The company has established a one gigawatt (GW) solar module manufacturing line near Jaipur, Rajasthan, which is currently under trial and is scheduled to be commercially operational by June. Commercial manufacturing is due to commence next month according to the firm’s timeline and the line will assemble modules from procured cells. Solar panels are assembled from modules that contain cells while cell production depends on ingots and wafers in the upstream value chain. Sunkind India has signed an agreement with a domestic solar cell manufacturer to source one GW of domestic content requirement (DCR) solar cells to ensure continuity of supply for the new vertical. The supply pact is intended to secure access to approved local cells mandated under the government’s ALMM-II rules which take effect on June first. The company indicated that the procurement order would cost in the region of Rs 12 bn to Rs 15 bn. The firm said in its statement that bringing module production in house will help it execute its EPC and IPP contracts more efficiently and reduce dependence on third-party module suppliers. Details of the cell supplier have not been disclosed because of a non-disclosure agreement with the manufacturer. The company remains focused on the commercial and industrial market and is positioning the new manufacturing capability as a strategic support for its project pipeline. Sunkind India is moving into solar module manufacturing and has placed an order worth Rs 12 bn to Rs 15 bn to procure solar cells for the new business vertical. The company has reported that the investment covers procurement of cells required for module assembly and will underpin the firm’s supply chain for modules. The move is intended to support the firm’s existing engineering, procurement and construction (EPC) and independent power producer (IPP) projects in the commercial and industrial segments. The company has established a one gigawatt (GW) solar module manufacturing line near Jaipur, Rajasthan, which is currently under trial and is scheduled to be commercially operational by June. Commercial manufacturing is due to commence next month according to the firm’s timeline and the line will assemble modules from procured cells. Solar panels are assembled from modules that contain cells while cell production depends on ingots and wafers in the upstream value chain. Sunkind India has signed an agreement with a domestic solar cell manufacturer to source one GW of domestic content requirement (DCR) solar cells to ensure continuity of supply for the new vertical. The supply pact is intended to secure access to approved local cells mandated under the government’s ALMM-II rules which take effect on June first. The company indicated that the procurement order would cost in the region of Rs 12 bn to Rs 15 bn. The firm said in its statement that bringing module production in house will help it execute its EPC and IPP contracts more efficiently and reduce dependence on third-party module suppliers. Details of the cell supplier have not been disclosed because of a non-disclosure agreement with the manufacturer. The company remains focused on the commercial and industrial market and is positioning the new manufacturing capability as a strategic support for its project pipeline. GTV Engineering reported audited financial results for the financial year ended 31 March 2026, recording total income of Rs 1,033.30 million (mn) and profit after tax of Rs 142.18 million, compared with Rs 110.46 million in FY25, reflecting healthy year-on-year growth in profitability. The company said annual performance was supported by continued execution across its fabrication and machining businesses. Management noted that the results demonstrate resilience in a project-driven business model. This performance follows sustained operational activity during the year. GTV has been engaged in h.. Burnpur Cement reported a standalone net loss of Rs 207.4 million (Rs 207.4 million) for the quarter ended March 2026. The company said the loss reflects its financial performance for the period and will be reflected in its results filed with regulators. The announcement followed routine quarterly reporting by the listed cement manufacturer. Burnpur Cement is a cement manufacturer operating in India and serving construction markets, with operations spanning production, distribution and sales across the domestic construction sector. The March 2026 quarter result marks a weakening in profitabili.. The meeting reviewed progress in limestone calcined clay cement (LC3) technology and its commercial adoption in India’s cement sector, focusing on low-carbon alternatives to conventional binders. JK Lakshmi Cement noted that limestone calcined clay cement can reduce carbon dioxide emissions by up to 40 per cent compared with conventional cement and said this reduction supports industry decarbonisation. The company highlighted that it was among the first two cement manufacturers in India to move LC3 into commercial production after the Bureau of Indian Standards approved the technology as a c.. Get daily newsletters around different themes from Construction world. Don’t miss out on valuable insights and opportunities to connect with like minded professionals A-303, Navbharat Estates, Zakaria Bunder Road, Sewri (West), Mumbai – 400 015, Maharashtra, India By creating an account, you agree to our Terms and Conditions and have read and acknowledge the Privacy Policy
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From pv magazine India India’s renewable energy transition has entered a decisive phase. This transition to renewable energy sources like solar has progressed from an experimental form of energy to the backbone of a cleaner and more reliable national power network. This moment is special not just because of the size of India’s growth in renewable energy, but it’s also noteworthy due to how advanced the technology has become. India’s strategy for solar energy and renewable energy as a whole, has evolved to be based largely on efficient, integrated and reliable ways to deploy renewable energy through a supportive policy environment that views renewable energy sources as long-term infrastructure instead of focusing simply on the capacity the energy can produce short term. India’s solar installed capacity grew from less than 3 GW in 2014 to just over 130 GW in 2023, a growth rate of over 40 times; however, the next phase of India’s growth will be driven much more by intelligent use of the megawatts of power that have been produced versus simply how many megawatts are generated. Efficiency as the New Commercial Baseline There has been a significant change in solar power technology. High-performance solar panels have moved from being mostly used in research facilities to being commonplace in commercial projects. In addition, new cell designs, advanced inverters, and artificial intelligence-based monitoring are contributing to greater than 23% efficiency levels of solar panels, allowing more power to be produced from a given amount of land. In India where there are issues with land availability and population density, high efficiency solar panels are not just a benefit, they’re a critical requirement for doing business. Increased panel efficiency helps to reduce the pressure on land, reduce the life-cycle cost of a solar installation, and improve project viability. India has taken an approach of leapfrogging incremental steps of technological adoption by utilizing proven global technologies on a large scale. This trend is not only an Indian phenomenon, but reflects a global trend of renewable energy investments exceeding fossil fuel investments and rapidly increasing the share of electricity produced with solar power. With the continued increase in efficiency of solar panels, this technology has transitioned from being an intermittent to a pivotal part of future electricity planning. Policy as a Catalyst for Quality Technology progress in India is not occurring in isolation; it is reinforced by deliberate policy design. Government incentives are increasingly tied to performance and manufacturing capability rather than basic assembly capacity. Production-linked incentive schemes for domestic solar manufacturing have mobilised tens of thousands of crores in investment, steering the industry toward high-value, high-efficiency production. This regulatory shift is critical. Solar infrastructure must perform reliably for decades. By embedding quality benchmarks into procurement and manufacturing incentives, policymakers are ensuring that India’s energy transition is durable, not disposable. The focus has moved from rapid installation to long-term reliability — a sign of sectoral maturity. Solar at the Household Level: A Structural Shift The PM Surya Ghar Muft Bijli Yojana is among the most ambitious solar projects for homes on a worldwide scale. By pursuing rooftop solar for 1 crore residential buildings, this plan is creating millions of different energy generating homes across India. This will install about 30 GW of rooftop generation capacity and provide relief to grid-connected power supplies while lowering energy bills for consumers. The project normalises home use of solar power outside of its numerical goals. It includes integrating renewable energy into everyday life and creating demand for compact, very high-efficiency manufactured goods designed mainly for urban use on rooftops. India is pursuing this project similar to markets such as Australia and parts of Europe, where distributed solar has established large numbers of “prosumers” — consumers that create their own energy. The End of Intermittency and the 24/7 Reality The criticism that long-term solar power has received due to its inconsistency will soon go away as developments are being made in both battery storage systems and hybridisation of existing electrical systems. The number of storage devices being deployed throughout the world at unprecedented rates, including in India where they have begun to include storage systems as part of their future technologies, will further cement this idea. Solar & storage projects are fundamentally changing our perception of what we can expect from the electrical grid. For large organisations and urban centres where uninterrupted supplies of electricity are critical to their operations, the use of renewable energy as an alternative to traditional forms of energy will become commonplace to them. As intelligent management of the electrical grid and integration of batteries into our existing system continues to develop, the gap between the definitions of intermittent and dependable energy will continue to disappear. A Blueprint for the Future India has already achieved 50% of its installed electricity capacity from non-fossil sources ahead of its 2030 target, underscoring the speed of its energy transition. Solar dominates new capacity additions and increasingly shapes national energy strategy. The lesson is clear: technology alone does not transform energy systems — policy alignment does. By combining efficiency-driven innovation with regulatory discipline and social inclusion, India is building a renewable ecosystem that is reliable, scalable, and equitable. As global energy markets evolve, India’s experience offers a blueprint for emerging economies. Solar is no longer an alternative energy experiment. It is infrastructure — central to economic growth, energy security, and climate responsibility. Comments Please login to comment Wednesday, June 3, 2026 4:00 pm – 5:00 pm CEST, Berlin, Paris, Madrid Wednesday, June 10, 2026 3:00 pm – 4:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 9, 2026 11:00 am – 12:00 pm CEST, Berlin, Paris, Madrid Thursday, June 11, 2026 5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid Monday, June 1, 2026 5:30 pm – 6:30 pm CEST, Berlin, Madrid, Paris Tuesday, June 16, 2026 6 am – 7:00 am CEST, Berlin Friday, June 12, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid The new pv magazine Global May issue is now available! Mountains to climb Available in print and digital formats. Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects. April 01 – August 31, 2026 A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution. Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy. Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way! We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team. June 23-25, 2026 | MUNICH, GERMANY
Copyright: Any unauthorized use or reproduction of The Daily Star content for commercial purposes is strictly prohibited and constitutes copyright infringement liable to legal action. In Molani village under Thakurgaon Sadar upazila, Soleman Ali, a self-taught innovator, is harnessing solar power to help Boro paddy farmers with a low-cost solution for irrigation. Relying on his own technical expertise, curiosity and persistence, Soleman developed a transportable solar-powered irrigation device that can lift water from deep underground, offering a practical, affordable, and portable alternative to commonly used irrigation pumps amid rising fuel prices and frequent power outages. Both local farmers and those from other areas are already being benefitted from the innovation, either by renting or purchasing the device. After Soleman had left primary school due to financial hardship, he took up work as a bicycle mechanic to support his family. Later, he learnt assembling instant power supply (IPS) systems, which sparked his interest in using solar powered electrical devices. Without any institutional or industrial support, Soleman began working on his solar-powered irrigation system in 2013. The prototype, built with solar panels and other components collected from market, was successfully developed following continuous trial and error. He later upgraded the system by adding a gearbox to the pump to regulate water flow, and a mechanism allowing the solar panels to be adjusted to the sun’s position. He also mounted the entire structure on wheels, making the system fully transportable. During a recent visit, Soleman demonstrated how the device operates on solar energy. The device ranges from 1,220-4,400 watts, priced at Tk 60,000-2,10,000. The popular 2,440-watt variant, priced at Tk 1.5 lakh, has a three-horsepower pump powered by four 610-watt panels, that can lift about 700 litres of water per minute, enough to irrigate 8-10 acres of Boro fields in a season. While conventional irrigation costs Tk 7,000–8,000 per bigha, his system reduces the cost to around Tk 2,500-3,000. Soleman has built and sold over 100 devices so far, including 25 this year. He also earns roughly Tk 36,000 annually by renting out each unit. At present, he struggles to meet growing demand, operating six units himself while 20 more are being rented. Farmers said they have been benefitted from using Soleman’s solar irrigation units. Md Babar Ali, 53, of Thakurgaon’s Baliadangi, has been using a 2,440-watt unit for nearly four years, irrigating about four bighas of Boro field every season and supplying water round-the-year to his fish hatchery without additional costs after an initial investment of around Tk 2 lakh. In Haripur, Md Shaheen, 30, purchased two units last year and is now irrigating around 65 bighas of land at a much lower cost than conventional methods. The affordable technology has also drawn interest from farmers beyond Thakurgaon, including in Panchagarh, Sunamganj, Natore, and Rangpur. Soleman’s use of solar energy, however, extends far beyond irrigation — from welding machines to livestock and poultry farm equipment, as well as electrical appliances. In fact, his home runs extensively on solar power. The innovator’s widespread adoption of solar-power has locally earned him the nickname “Solar-man Soleman”. He has also secured financial stability for his family. His four sons are now self-reliant in businesses, and his two daughters have been married off. Soleman said a non-government organisation, Eco-Social Development Organistion (ESDO), has distributed his solar-powered irrigation pumps among 18 beneficiaries under its agriculture-related projects, with 16 more units in the pipeline for delivery. He urged the government for support. Dr Muhammad Shahid-Uz-Zaman, executive director of ESDO, said Soleman’s device can irrigate croplands at significantly lower cost, benefitting small and marginal farmers. Mazedul Islam, deputy director of the Department of Agricultural Extension, recommended scaling up the technology to ensure affordable and accessible irrigation for farmers. Copyright: Any unauthorized use or reproduction of The Daily Star content for commercial purposes is strictly prohibited and constitutes copyright infringement liable to legal action.
Japanese medical equipment provider AirWater and Suichoku Solar K.K. have announced the completion of a 178.5 kW vertical PV system at a parking lot owned by Japanese telemarketing company JP Two-Way Contact Co., Ltd in Tottori, in the Chūgoku region of southern Japan. The system, based on Suichoku Solar’s proprietary Verpa design, incorporates vertical racks supplied by Germany’s Next2Sun and 525 W bifacial heterojunction PV modules. It began operation on November 20 under an on-site power purchase agreement. “All electricity generated by this system will be consumed by the center, covering approximately 25% of its total power consumption,” AirWater said in a statement. “This is the first vertical solar power generation system for parking lots in the San’in region and the largest in Japan.” The company noted that the vertical configuration eliminates the risk of snow damage, requires minimal space, and uses reflected and scattered light from the surrounding area to generate power comparable to rooftop systems.
“Furthermore, by installing the equipment more than 2 meters above ground level, the installation of protective fences, as required by the Ministry of Economy, Trade and Industry (METI) ordinance, is unnecessary, allowing the land to be used for other purposes such as parking lots, material storage areas, walkways, and green spaces,” it stated. “Verpa can be installed along property boundaries, in parking lots, green spaces, or along walkways, as long as there is a 2.5 m-wide space. Air Water recently announced it intends to deploy Verpa systems totaling 1.3 MW at 14 of its facilities and around 10 MW at other locations in Japan during 2026. Furthermore, it said it is currently developing Verpa-Mova, a portable version of the system that is being tested in the Nagano Prefecture. “This product features a laid-base type that does not require pile foundations, making it easy to install in locations where installation was previously difficult, such as on artificial ground or concrete structures,” the company said, without providing further details. *The article was updated on December 5 to reflect that the project developer was Suichoku Solar and not Luxor Solar, as we previously reported.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected]. Comments Please login to comment Nowadays, the electric vehicle is prosperously developing; this vertical PV system is where the trend lies. Designed to maximize the use of space and sunlight energy, the greater part is no longer worried about the dust, snow, or anything else, less maintenance. Reducing 25% of electricity cost and consumption is a great start to utilizing the PV module. Hope the expenditure will be more cost-effective and widely available. Wednesday, June 3, 2026 4:00 pm – 5:00 pm CEST, Berlin, Paris, Madrid Wednesday, June 10, 2026 3:00 pm – 4:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 9, 2026 11:00 am – 12:00 pm CEST, Berlin, Paris, Madrid Thursday, June 11, 2026 5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid Monday, June 1, 2026 5:30 pm – 6:30 pm CEST, Berlin, Madrid, Paris Tuesday, June 16, 2026 6 am – 7:00 am CEST, Berlin Friday, June 12, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid The new pv magazine Global May issue is now available! Mountains to climb Available in print and digital formats. Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects. April 01 – August 31, 2026 A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution. Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy. Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way! We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team. June 23-25, 2026 | MUNICH, GERMANY
Our goal with The Daily Brief is to simplify the biggest stories in the Indian markets and help you understand what they mean. We won’t just tell you what happened, we’ll tell you why and how too. We do this show in both formats: video and audio. This piece curates the stories that we talk about. You can listen to the podcast on Spotify, Apple Podcasts, or wherever you get your podcasts and watch the videos on YouTube. You can also watch The Daily Brief in Hindi. In today’s edition of The Daily Brief: Energy security is no longer a fuel problem India’s Missing Poor India is doing two seemingly contradictory things at once. On one side, we’re shifting to clean power as fast as any large economy on earth. Five years ago, for every rupee we put into fossil-fuelled electricity, we spent roughly a rupee and a half on renewables and nuclear power. Today, that has climbed to thrice as much. Solar and wind now make up more than half of our installed power capacity, with solar investment alone climbing about 25% a year. At the same time, however, we’re building oil refineries at the fastest pace in years. Our refining investments have grown at around 23% a year over the past five years, and we’re on track to add roughly 15% more by 2030. Almost all the crude we process is imported. And so, the more we expand our refining capacity, the deeper our reliance on imported oil grows. Why are we simultaneously trying to insulate ourselves from fuel imports, while also placing a bigger bet on the most import-dependent part of its energy system? Well, the energy world is in a weird inflection point, at the moment, all we can do is hedge. It isn’t just us, though. As a new report from the International Energy Agency (IEA) notes, what the world considers “energy security” is changing rapidly. For most of the last half-century, “energy security” meant you needed to keep fuel flowing. Fifty years ago, when the world had been hit by the oil shock of 1973, it responded by finding more oil in safer places — like the North Sea. It also moved to use each drop of fuel it had more carefully, with fuel-economy rules and efficiency standards. We now find ourselves in such a moment again, this year. This time around, the IEA describes a very different instinct taking hold. The most reliable defence against being held hostage for oil, countries are quickly discovering, is to not need any. If you can get energy in a form that you don’t have to import or burn though, you are immune to being embargoed, blockaded or priced out from another corner of the world. That logic is pointing the world to one direction: electricity. An electrified economy doesn’t perennially wait for fuel to reach its borders. It is already saving the world hundreds of billions in import costs. Which is why close to 60% of all energy investment in the world now goes into electricity in some form — generating it, moving it, storing it, or running things on it. The recent Hormuz crisis has given this trend wings. As oil prices jumped this year, interest in electric vehicles climbed across the world — from the European Union to Vietnam. Countries like Japan and Korea, which import nearly all their fuel, are now putting public money into electrifying buildings and heating. They’re selling this to voters as national security, not climate policy. As the IEA warns, however, electrification doesn’t end a country’s dependence on the outside world. It only changes how that dependence looks. An economy that runs on oil has to keep buying oil, often from far away. An electrified one, meanwhile, has to get two different things right: it needs to spend a large amount of money upfront, and it needs to source a steady supply of manufactured hardware — solar panels, batteries, transformers, and other equipment that makes up a grid. If those are built and paid for, running costs drop and the fuel risk mostly goes away. That comes with its own hurdles, however. The first is manufacturing. As we harp about endlessly on The Daily Brief, most of the world’s electrification hardware is overwhelmingly made in China. The country accounts for ~85% of the world’s solar manufacturing capacity, ~80% of its lithium-ion battery production, and ~95% of the capacity to make the wafers that go inside solar panels. It also controls more than 70% of the market for 19 out of the 20 minerals the IEA considers strategic. No single oil producer in the world enjoys anywhere near this level of dominance. The second hurdle is money. Clean power is capital-heavy and front-loaded. Almost all of your investment is compressed at the very beginning, while the savings only trickle in over decades. This makes electrification unusually sensitive to the cost of borrowing. If money is hard to raise at the time infrastructure is being set up, the future earnings it could bring matter for little. As our technology improves, the burden drops. Over the past decade, the cost of solar panels, batteries and electric vehicles fell by roughly 80%. Without these advancements, our current pace of electrification would cost twice as much. And yet, it is still not in everyone’s reach. Even if the equipment is cheap, if the loans needed to install it cost 12% a year, many are still unlikely to invest. That’s the deep irony of our moment. Electrification was supposed to be a great leveller: the sun and the wind fall on everyone, unlike oil, which sits under a lucky few countries. But the things that turn sun and wind into electricity — capital and factories — are even more concentrated than oil ever was. This has done something odd: there are few countries that can actually afford to make this switch, and they’re those that need it the least. A solar project in a rich country can be financed at a single-digit rate, often in the mid-single digits. The same project in India, Brazil, Indonesia or South Africa carries a cost of capital closer to 9 to 13%. In much of Africa, it runs above 20%. These higher interest rates add a recurring cost to the entire lifetime of a project, even though most investments are made upfront. As a result, places that have the most to gain from cheap, home-grown power are exactly the ones for whom it’s the most expensive to build. That gap is getting harder to cross. For one, the world is becoming reluctant to give these projects patient capital, instead tilting towards debt. While energy-related borrowing rose about 10% last year, projects funded out of equity, grants or subsidies edged down. The world is also doing away with the “green” premium it would once give electrification projects, shaving off some of their cost. The market for sustainable-labelled debt actually shrank about 14% last year, as lenders went back to judging projects on their economics rather than branding. And the philanthropic money that once backstopped risky projects in the poorest markets has thinned to a trickle. Instead, the capital for green infrastructure increasingly sits with a handful of very large institutions — pension funds, insurers, and asset managers. These now hold close to 30% of the biggest listed state energy companies and more than 85% of the largest private ones. These institutions chase low-risk returns, and are unlikely to fund moonshot projects somewhere in the developing world. The result: China, the United States and the European Union account for about two-thirds of the world’s clean-energy investment. The rest of the world gets under 30% of its energy investment. That has interesting knock-on effects. In a situation like the Hormuz crisis, countries that could already raise the money for green projects were the most capable of displacing its effects. Meanwhile, those most exposed to swings in fuel prices have the least capital to climb out of that exposure. How does the developing world break out of this grid-lock? IEA points to one idea: capital recycling. If developers can sell or refinance their finished, working projects, that could free up money for new ones. The same equity, then, can build several things over time instead of just one. This is already happening in the richer world. There, the market for buying and selling operating projects grew from around $220 billion in 2013 to roughly $960 billion a decade later. But once again, in the developing world — which needs this kind of secondary market the most — it barely exists. And without buyers for finished assets, money stuck in one project never reaches the second. This might sound dry, but consider this: if the cost of capital in developing economies comes down by a single percent, their yearly cost of financing their clean power and electrification falls by about $30 billion by 2035. Paradoxically, meanwhile, a moment like this pushes them further into coal and gas. Take coal. As recently as the beginning of this year, it seemed like coal would soon cease to be the world’s energy backbone. But the world has suddenly learnt just how unstable its oil supplies were, while green energy projects are unaffordable. At the moment, therefore, coal is being recast across much of Asia as a security asset. It is easy to find, plentiful, and relatively immune to the wrath of Hormuz-like chokepoints. The world’s coal supply investment is now at its highest in more than a decade, at around $180 billion. Meanwhile, investment in natural gas supply is at a ten-year high as well, but for very different reasons. This isn’t just an outcome of capital starvation — the United States, the world’s richest country, is ordering gas-fired power plants at the fastest rate in 25 years. Much of it is meant to feed the surging electricity demands of AI data centres. In just the last year, the country has placed ~$24 billion worth of gas-related orders for data centre projects alone. If American data centres were a country, they would be the world’s second largest destination for gas turbines. The timeline for AI demand is simply too short for grids and clean plants to catch up. And so, in a moment that’s pushing the world towards electrification, paradoxically, fossil fuels have suddenly had a second wind. India sits in an awkward spot through all this. We’re too big, and growing too fast, to stay still. Our appetite for energy is growing faster than almost anywhere on earth — with our total energy spending rising about 11% a year. We’re too short of cheap capital to outspend the problem. We can’t lean fully into clean energy, because that requires money we can’t afford, and hardware imports from China, who we don’t fully trust. And yet, we can’t afford to stand still either. Structurally, we need much more energy than we currently make. And so, we do both. As we recently covered, we’ve built out so much green energy infrastructure recently that our raw capacity for generating energy isn’t our biggest problem any more. Our bigger bottleneck, now, is finding a way to absorb all that energy. Some of our biggest investments are in things like the grid and batteries. Off late, even tenders for solar and wind energy are paired with storage — so that our energy can be made reliable around the clock. But we’re hedging this with a massive build-out of refineries. We’re increasingly a maker and exporter of finished fuels, capturing value and supply that would otherwise sit abroad. This naturally deepens our need for imported crude. But when the alternative is expensive finance and a dependence on China, oil can actually become insurance. We have to fend off two risks at once: the risk of being shut out of the clean future, and the risk of being caught short in the fossil-based present. Between 2004 and 2011, India pulled off something rarely seen in the world. Our poverty rate fell from 37% to 22%. Every single year, we would drop more than two percentage points. That is, every year, one in fifty Indians — tens of millions of people — would break out of poverty. And then, for about a decade, we struggled to even count how many poor people we had. Our economy kept growing through this period. Our GDP per capita rose by over 4% a year for the entire decade. By any normal logic, poverty should have kept falling, maybe even at a faster rate. But, did it? That’s a hard question to answer — because we simply stopped counting. But there’s some indication to be found in a 2025 Tinbergen Institute discussion paper, authored jointly by researchers at JNU, and the Vrije Universiteit in Amsterdam. The three of them set out to reconstruct what happened to Indian poverty after 2011, a period when the data had a hole in the middle. India counts its poor the simplest way imaginable: surveyors knock on doors and ask what a household spent last month. They then add up the answers, draw a line, and count who falls below it. One of these surveys was due around 2017. Although the survey itself happened, the government refused to release the results, citing concerns about data quality. And so, for roughly a decade, India had no official, trustworthy consumption numbers at all. There were some leaked tabulations, though. According to the economist S. Subramanian, they pointed to something unfortunate: poverty, it appeared, may have ticked up slightly between 2011 and 2017. A new survey finally arrived in 2022 and 2023. On the surface, it suggested a dream headline: India’s poverty had fallen 29.6% to 7.2%. That is, in a decade, poverty had fallen from nearly one in three Indians to barely one in fourteen. This didn’t settle things, however, because the survey changed how it measured people’s consumption. The new survey used a different recall method from the one in 2011, with different time windows for different goods. Surveyors now visited each household several times instead of once. They bundled the items differently, added new ones, and began putting a value on goods people received for free. This wasn’t a conspiracy; in fact, many of these were genuine upgrades. But when you overhaul a survey like this, ordinarily, you run a small bridge survey alongside it, done the old way, just once, so the new numbers can be lined up against the past. That step was skipped, and with that, we lost the ability to compare ourselves against older figures. Even though we had new numbers now, these changes made them incomparable with older surveys. You could tell how people responded to the new survey, but you couldn’t tell if there was real change in people’s lives, or if the new methods simply gave different answers. There was also a third problem: nobody agrees on the poverty line. You can think of a poverty line as a cutoff. Spend less than Rs. X in a month and you’re counted as poor. Spend more and you’re not. India has two of them, drawn by two different government committees. The lower of these, and therefore the one that’s harder to be considered “poor” under, was the “Tendulkar line”. By that line, 22% of Indians were poor in 2011. The Rangarajan line sat higher, considering more people as “poor”. Effectively, even if we had spending data from every single person in India, we wouldn’t know exactly how poor people were. With these problems in the data, all one could tell was that our level of poverty had moved. Only, it was much harder to estimate exactly how much it moved by. For that, we would need something more creative. To get around our many data challenges, the researchers attempted something called “survey-to-survey imputation” — a method that one of the same researchers had discovered previously. Instead of comparing spending, which the two surveys measured differently, they only looked for those things both surveys measured the same way. Both had household sizes, years of schooling, what kind of work people do, what they own — a fridge, a two-wheeler, a phone — and so on. That gave you a pattern: a household that looked a certain way tended to spend a certain amount. If you applied the pattern to the new survey, you could make an educated guess on what those households would have spent, counted by the old method. The authors then ran the model a thousand times over, giving them a proper range, instead of one fragile guess. In a richer version of the same exercise, they also tracked patterns alongside results from labour force surveys we carry out every year, getting a richer year-by-year picture rather than one frozen frame. Now, this method isn’t magic, as the authors themselves admit. There’s no guarantee that a statistical relationship from 2011 has any meaning today. They did test it backwards — between 2004 and 2011, for when we had good data. There, it tracked reality quite closely. But anything could have broken that correlation in the following years. This is a reconstruction. It is imperfect. But it does offer some sort of bridge between two data series with a massive gap in between, where none existed previously. This exercise gave the researchers a range. The quick method, that simply compared the two surveys, showed a drop in poverty from 22% to around 11-14%. In the richer method, which also used jobs data, the decline was less steep still — dropping to ~18% by 2017, and then hovering at 17-18% through to 2022. This is all, incidentally, under the Tendulkar line. The stricter Rangarajan line gives a poverty rate somewhere in the low twenties. A common finding that runs across is that our rate of poverty decline has slowed down. Against our old pace, where poverty came down by 2% every year, the pace fell to less than 1%, if not lower. In fact, chances are, we saw a quick enough decline until around 2017, but it stalled there — and some of our gains were wiped away in the COVID years of 2020 and 2021. This, however, is a national average. Underneath, they found a wildly uneven map, with different parts of India seeing completely different fates. Uttar Pradesh, India’s largest state, stands out with a clear and real fall in poverty across both villages and cities. In its immediate neighbourhood, though, the picture was very different. Bihar barely budged. Jharkhand and Madhya Pradesh weren’t much better off either. Whatever worked in UP wasn’t happening around it. That gap wasn’t some quirk of measurement; it was clearly a matter of policy. Poverty reduction in Maharashtra and Andhra Pradesh, too, flatlined. In fact, in some states that already had low urban poverty, urban poverty actually crept up. Villages and cities, too, saw very different fates. Rural India saw the bigger drop from 2011. At the same time, it also took the post-COVID hit. The bounce-back in poverty rates after 2020 is mostly a village story. Meanwhile, cities improved less, and more unevenly. But they also held onto their gains through the pandemic. One of the challenges with India’s poverty reduction is simply our size. Our population went from about 1.26 billion in 2011 to 1.43 billion in 2022 — with hundreds of millions of new people. Even when the poverty rate fell, the number of poor Indians barely saw a dent, dropping from ~270 million to maybe 250 million. The paper doesn’t attempt to tell one what happened. After all, it’s a measurement paper. It’s trying to get the number right, not to explain it. Here is our speculation, though: detached from the paper’s findings. The last decade has shored up serious headwinds that were beyond anyone’s control. COVID was a real external shock. In the years since, the world has been through a wider slowdown that no single country fully controls. None of this is good for an economy. But there are clearly choices involved. Consider the state gap. Why does UP pull ahead when Bihar — a culturally similar state that sits just next to it — cannot? Things like this signal that governance and state-level policy matter. As we recently covered, some of this ties back to India’s broken labour markets, where real wages for unskilled workers have gone nowhere for years, while people are sliding back into low-paid farm work because the factories never showed up. Many Indian economists — like Mody, or Ghatak and Kumar — have been making a similar point: that our headline GDP growth may overstate how good things really are on the ground. This new paper certainly gives them some firepower. There are other choices worth thinking about, though. Holding back the results of a survey is a choice. As was the lack of a bridge between our older consumption figures and the newest set. That, more than any single figure, is the real story. You might have whatever beliefs you do politically. You might completely disagree with their reading on India’s poverty rate. But it is unfortunate that a country of India’s stature doesn’t have detailed numbers on its own consumption, or that researchers have to reverse-engineer it with clever statistics out of a university in Amsterdam. That is a failure of our statistical plumbing. Good official statistics are infrastructure, as basic as roads or electricity. When they break down, everyone argues past each other with their own favourite numbers, while the truth dies a lonely death in between. [1] Govt to unveil coal-based urea policy within a month The government is finalising a new policy to promote coal gasification-based urea production, aiming to reduce dependence on imported natural gas and improve fertiliser self-sufficiency. The move has gained urgency amid global energy supply concerns and growing industry interest in coal-based urea projects. Source:BusinessLine [2] IBC has helped creditors recover over ₹4 lakh crore Creditors have realised more than ₹4 lakh crore through resolutions under the Insolvency and Bankruptcy Code (IBC), while over 30,000 cases involving ₹14 lakh crore were settled even before formal admission. The government says the IBC has significantly improved creditor-debtor discipline and accelerated resolution of stressed assets. Source:BusinessLine [3] West Asia conflict could slow growth and lift inflation: SBI Chairman SBI Chairman C.S. Setty warned that the ongoing West Asia conflict could weaken global growth, raise energy prices, and push up inflation. While India remains relatively resilient due to strong domestic demand and public investment, prolonged disruptions could weigh on FY27 growth and inflation. Source: Economic Times – This edition of the newsletter was written by Pranav and Bhuvan. Our team at Markets is always reading, often much more than what might be considered healthy. So, we thought it would be nice to have an outlet to put out what we’re reading that isn’t part of our normal cycle of content. So we’re kickstarting “What We’re Reading”, where every weekend, our team outlines the interesting things we’ve read in the past week. This will include articles and even books that really gave us food for thought. In India, the more educated you are, the more likely you are to be unemployed. Graduate unemployment among the youth sits at 40%. For those with no education, it’s 3%. We recently spoke to Rosa Abraham and Dr. Tamoghna Halder, two of the authors behind the Azim Premji University’s State of Working India 2026 report, to understand why. Our conversation goes into what’s really driving this paradox — the role of caste and social signalling in education choices, whether waiting for a good job is rational, why the “missing middle” of Indian firms matters, and what the demographic dividend window really means for policy. Do give it a listen! 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Australian solar manufacturer Tindo was first established in 2011 before opening its current factory in Adelaide in 2022, by which time it had already produced 284,000 solar panels for both domestic use and export sales. Its current solar manufacturing plant is situated in the heart of Technology Park, a 65-hectare site in the northern part of Adelaide. The company’s CEO, Richard Petterson, told pv magazine the factory’s current production line combines machinery, robotics, artificial intelligence and human expertise to produce solar panels. He added that as Australia’s lone solar manufacturer, the business is focused on staying current with global technology innovations while ensuring its own capacity to improve and innovate. “Tindo resources an internal design and engineering function which allows the company to remain at the forefront of technology developments and respond to users’ feedback,” he said. “To do this, we promote specific skills in management, production, finance, sales and marketing, distribution, installation and servicing.” Tindo’s product portfolio of Australian-made solar panels features products tailored to both households and businesses, with all panels designed to withstand the extremes of Australia’s climate. The panels are powered by N-Type TOPCon technology integrated into laser-cut 16 busbar bifacial cells. Its product range includes the Tindo Walara Series, a ninth generation of solar modules available in 440 W and a 475 W black panel option that uses black busbar technology. All of Tindo’s solar panels come with a 25-year warranty. According to data on the company’s website, Tindo solar panels fail once in every 200,000, compared to a global average of once in every 1,000. “Every Tindo panel is engineered, manufactured and tested in Adelaide using top quality componentry, and processes validated against Australian conditions,” Petterson said. “This approach delivers extraordinary field performance and durability that continues to set the benchmark for locally installed solar modules.” The company undertakes a zero-defect manufacturing process that guarantees each panel undergoes testing at seven predetermined quality checkpoints. Petterson added that the company also develops its own sealing technology, allowing the panels to perform excellently in humid and salt-mist environments, making them popular exports to the Southeast Asian and South Pacific markets. “Tindo panels are exported to Vietnam. They also power landing stations in the East Micronesia Cable (ESM) project in Nauru, and Tarawa in Kiribati,” Petterson said. “The ESM is a 2,250 km undersea data cable that links Tarawa in Kiribati to Nauru and to Kosrae and Pohnpei in the Federated States of Micronesia. Pohnpei already has an international data connection. Tindo panels will be used at the Pacific Technical and Further Education (TAFE) school in Suva, Fiji, which is training people of the Pacific in solar-battery installations.” Last year, Tindo was granted a $34.5 million (USD 24.5 million) assistance package from the Australian Renewable Energy Agency (ARENA), allowing the company to expand its production facilities and workforce towards an increased capacity of 180 MW. Petterson said Tindo is now readying and expanding the facility to produce even higher-rated solar panels that will be powered by upsized 210R cells. ARENA’s assistance package is also funding a feasibility study for a planned Gigafactory, a greenfield facility capable of producing up to 1 GW of high quality Australian-made panels per year that would employ more than 200 people. “This level of output will make Tindo a serious part of the Australian energy transition,” Petterson said. Previous articles in pv magazine‘s new series on solar manufacturing facilities around the world covered SoliTek’s fully-automated line in Lithuania, United Solar’s polysilicon factory in Oman, Belga Solar’s module production facility in Belgium and Midsummer’s CIGS factory in Italy. Comments Please login to comment Wednesday, June 10, 2026 3:00 pm – 4:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 9, 2026 11:00 am – 12:00 pm CEST, Berlin, Paris, Madrid Thursday, June 11, 2026 5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid Monday, June 1, 2026 5:30 pm – 6:30 pm CEST, Berlin, Madrid, Paris Tuesday, June 16, 2026 6 am – 7:00 am CEST, Berlin Friday, June 12, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid The new pv magazine Global May issue is now available! Mountains to climb Available in print and digital formats. Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects. April 01 – August 31, 2026 Energy-hungry data centers open new doors for solar and storage. Available in print and digital formats.
New user? Register Now! Industrial Manufacturing T1 Energy said recently one of the largest U.S. projects for manufacturing solar cells, the major component of solar modules (panels), is on track to begin construction by the end of the year
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The Victorian government in Australia has formally declared five onshore renewable energy zones (REZs) and a dedicated shoreline zone for offshore wind infrastructure. In doing so, this completes a process that began with the release of the draft Victorian Transmission Plan in May 2025 and involved more than two years of community consultation across the state. Get Premium Subscription The order sets the geographic boundaries and indicative transmission hosting capacities for each zone. It requires that solar, wind, and battery energy storage system (BESS) developers meet government expectations for community engagement and deliver social value and economic benefits. The five onshore zones, South West, Central Highlands, Gippsland, Western and North West, span regional Victoria from the state’s south-west pastoral country to the Latrobe Valley in Gippsland. The Gippsland region will host both the Gippsland REZ, located between Morwell and Sale, and the Gippsland Shoreline REZ, which will house the underground cables connecting Australia’s first offshore wind power plants to the main grid. VicGrid chief executive Alistair Parker said the declarations would improve coordination of solar, wind and battery projects while reducing the need for unnecessary transmission infrastructure. “Victoria’s coal-fired power stations are due to close over the next 10 years, and a significant amount of new renewable energy development is needed to make sure we can meet increasing demand for electricity,” Parker said. “Renewable energy zones will ensure better coordination of projects and signal to communities and industry where the development of renewable energy should occur.” The declared zones are the product of a framework that has evolved considerably since its earliest drafts. When the original Victorian Transmission Plan was published in May 2025, it proposed seven REZs to help achieve the state’s target of 2.7GW of utility-scale solar PV generation by 2040, alongside 5.8GW of new onshore wind and 9GW of offshore wind, with the zones collectively covering around 7% of Victoria’s land area. Following public consultation, the plan was revised: an updated transmission plan released in August 2025 added 200,000 hectares of designated renewable energy area, expanding the total footprint to 1.88 million hectares across nine distinct zones, up from seven in the original draft, as the government incorporated industry and community feedback. The final declared zones differ from both earlier drafts. The most recent round of consultation led to the refinement of zone boundaries and the removal of some areas, including a section of the South West zone, which was set aside in recognition that more work was required on its suitability. The northern section of the South West zone, known locally as the Dundas Tablelands, was excluded due to concerns about environmental and biodiversity values and land-use constraints. Changes were also made to the Central Highlands REZ, which covers areas to the west and south of Ballarat, with an area at the northern end removed and a small section added in the south. One proposed zone, the Central North REZ, which had been proposed in two sections covering areas between Bendigo and Tatura and between Shepparton and Glenrowan, was not declared and will be subject to a further formal consultation round before any decision is made. The declarations arrive as Victoria pushes toward legislated renewable energy targets of 65% by 2030 and 95% by 2035. The REZ framework is designed to coordinate private development within designated areas rather than to fund or build generation capacity directly. Each declaration sets a hosting capacity, the volume of renewable energy generation the planned transmission network within that zone can support, and establishes conditions around community engagement, First Nations consultation and the delivery of local economic benefits. Only a small proportion of the land within each zone will be needed for renewable energy development, with much less than 1% of Victoria’s total land area required for projects. Concurrent with the REZ declarations, VicGrid released for consultation the draft 2026 Victorian Transmission Plan Guidelines, which will inform the development of the next full transmission plan in 2027. That document will take a 25-year view of the state’s transmission and generation needs, covering a period that will see the closure of all remaining coal-fired power stations and the full build-out of Victoria’s offshore wind industry off the Gippsland coast.
0 Powered by : Solar Energy Corporation of India Limited, an India-based government enterprise, has reported its FY2026 audited financial results. Q4 revenue from operations was INR 4,623.40 Crore (~ $508.57 million), compared with INR 4,226.80 Crore (~ $464.95 million) in Q4 FY2025. Full-year consolidated revenue from operations was INR 18,446.80 Crore (~ $2,029.15 million), compared with INR 15,185.10 Crore (~ $1,670.36 million) in FY2025. Profit before tax rose to INR 915.22 Crore (~ $100.67 million) in FY2026, compared with INR 784.73 Crore (~ $86.32 million) in FY2025. Profit after tax was INR 721.75 Crore (~ $79.39 million), compared with INR 614.70 Crore (~ $67.62 million) in the previous financial year. SECI’s consolidated total assets rose to INR 10,094.98 Crore (~ $1,110.45 million) from INR 7,976.60 Crore (~ $877.43 million). The consolidated results included six joint ventures, including Andhra Pradesh Solar Power Corporation and Rewa Ultra Mega Solar. SECI also reported INR 600 Crore (~ $66.00 million) raised through non-convertible debentures, with no deviation in fund use.
Science and Technology One of the world’s most promising solar technologies has just proven it can work outside the laboratory. Researchers from China managed to combine perovskite, a material with high efficiency in converting light into electricity, with a silicon base already established in the photovoltaic industry, achieving 33% efficiency in a cell with an active area of about one square centimeter. The most significant advancement among next-generation solar technologies is not just efficiency, but durability: after a thousand hours of continuous operation, the cell maintained approximately 90% of its original performance, overcoming the fragility that until now prevented perovskite from commercially competing with conventional silicon. The technical obstacle that the researchers solved was known in the industry. The surface of industrial silicon presents pyramidal microstructures that hinder the uniform deposition of the perovskite layer and cause localized electrical leaks. Ye Jichun, one of the study’s authors, stated that “this strategy is simple and compatible with existing industrial production lines,” which brings multijunction perovskite and silicon solar technologies closer to real commercial applications. Perovskite is one of the most efficient materials for converting sunlight into electricity, but its main weakness has always been durability. Pure perovskite cells degrade rapidly when exposed to moisture, heat, and ultraviolet light, losing efficiency in weeks or months. Conventional silicon, on the other hand, lasts for decades, but its theoretical efficiency is already approaching the limit, which has led researchers around the world to seek combinations of the two materials as the most promising path among solar technologies. From a block of ice about six million years old extracted in Antarctica, scientists managed to capture bubbles with the oldest air ever recovered on Earth. Hubble Telescope captures irregular dwarf galaxy 23 million light-years from Earth using red giants as “standard candles” to map the invisible gravitational force of the universe. Flying cars become a reality: Eve’s prototype completes 59 successful flights, tests automatic landing, secondary fly-by-wire system, and exceeds battery expectations before the dangerous transition to horizontal flight scheduled for 2026. With only 300 kg and military-grade precision radar, the first Chinese satellite exclusively for engineering enters orbit to track dams and predict structural collapses 24 hours a day. The hybrid architecture places a layer of perovskite over a silicon base, taking advantage of the best of each material: perovskite captures light bands that silicon does not absorb well, and silicon provides structural stability. The problem was the uneven surface of industrial silicon: the micropyramids, designed to increase light absorption, created points where the perovskite did not adhere evenly, causing electrical leaks that reduced the efficiency of hybrid solar technologies. The Chinese team applied a thin layer of aluminum oxide only on top of the silicon micropyramids. This coating acts as an electrical insulator and blocks leakage points without significantly altering the device’s structure, allowing the perovskite to deposit more evenly and the cell to operate without localized current losses. The simplicity of the solution is what makes it relevant for commercial solar technologies. Adding a layer of aluminum oxide is a process compatible with existing industrial production lines, which means that factories already producing silicon cells could incorporate the technique without overhauling their equipment. For the photovoltaic industry, this compatibility is as important as efficiency because it determines whether an innovation can move from the lab to the rooftop. According to information released by Revista Fórum, the cell developed by the Chinese team achieved 33% energy conversion efficiency in an active area of approximately one square centimeter. For context, commercial pure silicon solar panels typically operate between 20% and 24% efficiency, meaning hybrid solar technologies can generate up to 50% more electricity with the same panel area. The durability of 90% performance after a thousand hours is the data that differentiates this result from previous announcements about perovskite. A thousand hours is equivalent to about 42 days of continuous operation, a period that, although far from the 25-year warranty of silicon panels, represents a considerable advance for a material that in previous versions lost performance in a few hundred hours. The trajectory of perovskite solar technologies shows that the gap between the lab and the market is narrowing with each study. Scale is the next challenge. The tested cell is one square centimeter, and translating this efficiency to square meter panels requires solving problems of uniformity, encapsulation, and mass production. The photovoltaic industry needs hybrid solar technologies to achieve a durability of at least 20 years to compete with conventional silicon in cost per kilowatt-hour over the lifespan. Even so, the combination of 33% efficiency with 90% retention after a thousand hours places the Chinese study among the most relevant results of new generation solar technologies published so far. The 90% mark is particularly significant for a technology that a few years ago lost half of its yield in days. If perovskite maintains this trajectory of advancement in efficiency and durability, the solar panels of the future may generate significantly more energy in the same space, changing the economics of photovoltaic generation worldwide. Did you know that a Chinese perovskite and silicon solar cell already converts 33% of light into energy? Do you think this technology will replace current solar panels or will conventional silicon still last for decades? Tell us in the comments.
Home solar Home batteries EV charging Heat pumps Smart home For your business Are solar panels worth it? Our company Our work Work with us EnergySage Releases 22nd Home Electrification Marketplace Report Home solar How much do solar panels cost? Updated May 22, 2026 You'll pay an average of $29,389 to install a 13.5 kilowatt (kW) solar panel system in San Antonio, TX before any available incentives.
Solar panels typically last 25-30 years, generating free electricity and protecting you from rising utility rates for decades.
Solar panels have been installed at Leighton Hospital with the aim of saving almost £10,000 a year. Mid Cheshire Hospitals NHS Foundation Trust is saving money through a scheme by Great British Energy. The NHS is the single biggest public sector energy user, with an annual energy bill of around £1.3 billion that has almost doubled since 2019. Last year, MCHFT was awarded £32,612 to install solar panels at Leighton Hospital to generate clean power and deliver reductions in energy costs. The 92 roof-top solar panels were installed last month and are now live. They will generate an estimated 34,000kWh of electricity a year for the site – equivalent of powering more than 12 average UK homes for a year. It is expected to save Mid Cheshire Hospitals NHS Foundation Trust around £9,500 annually, while also supporting the Trust’s Green Plan. Russ Favager, Board Senior Responsible Officer for Healthier Futures & Estates Redevelopment, said: “I’m very proud of everyone at the Trust who delivered this project, from initial application development and submission, right through to installation and commissioning. “Reducing our energy costs will help to support our long-term financial plans as we continue to provide the best possible standards of healthcare.” Chris Gormley, Chief Sustainability Officer, NHS England said: “As Great British Energy marks its first year, it’s fantastic that 162 NHS sites, including Leighton Hospital, have completed their solar installations. “This represents important progress in expanding solar generation across the NHS because every pound saved on energy bills is a pound that can go back into patient care. These solar panels are helping trusts across the country do exactly that. “Together with Great British Energy, we’re building an NHS that is greener, more sustainable and better placed to serve patients for years to come.” Great British Energy’s CEO Dan McGrail said: “One year on, Great British Energy is delivering what it was designed to do – backing clean power, supporting jobs, and helping to build an energy system that is fit for the future. “The second year is about turning momentum into legacy, setting the pathway so that every citizen can feel the benefit of public ownership with purpose.” Your email address will not be published.Required fields are marked * By using this form you agree with the storage and handling of your data by this website, to learn more please read our privacy policy.
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Adani Solar has become the only Indian company to feature in Wood Mackenzie’s Global Solar Module Manufacturer Ranking for the first half of 2025, highlighting India’s growing presence in clean energy manufacturing. The Adani New Industries arm was ranked eighth globally and received a Grade A classification, with a score of 81, based on shipments, bankability and performance.
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An official website of the United States government Here’s how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites. Funding Opportunities Strong federal and state support, declining system costs, and growing public and private sector demand for solar energy have all contributed to a rapid increase in solar deployment over the past decade. In order to support this growth, the solar workforce will need to grow significantly with jobs that are available to workers from all backgrounds, provide competitive wages and benefits, and offer opportunities for union membership. Solar workforce research and development at the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) supports efforts to prepare and sustain this skilled solar energy workforce. Workforce development initiatives funded by SETO include online and in-person training and education programs, work-based learning opportunities such as internships and apprenticeships, collegiate competitions, certification programs, and support services such as career counseling, mentorship, and job readiness. Program performance metrics are analyzed to assess the impact of different workforce strategies and partnerships, which can then inform target metrics and intended outcomes for future programming. Additionally, SETO solicits stakeholder feedback and analyzes industry growth and workforce trends in order to make informed decisions related to the solar workforce. Through these efforts, SETO supports a solar energy workforce ecosystem that provides affordable learning opportunities and pathways to stable careers and economic prosperity. A well-trained workforce supports solar energy that will safely and reliably generate power now and for decades to come. Learn more about SETO’s goals. Each SETO research topic areas targets an essential segment of the solar energy workforce and its unique training and skills requirements. Just as solar installation professionals must be trained to properly design, install, and maintain solar energy systems, power systems engineers must be trained to successfully integrate these new distributed resources into the grid and drive innovation. Additionally, many related professionals, such as architects and code enforcement officials, play a key role in easing solar adoption and ensuring system safety and integrity. SETO has supported a variety of workforce initiatives to assess and address the needs of a growing clean energy economy: Reports resulting from research projects can be found on the Office of Science and Technical Information (OSTI) website. Committed to Restoring America’s Energy Dominance. Follow Us
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. Cumulative solar PV capacity in the U.S. 2000-2024 In 2024, the United States’ cumulative solar photovoltaic (PV) capacity amounted to gigawatts. This was the peak from the period in consideration, and an increase of nearly gigawatts compared to 2010. Feel free to contact us anytime. We will respond to your inquiry as quickly as possible.
We may receive a commission on purchases made from links. A lot of gadgets proudly claim they’re eco-friendly or sustainably made, but that can often be as trivial as being made from a small percentage of recycled plastic. But is any of that actually sustainable, or is it just marketing noise to slap a green sticker on yet another disposable bit of tech? What does sustainable actually mean, anyway? For us, it means gadgets that are easily repaired, perhaps modular — that’s a great way of keeping your annual upgrades from becoming e-waste. Or, it can be gadgets that help you save energy, water, food, or power. Those have a measurable impact on your life and can actually save you money. So here’s our pick of gadgets that are sustainable — and actually work. But we’ll also mention that the most sustainable option is the one you already have: If it ain’t broke, don’t fix it, and consider the long-term environmental impact of the gadgets you buy. Prices may vary. The average laptop lasts around four years, per Business Insider. While the choice of OS certainly contributes to the stark difference (try these tips to speed up an aging PC), component quality, repairability and potential upgrades are also key. Even batteries are becoming difficult to replace now — never mind a new motherboard. Framework’s mission is to fix the broken consumer electronics market with truly modular laptops, which is absolutely a sentiment we can get behind. A key feature of the Framework laptop (starting at $1,249) is the expansion card slot. The Framework 16 features six slots to house anything from USB ports to an SD card slot or Ethernet port — or even additional storage drives. Finally: all the ports you actually want, and none that you don’t (goodbye dongle hell). Just because it’s modular doesn’t mean it’s underpowered, though, as users can opt to include either an NVidia RTX5070 or AMD Radeon RX7700S GPU for serious gaming performance. While the dream of the truly-modular phone is dead, the Fairphone 6 is about as close as you’ll get to one that’s ethically manufactured and easily repaired: iFixit awarded it 10/10 for repairability. With a five-year warranty and eight years of software support, the Fairphone 6 doesn’t feature modular upgrades, but it does have classic features like expandable microSD storage and a battery that can be swapped in two minutes; even the USB-C port can be replaced easily. European users can opt for either a standard Android experience or a de-Googled version of Android called e/OS, though expect some compatibility issues with apps that rely on Google Play services. Curiously, only the de-Googled version of the Fairphone 6 can be purchased directly from Murena in the U.S. ($749), though you can flash the standard Android version on if needed. Honestly, you had us at “replaceable battery” (but there other smartphone features that we want back). Figuring out exactly what’s using power and when can help you save money by identifying the energy hogs and unusual activity. The Refoss system is favored by smart home enthusiasts because of its local operation with no reliance on the cloud, as well as the open API for integration with Home Assistant. The current budget friendly $169.98 price of the Refoss Home Energy Monitor is appreciated, too, and it has an equally user-friendly app for those who don’t want to delve into complex DIY smart home systems. While there have been some attempts at whole home monitoring with AI identification from a single sensor, none of have been particularly reliable; Sense stopped sales in December of last year. The most reliable systems, like the Refoss, use individual circuit CT clamps (the package includes two main circuit sensors up to 200A, and 16 branch sensors for up to 60A), so installation can be a little more involved and will require a professional. Just want to keep an eye on a few appliances, or add smart controls to something you suspect might be drawing a lot of power? Use a smart plug with energy monitoring built-in to figure out exactly how much they’re using and when, with cost estimates once you’ve added your power pricing. You’ll be able automate your appliances’ power schedules, too, perhaps turning them on only when you have surplus solar or power is cheap. The smart plug itself draws a tiny amount of power compared to the cost savings you’ll make by turning devices off. At around $30 for a pack of four, the TP-Link Tapo P110M is an inexpensive and unobtrusive smart plug with full energy monitoring that runs over your regular home Wi-Fi. Thanks to Matter support, you’ll find it works with all the major smart home platforms, too, ensuring you’re not locked into any one ecosystem. And if you’re wondering, “what is Matter?”, we got you. Portable batteries can be handy, but the small ones will only recharge your phone a couple of times at best, and anything larger can be impractical most of the time. If you get lots of sun, a portable solar panel with direct USB charging can be a lot more sustainable than a battery. The average lifespan of a solar panel is measured in decades, not years. The key specification to look for is built-in USB-C PD charging; most panels are only designed to charge a battery (which has it’s own voltage conversion circuitry) — not a phone. Aim for at least 100-watt panel to consistently get 50-watts or more to charge your devices. The Mesuvida 100W portable solar charger ($86.99 on Amazon) is a well-reviewed option, currently sitting at 4.4 stars from 331 reviews. Avoid the smaller panels rated 50 watts or less; they will be unable to do anything other than trickle charge. If you have room for something bigger, the Vevor 200W Foldable Solar Panel ($132.90 on Amazon) also does direct USB charging. Also avoid “solar powered battery banks” — they’re a gimmick, and the panels don’t generate enough to actually charge the battery. For those renting or who simply can’t afford to deck their roof out in a traditional solar array, new balcony solar systems could be the answer. These grid-connected “micro-inverters” plug directly into a household socket and allow you to feed in around 800 watts of smaller panels, which can be easily mounted to a balcony without permanent fixings — or used with portable panels. There’s no complex installation, no permission needed from the grid operators, and it immediately reduces your household consumption, offering a three-to-four year payback period depending on your electric pricing. Full solar installations can often take a decade for payback. The Ecoflow Stream — $299 direct from Ecoflow — can even be connected to a battery to store excess solar in the daytime and discharge it at night. There’s only one snag: It’s a fairly new concept that’s currently only approved in the U.S. for the state of Utah. Europe has approved them for a while now, and the U.K. is following suit. But it’s one to keep an eye on in your locality. Any time we see a pile of disposable dollar tree batteries, we die a little inside. They’re cheap, but a wasteful use of resources that are rarely recycled (even if they technically can be). Eneloops — $28.99 for a pack of four with a charger — are our favorites. They’re good for 2,100 full recharge cycles, though there are a few quirks to be aware of when you use rechargeables, and some devices you should never use rechargeables in, like smoke alarms. Rechargable batteries have a slightly different voltage when fully charged: generally 1.2 volts versus 1.5 volts in disposables. That means if your device is calibrated to thinking 1.5 volts is full, it’ll likely show your rechargeables as being a depleted before you’ve even used them. But rechargeables tend to maintain their voltage for a longer time before dropping precipitously. So even when it says low, you probably still have a good bit of life left in them before you need to recharge. You can save even more by buying Amazon Basics, which are technically made by the same company as Eneloop, but testing shows slightly worse performance compared to the premium brand, so keep that in mind. Once you have a charger, you can expect to pay around $3 per battery when buying in multipacks; for something that can be reused over 2,000 times, that’s both sustainable and a bargain. A smart thermostat is a relatively low cost and simple DIY install gadget that can have a high impact on household energy consumption for those who don’t generally micromanage their climate control. If you already adjusting the dial every hour, it probably wont’ save you money, but it will make your life easier. In our review of Google’s fourth generation Nest Learning Thermostat, we called it the best smart thermostat for ease of use, aesthetics, and Matter compatibility. The fourth generation Nest Learning Thermostat is available from $129.99 on Amazon in four colors to suit your home. For European buyers with less complex home heating needs, individual radiator control using smart TRVs such as the Tado X system (£179.99 for a three-pack) can be a better option. Rather than setting a temperature for your whole home, they allow you to control and automate at a room level, bypassing radiators when not occupied. Here’s a truly horrific and unsustainable fact for you: The average U.S. household throws away 30% to 40% of all the food it buys, per the American Journal of Agricultural Economics. Some of that is leftovers, but a large proportion is never touched because it’s past its best buy or been left in the fridge to rot. Vitesy is on a mission to change that with the Shelfy gadget ($144.99 on Amazon), which keeps fresh fruit and vegetables in the refrigerator fresher for up to twice as long. It does this by filtering out harmful bacteria and ethylene gases to slow down the decomposition process, using a washable photocatalytic filter and blue light. This also reduces odors without the use of ozone. The only downside is that it’s yet another thing to keep charged; the battery should last three weeks on eco mode, though users report them lasting only a few days when running on full performance mode. Naturally, this elaborate deodorizer also connects to your home Wi-Fi so it can report back temperature and number of openings, giving you an overall refrigerator health score. It also lets you know when you should clean (but don’t worry, you can disable that if you want). What could be more sustainable than replacing a short car trip with a bike ride? If you’re riding for pleasure, you have a huge choice of e-bikes to suit every budget, rider, and terrain. But if you want something to actually replace car trips, your choices are far more limited and prices are significantly higher. You’ll need something that carry the groceries and perhaps a child or two and an adaptable accessory system that can be swapped around. The Aventon Abound LR (Long Rack) retails at $1,999 and can carry up to 143 pounds of cargo at the rear and 440 pounds overall with a powerful 750-watt motor. One highlight is the connected app with security features like an electronic kickstand lock and GPS, which allows you to render it inoperable if it leaves a particular area. Bicycling put the Aventon Abound LR through 500 miles of testing and found that despite the budget price point, it didn’t feel cheap and outperformed pricier rivals. Of course, the most sustainable choice is to not buy anything new at all. Buying used or refurbished gadgets gives a second life to something already made and keeps it out of the growing pile of e-waste that’s polluting the planet. However, there are some things you should probably never buy used. But be wary of sites like eBay or Facebook Marketplace, which can be rife with scammers. Remember if something seems too good to be true, it probably is. If you must, be sure to ask these things before buying refurbished. Instead, we’d recommend going direct to manufacturers for refurbished products that are often as good as new. Amazon’s Renewed storefront is is a certified refurbished program with discounts of up to 50%. Products are sold by third parties, but Amazon support is generally great if you do get a dud. Apple also sells “good as new” in its refurbished storefront, with savings of up to 15%.
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 Reportsvolume 16, Article number: 15094 (2026) Cite this article 1411 Accesses Metrics details This study presents the design and performance evaluation of a bidirectional electric vehicle charging system integrating solar photovoltaic energy with an Artificial Neural Network based control strategy. The proposed architecture employs a modified Single-Ended Primary Inductor Converter capable of supporting both Grid-to-Vehicle and Vehicle-to-Home operating modes while maintaining stable bidirectional power flow between the grid, photovoltaic source, and EV battery. The ANN controller dynamically regulates the duty cycle of the MOSFET switches using battery current feedback and reference current signals, enabling adaptive control under varying solar irradiance and grid conditions. Simulation results indicate that the proposed system achieves charging efficiencies above 90% while maintaining stable operation for both 72 V and 240 V EV battery configurations. Compared with conventional proportional–integral control approaches, the ANN controller demonstrates faster transient response and improved current regulation during dynamic operating conditions. The integration of solar photovoltaic energy further reduces reliance on grid power and enhances renewable energy utilization in EV charging infrastructure. These results indicate that the proposed ANN-controlled bidirectional charging system provides an efficient and flexible solution for renewable-integrated EV charging applications. From the recent literature review it is understood that significant progress in integrating Artificial Neural Networks (ANNs) with electric vehicle (EV) charging systems are used to improve efficiency, optimize power flow, and enhance sustainability. ANN-based controllers have consistently performed better as compared to conventional proportional-integral (PI) controllers in hybrid energy systems by regulating power distribution between batteries and ultra-capacitors1. Also, the Bidirectional chargers equipped with ANN control have shown improved grid voltage regulation during charging and discharging operations2. In addition to that, integrating photovoltaic (PV) systems with bidirectional chargers enables simultaneous PV energy conversion and battery charging/discharging while considering irradiance fluctuations and battery characteristics3. Energy management systems such as ANN-based Energy Management Systems (EMS) further strengthen such systems by reducing grid energy consumption by up to 28% in PV-powered charging stations with battery backup and V2G support4. All these developments lead to collectively highlighting the potential importance of ANN-driven control for achieving efficient, adaptive, and sustainable EV charging. Rajalakshmi and Nisha5 has studied the importance of role of ANNs into broader smart grid applications. Also, ANNs support intelligent control of energy storage systems, enabling precise, real-time regulation of charging and discharging cycles in microgrid environments. The ANNs ability to respond adaptively to intermittent renewable resources, particularly solar and wind-makes them valuable for maintaining system stability under highly variable conditions. ANN-controlled bidirectional chargers also contribute to grid resilience by providing reactive power support during peak demand periods and by reducing total harmonic distortion (THD), thereby improving overall power quality6. These ANNs systems have also been successful in integrating with dual-stage converters in standalone PV systems to maximize power output and compensate for irradiance fluctuations, enhancing conversion efficiency and ensuring reliable operation7,8. These above studies clearly demonstrate the benefits of ANN-based controllers and highlight challenges related to ANN training complexity, data requirements, and real-time implementation constraints. Apart from controller advancements, research in EV charging technologies emphasizes the need for rapid, grid-resilient infrastructure. In this context a comprehensive review by Sandeep and Anita9 examined the power quality and stability challenges associated with EV charging systems integrated with grid and photovoltaic energy sources. Their study highlighted that maintaining stable voltage and power quality is a critical requirement for large-scale EV adoption, particularly when renewable energy sources introduce variability in the charging infrastructure. The authors emphasized that continued research is required to develop improved control strategies capable of maintaining stable system operation under dynamic grid and generation conditions. These observations underline the need for intelligent control mechanisms in EV charging systems. In this context, the present study proposes an ANN-based control strategy for a bidirectional EV charger to enhance system stability, improve charging performance, and support effective integration of solar PV energy within the charging infrastructure10. The recent evolution of EV infrastructure, including standardized connectors and intelligent control strategies, is essential for improving user experience and charging performance11. Bidirectional V2G systems further enhance grid stability by enabling EVs to supply energy back to the grid, balancing supply-demand mismatches and supporting renewable energy penetration12. Along with these advanced control strategies such as model predictive control and adaptive virtual synchronous generator control will ensure stable and seamless bidirectional energy transfer13. Despite these technologies, issues of cost, interoperability, and grid stability continue to present major barriers that future research must address. Beyond EV-specific applications, ANNs have been widely implemented across multiple energy sectors to optimize system performance and reduce energy consumption. Their ability to process large datasets and learn non-linear patterns enables accurate prediction of energy demand influenced by environmental conditions, occupancy, and operational behavior14. Case-study evidence as shown by Martina et al.15 shows substantial savings when neural networks are used for machine-level optimization, such as a 50,000-kWh annual reduction achieved in a plastic manufacturing SME. The study by Sanjeeb et al.16 using functional optimization using Neural Networks (FONN) approach further demonstrates the potential of ANNs in minimizing energy functional at discrete interaction points, improving efficiency across complex engineering processes. ANNs have also been effectively used to model converter efficiencies in telecom networks17 and to improve building energy management using optimized feed-forward architectures18. The integration of Solar PV-EV in sustainable transportation represents an important pathway and solar-powered EV charging reduces energy costs19,20, mitigates voltage drops during peak charging21, and decreases greenhouse gas emissions by reducing dependence on fossil fuels22. Building-integrated PV (BIPV) models additionally enable structural energy generation, improving space utilization and promoting energy circularity19. Also, Advanced optimization algorithms further enhance PV-battery coordination, reducing grid stress and maximizing renewable utilization22. But challenges in widespread implementation include high installation costs, infrastructure limitations and variability in irradiance. Sandeep and anita23 have reviewed power quality and stability of EV charging with grid and PV solar and considered it is essential for widespread adoption since these systems have been improved based on generation and demands of the power grid. They concluded that continued research and development is necessary to upgrade the power quality. At the grid level, bidirectional power flow introduces both challenges and opportunities. Increased EV penetration can destabilize power systems, especially in hybrid microgrids where AC-DC interactions may trigger frequency deviations and negative capacitance effects22,24. Microgrids capable of independent operation offer enhanced resilience, especially during outages25. Load modelling research further reveals that traditional constant power models inaccurately represent EV charging behavior, while ZIP models incorporating state-of-charge-dependent voltage characteristics provide more realistic assessments of system losses and voltage profiles26. Sandeep and anita27 have studied optimized energy management in grid connected solar PV battery for enhancing the stability and power quality. They used the honey badger optimization algorithm for energy management in battery connected solar PV. They implement this proposed work in MATLAB/Simulink tool, and the results are evaluated. They concluded that the proposed method improved the power quality for voltage and current respectively. Indrajit and Provas28 have studied optimized design and hybrid photovoltaic (PV), wind turbine (WT) distributed generation system and battery energy storage system using dynamic arithmetic optimization algorithm (DAOA) which is commonly used in electrical engineering to solve optimization tasks. They concluded that DAOA is superior to several optimization methods. Sunanda et al.29 has studied power grid incorporating electric vehicles (EVs) to check its significant impact in safe and reliable operation. But if it applied to wind power then there is an unpredictable nature. So, they combined wind energy and solar energy as wind is always available as compared to solar energy. So, they concluded that renewable energy sources (RESs) perform better as compared to conventional systems. Sourav et al.30 has studied renewable energy sources (RESs) considering wind, solar photovoltaic (PV)and hydro to find the optimal design using hummingbird algorithm. They used the probabilistic optimal power flow (POPF) to address the inherent uncertainties related to power output of RESs. They concluded that POPF system has superior over sophisticated modern approaches. Chandan et al.31 has studied optimal power flow (OPF) of the combined heat and power economic dispatch (CHPED) problem integrated with renewable sources and using a new practical approach using chaotic oppositional sine cosine algorithm (SCA) (COSCA). They combined renewable energy sources such as wind-solar-EVs and integrated with the system for environmental sustainability. They concluded that the suggested COSCA algorithm has shown well established optimization techniques. Recent research highlights the increasing importance of integrating renewable energy resources and electric vehicles within modern power systems to enhance energy efficiency and grid stability. For example, studies on optimal power flow and economic dispatch frameworks demonstrate that combining wind, solar, and EV resources can reduce fuel costs, emissions, and transmission losses while improving voltage stability and system reliability through advanced optimization algorithms32. Similarly, optimization-based approaches such as whale optimization and other metaheuristic techniques have been applied to complex energy scheduling problems to improve convergence speed and solution quality in multi-objective power system optimization33. In addition, recent work on renewable-integrated grid systems emphasizes the role of intelligent optimization strategies in maximizing renewable energy utilization and managing the nonlinear constraints associated with economic load dispatch and grid operation34. The increasing penetration of renewable energy resources has become a key strategy for addressing global energy sustainability, reducing greenhouse gas emissions, and improving the resilience of modern power systems. Renewable technologies such as solar photovoltaic and wind energy provide clean and sustainable electricity generation, while their integration with advanced power electronic converters and intelligent control strategies enables improved energy efficiency and grid flexibility. Recent studies have shown that renewable-integrated power systems can enhance system stability, reduce dependency on fossil fuels, and support decentralized energy management within smart grids and electrified transportation infrastructures35,36,37,38,39. In particular, the integration of renewable energy with electric vehicle charging infrastructure has attracted increasing research attention because EVs can act not only as transportation devices but also as distributed energy resources that support grid balancing and renewable energy utilization. At the same time, battery technologies play a crucial role in enabling efficient energy storage, power balancing, and bidirectional energy exchange in renewable-integrated systems. Advances in battery thermal management, energy storage optimization, and lifecycle performance have significantly improved the operational reliability and energy density of modern electrochemical storage systems40,41,42,43. These developments highlight the importance of integrating renewable generation, intelligent control strategies, and advanced battery storage technologies in the design of next-generation EV charging architectures. Sustainability has become a central objective in modern energy systems due to the need to reduce carbon emissions, enhance energy efficiency, and support reliable integration of renewable energy resources. Recent studies highlight that intelligent management of distributed energy resources and smart grid technologies can significantly improve demand response, system reliability, and overall sustainability of power networks, particularly when renewable generation and electric mobility are integrated into grid operations. In this context, advanced optimization and control strategies are increasingly required to maintain stable power system operation while maximizing renewable energy utilization and reducing environmental impact. Artificial Neural Networks (ANNs) have emerged as powerful tools for such applications because they can learn nonlinear relationships, adapt to varying operating conditions, and provide accurate prediction and control capabilities in complex energy systems. Consequently, ANN-based approaches are widely used in power electronics, energy management, and intelligent control frameworks to enhance system performance, efficiency, and decision-making in renewable-integrated energy infrastructures44,45,46,47,48,49,50. Existing studies on EV charging systems have often examined intelligent control strategies and renewable energy integration as separate research streams (Refer Table 1). Several studies have applied Artificial Neural Network controllers to regulate charging current and enhance converter performance in EV charging systems. However, most of these implementations are designed primarily for grid-connected charging environments and do not explicitly prioritize renewable energy utilization. In parallel, photovoltaic-assisted EV charging systems have been proposed to reduce dependence on conventional grid electricity. Nevertheless, many of these systems rely on conventional control techniques such as proportional–integral controllers or rule-based energy management approaches, which may exhibit limited adaptability under rapidly changing solar irradiance and load conditions. Despite these advancements, only limited research has investigated the integration of intelligent control with renewable energy management within a unified bidirectional EV charging architecture. Many existing systems either focus on ANN-based control for battery charging processes or on photovoltaic-powered EV charging stations without incorporating adaptive control mechanisms for bidirectional power exchange. As a result, the interaction between renewable energy variability, EV battery dynamics, and bidirectional energy exchange with residential loads remains insufficiently addressed. Furthermore, several technical challenges remain in the development of efficient EV charging infrastructures. First, conventional EV charging systems frequently rely on proportional–integral control strategies that may not perform effectively under nonlinear and time-varying operating conditions introduced by solar photovoltaic generation and grid voltage fluctuations. Second, many charging architectures are designed for unidirectional energy transfer, limiting their ability to support advanced functionalities such as Vehicle-to-Home or Vehicle-to-Grid energy exchange. Third, the integration of renewable energy sources into EV charging systems is often constrained by the lack of intelligent control mechanisms capable of maintaining stable power flow and power quality under dynamic environmental conditions. These limitations highlight the need for improved control strategies and converter architectures that enhance system adaptability, efficiency, and renewable energy utilization. To address these research gaps, the present study makes the following contributions: A bidirectional EV charging architecture is developed that integrates solar photovoltaic generation with grid-connected charging infrastructure, enabling both Grid-to-Vehicle and Vehicle-to-Home operating modes. A modified SEPIC converter topology is introduced to facilitate stable bidirectional power transfer between the photovoltaic source, grid, and EV battery. An Artificial Neural Network based control strategy is proposed to regulate the duty cycle of the MOSFET switches, enabling adaptive control under varying solar irradiance and grid operating conditions. The proposed system supports multiple EV battery configurations, including 72 V and 240 V battery systems, demonstrating flexibility for different electric vehicle applications. The performance of the proposed architecture is evaluated through simulation analysis, demonstrating improved charging stability, efficient power transfer, and enhanced renewable energy utilization. The proposed system integrates a bidirectional battery charger for electric vehicles (EVs) with solar photovoltaic (PV) input, grid interaction, and an Artificial Neural Network (ANN)-based control scheme (refer Fig. 1). The architecture supports both Grid-to-Vehicle (G2V) and Vehicle-to-Home (V2H) modes and is composed of four major subsystems: EV Charging Unit: It enables controlled bidirectional energy flow between the EV battery, grid, and solar PV system in turn supports G2V and V2H functionality. Solar PV System: It generates renewable energy and supplies it to the charger during G2V operation, in turn helps to reduce reliance on the grid. Grid Interface: Supports power draw from the grid during low PV availability and enables controlled V2H power injection. ANN-Based Control Unit: Maintains charging and discharging operations by adjusting MOSFET duty cycles in real time based on battery current, reference current, and operating mode. A modified Single-Ended Primary Inductor Converter (SEPIC) forms the core power-processing stage, offering intrinsic bidirectional capability when configured with MOSFET switches. The system architecture of the ANN-controlled bi-directional EV charging system. The existing PI controllers perform satisfactorily for linear, steady-state operating conditions and require manual tuning and exhibit limited adaptability under various nonlinear and time-varying scenarios such as irradiance fluctuations, battery ageing, and grid disturbances. In contrast, the ANN: Adapts nonlinear relationships without explicit modelling. Adjusts control decisions dynamically. Ensures maintain stable current/voltage even under rapidly changing conditions. Helps to improve power quality by reducing waveform distortion. Enhances G2V and V2H performance through optimized duty-cycle generation. This adaptability makes ANN more suitable for real-time EV charging applications involving renewable energy and bidirectional operation. The solar PV subsystem used consists of four parallel strings, each with five series-connected modules. Irradiance (1000 W/m2) and cell temperature (25 °C) were used as standard test conditions for simulation. Real-time PV output was continuously monitored and supplied to the SEPIC converter. The ANN-managed controller prioritizes the use of solar energy during G2V operation and automatically switches to grid power during low irradiance, ensuring uninterrupted charger functionality. The solar PV subsystem consists of four parallel strings, each with five series-connected modules. Standard test conditions (irradiance of 1000 W/m2 and temperature of 25 °C) were used. Real-time PV output was supplied to the SEPIC converter. The ANN-managed controller prioritizes solar energy during G2V operation and automatically transitions to grid power during low irradiance, ensuring uninterrupted charger functionality. The modified SEPIC converter enables regulated and isolated bidirectional power flow. Important design features include: Bidirectionality: It is achieved by replacing the conventional SEPIC diode with an actively controlled MOSFET. Galvanic Isolation: It ensures using a high-frequency transformer for safety during grid interaction. Single-Stage Energy Conversion: Helps to reduce switching losses compared to dual-stage architecture. Wide Voltage Compatibility: It helps to support both 72 V (two-wheeler) and 240 V (four-wheeler) EV batteries. DCM Operation: Facilitates near-unity power factor and reduces harmonic content in the supply current. During G2V operation, the converter maintains constant battery current, and during V2H, it regulates the sinusoidal voltage at the point of common coupling. In a conventional SEPIC converter topology, a diode is typically used as the rectifying element to allow energy transfer from the input source to the output load. This configuration restricts power flow to a single direction and therefore cannot support bidirectional energy exchange. Such unidirectional operation is suitable for traditional DC–DC power conversion but is insufficient for EV charging systems that require both charging and discharging capabilities. To enable bidirectional power flow, the proposed system replaces the conventional diode with an actively controlled MOSFET switch. This modification allows the converter to operate in two modes depending on the switching strategy. During Grid-to-Vehicle operation, the converter functions similarly to a standard SEPIC converter, transferring energy from the grid or solar PV source to the EV battery. During Vehicle-to-Home operation, the switching sequence reverses the direction of energy transfer, allowing stored battery energy to supply the AC load through the inverter stage. This modification transforms the SEPIC converter into a bidirectional power processing unit capable of supporting both charging and discharging functions within the same converter structure. Compared with conventional unidirectional SEPIC designs, the proposed topology improves system flexibility and enables seamless integration of EV batteries as distributed energy storage resources within residential or grid-connected energy systems (refer Table 2). A high-gain DC-DC converter was employed to regulate voltage between the solar PV subsystem and the DC bus of the proposed EV energy management system. This converter helps to provide the required voltage boosting capability under different irradiance and load conditions, ensuring stable charging performance for both two-wheeler (72 V) and four-wheeler (240 V) battery configurations. This design includes inductive energy storage, active switching control, and intermediate capacitive buffering to achieve high step-up ratios while maintaining low ripple characteristics. The schematic of converter topology is presented in Fig. 2. Schematic representation of the high-gain DC-DC converter topology integrating an input source Vg, primary inductor L1, switching device S1, intermediate capacitor C1, secondary inductor L2, rectifying diode D, and output filter capacitor C2, supplying the regulated output voltage V0. The diagram illustrates the power transfer path and switching operation, where the controlled switching of S1 enables energy storage and release across the inductors and capacitors, achieving voltage boosting under varying load and input conditions. This converter configuration is suitable for electric vehicle charging systems and renewable energy interfaces requiring efficient, stable, and high step-up DC conversion. Two MOSFET switches manage directional power flow. A Pulse Width Modulation (PWM) generator produces high-frequency switching pulses whose duty cycle determines the charging or discharging rate. The ANN provides the real-time duty-cycle command to the PWM system, enabling: Fast transient response. Reduced switching losses. Optimal power transfer under variable load and grid conditions. The PWM subsystem receives the duty-cycle input generated by the ANN controller. The Simulink model shown in Fig. 3 illustrates the comparator-based PWM generation mechanism, where sinusoidal reference and high-frequency triangular waveforms produce the switching pulses supplied to the MOSFETs. The resulting PWM waveform behavior is shown in the combined scope traces. Simulink PWM generation model and corresponding switching waveforms. The left portion shows the Simulink block diagram used to generate the PWM control signals, including sinusoidal and carrier waveform inputs, comparators, and signal routing to the switching block. The right portion displays the resulting PWM pulses captured in Scope3, showing the modulation pattern and switching behavior over the sampled interval. This figure provides an integrated view of both the control logic and the generated gate pulses used for converter switching. The ANN serves as the primary controller for regulating G2V and V2H operations. The proposed EV charging architecture integrates three primary subsystems: the solar photovoltaic energy source, the bidirectional charging converter, and the grid interface. Effective coordination among these subsystems is essential for maintaining stable system operation and enabling seamless transitions between charging and discharging modes. The control framework therefore employs an ANN-based supervisory mechanism that continuously monitors system variables and determines the appropriate operating mode. During Grid-to-Vehicle operation, the EV battery is charged using energy supplied by the solar PV array and the utility grid. The control algorithm prioritizes the use of photovoltaic energy whenever available. When PV generation exceeds the charging demand, the ANN regulates the converter duty cycle to maintain the desired charging current while minimizing grid power consumption. If solar irradiance decreases and PV output becomes insufficient, the controller automatically increases grid contribution to maintain the target charging profile. This adaptive behaviour ensures uninterrupted charging while maximizing renewable energy utilization. Vehicle-to-Home operation is activated when the system detects a demand from the connected AC load and the EV battery state of charge remains above the minimum allowable threshold. In this mode, the bidirectional SEPIC converter reverses the direction of power flow and delivers energy from the battery to the load through the inverter stage. The ANN controller regulates discharge current by continuously comparing the measured battery current with the reference value, thereby maintaining stable output voltage at the load interface. The transition between operating modes is governed by a supervisory control logic based on system conditions such as battery state of charge, load demand, PV availability, and grid status. When the battery reaches the desired charging level or when external load demand increases, the control system shifts from G2V to V2H operation by adjusting switching signals and converter duty cycles. Conversely, when battery charge decreases below the predefined threshold or grid support becomes necessary, the system transitions back to G2V mode. This coordinated control strategy enables smooth and stable energy exchange among the PV system, EV battery, and grid infrastructure. Mode selection conditions. G2V mode activated when. Battery SOC below charging threshold. PV or grid energy available. V2H mode activated when. Residential load demand detected. Battery SOC above discharge threshold. Conventional proportional–integral controllers are commonly used in DC–DC converter applications because of their simple structure and straightforward implementation. However, their performance is highly dependent on fixed parameter tuning and accurate system modelling. In renewable-integrated EV charging systems, operating conditions frequently change due to variations in solar irradiance, grid voltage fluctuations, and dynamic battery characteristics. These nonlinear and time-varying conditions can reduce the effectiveness of PI controllers, resulting in slower dynamic response and reduced stability. Artificial Neural Network controllers provide an alternative approach capable of handling nonlinear system behaviour. ANN models can learn complex relationships between input variables and control actions during the training process, allowing the controller to adapt to varying operating conditions without requiring precise mathematical modelling of the system. In the proposed charging architecture, the ANN controller regulates the converter duty cycle based on real-time current measurements, enabling improved dynamic response and more stable operation during both Grid-to-Vehicle and Vehicle-to-Home modes. The Artificial Neural Network (ANN) controller dynamically regulates the switching duty cycle of the MOSFET devices in the bidirectional SEPIC converter based on real-time operating conditions. The ANN receives system measurements such as battery voltage, charging current, and reference current as input variables. During the training stage, the network learns the nonlinear relationship between these input parameters and the optimal duty cycle required to maintain the desired charging performance. During real-time operation, the trained ANN processes the instantaneous system measurements and generates an appropriate duty cycle command for the converter switches. This enables the controller to continuously adapt to variations in solar irradiance, grid voltage fluctuations, and changes in battery state of charge. As a result, the ANN-based controller provides adaptive current regulation and improved dynamic response compared with conventional proportional-integral controllers, which rely on fixed gain parameters and may exhibit reduced performance under nonlinear and time-varying operating conditions. The flowchart summarizes the closed-loop control procedure of the proposed ANN-controlled bidirectional EV charging system. Real-time measurements of battery current and the reference current are used to compute an error signal, which is processed by the ANN to generate the optimal MOSFET duty cycle and corresponding PWM signals. Based on the selected operating mode (G2V or V2H), the controller regulates charging or discharging current and continuously updates measurements to maintain stable bidirectional power flow (refer Fig. 4). Flowchart illustrates the operational procedure of the ANN-based control strategy used for regulating the bidirectional EV charging system during Grid-to-Vehicle and Vehicle-to-Home modes. Input Layer: Measured battery current (Ib). Reference current (Iref). Hidden Layers: Two hidden layers. 10 neurons each. Sigmoid activation functions for nonlinear mapping. Output Layer: Generates duty cycle (D). Linear activation to produce continuous control values. The selected neural network architecture consists of two hidden layers with ten neurons each. This configuration was chosen to provide sufficient representational capacity for modelling the nonlinear dynamics of the EV charging system while maintaining computational efficiency suitable for real-time control applications. Sigmoid activation functions were used in the hidden layers to capture nonlinear relationships between input variables, while a linear activation function was applied at the output layer to generate continuous duty-cycle values for PWM control. Preliminary simulations indicated that increasing the number of neurons beyond this configuration did not produce significant performance improvement while increasing computational complexity. In the proposed system, the duty cycle of the MOSFET switch in the modified SEPIC converter is determined using an ANN-based control strategy. The controller continuously monitors the converter output current and compares it with the reference current required for the desired operating mode. The resulting error signal is used as an input to the ANN controller, which processes the system state and generates an appropriate control signal corresponding to the required duty cycle. This duty cycle is then used to generate pulse width modulation signals that drive the MOSFET switch of the converter. By dynamically adjusting the switching duty cycle in response to real-time system conditions, the ANN controller ensures stable current regulation and efficient power transfer during both Grid-to-Vehicle and Vehicle-to-Home operating modes. The Artificial Neural Network (ANN) controller determines the optimal duty cycle of the converter switches based on the current regulation error. The error signal is defined as the difference between the reference current and the measured battery current, which represents the deviation that the controller must minimize. This formulation is commonly adopted in feedback control systems used in electronic power converters. The ANN determines the optimal duty cycle based on the Error signal generated as52: where (:{I}_{ref})represents the reference charging current and (:{I}_{b})denotes the measured battery current. During Grid-to-Vehicle (G2V) operation, the ANN increases the converter duty cycle when the battery current is lower than the reference current and gradually reduces the duty cycle as the battery current approaches the desired reference value. During Vehicle-to-Home (V2H) operation, the ANN regulates the discharge current while maintaining stable voltage at the point of common coupling. The ANN model is trained using datasets obtained from simulated G2V and V2H operating conditions. The training process uses the backpropagation learning algorithm with gradient descent optimization to update network weights and minimize prediction error. The performance of the ANN during training is evaluated using the Mean Squared Error (MSE) loss function, which measures the squared difference between the reference current and the predicted current. Training Data: Collected from simulated G2V and V2H operations. Learning Algorithm: Backpropagation with gradient descent. Loss Function: Mean Squared Error (MSE). The MSE loss function penalizes larger deviations between reference and measured current, enabling the ANN to converge toward accurate current regulation53. where (:n) represents the number of training samples. Learning rate, epochs, and batch size are fine-tuned via cross-validation. The trained ANN consistently generates faster, more accurate control decisions compared to fixed-gain PI controllers. The training dataset used for the ANN controller was generated from dynamic simulations of the EV charging system operating under both Grid-to-Vehicle (G2V) and Vehicle-to-Home (V2H) modes. The dataset includes variations in grid voltage, battery state of charge, and solar irradiance levels to capture realistic operating conditions encountered in renewable-integrated EV charging systems. Input variables consisted of measured battery current and reference current values, while the desired output corresponded to the optimal duty cycle required for converter switching. Approximately several thousand data samples were generated across different operating states to ensure sufficient representation of system dynamics. The ANN was trained using a supervised learning approach based on the backpropagation algorithm with gradient descent optimization. Training was performed over multiple epochs until the mean squared error converged to an acceptable threshold. The learning rate was selected to balance convergence speed and stability, and the dataset was divided into training and validation subsets to avoid overfitting. This training procedure enabled the ANN to learn the nonlinear relationship between current error and the required duty cycle for maintaining stable charging performance. The input data required for training the ANN controller were generated using the MATLAB/Simulink model of the proposed EV charging system. The simulation environment was used to replicate different operating scenarios for both Grid-to-Vehicle (G2V) and Vehicle-to-Home (V2H) modes. During these simulations, electrical variables such as the reference charging current, battery current, battery voltage, and system operating conditions were recorded. These parameters represent measurable quantities that can be obtained in practical EV charging systems through current and voltage sensors. The recorded simulation data were organized into input-output pairs for ANN training. The input variables include the reference current and the measured battery current, while the output variable corresponds to the duty cycle required to control the MOSFET switches of the bidirectional SEPIC converter. By exposing the ANN to multiple operating conditions during training, the network learns the nonlinear relationship between system variables and the optimal control action required to maintain stable charging and discharging performance. The dataset was pre-processed and normalized before training to improve convergence and stability of the neural network learning process. The system was simulated in MATLAB/Simulink 2023a, modelling: Dynamic solar irradiance. Grid voltage fluctuations. G2V/V2H power flow. Two battery configurations (72 V, 240 V). Performance Metrics Evaluated. Charging and discharging efficiency. ANN response time. Grid power quality compliance. Voltage and current waveform distortion. Battery safety parameters (SOC behavior). The comparative evaluation as compared to conventional unidirectional chargers demonstrated: Higher efficiency. Lower charging time. Better handling of grid disturbances. Reduced reliance on grid power due to solar contribution. The simulation environment was developed in MATLAB/Simulink 2023a to emulate realistic EV charging conditions. The PV subsystem was modelled using standard test conditions with an irradiance of 1000 W/m² and a cell temperature of 25 °C. Dynamic irradiance variations were introduced during simulation to evaluate controller robustness under renewable energy fluctuations. The SEPIC converter switching frequency was set in the high-frequency range typical for power electronic converters to ensure efficient energy transfer and reduced ripple characteristics. The EV battery models included two representative configurations commonly used in electric mobility applications: a 72 V battery pack representing electric two-wheelers and a 240 V battery pack representing electric four-wheelers. Battery state-of-charge dynamics were incorporated into the model to simulate realistic charging and discharging behaviour. Grid conditions were also varied to include voltage fluctuations, enabling evaluation of controller performance under disturbed operating conditions. These simulation parameters allowed the proposed ANN-controlled charging system to be evaluated under a wide range of operating scenarios representative of real EV charging infrastructure. The system supports: 72 V e-2 W battery. 240 V e-4 W battery. An AC load was connected during V2H mode to demonstrate real-time home power support. This section presents experimental results and a systematic evaluation of the proposed bidirectional EV charging system integrated with solar PV and ANN-based control. The main objective is to validate the system’s performance against the operational goals established in earlier sections. Empirical validation is also essential, as it provides quantitative evidence of how the system behaves under simulated real-world operating conditions, thereby enabling a comprehensive assessment of reliability, efficiency, and control effectiveness. The key performance metrics examined include overall system efficiency, charging and discharging times, grid power quality, and stability during G2V and V2H operations. These indicators demonstrate how effectively the converter regulates power flow, how rapidly and safely the batteries reach the desired state of charge, and how well the ANN controller maintains voltage and current quality under varying irradiance and load profiles. Also, a comparative analysis is conducted against conventional PI-controlled charging architectures and traditional unidirectional converters. This comparison highlights the improvements achieved through ANN-based duty-cycle regulation, renewable energy prioritization, and bidirectional energy flow, emphasizing gains in energy efficiency, dynamic response, and sustainability. The literature from previous studies has individual components such as ANN optimization in charging systems or PV-assisted charging techniques and this existing literature has not offered a unified model combining solar PV generation, a bidirectional EV charger, and ANN-driven control into a single integrated framework. This present research bridges this gap by demonstrating a complete, coherent system capable of intelligent energy management and enhanced operational performance. This integration represents a meaningful advancement in the domain of EV charging technologies, supporting both sustainable energy utilization and improved power-electronic control. To further highlight the contribution of the proposed system, the developed ANN-controlled charger was conceptually compared with previously reported EV charging architectures that employ either conventional controllers or renewable energy integration individually. Traditional EV charging systems commonly rely on proportional-integral controllers for regulating converter duty cycles. While these controllers provide satisfactory performance under steady-state conditions, they exhibit limited adaptability when system parameters change due to renewable energy variability, battery ageing, or grid disturbances. In contrast, ANN-based controllers can learn nonlinear system behaviour and dynamically adjust control signals without requiring explicit system modelling. In addition, many photovoltaic-assisted EV charging systems operate as unidirectional chargers and primarily focus on utilizing solar energy for battery charging. Such systems typically lack bidirectional capability and therefore cannot support vehicle-to-home or vehicle-to-grid energy exchange. The proposed architecture differs from these approaches by combining renewable energy integration with intelligent control and bidirectional power flow within a single power conversion framework. The modified SEPIC converter enables efficient bidirectional energy transfer, while the ANN controller dynamically regulates duty cycles to maintain stable operation under varying grid and irradiance conditions. This integrated configuration enhances renewable energy utilization, improves system adaptability, and enables EV batteries to function as distributed energy storage resources. Consequently, the proposed system provides a more flexible and sustainable EV charging solution compared with conventional grid-dependent chargers and previously reported PV-assisted charging systems (refer Table 3). The performance of the proposed ANN-controlled bidirectional charger was evaluated under multiple operating scenarios, specifically focusing on Grid-to-Vehicle (G2V) and Vehicle-to-Home (V2H) modes. This evaluation considered dynamic changes in solar irradiance, grid fluctuations, and variations in battery state-of-charge (SOC) for both 72 V (two-wheeler) and 240 V (four-wheeler) battery configurations. During G2V mode, the system’s ability to maintain a constant current charging profile was assessed under fluctuating grid input. The ANN-based controller adjusted the duty cycle in real time, ensuring smooth charging even when input voltage varied (refer to Figs. 5 and 6). The measured charging waveforms demonstrate that the controller successfully minimized ripples while achieving the target charging profile. Battery voltage and SOC progression were also monitored (Figs. 7 and 8), confirming that the ANN provided a stable charging trajectory with improved transient response compared to conventional PI based approaches. Simulink implementation of the proposed ANN-controlled bidirectional EV charging system incorporating a modified SEPIC converter, solar PV integration, and battery current feedback for duty-cycle control of MOSFET switches. Details of Simulink framework for the 240 V four-wheeler charging configuration, integrating the SEPIC converter, ANN controller, switching logic, and battery-side measurement blocks required for dynamic control under G2V and V2H operating conditions. Representation of the Vehicle-to-Home (V2H) discharge operation. This figure shows the reverse-power control network, switching blocks, current and voltage sensing units, and ANN control integration enabling the EV battery to supply AC loads. EV battery (SOC Profile) during grid-to-vehicle operation. The ANN-controlled converter maintains a stable charging pattern, showing SOC increasing smoothly over the simulation period, demonstrating robust charging control under dynamic conditions. During V2H operation, the system was evaluated for its capability to sustain power delivery to the AC load while maintaining system stability. The SEPIC converter and bidirectional interface were tested under varying residential load conditions (Fig. 9). The ANN ensured controlled discharging by regulating the duty cycle according to the SOC feedback and AC load demand. The resulting discharge curves (Fig. 10) verify that the system can reliably supply energy back to the AC side without compromising battery protection or power quality. Overall, the results demonstrate that the integrated PV-EV-ANN framework achieves high operational efficiency, stable mode transitions, improved charging accuracy, and reliable V2H energy support. This performance validates the effectiveness of the proposed architecture for sustainable energy utilization in residential EV charging applications. Figure 8 illustrates the state of charge (SOC) profile of the electric vehicle battery during Grid-to-Vehicle (G2V) charging operation. The SOC represents the percentage of the battery capacity that is currently stored as electrical energy. During the charging process, the SOC gradually increases as power is transferred from the grid to the battery through the bidirectional SEPIC converter. The SOC curve shown in Fig. 8 remains close to the upper charging range, indicating that the battery is approaching full charge during the simulation period. Minor fluctuations are observed in the SOC trajectory, which are associated with transient responses of the power converter and adjustments in the duty cycle generated by the ANN-based controller. These small variations demonstrate the adaptive behaviour of the controller when responding to dynamic operating conditions. The SOC profile confirms that the proposed ANN-controlled charging system maintains stable and efficient energy transfer during the G2V mode. The smooth progression of SOC and the absence of significant oscillations indicate effective regulation of the charging current and reliable operation of the bidirectional EV charging architecture. Measured output voltage characteristics of the high-gain DC-DC converter during charging operation. The waveform shows the boosted DC-bus voltage with periodic switching behavior, highlighting the converter’s ability to maintain regulated output under varying input and load conditions. Battery diagnostic monitoring block displaying the measured state-of-charge (SOC), charging current, and terminal voltage. These measurements are fed to the ANN controller to ensure safe and optimized charging behavior across G2V and V2H modes. Efficiency was one of the primary performance indicators evaluated in the proposed charger. Operating the SEPIC converter in discontinuous conduction mode (DCM) enabled high power-factor operation while reducing switching and conduction losses. Empirical results confirmed that the system consistently achieved above 90% efficiency across varying input and load conditions, demonstrating strong robustness against grid voltage fluctuations (refer Figs. 11, 12 and 13). The charger exhibited smooth adaptability when switching between 72-V and 240-V battery configurations, with charging durations comparable to modern commercial EV chargers. This highlights the suitability of the ANN-controlled architecture for practical implementation in multi-battery EV environments. Battery charging profile of the EV battery under Grid-to-Vehicle (G2V) operation. The upper plot shows the charging voltage rising smoothly and stabilizing at the regulated set point, while the lower plot illustrates the corresponding charging current behavior over time. This combined waveform demonstrates that the ANN-regulated SEPIC converter maintains a consistent charging pattern with minimal fluctuations, ensuring safe and efficient charging performance. Simulink implementation of the V2H operation illustrating the SEPIC converter, switching network, ANN controller, and AC load connection. The figure highlights the bidirectional flow path from the battery to the AC load, showing how the inverter and control logic regulate power delivery during V2H operation. This model validates the system’s ability to support residential loads using EV battery energy stored. Output voltage waveform measured across the AC load during V2H mode. The stable sinusoidal pattern demonstrates effective inverter operation and ANN-based control, ensuring that the AC load receives a regulated and continuous power supply. The waveform confirms grid-compatible quality and minimal distortion under varying load conditions. The increasing penetration of electric vehicles introduces significant challenges for electrical distribution networks, particularly in terms of voltage stability, harmonic distortion, and load variability. Conventional EV charging systems typically rely on fixed-gain proportional–integral controllers to regulate converter operation. While such controllers can maintain acceptable performance under steady-state conditions, their ability to respond to dynamic grid disturbances and renewable energy variability is limited. In contrast, intelligent control approaches such as artificial neural networks provide adaptive decision-making capability that allows the charging system to adjust operating parameters in real time. This adaptability is particularly important for grid-resilient charging infrastructure, where rapid response to grid fluctuations and renewable energy intermittency is essential for maintaining stable system operation. The performance of the proposed ANN-integrated SEPIC charger was compared against existing commercial EV chargers to determine its competitive advantages. The system exhibited marked improvements in both energy efficiency and adaptability to variable input conditions, largely due to its integration with solar PV and intelligent duty-cycle regulation. As shown in Fig. 14, the PV subsystem provided stable and sufficiently high DC output even under fluctuating irradiance, contributing to reduced dependency on the grid during G2V operation. Comparative evaluation indicated that the modified SEPIC converter delivers superior performance relative to conventional two-stage converters. Its wide input voltage tolerance and single-stage topology reduced conversion losses, while the ANN-based control ensured tighter regulation of charging and discharging currents. These characteristics collectively enhance system robustness, especially during V2H mode where maintaining voltage stability is critical. Overall, the integrated approach offers a more energy-efficient, stable, and flexible alternative to commercially available chargers. The figure illustrates the stabilized DC output voltage obtained from the PV array, demonstrating its ability to maintain consistent power delivery under varying operating conditions. This stable PV output supports reduced grid dependency during G2V charging and enhances the overall efficiency of the proposed energy management system28. To evaluate the advantages of the proposed architecture, the ANN-controlled bidirectional charger was conceptually compared with conventional EV charging systems that employ PI-based control strategies. Traditional PI controllers regulate the duty cycle of the converter based on predetermined gain parameters. Although effective for linear systems with predictable operating conditions, PI controllers require careful tuning and may exhibit slower dynamic response when system parameters change due to grid disturbances, renewable energy fluctuations, or battery dynamics. In contrast, the ANN-based controller implemented in the proposed system learns the nonlinear relationship between charging current error and converter duty cycle during the training process. This enables the controller to adapt its switching decisions dynamically during operation. As a result, the ANN provides improved transient response and maintains more stable current and voltage profiles during both Grid-to-Vehicle and Vehicle-to-Home modes. From a power quality perspective, the proposed architecture also benefits from the combination of ANN-based control and discontinuous conduction mode operation of the SEPIC converter. This configuration contributes to reduced current ripple and improved power factor characteristics when interacting with the grid. The ANN controller further enhances stability by rapidly compensating for variations in input voltage or load demand. Consequently, the charging system demonstrates improved resilience to grid disturbances compared with conventional charging infrastructures that rely solely on fixed-parameter control strategies. The analysis conducted in this study demonstrates clear improvements across multiple performance dimensions, particularly in charging efficiency, grid adaptability, and overall system stability. These outcomes align closely with the research objectives established at the outset. The system effectively regulates bidirectional power flow while integrating renewable energy from the solar photovoltaic (PV) array, confirming the success of the proposed architecture. A key result is the superior performance of the Artificial Neural Network (ANN)-based control system compared with traditional proportional-integral (PI) controllers. The ANN consistently delivered more accurate duty-cycle regulation during both charging and discharging processes, enabling smoother transitions, reduced oscillations, and faster dynamic response. The system also displayed stronger resilience to grid disturbances than initially anticipated. Simulation results indicate that stable voltage levels were maintained across a wider range of input fluctuations, suggesting that the combined ANN-SEPIC configuration offers greater robustness than originally hypothesized. However, performance limitations emerged under low-irradiance conditions. Despite being designed to handle variability in solar input, empirical testing showed that partial shading and reduced irradiance caused minor inconsistencies in charging rates. These deviations highlight the need for further optimization of the ANN controller and the PV charge management strategy to maintain full stability under adverse environmental conditions. The findings confirm that the system successfully meets its primary functional targets while revealing areas where refinement is warranted. Notably, the SEPIC converter demonstrated the ability to sustain stable voltage regulation even when the input voltage dropped to approximately 100 V significantly below nominal single-phase AC levels. This capability supports reliable charging performance under weakened or fluctuating grid conditions, reinforcing the system’s suitability for practical deployment across diverse operating environments. The performance of renewable-integrated EV charging systems can be significantly influenced by environmental and grid-related uncertainties. Therefore, it is important to evaluate the potential behaviour of the proposed system under extreme operating conditions such as partial shading of photovoltaic modules, very low solar irradiance, and significant grid voltage fluctuations. Under conditions of very low irradiance, the power output of the solar PV subsystem decreases substantially, which limits the availability of renewable energy for battery charging. In such cases, the ANN-controlled energy management system prioritizes grid power to maintain stable charging operation. Although the proposed controller effectively maintains current regulation under these conditions, the overall renewable energy contribution to the charging process is reduced. Consequently, system efficiency may decline slightly due to increased reliance on grid power. Another potential edge case involves partial shading of PV modules, which can introduce nonlinear variations in PV output voltage and current. These variations may affect the stability of the DC input supplied to the converter. While the ANN controller helps maintain stable duty-cycle control under moderate fluctuations, severe shading conditions may still produce irregular charging patterns. Incorporating advanced maximum power point tracking strategies or module-level power electronics could further enhance system resilience under such scenarios. High grid voltage fluctuations represent another important challenge, particularly in regions with weak distribution infrastructure. In the proposed architecture, the ANN controller dynamically adjusts the converter duty cycle in response to changing current and voltage measurements, allowing the system to maintain stable operation during moderate grid disturbances. However, extreme voltage deviations beyond typical distribution limits may affect converter performance and reduce charging stability. Additional protection mechanisms such as voltage regulation units or grid-supportive control strategies could be implemented in future designs to mitigate such effects. These considerations highlight that while the proposed ANN-controlled bidirectional charging system demonstrates strong adaptability to dynamic conditions, its performance may still be influenced by extreme environmental or grid-related disturbances. Future work should therefore explore hybrid control strategies, advanced PV management techniques, and improved grid-interaction algorithms to further enhance robustness and reliability in renewable-integrated EV charging systems. During the development and testing of the proposed EV charging system, several technical challenges emerged, each requiring targeted modifications to ensure reliable and efficient performance. One of the most significant issues involved the operation of the AC load during Vehicle-to-Home (V2H) mode. The 48-Ohm resistive load failed to energize correctly because the battery supplied a voltage that exceeded the permissible limit for direct load operation. To resolve this, a buck converter was integrated after the battery to step down the voltage to an appropriate level. This addition ensured stable load operation and allowed the V2H functionality to be demonstrated without compromising safety or power quality. Grid voltage fluctuations presented another major challenge, as variable grid conditions made it difficult to maintain a constant charging current and comply with power quality standards. These fluctuations had the potential to introduce inefficiencies or instability in the charging process. The issue was addressed by employing the ANN-based control algorithm, which continuously monitored grid conditions and dynamically adjusted switching decisions in real time. This adaptive behavior allowed the system to compensate for voltage variations and sustain a stable charging profile. Harmonic distortion also required careful attention. Without appropriate control, harmonics generated by the converter could have violated power quality limits. Operating the SEPIC converter in discontinuous conduction mode (DCM) proved essential, as DCM operation inherently supports near-unity power factor and reduces harmonic content, enabling the system to meet the required standards. Compatibility across different battery types such as 72 V for electric two-wheelers and 240 V for electric four-wheelers posed an additional challenge. Early tests showed that maintaining uniform performance across these voltage levels was difficult. This issue was mitigated by fine-tuning the SEPIC converter’s wide input-output voltage range, allowing both battery types to charge efficiently and safely under varying conditions. Thermal management emerged as another critical concern due to the high-power levels involved in rapid EV charging. Components such as MOSFETs and inductors experienced elevated temperatures during operation. The design was strengthened through the implementation of heat sinks and real-time temperature monitoring, preventing overheating and ensuring long-term component reliability. Tuning the artificial neural network (ANN) required additional effort as well. Initial ANN output did not consistently yield optimal MOSFET switching behavior across all operating scenarios. This challenge was addressed by expanding the training dataset and refining the ANN architecture, resulting in significantly improved switching accuracy and overall system performance. Synchronizing the solar PV array with the grid supply also presented difficulties, particularly in ensuring smooth transitions between sources. Although the prototype relied on a manual switching mechanism, the results indicate that future implementations should incorporate an automatic transfer control to allow seamless, disturbance-free switching. Finally, the integration of multiple subsystems-including SEPIC converters, MOSFET switches, PWM generators, and the ANN controller-introduced interoperability issues during early testing. These challenges stemmed from mismatched signal timings and inconsistent responses between components. Extensive calibration of PWM signals and control interfaces resolved these issues and ensured cohesive system operation. Through systematic troubleshooting and iterative refinement, each challenge was effectively addressed. The resulting system meets its design objectives and demonstrates a robust, efficient, and adaptable solution for sustainable EV charging applications. The integration of solar photovoltaic (PV) energy and Artificial Neural Network (ANN) based control significantly enhances both the efficiency and sustainability of the proposed EV charging system. Solar PV provides a clean, renewable, and locally available energy source, reducing dependence on conventional grid power that is predominantly generated from fossil fuels. By utilizing solar energy for EV charging, the overall carbon footprint associated with transportation and electricity consumption is substantially lowered. The ANN plays a central role in maximizing the utilization of available solar energy. Through real-time analysis of system parameters such as battery state of charge (SOC), current demand, and grid conditions, the ANN optimizes charge-discharge cycles and minimizes energy losses within the power conversion stages. This intelligent decision-making capability ensures that solar power is prioritized whenever available, while grid power is used only when necessary. As a result, the system operates with greater overall efficiency, particularly under varying irradiance levels or fluctuating load conditions. The combined use of solar PV and ANN-based optimization contributes to more sustainable energy consumption patterns. By reducing reliance on the grid during peak times, the system alleviates stress on the electrical infrastructure and supports broader goals of energy security and resilience. Enhanced control over energy flow also improves power quality, ensuring that voltage and current remain within acceptable limits even when environmental or grid disturbances occur. From a technological perspective, this integration represents a forward-looking approach to EV charging. It demonstrates how renewable energy sources and intelligent control algorithms can be combined to create adaptive, energy-efficient, and environmentally responsible charging systems. These advancements lay the groundwork for future innovations in smart grid technologies, demand-side energy management, and next-generation charging infrastructure. The system also holds relevance for policy development. As governments and regulatory bodies prioritize clean energy initiatives, solutions that effectively integrate renewable energy with EV charging can guide the creation of supportive regulations, incentives, and infrastructure investments. Policies promoting the pairing of solar PV with EV charging networks could accelerate the transition to low-carbon mobility and support long-term sustainability goals. At a broader level, the scalability of the proposed system enables adoption in diverse environments. Residential users can benefit from reduced electricity costs and greater energy independence, while commercial and public charging stations can improve efficiency and reduce grid burden. As EV adoption increases globally, such intelligent, renewable-integrated charging systems can offer a reliable and environmentally sustainable approach capable of meeting large-scale demand. In general, integrating solar PV with an ANN-controlled charging strategy significantly advances efficiency, sustainability, and system resilience. The approach not only reduces carbon emissions but also establishes a foundation for the future of intelligent, renewable-powered EV charging, with substantial implications for technology development, policy planning, and large-scale adoption. Although the proposed ANN-controlled charging architecture demonstrates promising performance in the simulation environment, the present study relies exclusively on MATLAB/Simulink modelling for system validation. Simulation-based evaluation enables controlled investigation of system dynamics under varying solar irradiance, grid fluctuations, and battery conditions; however, it does not fully capture practical hardware constraints such as switching losses, sensor noise, thermal effects, and real-time controller implementation challenges. Consequently, the results presented in this study should be interpreted as a proof-of-concept validation of the proposed control strategy. Future work will focus on developing a hardware prototype of the bidirectional charger and implementing the ANN controller on an embedded control platform to experimentally evaluate system performance under real operating conditions. Although the proposed ANN-controlled charging system demonstrates promising performance in the MATLAB/Simulink simulation environment, the present study is limited to simulation-based validation. While simulation allows controlled evaluation of system behavior under varying solar irradiance, grid fluctuations, and battery operating conditions, it does not fully capture practical hardware constraints such as switching losses, sensor inaccuracies, thermal effects, and real-time controller implementation challenges. Therefore, the results presented in this work should be interpreted as proof-of-concept validation of the proposed control architecture. Future research will focus on developing a hardware prototype of the bidirectional charger and implementing the ANN controller on an embedded platform to experimentally evaluate system performance under real operating conditions. Such experimental verification will help assess the practical feasibility, reliability, and real-world applicability of the proposed renewable-integrated EV charging system. This study developed and evaluated a bidirectional electric vehicle (EV) charging architecture integrating solar photovoltaic (PV) generation with an Artificial Neural Network (ANN)-based control strategy. The proposed system employs a modified SEPIC converter to enable bidirectional energy exchange between the EV battery, the utility grid, and residential loads, thereby supporting both Grid-to-Vehicle (G2V) and Vehicle-to-Home (V2H) operating modes. Simulation results demonstrate that the ANN controller effectively regulates the duty cycle of the converter switches under varying grid and solar conditions, resulting in stable charging performance and improved dynamic response compared with conventional PI-based control approaches. The proposed system supports multiple battery configurations, including 72 V two-wheeler and 240 V four-wheeler EV batteries, indicating flexibility for different electric mobility applications. In addition, the integration of solar PV prioritizes renewable energy utilization during charging operations, thereby reducing dependence on grid electricity. Overall, the results indicate that the ANN-controlled bidirectional charging architecture provides an adaptive and energy-efficient framework for renewable-integrated EV charging systems. The charging dynamics of EV batteries can influence the performance of the proposed system because parameters such as battery capacity, nominal voltage, and internal resistance affect current regulation and energy transfer characteristics. In this study, the battery model represents typical lithium-ion EV batteries operating under nominal conditions. Although stable charging behaviour is observed for the investigated configurations, variations in battery characteristics may influence charging profiles and SOC evolution in practical applications. Battery degradation may also affect long-term system performance. Over repeated charging cycles, lithium-ion batteries typically experience gradual capacity reduction and increased internal resistance, which may alter charging efficiency and current regulation behaviour. These degradation mechanisms were not explicitly modelled in the present simulation study. However, the ANN-based controller dynamically adjusts the converter duty cycle based on real-time system measurements, which may help maintain stable control performance even when battery parameters change over time. Future work will focus on experimental validation of the proposed charging architecture through hardware implementation to evaluate controller performance, switching losses, converter efficiency, and thermal behavior under real operating conditions. Hardware-based testing will also allow verification of power quality and dynamic response in practical grid-connected environments. Further research may explore advanced control strategies to enhance the adaptability of the charging system under highly dynamic grid and renewable energy conditions. Techniques such as deep learning or reinforcement learning could be investigated to improve real-time control and energy management capabilities. Another important research direction involves large-scale deployment scenarios in which multiple EV chargers interact with distribution networks or microgrids. In such environments, coordinated charging strategies and grid-supportive control mechanisms will be required to maintain power quality, voltage stability, and efficient utilization of renewable energy resources. Future studies should also consider incorporating detailed battery aging models to evaluate the impact of capacity degradation and internal resistance growth on charging performance and long-term system reliability. Investigating the behaviour of different battery chemistries and energy storage technologies may further improve the robustness of renewable-integrated EV charging architectures. So, finally advanced charging technologies such as wireless or inductive bidirectional charging and hybrid systems combining EV batteries with stationary energy storage may be explored. These developments could enhance system flexibility, improve peak load management, and increase renewable energy utilization in future smart grid environments. Data sets generated during the current study are available from the corresponding author on reasonable request. 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The authors declare no competing interests. This study did not involve human participants, animals, or any sensitive data, and thus did not require ethical approval. All experimental procedures followed institutional and national guidelines for research integrity and ethics. 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. 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From pv magazine Australia Australian households and businesses installed more rooftop solar last month than in any other month on record with new data from solar and storage market analyst SunWiz showing 442 MW of sub-100 kW rooftop PV capacity was registered nationwide in April 2026. This marked a 31% increase on the 341 MW registered across the country in March and is almost double the 225 MW of new capacity registered in April 2025. “We have now reached the strongest month in the history of STC (small-scale technology certificates),” SunWiz Managing Director Warwick Johnston said, adding that the market is now running 35% ahead of the same point in 2025. Johnston said the surge in solar registrations was largely a byproduct of changes to the federal government’s Cheaper Home Batteries Program, which has supported the installation of more than 350,000 small-scale battery energy storage systems over the past 10 months. Changes to the rebate scheme, that provides discounts of up to 30% on the upfront cost of installing small-scale battery systems alongside new or existing rooftop solar, were introduced on 1 May 2026. In the wake of the changes, systems installed through the program will continue to receive the full discount on the first 14 kWh of usable capacity, while 14-20 kWh batteries will get 60% of the discount and 28-50 kWh batteries will get 15% of the rebate. Johnstone said the adjustments to the battery rebate scheme had “triggered a surge in battery demand with a meaningful flow-on effect to solar.” “The rebate cut sent households scrambling for large-format (40–50 kWh) batteries, and the bigger solar arrays needed to run them followed, turning the Cheaper Home Battery Program into a multiplier well beyond its original scope,” he said. Every state posted growth in rooftop solar installations in April with 143 MW of new capacity registered in New South Wales alone, up 35% on the previous month. The Australian Capital Territory reported a 62% increase while Queensland delivered a 36% month-on-month increase. SunWiz said most rooftop PV segments had recorded growth over the month with the 20-30 kW segment the standout, delivering almost double the installed capacity compared to March, up 98%. The 15-20 kW segment increased by 61% while the 30-50 kW segment recorded growth of 45%. The 3-6 kW and 6-8 kW segments showed minor dips in month-on-month capacity growth. The growth in the larger segments saw the national average system size bump up to 11.35 kW. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected]. Comments Please login to comment Wednesday, June 3, 2026 4:00 pm – 5:00 pm CEST, Berlin, Paris, Madrid Wednesday, June 10, 2026 3:00 pm – 4:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 9, 2026 11:00 am – 12:00 pm CEST, Berlin, Paris, Madrid Thursday, June 11, 2026 5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid Monday, June 1, 2026 5:30 pm – 6:30 pm CEST, Berlin, Madrid, Paris Tuesday, June 16, 2026 6 am – 7:00 am CEST, Berlin Friday, June 12, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid The new pv magazine Global May issue is now available! Mountains to climb Available in print and digital formats. Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects. April 01 – August 31, 2026 A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution. Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy. Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way! We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team. June 23-25, 2026 | MUNICH, GERMANY
Readers who have been following me know that the Bluetti Elite 200 V2 was my favorite portable power station (and I’ve tried a lot of portable power stations). But notice I used the word “was.” That’s because Bluetti has a new version—the Bluetti Elite 300—and it has now claimed my top spot. What makes the 300 so much better than the 200? In two words: more power. But beyond that, it’s the ability to plug a 30-amp RV cord directly into it. Over the years, people have often asked whether I could charge my trailer from my portable power stations. Usually, the answer was no. My power stations could run appliances and devices, but they couldn’t charge the trailer batteries. This one changes that. That’s right—You can charge your RV directly using the TT-30 port and run your RV devices with the 12V/30A DC output. Keep your fridge, lights, fans, and water pump running while also charging phones, laptops, and routers—all without complicated adapters. To be fair, my OUPES Mega 2 power station can do that, as well, and it’s also a quality unit. However, I prefer the Bluetti because it takes up slightly less space, and, on a personal note, I like the way the solar panels connect better. Having that 30-amp connection means you can boondock, recharge the power station with solar panels, use it to recharge your RV batteries, and keep the adventure going. With the available Charger 2 accessory, which pulls up to 1,200 W from both your vehicle’s alternator and solar panels, you can far outpace a standard 12 V car socket, charging your Elite 300 13 times faster while driving than just the 12 volt plug alone. For those with older rigs, like mine, that don’t already have solar installed, this portable power station offers an easy, portable, and more affordable solution that can move with you to your next RV. • Battery type: LiFePO₄ (Lithium Iron Phosphate) • Battery capacity: 3,014.4 Wh (314 Ah) • Cycle life: 6,000+ cycles to 80% capacity • Surge power: 4,800W • Lifting power: 4,800W • Charging temperature: 32°F to 104°F (0°C to 40°C) • Discharging temperature: -4°F to 104°F (-20°C to 40°C) • AC input: 1,800W, 15A max, 120V, 50/60Hz • Solar input: 1,200W max, 12V–60V, 22A max Total outlets: • 4 × Standard AC outlets • 1 × NEMA TT-30 • 1 × 12V/30A port • 2 × 15W USB-A • 1 × 100W USB-C • 1 × 140W USB-C • 1 × Cigarette lighter port (120W max) AC output: • 2,400W max (discharging) • 120V, 50/60Hz AC output (bypass) • 1,800W max, 120/60Hz • Quick charging! When I took this power station out of the box to charge it the first time, it went from 30% to 100% in about two hours on AC using the standard mode, meaning it can charge even quicker if using turbo mode. • There is even a silent charging mode. It takes a little longer but makes virtually no noise—not that there is much noise in regular mode, but power stations do have fans that come on automatically. • Power lifting mode allows you to run high-power heating devices, such as hair dryers or electric kettles. While the Elite 300’s actual power output is 2,400W, power lifting mode can handle appliances rated from 2,400W to 4,800W. • UPS mode allows you to plug the power station into a wall outlet, which powers any appliances plugged into the unit. If the power goes out, those appliances automatically switch to battery power, which is especially useful in places with unstable electricity. You can also personalize this mode with your desired charging and discharging schedule. • Self-grid adaptation mode automatically adjusts to handle power fluctuations when charging from unstable sources, such as a generator or unreliable grid power. • The sturdy built-in handles are located on the sides, which means the top of the power station remains flat (unlike some competitors, such as Jackery). That makes it more space-efficient and easier to pack when it’s time to move. • For the amount of power it provides, this power station is surprisingly compact (though still heavy—see below), measuring just 14.41 × 12.01 × 11.71 inches. • There are four ways to charge the power station: AC outlet, solar panels, your vehicle’s 12V or 24V outlet, or a traditional generator. • Control via the device itself or remotely through the app. • Supports pass-through charging. • Advanced settings allow you to adjust sleep time, grid self-adaptation mode, ECO mode, and more. • Comes with a 5-year warranty. It comes with the territory, but for this much power, a portable power station is going to be heavy. This one weighs in at 57.98 pounds. That said, the sturdy handles and smart design make it manageable. I’m a 60-something woman and can handle it myself. With two people, it’s a breeze. Also, it’s a minor issue, but it would be nice if Bluetti included the necessary accessories and cords along with a carrying case for them with the power station. Sadly, unlike most other manufacturers, they don’t. So, budget a little extra for the cords and accessories you will need and find a bag to keep them all together. RVT1263 The FREE RVtravel.com newsletter is filled with great RV information, advice, and news written by RV experts, delivered right to your inbox. Never any SPAM and we will NEVER sell your information! When you subscribe, you’ll get three checklists that every RVer should have as a thank you!
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From pv magazine India India installed 15.3 GW of solar capacity in the first quarter of 2026, the highest quarterly addition on record and a 143% increase year on year from the 6.3 GW installed in Q1 2025, according to Mercom India’s Q1 2026 India Solar Market Update Report. Large-scale projects accounted for 82% of total quarterly solar installations, with 12.6 GW added in the quarter. Open access projects contributed 21% of large-scale solar capacity additions. Mercom said record commissioning activity was driven by a combination of approaching policy deadlines and improved transmission readiness in key solar markets. One of the main drivers was the upcoming implementation of Approved List of Models and Manufacturers (ALMM) List-II from June 2026, which prompted developers to accelerate project commissioning under the existing procurement framework amid concerns over limited domestic cell availability and rising module procurement costs. Installation activity was also supported by stronger execution under the Pradhan Mantri Kisan Urja Suraksha evam Utthaan Mahabhiyan (PM-KUSUM) scheme — India’s government solar program targeting farmers and rural communities — along with accelerated commissioning of open access projects ahead of the next phase of Inter-State Transmission System (ISTS) waiver reductions. “India’s solar sector recorded its strongest quarter ever in Q1 2026, driven by accelerated project execution ahead of the June ALMM-II deadline and the reduction in ISTS charge-waiver benefits,” said Raj Prabhu, CEO of Mercom Capital Group. “However, transmission bottlenecks could still play the spoiler in what is expected to be a record year for solar installations. While project execution and commissioning activity remain strong, transmission readiness and evacuation infrastructure are struggling to keep pace with the rapid growth in renewable capacity. As renewable penetration increases, curtailment, grid flexibility, and storage integration are becoming critical to sustaining future growth.” As of March 2026, India’s cumulative installed solar capacity stood at 152 GW, with large-scale projects accounting for 85% and rooftop solar 15%. Solar energy accounted for 28% of India’s total installed power capacity and 55% of total installed renewable energy capacity. Rajasthan had the highest cumulative installed large-scale solar capacity at 32% of total PV installations, followed by Gujarat at 21% and Karnataka at 11%. In the first quarter of 2026, Gujarat and Rajasthan led quarterly large-scale additions with approximately 40% and 39% of capacity additions respectively. Maharashtra ranked third with 6%. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected]. Comments Please login to comment Wednesday, June 3, 2026 4:00 pm – 5:00 pm CEST, Berlin, Paris, Madrid Wednesday, June 10, 2026 3:00 pm – 4:00 pm CEST, Berlin, Paris, Madrid Tuesday, June 9, 2026 11:00 am – 12:00 pm CEST, Berlin, Paris, Madrid Thursday, June 11, 2026 5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid Monday, June 1, 2026 5:30 pm – 6:30 pm CEST, Berlin, Madrid, Paris Tuesday, June 16, 2026 6 am – 7:00 am CEST, Berlin Friday, June 12, 2026 2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid The new pv magazine Global May issue is now available! Mountains to climb Available in print and digital formats. Be part of the high-level European conference on solar and energy storage, exploring bankable BESS projects, warranties, and energy management for residential and C&I sectors Entries open in seven categories: Modules, Inverters, BoS, BESS, Manufacturing, Sustainability, Projects. April 01 – August 31, 2026 A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution. Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy. Showcase your brand across all our platforms: from 13 websites in 7 languages to our magazines, daily newsletters, industry events and more. Reach your audience the right way! We are participating in Intersolar 2026 again this year! Visit us at our Booth Hall 2 A2.250 to discuss the latest trends within the photovoltaic industry with the pv magazine team. June 23-25, 2026 | MUNICH, GERMANY
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