD. ACS Appl. Mater. Interfaces 7, 11107–11116 (2015).
Article  Google Scholar 
Sanehira, E. M. et al. Influence of electrode interfaces on the stability of perovskite solar cells: reduced degradation using MoOx/Al for hole collection. ACS Energy Lett. 1, 38–45 (2016).
Article  Google Scholar 
Ye, L. et al. Superoxide radical derived metal-free spiro-OMeTAD for highly stable perovskite solar cells. Nat. Commun. 15, 7889 (2024).
Article  Google Scholar 
Cho, Y. et al. Elucidating mechanisms behind ambient storage-induced efficiency improvements in perovskite solar cells. ACS Energy Lett. 6, 925–933 (2021).
Article  Google Scholar 
Shen, Y., Deng, K., Chen, Q., Gao, G. & Li, L. Crowning lithium ions in hole-transport layer toward stable perovskite solar cells. Adv. Mater. 34, 2200978 (2022).
Article  Google Scholar 
Laoire, C. O., Mukerjee, S., Abraham, K. M., Plichta, E. J. & Hendrickson, M. A. Influence of nonaqueous solvents on the electrochemistry of oxygen in the rechargeable lithium−air battery. J. Phys. Chem. C 114, 9178–9186 (2010).
Aetukuri, N. B. et al. Solvating additives drive solution-mediated electrochemistry and enhance toroid growth in non-aqueous Li–O₂ batteries. Nat. Chem. 7, 50–56 (2015).
Article  Google Scholar 
Lamberti, F. et al. Evidence of spiro-OMeTAD de-doping by tert-butylpyridine additive in hole-transporting layers for perovskite solar cells. Chem 5, 1806–1817 (2019).
Article  Google Scholar 
Kim, S. et al. Enhancing thermal stability of perovskite solar cells through thermal transition and thin film crystallization engineering of polymeric hole transport layers. ACS Energy Lett. 9, 4501–4508 (2024).
Article  Google Scholar 
Yang, J. & Kelly, T. L. Decomposition and cell failure mechanisms in lead halide perovskite solar cells. Inorg. Chem. 56, 92–101 (2017).
Article  Google Scholar 
Divitini, G. et al. In situ observation of heat-induced degradation of perovskite solar cells. Nat. Energy 1, 15012 (2016).
Article  Google Scholar 
Li, H. et al. Dynamics parameter correction for predicting the long-term stability of organic photovoltaics. Macromolecules 57, 6548–6558 (2024).
Article  Google Scholar 
Hawash, Z., Ono, L. K., Raga, S. R., Lee, M. V. & Qi, Y. Air-exposure induced dopant redistribution and energy level shifts in spin-coated spiro-MeOTAD films. Chem. Mater. 27, 562–569 (2015).
Article  Google Scholar 
Pan, H. et al. Advances in design engineering and merits of electron transporting layers in perovskite solar cells. Mater. Horiz. 7, 2276–2291 (2020).
Article  Google Scholar 
Yao, Y. et al. Organic hole-transport layers for efficient, stable, and scalable inverted perovskite solar cells. Adv. Mater. 34, 2203794 (2022).
Article  Google Scholar 
Dong, Y. et al. Dopant-induced interactions in spiro-OMeTAD: advancing hole transport for perovskite solar cells. Mater. Sci. Eng. R 162, 100875 (2025).
Article  Google Scholar 
Euvrard, J. et al. p-Type molecular doping by charge transfer in halide perovskite. Mater. Adv. 2, 2956–2965 (2021).
Article  Google Scholar 
You, S. et al. Bifunctional hole-shuttle molecule for improved interfacial energy level alignment and defect passivation in perovskite solar cells. Nat. Energy 8, 515–525 (2023).
Article  Google Scholar 
Li, W. et al. Montmorillonite as bifunctional buffer layer material for hybrid perovskite solar cells with protection from corrosion and retarding recombination. J. Mater. Chem. A 2, 13587–13592 (2014).
Article  Google Scholar 
Domanski, K. et al. Migration of cations induces reversible performance losses over day/night cycling in perovskite solar cells. Energy Environ. Sci. 10, 604–613 (2017).
Article  Google Scholar 
Christians, J. A. et al. Tailored interfaces of unencapsulated perovskite solar cells for >1,000 hour operational stability. Nat. Energy 3, 68–74 (2018).
Article  Google Scholar 
Khenkin, M. V. et al. Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures. Nat. Energy 5, 35–49 (2020).
Article  Google Scholar 
Lin, Y.-H. et al. A piperidinium salt stabilizes efficient metal-halide perovskite solar cells. Science 369, 96–102 (2020).
Article  Google Scholar 
Lang, F. et al. Radiation hardness and self-healing of perovskite solar cells. Adv. Mater. 28, 8726–8731 (2016).
Article  Google Scholar 
Tu, Y. et al. Perovskite solar cells for space applications: progress and challenges. Adv. Mater. 33, 2006545 (2021).
Article  Google Scholar 
Miyazawa, Y. et al. Tolerance of perovskite solar cell to high-energy particle irradiations in space environment. iScience 2, 148–155 (2018).
Article  Google Scholar 
Yang, J., Bao, Q., Shen, L. & Ding, L. Potential applications for perovskite solar cells in space. Nano Energy 76, 105019 (2020).
Article  Google Scholar 
Dong, B. et al. Self-assembled bilayer for perovskite solar cells with improved tolerance against thermal stresses. Nat. Energy 10, 342–353 (2025).
Article  Google Scholar 
Bulut, M. Thermal design, analysis, and testing of the first Turkish 3U communication CubeSat in low earth orbit. J. Therm. Anal. Calorim. 143, 4341–4353 (2021).
Article  Google Scholar 
Lamb, D. A., Irvine, S. J. C., Baker, M. A., Underwood, C. I. & Mardhani, S. Thin film cadmium telluride solar cells on ultra-thin glass in low earth orbit—3 years of performance data on the AlSat-1N CubeSat mission. Prog. Photovolt. Res. Appl. 29, 1000–1007 (2021).
Article  Google Scholar 
McAndrews, G. R. et al. Why perovskite thermal stress is unaffected by thin contact layers. Adv. Energy Mater. 14, 2400764 (2024).
Article  Google Scholar 
Valiev, M. et al. NWChem: a comprehensive and scalable open-source solution for large scale molecular simulations. Comput. Phys. Commun. 181, 1477–1489 (2010).
Article  Google Scholar 
Becke, A. D. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A 38, 3098–3100 (1988).
Article  Google Scholar 
Lee, C., Yang, W. & Parr, R. G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B 37, 785–789 (1988).
Article  Google Scholar 
Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).
Article  Google Scholar 
Jorgensen, W. L., Maxwell, D. S. & Tirado-Rives, J. Development and testing of the OPLS All-Atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 118, 11225–11236 (1996).
Article  Google Scholar 
Jensen, K. P. & Jorgensen, W. L. Halide, ammonium, and alkali metal ion parameters for modeling aqueous solutions. J. Chem. Theory Comput. 2, 1499–1509 (2006).
Article  Google Scholar 
Yeh, I.-C. & Berkowitz, M. L. Ewald summation for systems with slab geometry. J. Chem. Phys. 111, 3155–3162 (1999).
Article  Google Scholar 
Download references
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00338765, 2021R1A2C3004202), the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (RS-2025-02309702) and the Nano and Material Technology Development Program through the NRF funded by the Ministry of Science and ICT (RS-2025-25442266).
These authors contributed equally: Kihoon Kim, Sangjin Yang.
KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, South Korea
Kihoon Kim, Chanhyeok Kim, Youngmin Kim, Jinsoo Park & Hanul Min
School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
Sangjin Yang, Jeewon Park, Seokhwan Jeong, Zhe Sun, Seung-Jae Shin & Changduk Yang
Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology, Ulsan, South Korea
Minseok Kang & Changduk Yang
Division of Advanced Materials, Korea Research Institute of Chemical Technology, Daejeon, South Korea
Bong Joo Kang
Space Testing Center, Korea Testing Laboratory, Jinju, South Korea
Juhong Oh
Department of Electrical and Electronic Engineering, Advanced Technology Institute, University of Surrey, Guildford, UK
Jae Sung Yun
Department of Integrative Energy Engineering, Korea University, Seoul, South Korea
Hanul Min
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
C.Y., H.M. and S.Y. conceived the idea. K.K. fabricated the PSCs and performed characterization of the perovskite films. S.Y. synthesized the 4CP and performed the related characterizations. C.K. carried out the model PSC fabrication. Jeewon Park carried out PL characterizations. S.J. carried out TGA and DSC characterization. Y.K. and Jinsoo Park performed SEM and conductivity measurement. Z.S. carried out in situ PL measurement. M.K. conducted ATR-FTIR measurements. B.J.K. carried out PLQY measurements. J.O. and J.S.Y. performed the thermal shock test. C.Y. supervised the project. S.-J.S. performed the theoretical simulation. The paper was mainly written by S.-J.S., C.Y. and H.M., and all authors commented on the paper.
Correspondence to Seung-Jae Shin, Changduk Yang or Hanul Min.
The authors declare no competing interests.
Nature Energy thanks Thomas J. Macdonald, Jovana Milic and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–19 and Tables 1 and 2.
Source Data for Supplementary Fig. 18.
Statistical source J–V data.
Statistical source J–V data and source J–V curve.
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
Kim, K., Yang, S., Kim, C. et al. Non-volatile solid-state 4-(N-carbazolyl)pyridine additive for perovskite solar cells with improved thermal and operational stability. Nat Energy 10, 1427–1438 (2025). https://doi.org/10.1038/s41560-025-01864-z
Download citation
Received: 12 February 2025
Accepted: 11 August 2025
Published: 10 September 2025
Version of record: 10 September 2025
Issue date: December 2025
DOI: https://doi.org/10.1038/s41560-025-01864-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
Journal of Molecular Modeling (2026)
Nature Energy (2025)
Advertisement
Nature Energy (Nat Energy)
ISSN 2058-7546 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source

5W 6V Solar Panel – Portable Polycrystalline Charger For Phones, Batteries, Outdoor Use  ruhrkanal.news
source

Ambient Photonics Opens Low-Light Photovoltaic Cell Factory | Industry News | Jul 2023  Photonics Spectra
source

© iStock
Click play to listen to this article.
The idea of making solar part of your home’s power source mix is to keep your rising electricity bill in check but analysts said up-front costs complicate planning, and Minnesota wants to be part of an emerging market featuring less expensive plug-in models.
The units, also known as “balcony solar,” typically cost several hundred dollars. Traditional rooftop solar panels have long enjoyed tax credits but still come with price tags well into the thousands.
Will Mulhern, electricity program director for the advocacy group Fresh Energy, said balcony solar setups are much smaller and can be easily transported, allowing more renters to adopt the technology.
“When you think about rooftop solar, a lot of folks will generate electricity and then they’ll sell that back to the power grid,” Mulhern observed. “But with balcony solar, it’s really just going to serve the devices that are in your home.”
Appliances and digital devices will directly soak up the captured solar power flowing into a wall outlet. Backers said it helps ease pressure on the power grid as electricity demand soars. Fresh Energy supports a legislative plan to set standards for balcony solar in Minnesota, including safety and assurances customers would not have to contract with a utility, like they do with rooftop connections.
Utilities in other states oppose the moves, arguing unused electricity could flow back to the grid, overwhelming the system.
Supporters countered electricity generated from plug-ins is minimal compared to rooftop panels and new product certifications from a nationally recognized testing lab can help prevent backflow.
Cora Stryker, cofounder of the national nonprofit Bright Saver, which helps expand access to balcony solar, said following massive popularity in Europe, nearly 30 other U.S. states are considering bills similar to Minnesota’s. Stryker added consumers are making it clear they do not want to be left on the sidelines in the clean energy transition.
“Solar is the cheapest energy on the planet, full stop,” Stryker emphasized. “We need all forms of it everywhere, all at once. We are not in a position where we can wait any longer to reduce ordinary folks’ utility bills.”
Some GOP lawmakers in Minnesota have concerns about the balcony solar bill but nationally, Stryker pointed out there is bipartisan support for cementing a regulatory framework. It includes the Republican-controlled legislature in Utah, which passed the nation’s first plug-in law. The push for balcony solar comes as most monthly electricity bills keep climbing, with an average increase of 6.7 percent last year.
Kiowa County Press – 1208 Maine Street, Eads, Colorado 81036.

Email editor@KiowaCountyPress.net

Copyright © 2002-2026. All Rights Reserved. An independently-owned newspaper serving rural Colorado communities.
Privacy
.

source

Solar panel makers are aggressively reducing silver content to combat high costs, despite a persistent structural supply deficit projected at 67M oz for 2026.
The relentless expansion of the global solar industry is colliding with a harsh economic reality: its dependence on an increasingly expensive key material. Photovoltaic manufacturers, facing soaring costs, are now aggressively pursuing strategies to drastically cut the silver content in their solar panels. This fundamental shift is occurring within a market paradoxically gripped by a deep and persistent structural supply deficit.
Despite the solar sector—the largest industrial consumer—applying the brakes, the underlying scarcity of silver remains acute. Analysis from The Silver Institute points to a projected shortfall of 67 million ounces for 2026. Since 2021, a cumulative supply gap approaching 820 million ounces has accumulated.
The root of the problem lies in production dynamics. Approximately 72% of newly mined silver is extracted only as a by-product of copper, lead, and zinc mining. Consequently, higher silver prices do not directly incentivize increased primary output, as mining firms focus on their target base metals. The industry’s serious concern for long-term supply is highlighted by a landmark agreement in February, where Wheaton Precious Metals committed $4.3 billion to secure the future silver stream from BHP’s Antamina mine in Peru.
Should investors sell immediately? Or is it worth buying Silber Preis?
Historically, the precious metal constituted less than 5% of a solar module’s total cost. That share has now ballooned to an estimated 17-29%. Researchers at J.P. Morgan now describe the current price environment as an existential challenge for solar manufacturers.
In direct response, silver demand from photovoltaic installations is forecast to drop by 7% this year to approximately 194 million ounces. The striking detail is that this decline is happening alongside continued robust growth of about 15% in global solar capacity. The sector is successfully decoupling its expansion from reliance on the costly commodity.
This fundamental uncertainty is mirrored in recent price action. Following a sell-off last week fueled by a restrictive U.S. Federal Reserve and heightened geopolitical tensions in the Middle East, prices found a footing on Tuesday around $69.40 per ounce. Silver’s relative weakness is particularly evident against gold: the gold-silver ratio recently expanded to 64.6, underscoring the industrial metal’s underperformance.
Looking ahead, the market faces a clash between a modestly rising global mine output—projected at 820 million ounces—and the declining appetite from the solar industry. The trajectory for prices now hinges critically on whether the accelerating technological thrifting by PV manufacturers can outpace the enduring supply deficit.
Ad
Silber Preis Stock: New Analysis – 24 March
Fresh Silber Preis information released. What’s the impact for investors? Our latest independent report examines recent figures and market trends.
Read our updated Silber Preis analysis…

source

© 
Dear EarthTalk:
Is it true that we could meet all of our electricity needs in the U.S. with floating solar panels on reservoirs and other water bodies across the country?
P.L., via e-mail
Energy usage in the U.S. has surged in recent years, with electric power sector 96 percent of the country’s utility-scale electricity. To address this, floatovoltaics—solar panel systems that float on water—have the potential to combat growing energy demands. “Floatovoltaics are one of the fastest-growing power generation technologies today and a promising low-carbon energy source,” University of Texas aquatic ecosystem ecologist Rafael Almeida told Eos.
Floating solar farms operate similarly to ground-mounted farms. Devices keeping the system buoyant sit on top of the water, cooling the panels, which increases efficiency. Also, using water surfaces frees up land for other uses. Germany, Russia and China are already benefiting from floating solar farms, suggesting that the U.S. could follow suit.
© Eyematrix – iStock-453911335
However, implementing floatovoltaics has issues. As the idea is relatively new, long-term durability is uncertain. Harsh weather, declining performance and maintenance needs must be studied as systems age. Connecting from water to land adds more complexity, as scientists must ensure efficient power transmission. With time will come a better understanding of the potential to implement them nationwide.
But floatovoltaics do show great promise, though they are unlikely to meet all of the U.S. energy needs alone. Waterbody availability, environmental and logistical concerns mean that floating solar farms could be a complementary solution rather than a standalone one. More research on renewable energy, along with careful system design, will be need to ensure successful implementation. When combined with other efforts, floatovoltaics could help reduce reliance on fossil fuels and meet a large portion of U.S. energy demands. “On one hand, we can’t put too many barriers to this potentially important sector to advance,” Almeida added. “But on the other hand, we need to understand the trade-offs and fill our prevailing knowledge gaps with more studies.”
Floatovoltaics present a revolutionary approach to addressing the U.S. energy needs. Through more research and design, these systems could become key in future renewable energy solutions. Support the adoption of renewable technologies by engaging with community leaders, advocating for local initiatives, and supporting green energy providers. By staying informed and spreading awareness about innovations like floating solar farms, you can help shape the future of renewable energy in the United States.
EarthTalk® is produced by Roddy Scheer & Doug Moss for the 501(c)3 nonprofit EarthTalk. See more athttps://emagazine.com. To donate, visit https://earthtalk.org. Send questions to: question@earthtalk.org.
Kiowa County Press – 1208 Maine Street, Eads, Colorado 81036.

Email editor@KiowaCountyPress.net

Copyright © 2002-2026. All Rights Reserved. An independently-owned newspaper serving rural Colorado communities.
Privacy
.

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.
A novel type of smart window has been developed by researchers at the City University of Hong Kong, as reported by pv magazine. The system combines a thermochromic hydrogel layer with bifacial solar cells to manage solar heat and generate electricity simultaneously.
The thermochromic technology allows the glazing to change its optical properties based on temperature. The hydrogel layer shifts from transparent to translucent as temperatures rise, reducing solar heat entering a building. A key innovation is the bifacial photovoltaic component, which captures solar energy from both sides. This design specifically harvests light reflected by the hydrogel when it is in its hot, translucent state, addressing a common limitation where that energy would otherwise be wasted.
The prototype system is constructed with a bifacial photovoltaic glass pane on the exterior, an air gap, and an interior pane containing the hydrogel layer. This configuration allows for independent maintenance of each component. Experimental testing on a summer day demonstrated several performance benefits. Compared to thermochromic glazing alone, the hybrid system reduced direct solar heat gain by an estimated 30% and lowered indoor air temperature by several degrees. When compared to conventional bifacial photovoltaic glazing, it reduced direct solar heat gain by approximately 62.6%, lowered indoor temperature more significantly, and increased electricity generation by about 16.5%.
Annual simulations for tropical climates indicate the system provides a higher bifacial energy gain compared to standard bifacial photovoltaic units for both skylight and vertical window installations. The simulations also show substantial reductions in annual indoor heat gain for both application types when compared to either standard bifacial photovoltaic or thermochromic glazing alone. The researchers identified the ratio of photovoltaic cell coverage and the specific transition temperature of the hydrogel as critical design parameters for optimizing performance.
The system is described as a passive, scalable solution for improving building energy efficiency in warm climates, aiming to lower cooling demands while boosting on-site power generation. The findings were detailed in the academic journal Building and Environment.
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
Instant access. No credit card needed.
Online access to 2M+ reports, dashboards, and tables. Trusted by Fortune 500 teams.

source

Enervest kicks off 0.5-MW floating solar project in Australia  Renewables Now
source

Language
Change font
A
A
A
A
Share
TOP
Most read
Language
Change font
A
A
A
A
Share
Thanks to its high efficiency, perovskite solar cell-related technology, called the next-generation solar cell, is attracting attention, and the stock price of Jusung Engineering is rising.
At 10:26 a.m. on the 24th, Jusung Engineering was trading at 79,000 won, up 7,500 won (10.49%) from the previous trading day.
Jusung Engineering set a new 52-week high, rising to 85,700 won during the day.
According to the analysis by Zyant Telegram every year, the upward factors of Jusung Engineering were strengthening performance momentum by mass-producing ALD, a key equipment for perovskite solar cells, and the prospect of expanding the market by securing semiconductor and solar customers through atomic layer growth (ALG) technology.
Shares of other companies besides Jusung Engineering are also rising.
Shares of semiconductor-related stocks such as AD Technology (19.4%), CoAsia (13.1%), Neotis (11.5%), and Wonik IPS (11.3%) are rising.
Woori, Ace Tech, Mercury, and Solid, which are considered communication equipment-related stocks, broke their 52-week high.
☞If you’re curious about more investment information?Enter every day on Zayant Telegram! Everyday, the Gyant Telegram service is operated by receiving information from AWAKE – Market Briefing, a data platform that analyzes various market data, including electronic disclosures, based on AI.
Every Gyeong Giant Telegram channel analyzes the disclosures of the last seven years to select disclosures that are likely to affect stock prices, and provide only key market information in real time so that users can check them at a glance, so be sure to check them out.
2026-03-24 08:42:40
2026-03-24 08:27:14
2026-03-24 06:45:02
2026-03-24 14:11:41
2026-03-24 14:51:17
2026-03-24 15:14:49
2026-03-24 19:59:55
2026-03-23 23:01:25
2026-03-24 06:13:46
2026-03-24 23:07:00
※ This article was translated using AI technology for reader convenience.
Maeil Business Newpaper(MK) provides these translations “as they are” and makes no warranties of any kind, either explicitly or implicitly, regarding accuracy, reliability and marketability, suitability for a particular purpose, etc. of translation. Please be informed that the content provided may not be translated accurately due to limitations in machine translation before using this service.
Copyright (c) 매경AX. Maeil Business News Korea & mk.co.kr, All rights reserved.
Prohibition of unauthorized reproduction, redistribution, and use of AI learning

source

Solar First Completes 1MWp Rooftop Solar Project In Thailand With Advanced Mounting Solution  SolarQuarter
source

The firm behind a major renewable energy development in Clare says a new multi-million euro investment will have a tangible impact on this county.
The European Investment Bank has agreed to give a €100 million project finance loan to a holding company of Power Capital which is carrying out four utility-scale photovoltaic projects across Ireland.
One of these is the 99.5-megawatt Manusmore Solar Farm west of Quin village, for which shovels are expected to enter the ground this month.
Power Capital CEO Justin Brown says it’s already creating local jobs.
Listen to the full interview here

Make sure to keep up to date and follow us on all platforms
© 2025 Clare FM. All Rights Reserved.

source

600W Portable Solar Panel Kit With 100A MPPT Controller – Monocrystalline For Car, RV, Caravan Charging  ruhrkanal.news
source

California-based software provider SiteCapture has launched an AI platform for the US solar market that automates job-site documentation, quality control, and reporting workflows for installers.
Image: US Department of Energy
From pv magazine USA
California-based software provider SiteCapture has announced the release of SiteCaptureAI for solar, a new artificial intelligence product designed to automate jobsite documentation, quality control and reporting workflows.
The company launched the offering for the US solar market to help installers eliminate the need for manual review of job-site photos and documentation after the installation crew has already left the site.
The SiteCaptureAI platform utilizes photo and video intelligence to analyze field documentation. According to the company, the software can detect system components, identify equipment conditions, extract text from labels and transcribe video narrations from technicians.
The system also includes real-time field alerts to notify crews of issues with missing images and documentation and even photos that don’t show a complete picture of certain components, before they leave the jobsite.
Graham Horne, installation QA/QC manager at Powur – a solar sales and fulfillment provider that was involved in SiteCaptureAI beta testing – says the real-time notifications were especially important to his team.
“Ensuring our quality control and finance partner documentation requirements are met the first time will drive significant efficiency gains for our team, reduce truck rolls, minimize rework, and improve first-time financing approval rates,” Horne said.
SiteCaptureAI is available now to installers across the United States, and interested parties can schedule a demo on the company’s website.
Essential reporting for TPO projects
The documentation workflows supported by the SiteCaptureAI software are especially relevant to installers that work with third-party ownership (TPO) financing providers, who often have strict requirements for installation quality reporting that must be met before payments are made to the contractor.
A recent forecast from research firm Ohm Analytics estimates third-party solar agreements will account for about 64% of the U.S. residential market in 2026, up from 43% in 2025.
SiteCapture says its AI platform can automatically identify missing financing documentation and detect non-compliant images before submissions go to review, which can improve first-time approval rates.
A representative from SiteCapture told pv magazine USA that the AI system increased the proportion of complete, error-free documentation submissions from 30 to 80 percent in beta testing.
The company currently has a direct integration with TPO provider Palmetto Lightreach, but the SiteCapture representative said its application engineers can help create custom workflows to help installers match any provider’s documentation requirements.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
More articles from Ben Zientara
Please be mindful of our community standards.
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.


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

source

source

Column | How to get Big Tech to pay your energy bills  The Washington Post
source

Become A Donor
Subscribe To Our Newsletter
A Nonprofit, Reader-Supported News Organization

With federal funding gone, tribes are turning to philanthropy, alternative lenders, and their own institutions.
Across tribal nations, hosting a convening with dinner and a tour of an ambitious new project is a familiar scene. But for David Harper, a member of the Colorado River Indian Tribes and CEO of the newly created tribal energy financing organization Huurav, a recent gathering felt different. Last week, at the Bluewater Resort and Casino on the Colorado River Indian Tribes reservation in western Arizona, Huurav met with tribal leaders, investors, and farmers to kick off the tribe’s first agrivoltaics project: a practice that allows for growing crops beneath solar panels.
The project marks a significant breakthrough for the tribe and the broader tribal clean energy landscape, arriving on the heels of a devastating blow to federal support. In October of last year, the passage of President Donald Trump’s tax bill, colloquially known as the “One Big Beautiful Bill Act,” stripped roughly $1.5 billion in federal funding earmarked for tribal renewable energy and climate resilience projects.
“Were we surprised by the claw back? No, they’ve done it before,” said Huurav’s Harper. “Have they reneged on their promise of our treaties? Yes, of course. So does that immobilize us and not be able to survive? No, what it does is it helps us, it makes us create a better pathway for ourselves.”
With nearly 1,600 projects by tribal governments and Native entities losing some or all of their federal funding, tribes have been forced to get creative. To keep clean energy projects alive, tribes are turning to philanthropy, low-interest loans, and nonprofits to bridge the massive financial gap.
The Biden-era Inflation Reduction Act of 2022 represented a historic investment in tribes and renewables, but experts and tribal leaders felt it was still insufficient to address historic inequities in Indian Country. “Given the role that the federal government itself played in generating these investment needs through things like land theft, disinvestment, [and] cultural destruction in Indian Country, a one-time infusion of cash isn’t enough,” said Robert Maxim, a member of the Mashpee Wampanoag Tribe and Brookings fellow who recently co-authored a report on the One Big Beautiful Bill Act’s impact. Maxim emphasized that federal funding for Indian Country is a fundamental part of trust and treaty obligations.
“Things like a clean environment, adequate energy to supply homes, basic investments in electricity, and the ability to do all that without higher levels of pollution and environmental degradation than the US population as a whole are all key to that trust and treaty relationship,” said Maxim.
To fill the void, nonprofits like the Alliance for Tribal Clean Energy are stepping in. Through its Indigenous Power and Light Fund, backed by philanthropic partners like the MacArthur Foundation, the Alliance serves as a critical lifeline. Huurav is taking a lead role as well, drawing on its expertise gained through its participation in the inaugural cohort of the Agrivoltaics Growth through Resilience and Innovation program, run by the National Laboratory of the Rockies and the Farmland Trust, a conservation-focused agriculture nonprofit.
While commercial lenders are difficult to find across tribal nations, community development financial institutions, known as CDFIs, are meant to bridge that gap by offering funding to Native-led organizations and tribes seeking to invest in renewables on tribal land. 
A recent survey found that more than half of Native-operated CDFIs, financial institutions that provide credit to underserved populations, cite lack of funding as one of their biggest challenges, and that financial barrier compounds existing energy inequalities. A 2023 Department of Energy report revealed that tribal households face an energy burden 28 percent higher than the national average. In the Southwest, for example, the Hopi Tribe and Navajo Nation have the highest rates of unelectrified homes and often rely on coal or propane for heat. Many tribes lack reliable transmission lines, leaving them vulnerable to extreme weather events, like floods and wildfires, fueled by climate change.
Despite these obstacles, Indigenous communities are pushing forward by courting sustainable investors to achieve energy independence and lower utility costs. On the Hawaiian island of Molokai, for instance, Native Hawaiians are leveraging profitable renewable energy projects to reduce exorbitant utility bills while simultaneously advancing their landback initiatives. “They’re making the case that they can get investors to invest in their landback projects,” said Kyle Whyte, Citizen Potawatomi Nation and University of Michigan professor of environmental justice. “One of the ways that they’ll be able to succeed in governing that land is through profitable renewable energy projects that would reduce people’s utility costs.”
However, some tribes have turned away from solar and wind altogether, opting instead to pursue active federal grants aligned with the Trump administration’s agenda to expand domestic energy. This includes tapping into almost $172 million in Department of Energy geothermal funding or seeking transmission upgrades backed by a $1.6 billion federal loan guarantee. 
Ultimately, the push for clean energy is about self-determination and resilience. As David Harper of Huurav puts it: “We don’t trust the federal government, but we have to work with them to understand that we have to continue in our process of survival and self-sustainability.”
This story by Miacel Spotted Elk was originally published by Grist and is part of Covering Climate Now, a global journalism collaboration strengthening coverage of the climate story. WhoWhatWhy has been a partner in Covering Climate Now since its inception in 2019.

source

Sign up to the GCR newsletter for free, delivered direct to your inbox.
Reach 97,000 UK and global construction experts.
The project consists of three large-scale photovoltaic power plants: Sidłowo plant with a 290MWp capacity, Kikowo with 235MWp and Dobrowo with 197MWp.

It will be connected to Poland’s national grid via the STR LKO 400/110 kV intermediate substation.
Goldbeck Solar Polska will provide engineering, procurement and construction of all three plants, plus associated substations, high-voltage cable routes and grid connection infrastructure.
Steffen Emmerich, Goldbeck Solar Polska’s managing director, said: “The Sidłowo–Kikowo–Dobrowo solar farm is part of a broader strategic initiative by Optima Wind, Virya Energy and the European Bank for Reconstruction and Development, who are launching Virya Renewables Poland, a new renewable energy platform with a project pipeline exceeding 2GW in potential capacity across the country.”
Further Reading:
Your email address will not be published. Required fields are marked *
Comment *
Email me when someone replies to my comment
Name *
Email *
Website
Save my name, email, and website in this browser for the next time I comment.

source

Solar panels to be compulsory on all new homes in England  Anadolu Ajansı
source

notices – General Notices  News24
source

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Scientific Reports volume 16, Article number: 9682 (2026) Cite this article
Photovoltaic (PV) systems are increasingly significant in modern electrical energy applications. Extracting the maximum power from PV modules with high efficiency requires measuring temperature (T) and irradiance (G), which often demands sensors that increase the overall system cost. Furthermore, tracking the PV maximum power point (MPP) under varying T and G presents a considerable challenge. Conventional MPPT techniques require a long time to reach the MPP and can exhibit fluctuations during operation. To address these challenges, this work proposes a novel two-stage maximum power point tracking (MPPT) strategy. In the first stage, T and G are estimated using an artificial neural network (ANN) based on the measured PV open-circuit voltage and short-circuit current, thereby reducing system cost. The first proposed stage is compared with Newton Raphson and Open circuit voltage methods (VOC) in terms of T and G errors. In the second stage, the MPP is determined directly by ANN under varying T and G, minimizing tracking time and fluctuations. This stage is compared with Fuzzy logic control (FLC), Perturb and observe (P&O), Fixed increment conductance (FIC) and Variable increment conductance (VIC) in terms of efficiency, time capture (TC), and steady-state error. Simulation results demonstrate high tracking efficiency (99.99%), fast settling time (0.007 s), and low voltage/current ripples (0.018/0.12). Comparison with FLC (99.1%, 0.0275s), P&O (98.7%, 0.0322s), FIC (98.78%, 0.0517s), and VIC (98.81%, 0.0342s) confirms the best performance of the proposed method. The proposed ANN-based method is applied to simulate the system for three case studies. In the first case, predefined data are utilized, while in the second case, real T and G data from Hurghada, Egypt are employed. Third case is an experimental setup established to validate the performance of the proposed ANN strategy. The result of the proposed system was evaluated using MATLAB/Simulink.
The changes in atmospheric conditions, such as T and solar G throughout the day have a great impact on the PV array efficiency. Therefore, studying the effect of temperature and solar irradiance on PV panels is crucial. The measurements of T and G for the PV array are essential for effectively extracting MPP. In some circumstances, measurements are inaccurate. Therefore, alternative measurement methods should be used instead¹. The usage of the pyranometer or reference solar cells is limited. This is owing to the pyranometer’s limitations due to its cost, installation, and maintenance difficulty in providing accuracy^2,3. To better measure performance, it is occasionally required to use an extra number of sensors. Therefore, techniques for lowering sensors and costs as well as maintaining sufficient solar radiation detection are needed efficiently⁴.
In², a method for estimating solar irradiance in W/m² was provided. This method was based on the short circuit current that the PV module produces. In⁴, mathematical methods were used to calculate sun irradiance using the PV model. As a result, a relationship between solar irradiation and the PV module’s output voltage was established; however, because the estimation relied on an accurate PV model and parameters that change with temperature and aging, its accuracy decreases under dynamic environmental conditions.
In⁵, the conventional method such as Newton-Raphson was used to estimate solar radiation. The Thévenin equivalent circuit for PV modules has several limitations: it is a linear or quasi-linear approximation that cannot fully capture the nonlinear behavior of PV cells, leading to reduced accuracy under changing irradiance, temperature. It is also difficult to derive accurate I–V characteristics and is valid only within a limited linear range, making it unsuitable for precise real-time solar irradiance estimation.
In², the conventional open circuit voltage (VOC) method was used to estimate The Open-Circuit Voltage method, while theoretically reliable, faced practical limitations: it was difficult to apply in real-world conditions, its accuracy depended on precise knowledge of key parameters such as Voc, its temperature coefficient, and the diode quality factor, and achieving high accuracy (uncertainty ≤ 1 °C) required controlled laboratory measurements, making it less suitable for real-time applications⁶. This literature is provided for the first stage.
The following literature is included to support the second step of the proposed method. Due to the continuous change in the environmental condition, primarily T and G, the P-V characteristics curve shows a non-linear maximum power point (MPP). To guarantee that the maximum power is always extracted, the MPPT is employed in conjunction with the power converter. Exceptional forms of MPPT strategies were advanced and employed. These techniques may be differentiated depending on the used sensors, convergence speed, cost, variety of effectiveness, implementation experimental necessities, and popularity.
These techniques are categorized into two categories: one is classical, which consists of direct and indirect strategies. The second is modern, which is financed by Artificial intelligence techniques^7,8. Direct methods are classified as Open-Circuit Voltage (OCV), Short Circuit Current (SCC), and Hill Climbing (HC). Indirect methods such as Perturb and Observe (P&O) and Increment Conductance (IC)⁸. Some of the modern methods are ANN, Genetic algorithms (GA), and fuzzy logic.
Surveyed previous proposals for classical methods to extract MPPT are presented. OCV and SCC methods have been analyzed in⁶. They are simple methods that need only one current or voltage sensor but have the same disadvantage of low accuracy. For the indirect method, in⁸, the perturbation method has been presented. The disadvantages of this method are that it cannot reach MPP with high precision and oscillates near MPP, making steady state challenging^9,10.
In¹¹, the IC method is presented. It depends on the power curve slope of the PV. However, the disadvantages are the same for P&O. In¹²modified P&O is analyzed. In^12,13, a modified IC is studied. The modification depends on the variation of step size. In¹⁴, the study presents valuable insights into hill-climbing MPPT algorithms under low irradiance; however, several limitations exist. The experiments were conducted on a small-scale prototype, additionally, the focus on thin-film PV modules limits the generalization of results to other PV technologies, while environmental factors such as temperature fluctuations not considered. ADC resolutions on algorithm performance was not extensively tested. In¹⁵, the study highlights several limitations of hill-climbing MPPT algorithms under rapidly changing environmental conditions. The modified algorithms fail to track the true maximum power point under low irradiance, while conventional algorithms such as P&O and INC, although providing satisfactory dynamic response, suffer from significant steady-state power losses. The performance of these algorithms is particularly sensitive to sudden changes in irradiance and temperature, which are common in practical applications like rooftop, building-mounted, or vehicle-mounted PV panels. Moreover, the effectiveness of variable step-size algorithms (P&O, INC) relies heavily on the proper selection of step size, making their performance sensitive to environmental fluctuations. In¹⁶, the modified P&O algorithm is sensitive to the step size, which affects its performance under significant changes in irradiance or temperature.
In contrast to traditional MPPT approaches, improved performance of PV systems to track the highest power is achieved using artificial intelligence-based MPPT approaches such as ANN, particularly in quickly changing environmental circumstances. In¹⁷, two hidden layers of ANN with a back-propagation network were used. The input to ANN is only Irradiance, and the output is only PV voltage V_PV. Using nonelectrical components, G, results in high costs. In¹⁸, the inputs to ANN are T and G while the output is PPV. This system is more accurate because it uses G and T as inputs that have more effect on the maximum power point of PV. However, this system suffers high costs in addition to using voltage and current sensors in output and indirect control for duty cycle values. In¹⁹, the ANN has been achieved by direct control. Measurements of current and voltage weren’t needed for comparative output. This reduced cost and was simple to deploy. However, because ANN was not properly trained, a significant error in output power was discovered. In²⁰, T, G, and V_OC are employed as inputs to ANN while the output is V_PV. Achieved high performance technique is obtained, and insertion of V_OC enhances the tracking. However, this system is expensive, needs extra two voltage sensors, and uses indirect control for duty cycle values.
Since solar plants in Egypt are generally large-scale, the proposed method was designed from a large-scale perspective. By measuring Voc and Isc from only one module—rather than the entire PV array—the sensor ratings and overall cost of proposed method are significantly reduced.”
The objectives and main contributions of this work are to:
Using a first stage of ANN to estimate solar irradiance (G) and module temperature (T) reduced costs by eliminating the need for a pyranometer, which normally requires additional sensors and a controller for irradiance control, as well as a separate temperature sensor.
Comprehensive simulation study and comparison of the obtained results using proposed ANN with respect to use conventional methods such as Newton-Raphson (NR) and open circuit voltage (VOC).
The second stage of the proposed ANN is used to track the Maximum Power Point (MPP) under variations of T, G, and load.
The variable D calculation block is used to compute different D values at different loads, simplifying the implementation of the proposed ANN.
The proposed method was simulated under different levels of T, G, and load.
The proposed method was simulated for a case study in Hurghada.
Experimental validation of the proposed ANN on PV system in two cases. The first case is implemented inside the laboratory using PV emulators, while the second case is carried out outside the laboratory using three 100 W PV panels.
The paper is organized as follows: “System description” presents the system description. The process of T and G estimation and the tracking stage are investigated in “Methodology”. In “Simulation results and discussion”, two cases of T and G estimation and MPP determination are simulated using MATLAB neural network toolbox. In “Experimental results and discussion”, the proposed technique in this study is verified in two cases experimentally.
The block diagram for the proposed system is shown in Fig. 1. The system consists of a PV panel, DC–DC boost converter, DC load, and two estimation stages. In the first stage, (:{V}_{OC}) and (:{I}_{SC}) are measured to determine the corresponding T and G values using the ANN algorithm. The optimum power, using ANN MPPT, is then extracted in the second stage by estimating the converter duty cycle corresponding to the obtained T and G values in the previous stage. Moreover, to track load change, the load voltage and current are measured and considered in converter duty cycle determination.
The proposed system block diagram.
The mathematical representation of the PV system using a single-diode model is introduced. The electrical equivalent circuit is shown in in Fig. 2, where (:{R}_{p}) is shunt resistance, and (:{R}_{s}) is series resistance²¹. Kirchoff’s current rule specifies that the anti-parallel branch to (:{I}_{ph}) is substituted by an (:{I}_{d}) as in Eq. (1)²²:
Where (:{I}_{ph}) is the photocurrent, which is generated when a cell is exposed to sunlight. The current traveling through the diode that creates the non-linear features of the solar cell is called (:{I}_{d}.{I}_{p}) is the shunt current. The output current is obtained by substituting for (:{I}_{d}) and (:{I}_{p}) as in Eq. (2)²³:
(:q): The electric charge (:left(q={1.60210}^{-19}Cright)). (:k:::text{Boltzmann}:text{constant}:left(k={1.380650310}^{-23}J/Kright).) (:n) : The ideality factor. (:T) : Temperature of a cell (:left(Kright)). (:{I}_{o}): Diode Saturation current ((:A)). (:{R}_{S},{R}_{p}) : Series, and shunt resistance(:left({Omega:}right)).
Several solar cells are commonly connected in series to make a PV module. (:{N}_{S}) denotes the number of series cells of a single module. (:{I}_{M}), is the module output current, is presented in Eq. (3)²²:
Where (:{V}_{M}) is the output voltage of a module. The PV array consists of groups of shunt and series connection of PV modules. The output current of array (:{I}_{A}) can be computed using Eq. (4):
The standard model of the PV module.
For the considered system configuration given in Fig. 1, the process of T and G estimation is investigated. Then, the optimum value of the converter duty cycle corresponding to PV MPP is obtained as presented in this section.
The photovoltaic (:{V}_{OC}) and (:{I}_{SC}) are changed with T and G variation as shown in Fig. 3. In contrast to conventional methods of measuring T and G to determine MPP, in this study T and G are estimated to find MPP directly by only measuring (:{V}_{OC}) and (:{I}_{SC}) by low-cost sensors.
To achieve this objective, ANN is used in this stage. The ANN receives two inputs and generates two outputs. The two inputs are (:{V}_{OC}) and (:{I}_{SC}) of the PV module, obtained from the PV characteristics under different temperature and irradiance conditions by using PV module equation. These generated data were then used to train the neural network as shown in Fig. 4. Moreover, the two outputs are the corresponding T and G. To ensure a robust and accurate model, we adopted a backpropagation (BP) approach. This type of ANN is particularly suitable for quantitative studies with smaller datasets as it can handle complex relationships without compromising power or precision.
PV array characteristics at various G and T: a I–V characteristics at various G, b P–V characteristics at various G, c I–V characteristics at various T, and d P–V characteristics at various T.
The proposed ANN architecture of estimate T and G.
To assess the model performance, the dataset was divided into training, validation, and test sets, with a recommended split ratio of 70%, 15%, and 15%, respectively. The optimal ANN structure, comprising two hidden layers with eight neurons as in Fig. 4, was determined through iterative adjustments during training to minimize the mean squared error (MSE). After 100 iterations, the best-performing ANN achieved a validation MSE of 0.00089, demonstrating its accuracy in estimating T and G values for the corresponding PV({V}_{OC}) and ({I}_{SC}).
In this stage, we employed a second ANN, establishing the relationship between (:T) and (:G) as two inputs and the optimum value of the converter duty cycle corresponding to PV MPP as output. The data set for ANN are calculated using Eq. (5)¹⁹:
In which (:{R}_{in}) denotes the typical input resistance of a solar cell and (:{R}_{l}) denotes the resistance of the load. When (:{V}_{text{m}text{a}text{x}}) is divided by (:{I}_{max},{R}_{in}) is fulfilled. By noting the (:{V}_{mpp}) and (:{I}_{mpp}) sites in the (:V-I) characteristics in various atmospheric circumstances, the various (:D) is determined and utilized to train the ANN.
The training function of the backpropagation is Traingdx which uses adaptive learning rate and gradient descent momentum to adjust bias and weight values and speed up learning. Applying the generalized delta rule, accelerate the momentum concept as given in Eq. (6)²⁴:
When the momentum fixed parameter (:left(beta:right)) is represented by a positive value. Two learning criteria are utilized with adaptive learning rate (:left(alpha:right)). The optimal ANN structure, comprising two hidden layers with ten neurons as in Fig. 5, was determined through iterative adjustments during training to minimize the mean squared error (MSE). After 135 iterations, the best-performing ANN achieved a validation MSE of 0.0075, demonstrating its accuracy in estimating the optimum value of the converter duty cycle corresponding to T and G values.
The proposed ANN architecture of track MPP.
The actual duty cycle for MPP can really be derived using Eq. (7). For predetermined PV solar cells with constant temperature and irradiance, (:{R}_{in}) is constant regardless of the load value. Variations in (:{V}_{mpp}) and (:{I}_{mpp}) for PV characteristics cause modifications in the value of (:{R}_{in}) when T and G are altered. According to the following formula, the new duty cycle for various loads is determined:
The value of the optimal (:D) for various loads will be determined by inserting Eq. (7) within Eq. (5) and arriving at Eq. (8).
In this section, two cases of T and G estimation are simulated using the MATLAB neural network toolbox to verify the analysis. Moreover, the simulation of two cases of MPP determination is presented.
The first case of T and G estimation is simulated for data obtained from the PV data sheet, while the second case is simulated for data collected for one day in August in Hurghada City, Egypt.
In the first case, T and G are estimated using the proposed ANN at predefined (:{V}_{OC}) and (:{I}_{SC}) from PV data sheet. The obtained results from ANN are compared to actual data and presented in Fig. 6. The estimated G and T values are compared with Newton-Raphson (NR) and open-circuit voltage (OCV) methods, respectively, and given in Tables 1 and 2. The results from ANN method ((:{G}_{ANN}) and (:{T}_{ANN})) are so close to actual values, (:left({G}_{ACT}right.) and (:left.{T}_{ACT}right)), than NR (:left({G}_{NR}right)) and OC V ((:left.{T}_{OC}right)) methods. The maximum error using the proposed ANN for (:text{G}left({E}_{{G}_{ANN}}right)) is (:0.0001text{%}), and the error obtained by the NR (:left({E}_{{G}_{NR}}right)) method is about 2.5%. The maximum error using proposed ANN for(::text{T} ({E}_{{T}_{ANN}})), is 0.24% and (:left({E}_{{T}_{OC}}right)) is 8.8% by OC method.
Case I: ANN’s inputs at variance in T and G: a (:{I}_{SC}) (A), b (:{V}_{OC}left(text{}text{V}right)) & ANN’s outputs: c (:text{G}left(text{k}text{W}/{text{m}}^{2}right)), d T (°C).
In the second case, T and G are estimated using the proposed ANN for real data collected for one day in August in Hurghada City, Egypt. It is located at 27015.5′ N latitude and 33048.7′ E longitude. The data in this study are obtained from NASA surface²⁵.
In Table 3, the ANN outputs ((:{T}_{ANN}) and (:{G}_{ANN})) are given and compared with real data ((:{T}_{ACT}) and (:{G}_{ACT})). The maximum and average error in (:T:left({{E}_{:}}_{{T}_{ANN}}right)) are 1.9% and 0.87% respectively and in (:G:left({{E}_{:}}_{{G}_{ANN}}right)) are 1.25% and 0.25% in respective order. Thus, the obtained results by ANN are approximately equal to the actual data.
In this section, the proposed ANN is applied to find the corresponding D for two cases. In the first case, the system is simulated at different radiation levels at a constant temperature of 25 °C. The irradiance level is changed from 1000 W/m² to 400 W/m² with a decreasing step of 100 W/m², and subsequently increased back to 1000 W/m² using the same step size. The output power is given in Fig. 7 while the output power for each radiation level is shown in Fig. 8, and it is clear that the output power obtained using ANN is the closest one to optimal power. The second case is a study of the system for real data obtained from Hurghada City, Egypt. The PV output power using ANN is the best compared to other methods, as shown in Fig. 9. In both cases, the proposed ANN method results are compared to three traditional methods (P&O, FIC,,VIC, FLC, and other techniques) and extract the maximum power. The system’s performance is summarized in Table 4. For the first case, the time capture (TC) of output power is between 0.008 and 0.02, the tracking efficiency (μ) is between 99 and 99.99%, the ripple current (∆I_RP) is between 0.25 and 0.52 and the ripple voltage (∆V_RP) is between 0.03 and 0.07. Under the EN50530 irradiance test profile, the dynamic efficiency is calculated according to the method presented in²⁶. Capture time is defined as the time interval required by the MPPT algorithm to converge and remain within a ± 2% tolerance band around the theoretical maximum power point following a step change in irradiance or load conditions. This metric quantitatively reflects the dynamic tracking capability of the proposed controller. Moreover, steady-state error is defined as the normalized mean deviation between the extracted output power and the theoretical maximum power under steady environmental conditions, computed over a predefined observation window after convergence. This metric is employed to assess the tracking accuracy and stability of the MPPT algorithm in steady-state operation.
PV output power at constant T and Variance in G.
The output power at each stage compared with MPP at T = 25 °C and a G = 1000 W/m², b G = 900 W/m², c G = 800 W/m², d G = 700 W/m², e G = 600 W/m², f G = 500 W/m², g G = 400 W/m², h G = 1000 W/m².
PV output power (real data at Hurghada City).
In this section, the proposed technique in this study is verified experimentally in two cases. The experimental power and control circuits are shown in Fig. 10. The first case is implemented inside the laboratory using programmable power supplies as PV emulators, while the second case is carried out outside the laboratory using three 100 W PV panels.
Experimental power and control circuit.
The components used to implement the experimental study here are three power supplies; two of them are power supplies (18–20 A) for open circuit voltage and short circuit current measurement instead of using two PV sources to get different output power values at different temperatures and irradiance. One programmable power supply (30 V–3 A) equivalent to the main PV module, an oscilloscope with two channels, variable power resistance (100 Ω–100 W), and an Arduino duo for the control circuit. The following diagram, shown in Fig. 11, illustrates how the experimental setup was connected in the laboratory.
The experimental indoor hardware.
There are six cases for irradiance and temperature variation are studied and given in.
Table 5. The error for output power is between 0.06 to 0.9%, the error for temperature is between 0.2 to 1.4%, and the error for irradiance is between 0.1 to 1.4%. Two cases of them are shown in Figs. 12 and 13. From the results, the temperature, irradiance, and output power of the experimental system are nearly equal simulation output results.
Indoor experimental setup: a the output P-V-I of simulation at G = 0.6 kW/m² and T = 30 °C. b The experimental output of T and G, c the output P-V-I of experimental work.
Indoor experimental setup: a the output P-V-I of simulation at G = 0.7 kW/m² and T = 40 °C. b The experimental output of T and G. c the output P-V-I of experimental work.
In this case, three PV panels (each 100 W) are used²⁵. The first PV module is used as a source for(:{:V}_{oc}), the second PV module is used as a source for(::{I}_{sc}), and the third PV module is used as the main PV module. A current sensor was utilized to measure (:{I}_{sc}), whereas a voltage sensor was employed to capture (:{:V}_{oc}). The parameters of the components used in this case are given in Table 6. This study was implemented in October (:{5}^{th}), 2022, at EAET academy in EL-salam First, Egypt. The actual values of temperature and irradiance are taken from solar irradiance data (SODA) from 9 AM to (:2:text{P}text{M})²⁹. The selected location latitude is 30.107 N, and the longitude is 31.565 E.
Irradiation values from HelioClim-3, meteorological data, and temperature are given in Table 7. The experimental output voltage, current, and power are presented in Figs. 14 and 15. The output of current is taken by a current sensor. So, the output must be scaled by the values 1.64 and 0.1 as given in Eq. (9). Where (:{V}_{text{sensor:}}) is the voltage measurement of the current sensor. The number 1.64 is the reference or zero value of the current sensor and 0.1 is the accuracy of the sensor.
The error in the experimental output power is observed at different times of the day as presented in Figs. 14, 15 and 16. From 9:00 AM to 11 AM, the error is between 0.2% and 1%. A significant deviation is recorded at 12:00 PM, where the error reaches 18%. This sharp increase is likely due to the elevated ambient temperature around noon, which negatively impacts the efficiency of solar panels, as their performance generally declines with rising temperatures. After this peak, the error decreases to 2.5% at 1:00 PM and 1.5% at 2:00 PM. These results proved the success of the proposed strategy to estimate the irradiance and temperature as well as extracting MPP.
Experimental and simulation results: a Experimental data at 9:00:00, b Simulation data at 9:00:00. c Experimental data at 10:00:00. d Simulation data at 10:00:00. e Experimental data at 11:00:00. f Simulation data at 11:00:00.
Experimental and simulation results: a experimental data at 12:00:00. b Simulation data at 12:00:00. c Experimental data at 13:00:00. d Simulation data at 13:00:00. e Experimental data at 14:00:00. f Simulation data at 14:00:00.
Experimental and simulation results: a the simulation and the experimental values of (:{V}_{oc}). b The simulation and the experimental values of (:{I}_{sc}). c The simulation and the experimental values of (:{V}_{o}). d The simulation and the experimental values of (:{I}_{o}). e The simulation and the experimental values of (:{P}_{o}).
This paper presented an ANN-based MPPT approach for photovoltaic systems operating under varying environmental conditions. The proposed method demonstrated fast convergence, high tracking efficiency, and reduced steady-state oscillations compared with conventional and fuzzy logic–based MPPT techniques. Simulation results confirmed the effectiveness and robustness of the proposed approach under different irradiance and temperature profiles, making it a promising solution for efficient photovoltaic energy extraction. A proposed MPPT system based on two artificial intelligence techniques was employed to maximize the extracted power from photovoltaic systems. The first AI-based estimation block was used to accurately estimate the cell temperature and solar irradiance, providing high accuracy with low computational cost. The estimated temperature and irradiance values were compared with Newton–Raphson and open-circuit voltage methods, showing best performance. The second ANN block was utilized to control the duty cycle under varying temperature and irradiance conditions, while load variations were also considered in the analysis. The proposed estimation and MPPT strategy was validated using both simulation and experimental studies with real data obtained from photovoltaic projects in Hurghada and El-Salam cities in Egypt. Two methods are used to carry out the experimental study. The first study employs PV modules in EL-Salam city under real-world T and G values. The second experimental investigation uses DC supplies as the system’s power source rather than PV panels. The results for estimates T and G are much closer to real values than NR and VOC methods. The output power is highest, with lower oscillation and fast response of current and voltage compared with VIC, FIC, and P&O. For the simulation cases, the error for T is 0.001% by the proposed ANN. The error for G is 0.0001% by the proposed ANN. The Time capture is 0.007, the efficiency is 99.99%, the ripple in voltage is 0.018, and the ripple in current is 0.12. For real cases, the temperature or irradiance error is 0.02%. For the first experimental study, the error for output power for most results is 0.01 to 1.1%. The error for temperature is 0.2 to 1.7%, and for irradiance is 0.2 to 1.4%. For the Second experimental study, the error in output power is very small. The error is between 0.2 and 2.5% except at 12 PM.
The effect of partial shading on the PV system’s output power was not considered in this study, but it will be included in subsequent work.
Machine learning can be used to enhance duty cycle accuracy, and hybrid systems may be explored instead of neural networks.
The system performance and accuracy could be further enhanced by employing machine learning or deep learning models trained on a larger and more diverse dataset, allowing for better generalization under various environmental and operating conditions.
The current study does not include experimentally comparison with state-of-the-art methods; however, this will be addressed in subsequent work, including additional experimental validation, to further evaluate and benchmark the proposed approach.
integration of advanced optimization techniques, such as hybrid or metaheuristic algorithms, to enhance the performance of the proposed MPPT.
The integration of the proposed ANN-based MPPT system with grid-connected PV installations.
Improved DC–DC converter topologies to enhance MPPT efficiency and dynamic performance under varying operating conditions.
Hybrid energy system incorporating a fuel cell will be explored to improve power reliability and overall system performance.
All data generated or analyzed during this study are included in this published article.
Duty cycle
New duty cycle
Irradiance estimation error using ANN
Irradiation estimation error using NR
Temperature estimation error using ANN
Temperature estimation error using VOC method
Irradiance of a cell (W/m²)
Actual radiation (W/m²)
Estimated irradiance using ANN (W/m²)
Measured radiation (W/m²)
Estimated irradiance using NR (W/m²)
Output current of array (A)
Maximum PV current (A)
Measured current (A)
Diode Saturation current (A)
Short circuit current of PV (A)
Simulated current (A)
Boltzmann constant (k = 1.380650310⁻²³ J/K)
Ideality factor
Number of series cells of a single module
Simulated power (W)
Artificial neural network
Fixed increment conductance
Fuzzy logic control
Hill Climbing
Maximum Power Point Tracking
Mean Squared Error
Electric charge (q = 1.60210⁻¹⁹ C)
Typical input resistance of a solar cell (Ω)
Load resistance (Ω)
Series, and shunt resistance (Ω)
Temperature of a cell (K)
Actual temperature (K)
Estimated temperature using ANN (K)
Measured temperature (K)
Estimated temperature using VOC method (K)
Maximum PV voltage (V)
Output voltage of a module (V)
Measured voltage (V)
Open circuit voltage of PV (V)
PV voltage (V)
Simulated voltage (V)
Adaptive learning rate
Momentum fixed parameter
Tracking efficiency
Ripple current
Ripple voltage
Newton–Raphson
Open-circuit voltage method
Perturb and observe
Short-circuit current
Time capture
Variable incremental conductance
Singh, B. P., Goyal, S. & Siddiqui, S. A. A comparative analysis of varying weather patterns effect on the performance of the MPPT techniques. Discov. Appl. Sci. 7, 1167. https://doi.org/10.1007/s42452-025-07667-x (2025).
Article  Google Scholar 
Mavromatakis, F., Kavoussanaki, E., Vignola, F. & Franghiadakis, Y. Measuring and estimating the temperature of photovoltaic modules. Sol. Energy 110, 656–666. https://doi.org/10.1016/j.solener.2014.10.009 (2014).
Article  ADS  Google Scholar 
Oliveira, M., Silva, J., Santos, P. & Costa, L. Solar radiation measurement tools and their impact on in-situ testing: a Portuguese case study. Buildings 14 (7), 2117. https://doi.org/10.3390/buildings14072117 (2024).
Article  Google Scholar 
Ma, X., Wang, Y. & Li, Q. A solar irradiance estimation technique via curve fitting based on dual-mode Jaya optimization. Front. Energy Res. (2023). https://doi.org/10.3389/fenrg.2023.1173739
Mohamed, A. M. Solar irradiance estimation of photovoltaic module based on thevenin equivalent circuit model. Int. J. Renew. Energy Res. (IJRER). 5 (4), 971–972 (2015). https://dergipark.org.tr/tr/pub/ijrer/issue/16069/167881
MathSciNet  Google Scholar 
Jordehi, A. R. Maximum power point tracking in photovoltaic (pv) systems: a review of different approaches. Renew. Sustain. Energy Rev. 65, 1127–1138. https://doi.org/10.1016/j.rser.2016.07.053 (2016).
Article  Google Scholar 
Kiran, S. R. & Basha, H. Reduced simulative performance analysis of variable step size ANN based MPPT techniques for partially shaded solar PV systems. IEEE Access 10, 48875–48889. https://doi.org/10.1109/ACCESS.2022.3172322 (2022).
Article  Google Scholar 
Hussaian Basha, C. H. & Rani, C. Performance analysis of MPPT techniques for dynamic irradiation condition of solar PV. Int. J. Fuzzy Syst. 22 (8), 2577–2598 (2020). https://doi.org/10.1007/s40815-020-00974-y
Elgendy, M. A., Zahawi, B. & Atkinson, D. J. Assessment of perturb and observe Mppt algorithm implementation techniques for pv pumping applications. IEEE Trans. Sustain. Energy. 3, 21–33. https://doi.org/10.1109/TSTE.2011.2168245 (2011).
Article  ADS  Google Scholar 
Selmy, M., El sherif, M. Z., Noah, M. S. & Abdelqawee, I. M. Optimized and sustainable PV water pumping system with three-port converter, a case study: the Al-Kharijah Oasis. Electricity 5, 227–253. https://doi.org/10.3390/electricity5020012 (2024).
Article  Google Scholar 
Bharti, S., Kumar, R., Monika & Sinha, U. K. Analysis and comparison of the P&O and INC MPPT techniques for solar energy systems when compared to various atmospheric temperatures. In Control Applications in Modern Power Systems 365–378 (Springer Nature, Singapore, 2024). https://doi.org/10.1007/978-981-99-9054-2_23.
Derbeli, M. A., Napole, C., Barambones, I., Sanchez, P. & Calvo, P. A comparative analysis of varying weather patterns effect on the performance of the MPPT techniques. Discov. Appl. Sci. 7, 67. https://doi.org/10.1007/s42452-025-07667-x (2025).
Article  Google Scholar 
Chellakhi, A. & El Beid, S. Optimizing solar photovoltaic systems: advances in MPPT techniques for enhanced energy efficiency (Springer Nature, 2025). https://doi.org/10.1007/978-3-031-93283-0
Periasamy Jately, V. et al. Experimental analysis of hill-climbing MPPT algorithms under low irradiance levels. Renew. Sustain. Energy Rev. 150, 111467. https://doi.org/10.1016/j.rser.2021.111467 (2021).
Article  Google Scholar 
Jain, Jately, V. & Arora, S. Performance investigation of hill-climbing MPPT techniques for PV systems under rapidly changing environment. Intelligent communication, control and devices, advances in intelligent systems and computing 624, 1145–1157 (Springer, Singapore, 2018). https://doi.org/10.1007/978-981-10-5903-2_120.
Cheah, A. R. C., Yeap, K. H., Hirasawa, K., Yeong, K. C. & Nisar, H. Optimizing the design parameters of a wireless power transfer system for maximizing power transfer efficiency: a simulation study. In: 2016 IEEE Power India International Conference (POWERI), New Delhi, India, 1–6 (2016). https://doi.org/10.1109/POWERI.2016.807732
Abdelrahman, S., Hasaneen, K. M., Abdel-Rahim, N. & Selmy, M. A comprehensive analysis and closed-loop control of a non-isolated boost three-port converter for stand-alone PV system. Eng. Sci. Technol. Int. J. https://doi.org/10.1016/j.jestch.2024.101786 (2024).
Article  Google Scholar 
Pachauri, R. K. & Chauhan, Y. K. Hydrogen generation/pressure enhancement using fc and ann based mppt assisted pv system. In 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH) 427–432 (IEEE, 2014). https://doi.org/10.1109/CIPECH.2014.7019117
Periasamy, P., Jain, N. & Singh, I. A review on development of photovoltaic water pumping system. Renew. Sustain. Energy Rev. 43, 918–925. https://doi.org/10.1016/j.rser.2014.11.019 (2015).
Article  Google Scholar 
International Conference on Advanced Intelligent Systems for Renewable Energy Applications. ANN-Based MPPT with inputs temperature, irradiance, VOC, and Isc for photovoltaic systems. In Lecture Notes in Electrical Engineering (Springer, Cham, 2024). https://doi.org/10.1007/978-3-031-54288-6.
Abdulrazzaq, A., Bognár, G. & Plesz, B. Enhanced single-diode model parameter extraction method for photovoltaic cells and modules based on integrating genetic algorithm, particle swarm optimization, and comparative objective functions. J. Comput. Electron. 24, 44. https://doi.org/10.1007/s10825-025-02282-w (2025).
Article  Google Scholar 
Tian, H., Mancilla-David, F., Ellis, K., Muljadi, E. & Jenkins, P. A cell-to-module-to-array detailed model for photovoltaic panels. Sol Energy. 86, 2695–2706. https://doi.org/10.1016/j.solener.2012.06.004 (2012).
Article  ADS  Google Scholar 
Kolsi, S., Samet, H. & Amar, M. B. Design analysis of dc-dc converters connected to a photovoltaic generator and controlled by Mppt for optimal energy transfer throughout a clear day. J. Power Energy Eng. 2014 https://doi.org/10.4236/jpee.2014.21004 (2014).
Zhao, Y., Li, X. & Wang, J. Using adaptive learning and momentum to improve generalization. Neural Comput. Appl. 37, 14399–14426. https://doi.org/10.1007/s00521-025-11220-7 (2025).
Article  Google Scholar 
Mohamed, H. N. & Mahmoud, S. A. Temperature dependence in modeling photovoltaic arrays. In 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS) 747–750 (IEEE, 2013). https://doi.org/10.1109/ICECS.2013.6815522
Jately, V. et al. Voltage and current reference based MPPT under rapidly changing irradiance and load resistance. IEEE Trans. Energy Convers. 36 (3), 2297–2307. https://doi.org/10.1109/TEC.2021.3058454 (2021).
Article  ADS  Google Scholar 
Fathi, M., Gad, A., Mohamed, A. & El-Sayed, A. Intelligent MPPT for photovoltaic panels using a novel hybrid particle-swarm-optics optimization technique. Mater. Today: Proc. 43(A), 112–121. https://doi.org/10.1016/j.matpr.2021.03.546 (2021).
Article  Google Scholar 
Jately, V. & Arora, S. Development of a dual-tracking technique for extracting maximum power from PV systems under rapidly changing environmental conditions. Sol. Energy 155, 1170–1180. https://doi.org/10.1016/j.solener.2017.07.034 (2017).
Article  Google Scholar 
Solar Radiation Data (SoDa.) (2022). https://www.soda-pro.com/soda-products.
Download references
Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
Islam M. Abdelqawee, Mohamed Selmy, Mahmoud N. ALI & Alzhraa A. Abdelfattah
Egyptian Academy for Engineering and Advanced Technology, Cairo, Egypt
Alzhraa A. Abdelfattah & Wael Mamdouh
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.S.: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Visualization. I.A: Conceptualization, Methodology, Formal analysis, Validation, Investigation, Resources, Data curation, Writing – review and editing, Visualization. M.N.A: Review and editing, Visualization, Supervision. A.A.A: Resources, Software, Data curation, Validation, Investigation, Writing – original draft, Visualization. W.M: Review and editing, Visualization, Supervision.
Correspondence to Alzhraa A. Abdelfattah.
The authors declare no competing interests.
Not applicable.
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
Abdelqawee, I.M., Selmy, M., ALI, M.N. et al. Harnessing artificial neural networks for accurate PV system parameters determination: radiation, temperature, and MPPT. Sci Rep 16, 9682 (2026). https://doi.org/10.1038/s41598-026-40175-5
Download citation
Received: 14 October 2025
Accepted: 11 February 2026
Published: 23 March 2026
Version of record: 23 March 2026
DOI: https://doi.org/10.1038/s41598-026-40175-5
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

European Energy Divests High-Capacity 151 MW Solar PV Project In Sicily  SolarQuarter
source