Back Nanyang Technological University, Singapore Perovskite solar cells are a promising alternative to silicon-based photovoltaic technologies. However, their widespread adoption is limited by poor environmental stability, as perovskite materials degrade easily when exposed to oxygen, moisture, heat or light.
An innovation by scientists at Nanyang Technological University, Singapore (NTU Singapore) has made perovskite solar cells more stable and efficient, bringing the technology one step closer to market.
Their method expands the possibilities of using chemically inert materials to improve the stability of perovskite solar cells without compromising efficiency.
The research was published in Nature Energy in August 2025 and led by Prof Sum Tze Chien, Director of the Institute of Advanced Studies at NTU and Associate Dean (Research) of NTU’s College of Science, and Prof Lam Yeng Ming of NTU’s School of Materials Science and Engineering.
Engineering protective layers for perovskite solar cells To protect perovskite solar cells from environmental degradation, an ultrathin interface layer typically made of highly reactive bulky cations – large positively charged ions – is often applied to the perovskite film. Although the cations readily react with perovskites to form a coating that provides good electrical conductivity, such interface layers have low stability due to their high reactivity.
On the other hand, chemically inert bulky cations can be integrated into the interface layers to produce a protective coating that offers both high stability and good electrical conductivity. However, this integration is limited by the low reactivity of such cations.
To overcome this challenge, the NTU team developed a strategy called selective templating growth (STG) to create chemically inert interface layers that combine high stability with good conductivity.
In this strategy, the team first deposited a layer of phenylammonium lead iodide (PA2PbI4) onto the perovskite surface. PA2PbI4 is usually used to protect the underlying perovskite layer to improve the performance of perovskite solar cells.
Then, a chemically inert bulky cation – 2-piperidin-1-ium-1-ylethylammonium (PiEA2+) – was introduced by spin-coating an alcohol-based PiEA2+ solution onto the PA2PbI4 layer. Through a controlled organic cation exchange process, in which PA+ is replaced by PiEA2+, a more stable ultrathin layer of (PiEA)PbI4 is formed.
This method of boosting the stability of perovskite solar cells with inert materials is one of several innovations that have emerged from the more than ten years of research collaboration between Prof Sum and Prof Lam.
“Our strategy enables access to a class of chemically inert interface materials that previously could not be used due to reactivity and solubility limitations, opening a new avenue for interface engineering in perovskite devices,” said Prof Sum.
Manufacturing highly efficient and stable perovskite solar cells Using the strategy, the team fabricated a 1-cm2 perovskite solar cell prototype that achieved a power conversion efficiency of 25.1%, one of the highest reported for perovskite solar cells of this size. The device retained over 93% of its initial efficiency after 1,000 hours of operation, and 98% after 1,100 hours at 85 °C.
Beyond the prototype (PiEA)PbI4 interface, the strategy also enables the formation of a wide variety of chemically inert interfaces. Importantly, being fully solution-based, the approach is compatible with industrial techniques for coating large areas, such as blade-coating, paving the way for large-scale fabrication and practical deployment.
“Our strategy provides a versatile and scalable interface design platform. It can be extended not only to the manufacturing of lead-free perovskite solar cells, but also to other perovskite optoelectronic devices such as light-emitting diodes and photodetectors.” added Prof Lam.
The researchers are collaborating with companies to manufacture full-sized solar panels and bring the technology one step closer to commercialisation.
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The project spans 12 sites and is projected to deliver $48 million in cost savings over the next 20 years. Image: Opterra / Yucaipa-Calimesa Joint Unified School District The Yucaipa-Calimesa Joint Unified School District has deployed 3.5 MW of solar across 12 sites, leveraging a performance-based contract to guarantee $48 million in savings while integrating clean energy into the K-12 curriculum. The Yucaipa-Calimesa Joint Unified School District has announced the completion of a district-wide energy transformation, marking a significant milestone for distributed generation in California’s Inland Empire. In partnership with OpTerra Energy Services, the $33 million initiative combined on-site solar PV with deep energy efficiency retrofits to hedge against rising utility rates and provide a living laboratory for its 8,600 students. The centerpiece of the project is the installation of 3.5 MW of solar capacity, primarily installed on parking lot canopies. The structures serve a dual purpose of generating clean electrons and providing much-needed shade in the high-heat environment of San Bernardino and Riverside Counties. Beyond the arrays, the district implemented a whole-building efficiency strategy including comprehensive LED retrofits across all 14 campuses, the replacement of aging heating and cooling units with high-efficiency systems, and advanced irrigation controls to mitigate drought-related costs. To fund the $33 million project without increasing the burden on local taxpayers, the district utilized a lease-purchase agreement with Banc of America Public Capital Corp at a fixed interest rate of 4.373%. Crucially, the district secured interconnection under NEM 2.0 rules before the transition to the more restrictive Net Billing Tariff, ensuring higher compensation for the solar energy exported to the grid. This timing contributes to a projected $48 million in total savings over the 20-year term of the program. Following the trend of solar-powered schools acting as educational hubs, the district has integrated the physical infrastructure into the classroom experience. Students use data from the solar arrays to study energy production and consumption patterns in math and science modules, while the project provides a pathway for STEM education and career exposure in the renewable energy sector. The district board recently recognized student interns for their active contributions to the energy project, bridging the gap between facility management and student learning. In future developments, the district plans to add battery energy storage system technology to its existing solar infrastructure. This system will allow the district to store excess clean energy generated during the day and deploy it during peak evening hours or during utility outages, further driving down costs and ensuring campus reliability. The expansion is made possible through a strategic grant received via So Cal West Coast Electric. A detailed site-based analysis on the school budget savings and other relevant project details can be found here. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Ryan Kennedy Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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Solar panels seen at a solar energy facility in the Joliet area. (Gary Middendorf – gmiddendorf@shawmedia/Gary Middendorf) A Will County judge will issue a ruling by Wednesday afternoon that could delay a vote on the 6,100-acre Pride of the Prairie solar project. The Will County Board has moved its Thursday morning meeting to the Clarion Hotel and Convention Center in Joliet to accommodate an expected large public turnout for the vote needed to move the controversial project forward. But an attorney for potential neighbors of the solar complex was in court on Tuesday arguing for an emergency order to stop the vote as a legal fight over the project continues. Attorney Steve Becker said a vote on Thursday could lead to “years in litigation that can be settled here right now.” Becker wants a new public hearing on the solar complex, saying his clients were denied rights under state and county law to present their own case against the project and cross-examine representatives from developer Earthrise Energy. People line up to speak on March 30, 2026 on what was the first of two nights of public hearings held at the Renaissance Center in Joliet for the Pride of the Prairie solar project. (Bob Okon) The Will County Board Planning and Zoning Commission in an advisory vote recommended against the project after a public hearing that ran March 30 and 31. But Becker said his client’s case needs to be in the public record for potential future litigation on the project. “It’s an irreparable harm because the record will now be silent,” Becker said. Attorneys for the Will County State’s Attorney’s Office and Earthrise argued that Becker and his clients had their chance to make a case during the public comment at the public hearing in which individuals had five minutes each to speak on the project. Assistant State’s Attorney Scott Pyles contended that Becker wanted “a Perry Mason concept” of cross-examination that would not be suitable for zoning hearings. Assistant State’s Attorney Scott Pyles is seen in this file photo. (Gary Middendorf – gmiddendorf@shawmedia.com/Gary Middendorf) Pyles described what happened instead. “Eighty-nine witnesses at five minutes a pop and answering every question that was asked. I don’t know how much more process we could have had unless we wanted to still be conducting the hearings today,” he said. The public hearing stretched over two nights with hours of public comment on the plan to add solar panels to 96 different properties in Manhattan, Green Garden and Wilton townships. But Becker said the hearing process did not allow for the kind of questioning allowed by law for adjoining property owners. “These are critical constitutional questions, and they cannot be pigeon-holed into a five-minute comment period,” he said. Will County Judge Victoria Breslan said she would review filings on the case, some of which came in as the case was heard, and make her decision by 2 p.m. Wednesday. The Clarion Hotel & Convention Center in Joliet will be the site of a Will County Board meeting on Thursday. “I know this has to be ruled on tomorrow because Thursday is the day that there is a vote,” Breslan said. The meeting is scheduled for 9:30 a.m. at the Clarion Hotel and Convention Center, located at 411 S. Larkin Ave., Joliet. Bob Okon covers local government for The Herald-News
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Tuesday, April 14, 2026 Jenkins County’s first solar farm, constructed in 2015, is expected to begin expansion soon. Patrick Hutchinson, owner of Cantsink Manufacturing of Lilburn, confirmed last week that the solar farm, located on Dairy Road, will expand the existing site by 6,000 panels, generating an additional 2 megawatts of power. The Jenkins County Development Authority approved the sale of an additional 5.41 […] You must be a paid subscriber to access this content! Please login or Subscribe now! Loading Comments Our HometownDMCA Notices Newspaper website content management software and services
Report by Common Wealth argues rest of the world should pay for country’s transition as reparative climate finance Cuba could beat the US’s crippling energy blockade for ever with just an $8bn investment in renewable energy. And the rest of the world should pay for it. Those are the bold claims of a thinktank analysis of the embattled socialist republic’s energy policy, which claims that Cuba could show its Caribbean neighbours the way to a green energy future. Just $8bn (£5.9bn) could fund the buildout of enough renewable energy to cover 93.4% of Cuba’s electricity generation needs, the report claims. For less than $20bn, Cuba could become the first country in the Caribbean to have a grid powered entirely by renewables. The proposals come as Cuba endures weeks of an energy blockade imposed by the US on the island and its communist-run government, which Washington claims has a “malign influence” on the region. Since January, Cuba has received just one shipment of oil, from Russia, after Donald Trump signed an executive order threatening trade tariffs on any country that sold oil to the island nation. By March, its national electric grid had collapsed, with its 10 million people enduring repeated blackouts. Hospital intensive care units lost power, and transport and industry ground to a halt, as Trump boasted: “I do believe I’ll be … having the honour of taking Cuba.” Analysis by the Common Wealth thinktank’s Transition Security Project (TSP) outlines how Cuba could gain complete energy independence from its volatile neighbour by transforming its grid to run from renewable energy, which would not only eliminate its vulnerability but also serve as a model for the region. “The US’s energy dominance strategy seeks to entrench dependence on fossil fuels, stall the green transition and strengthen US power,” said Kevin Cashman, a researcher with TSP, who wrote the analysis. But increasingly cheap and scalable solar power and battery storage weaken such a strategy. “For countries like Cuba – with enormous renewable potential, but suffering blackouts and widespread suffering under a cruel and illegal US-imposed energy blockade – a transition to green electricity would reduce US leverage and provide a shining example to the world.” Modelling four different scenarios, the TSP analysis found that a fully renewable grid for Cuba would cost $19.2bn, but an $8bn investment would be sufficient to end the country’s reliance on imported fossil fuels. Even a $5bn rollout would reduce Cuba’s reliance on fossil fuels to just a fifth of electricity generation. Under the most ambitious proposal, three-quarters of electricity generation would be provided by solar, with a fifth coming from wind and the remainder provided by hydropower and bioenergy. Cheaper scenarios would have greater reliance on bioenergy and wind. “Electricity is cheaper in every renewable investment scenario than in business as usual: the cost per unit of energy falls from 14.3¢ per kWh in the baseline scenario to 12.1¢ with $1bn of investment, 7.3¢ with $5bn, 6.5¢ with $8bn, and 9.9¢ in the fully renewable case,” the report said. The transition would require a society-wide transformation, but Cuba has managed that before: after the collapse of the Soviet Union in the 90s, the country rapidly transformed its agricultural system towards agroecology and self-sufficiency. In the past year, the Cuban government has already brought more than 1,000MW of solar online with Chinese financing and assistance. Which leaves the question: who would pay? “Financing this transition should … be understood as reparative climate finance,” the report argues. Not only would Cubans be able to pay back investments through savings on cheaper energy, but the transformation “would set an important example of a rapid energy transition under conditions of external constraint”.
Luis Alvarez/DigitalVision via Getty Images SolarEdge Technologies, Inc. (SEDG) – founded in 2006 and based in Herzliya, Israel – has been one of the most successful turnaround stories of the past 12 months. In the lead-up to 2024/25, the company had an This article was written by Analyst’s Disclosure: I/we have a beneficial long position in the shares of ENPH, ARRY either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. Seeking Alpha’s Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.
LAURENS COUNTY, S.C. (FOX Carolina) – A U.S.-owned and operated solar cell manufacturer is looking at Laurens County to open its first South Carolina manufacturing facility. Suniva Inc.’s $350 million investment will create 564 new jobs, according to a release. The release said Suniva plans to lease a 620,000-square-foot building at 1200 Commerce Boulevard in Laurens to produce advanced solar cells. “By selecting its location in Laurens County, Suniva joins a growing number of manufacturers in Upstate S.C. whose products help to power the world, deepening our expertise in advanced energy. We’re excited for the opportunities they will create in our region and look forward to watching them grow,” said Upstate SC Alliance President and CEO John Lummus. The manufacturing facility will support Suniva’s commitment to independent clean energy production. The Laurens County facility, in addition to the Georgia facility, will produce over 5.5 gigawatts of solar cells annually, one of the largest such capacities in the nation. Operations are set to start in 2027. “Since its founding in 2007, Suniva has championed U.S. leadership in solar energy manufacturing. Solar is the fastest and most economical way to grow our nation’s energy supply — and at this critical juncture, access to energy will determine how America competes for generations to come. Our expansion in South Carolina means that renewable energy, made right here at home, will now do more than ever to secure that future,” said Suniva CEO Tony Etnyre. Anyone interested in joining the company can click here. Feel more informed, prepared, and connected with FOX Carolina. For more free content like this, download our apps. Copyright 2026 WHNS. All rights reserved.
The order underscores the growing confidence of power sector players in Saatvik’s manufacturing capabilities, product quality, and timely execution track record. With its 4.8 GW module manufacturing facility in Ambala, Haryana, Saatvik is positioned to fulfil this order while maintaining its standards of quality and performance across large-scale solar deployments. Prashant Mathur, CEO, Saatvik Green Energy Limited said, “This order win is a strong endorsement of our product quality and execution capabilities. As India continues to accelerate its renewable energy capacity addition, we remain committed to being a trusted supply partner for leading power producers and contributing meaningfully to the country’s clean energy goals.” This order further consolidates Saatvik’s position as a domestic supplier in the utility-scale solar segment and adds to the company’s growing order book. It also aligns with India’s push for energy self-sufficiency under the ‘Make in India’ initiative by driving demand for domestically manufactured solar components. The win reflects the increasing preference among Independent Power Producers for high-quality, reliable, and locally manufactured solar modules, a trend that Saatvik is placed to capitalise on, backed by its expanding manufacturing footprint and ongoing investments in next-generation solar technologies.
YORK, Neb. (KLKN) — It was standing room only inside the York County Courthouse on Tuesday as commissioners discussed zoning regulations for a 3,000-acre solar farm. Community testimony lasted almost two hours as dozens voiced their concerns about the impact of the solar panels on the land. “How do you know the long-term impacts when a majority of these facilities are new?” said Hunter Johnson, a York County resident. “Are you so sure that solar is safe that you’re willing to bet the health and safety of my family for the next 30 years?” SEE ALSO: York County Commissioners revisit 3,000-acre solar farm zoning rules After several long years of discussion, the zoning regulations were finally set. Commissioners voted to set a half-mile distance from the solar farm for any homeowner who is not a willing partner in the project. That’s 2,640 feet. Some say this move makes it even harder, if not impossible, to build the solar farm. “You know how unpredictable and unforgiving our weather can be,” said Jim Edmundson, who lives in York County. “Not only tornadoes, but we also get huge gusts of wind, hail, and all kinds of storms. All kinds of conditions that could easily damage solar panels, and possibly make that risk a reality. No matter what your setbacks are, 3,000 acres is a lot of solar panels. It’s going to be almost impossible to carefully monitor them.” SEE ALSO: York County Board adopts zoning rules that make it harder to build large solar farms Other setbacks include 660 feet from any church or school, 330 feet from cemeteries, 660 feet from state recreational areas and 2,640 feet from platted subdivisions. Representatives from the Omaha Public Power District sat through the meeting and now have to adjust the project. “Today’s discussion gives us a lot to take away, and we have to evaluate, as every meeting does,” said Dustin Marvel, the Government & Community Relations Manager at OPPD. “I know there’s some discussion around putting all of what was agreed upon today into a document, so our project team is going to have to evaluate our final conversation and see what that has toward implications for the prospective project we’re trying to build.” County commissioners are inviting the public to another hearing on April 27 to voice any additional concerns before the Planning and Zoning Board. SEE ALSO: Public sounds off on proposed 3,000-acre solar farm in York County
The Little Engine that Could is the first book all children receive when they sign up to receive books from Dolly Parton’s Imagination Library. Monday, July 28, 2025. Earthrise Energy, the company currently trying to create over 8,500 acres of solar farms in Will County, announced a $15,000 grant to expand Will County’s Imagination Library. Imagination Library is the early childhood literacy program created by Dolly Parton to help get books to the families of children under the age of 5. Each month the organization, sends out approximately 3 million books to families around the U.S., Canada, the UK, Ireland, and Australia. Illinois became the 16th U.S. state to launch a statewide Imagination Library program in 2024, which offers a 50% match to funds raised by local counties for their chapters. Will County launched it’s chapter last summer with support of the Will County Center for Economic Development Foundation. The free program sends new books each month to every enrolled child until their 5th birthday. The RISE Grant from Earthrise Energy will help supply an additional 11,000 books; enough to supply 937 children with their books for a year. Earthrise Energy’s grant program is meant to support initiatives that “strengthen communities through investments in education, mental health, and community development,” according to the grant announcement. The company, which is in the process of developing 1.5 gigawatts of solar projects in the Midwest, has already awarded nearly $2 million in grants to Illinois organizations funded by the company’s profits. A solar farm under construction at the intersection of County Road 1800 North and County Road 2100 East Street on Monday, March 30, 2026 north of Princeton. (Scott Anderson) “Early literacy is foundational – not just for a child’s education, but for their long-term wellbeing,” said Earthrise Energy’s Director of Community Engagement Talya Tavor. “We’re proud to support a program that brings joy, opportunity, and imagination into the homes of so many young readers in Joliet. And we’re huge Dolly fans here at Earthrise, her music, her leadership, and her vision for a kinder world.” Doug Pryor, president and CEO of Will County Center for Economic Development, speaks at the celebration of the first Will County Summer Internship Program organized by the Will County Center for Economic Development on Wednesday, Aug. 7, 2024 in Joliet. (Gary Middendorf) Since the Will County chapter of the program launched in July 2025, the county reports that over 5,200 children have signed up. Those children will have received more than 50,000 books collectively by the end of this year. “Putting books into the hands of children is one of the most powerful investments we can make in our communities,” said Will County Center for Economic Development Foundation President and CEO Doug Pryor. “Thanks to Earthrise Energy, we’re one step closer to giving every child the gift of early literacy through this county-wide program.” Jessie has been reporting in Chicago and south suburban Will and Cook counties since 2011.
0 Powered by : Mulilo, South African renewable energy developer,has reached financial close for the 337 MW (DC) Middlepunt Solar PV project near Welkom in South Africa’s Free State Province. The project has a contracted export capacity of 240 MW (AC) and is the first Bid Window 7 project under REIPPPP to close. Once operational, Middlepunt is expected to generate about 770 GWh annually. The project will connect to the Everest Main Transmission Substation for grid integration. Electricity is priced at ZAR 458/MWh under a 20-year PPA with the National Transmission Company of South Africa. The project is expected to power about 325,000 households and avoid 813,000 tons of CO₂ annually. Mulilo targets delivering 1 GW of new generation capacity annually through solar, wind, and storage.
Researchers have found that PV plants in arid regions create a measurable cool island effect that varies strongly with season, location, and plant design, influencing surrounding vegetation in complex and spatially uneven ways. They showed that cooling intensity and distance differ widely across sites, are driven mainly by plant morphology, Image: Image: Longi Researchers from the Chinese Academy of Sciences (CAS) have investigated the solar plant–induced cool island effect (CIE) in arid regions and found that it significantly influences surrounding vegetation, with the direction and magnitude of its impact governed by geographical context and seasonal factors. CIE refers to a condition in which a specific area is cooler than its surroundings due to differences in surface properties and energy balance. In PV plants, this may occur due to panel shading, reduced ground-level solar absorption, conversion of sunlight into electricity, and enhanced convective heat dissipation. “We analysed eight PV plants in the arid regions of China using Landsat-8 land surface temperature, kernel normalised difference vegetation index, buffer analysis, and partial least squares structural equation modeling (PLS-SEM),” the group said. “Eight PV power plants were selected for this study, which are located in the arid regions of China, specifically in Xinjiang, Inner Mongolia, Gansu, and Qinghai.” The scientists used land surface temperature (LST) data from 2022, derived from seasonal imagery captured by the Landsat 8. These LST datasets were used to quantify the PV plant–induced cool island effect through two key metrics: cooling intensity (XD), defined as the temperature difference between the solar plant area and its surrounding environment, and cooling distance (Dist), which describes how far the cooling influence extends outward from the installation. In addition, the same remote sensing data were used to calculate vegetation indices, particularly kernel-normalised difference vegetation index (kNDVI), to evaluate vegetation responses both within the cooled zone and in adjacent areas beyond its influence. This allowed the researchers to assess not only the spatial extent of the cooling effect but also its ecological impact on plant growth dynamics across different zones. The results showed that the cooling intensity reached its highest value of 3.1 C in summer in Wuzhong City, while the lowest value of 0.02 C was observed in autumn at Hongshagang Town, Minqin County, Gansu Province. In addition, the cool island effect was not present in certain seasons at several sites, including Urad Banner in spring, Huanghuatan Town in autumn, and Hami in winter. Moreover, the results indicated that summer generally exhibited elevated cooling intensity values, including 2.1 C at Dalad Banner and a peak of 3.1 C at Wuzhong City. In contrast, winter conditions showed greater spatial variability: Gonghe County recorded a relatively high cooling intensity of 2.6 C, whereas Huanghuatan Town and Dalad Banner remained considerably lower, at 0.31 C and 0.9 C, respectively. Across all eight study locations, the cooling distance was found to vary substantially, ranging from 120 m to 540 m, highlighting strong site-specific differences in the spatial extent of the cool island effect. Partial least squares structural equation modeling further revealed that morphological complexity is the dominant driver of the cooling effect, while larger solar plant size exerts a strong suppressing influence. Climatic conditions were also found to contribute positively, albeit to a lesser extent. Collectively, these factors explained approximately 63% of the observed variation in cooling intensity and extent. The analysis additionally suggested that vegetation responses are highly heterogeneous across sites and seasons, depending on both local climatic conditions and the strength of the cooling effect. “We proposed a geographically differentiated ‘PV CIE–vegetation response’ framework. Medium-scale, decentralized plants with superior shape complexity are preferable in relatively dry and warm regions,” the academics said. “However, in cold, high-altitude areas, adjusting tilt and reducing panel density may mitigate vegetation risks.” Their findings appeared in “Quantifying photovoltaic power plant–induced cool island effect and vegetation response in arid regions,” published in Ecological Indicators. Researchers from the Chinese Academy of Sciences, China’s Huadian Gansu Energy Corporation, PowerChina Beijing Engineering Corporation, and the United Kingdom’s University of Reading have contributed to the study. From pv magazine Global This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Lior Kahana Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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A relative newcomer to the home appliance industry is Chinese appliance maker Mova, which along with its subsidiary brand Dreame, has a wide range of kitchen appliances and robots, ranging from robot vacuums to lawn mowers to pool cleaners and more, which it pitches under the slogan of intelligent home living. At CES 2026, some of the largest booths on display seen by ESS News were the Mova and Dreame booths, taking up huge floor space in both of the main convention locations, with Dreame even launching an electric hypercar car. Now Mova is joining the ranks of residential-sized energy storage, offering solar-plus-storage as a newcomer, with the launch this week of its LumeGret A2000 and A4000 all-in-one solar and storage options. With those with existing and new balcony solar systems and larger scale residential PV systems increasingly adding storage for both self-consumption, cost-savings, and energy security, it’s already strongly competitive market. Just in the past weeks, Anker Solix launched a new Solarbank storage product with a promise to ESS News of more to come, Zendure launched its new SolarFlow products just in February, EcoFlow has new options via the EcoFlow Ocean 2 launch plus its existing products, Jackery just launched its new SolarVault 3 range, among others, all competing in the space of smart solar and storage in and around the balcony kit level up to small and medium-size residential energy storage. Now Mova emerges somewhere in the middle with two options. LumeGret A4000, A2000 Dubbed AI-powered plug-and-play, the LumeGret A4000 is a 4kWh LFP-type hybrid unit, expandable up to 20 kWh, with a bi-directional hybrid inverter that supports up to 3.6 kW solar PV input, charging from the grid, and delivers an AC output of up to 2.5 kW. Mova says it offers up to 10,000 charge cycles, a 20-year design lifespan, and a 10-year warranty, and in the event of a grid outage, it can seamlessly switch to backup mode rather than a standalone storage device. The A2000 is the same idea, with a lower capacity battery and inverter. Storage ranging from 1.92 to 9.6 kWh. It delivers 1.5 kW AC output via the bi-directional inverter. One feature only on the A2000 is an increased safety function, with an apparent four-layer battery safety protection system including aerosol fire suppression. Mova didn’t supply a photo of the A2000. AI claims A differentiating factor Mova is pushing is what it calls LumeGret Orbit, an AI tariff optimization tool that can attempt to both optimize usage and forecast upcoming usage. Mova says it has monitoring and forecasting of solar generation, battery status, home loads, and grid flow, with users still able to adjust operating modes, set backup reserves, and optimize solar usage. Another factor is smart tariff optimization across a wide range of providers, and compatibility with smart meters and third-party ecosystems like Shelly via app. Ultimately, most competitors to Mova releasing products in 2026 have claimed similar functionality, including adjusting systems to weather and dynamic tariffs when available. The competition is then on the quality of AI, ease-of-use, service and support, and how attractive the products are both in design, implementation, and through the months and years. Still, Mova has one other trick with the LumeGret that hasn’t been mentioned by competitors: a direct EV charging concept. The company says something it calls FluxCharge enables “solar-adaptive EV charging by dynamically adjusting charging power to real-time PV output.” The company says this prioritizes clean solar energy for maximum efficiency with a 2.5 kW charging capacity that aims for max charging during “optimal sunlight”. Price, availability Mova said at its launch in Hamburg, Germany that the new LumeGret series lineup will roll out across Europe “in Q2 2026,” with entry pricing expected to begin at “approximately €1,000.” Your email address will not be published.Required fields are marked *
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NewsNews | editor@tahoedailytribune.com SOUTH LAKE TAHOE, Calif. – The South Tahoe Public Utility District (STPUD) is excited to invite community members, partners, and stakeholders to celebrate the completion of its new solar array with a ribbon-cutting ceremony on Wednesday, April 29 from 2-3:30 p.m. at STPUD on 1275 Meadow Crest Drive, South Lake Tahoe. This milestone project marks a major step forward in STPUD’s commitment to sustainability, cost efficiency, and reliable service for the community. Attendees will enjoy light refreshments, brief remarks, and a ceremonial “switch-on” moment to officially launch the system. The new solar array, the largest in the Tahoe Basin, was developed to provide long-term, stable, and cost-effective energy for STPUD’s wastewater treatment plant. By harnessing renewable energy, the system is expected to cut electricity costs by locking in predictable energy rates for decades. “This project reflects our responsibility to both our ratepayers and the environment,” said Shane Romsos, STPUD Board President. “By investing in proven solar technology, we are reducing costs, increasing energy independence, and supporting a cleaner future for our region.” Designed specifically for mountain conditions, the system incorporates features to maximize performance year-round. Solar panels are elevated and angled to shed snow naturally, while bifacial solar panel technology captures sunlight reflected off snow surfaces to boost winter energy production. The array is expected to generate approximately 2M kilowatt-hours annually, enough to offset about one-third of the wastewater treatment plant’s annual energy use. The project was made possible through strong regional collaboration, including partnerships with the Tahoe Regional Planning Agency, the City of South Lake Tahoe, El Dorado County, and Liberty Utilities. “STPUD’s solar project is a community success. By embracing renewable energy, we’re not only reducing greenhouse gas emissions, but also taking meaningful steps toward a more sustainable and resilient future,” said Nick Exline, STPUD Board Member. “It’s exciting to see the District lead by example, protecting the environment while delivering long-term value to our ratepayers.” Notably, STPUD entered into a Power Purchase Agreement for the project, meaning there were no upfront costs to ratepayers. STPUD will pay only for the energy produced, at approximately half the current utility rate, resulting in significant long-term savings. Beyond cost benefits, the project supports STPUD’s broader sustainability goals. STPUD continues to explore future enhancements such as battery storage and additional efficiency upgrades at the wastewater treatment plant. Community members are encouraged to attend the solar ribbon cutting and learn more about how this innovative project supports both environmental stewardship and responsible financial management. Event Details: What: Solar Array Ribbon Cutting When: Wednesday, April 29, 2:00 – 3:30 p.m. Where: South Tahoe Public Utility District, 1275 Meadow Crest Drive, South Lake Tahoe For more information, visit http://www.stpud.us/2026-04-29-solar-ribbon-cutting.
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April 14, 2026 By Sheryl Swingley— MUNCIE, IN—“Sustainable Land Management on Ground Mounted Solar Projects” is the title of the League of Women Voters of Muncie-Delaware County’s program to celebrate Earth Day 2026 at 2 p.m. Saturday, April 25, at the Kennedy Branch Library. The program is free and open to the public. The guest speaker will be Breanna Reed, the owner and operator of Bee-Ewe-tiful Farms in Walkerton, Indiana. Reed’s specialty is managing sheep on solar fields, and she is an advocate for dual-use solar at the local and state levels. In the summer of 2023, Reed attended a solar grazing workshop hosted by the Indiana Sheep Association. Afterward, she found local solar arrays that needed vegetation management. Instead of mowing the fields, the owners and operators of the solar fields contracted with her for her sheep to graze under the solar panels. Reed says this cooperative relationship has saved her family farm. Reed is a member of the American Solar Grazing Association, Indiana Farmers Union and board member for the Indiana Sheep Association. She participated in the German Aspen Institute’s 2025 policy forum on renewable energy and agriculture. She attended the 2025 Lamb Summit hosted by the American Lamb Board in Idaho. The League of Women Voters is a nonpartisan, grassroots organization working to protect and expand voting rights and ensure everyone is represented in the country’s democracy. The League, since its founding in 1920, strives to empower voters and defend democracy through advocacy, education, and litigation at the local, state and national levels. In addition, the League never supports or opposes political parties or candidates. Instead, it takes positions on issues that affect voters. Positions might align with a party or a candidate at times but diverge at other times. The League’s focus is always on policies and measures that serve the public interest – not party affiliation. The League of Women Voters Education Fund and local leagues work to register and inform voters through the election resources of VOTE411.org and candidate forums.
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Show all news, opinion, videos and press releases matching → International Edition It was 27 February. A herd of around 20 cows drank from the Biprasar pond, while a flock of sheep grazed nearby. Around 13 camels straddled in. “Every day, thousands of animals, birds, and humans come to quench their thirst here. And this water is two years old because there was not much rain last year. Even when the seasonal rainfall is low, the vast aagor (catchment) helps us collect it here,” said Lal Singh, spreading his arm to indicate the extent of the land before growing sombre. “There is a proposal to set up a 400 MW solar energy park in the catchment. Where will all these animals go? How will we survive without water?” Growing up in Ramgarh village of Jaisalmer district, Singh has imbibed the language of the desert ecosystem where people thrive on an average annual rainfall of around 100 mm spread over just eight days. This region has some of the lowest intensity of rainfall. For comparison, the average annual rainfall in India is around 1,200 mm. People here use traditional wisdom to harvest this little water from ponds, shallow and deep wells, and khadeens, and to rear animals on desert grasses and shrubs in orans (sacred groves) and gochars (pastures). But a growing number of large solar power and mining projects in the region are now taking over these traditional community lands, threatening the traditional way of life and sparking conflicts that have grown into a broader movement in the last five years. Solar parks don’t generate jobs for the locals, except a few who are hired as security guards or cleaners of solar panels. If the government is really serious about the welfare of people, they should promote small, decentralised solar plants owned by communities. Bhopal Singh, leader, Save Oran group Orans are sacred groves dedicated to local deities or martyrs, conserved by local communities under strict rules governing extraction. While livestock are allowed to graze, tree cutting is not allowed, turning these into oases in the desert, harbouring a large number of indigenous trees like khejri and rohida, as well as the critically endangered great Indian bustard, caracal, and desert fox. On 21 January, around 100 villagers started a protest march from Tanot Mata temple near the India-Pakistan border in Jaisalmer, planning to reach the state capital Jaipur, a distance of around 700 km, by the end of March to press upon the state government for protection of orans, pastures, and catchment areas of water sources. Along the way, several thousand others are joining them in cities like Jaisalmer and Jodhpur, while villages en route offer a warm welcome with shelter and food. Several political leaders, cutting across party lines, have supported the campaign and raised the issue in the state assembly as well. “The march is raising public awareness on the issue. We are expecting thousands of supporters from all over Rajasthan to enter Jaipur,” said Sumer Singh Bhati, a conservationist and activist who is leading the protest under the banner of ‘Save Oran.’ “We are not against development, but the focus on large-scale solar energy projects, requiring thousands of hectares, is taking away our sources of survival and livelihood.” At Bandha village, for instance, the state government allotted 2,397 hectares for a 1 GW solar power project, forcing livestock owners to look for alternatives to the grassland that is now enclosed. “Earlier, the animals could graze freely, but now there is limited land. This has forced people to reduce their herd size,” said Swaroop Ram, a resident of Bandha village. “In records, our pasture was classified as wasteland, thus making it easier for the government to allot it to the companies.” The Rajasthan Tenancy Act 1955 and the Rajasthan Land Revenue Act 1956 restrict the use of pastures and catchment of water resources for industrial and infrastructural purposes, and subsequent judgments have reinforced the rule. But wastelands can be easily allocated, which is why the locals are pressing for accurate classification of their community lands. “Our estimate suggests that around 5.8 lakh (580,000) hectares of orans in Jaisalmer district are classified as wasteland in government records,” said Bhati. “We did not know about this wrong classification and had no reason to worry because there were negligible industrial projects in the desert, and they usually required just a few acres. Solar parks, however, are different. They are being set up in thousands of hectares, and so many of them are coming up now.” Mongabay-India reached out via email to the Rajasthan Rajya Vidyut Utpadan Nigam Limited (RRVUNL), the Rajasthan Renewable Energy Corporation Limited, and the Jaisalmer district collector to inquire about the safeguards employed when allocating land for solar energy parks. No response was received at the time of publishing. With over 325 sunny days a year, Rajasthan has emerged as India’s renewable energy hub. The state ranks first in solar power, boasting an installed capacity of 22,860.73 MW. The Rajasthan Clean Energy Integrated Policy aims to achieve a target of 125 GW Renewable Power Projects by 2029-30, including 90 GW solar. Some 44,247 hectares of land were allotted for solar parks with a capacity of 23 GW between 2023 and 2025. The conflicts arising out of such expansion have also reached court. Residents of Nedan village, for instance, filed a case in 2018 arguing that a 600-MW hybrid solar-wind project by the Adani group had restricted access to orans, leading the Rajasthan High Court to cancel the allotment of land to the group. In another case, the Adani group had to return 205.3 hectares of oran land it had acquired for a solar power project at Baiya village, following vehement opposition from the locals last year. “Solar parks don’t generate jobs for the locals, except a few who are hired as security guards or cleaners of solar panels. If the government is really serious about the welfare of people, they should promote small, decentralised solar plants owned by communities,” said Bhopal Singh, a leader of the Save Oran group. “Large solar parks and mining projects only benefit a few businessmen while villagers are forced to either migrate to cities or resort to poorly paid labour work. In contrast, livestock rearing has helped people survive in this harsh region for generations.” According to the 20th Livestock Census 2019, Jaisalmer district had around 24 lakh cows, goats, sheep, and camels, but activists say the recorded pasture area is not enough for their survival. A tehsildar can earmark pasture land in consultation with the village panchayat by roughly allocating 0.12 hectares for each cattle head, says the Rajasthan Tenancy (Government) Rules 1955. “Our assessment of 45 villages based on livestock census shows that the pasture land in records is invariably short of the requisite area. We have written to the Jaisalmer district collector to do similar assessments for all villages of the district and allocate the pasture area accordingly,” said activist Balwant Singh Jodha. “A cow consumes 5 kg of dry fodder daily. If we buy from the market, it will cost ₹2,800 every week. This is why it’s essential to have orans and gochar for every village.” In 2005, the Supreme Court’s Central Empowered Committee recommended detailed mapping of orans and their classification as forests. The recommendations, however, remained unimplemented, and after several follow-up interlocutory applications, the court directed the Rajasthan government in December 2024 to enforce the recommendations and to form an expert committee to identify various forms of desert ecosystems, such as grassland, rocky outcrops, and stony desert, and to consider them as forest land. In December 2025, the state government-formed committee proposed 11,313 bigha (2,977 hectares) of land in three villages of Jaisalmer district for classification as oran. Many other villages, however, are yet to be surveyed. “No orders have yet been issued to the local revenue officers to carry out this exercise, and hence most villages are not able to take up new proposals in their panchayats,” said Parth Jagani, a Jaisalmer-based environmentalist and farmer. “Until this mapping is done, no land should be allotted or leased out for any commercial activity.” Mongabay-India reached out to the Principal Chief Conservator of Forests and the Jaisalmer district collector to inquire about ground mapping of the orans and pasture lands. Their responses are awaited. This story was published with permission from Mongabay.com. Your support helps to strengthen independent journalism, which is critically needed to guide business and policy development for positive impact. Unlock unlimited access to our content and members-only perks.
Through the window of his combine, Wayne Greier watches his teenage son Blake drive a tractor across an empty field, towing a plow into position for another uncertain season of spring planting. Greier would be worrying less if the solar farm he wanted on his land had come to pass. But local officials blocked it in 2023 under an Ohio state law, and Greier — facing a heavy medical debt — had to sell part of his land to stay afloat. The deal that was killed would have brought him about $540,000 in lease payments every year. “It was our saving grace,” he said. “It wasn’t a scary picture that everybody likes to paint about solar and the loss of farmland.” Local opposition to solar has long been an obstacle for green energy developers. But some communities are working to reverse local restrictions, citing the tax benefits and jobs the projects bring and the lease payments from energy companies that can provide stable income to farmers in a volatile industry. When a solar company approached him wanting to build panels on part of his land, Greier, 42, and a sixth-generation farmer, hesitated. But facing $1 million in medical debt from a long battle with COVID and related complications, he saw a chance to save his farm. Some in the community thought differently. Greier said he and his family were ostracized as debate over the project played out in public meetings. His mental health plummeted. And the project was eventually blocked under a state law that allows counties to block construction of wind and solar farms on land they deem “restricted.” “I was the one that was going to lose the sixth-generation farm. I was the one that couldn’t provide for my family,” he said. President Donald Trump’s hostility to green energy has battered the industry by wiping away subsidies, loans and tax incentives. But even before his return to the White House, local bans on renewable energy were becoming more common. A 2025 study from Columbia University found that from 2023 to 2024, there was a 16% increase in local laws across 44 states that restricted such projects. “Many communities want to decarbonize and probably theoretically support renewable energy,” said Juniper Katz, an assistant professor at the University of Massachusetts who focuses on environmental policy. But, she added, “When it’s your community and your backyard, balancing these processes so people feel like they’ve had a say without creating so many veto points that nothing can get done, I think is the trick. And it’s not easy to do.” In February, Dearborn County, Indiana, officials paused solar development for a year after concern from residents over the proximity of solar panels near homes and potential environmental impact of panel materials. Bobby Rauen, who lives near part of a proposed 1,200-acre (486-hectare) solar project in that county, is among residents who petitioned for the pause. He said he hopes officials use this time to create better protections for residents living near potential solar projects. He said he was also concerned that farmland may not go back into production if solar panels are eventually removed. After officials in Mahoning County, Ohio, halted Greier’s planned 675-acre (273-hectare), 150-megawatt project, he decided to help others who wanted solar on their land, saying he “didn’t want to be a victim.” As a member of the Renewable Energy Farmers of America, Greier, who primarily farms corn and soybeans, has shared his experience with lawmakers, advocacy groups and in communities debating green energy development. He recently spoke to government officials at a public meeting in Richland County, Ohio, about 100 miles (161 kilometers) from his home. Advocates there got a referendum on the ballot this May to reverse the county’s ban on wind and solar projects. Morgan Carroll, a lifelong county resident, has been working since last summer to rally support to drop the ban. Though she is not a farmer or landowner, Carroll said she supports the jobs and tax revenue these projects can bring and thinks the ban takes the decision away from residents — and may someday affect her two young children. “I want them to be in a county that can provide jobs, can provide a good school for them,” she said. “I don’t want to have to move.” Congressional Republicans and the Trump administration moved up deadlines for utility-scale solar projects to qualify for tax incentives after the passage of a big tax breaks and spending cuts bill last July. Now, utility-scale solar projects have to be in service by the end of 2027 to qualify. Last year, Lita Leavell and her husband, Joe, who operate a 1,000-acre (405-hectare) cattle farm in Lancaster, Kentucky, had hoped to host a utility-scale solar project on about half their land that would have brought them an estimated $60,000 per year. Like Greier, the lease payments would have ensured the land could stay in their family. But after a Garrard County ordinance was passed in 2023 restricting the development of solar, the energy company Leavell was working with decided to end the project. Part of her county’s rationale for the ordinance was the federal government’s opposition to solar energy and the Trump administration’s desire to stop utility-scale projects on farmland, county leaders said during an August 2025 meeting. Leavell, who said she is a Republican, questioned why lack of federal support for green energy projects should affect her ability to pursue these projects on her own land. She and a group of six other landowners are suing to overturn the ordinance. “The thing I guess that perplexed me so much is that there’s so many more worse things that could be next to you,” she said. Carroll, who helped gather signatures for the referendum in Richland County, Ohio, found that when the debate over solar projects was framed as a property rights issue, people in the community were more receptive. Greier also focuses on property rights when speaking on the issue. His farm is his retirement plan, and he should have the right to use it to support his family, he said. “There’s families that are relying on this and looking for this,” he said. “And it’s been taken away, this opportunity.” Photo: A sign opposing a nearby solar development sits near a pasture Friday, April 3, 2026, in Manchester, Ind. (AP Photo/Joshua A. Bickel) TopicsAgribusiness Was this article valuable? Thank you! Please tell us what we can do to improve this article. 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0 Powered by : Spain based Iberdrola has agreed to acquire a 42 MW solar photovoltaic plant in Lazio, Italy, from CCE. The asset was commissioned less than 6 months ago and is backed by long-term PPAs. Following the deal, Iberdrola’s Etruria Complex will reach 174 MW in total capacity. The complex includes Montalto di Castro at 23 MW, Tarquinia at 33 MW, Montefiascone at 7 MW, Limes 15 at 33 MW, Limes 10 at 18 MW and Tuscania at 18 MW. The acquisition also adds to Iberdrola’s 243 MW Fenix photovoltaic project in Italy. With this transaction, Iberdrola’s installed renewable capacity in Italy will rise to approximately 400 MW. The deal remains subject to customary closing conditions.
GAIL (India) Ltd has approved the development of a 600 MW greenfield solar project integrated with a 550 MWh co-located battery energy storage system (BESS) in Uttar Pradesh. The estimated project cost is INR 3,294.86 crore, to be financed through a mix of debt and equity. Image: GAIL GAIL (India) Ltd has approved the development of a 600 MW greenfield solar project integrated with a 550 MWh co-located battery energy storage system (BESS) in Uttar Pradesh. The estimated project cost is INR 3,294.86 crore, to be financed through a mix of debt and equity. GAIL currently has around 29 MW of installed solar capacity. The new project is expected to be commissioned within 15 months from the award of the EPC contract. The company views renewable energy as a strategic growth opportunity and is expanding its clean energy portfolio as part of its decarbonisation strategy. It aims to achieve Net Zero Scope 1 and Scope 2 greenhouse gas emissions by 2035 through a combination of electrification of natural gas–based equipment, deployment of renewable energy, battery energy storage systems (BESS), compressed biogas (CBG), and green hydrogen initiatives. Several large-scale projects are currently under various stages of development, including 100 MW and 600 MW solar projects in Uttar Pradesh, along with captive solar installations across multiple GAIL facilities. This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com. More articles from Uma Gupta Please be mindful of our community standards. Your email address will not be published.Required fields are marked *
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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: 9200 (2026) Cite this article 1251 Accesses 1 Citations Metrics details This study presents an advanced control strategy for a standalone photovoltaic (PV) system integrated with a hybrid energy storage system (HESS) comprising batteries and supercapacitors (SCs). The proposed system employs a novel Fuzzy Logic-based Two-Degree-of-Freedom Proportional-Integral (Fuzzy 2DOF-PI) controller, optimized using the Hippopotamus Optimization (HO) algorithm, to enhance power management and stability. The batteries address long-term energy demands, while SCs handle instantaneous power fluctuations, mitigating stress on the batteries and extending their lifespan. The control strategy ensures optimal power distribution, maintains DC bus voltage stability, and prevents battery overcharging by regulating the State of Charge (SOC) within safe limits. The system’s performance is validated through MATLAB/Simulink simulations under varying solar irradiance and load conditions. Comparative analyses with classical PI, Fuzzy PI-based Teaching-Learning-Based Optimization (TLBO), and Particle Swarm Optimization (PSO) demonstrate the better dynamic response, reduced transient time, and minimized overshoot of the proposed approach. Results indicate improvements of at least 15% in peak overshoot and 10% in transient duration, highlighting the robustness and efficiency of the Fuzzy 2DOF-PI controller in hybrid energy storage applications. Recently, there has been an increasing focus on integrating renewable energy sources (RESs) into power generation systems to move towards a more sustainable and environmentally friendly energy mix. This worldwide transition is propelled by the pressing necessity to alleviate climate change, diminish greenhouse gas emissions, and strengthen energy security1. Governments and organizations around the world have enacted regulations and offered incentives to accelerate the adoption of RESs, including solar photovoltaic, wind, hydropower, and biomass. These programs have markedly augmented the integration of RESs into power networks, resulting in diversification of energy sources, improved grid resilience, and economic prospects for stakeholders2. The inherent variability and fluctuations of RESs pose significant problems for grid stability and energy management3. As a result, innovations in energy storage technologies, smart grid infrastructure, and energy management systems have become essential solutions to address these challenges and ensure reliable power supplies4. One viable strategy for the effective integration of RESs into power grids is the construction of DC microgrids5. DC microgrids have attracted heightened interest owing to their efficiency, reliability, and many uses, such as electric vehicles (EVs), uninterruptible power supplies, and distributed power systems. Unlike traditional AC systems, DC microgrids provide enhanced power conversion efficiency, reduced transmission losses, and streamlined integration with RES and energy storage systems (ESSs). These benefits make DC microgrids a compelling option for improving the sustainability and stability of modern energy infrastructure6. A fundamental component of DC microgrids is the incorporation of hybrid ESSs, which combines multiple storage technologies to improve performance. ESSs can be implemented using several storage technologies, including batteries, supercapacitors, flywheels, and ultracapacitors7. Batteries are the most common because of their considerable energy capacity and ability to store large amounts of energy for longer periods. However, sole reliance on batteries in an ESS may lead to reduced battery lifespan and performance degradation, particularly in environments with variable power demands. This limitation arises from the relatively slow response time of batteries to rapid power fluctuations, which may result in increased stress and thermal degradation8. To address these challenges, hybrid ESSs integrate multiple storage devices with complementary characteristics, hence enhancing overall system efficiency and reliability9. A common HESS configuration involves a combination of batteries and supercapacitors. In this arrangement, supercapacitors, noted for their high-power density and rapid response capabilities, regulate short-term power fluctuations and transient loads. Simultaneously, batteries, noted for their high energy density, provide sustained power over lengthy durations. This synergistic relationship reduces battery strain, extends their lifespan, and improves the overall efficiency of the ESS10. Advanced optimization-based control and planning strategies play a critical role in enhancing voltage regulation and power quality in renewable-integrated distribution systems. Their two-stage reactive power optimization approach demonstrates how coordinated control actions can effectively mitigate voltage deviations and reduce system losses under varying operating conditions. In a related work, the authors extended this concept to a multi-objective, multi-period framework, highlighting the importance of time-varying optimization in accommodating renewable intermittency and load dynamics11,12. As well, advanced energy management systems (EMS) are necessary for integrating RESs and energy storage technologies into DC microgrids to maximize energy flow and preserve system stability. EMS are crucial for optimizing operations via real-time monitoring, demand-side management, and adaptive control strategies. Recent EMS solutions incorporate smart grid technology, artificial intelligence (AI), and predictive machine learning techniques to forecast energy consumption, enhance storage efficiency, and bolster grid reliability13. Furthermore, the EMS facilitates seamless coordination across RESs, storage devices, and grid infrastructure, mitigating power fluctuations and improving energy efficiency. Despite the numerous advantages of DC microgrids and HESS, certain challenges remain in their widespread implementation. The fluctuation of RESs necessitates suitable control systems to equilibrate supply and demand. Research concentrates on enhancing power interface technology, dynamic energy distribution strategies, and adaptive control methods to improve the reliability of DC microgrids14. HESS consisting of batteries and Supercapacitors (SC) may exhibit various topologies, including passive, semi-active, and active configurations15. Active topologies have enhanced controllability that allows the full utilization of the storage capacity and power dispatch capabilities of the HESS devices. Each element of the HESS is independently connected to the system bus through a power electronic converter and has a separate control system16. Recent studies have demonstrated that metaheuristic optimization-based MPPT algorithms can significantly enhance power extraction, dynamic response, and system stability compared to conventional methods. In particular, advanced bio-inspired optimizers, including Ali Baba and Forty Thieves Optimization (ABFTO) and the Hippopotamus Algorithm (HA), have shown better capability in tracking the global maximum power point under complex operating conditions such as partial shading and rapid irradiance or temperature variations. These intelligent techniques ensure stable power delivery, fast convergence, and effective bidirectional energy management, thereby improving the resilience, efficiency, and sustainability of PV-integrated DC microgrids and EV charging systems17,18. The study19presents a novel metaheuristic-based control framework that integrates a two-degree-of-freedom PID acceleration (2DOF-PIDA) controller with the recently developed Starfish Optimization Algorithm (SFOA) for temperature regulation of the CSTH process. The 2DOF-PIDA structure improves control performance by independently addressing setpoint tracking and disturbance rejection, whereas the SFOA effectively optimizes the controller parameters through its balanced exploration and exploitation mechanisms. Simulation results confirm the superiority of the proposed approach in terms of tracking precision, disturbance attenuation, and robustness when compared to conventional control techniques20. Advanced studies have demonstrated the effectiveness of learning-based frameworks across load forecasting and battery state estimation. Specifically, in21, a spectral attention–enhanced bidirectional memory network showed superior performance in short-term load forecasting by capturing both temporal and spectral features of power demand signals. Meanwhile, the EBWO–GRU–ACKF framework presented in22highlighted the integration of optimization algorithms with recurrent neural networks for accurate state-of-charge (SOC) estimation. A multi-task learning (MTL) framework was created in this study to enhance SOH assessment of lithium-ion batteries (LIBs). The framework successfully captures both domain-invariant and target-specific features by using health-dependent pseudo-labels (PLs) and a multi-task strategy, which improves the model’s robustness and generalization abilities23,24. Following the same trend, hybrid machine learning methods combining Random Forest, Soft Weight K-Nearest Neighbors, and Levenberg–Marquardt Backpropagation within a variance–covariance weighted framework have been proposed for adaptive parameter tuning. As reported in25, incorporating meteorological and temporal variables in these hybrid models reduces errors by 8%–38% and improves forecasting accuracy by 12%–24% compared to single models. Researchers have developed various methodologies for using combined energy sources to send power from a battery and supercapacitor (SC) to the load26. Three main approaches exist for HESSs to control their power flow: optimization, filtering, and rule-based models as exhibited in Fig. 127. HEES Control Strategies. The sophisticated techniques encompass data-driven methodologies, including machine learning, artificial neural networks (ANN), and evolutionary algorithms28. Following this trend, in Ref29., an energy management system utilizing a combination of dynamic programming and neural networks is presented for the HESS, demonstrating near-optimal performance. Nevertheless, the neural network model requires a substantial quantity of sample data for training. Ref30. formulated a mathematical model to optimize a hybrid system employing a genetic algorithm (GA). The findings indicate that GA necessitates less time for simulation and demonstrates greater accuracy in delivering outcomes. A notable deficiency of HOMER software is its limited flexibility in model creation. This study analyzed two systems with varying turbine sizes, revealing that turbine size has minimal impact on the outcomes. Authors of31employed a multi-objective algorithm to ascertain the dimensions of a HESS in Tanzania. Their findings indicated that incorporating the electrochemical storage system into the HESS enhances its economic viability, particularly in configurations characterized by poor cyclability and shallow depth of discharge. Recent advancements show that combining hybrid deep learning architectures with metaheuristic optimization significantly enhances temperature prediction accuracy in power system components, thereby improving thermal monitoring and strengthening operational safety and reliability32. In addition, accurate wind speed forecasting remains crucial for renewable energy integration, where optimized machine learning frameworks enhance prediction robustness and support stable smart grid operation under varying environmental conditions33. To reduce the standardized cost of energy and the corresponding carbon dioxide (CO2) emissions that occur throughout the life cycle of the energy system, Ref34. used a multi-objective function. For this purpose, they used the Strength Pareto Evolutionary Algorithm. According to the results, photovoltaic (PV) generators have the potential to be a major electrical energy source in Spain. To maximize the size of a hybrid system that combines solar and wind power, Ref35. used the Linear TORSCHE optimization technique. According to the results, the cost-effectiveness of the wind, solar, and battery systems together was higher than that of any of the individual systems. This work introduces a hybrid optimization approach, termed DE–HHO, which integrates Differential Evolution (DE) with Harris Hawks Optimization (HHO) to address microgrid scheduling problems under a multi-objective optimization framework that simultaneously minimizes operating costs and environmental impacts. Simulation studies involving wind, photovoltaic, micro-gas turbine, and battery system models demonstrate the superior convergence behavior and global search capability of the proposed DE–HHO algorithm36. Moreover, an enhanced Snow Ablation Optimizer incorporating adaptive T-distribution control and Cauchy mutation has been reported to effectively mitigate premature convergence and accelerate convergence speed, highlighting its potential applicability to complex microgrid optimization and energy management problems37. A novel controller FOPI-PI with self-adaptive bonobo algorithm (SABO) and Puma Algorithm (PO) is presented in38,39with HESS to reduce the stress on the batteries with load and temperature variations. For a HESS consisting of wind power, photovoltaics, fuel cells, and batteries40,41, presented a multi-objective optimization framework using an elephant herding optimization algorithm. To reduce capital costs and improve electrical efficiency and power supply reliability, the proposed approach was studied. The results showed that the recommended approach is suitable for solar photovoltaic system design. The study42presents a multi-objective optimization model for microgrid energy management incorporating degradation costs and a carbon trading mechanism to reduce emissions. A hybrid energy storage system smooths renewable fluctuations, while demand response optimizes load. Two novel algorithms, an artificial hummingbird optimizer and a coati optimizer enhanced with advanced ranking and archiving techniques, are proposed to solve the optimization problem. Tested on benchmark functions and IEEE test systems, the coati algorithm improved network loss, voltage deviation, and minimum voltage by up to 56%. Optimal strategies are selected via TOPSIS, demonstrating the model’s effectiveness in managing active distribution networks with renewable integration43. In most microgrid applications, the power management of hybrid energy storage systems is conducted using filtration-based techniques44. The established protocol for implementing these techniques involves dividing the current input of the HESS into high-frequency (HF) and low-frequency (LF) components. Subsequently, the HF components get designated for the SC. While using linear time invariant (LTI) low-pass filters (LPF) for power smoothing reduces system complexity, efficiency is sacrificed in the process. On the other hand, sophisticated filtering methods like wavelet transformations can be used to improve system efficiency, but doing so comes at the cost of the charge control system’s computing complexity45,46. Using less-than-ideal filters in practice could cause the supercapacitor to fully charge or discharge. Furthermore, unexpected variations in the HESS’s input power may place a lot of strain on the SC, which has the ability to instantly fully charge or discharge the SC. Adaptive filtering techniques can be used to improve system efficiency and stop state of charge (SOC) violations in SC47. A rule-based controller is usually used in adaptive rule-based filters to relax the filter in the event that the SC’s SOC exceeds a predetermined threshold. To avoid SOC violation in this instance, extra HF components of the HESS input power are delivered to the BESS. As a result, the filter’s bandwidth and net power variations should be taken into account while designing the SC’s size. Otherwise, the filter is frequently turned off, which could reduce the effectiveness of the system. Model predictive control (MPC) can regulate the output voltage and current of power converters at the primary control level of microgrids. For instance, a rapid model predictive control (MPC) is proposed in research48. This MPC controller increases the robustness of DCMGs against a variety of disturbances by using just local information in the HESS. Simplified switching states and a one-step prediction horizon allow for rapid regulation of the DC bus voltage. Additionally, the residual capacity prompted activating sequence of various ESS types based on a dynamic voltage control optimizes the power allocation command. Conversely, rule-based approaches exhibit reduced computing complexity and are better appropriate for real-time applications. There are two types of rule-based approaches: fuzzy rule-based systems and finite state machines (FSMs). The rules in these approaches could be developed by a specialist or taken from mathematical models. Table 1 summarizes the latest techniques of fuzzy logic control (FLC) in HESS. This study employs a novel control architecture to guarantee the stability and robustness of interconnected micro-DC grids. The suggested controller parameters can be modified via Hippopotamus Optimization (HO) technique61. This study’s unique contributions, in contrast to prior research, are distinctly apparent in the following main aspects: Proposing an innovative control method that combines fuzzy logic with 2DOF-PI controller to manage the power of solar panels, batteries, and supercapacitors. With sophisticated modeling for both SC and batteries, this study suggests a novel optimized EMS for a battery–SC that is executed in a full-active configuration utilizing dual converters. The adoption of a 2DOF-PI control structure, allowing independent tuning of reference tracking and disturbance rejection, which is rarely considered in existing HESS fuzzy–PI designs that typically rely on 1DOF structures. The coordinated integration of a fuzzy supervisory layer with the 2DOF-PI controllers governing dual bidirectional converters in a fully active HESS. The suggested F2DOF-PI controller employs a HO method to refine its parameters. This novel optimization technique is being implemented for the first time in the domain of micro-DC grids. The novel control architecture presents numerous benefits compared to existing controllers by integrating the merits of fuzzy logic with 2DOF-PI. Consequently, enhanced stability, reliable performance, resilience, and better transient response can be attained. Moreover, in contrast to the classical methodology illustrated in62, and Fuzzy logic with PI controller based PSO and TLBO illustrated in52,53, the suggested controller distinctly surpasses all other controllers in essential aspects, including transient response attributes such as transient time, and overshoot/undershoot. The simulation encompasses four different scenarios pertaining to solar radiation and load variance. The outcomings show an improvement in peak overshoots by at least 15% in all cases and 10% in transient duration. The paper is organized in the following way: Sect. 2 outlines the detailed configuration and modelling of the system. Section 3 outlines the suggested control scheme, the DC bus configuration, the suggested controller, and the proposed optimization technique (HO) and its many strategies. Section 4 elucidates the simulation outcomes, thoroughly examining solar radiation and load variations. Section 5 ultimately delineates the research conclusions and findings. Figure 2 shows a complete design for a solar-powered hybrid energy management system that is meant to make power distribution and storage in DC microgrids more efficient. A MPPT controller controls this power by dynamically changing the operating point to get the most energy out of the PV voltage (ₚv) and current (ₚv). Then, the regulated DC power is sent to a centralized DC bus. There are a lot of parts connected to the DC bus, such as the DC load and an ESS, which is made up of a battery bank and a supercapacitor (SC) bank. Both storage units connect to the DC bus using separate buck-boost converters, which let energy flow in both directions for charging and discharging. The Power Management System (PMS) is in charge of the whole system and makes smart choices to keep the system stable and running at its best by balancing the generation, storage, and use of energy. An active topology’s main benefit is that it actively controls each ESS’s power. Active topologies fall into two categories: parallel and cascaded. A battery and supercapacitor (SC) ESS with a parallel active architecture was suggested in63. In microgrids (MG), the parallel active topology is widely adopted due to several key advantages. This configuration offers enhanced flexibility by allowing independent control of HESS units, enabling a wide range of control techniques to be implemented. Moreover, the voltage levels of the Energy Storage System (ESS) units do not directly affect the system voltage, which simplifies system integration and design. Additionally, the parallel active topology improves the system’s inherent fault tolerance, contributing to increased reliability and stability of the microgrid64. Complete architecture of a HES with PV. The constructed model of a photovoltaic cell entails the computation of current-voltage and power-voltage characteristics utilizing exact formulae. Researchers have developed models utilizing one to five factors. The five-parameter approach is the most favored and dependable, particularly in outdoor environments65. Figure 3 depicts the execution of the photovoltaic model. The model for a photovoltaic cell comprises many components: Iph denotes the sunlight current, ID signifies the diode current, and Ish represents the shunt-leakage current. Furthermore, Ipv denotes the output current supplied by the panel, while Rs represents the series resistance66. The output current is calculated from a series of equations from (1) to (4): Where Np represents the quantity of solar cells arranged in parallel,, Electron charge (q), cell output voltage (VPV), cell output current (IPV), number of series-connected cells (Ns), ideality factor (A), Boltzmann constant (K), and temperature (T) are all variables in this equation. A DC-DC buck-boost converter has been employed for the regulation of the PV array linked to the DC bus, enabling the elevation of voltage from the PV module to sustain the load voltage at the specified level. The solar panel under consideration has a peak power output of 120 W, achieved at a maximum power point (MPP) current of 7.1 A and a voltage of 17 V. Under no-load conditions, the panel exhibits (Voc) of 21 V, while (Isc) reaches 8 A. The panel’s electrical performance is also influenced by temperature variations, with a short-circuit current temperature coefficient of + 0.052%/°C, indicating a slight increase in current with rising temperature, and an open-circuit voltage temperature coefficient of − 0.358%/°C, reflecting a typical decrease in voltage as temperature increases. These characteristics are essential for accurately modeling the panel’s behavior under varying environmental conditions and optimizing its integration within solar energy systems. Circuit diagram of PV panel with boost converter. The SC operates as an electrical element with a high-power density and a quick dynamic response. The hybrid system may either release excess power or store additional energy from regeneration to make up for the large variation in power consumption. In this study, a SC model is constructed using the Stern model67. The SC model’s circuit is shown in Fig. 4. The SC voltage can be expressed as follows: where ISC is the current flowing through the SC, RSC is the internal resistance, NS and NP are the cells in series and parallel, respectively, and QT is the total electric charge (in coulombs). The SC energy ESC is determined by two factors: the SC voltage VSC and the SC capacitance QSC68: As a result, the quantity of energy stored will fluctuate in proportion to changes in the capacitor’s voltage, and the SOCSC may be computed as follows: SC is linked to the DC bus using a standard buck-boost converter. This converter is made by replacing the unidirectional switches of a normal buck and boost converter with bidirectional power switches. The final product is a BDC that can be used as a buck converter in the opposite direction and as a boost converter from Vsc to Vdc69. The parameters of the SC utilized in this model are presented briefly in Table 2. Circuit diagram of SC with buck-boost converter. ESTs are often governed to monitor the energy exchange between the generating and load sectors under both normal and abnormal circumstances. Furthermore, the role of ESTs becomes crucial, especially when the optimal utilization of renewable energies is implemented. The current work used a typical battery model in which the state of charge (SOC) is treated as a state variable to mitigate arithmetic loop complexity and to enable the representation of four battery varieties, including the lead-acid variant employed in this research70. The model characterizes the battery as a regulated voltage source in conjunction with constant resistance, as illustrated in Fig. 5 and highlighted by Eqs. (8) and (9). The no-load voltage, constant voltage of the battery, polarization voltage, battery capacity, real battery charge, amplitude of the exponential zone, and inverse of the time constant of the exponential zone are represented by V, V0, VPol, Cbat, ∫iB dt, A, and B, respectively, in the relationships given above. VB denotes the battery voltage, Rin represents the internal resistance, and iB indicates the real battery current. The maximum capacity and the change of current charge can be used to identify the battery’s state of charge (SOC). The parameters of the battery utilized in this model are presented briefly in Table 3. Circuit diagram of battery with buck-boost converter. An illustration of the proposed control technique may be found in Fig. 6. With this approach, the goal is to reduce the amount of strain that is placed on batteries throughout the charging and discharging cycles, hence extending the lifespan of the batteries. It is anticipated that the state of charge (SOC) of the batteries would continually remain within a range that is considered to be acceptable. In order for the method to function, it first compares the mean value of Vdc with a reference voltage (Vref), and then it sends the error to a proposed controller. The output signal of the proposed controller is represented by the total current (ΔI). Using Eq. (11), one can get the total current that is required from the HESS, which is comprised of both batteries and supercapacitors (SCs)62. Based on frequency, the reference current Itot_ref is separated into a (ILF_ref) and a (IHF_ref). The current (ILF_ref) is fulfilled by the batteries following the rate-limiting operation, which may be achieved through the use of a low-pass filter. In contrast, the SCs may satisfy the (IHF_ref). The LF component can be defined as: Where fLPF is the low-pass filter TF. So, the current of the battery may be: Where fRL is the rate limiter TF. In the proposed control framework, the rate limiter applied to the battery reference current in (14) is introduced to account for the inherently slower dynamic characteristics of batteries compared to supercapacitors and to mitigate excessive current stress. As indicated by (12) and (13), the total reference current is first decomposed into low and high-frequency components using a low-pass filter with a time constant of 0.015 s, and the resulting low-frequency component is then processed through a rate-limiting function. This ensures that the battery supplies only the slowly varying component of the load demand, whereas rapid current transients and high-frequency power fluctuations are primarily absorbed by the supercapacitor, thereby alleviating potential current stress on the battery and contributing to reduced degradation. The control method that has been suggested involves comparing the (IB_ref) with the actual (IB) and then entering the error signal into the fuzzy controller that has been provided. Following that, the 2DOF-PI does the calculation necessary to determine the duty ratio (DBat) that is generated from the error signal. This duty ratio is then sent to the PWM. For the purpose of controlling the flow of electricity into or out of the batteries, the pulse width modulation (PWM) may be used to generate the switching pulses for the battery switches (S1 and S2). While this is going on, the HF component can be calculated as follows: Proposed HESS Control Scheme. The battery’s slow reaction time may prevent it from promptly aligning with reference current (IB_ref). Consequently, the control method accommodates this delay by determining the uncompensated battery power, which is articulated as: The control approach uses Eq. (16) to set a reference current for the SC in order to equalize the uncompensated battery power. The fundamental step in the control procedures is achieved by comparing (ISC_ref) with the actual ISC. Any error resulting from the two previously stated signals is thereafter managed by the fuzzy controller and 2DOF-PI, which generates the relevant DSC depending on the error signal, subsequently relayed to the PWM generator. The PWM generator is responsible for producing switching pulses that are in sync with the switches of the SCs (S3 and S4). This allows the PWM generator to effectively regulate the power delivered or consumed by SCs. Through the process of modifying the duty cycle in response to the error signal, the control technique has the potential to guarantee that the actual current of the SCs is in accordance with the reference current and that an equitable distribution of power is maintained over the load. The Hippopotamus Optimization Algorithm (HOA) is a population-based metaheuristic inspired by the social organization and defensive behaviors of hippopotamuses in their natural habitats. Hippos typically form structured groups consisting of a dominant male, females, and calves, and they exhibit distinct responses such as confrontation and rapid escape when threatened. These behavioral patterns are abstracted in HOA into three main phases that guide the exploration and exploitation processes. Accordingly, candidate solutions (hippopotamuses) are initialized and iteratively updated within the search space based on position update rules, as formulated in Eq. (17)71. where LLj and ULj specify the bottom and upper bounds of the jth decision variable, and Xhi indicates the location of the hith candidate solution. r is random number between 0 and 1, N represents the overall population size inside the herd, and M is the total number of decision factors. Using the known CF, the dominating hippopotamus or herd leader is chosen at this stage, and the herd is protected from danger by the prevailing solution. Once they reach maturity, male hippos are kicked out of the herd by the dominant male. From that point on, they have to find a way to establish their own dominance, which is outlined in Eq. (18). Here, Dhippo denotes the location of the dominant hippopotamus, XiMhippo denotes the position of the male hippopotamus, y1 is a random value between 0 and 1, and I1, I2 are integer integers between 1 and 2. Vectors r1, r2, r3, and r4 are randomly created within the range of 0 to 1, whereas r5 is a random number also between 0 and 1. Q1 and Q2 are random integers, either 0 or 1. The behavior of female and juvenile hippopotamuses is influenced by two random vectors, h1 or h2, derived from five distinct circumstances as stated in the Eq. (19)71. Hippopotamuses inhabit herds for protection, using their bulk to dissuade predators; nevertheless, juvenile and ailing members remain susceptible. Their principal defense mechanism involves facing the predator and emitting loud vocalizations to repel dangers. Equation (20) delineates the protective distance between the predator and the hippopotamus, whereas Eq. (21) illustrates the processes of evasion and predation. where XiRhippo indicates the hippopotamus’s position relative to the predator, (:overrightarrow{RL}) signifies a random vector following a Lévy distribution, ϑ is a random variable that varies between 2 and 4, while c and d are random variables limited to the intervals [1, 1.5] and2,3, respectively. g is a uniformly distributed random value within the interval of -1 to 1, whereas (:overrightarrow{{r}_{9}}) denotes a random vector. Since predators like lions and hyenas tend to stay away from water, a hippopotamus will typically seek refuge near a body of water if it is attacked by multiple enemies or is unable to fight them off. This method improves local search utilization in the HOA model, as delineated in Eqs. (22) and (23). (:{X}_{i}^{{H}_{Hippo}}) denotes the location of the hippopotamus in pursuit of the nearest secure area, constrained by the lower and upper limits: (:text{L}{text{L}}_{text{j}}^{text{local}})and (:text{U}{text{L}}_{text{j}}^{text{local}}), respectively. iter represents the current iteration, while (:{text{iter}}_{text{max}}) signifies the total number of HOA iterations; (:text{α}) and r10 are randomly generated vectors. The HOA process flow is shown in Fig. 7. Flowchart of the HO optimizer. Fuzzy logic was chosen as the control architecture for managing both DC/DC converters due to its capability to operate effectively without requiring an exact mathematical model or transfer function of the system, thereby simplifying the design process and enhancing adaptability. Its inherent tolerance to imprecise or uncertain input data makes it highly robust under varying operating conditions and system nonlinearities. Furthermore, FLCs have been shown to deliver performance levels comparable to those of conventional PI or PID controllers, while offering improved flexibility in handling complex, nonlinear, and time-varying systems. This makes fuzzy logic a suitable and reliable control strategy for achieving stable and efficient power management in DC/DC converter applications72. The FLC structure with 2DOF-PI is illustrated in Fig. 8. Configuration of Fuzzy Logic with 2DOF-PI Controller. For the two different inputs to the controller, two input membership functions are required. Membership functions are clear curves that define the correspondence between each input value and a certain value, or the degree of truth related to that value. The preliminary membership function is the error as seen in Fig. 9. Error membership function. The error membership function’s rate of change is represented by the second membership function, as shown in Fig. 10. This function assesses whether the mistake diminishes at an acceptable rate. Rate of Error membership function. Zero (Z), positive small (PS), positive medium (PM), negative large (PL), negative medium (NM), negative small (NS), and negative large (NL) are the seven categories that make up each membership function. Due to its singular output, the FLC requires just one output membership function. Figure 11 shows the membership function that was produced. Output membership function. Throughout the simulation process, the membership functions’ input ranges were modified until the controller functioned as intended. Gain and, conversely, input function sensitivity can be changed by adjusting the membership functions’ input range. The suggested fuzzy logic rules are delineated in Table 4 below. The regulations were instituted to guarantee that the controller evaluates both the deviation between the measured value and the reference value and, by examining the error’s derivative, determines if the error is decreasing at an appropriate rate, thereby adjusting the duty cycle as necessary. FLC utilized the maximum method for aggregation and the centroid technique for defuzzification. The Mamdani inference method was employed49. Figure 12 illustrates the control surface that delineates the input-output correlation of the (FLC). Determining the appropriate input and output values and configurations for FLC is a formidable problem. FLC Rule Surface Viewer. The proposed controller integrates the advantages of Fuzzy logic with 2DOF-PI controllers, resulting in enhanced power regulation. The 2DOF-PI controller configuration mirrors that of the PI controller, including an additional weight component to the reference elements. Figure 13 illustrates the configuration of the 2DOF-PI regulator. Equation (24) is the transfer function of the 2DOF-PI controller73. Structure of 2DOF-PI controller. b represents the proportionate set-point weighting adjustment. The system parameters are constrained as follows: A suggested controller is intended to distribute power between the battery and the SC. The cost function (:J:)is now defined as the Integral of Squared Error (ISE) of the main HESS control variables and is given by: where (:{stackrel{prime }{e}}_{Vdc}), (:{stackrel{prime }{e}}_{ISC}), and (:{stackrel{prime }{e}}_{IB})denote the normalized DC-bus voltage error, supercapacitor current error, and battery current error, respectively. These variables represent the key performance indicators governing DC-link stability, transient power compensation, and battery current regulation within the hybrid energy storage system. This section verifies the constructed Fuzzy 2DOF-PI based HO controller under varied load situations and fluctuations in solar irradiation. The simulations in this study are performed under idealized conditions, without explicitly modeling practical non-idealities such as converter switching losses, measurement noise, communication delays, SOC estimation errors or component aging. The main objective is a fair comparative evaluation of control strategies under identical assumptions to isolate the effect of the proposed method. The objective is to diminish peak power and extend battery life to comply with the state of charge limitations of the battery and SC by optimizing the controller settings. To assess the efficacy of Fuzzy with 2DOF-PI, a comprehensive comparison will be conducted between the fuzzy PI-based TLBO, PSO, and non-optimization fuzzy methods, including conventional PI. To examine its performance, the planned system has been simulated using the MATLAB/Simulink® (2024b) environment. The convergence characteristics of the optimization algorithms HO, TLBO, and PSO are illustrated in Fig. 14. At the final iteration, the HO-based optimization achieves the lowest fitness value of 5307.7, compared to 5368.8 for TLBO and 5531.7 for PSO. This demonstrates that the HO-based offline parameter tuning not only converges more rapidly but also attains a higher-quality optimal solution, indicating superior exploitation capability and greater efficiency in tuning the proposed Fuzzy 2DOF-PI controller compared with the benchmark optimization algorithms. All algorithms are conducted based on 30 search populations and 100 iterations. Table 5 below lists the optimal values of the utilized controllers. Convergence rate of the three optimization techniques. In this case, the battery’s state of charge was originally at 50%. The PV system and HESS carry over the entire load requirement. Figure 15 illustrates how the amount of solar radiation is thought to fluctuate. The irradiance remains at 200 W/m² from 0 to 0.5 s, then increases to 400 W/m² from 0.5 to 1.0 s. At 1.0 s, there is a further increase to 700 W/m², maintained until 1.5 s. Subsequently, it decreases to 500 W/m² and remains stable for 2.0 s. This stepped irradiance profile is frequently employed to evaluate the dynamic response of photovoltaic systems and (MPPT) algorithms under fluctuating solar conditions, such as changing cloud cover or varied weather. The sudden alterations facilitate the assessment of tracking efficacy, control responsiveness, and system stability. The graph highlights how the battery and solar system work together to maintain a constant load power requirement by showing the power distribution fluctuations over time among the solar source, battery, and load. While the solar power production shows a stepwise increase in response to variations in sun irradiation, the load power stays roughly constant at 500 W over the 2-second interval. Initially, when there is not enough solar input, the battery makes up the difference by giving the load the extra power it needs. The battery contribution correspondingly decreases as solar power increases at approximately 0.5 and 1.0 s, demonstrating effective load distribution. Negative battery power levels, which indicate charging activity, occur when solar generation exceeds load demand during the peak solar irradiance period (roughly 1.0 to 1.5 s). When the amount of solar input decreases after 1.5 s, the battery switches back to discharging mode to make up for the lost solar generation and keep the load powered continuously. Figure 16 highlights the cooperative behavior of the battery and solar system in maintaining a constant load power demand by showing the dynamic power sharing between the solar source, battery, and load over time. Figures 17 and 18 depict the comparative analysis of power responses for various control strategies, including classical PI62, fuzzy PI based on TLBO53, fuzzy PI based on PSO52, and the proposed fuzzy 2DOF-PI based on HO. Figure 19 illustrates the battery state SoC. The peak overshoot and transient time for the various controllers are illustrated in Figs. 20 and 21, respectively. The comparative results of peak overshoot and transient time for the four control strategies clearly demonstrate that the F2DOF-PI based HO outperforms the other methods in both stability and dynamic response. The F2DOF-PI based HO achieves the lowest values across all power sources, with the battery power peak overshoot reduced by about 20% and the supercapacitor power peak overshoot lowered by nearly 23% compared to the classical PI controller. Meanwhile, the FPI-based TLBO and FPI-based PSO show moderate improvements over the classical PI, yet their overshoot levels remain considerably higher than those of the F2DOF-PI based HO. The proposed method also excels, reducing solar power transient time by approximately 40% and load power transient time by around 50% relative to the classical PI, which means it responds faster to system disturbances. Although the FPI-based TLBO and PSO exhibit some gains in transient performance compared to the classical PI, they still lag behind the F2DOF-PI based HO. Solar Irradiance Variation. Power Responses of the Proposed Control Strategy. Responses of Solar and Load Powers for different controllers. Responses of Battery and SC Powers for different controllers. Battery State of Charge. Peak overshoot for different controllers. Transient time for different controllers. To assess the system’s dynamic response and load-sharing efficiency, a step load increase is implemented in this scenario. First, the (HESS), which includes a battery, and the photovoltaic (PV) array work together to keep the overall system load constant. A realistic scenario, like turning on an extra appliance or piece of equipment, is represented by a sudden step increase in load demand that happens at a particular point in the simulation. The solar array provides a significant amount of power before the load increases, with the battery making up the difference. The battery can lower its discharge rate or even recharge if there is excess solar energy available as the PV system gradually takes on more of the load burden as it adapts to the new load condition, possibly using maximum power point tracking (MPPT) mechanisms. Figures 22 and 23 depict the comparative analysis of power responses for various control strategies. Figure 24 illustrates the battery SoC. The peak overshoot and transient time for the various controllers are illustrated in Figs. 25 and 26, respectively. The presenented outcomes reveals that the Fuzzy-2DOF-PI based HO delivers the best performance in terms of both stability and dynamic behavior. While all methods keep the SoC close to 50%, the Fuzzy-2DOF-PI based HO exhibits the smallest deviation, enhancing overall stability. In terms of peak overshoot, the highest supercapacitor (SC) power overshoot is observed in the Classical PI at about 175 W, followed by FPI based PSO (165 W), FPI based TLBO (135 W), and the lowest in F2DOF-PI based HO (125 W). Likewise, battery power overshoot is greatly minimized with F2DOF-PI based HO (15 W) compared to the Classical PI (70 W). For transient performance, the SC power transient time drops from 0.036 s in Classical PI to 0.023 s in F2DOF-PI based HO, while the battery power transient time decreases from 0.028 s to 0.013 s. Overall, the results demonstrate that Fuzzy-2DOF-PI based HO achieves faster settling, lower overshoot, and improved stability over conventional and other optimized PI-based techniques. Responses of Solar and Load powers for different controllers. Responses of Battery and Supercapacitor Responses for different controllers. Battery State of Charge. Peak overshoot for different controllers. Transient time for different controllers. In this scenario, a step load decrease is introduced to assess the system’s dynamic response and the effectiveness of power redistribution between the photovoltaic (PV) system and HESS. Initially, the total system load is stable, and power is jointly supplied by the PV array and the battery. At a defined moment during the simulation, the load demand experiences a sudden drop, simulating a real-world event such as the disconnection of a heavy appliance or reduction in operational demand. Prior to the load reduction, the battery supports the solar array by supplying the necessary deficit to maintain load power. However, following the step decrease, the total load demand falls below the available solar generation. As a result, the battery transitions from discharging to charging mode, effectively absorbing the excess power produced by the PV array. Figures 27 and 28 depict the comparative analysis of power responses for various control strategies. Figure 29 illustrates the battery SoC, indicating the periods of charging and discharging in relation to load demand and available solar irradiation. The peak overshoot and transient time for the various controllers are illustrated in Figs. 30 and 31, respectively. The presented results demonstrate that the proposed F2DOF-PI based HO consistently outperforms the others in terms of State of Charge (SoC) regulation, peak overshoot minimization, and transient performance. As shown in the SoC response, all controllers maintain values close to 50%, yet the HO-based method exhibits smaller dips during transient phases and faster recovery compared to the slower Classical PI. In peak overshoot evaluation, the HO approach achieves the lowest values across solar, battery, load, and supercapacitor (SC) power, with significant reductions in load power peaks compared to the excessive overshoot observed in the Classical PI. For battery and SC power regulation, HO further minimizes stress on energy storage components, enhancing system reliability. In terms of transient time, all methods maintain solar power settling near 0.03 s; however, HO achieves the shortest load power transient (about 0.015 s) and faster SC stabilization (near 0.04 s), confirming its good dynamic adaptability. Responses of Solar and Load powers for different controllers. Responses of Battery and Supercapacitor Responses for different controllers. Battery State of Charge. Peak overshoot for different controllers. Transient time for different controllers. In this scenario, the system is subjected to simultaneous variations in both load penetration and solar irradiance to evaluate the robustness and adaptability of the control strategies under more complex and realistic operating conditions. This mixed disturbance scenario mimics practical situations such as fluctuating consumer demand coupled with intermittent solar energy availability due to passing clouds or weather changes. Initially, the PV system and battery within HESS operate together to meet stable demand. As the simulation progresses, both a step change in solar irradiance and a variation in load demand are introduced. These concurrent changes challenge the system’s ability to maintain power balance and ensure uninterrupted load supply. The battery plays a critical compensatory role, dynamically shifting between charging and discharging modes in response to the net power imbalance resulting from fluctuating solar input and load variations. Figures 32 and 33 present the comparative analysis of power responses under various control techniques, while Fig. 34 illustrates the battery’s state of charge, showcasing its smooth behavior during simultaneous changes. The system’s transient response and peak overshoot under these compounded conditions are depicted in Figs. 35 and 36, respectively. The analysis of both transient time and peak overshoot results highlights the superior performance of the F2DOF-PI based HO controller. In terms of transient time, it achieves fast responses of approximately 0.021 s for solar power and 0.035 s for battery power, outperforming all other controllers. The classical PI, on the other hand, shows significantly slower responses, with 0.023 s for load power and 0.049 s for supercapacitor power, indicating delayed system settling. For peak overshoot, the proposed method records notably low values, such as 40 W for solar power and 230 W for supercapacitor power, reflecting reduced transient stress. By contrast, the classical PI reaches overshoots of 95 W and 490 W in the same categories, which can accelerate component degradation. The observed differences confirm that the proposed approach improves both dynamic stability and steady-state accuracy in PV-HESS control. Compared with optimization-based FPI controllers, the F2DOF-PI based HO achieves a better trade-off between response time and overshoot minimization. Responses of Solar and Load powers for different controllers. Responses of Battery and Supercapacitor Responses for different controllers. Battery State of Charge. Peak overshoot for different controllers. Transient time for different controllers. Table 6 presents a quantitative comparison of the percentage steady-state errors of solar power (:{P}_{text{solar}}), load power (:{P}_{text{load}}), and battery power (:{P}_{B})under four operating scenarios for all investigated controllers. The results clearly demonstrate that the proposed F-2DOFPI-based HO controller consistently achieves the lowest steady-state errors across all scenarios and power components. In Scenario 1, the proposed method reduces the steady-state error of (:{P}_{text{solar}})to 0.21%, compared with 1.81% for the conventional PI and 0.37% for the TLBO-based fuzzy PI. Similar trends are observed in Scenarios 2–4, where the proposed controller maintains smaller deviations in both (:{P}_{text{load}})and (:{P}_{B}), indicating improved power tracking accuracy and more effective energy sharing within the hybrid energy storage system. Overall, the results confirm that integrating a 2DOF-PI structure with fuzzy supervision and HO-based optimization significantly enhances steady-state performance and robustness compared to classical and other optimization-based fuzzy PI controllers. The stability performance of the examined controllers is evaluated under progressive load increase scenarios of 60%, 65%, 68%, 71%, and 73%, as shown in Table 7. With 60% load increase, all controllers continue to operate steadily, demonstrating nominal performance under moderate loading circumstances. However, all optimized fuzzy based controllers maintain stable operation when the load reaches 65%, demonstrating the efficacy of intelligent tuning strategies in enhancing disturbance rejection capability. In contrast, the conventional PI controller is unable to maintain system stability at this point. While the FPI-based TLBO and the F-2DOFPI based HO controllers continue to maintain stable system behavior, the FPI-based PSO controller becomes unstable at a 68% load increase. This outcome shows that TLBO and HO optimization techniques are more robust than PSO-based tuning. The suggested F-2DOFPI-based HO controller is the only one that maintains stability when the load increase exceeds 71%, demonstrating its capacity to improve system stability margins under extreme loading circumstances. Finally, all controllers lose stability at a 73% load increase, revealing the system’s operational stability limit under the control techniques under consideration. When compared to traditional PI, FPI-PSO, and FPI-TLBO controllers, the comparison study clearly shows that the suggested F-2DOFPI-based HO controller greatly expands the stability region and offers improved robustness against major load perturbations. To further evaluate the contribution of the optimization technique to controller performance, an ablation study was conducted by comparing the conventional 2DOF-PI controller with the optimized F-2DOF-PI controller based on HO. The optimized F-2DOF-PI-based HO achieved a lower objective function value (5307.7) compared with the conventional 2DOF-PI controller (5687.4), indicating improved control performance. This performance enhancement confirms the effectiveness of the optimization process in refining controller parameters. Therefore, the ablation analysis highlights the positive impact of integrating HO optimization within the F-2DOF-PI control structure compared with the non-optimized counterpart. This study investigated the design, control, and performance evaluation of a photovoltaic (PV) system integrated with a parallel active hybrid energy storage system (HESS) composed of a battery pack and a supercapacitor. The HESS was shown to play a critical role in maintaining DC-link voltage stability and balancing power generation and demand. To enhance system performance, an advanced control strategy combining fuzzy logic with a two-degree-of-freedom PI (2DOF-PI) controller, optimally tuned using the Hippopotamus Optimization (HO) algorithm, was proposed. Acting as the main regulator, the proposed fuzzy 2DOF-PI controller ensured stable bidirectional power exchange through DC–DC converters and effective DC bus voltage regulation with reduced computational complexity under fluctuating operating conditions. Simulation results demonstrated that the proposed control scheme effectively maintains reliable operation during sudden variations in solar irradiance and load demand. The battery was responsible for supplying the steady-state power component, while the supercapacitor absorbed fast transient fluctuations, enabling efficient power sharing within the HESS. Moreover, the control strategy ensured appropriate battery charging and discharging behavior, with the supercapacitor mitigating high-frequency disturbances and supporting stable, uninterrupted power delivery to the load. Overall, the results confirm that integrating a fuzzy 2DOF structure with HO-based optimization yields better power regulation performance compared to conventional and other optimized PI-based controllers. Despite the encouraging simulation results, the proposed approach has not yet been validated through experimental or hardware-in-the-loop testing, and the component aging were not explicitly considered. 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Design and analysis of 2dof-PID controller for frequency regulation of multi-microgrid using hybrid dragonfly and pattern search algorithm. J. Control Autom. Electr. Syst.31 (3), 813–827 (2020). Article Google Scholar Download references Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt Hossam Kotb, Ahmed G. Khairalla, Hesham B. ElRefaie & Kareem M. AboRas Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Search author on:PubMedGoogle Scholar Hossam Kotb: Conceptualization, Methodology, Supervision, Writing – Review & Editing. Ahmed G. Khairalla: Software, Validation, Formal Analysis, Writing – Original Draft. Hesham B. ElRefaie: Investigation, Data Curation, Visualization. Kareem M. AboRas: Resources, Conceptualization, Methodology, Supervision, Writing – Review & Editing. All authors contributed to the discussion of results and approved the final manuscript. Correspondence to Hossam Kotb. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Kotb, H., Khairalla, A.G., ElRefaie, H.B. et al. Enhanced power management in PV-Integrated hybrid energy storage systems using fuzzy 2DOF-PI control optimized by hippopotamus algorithm. Sci Rep16, 9200 (2026). https://doi.org/10.1038/s41598-026-40106-4 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-026-40106-4 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
India's solar development represents more than large-scale solar installations in Rajasthan and Gujarat; the entire country will participate through a well penetrated distribution network, which connects small shops, warehouses, service vans, and distributors and channel partner network. April 14, 2026. By News Bureau We Aim to Build 5 GW Capacity Across the Entire Solar Value Chain, Says Future Solar's Ravi Rao Solar to BESS: Reliability Begins with Advanced Sealants, Explains Manish Gupta, Fasto Adhesive Anand Jain of Aerem Solutions on Scaling Solar, Storage, and Finance for Sustainable India JIRE CEO Amit Kumar Mittal Explains Rising Role of Energy Storage and Green Hydrogen in India Icon Solar Modules Are Engineered for India’s Harsh Conditions, Says Rajat Shrivastava
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0 Powered by : National Solar Energy Federation of India (NSEFI), an India-based solar industry body, has said India is set to become the world’s second-largest solar market in 2026 by annual installations. According to the statement, India has installed 50 GW of additional solar capacity in just 14 months, raising total installed solar capacity to 150 GW. The statement has also added that the first 50 GW had taken 11 years to materialize, while the rise from 100 GW to 150 GW had taken nearly three years. NSEFI said that the solar capacity is expected to reach to 280-300 GW to help India attain its 500 GW non-fossil capacity target by 2030, with yearly additions approaching 50 GW. It added that PM Surya Ghar, the upcoming PM KUSUM 2.0, floating solar policies, and demand associated with the National Green Hydrogen Mission are supporting this growth. The industry body further said DRE and C&I solar are likely to lead expansion during the next three years.
German research organisation Fraunhofer ISE has launched a new consultancy spin-off—NEXUS GreenTech—to support companies active in the solar PV industry. NEXUS GreenTech was founded at the end of March, and is headquartered in Freiburg, Germany. The new company is led by Dr Jochen Rentsch, Dr Sebastian Nold and Dr Nico Wöhrle, who were previously working with the PV Technology Transfer unit at Fraunhofer ISE, and nave more than 60 years of cumulative experience in PV research, development and technology. Get Premium Subscription Rentsch said that the spin-off aims to address “a great need for consulting” in an increasingly complex global PV industry. “During our collaboration with PV companies in the field of technology transfer, we repeatedly noticed that many of the inquiries were not about a research question in the strict sense,” said Rentsch. “At the same time, there is a great need for consulting: Which cell technology should I choose, which suppliers are available, which factory layout makes sense—to name just a few issues.” Fraunhofer ISE said that the company would focus on several key areas, including technical and commercial due diligence, feasibility studies, layout planning for factories and technology consulting. NEXUS GreenTech will use scientific methods from Fraunhofer ISE, secured through cooperation and licensing agreements. The spin-off will start work with US solar cell manufacturer Talon PV, and support “the establishment and operation” of a new production line of tunnel oxide passivated contact (TOPCon) cells. Last year, Talon PV CEO Adam Tesanovich spoke to PV Tech Premium about some of the legal barriers that have impeded domestic TOPCon production in the US, and how the company aims to overcome them. In the months since, the company signed a wafer supply agreement with German solar wafer manufacturer NexWafe. This is also not the first collaboration between Talon PV and Fraunhofer ISE. Last year, the latter announced plans to build a pilot TOPCon cell production line in Germany to support the former’s development of its own manufacturing capacity in the US, and the launch of NEXUS GreenTech follows on from this cooperation.
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 15, Article number: 40405 (2025) Cite this article 1775 Accesses 3 Citations Metrics details The concept of Islanded Hybrid Power System (IHPS) has attracted considerable interest lately, especially for energizing remote or energy-poor locations. IHPS are more dependable and cost-effective alternatives to systems using only one energy source when properly constructed. IHPS configuration, including Diesel Engine Generator (DEG), Photovoltaic (PV) systems, and Battery Storage (BATT) elements, are desirable for islanded systems about price and dependability. IHPS mostly use Renewable Energy Sources (RES) for power production, which is variable. Consequently, these variations often make it difficult for traditional control systems to maximize efficiency across various operating environments. The current research discusses the requirement for more effective frequency control in IHPS by suggesting a Model Reference Adaptive Control-Fuzzy Proportional Integral based Whale Optimization Algorithm (MRAC-FPI-WOA) controller. The proposed controller can efficiently manage a range of disturbances by dynamically adjusting its control techniques. The current research conducts an evaluation study comparing the effectiveness of the suggested MRAC-FPI-WOA controller against FPI-WOA, PI-WOA, and PI-PSO controllers. The key evaluation criteria are the ability to maintain stability in frequency within the IHPS and the effectiveness of power production in the overall system. The results demonstrate the superior performance of the MRAC-FPI-WOA controller across diverse operational scenarios. Notably, during a three-phase fault at Bus2, the MRAC-FPI-WOA controller achieves significant performance enhancements over the PI-PSO controller, with reductions of 59.05% in maximum overshoot (%(:{text{M}}_{text{p}})), 72.83% in maximum undershoot (%(:{text{M}}_{text{u}text{s}})), 32.07% in settling time ((:{text{T}}_{text{s}})), and 34.81% in the integral of time-weighted absolute error (ITAE). A similar trend is observed during a three-phase fault at the tie-line, where the MRAC-FPI-WOA controller yields improvements of 57.47% in %(:{text{M}}_{text{p}}), 79.36% in %(:{text{M}}_{text{u}text{s}}), 40.9% in (:{text{T}}_{text{s}}), and 78.08% in ITAE. Furthermore, the controller exhibits exceptional dynamic responsiveness to ramp variations in solar radiation, substantially reducing %(:{text{M}}_{text{p}}:)by 96.72%, %(:{text{M}}_{text{u}text{s}}) by 95.24%, (:{text{T}}_{text{s}}:)by 22.79%, and ITAE by 89.69%. Additionally, it demonstrates robust adaptability to random solar radiation fluctuations, consistently optimizing transient response with reductions of 96.63% in %(:{text{M}}_{text{p}}), 99.58% in %(:{text{M}}_{text{u}text{s}}), 22.07% in (:{text{T}}_{text{s}}), and 95.23% in ITAE. Sustainable energy solutions are being widely adopted in modern power systems to reduce environmental impact and enhance grid performance. While they improve efficiency, voltage stability, and ecological benefits, their excessive integration can challenge grid operation, protection, and control1. A microgrid (MG) represents a localized power network that integrates renewable generation sources (e.g., photovoltaic arrays, wind turbines) with energy storage components (e.g., battery banks) to form a self-contained electrical system2. Hybrid Power System (HPS) operation can switch between two key modes: independent (islanded) and grid-tied operation. IHPS are considered the most effective approach for supplying electricity to remote and rural areas due to their technical feasibility and cost-efficiency3. The intermittent and unpredictable nature of RES in HPS can cause voltage instability and oscillations, potentially affecting connected loads. To ensure system reliability and the quality of electrical supply, an effective control strategy must be developed, allowing the HPS to operate efficiently despite uncertainties in weather conditions and load variations during the system runs in real-time4. As a result, IHPS operations necessitate BATT to retain surplus energy generated by the HPS, ensuring power availability when production is insufficient to meet demand. This study examines the dynamic performance of IHPS under various operating conditions. The efficient control and management of HPS require advanced strategies and algorithms to optimize the utilization of RES, manage BATT, and ensure a stable and reliable power supply5. One of the most critical aspects of HPS operation is frequency stability, which is essential for maintaining high-quality electricity for connected loads. Fluctuations in frequency arise from variations in power generation and consumption, highlighting the necessity for robust frequency regulation mechanisms to maintain HPS stability and performance6. Several approaches can be applied to frequency regulation in IHPS. One widely used method involves BATT to compensate for fluctuations in RES generation, ensuring a steady and secure system frequency. Other techniques include advanced control strategies and demand-side management approaches. Extensive research has explored various control methodologies for regulating the operation of standalone hybrid MG7. A control strategy proposed in8 focuses on biogas-based MG, allowing the system to increase or decrease power generation in response to disturbances caused by fluctuations in RES input or load demand. However, a key drawback of this approach is its inability to respond swiftly to sudden changes, potentially leading to transient instability. Additionally, the controller may lack robust fault detection and isolation capabilities, and its effectiveness could decline when scaling up or integrating with larger power grids. To enhance frequency regulation and stability, Ref9 suggests using an adaptive active power droop controller along with voltage setpoint adjustment in IHPS. These control mechanisms aim to improve the overall performance of HPS systems. Furthermore, Ref10 explores a control technique for BATT designed to mitigate frequency variations and enhance the dynamic response of IHPS. To achieve superior frequency stability during transient disturbances, they propose the use of a Piecewise Linear-Elliptic (PLE) droop characteristic in BATT control systems. This control characteristic enables a faster equilibrium between consumption and power generation, leading to improved frequency regulation in HPS. However, while the PLE controller can reduce frequency variations, it does not fully eliminate them. Additionally, it may be less effective when load demand is lower than power generation, potentially causing sudden fluctuations in BATT output power. In11, a voltage regulation strategy for IHPS incorporating PV and BATT was examined. Ref12 deals with the control of the Vehicle Cruise Control System (VCCS) based on a Model Predictive Controller (MPC) in parallel with the conventional PID controller. The study evaluates the technique’s effectiveness in improving HPS performance, but it does not fully address key challenges related to islanded mode regulations, frequency stability, protection settings, power management, and load diversity handling in HPS. Optimization algorithms inspired by biological and natural phenomena are classified as metaheuristic approaches. Unlike traditional mathematical optimization techniques, which often struggle with complex search spaces, metaheuristic algorithms effectively explore potential solutions to high-dimensional, nonlinear, and multi-modal optimization problems. As a result, techniques like the WOA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) have gained widespread attention across various fields. These techniques are commonly used to optimize system performance by fine-tuning control parameters in advanced control systems, including Proportional-Integral (PI), Proportional-Integral-Derivative (PID), Fuzzy Proportional-Integral (FPI), Fractional-Order PI (FOPI), and Fuzzy-Fractional Order PID (FFOPID) controllers. Recent studies highlight innovative bio-inspired optimization techniques for power systems. In13 Bio-Dynamic Grasshopper Optimization Algorithm (BDGOA) is used to optimize Tilt-Derivative with N-filter plus PI controllers for frequency/tie-line oscillation damping. In14 Diligent Crow Search Algorithm (DCSA) is employed for solar cell parameter identification to maximize PV output. In15 Hybrid Adaptive Ant Lion Optimization (HAALO) with PI/FOPID controllers is developed to enhance Switched Reluctance Motor performance through adaptive mutation and torque ripple reduction. In16 BDGOA is applied for precise parameter estimation across five solar module technologies. In17 Crow-Search Algorithm (CSA) is employed to optimize Type-2 Fuzzy Cascade (T2F-CPIF) controllers for robust frequency/tie-line error mitigation in hybrid systems under contingency scenarios. In18 WOA is utilized to enhance Fuzzy Cascade PD-PI controllers, substantially improving microgrid transient response during operational disturbances. For secondary frequency regulation. In19 Improved Salp Swarm Optimization (I-SSO) tuned Type-II Fuzzy PID controller is implemented to maintain nominal frequency and tie-line power despite uncertainties. Complementing these approaches. In20 advanced Sine Cosine Algorithm (a-SCA) is implemented to optimize the Fractional-Order Fuzzy for precise generation-demand balancing in fluctuating conditions. In21 Coati Optimization Algorithm (COA) was implemented to optimize the parameters of Fuzzy-PI (FPI) and conventional PI controllers, significantly improving the frequency regulation performance in a two-area power system. In22 modified Sea-horse Optimization (SHO) method is developed for tuning Proportional-Integral-Derivative-Tilt (PID-T) controllers in renewable-integrated multi-area systems. In23 SHO is enhanced to optimize Model Predictive Control (MPC), PID, Fractional order proportional integral derivative (FOPID), and Tilted Integral Derivative (TID) controllers for complex power networks. For cyber-resilient operation. In24 Chaos Quasi-Oppositional SHO (CQOSHO) proposes to tune a novel Cascaded tilted-FO derivative with filter ((:{text{C}text{P}text{D}}^{{upmu:}})F − TI) controller with deep learning capabilities. Complementing these advances. In25 Opposition-based SHO (OSHO) is developed for hybrid systems, optimizing TID-MPC controllers to manage renewable penetration and virtual inertia challenges. In26 Dragonfly Search Algorithm (DSA) is employed to optimize an Adaptive Fractional Order PI (AFOPI) controller for precise motor speed regulation. In27 DSA is utilized for tuning a novel cascaded PI-(FOP + PD) structure to mitigate frequency fluctuations in power systems. Complementing these approaches. In28 Tunicate Search Algorithm (TSA) is implemented to enhance transient stability in hybrid grids through optimized Tilt Fractional Order PID (TFOPID) control. These developments showcase the effectiveness of bio-inspired optimization in addressing diverse control challenges across electromechanical and power system applications. The proposed WOA has demonstrated remarkable efficacy across diverse domains, especially in enhancing control system configurations29. For instance, when applied to PID controllers, WOA-optimized systems achieve rapid transient responses, minimized steady-state deviations, and enhanced oscillation damping in contemporary power grids, outperforming GA and Artificial Bee Colony (ABC) approaches30. In renewable energy applications, WOA-driven Fractional-Order Proportional-Integral Controllers (FOPIλ) excel within sensor-free speed control applications for solar-fed permanent-magnet brushless DC motors. These systems surpass Bat Algorithm (BA) and Grey Wolf Optimizer (GWO) implementations by reducing tracking errors and shortening convergence intervals31. Similarly, WOA-enhanced FFOPID controllers integrated into active vehicle suspension models significantly attenuate driver vibrations relative to Fractional-Order PID (FOPID) and PSO-tuned counterparts32. Furthermore, WOA-based Maximum Power Point Tracking (MPPT) techniques applied to Proton Exchange Membrane Fuel Cells (PEMFC) dynamically adapt to electrolyte hydration fluctuations, securing optimal power extraction with greater efficiency than Perturb-and-Observe (P&O), Fuzzy Logic Controller (FLC), and PSO methodologies33. These advancements underscore WOA’s versatility in resolving nonlinear, multi-variable challenges across energy and mechanical systems. The Research gap of this study includes: Limitations of Traditional Controllers: Existing IHPS studies rely on PI, PID, FOPI, and FPI controllers, which face challenges in handling nonlinear system dynamics and severe grid disturbances. These controllers show slow transient recovery, increased frequency overshoot, and prolonged settling times, compromising system stability. Inadequate Handling of Diverse Disturbances: Prior research does not sufficiently address the combined impact of gradual fluctuations (e.g., solar irradiance) and severe grid anomalies (e.g., three-phase faults, load shedding), causing instability in IHPS. Lack of Adaptive Frequency Control: Many existing controllers do not adapt to varying renewable energy fluctuations and load changes, leading to poor frequency regulation and reduced system efficiency. Deficiencies in Power Coordination and Scalability: Conventional methods do not effectively coordinate power generation, storage, and demand, limiting overall system reliability and scalability for real-world applications. Underutilization of Intelligent Optimization in Control Tuning: Automated gain calibration for frequency controllers is underdeveloped, and no framework integrates nonlinear adaptive control with swarm-based optimization for dynamic tuning. Need for an Advanced Control Strategy: A novel approach is crucial for optimizing frequency regulation, transient stability, and operational robustness in IHPS. The integration of MRAC-FPI-WOA gives a promising answer by enabling adaptive tuning in real time and intelligent power coordination in IHPS. The contributions of this study include: Methodological innovations: Investigates the transient behavior and operational robustness of integrated PV-BATT-DEG power systems under both gradual environmental perturbations (e.g., incremental solar irradiance shifts) and severe grid anomalies (e.g., three-phase faults, abrupt load shedding). Proposes a load frequency control to synchronize power generation, storage, and demand in IHPS. This strategy strengthens inter-component coordination, adapts to real-time grid dynamics, and ensures voltage/frequency stability during fluctuating renewable outputs and load transitions. Develops a non-linear adaptive controller (MRAC-FPI-WOA). This innovation optimizes transient frequency recovery across diverse operating regimes, outperforming PI-PS0, PI-WOA and FPI-WOA controllers in damping oscillations and minimizing settling times. Enhances the technical feasibility of large-scale renewable adoption by mitigating frequency volatility in IHPS. This advancement aligns with global sustainability agendas, reducing fossil dependency while improving energy distribution reliability in decentralized grids. Algorithmic implementations: Proposes Beta-based MPPT technique, which enhances the tracking accuracy and dynamic performance of the PV system by adaptively controlling power extraction based on a novel intermediary variable (β), rather than relying solely on conventional power change methods. The WOA is integrated with the beta-based MPPT controller to enhance the total efficiency of the PV system. Leverages the PSO and WOA to automate gain calibration for proposed controllers. WOA effectively resolves nonlinearities and component interdependencies, ensuring the best dynamic response in variable operating conditions. Simulation/experimental findings: Demonstrates the MRAC-FPI-WOA’s superiority through rigorous metrics: lower maximum overshoot (%(:{text{M}}_{text{p}})), and trough undershoot (%(:{text{M}}_{text{u}text{s}})) at lower frequencies, faster settling time ((:{text{T}}_{text{s}})), and a decrease in the integral of time-weighted absolute error (ITAE) in contrast to benchmarks. These results validate its capability to sustain grid stability during both minor and catastrophic disturbances. This paper’s remaining sections are arranged as follows: This paper systematically explores the design and control of IHPS components PV systems, DEG, and BATT in “Modeling of islanded hybrid power system“, proceeding to evaluate four frequency control strategies, including MRAC-FPI-WOA, FPI-WOA, PI-WOA, and PI-PSO controllers in “Frequency Control“. A detailed simulation-based analysis in “Results and discussion” compares controller performance under seven scenarios, including three-phase faults, step/ramp/random solar irradiance fluctuations, as well as abrupt load changes and composite disturbances. Cases 3 (step irradiance) and 6 (sudden load shift) are tested concurrently to assess robustness under hybrid stresses. The study concludes in “Conclusions” that the MRAC-FPI-WOA controller, enhanced by metaheuristic tuning, outperforms conventional methods in maintaining frequency stability and power quality across all disturbances, underscoring its potential to enhance HPS resilience in real-world applications characterized by renewable intermittency and operational uncertainties. This research undertaking centers its analytical scope on the architectural design and functional dynamics of Alternating current (AC) IHPS, integrating multiple distributed energy resources, including DEG, PV, AC consumer loads, and advanced BATT solutions. Figure 1 delivers a refined schematic overview of the IHPS infrastructure, emphasizing the interconnection of the DEG to the primary AC distribution backbone through sophisticated power electronic interfaces. These components perform dual critical functions: harmonizing the phase and frequency characteristics of disparate AC power sources while enabling efficient conversion of Direct Current (DC) electricity harvested from solar panels into HPS-compatible alternating current waveforms. The BATT incorporates a bidirectional power conversion apparatus, engineered to transition seamlessly between AC-to-DC operational modes during energy accumulation cycles and DC-to-AC modes during discharge phases. This dual functionality not only stabilizes the HPS against voltage fluctuations and transient load imbalances but also enhances operational flexibility during system upkeep or component servicing. This comprehensive framework underscores HPS’s resilience in maintaining uninterrupted power delivery while accommodating diverse energy inputs and dynamic load profiles. Block diagram of the proposed IHPS. This section introduces a detailed and robust simulation framework designed to be a high-performance PV system. The system architecture encompasses several critical elements: a 100-kilowatt solar panel array, a step-up DC-DC converter, a power inversion unit, and a voltage adjustment transformer. A methodically structured schematic diagram and computational model, illustrated in Fig. 2, offer a comprehensive and logically organized visualization of the entire configuration. Sunlight is harvested by a solar array and converted into DC electricity. To enable compatibility with standard power distribution networks, this DC output must undergo conversion to AC. This critical transition is eased by the inverter module, which transforms the unidirectional electrical flow into a three-phase AC output synchronized with grid specifications. Subsequently, a voltage-elevating transformer amplifies the AC voltage to match the grid’s operational requirements, ensuring seamless energy transfer. Each component operates synergistically: the Boost converter optimizes the DC voltage from the solar panels to maximize efficiency, the inverter ensures waveform compatibility with HPS standards, and the transformer bridges voltage disparities to enable stable power injection. This integrated approach highlights the system’s capability to efficiently harness, process, and deliver renewable energy while adhering to technical and operational benchmarks for grid integration34,35. Schematic of a Solar PV System. Various mathematical representations describing the functionality and efficiency of solar panels have been extensively documented in previous studies. For real-time simulation, it is necessary to develop an equivalent circuit model of PV cells. Among the different approaches, the single-diode model is the most widely adopted by researchers. This circuit configuration comprises, at a minimum, four key elements: a photocurrent source ((:{I}_{ph})), a diode (D), a shunt resistance ((:{R}_{sh})), and a series of resistance ((:{R}_{ser})). Based on the equivalent single-diode model of a PV cell depicted in Fig. 3, the output current ((:{I}_{out})) can be expressed mathematically in the following way36,37. Where(::left({N}_{P}right)) is the number of PV cells arranged in parallel, ((:{I}_{rs})) is The PV cell’s reverse leakage current, (q) is the electric charge of an electron,(:{(V}_{out})) is the cell’s output voltage, (A) is the diode ideality factor, (K) is the Boltzmann constant, (T) is the temperature measured in Kelvin, (:{(N}_{S})) is the total PV cells wired in a series connection,(:{:(text{I}}_{text{s}text{c}})) is the short-circuit current, (:{(k}_{i})) is the short circuit current factor, (:left({T}_{r}right)) is the cell reference temperature and (E) is the solar irradiance. Schematic representation of a basic diode-based model used for PV solar cells. Figure 4(a) and Fig. 4(b) depict the I-V and P-V characteristics of the PV cell, derived from a MATLAB-based computational model. These findings provide critical insights into the operational dynamics of the solar module under fluctuating irradiance scenarios, revealing how variations in solar intensity influence electrical output characteristics such as Maximum Power Point (MPP), open-circuit voltage (:{(V}_{oc}), and (:{I}_{sc})). The simulations show the nonlinear relationship between irradiance levels and energy conversion efficiency, emphasizing the importance of adaptive control strategies for optimizing solar harvesting in real-world environmental conditions. (a) I-V curve and (b) P-V characteristics of solar cells at varying irradiation levels. A basic DC-DC boost converter is employed to deliver power from the PV to the DC link and the inverter once the matching condition between them is met. This matching is achieved by applying a suitable duty cycle (ranging between 0 and 1). The converter’s switching element, typically an IGBT, is regulated using a PWM signal. Figure 5 displays the Simulink model layout of the boost converter. The mathematical relationships governing the converter’s input and output parameters are expressed through the following Eqs35,36. Here, the input and output voltages, along with the duty cycle, are represented as(:{::(V}_{o:}), (:{V}_{in}), and D), respectively. The roles of the boost converter’s inductor (L) and capacitor (C) elements are specified as follows35,36: Where ((:f)) is the frequency, (:(varDelta:I) and (:varDelta:V)) are the current and voltage ripple. Circuit diagram of a boost converter. The β-MPPT method involves observing an intermediate variable called ((:beta:)), rather than directly tracking power variations, as outlined in Eqs. (7) and (8)36,37. Here, (:left({I}_{pv}right)) is the output current, (C) is the diode constant, and (N) is the total count of solar cells contained in the module. This method uses a hybrid step-size strategy, applying a variable step during dynamic changes and a fixed step during stable operating conditions. As outlined in Fig. 6, the algorithm begins by continuously observing voltage and current values to compute the intermediary beta parameter. If the calculated beta lies within a designated threshold range ((:{beta:}_{min}) to (:{beta:}_{max})), the system is in a steady state, and a fixed step is applied. If beta falls outside this range, the algorithm identifies a transient phase and switches to a P&O approach. In this stage, the variable step size, denoted as ΔD, is adjusted based on a reference parameter called (:{(beta:}_{g})), which is defined mathematically in Eq. (9)36,37. Where (F) is the scaling factor. Flow chart of β-MPPT. The WOA discussed in Sect. 4 is integrated with the beta-based MPPT controller to enhance the total efficiency of the PV system. Within this hybrid framework, the scaling factor (F) is essential for adaptively regulating the step size (∆D) during the dynamic response phase of the Beta MPPT method. Selecting the perfect value for (F) is key to achieving: Rapid tracking of the Maximum PowerPoint (MPP). Minimized fluctuations during steady-state operation. Enhanced performance across various levels of sunlight and temperature conditions. Since the scaling factor (F) significantly affects MPPT efficiency but does not have an exact analytical expression, a metaheuristic optimization method can be applied to find its best value. The WOA offers a reliable control mechanism across various load scenarios and system parameters. This enhances both the flexibility and resilience of the control framework, ensuring that the Beta MPPT method consistently performs at its best under changing operational conditions. The objective function aims to find the ideal value of the scaling factor in a way that enhances power extraction efficiency (η) while simultaneously reducing both convergence time (CT) and Steady-State Oscillations (SSO). The goal is to minimize J(F) and obtain the best value of the scaling factor as outlined in Fig. 7. Where: MPPT Efficiency (η) is expressed as the ratio of the power obtained using the MPPT method ((:{P}_{MPPT})) to the ideal power ((:{P}_{ideal})). (SSO) is the Root Mean Square (RMS) value of the power fluctuations in the steady state. CT refers to the duration needed for the system to reach 98% of the (:{text{P}}_{text{i}text{d}text{e}text{a}text{l}}). (W₁, W₂, and W₃ ) are the weighting coefficients assigned to balance the impact of each parameter in the optimization process. Flow chart of the WOA to calculate the best value of the scaling factor (F). As illustrated in Fig. 8, the control framework of the voltage source inverter (VSI) includes two inner loops for managing current and two outer loops for managing voltage. The d-axis current ((:{text{I}}_{text{d}})) controls active power, which directly influences the DC bus voltage. On the other hand, controlling the q-axis current ((:{text{I}}_{text{q}})) allows for the regulation of reactive power, thus stabilizing the AC load voltage. The PI controller is employed to evaluate and enhance the dynamic response of the external voltage regulation loops on the DC and AC sides3,21. The mathematical expressions governing the VSI voltage are outlined in Eq. (11). To operate in the (dq) rotating reference frame (synchronous frame), the original three-phase (abc) signals are converted using transformation matrices, as described in Eq. (12). Assume that (:({V}_{as}), (:{V}_{bs}), (:{V}_{cs})) are the phase voltages produced by the VSI, and (:{(I}_{as}), (:{I}_{bs}), (:{I}_{cs})) correspond to its output currents. The filter’s resistance and inductance are denoted by (:{(R}_{f}) and (:{L}_{f})) respectively. (:{(V}_{aL}), (:{V}_{bL}), (:{V}_{cL})) are the voltages across the connected load. In the synchronous dq reference frame, (:({V}_{dqs}), (:{V}_{dqL}), (:{I}_{dqs})) are the inverter’s output voltages, the load-side voltages, and the inverter output currents, respectively. According to the described approach, the control of reactive power is managed through the q-axis current component, as detailed in Eq. (13), while the regulation of active power is managed through the d-axis current, as specified in Eq. (14)3,21. Here, (:{(text{Q}}_{s}) and (:{text{P}}_{s}):)are the delivered reactive and active power, respectively. The responses generated by the current controllers aligned with the d-axis and q-axis are computed using the expressions provided in Eqs. (15) and (16)3,21. VSI control. The integration of electrochemical storage units, such as lithium-ion battery banks, plays a pivotal role in HPS incorporating variable RES like PV arrays. These storage systems address imbalances between electricity production and consumption that arise from rapid fluctuations in solar insolation. During periods of diminished solar generation, when PV output falls short of the inverter’s target power level, the battery discharges to supplement load requirements38,39. Conversely, when PV generation exceeds demand, surplus energy is stored within the battery for next use. Solar installations inherently cease operation during nocturnal intervals due to the absence of sunlight40,41. Here, BATT synergizes with DEG to enhance system reliability and cost-effectiveness compared to standalone DEG configurations, reducing fuel consumption and operational expenses. The operational framework of the BATT, illustrated in Fig. 9, is governed by critical performance metrics including terminal voltage, energy capacity, and charge retention level State of Charge (SOC). The battery is mathematically represented as a tunable voltage source paired with an internal impedance component. Where (:{(text{C}}_{text{R}}) ) is the rated capacity and (:left({text{I}}_{text{B}text{A}text{T}text{T}}right)) is terminal current flow. Additional governing equations account for electrochemical reactions, gas evolution phenomena, thermal dynamics, and voltage-current relationships. Key variables include (:{text{V}}_{text{B}text{A}text{T}text{T}}) (battery terminal potential), (:{text{I}}_{text{R}}) (internal reaction current), (:{text{I}}_{text{G}}) (parasitic gassing current), and (:{text{T}}_{text{B}text{A}text{T}text{T}}) (operating temperature). Battery Model. The battery management strategy enforces specific operational constraints to ensure safe and efficient usage. Firstly, it restricts both the charging and discharging power levels, ensuring they do not exceed the maximum threshold specified by Eq. (17). Secondly, as outlined in Eq. (18), it regulates the battery’s SOC, keeping it within acceptable boundaries to avoid risks associated with overcharging or excessive depletion38,39,40,41. In the proposed system, batteries are utilized to mitigate the effects of the intermittent nature associated with PV sources. Due to their high energy density, batteries can deliver power at nearly constant voltage when their charging and discharging cycles are appropriately managed. The modeled battery is integrated into the DC link through a bi-directional DC-DC converter, as illustrated in Fig. 10. This converter facilitates the charging and discharging of the battery while maintaining the DC link voltage at 500 volts. When the battery supplies power to the microgrid, the converter operates in boost mode; conversely, when it absorbs power from the grid or PV panels, it operates in buck mode. The control loop regulates the DC link voltage by adjusting the duty cycle of the bi-directional DC-DC converter. It continuously measures the DC link voltage, compares it to a reference value, and processes the error through a voltage mode compensator to determine the necessary duty ratio. This control approach is agnostic to the direction of power flow and generates appropriate switching signals for the buck and boost operations. As shown in Fig. 11, an intelligent controller determines the operational mode and transmits the control pulses to a designated semiconductor switch. The decision to operate the converter in a buck or boost mode is based on the command signal received from the HPS. In the absence of a regulation signal, the battery’s SOC determines whether the converter should operate in buck mode to facilitate charging. Bi-directional DC-DC converter. Battery controller. The DEG assumes a crucial role as a backup power solution, particularly in scenarios where RES such as PV is insufficient due to intermittent availability or environmental factors. Additionally, the system activates in island mode when the main grid experiences instability, such as voltage sags, frequency deviations, or unforeseen disconnections. In this isolated operational state, the DEG autonomously sustains power supply to critical loads, preventing blackouts and enabling seamless transitions until grid conditions stabilize or renewable generation resumes. This dual functionality underscores the DEG’s importance in hybrid energy systems, bridging gaps between renewable intermittency and grid reliability while ensuring uninterrupted electricity access during emergencies42,43. The DEG system illustrated in Fig. 12 is composed of multiple interconnected elements designed to ensure reliable power generation and grid stability. At its core, the system includes a governor mechanism for the diesel engine, an excitation system, and a synchronous machine integrated with the engine. The governor operates through a closed-loop feedback control strategy, which continuously monitors and adjusts the engine’s rotational speed. By dynamically aligning the engine’s output with a predefined reference speed, the governor guarantees the stabilization of the electrical grid’s frequency, even under fluctuating load demands. This precision in speed regulation is critical for maintaining synchronization between the generator and the grid, thereby preventing disruptions in power quality. Diesel Engine Generator model. The primary objective of stabilizing an islanded AC HPS lies in regulating the electrical supply to preserve system frequency at its predefined operational standard. This process hinges on frequency stability management, which entails dynamically modulating generator output levels to equilibrate power consumption needs while sustaining consistent grid oscillations. Within such systems, the cumulative energy contribution from distributed resources—comprising DEG, PV, and BATT—must collectively satisfy load requirements, as expressed by the relationship: Given the inherent variability of PV generation due to weather-dependent intermittency, this analysis prioritizes DEG as the primary actuator for frequency correction. The control framework compensates for deviations caused by fluctuating loads and PV generation by adaptively scaling DEG output. Conventional PI regulators remain widely adopted for such stabilization tasks, while FPI systems introduce rule-based adaptability, enhancing responsiveness to dynamic operational shifts. To address limitations in existing hybrid energy systems, this work proposes an MRAC-FPI-WOA framework, which synergizes adaptive reference tracking with fuzzy logic to optimize disturbance rejection across diverse instability scenarios. PI-PSO, PI-WOA, and FPI-WOA architectures have proved efficacy in grid frequency management, yet the MRAC-FPI-WOA hybrid appears as a superior solution, using real-time parameter adaptation to maintain precision under abrupt load transitions, resource volatility, and compound disruptions. This innovation underscores the critical need for advanced control paradigms in modernizing HPS resilience against the uncertainties of renewable integration. The study specifically examines the PI controller’s effectiveness in maintaining system frequency stability and enhancing proposed IHPS operational performance, utilizing a control law expressed as (Eq. 20), with particular focus on its PI controller dynamic response characteristics and stabilization capabilities under varying load conditions4. This equilibrium enables accelerated convergence and superior precision compared to conventional optimization frameworks. By defining frequency control as an optimization problem, the ITAE performance metric can be minimized44,45. Where (t) is time, while e(t) is the deviation between (:{F}_{m}:)and (:{F}_{ref}). The system configuration depicted in Fig. 13 presents the closed-loop control structure employing the PI-PSO controller. PSO is popular for its simplicity and fitness-based approach, effective for diverse optimization problems. However, it risks premature convergence due to declining swarm diversity. The methodology incorporates three fundamental components46: Individual Best ((:{:P}_{pest:})): The optimal solution encountered by particle (i) during its search history. Global Best ((:{:g}_{pest:})): The most favorable solution discovered by the entire particle collective. Dynamical Update Rules: Governing equations directing particle movement through the solution space. The particle’s velocity vector is modified following Eq. (22), while its positional coordinates are recomputed via Eq. (23) through vectorial addition of the updated velocity to its prior location47. The PSO algorithm updates each particle’s velocity and position through three key components: (1) an inertia term ((:{:wv}_{id})) that preserves momentum from previous movements, (2) a cognitive component ((:{:r}_{1}{C}_{1}left({:P}_{pest,id}left(tright)-:{:X}_{iid}left(tright):right)))) that attracts particles toward their personal best positions ((:{P}_{pest,id})), and (3) a social component ((:{:r}_{2}{C}_{2}left({:g}_{pest,id}left(tright)-:{:X}_{id}left(tright):right)))) that guides particles toward the swarm’s global best solution ((:{g}_{pest,id})), where (w) represents the inertia weight, C₁ and C₂ are cognitive and social learning rates, respectively, and (r1,r2) are random numbers that maintain stochastic exploration. This balanced combination of individual experience (cognitive) and collective knowledge (social) enables effective search-space exploration while progressively converging toward optimal solutions. Figure 14 illustrates the algorithm’s operational flowchart48. PI-PSO Controller-Based Control System Structure. PSO flowchart. PI-WOA controller illustrated in Fig. 15, for frequency stabilization. By framing the controller tuning process as an optimization problem, WOA dynamically minimizes frequency deviations through iterative adjustments to the gain values, ensuring robust adaptability to grid disturbances. This hybrid approach synergizes the simplicity of PI control with the intelligence of bio-inspired optimization, enabling enhanced precision in frequency regulation for modern power networks characterized by intermittent renewable integration and complex load dynamics. The methodology aims to elevate grid resilience, reduce oscillations, and maintain nominal frequency stability under heterogeneous operating conditions. PI-WOA Controller-Based Control System Structure. WOA technique is a robust nature-inspired computational method modeled after the foraging strategies of humpback whales. It shows exceptional ability in addressing intricate optimization problems by harmonizing the search for novel solutions (exploration) with the refinement of existing ones (exploitation)49,50. Figure 16 illustrates the flowchart of the WOA, which can be mathematically expressed using the following Eqs49,50. The symbols X(t), (:{X}_{p}left(tright)), and (:{X}_{r}left(tright)), correspond to the position vectors of the whale, prey, and random whale, respectively. (t) is the current iteration. (A and C) are the coefficient vectors. Over the number of rounds, (a) constantly decreases linearly from 2 to 0. The random integer (l) is between − 1 and 1, the random vector (r) is between 0 and 1, the (p) is the probability number ε [0, 1], and the constant that determines the spiral logarithmic form is represented by (b). Figure 17 demonstrates the convergence behavior of the objective function for both optimization methods, with Table 1 detailing the corresponding algorithmic parameters and optimized PI controller gains obtained through WOA and PSO implementations. WOA flow chart. Convergence of the objective function. This research explores the FPI-WOA controller, illustrated in Fig. 18, as a hybrid control strategy that merges essential aspects of both FLC and PI-WOA control frameworks, aiming to enhance the capabilities of the PI controller by incorporating the advantages of FPI control. FLC outperforms classical methods in complex power systems due to its adaptability to nonlinearities and uncertainties without precise modeling. They maintain robust performance amid variable conditions like renewable generation fluctuations and load changes. Their rule-based heuristic approach enables intuitive tuning using operational expertise rather than complex math. Additionally, they are less sensitive to parameter variations than PID controllers, making them ideal for real-world applications with drifting system parameters2,20,21. As outlined in51,52 the fuzzy inference process consists of three main phases. The first step, fuzzification, transforms precise input values into fuzzy variables within their respective fuzzy sets. In this study, two input Errors (E), depicted in Fig. 19(a), and a Change in Error (CE), illustrated in Fig. 19(b), along with one output, shown in Fig. 19(c), are represented through triangular membership functions. Each input and output is characterized through a set of seven linguistic levels: NB (Negative Big), NM (Negative Medium), PB (Positive Big), PM (Positive Medium), PS (Positive Small), NS (Negative Small), and ZO (Zero). At the fuzzy logic rule inference stage, decisions are formulated through the integration of aggregation and implication techniques within the framework of fuzzy inference rules. The fuzzy rules, detailed in Table 2, can be linguistically described as follows: If both error (E) and (CE) are categorized as (PB), then the corresponding output is also classified as (PB). The parameters of PI-PSO, PI-WOA, and FPI-WOA controllers are displayed in Table 3. FPI-WOA Controller-Based Control System Structure. The MFs (a) E, (b) CE, (c) ΔD. To enhance the accuracy of the FPI-WOA controller, it has been integrated with MRAC to enhance its efficiency and adaptability. Implementing MRAC in IHPS offers significant benefits, particularly in managing the unpredictable nature of RES. By dynamically adjusting to changes in generation and load variations, MRAC strengthens frequency stability and voltage regulation, optimizing system performance through continuous tuning of control variables adjusted during real-time operating conditions. This integration results in a more robust and dependable energy system, enabling seamless RES integration while improving the overall efficiency and reliability of IHPS. Significant applications include maintaining stable output voltage in DC-DC converters used in IHPS53, implementing a tailored MIT-rule-driven MRAC for boost-type DC-DC converters54, and improving conventional droop-based regulation in marine power systems55. Additionally, MRAC has been applied in HPS to regulate the unified interphase power controller (UIPC)56, and develop a fractional-order MRAC control strategy to stabilize voltage and current in multi-source power configurations using DC-DC converters57. As depicted in Fig. 20, the MRAC-FPI-WOA controller consists of three main components: the FPI controller, the reference model, and the adjustment mechanism. MRAC-FPI-WOA Controller-Based Control System Structure. In this study, simulations were conducted using MATLAB Simulink as shown in Fig. 21 to introduce an MRAC-FPI-WOA controller designed to ensure frequency stability within the system while facilitating fundamental control processes. A comparative analysis was performed between the MRAC-FPI-WOA, FPI-WOA, PI-WOA, and PI-PSO controllers across various scenarios to analyze the controllers’ effectiveness. These scenarios are essential for a thorough assessment of performance. For example, Case 1, which focuses on a three-phase fault at Bus 2, offers insights into the system’s robustness across different network configurations. Case 2 analyzes a three-phase fault occurring at the center of the tie-line, further evaluating the system’s capacity to manage faults that impact multiple components at once. Additionally, Cases 3, 4, and 5 address fluctuations in solar radiation, including step changes, ramp changes, and random variations, respectively. These scenarios are crucial for understanding how dynamic solar input influences overall system performance, given the inherent variability of solar energy due to environmental factors. Case 6 introduces a rapid load change, testing the system’s responsiveness to sudden alterations in energy demand, a frequent challenge in practical applications. Finally, Case 7 merges Cases 3 and 6, running them simultaneously to evaluate how the system performs under various conditions of concurrent changes in solar radiation and load demands. This integrated approach offers a comprehensive understanding of how various disturbances interact and impact frequency regulation, ultimately informing more efficient design and control strategies for the proposed IHPS. This section presents an in-depth analysis that includes a variety of responses and numerical results, demonstrating the findings and implications of our research. The nominal specifications for the PV, DEG, BATT, and loads are detailed in Table 4. MATLAB/Simulink model. In this situation, a fault involving a three-phase short circuit at BUS 2 occurs, lasting 0.1 s. This fault starts at the terminals of the AC load after a time interval of 1 s and is identified by a fault resistance of 0.001 ohms. Figure 22(a) visually illustrates the output power generated by both the PV and DEG sources. Additionally, Table 5; Fig. 22(b) assess the overall efficiency of the system’s frequency by analyzing various control strategies, including the MRAC-FPI-WOA, FPI-WOA, PI-WOA and PI-PSO controllers. This evaluation considers several Key performance indicators like ITAE, (:{text{T}}_{text{s}}), %(:{text{M}}_{text{p}}), and %(:{text{M}}_{text{u}text{s}}:)during instances of three-phase faults. The findings from this case prove that the MRAC-FPI-WOA controller surpasses the PI-WOA, PI-PSO and FPI-WOA controllers in every evaluated aspect. Furthermore, Fig. 22(c) shows the voltage values existing in the system. System behavior in case 1. (a) Output power of the sources, (b) System Frequency, (c) System Voltage. In this case, a three-phase short circuit starts at the central point of the tie, lasting for a total duration of 0.11 s. This fault event starts at the terminals of the AC load after 2 s and shows fault resistance as low as 0.001 ohms. Figure 23(a) visually depicts the output power generated by both the PV and DEG sources. Meanwhile, Table 5; Fig. 23(b) provide an assessment of the system’s efficiency by examining various control methodologies, including the MRAC-FPI-WOA, FPI-WOA, PI-WOA and PI-PSO controllers. The evaluation process considers several important performance metrics, such as ITAE, %(:{text{M}}_{text{u}text{s}}), %(:{text{M}}_{text{p}}), and (:{text{T}}_{text{s}}), specifically during instances of three-phase faults. The results from this analysis confirm the enhanced effectiveness of the MRAC-FPI-WOA controller over other controllers in all evaluated performance aspects. Additionally, Fig. 23(c) illustrates the voltage values existing in the system. System behavior in case 2. (a) Output power of the sources, (b) System Frequency, (c) System Voltage. Figure 24(a) illustrates the stepped variation in solar irradiance over time. One must note that changes in solar radiation levels can significantly affect the frequency within the system. The implementation of the MRAC-FPI-WOA controller plays a vital role in ensuring effective frequency regulation under these varying conditions. When compared to PI-PSO, PI-WOA and FPI-WOA controllers, the MRAC-FPI-WOA controller demonstrates a higher level of accuracy in responding to abrupt changes in solar radiation, notably when efficiency is high and weather conditions shift rapidly. The performance efficiency is assessed using the control strategies, taking into account several key parameters, including %(:{text{M}}_{text{u}text{s}}), ITAE, (:{text{T}}_{text{s}}), %(:{text{M}}_{text{p}}). Table 5; Fig. 24(b) present an in-depth analysis of the IHPS frequency’s behavior in response to a step change. Furthermore, Fig. 24(c) visually represents the output power produced by both the PV and DEG sources. In this scenario, when solar radiation diminishes from 1000 to 800 W/m² after two seconds, the PV power decreases from 94 kW to 73 kW. This reduction in PV power generation prompts an increase in the power output from the DEG, which grows from 56 kW to roughly 70 kW to meet the energy demand. Conversely, when solar radiation declines further to 600 W/m² after an additional four seconds, while maintaining a constant ambient temperature, the PV power output declines from 73 kW to about 54 kW. In response, the DEG power generation escalates from 70 kW to approximately 84 kW to satisfy the demand. Additionally, when solar radiation increases from 600 to 900 W/m² at the six-second mark, PV power rises from 54 kW to about 82 kW, causing the DEG output to decline from 84 kW to roughly 63 kW. The proposed controller successfully stabilizes the system frequency, even amidst fluctuations in solar radiation levels. Lastly, Fig. 24(d) provides further insight by presenting the distribution of voltage across the system. System behavior in case 3. (a) Radiation is a step-changed profile, (b) Output power of the sources, (c) System Frequency, (d) System Voltage. Figure 25(a) illustrates the ramp-shaped trend of solar irradiance over a specific period. The MRAC-FPI-WOA controller plays a crucial role in ensuring efficient frequency control. Compared to other controllers, the MRAC-FPI-WOA controller proves a significantly higher level of accuracy and responsiveness to ramp variations in solar irradiance levels. Figure 25(b) depicts the output power of the sources. As solar radiation decreases, there is a corresponding increase in the DEG power. Alternatively, as the solar radiation increases, the DEG power also rises to accommodate the changing power consumption needs. To evaluate the efficiency of the system, A comparative analysis is performed on the MRAC-FPI-WOA, PI-WOA, PI-PSO and FPI-WOA controllers. Table 5, along with Fig. 25(c), provides a comprehensive overview of the controlled response of the system frequency under ramp shifts in solar irradiance. The MRAC-FPI-WOA controller ensures stable control of system frequency, even in the face of ramp variations in solar radiation. This proves the controller’s ability to sustain stable performance across different conditions. Moreover, Fig. 25(d) presents additional further by illustrating the voltage levels across the system, further contributing to the general comprehension of the system’s performance. System behavior in case 4. (a) Radiation is a ramp-changed profile, (b) Output power of the sources, (c) System Frequency, (d) System Voltage. Figure 26(a) shows the erratic behavior of solar irradiance levels over a defined time. In these situations, the MRAC-FPI-WOA controller is crucial for ensuring effective frequency control. When contrasted with other controllers, the MRAC-FPI-WOA controller shows a notably greater degree of precision and responsiveness to unpredictable fluctuations in solar irradiance levels. Figure 26(b) illustrates the output power of the sources. As the intensity of solar irradiance declines, the DEG power grows proportionally. However, when solar radiation increases, the DEG power rises to match the fluctuating energy requirements. Table 5, along with Fig. 26(c), offers an in-depth examination of the system frequency behavior throughout instances of random fluctuations in solar irradiance. The MRAC-FPI-WOA controller successfully maintains the stability of the system frequency, even amidst unpredictable fluctuations in solar irradiance. This highlights the controller’s ability to offer consistent functionality across a range of conditions. Additionally, Fig. 26(d) offers additional insights by illustrating the system’s voltage levels, improving the overall comprehension of system’s operational performance. System behavior in case 5. (a) Radiation is a random-changed profile, (b) Output power of the sources, (c) System Frequency, (d) System Voltage. Figure 27(a) illustrates the power output from both the PV and DEG sources during instances of abrupt load changes. At the 2-second mark, there is a notable decrease of 18.3% in the demand for the AC load within the system, dropping from 120 kW to 98 kW. During this period, the power output from the PV systems remains unchanged at 94 kW, while the output from the DEG declines from 56 kW to 35 kW. At the 4-second mark, as depicted in Fig. 27(a), there is an increase of 11.2% in the AC load demand, rising from 98 kW to 109 kW. Throughout this time, the PV power continues to hold steady at 94 kW, while the DEG power rises from 35 kW to 44 kW to satisfy the additional energy requirements. Figure 27(b) and Table 5 present a detailed comparison of the performance of the MRAC-FPI-WOA, FPI-WOA, PI-WOA, and PI-PSO controllers in managing sudden changes in load, emphasizing the MRAC-FPI-WOA controller’s effectiveness in ensuring effective frequency regulation under these conditions. Furthermore, Fig. 27(c) provides a sequential representation of the system’s voltage measurements, offering additional insights into its operational dynamics. System behavior in case 6. (a) Output power of the sources, (b) System Frequency, (c) System Voltage. In this case, scenarios (3) and (6) are interconnected and run simultaneously. Figure 28(a) visually depicts the output power of the sources. When solar irradiance reduces from 1000 to 800 W/m² after two seconds, there is also a concurrent 18.3% reduction in the demand for AC load within the system, which drops from 120 kW to 98 kW. At precisely 2 s, the output from the PV systems reduces from 94 kW to 73 kW. This decline in PV power output, combined with the reduced load, leads to a decrease in power generation from the DEG, which falls from 56 kW to about 48 kW to meet the adjusted energy requirements. Subsequently, when solar radiation further decreases to 600 W/m² after an additional four seconds, there is an 11.2% increase in the AC load demand, rising from 98 kW to 109 kW. At this 4-second mark, the PV power drops from 73 kW to 54 kW. In response to this change, DEG’s power generation rises from 48 kW to 72 kW to fulfill the new demand. Additionally, when solar radiation increases from 600 to 900 W/m² at the six-second mark, the AC load demand within the system remains constant at 109 kW. The PV power rises from 54 kW to about 82 kW, resulting in decreased production from the DEG, which reduces from 72 kW to 62 kW. Table 5; Fig. 28(b) provide a comprehensive overview of the system frequency response. The system’s performance efficiency is assessed based on the MRAC-FPI-WOA, PI-WOA, PI-PSO and FPI-WOA controllers, considering several critical parameters, including (:{text{T}}_{text{s}}), ITAE, %(:{text{M}}_{text{p}}), and %(:{text{M}}_{text{u}text{s}}). Finally, Fig. 28(c) gives deeper insights into displaying the system’s voltage levels. System behavior in case 7. (a) Output power of the sources, (b) System Frequency, (c) System Voltage. This study proposes a robust technique for controlling the frequency of an IHPS, utilizing MRAC-FPI-WOA, FPI-WOA, PI-WOA, and PI-PSO controllers to maintain system stability amid disturbances. The findings highlight the substantial benefits of the MRAC-FPI-WOA controller compared to the FPI-WOA, PI-WOA, and PI-PSO controllers across multiple scenarios. For instance, in Case 1, during a three-phase fault for 100 ms at Bus2, the MRAC-FPI-WOA controller lowers %(:{text{M}}_{text{p}}) by 59.05%, %(:{text{M}}_{text{u}text{s}}) by 72.83%, (:{text{T}}_{text{s}}) by 32.07%, and ITAE by 34.81% compared to the PI-PSO controller. In Case 2, with a three-phase fault at the tie-line lasting 110 ms, similar improvements are observed, including lowering %(:{text{M}}_{text{p}}) by 57.47%, %(:{text{M}}_{text{u}text{s}}:)by 79.36%, (:{text{T}}_{text{s}}) by 40.9%, and ITAE by 78.08%, reinforcing the MRAC-FPI-WOA controller’s superior performance in dynamic situations when compared to the PI-PSO controller. In Case 3, MRAC-FPI-WOA showcases its superior adaptability under varying solar irradiance conditions. When irradiance drops from 1000 to 800 W/m², the controller significantly enhances performance by reducing overshoot by 100%, undershoot by 94.12%, settling time by 75.14%, and ITAE by 82.8%. A further decrease from 800 to 600 W/m² yields even better results, undershoot improved by 94.06%, overshoot cut by 100%, settling time improved by 78.05%, and ITAE reduced by 89.47%. Conversely, when solar radiation increases from 600 to 900 W/m², MRAC-FPI-WOA maintains strong performance, decreasing overshoot by 95.38%, undershoot by 100%, settling time by 83.96%, and ITAE by 92.24%. Furthermore, the MRAC-FPI-WOA controller proves improved dynamic responsiveness to ramp changes in solar radiation in Case 4, achieving reductions in %(:{text{M}}_{text{p}}), %(:{text{M}}_{text{u}text{s}}), (:{text{T}}_{text{s}}), and ITAE by 96.72%, 95.24%, 22.79%, and 89.69%, respectively. In addition, it also shows enhanced adaptability to random fluctuations in solar radiation in Case 5, consistently lowering %(:{text{M}}_{text{p}}), %(:{text{M}}_{text{u}text{s}}), (:{text{T}}_{text{s}}), and ITAE by 96.63%, 99.58%, 22.07%, and 95.23%, respectively. The MRAC-FPI-WOA controller also proves effective during load variations in Case 6, significantly improving dynamic performance when the load decreases by 18.3% from 120 kW to 98 kW, with reductions in %(:{text{M}}_{text{p}}) by 93.38%, %(:{text{M}}_{text{u}text{s}}) by 100%, (:{text{T}}_{text{s}}) by 55.19%, and ITAE by 83.08%. Likewise, with a load increase of 11.2% from 98 kW to 109 kW, the MRAC-FPI-WOA controller enhances performance by cutting %(:{text{M}}_{text{p}}) by 33.33%, %(:{text{M}}_{text{u}text{s}}) by 93.48%, (:{text{T}}_{text{s}}) by 77.24%, and ITAE by 86.79%. In Case 7, MRAC-FPI-WOA exhibits exceptional adaptability under varying operating conditions: when solar irradiance decreases from 1000 to 800 W/m² alongside an 18.3% load reduction (120 kW to 98 kW), it reduces overshoot by 92.45%, undershoot by 100%, settling time by 69.81%, and ITAE by 87.46%; during a further irradiance drop to 600 W/m² with an 11.2% load increase (98 kW to 109 kW), it achieves even better performance with 100% overshoot reduction, 93.94% undershoot reduction, 75.3% settling time improvement, and 88.22% ITAE reduction; and finally, when irradiance rebounds to 900 W/m² at a steady 109 kW load, it maintains superior control with 95.4% overshoot reduction, 100% undershoot suppression, 72.9% faster settling, and 90.4% lower ITAE, demonstrating consistent excellence across all test scenarios. The simulation results confirm that the MRAC-FPI-WOA controller effectively sustains system stability and quality by balancing generation and consumption across diverse operating conditions. While the current study demonstrates the controller’s effectiveness through comprehensive MATLAB/Simulink simulations, we acknowledge that real-time hardware validation, such as HIL (Hardware-in-the-Loop) and Processor-in-the-Loop (PIL) validation, would be necessary to fully verify its performance in practical implementations. Future work will focus on experimental validation using microgrid testbeds with actual power electronics interfaces, robustness testing under real-world communication delays and measurement noise, and comparative analysis with physical benchmark controllers. Additionally, we plan to integrate advanced control techniques, such as machine learning, to further improve adaptability and explore hybrid energy systems that incorporate additional renewable sources, with parallel development of hardware prototypes for field testing. All data generated or analyzed during this study are included in this published article. Artificial bee colony Alternating current Adaptive fractional order PI advanced sine cosine algorithm Battery storage Bat algorithm Bio-dynamic grasshopper optimization algorithm Coati optimization algorithm Cascaded tilted-FO derivative with filter Chaos quasi-oppositional SHO Crow-search algorithm Direct current Diligent crow search algorithm Diesel engine generator Dragonfly search algorithm Fuzzy-fractional order PID Logic Controller Model reference adaptive control-fuzzy proportional integral based whale optimization algorithm Proportional-integral-derivative-Tilt Piecewise Linear-Elliptic Particle swarm optimization Perturb and observe Photovoltaic Renewable energy sources Sea-horse optimization State of Charge Type-2 Fuzzy Cascade Tunicate search algorithm Tilt fractional order PID Whale optimization algorithm P-N junction ideality factor Solar irradiance Battery rated capacity Short-circuit current Battery internal reaction current Parasitic gassing current Battery current PV cell’s Reverse leakage current Fractional-Order PID Fractional-Order PI Fuzzy Proportional-Integral Genetic Algorithm Grey Wolf Optimizer Hybrid Adaptive Ant Lion Optimization Fractional-Order Proportional-Integral Islanded Hybrid Power System Improved Salp Swarm Optimization Model Predictive Control Maximum Power Point Tracking Maximum Power Point Model Reference Adaptive Control Opposition-based SHO Proton Exchange Membrane Fuel Cells Proportional-Integral Proportional-Integral-Derivative PV cell’s output current Photocurrent source Boltzmann constant Short-circuit current coefficient Number of PV cells connected in series Number of PV cells arranged in parallel Load power Diesel engine generator power Photovoltaic power Electron charge Series resistance Shunt resistance P-N junction temperature Battery operating temperature Cell reference temperature Settling time Battery terminal voltage PV cell’s terminal voltage Open-circuit voltage Maximum overshoot Maximum undershoot Elborlsy, M. 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S: original draft, Writing – review & editing. R.M: Formal analysis, Software, Supervision. H.E: Investigation, Formal analysis, Software. All authors reviewed the manuscript. Correspondence to Mohamed A. Ghalib. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions Ghalib, M.A., Elbrolsy, M., Mostafa, R. et al. Adaptive Control-based frequency control strategy for PV/ DEG/ battery power system during islanding conditions. Sci Rep15, 40405 (2025). https://doi.org/10.1038/s41598-025-19341-8 Download citation Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41598-025-19341-8 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Suniva Inc., a U.S.-owned and -operated solar cell manufacturer, will invest $350 million to establish its first South Carolina manufacturing facility in Laurens, the company announced April 14. The investment at 1200 Commerce Blvd. is expected to create 564 jobs and Suniva’s 620,000-square-foot building will be used to produce advanced solar cells. “Since its founding in 2007, Suniva has championed U.S. leadership in solar energy manufacturing,” said Suniva CEO Tony Etnyre. “Solar is the fastest and most economical way to grow our nation’s energy supply — and at this critical juncture, access to energy will determine how America competes for generations to come. Our expansion in South Carolina means that renewable energy, made right here at home, will now do more than ever to secure that future.” Operations in Laurens are expected to be online in 2027.
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Partly cloudy early with increasing clouds overnight. Low 59F. Winds WSW at 5 to 10 mph.. Partly cloudy early with increasing clouds overnight. Low 59F. Winds WSW at 5 to 10 mph. Updated: April 14, 2026 @ 6:35 pm
LEWISBURG — The East Buffalo Township supervisors approved a feasibility study to determine the economic benefits of installing solar panels on the roof of the township garage. At Monday night’s public meeting, the supervisors said the study from the Pennsylvania Solar Center, a Pittsburgh-based nonprofit organization, will be at no cost to the township. The study is expected to take 15 days. Javascript is required for you to be able to read premium content. Please enable it in your browser settings. {{description}} Email notifications are only sent once a day, and only if there are new matching items. Sign up now to get our FREE breaking news coverage delivered right to your inbox. First Amendment: Congress shall make no law respecting an establishment of religion, or prohibiting the free exercise thereof; or abridging the freedom of speech, or of the press; or the right of the people peaceably to assemble, and to petition the Government for a redress of grievances. Your browser is out of date and potentially vulnerable to security risks. We recommend switching to one of the following browsers:
A map of the Clean Air Solar Farm proposals(Image: Clean Air Solar Farm) Fresh proposals for a 500MW solar farm near Beverley have been announced by renewable energy specialists and joint partners, PS Renewables and Ørsted Onshore. The proposed 'Clean Air Solar Farm' represents a revised version of the Kingfisher Solar Farm, which was first announced in January 2025. The new scheme features a revised site boundary and would generate sufficient electricity to power approximately 160,000 UK homes. It would make it one of the biggest planned, coming just a week after the Government gave the green light to what is set to become the UK's largest solar farm, rated at 800MW. The project would be spread across two sites near Beverley. A northern site would sit roughly three miles north of Beverley, to the east of the A164. Plans for this land were put before the public during a consultation in February 2025 under the Kingfisher Solar Farm name. The southern site would be positioned to the southwest of the A1079. The project would tie into the planned Wanlass Beck substation, which forms an extension of the existing Creyke Beck substation. Given the volume of electricity the Clean Air Solar Farm would produce, it is classified as a Nationally Significant Infrastructure Project (NSIP). This means the decision on whether to grant final consent for the development would rest with the Secretary of State for Energy Security and Net Zero, rather than the local council, as would ordinarily be the case with planning matters. A planning ruling is anticipated in 2028. Should consent be granted, the Clean Air Solar Farm is projected to be operational by 2033, reports Hull Live. Randall Linfoot from the Clean Air Solar Farm team said: "Since we first introduced Kingfisher Solar Farm, there have been significant changes. The project was originally developed to make use of spare grid capacity associated with Ørsted's Hornsea 4 offshore wind project. Since then, Hornsea 4 has returned to development, and we have been following the statutory National Energy System Operator (NESO) Gate 2 process to secure a new grid connection. The project would include two sites near Beverley(Image: Clean Air Solar Farm) "New project partners PS Renewables are a highly experienced, UK renewable energy developer. Together with Ørsted Onshore, the project proposals and site boundary have since evolved. To reflect these collective changes and a fresh start to our proposal, we took the decision to rename to Clean Air Solar Farm. "Clean Air Solar Farm will be able to power approximately 160,000 UK homes, making a significant contribution toward meeting the country's ambitious plans to achieve net-zero carbon emissions by 2050. We are committed to making a long-term, positive impact with these proposals and feedback from the community is critical. We would like to thank everyone for the time taken to engage with Kingfisher Solar Farm. All the feedback received to date has been carefully reviewed and fed into our plans." A series of Public Information Days regarding the scheme will take place in the local area during June 2026. These drop-in sessions will give local communities near the site an opportunity to discover more about the proposals, speak directly with the project team and share their views on the developing design. This will be followed by a consultation period in Autumn 2026. Drop in sessions take place in June in Lockington, Beverley and Walkington. To find all the planning applications, traffic diversions, road layout changes, alcohol licence applications and more in your community, visit the Public Notices Portal. At Reach and across our entities we and our partners use information collected through cookies and other identifiers from your device to improve experience on our site, analyse how it is used and to show personalised advertising. You can opt out of the sale or sharing of your data, at any time clicking the "Do Not Sell or Share my Data" button at the bottom of the webpage. Please note that your preferences are browser specific. Use of our website and any of our services represents your acceptance of the use of cookies and consent to the practices described in our Privacy Notice and Terms and Conditions.
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