Solar Now

OR
Date: 1 September 2026
Wavelength-dependent transparency, in particular, is a major strength of organic photovoltaics. Researchers at the University of Freiburg and the Fraunhofer Institute for Solar Energy Systems ISE have now succeeded in building 14.5 by 14.5- centimeter organic PV modules with an average transparency of 43.2 percent and an efficiency of up to 9.26 percent. To achieve this, they used a combination of sputtering and slot-die coating – both processes that are industrially proven and highly scalable to large module areas.
“Scaling up to larger areas is one of the major challenges in organic photovoltaics,” explains Dr. Uli Würfel, head of the Organic and Perovskite Photovoltaics Department at Fraunhofer ISE. Good laboratory results with small, hand-made solar cells often cannot be transferred to industrial production because their manufacturing process does not work on larger areas.
“The fact that we have now, for the first time, successfully and with virtually no loss applied all the layers of the solar cells using the slot-die process is a major breakthrough for us.” The back electrodes of the solar cells are applied in the preceding process step using a sputtering process, which is also an established manufacturing method.
In the development of semi-transparent organic photovoltaics, a trade-off is always made between light transmittance and the inevitable loss of efficiency associated with it, expressed as light utilization efficiency (LUE). With the 210.25-square-centimeter semi-transparent organic photovoltaic modules, the researchers achieved an efficiency of up to 9.26 percent with an average visible-light transmittance of 43.2 percent. This corresponds to a LUE of up to 4.0 percent. The project’s results were published in the leading journal “Joule” in early August.
“Now that we have the manufacturing process under control, we are optimistic that we can significantly increase transparency without compromising efficiency,” adds Uli Würfel. 
Organic solar modules with light transmittance well over 50 percent could be used in place of window glass in building facades or greenhouses. In contexts where tinted glass is desirable – for example, in car roofs and façade elements – lower transparency can even be an advantage.
In each module, more than 100 solar cells were interconnected using laser structuring. The solar cells consist of a back electrode that reflects near-infrared light, which was deposited onto a glass substrate via sputtering; an absorber layer made of organic semiconductors; and a metal-free top electrode made of the polymer PEDOT:PSS, which was applied in multiple layers using a slot die. Heraeus Epurio developed a new PEDOT:PSS formulation for this top electrode, which helped the PV modules achieve higher transparency.
Slot-die coating is compatible with roll-to-roll processes and is therefore also suitable for the production of solar modules on film. “As part of the project, we have already produced the first flexible, organic PV modules that retain 100 percent of their original efficiency after 1,274 bending cycles over a rod with a diameter of 15 millimeters,” explains Dr. Mathias List, research associate for organic and perovskite photovoltaics at Fraunhofer ISE. The company ROWO Coating manufactured the films for this purpose. “The next step is to achieve larger module areas here as well.”
The research findings are part of the project “Transparent PV – Development of Organic Solar Modules with High Visual Transparency,” supported by the Federal Ministry for Economic Affairs and Climate (BMWK, BMWE). Projects partners included Heraeus Epurio, ROWO Coating, ASCA, and the University of Freiburg.
MORE…

• Log in or register to post comments

source

Sibi Arasu
Associated Press
Published: September 1, 2026 at 9:31 PM
Sibi Arasu
Associated Press
Copyright 2023 The Associated Press. All rights reserved
FILE – Workers walk through a swamp to install electric transmission towers for the Adani Renewable Energy Park near Khavda, Bhuj district, near the India-Pakistan border in the western state of Gujarat, India, Sept. 21, 2023. (AP Photo/Rafiq Maqbool, File)
BENGALURU – When India’s power demand surged at the height of summer, the country struggled to meet evening needs as air conditioners ran longer amid hotter nights. Despite this demand, some renewable energy providers were told to limit their output because the country had more clean electricity available than its grid could safely handle.
In the last 15 months, India curtailed nearly 11 terawatt-hours of solar generation — enough electricity to power about 10 million homes, according to government data and research by energy think tank Ember. That solar power went unused even as extreme heat and poor monsoon rains drove up demand for power in India for cooling and pumping groundwater for agriculture.
Recommended Videos
India, the world’s most populous country and one of the largest emitters of climate-polluting gases, is rapidly adding clean energy, especially solar, to its power mix. However, it can’t use all the clean power it could generate because of insufficient transmission and storage capacity and the technical difficulty of shifting between fossil power and renewables.
Energy experts say that contradiction points to the next big challenge for India’s energy transition. Building solar and wind farms is no longer enough. India also needs more transmission lines to move electricity across the country, batteries to store renewable power until it is needed and a more flexible power system that can quickly adjust as wind and solar output rises or falls.
“We’re in a stage where some of the biggest hurdles in renewables are starting to hit us,” said Neshwin Rodrigues, an energy analyst at Ember.
Clean power gets switched off despite record demand
India has more than 300 gigawatts of clean power capacity, more than half its total installed electricity capacity. But coal still produces most of the country’s electricity.
Experts said the main reason for this is curtailment — when a wind or solar plant could produce electricity but is ordered to reduce or stop generation as the grid cannot take the power.
Rodrigues said that when solar generation surges in the afternoon, it’s difficult for coal units to reduce their output because they are relatively inflexible and cannot ramp down quickly without compromising efficiency, increasing costs or risking operational problems.
Trying to make thermal power plants flexible and adapt to increasing supply from clean energy sources “is like asking an elephant to dance,” said Vinay Pabba, CEO of Hyderabad-based renewable energy company Vibrant Energy.
Pabba said curtailment results in losses for clean power developers. “We get paid only for what we put on the grid,” he said.
The high concentration of renewable generation in western India has also meant that transmission lines in that part of the country get congested quickly. The western states of Gujarat and Rajasthan account for nearly 50% of India’s solar power capacity.
“When solar peaks, usually in the afternoon, there is a limited pipe to evacuate it,” said Pabba.
Another risk of not building storage quickly is that dirty fuels get used more. “Without enough storage, India risks keeping coal plants running even when cheap renewable power is available,” said Vibhuti Garg, South Asia director at the Institute for Energy Economics and Financial Analysis.
While a solar or wind farm can sometimes be completed within two years, building new power lines can take a minimum of three years, according to energy experts.
India has achieved only about 80% of its annual transmission construction targets over the past five years, research by Ember has found.
Spreading more renewable development across other parts of the country, while adding more wind and smaller local solar projects, could reduce pressure on crowded transmission corridors and make the electricity supply more balanced throughout the day, said Disha Agarwal, an energy analyst at the New Delhi-based Council on Energy, Environment and Water.
Agarwal said the challenge is likely to become more difficult as renewable capacity keeps rising.
India is aiming for 500 gigawatts of clean electricity capacity by 2030. Also, Indian policymakers expect nearly 70% of India’s installed power capacity to come from nonfossil sources by 2036.
Batteries could help save power for when it is needed
A study released earlier this month by the India Energy and Climate Center at the University of California, Berkeley, found that renewable power backed by batteries can provide electricity with reliability approaching that of conventional power plants at a price researchers said is lower than the price of power from new coal-fired plants.
Batteries make it possible to store solar electricity when it is abundant in the afternoon and discharge it after sunset, when demand remains high.
But India’s storage sector remains far smaller than what planners said will eventually be required.
“If we try to increase the solar installations without solving for energy storage, it is only going to lead to curtailment,” said Ankit Mittal, CEO of battery storage company Ingro Energy.
Mittal said India is trying to transform several parts of its electricity system simultaneously as power demand rises. “Things that were supposed to happen over decades” are now happening at once, he said.
The government said in July that it had about 3 gigawatts of battery storage and 7.4 gigawatts of operational pumped-storage capacity. It expects India’s storage needs to reach about 74 gigawatts by 2032.
“If high quality energy storage projects are not built, we could be adding an additional layer of risk to grid operations while also decelerating our nation’s ability to achieve our energy transition targets,” said Avinash Rao, CEO of Mahindra Susten, a leading renewable energy company in India.
Rodrigues, the energy analyst, said batteries can be built much more quickly than major transmission infrastructure, making them one of the fastest options for easing some immediate constraints.
Industry stakeholders said that few, if any, foresaw the incredible increase in demand happening as the country’s transportation and other major sectors electrify and data centers are built.
“None of us saw it coming. If we had seen it coming, we would have probably planned our way around it,” said Pabba of Vibrant Energy.
___
Sibi Arasu can be followed on X at @sibi123. Reach him at sarasu@ap.org.
___
The Associated Press’ climate and environmental coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP’s standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org.
Copyright 2026 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed without permission.
TV Listings
Email Newsletters
RSS Feeds
Contests and Rules
Contact Us / Follow on Social Media
Careers at WKMG
Closed Captioning / Audio Description
Public File
Current EEO Report
Terms of Use
Privacy Policy
Do Not Sell My Info
FCC Applications
EEO Report
Disability Assistance
Cookie Preferences
If you need help with the Public File, call (407) 291-6000
At WKMG, we are committed to informing and delighting our audience. In our commitment to covering our communities with innovation and excellence, we incorporate Artificial Intelligence (AI) technologies to enhance our news gathering, reporting, and presentation processes. Read our article to see how we are using Artificial Intelligence.
Copyright © 2026 ClickOrlando.com is managed by Graham Digital and published by Graham Media Group, a division of Graham Holdings.

source

New Jersey residents have a new option to save on their electric bills.
Portable solar panels that meet safety standards are now legal to buy online or at local home improvement stores, Gov. Mikie Sherrill announced on Tuesday.
The law exempts these plug-in energy generators under 1,200 watts from utility interconnection and metering requirements. Depending on wattage, models can range in price between a few hundred dollars and $1,500.
"These units cost a fraction as much as rooftop solar, but they can still shave up to $50 off the typical monthly bill," Sherrill said. That's savings of $600 a year. She said there are more than 1 million of these panels in Germany alone.
Often placed on balconies or backyards, most portable solar panels have attached microinverters that go out to a regular plug. They plug into standard 120-volt wall outlets, allowing other appliances and devices in the home to draw from that power.
Under the new law, residents who use balcony solar panels don't have to notify or get approval from their power companies. The law also stops landlords and homeowner associations from banning them. Municipalities can't ban or require permits for them either.
The change gives New Jersey homeowners and renters a new option for generating some of their own electricity without taking on the cost and complexity of a traditional rooftop solar installation.
It also removes several potential roadblocks that could otherwise prevent residents from using the systems.
Only six months passed between when the bill (S2368) was introduced and its signing on Tuesday, a relatively speedy journey in Trenton. It passed unanimously. This makes New Jersey the 9th state to legalize balcony solar, according to PlugInSolarUS.
The Garden State Balcony Solar Act is one of several laws the Sherrill administration has passed to counter the spike in New Jersey energy bills, which was the highest increase in the nation last year.
Gallery Credit: New Jersey 101.5
Report a correction 👈 | 👉 Contact our newsroom
Gallery Credit: Erin Vogt

source

source

Professor Kwon Tae-hyuk's Team and University of Cambridge Swap Solar Cell Modules to Match Light Levels Fast Charge to 70% in 10 Minutes Points to Maintenance-Free IoT Power Source
ULSAN — A modular battery technology that switches between sunlight and indoor lighting to charge around the clock has been developed. The advance is expected to accelerate commercialization of standalone power sources for Internet of Things (IoT) sensors that require neither external power lines nor periodic battery replacement.
The Ulsan National Institute of Science and Technology (UNIST) said on the 2nd that a research team led by Professor Kwon Tae-hyuk of the Department of Chemistry, working with Professor Michael De Volder's team at the University of Cambridge, has developed a modular photo-rechargeable battery architecture whose configuration can be changed according to light intensity.
Photo-rechargeable batteries combine the power-generating function of solar cells and the energy-storage function of batteries into a single device. Existing photo-charging systems had a limitation: when light intensity changed, the voltage produced by the solar cells no longer matched the battery's charging requirement, halting the charge or sharply cutting efficiency.
The team devised an approach that keeps the battery body fixed while swapping only the solar cell modules to suit light intensity. Solar cells that add voltage are wired in series next to a battery cell containing a lithium iron phosphate (LFP) cathode, allowing the voltage of one, three or five segments to be selected depending on light conditions. When charging a lithium metal battery that requires higher voltage, all five segments are connected to secure sufficient driving force for the charge.
The battery materials were also overhauled to raise energy density. In the conventional approach using an iodine-based liquid cathode electrolyte, the separator needed to prevent side reactions increased internal resistance and slowed charging and discharging. The team applied a solid LFP cathode requiring no separator, together with a lithium metal anode, sharply raising volumetric energy density. It also adopted a solid polymer (PEDOT) as the charge transport material under sunlight and a copper complex electrolyte under weak indoor lighting, maximizing generation efficiency.
In performance testing, the lithium metal battery fitted with the sunlight module charged rapidly to 70% in 10 minutes under standard solar conditions and recorded a discharge energy density of 327.8 mWh/g. Complete charging and discharging was also achieved using light alone at 1,000 lux, the illumination level of a typical office. The researchers also observed that available battery capacity increased when light was shone on the device while power was being drawn.
The technology is expected to find broad use in indoor energy harvesting, recovering lighting energy otherwise wasted inside buildings to power wireless sensors and IoT devices indefinitely. On a European basis in 2016, annual energy consumption by buildings accounted for roughly 40% of total energy consumption, and about 20% of the energy consumed in buildings went to lighting.
"By making the number of solar cell segments and the materials variable, we secured optimal driving force matched to the charging voltage of each battery material," said Kim Byung-man, a UNIST researcher and the paper's first author. "This will serve as a design strategy applicable to the various battery materials now in commercial use."
"By establishing a structure capable of stable charging under both indoor and outdoor light conditions, we have cleared a major obstacle to commercializing photo-rechargeable secondary batteries," said Professor Kwon of UNIST. "This can fundamentally resolve the maintenance cost problem of IoT devices in which battery replacement is difficult."
The findings were published in the August issue of Energy Storage Materials, an international journal in the field of energy storage. The research was supported by KEPCO and UK Research and Innovation (UKRI).
Original reporting by Jang Ji-seung for Seoul Economic Daily.
AI-translated from Korean. Quotes from foreign sources are based on Korean-language reports and may not reflect exact original wording.
Watch · Seoul Economic Daily
◆ SIGNAL English Edition
Korean capital markets, translated. Subscribers see deals before the wires.
Free to browse · 50% intro rate
Seoul Economic Daily
the Editorial Board (Opinion)
the Editorial Board (Opinion)
Editorial Board (Opinion)
Hong Byung-moon (Commentary)
An independent data project
Your chart, luck cycles, today’s flow, wealth, career, and ideal match — from one date of birth.
Saju · sedaily.ai
A live, cap-weighted view of every KOSPI and KOSDAQ sector, with same-day Korean reporting distilled by company — built for foreign investors, correspondents and analysts who need to scan Korea before the next session.
An English ontology of the Korean market — how companies, the media, the government and the National Assembly move each other in a loop. Korea's named controlling persons and designated business groups are a mechanism, not a risk to be priced blind.
SIGNAL English Edition is live — Korea's deal desk reporting in English. M&A, IPOs, private equity and fund flows, covered daily for global institutional investors. Browse free; subscriber-only scoops at the 50% intro rate.
Copyright ⓒ Sedaily, All right reserved

source

Both small- and grid-scale solar and battery energy storage systems (BESS) will be dominant in achieving Australia’s least cost energy transition to 2050, according to the Australian Energy Market Operator (AEMO) 2026 Integrated System Plan (ISP).
Targeting a total generation and storage capacity increase from 99 GW in 2026 to 308 GW over the next 24 years to 2050, would require from grid-scale wind and solar, a  5-fold increase from 23 GW in 2026, to 117 GW in 2050, or 3.9 GW per year.
Similarly, distributed solar would need to increase 20 GW to 87 GW, and dispatchable storage capacity from batteries, virtual power plants (VPP) and pumped hydro, would need an 11-fold increase from 6 GW to 64 GW, or an average of 2.6 GW per year to 2050.
The 117 GW of wind and solar will replace vanishing coal fleet capacity, which is projected to be 0 GW by 2049, when all plants are scheduled to be retired.
It will also be needed to meet rising demand, which the ISP forecasts will nearly double from 205 TWh in 2026 to 390 TWh in 2050.
Investment by consumers is forecast to contribute 87 GW of rooftop and other small-scale solar by 2050, and 35 GW of BESS.
In the 2050 Step Change scenario AEMO least-cost forecast, rooftop and other small-scale solar would have a 28% share of total National Electricity Market (NEM) capacity and, supported by consumer batteries and distribution networks, deliver a similar share of annual generation.
Similarly, in 2050, grid-scale solar would have a 21% share of NEM capacity and, supported by grid-scale batteries, deliver 29% of annual generation.
“This ISP projects a higher share of grid-scale solar and battery storage in the NEM capacity than previously, as their relative costs decline and battery connections increase,” the ISP says.
Transmission
Under the Step Change scenario, the plan forecasts around $106 billion (USD 73 billion) in annualised capital investment to 2050 (in today’s dollars) in transmission projects to connect to renewable energy generation and distribution sources.
“Around $6 billion of this is for transmission, which would deliver significant benefits, saving consumers $30 billion in avoided capital, operating and fuel costs compared to a pathway without these transmission investments,” the ISP says.
“Transmission is a relatively small share of overall system investment but delivers substantial benefits for consumers by unlocking lower-cost energy across the National Electricity Market,” Westerman said.
“The direction for Australia’s energy future remains clear, it’s renewable energy, supported by storage, connected by transmission and distribution, and backed up by gas.”
Smart Energy Council Chief Executive Officer David McElrea said “the more we delay, the more we pay”. 
“That’s almost $30 billion Australians stand to lose if we prolong our reliance on expensive, unreliable, ageing, and polluting fossil fuels like coal and gas.”
“Without a continued, rapid rollout of new transmission, grid-scale investment costs will balloon by $17 billion and system operating costs will shoot up by $12 billion. This infrastructure isn’t just about generating clean energy; it’s about moving it to where it is needed most – our regional manufacturing hubs, mining centers, cities, and electrified transport networks,” McElrea said.
AEMO Group System Planning Group Manager Eli Pack asked on LinkedIn what if the transition keeps moving, but delivery is harder, slower and more expensive than we’d like?
“That matters because this transition is happening in the real world, with real people, and not in a perfect model or giant spreadsheet. Even in that tougher world, there would still be around 45 GW of renewables and 31 GW of storage needing to connect by 2030, making transmission even more important across the NEM,” Pack said.

This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

SA Power Networks, the electricity distribution network operating in the state of South Australia, has announced its annual curtailment test will take place next Tuesday (August 25).
A statement published on the network’s website describes the annual test as “like a fire drill for the grid” that ensures its ability to temporarily curtail rooftop solar generation.
“[The test] simulates a rare but urgent situation so we can confirm that all systems and people are ready to respond if an actual emergency occurs,” the statement adds.
Around 100,000 customers are expected to be impacted on the day, with the curtailment expected to last less than an hour. SA Power Networks says solar systems will ramp down to 0 kW before ramping back up, with customers expected to miss around 1.5 kWh of generation on average.
The network’s update explains that the Australian Energy Market Operator (AEMO) can direct it to use curtailment to help keep the electricity system stable during a system security emergency. 
“AEMO monitors grid stability nationally and works to maintain system security – balancing electricity supply with demand,” the statement continues. “In South Australia, that balance can be challenging because we have more rooftop solar per capita than almost anywhere in the world.”
The government of South Australia passed legislation in 2020 requiring all solar systems installed after September that year to be able to be remotely disconnected during a system security emergency.
Curtailment is considered as one of the biggest challenges facing the development of Australia’s solar market, across all market segments. Analysis from February found South Australia typically sees relatively moderate curtailment through autumn and winter followed by a sharp escalation in spring and early summer.
Earlier this year, the Australian Energy Market Commission unveiled plans to modernize its distribution network planning, which it says will help to lower curtailment of rooftop solar.
According to figures shared by the International Solar Energy Society, the number of dwellings with rooftop PV in South Australia has now passed 50%.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

New Jersey Governor Mikie Sherrill has signed the the Garden State Balcony Solar Act (S2368/A4836) into law, enabling New Jerseyites to install and use portable solar generation devices of up to 1,200 watts without the need to apply for an installation permit or obtain their utility’s approval.
The law, which was passed by the state’s two legislative bodies on unanimous votes in late June, would require the portable solar devices to comply with provisions of the most recent versions of the National Electrical Code (NEC) and the State Uniform Construction Code, in addition to becoming listed or certified under the UL 3700 Outline of Investigation for Interactive Plug-In PV (PIPV) Equipment and Systems.
The bill creates an exemption for devices with power output of 400 watts from the need to obtain the UL listing or comply with the NEC and state code.
“From day one, I’ve been laser-focused on driving down energy costs through an all-of-the-above approach, and that includes putting clean, affordable solar power that you can simply plug in directly into the hands of New Jerseyans,” said Governor Sherrill in a statement. “Balcony solar is a practical, easy-to-use tool that can help families save money while allowing more people to participate in our clean energy future. This bill cuts unnecessary red tape, expands access to affordable solar power, and proves that affordability and sustainability can go hand in hand.”
Notably, the bill also contains provisions that restrict landlords and homeowners’ associations (HOAs) from prohibiting the use of portable solar generation devices, so long as renters (or homeowners subject to HOA oversight) abide by “reasonable restrictions concerning the size, placement, or manner of placement of a portable solar generation device on the exterior of a unit owner’s or tenant’s premises.”
News of the law was celebrated widely among advocates and industry representatives. “By making solar more accessible, New Jersey is building a fairer, more affordable energy system where everyone can share in the benefits of clean power,” said Elowyn Corby, Senior Regional Director for the Mid-Atlantic, Vote Solar Action Fund. “We are grateful Governor Sherrill has stood with New Jersey families and taken a major step toward a clean energy future that delivers greater energy affordability and access to solar.”
“By signing this law, Governor Sherrill and legislative leaders have taken another big step in making solar energy more affordable and accessible for New Jerseyans,” said Stephan Scherer, CEO and co-founder of CraftStrom, a company that sells balcony solar equipment. “As the most densely populated state in the nation, New Jersey is built for plug-in solar: it takes just an hour to install, fits on apartment and condominium balconies, and cuts utility bills immediately. New Jersey is sending a clear signal that the future of solar is portable, affordable, and consumer-led.”
Plug-in solar bills in other states (such as the recently-passed California Plug and Play Solar Act) do not contain similar protections for renters and HOA members. 
With Sherrill’s signature, New Jersey becomes the ninth state in the nation to enact a plug-in solar law. Laws in two other states — New York’s SUNNY Act and the aforementioned California legislation — await action from governors in those states.
The Garden State Balcony Solar Act bill will take effect on March 1, 2027, giving the state Board of Public Utilities time to take action necessary to implement the law’s provisions.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Monday, October 26, 2026
10:30 am – 11:30 am CEST, Berlin, Paris, Madrid
Thursday, September 10, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Tuesday, September 15, 2026
5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
pv magazine USA hosts its multi-day virtual event on U.S. solar and energy storage, covering domestic manufacturing, distributed energy and the growing role of solar-plus-storage in meeting AI-driven power demand.

You have no items in your basket.

source

Wind farm proposal ‘vandalism’ says deputy mayor  The Southern Wire
source

Subscribe Now! Get features like
The Delhi government announced an ambitious plan on Tuesday to raise the state subsidy for residential rooftop solar installations in the national capital to ₹78,000, a move that would slash the upfront cost of setting up the clean energy system.
Chief minister Rekha Gupta said households with a monthly consumption of 400 units of electricity or less would effectively be eligible for a fully funded 3-kilowatt rooftop solar system under the revised Delhi Solar Policy.
Under the changes made to the 2023 policy, the Delhi government will offer subsidies of up to ₹78,000 for a 3-kw solar panel system. Paired with equal funding from the central government’s PM Surya Ghar initiative, the total incentives will effectively cover the setup cost for standard 2- and 3-kw units.
Gupta said the government has targeted installing rooftop solar systems in 230,000 households across Delhi by March 2027.
Also Read: The supply chain behind solar panels
“The government not only wants to reduce people’s electricity bills but also to support the installation cost of solar panels. The government will provide free rooftop solar panels to all households with monthly electricity consumption of 400 units,” Gupta said at a press conference.
Apart from the state subsidy of ₹78,000 for a 3-kW system, the policy also proposes an additional state top-up of ₹19,000 for consumers using up to 400 units of electricity a month. This would cover the installation cost of ₹1.75 lakh for a 3-kw system.
Also read | Read More

source

In a 2023 China tea-field trial, bushes grew beneath…  inkl.com
source

Saatvik Solar Industries, a material subsidiary of Saatvik Green Energy, has applied for enlistment of its 2.4 GW solar cell manufacturing facility in Ganjam, Odisha, under the Ministry of New and Renewable Energy’s (MNRE) Approved List of Models and Manufacturers (ALMM) List-II for solar PV cells. 
The facility is designed to manufacture high-efficiency, n-type TOPCon G12R solar cells under the Domestic Content Requirement (DCR) category. The application covers Model No. SS-NTP-210R-16BB-BF.
Subject to MNRE’s approval and enlistment, the facility will be eligible for deployment in projects requiring compliance with applicable domestic sourcing requirements.
Prashant Mathur, CEO, Saatvik Green Energy said, “The advancement of our Odisha cell manufacturing facility towards ALMM List-II enlistment is an important milestone in our ambition to build advanced and competitive solar manufacturing capabilities in India. With a planned capacity of 2.4 GW and a focus on high-efficiency N-TOPCon technology, the facility will strengthen our ability to offer domestically manufactured solar cells while supporting the industry’s broader localisation journey.”
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

The Indonesian government has officially launched its 100 GW solar power plant program.
Speaking during a groundbreaking ceremony, President Prabowo Subianto said the government is serious about realising the program and is aiming to develop 100 GW within three years.
According to a statement published by the government, the program will be carried out in phases with the initial phase targeting 17 GW of new capacity.
Under this first phase, 14 solar plants with a combined capacity of 5.2 GW are already in various stages of development. Reports from Reuters add that two of these plants are already in operation, with six in the construction phase and another six to be offered to investors.
Indonesia’s flagship solar program will target the deployment of ground-mounted, rooftop and floating solar power plants, as well as decentralised, smaller-scale solar systems, in order to replace diesel generation, electrify rural areas and villages and strengthen the country’s energy security and independence.
The program will also deploy battery energy storage systems (BESS) in efforts to support a more reliable power system. The official launch of the program coincided with the start of construction of a 305 MW solar project alongside a 1 GWh BESS in the port town of Gilimanuk, west Bali. The project belongs to Indonesia’s power utility PLN.
The 100 GW target surpasses Indonesia’s current electricity generation capacity, which stands at around 88 GW. According to the country’s Minister of Energy and Mineral Resources, Bahlil Lahadalia, it will require approximately $73 billion in investment and has the potential to create over 5.5 million jobs.
In a statement sent to pv magazine, Indonesian think tank Institute for Essential Services Reform (IESR) said that achieving the 100 GW target in less than four years is highly ambitious and will require innovative approaches to implementation.
“For the program to succeed, Indonesia needs a strong and consistent national implementation architecture backed by regulatory certainty,” IESR said. “The government must immediately establish clear program leadership with the authority to coordinate across ministries, PLN, local governments, industry, financial institutions, and businesses.”
IESR’s Chief Executive Officer, Fabby Tumiwa, added that success of the program should not be measured by how many projects break ground, but by how many gigawatts of solar can be built, connected to the grid, and reliably generate electricity before 2029.
“The biggest challenge now is to translate the President’s political commitment into an implementation engine capable of delivering tens of gigawatts of solar PV every year,” Tumiwa added.
IESR is recommending six priority measures to ensure the program achieves its 100 GW target, beginning with immediately finalzing a presidential regulation as the legal framework for the program, and aligning the country’s national energy general plan and PLN’s electricity supply business plan to accommodate the additional capacity.
It also recommends developing a clear annual project pipeline through 2029, accelerating procurement through competitive, transparent, and bankable tenders, ensuring power system readiness, developing a national supply chain and adopting a multi-track implementation so the program is not solely dependent on PLN projects.
Earlier this year, IESR released a report exploring how Indonesia can mobilize its 100 GW solar target as it warned against trying to adopt a one-size-fits all approach to electrifying the 80,000 villages targeted by the program.
Indonesia surpassed 1 GW of solar capacity in 2025, with total capacity reaching 1.49 GW by the end of the year.
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: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

China’s solar power capacity surpassed coal-fired power capacity for the first time, marking a milestone in the country’s transition toward cleaner energy, official data showed Tuesday.
Solar power capacity reached 1.286 billion kilowatts at the end of July, edging past coal-fired capacity of 1.285 billion kilowatts, according to figures announced by China’s National Energy Administration and reported by state-run Xinhua.
Solar power has consequently become China’s largest electricity source in terms of installed capacity.
Centralized solar plants accounted for 704 million kilowatts of the total, while distributed photovoltaic systems made up 582 million kilowatts.
“This marks a milestone in China’s green and low-carbon energy transition,” said Liu Zhiqiang, an expert at the China Electricity Council.
China’s total installed power-generation capacity stood at 4.08 billion kilowatts at the end of July, with solar power accounting for more than 30% of the total.
The country added 193.97 million kilowatts of generation capacity during the first seven months of the year. Solar accounted for 85.65 million kilowatts, or more than 40% of the newly installed capacity.
Installed capacity, however, does not directly correspond to electricity production because solar generation is affected by daylight hours and weather conditions.
Photovoltaic facilities generated 802.4 billion kilowatt-hours of electricity between January and July, equivalent to about 13% of China’s total power consumption.
Coal-fired power is therefore expected to remain a key source of reliable electricity and provide support for grid stability in the short term.
China supplies more than 80% of the world’s photovoltaic modules and about 70% of its wind-power equipment, according to Xinhua.

source

Australia Accelerates Next-Generation Solar R&D with $105.6 Million Technology Push  solarquarter.com
source

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Nature Materials (2026) Cite this article
Tin halide perovskites (THPs) offer narrower bandgaps and improved environmental safety compared with the widely studied lead-based perovskites, but their air sensitivity has hindered their progress and demands new material design to enable their practical applications. Here we report stable and efficient 2D/3D tin perovskite solar cells enabled by new ultrastable 2D and quasi-2D THPs based on the 4-chloro-phenethylammonium (4ClPEA) cation. The stronger π-stacking interactions and tighter interlayer packing in (4ClPEA)₂SnI₄ among (4XPEA)₂SnI₄ structures (X = H, F, Cl, Br) substantially impede oxygen and water diffusion, enabling superior air and moisture stability and bright photoluminescence lasting several months in ambient air. The addition of 4ClPEA markedly improves 2D/3D THP film crystallinity and orientation, leading to 16.2% efficient 2D/3D THP solar cells that show prolonged storage stability and operational stability at 55 °C surpassing 1,000 h. This study establishes a new strategy for designing stable and efficient THPs towards their practical applications.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
USD 39.95
Prices may be subject to local taxes which are calculated during checkout
Cambridge Crystallographic Data Centre (CCDC) deposition numbers 2496775–2496777 and 2538309 contain the supplementary crystallographic data for this paper. These data can be obtained free of charge via the CCDC available at www.ccdc.cam.ac.uk/data_request/cif. All data needed to evaluate the conclusions of the study are available in the Article or Supplementary Information.
Chen, H. et al. Improved charge extraction in inverted perovskite solar cells with dual-site-binding ligands. Science 384, 189–193 (2024).
Article  CAS  PubMed  Google Scholar 
National Laboratory of the Rockies. Best Research-Cell Efficiency Chart. https://www.nlr.gov/pv/cell-efficiency (2026).
Xiong, Z. et al. Homogenized chlorine distribution for >27% power conversion efficiency in perovskite solar cells. Science 390, 638–642 (2025).
Article  CAS  PubMed  Google Scholar 
Li, J. et al. Biological impact of lead from halide perovskites reveals the risk of introducing a safe threshold. Nat. Commun. 11, 310 (2020).
Article  PubMed  PubMed Central  Google Scholar 
Stoumpos, C. C., Malliakas, C. D. & Kanatzidis, M. G. Semiconducting tin and lead iodide perovskites with organic cations: phase transitions, high mobilities, and near-infrared photoluminescent properties. Inorg. Chem. 52, 9019–9038 (2013).
Article  CAS  PubMed  Google Scholar 
Zhou, H. et al. Robust excitonic light emission in 2D tin halide perovskites by weak excited state polaronic effect. Nat. Commun. 15, 8541 (2024).
Article  PubMed  PubMed Central  Google Scholar 
Li, Y. et al. Photo-excited carrier behaviors of two-dimensional tin halide perovskite single crystals. Cell Rep. Phys. Sci. 5, 102020 (2024).
Article  CAS  Google Scholar 
Mihalyi-Koch, W. et al. Tuning structure and excitonic properties of 2D Ruddlesden–Popper germanium, tin, and lead iodide perovskites via interplay between cations. J. Am. Chem. Soc. 145, 28111–28123 (2023).
Article  CAS  PubMed  Google Scholar 
Jiang, X. et al. Ultra-high open-circuit voltage of tin perovskite solar cells via an electron transporting layer design. Nat. Commun. 11, 1245 (2020).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Chen, L. et al. On the durability of tin-containing perovskite solar cells. Adv. Sci. 11, 2304811 (2024).
Article  CAS  Google Scholar 
Awais, M., Kirsch, R. L., Yeddu, V. & Saidaminov, M. I. Tin halide perovskites going forward: frost diagrams offer hints. ACS Mater. Lett. 3, 299–307 (2021).
Article  CAS  Google Scholar 
Fu, Y., Jin, S. & Zhu, X. Y. Stereochemical expression of ns² electron pairs in metal halide perovskites. Nat. Rev. Chem. 5, 838–852 (2021).
Article  CAS  PubMed  Google Scholar 
Lanzetta, L. et al. Degradation mechanism of hybrid tin-based perovskite solar cells and the critical role of tin (IV) iodide. Nat. Commun. 12, 2853 (2021).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Chen, J. et al. Spatially isomeric fulleropyrrolidines enable controlled stacking of perovskite colloids for high-performance tin-based perovskite solar cells. Angew. Chem. Int. Ed. Engl. 64, e202420150 (2025).
Article  CAS  PubMed  Google Scholar 
He, D. et al. Homogeneous 2D/3D heterostructured tin halide perovskite photovoltaics. Nat. Nanotechnol. 20, 779–786 (2025).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Li, T. et al. Tin-based perovskite solar cells with a homogeneous buried interface. Nature 648, 84–90 (2025).
Article  CAS  PubMed  Google Scholar 
Shao, S. et al. Highly reproducible Sn-based hybrid perovskite solar cells with 9% efficiency. Adv. Energy Mater. 8, 1702019 (2018).
Article  Google Scholar 
Wang, F. et al. 2D-quasi-2D-3D hierarchy structure for tin perovskite solar cells with enhanced efficiency and stability. Joule 2, 2732–2743 (2018).
Article  CAS  Google Scholar 
Jiang, X. et al. One-step synthesis of SnI₂·(DMSO)_x adducts for high-performance tin perovskite solar cells. J. Am. Chem. Soc. 143, 10970–10976 (2021).
Article  CAS  PubMed  Google Scholar 
Nakamura, T. et al. Sn(IV)-free tin perovskite films realized by in situ Sn(0) nanoparticle treatment of the precursor solution. Nat. Commun. 11, 3008 (2020).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Shi, Y., Zhu, Z., Miao, D., Ding, Y. & Mi, Q. Interfacial dipoles boost open-circuit voltage of tin halide perovskite solar cells. ACS Energy Lett. 9, 1895–1897 (2024).
Article  CAS  Google Scholar 
Liao, Y. et al. Highly oriented low-dimensional tin halide perovskites with enhanced stability and photovoltaic performance. J. Am. Chem. Soc. 139, 6693–6699 (2017).
Article  CAS  PubMed  Google Scholar 
Lee, D. S. et al. Passivation of grain boundaries by phenethylammonium in formamidinium–methylammonium lead halide perovskite solar cells. ACS Energy Lett. 3, 647–654 (2018).
Article  CAS  Google Scholar 
Li, P. et al. Phase pure 2D perovskite for high-performance 2D–3D heterostructured perovskite solar cells. Adv. Mater. 30, 1805323 (2018).
Article  Google Scholar 
Knutson, J. L., Martin, J. D. & Mitzi, D. B. Tuning the band gap in hybrid tin iodide perovskite semiconductors using structural templating. Inorg. Chem. 44, 4699–4705 (2005).
Article  CAS  PubMed  Google Scholar 
Li, X., Hoffman, J. M. & Kanatzidis, M. G. The 2D halide perovskite rulebook: how the spacer influences everything from the structure to optoelectronic device efficiency. Chem. Rev. 121, 2230–2291 (2021).
Article  CAS  PubMed  Google Scholar 
Gu, J. et al. Correlating photophysical properties with stereochemical expression of 6s² lone pairs in two-dimensional lead halide perovskites. Angew. Chem. Int. Ed. 62, e202304515 (2023).
Article  CAS  Google Scholar 
Triggs, C. T. et al. Cage balancing enhances optoelectronic and lasing performance in stable quasi-2D tin iodide perovskites. J. Am. Chem. Soc. 147, 34706–34720 (2025).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Gong, X. et al. Electron–phonon interaction in efficient perovskite blue emitters. Nat. Mater. 17, 550–556 (2018).
Article  CAS  PubMed  Google Scholar 
Zhang, T. et al. Regulation of the luminescence mechanism of two-dimensional tin halide perovskites. Nat. Commun. 13, 60 (2022).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Park, I. H. et al. Highly stable two-dimensional tin(II) iodide hybrid organic–inorganic perovskite based on stilbene derivative. Adv. Funct. Mater. 29, 1904810 (2019).
Article  Google Scholar 
Triggs, C. T. et al. Spacer cation design motifs for enhanced air stability in lead-free 2D tin halide perovskites. ACS Energy Lett. 9, 1835–1843 (2024).
Article  CAS  Google Scholar 
Yu, D. et al. Electron-withdrawing organic ligand for high-efficiency all-perovskite tandem solar cells. Nat. Energy 9, 298–307 (2024).
Article  CAS  Google Scholar 
Mitzi, D. B., Dimitrakopoulos, C. D. & Kosbar, L. L. Structurally tailored organic–inorganic perovskites: optical properties and solution-processed channel materials for thin-film transistors. Chem. Mater. 13, 3728–3740 (2001).
Article  CAS  Google Scholar 
Zhang, F. et al. Enhanced charge transport in 2D perovskites via fluorination of organic cation. J. Am. Chem. Soc. 141, 5972–5979 (2019).
Article  CAS  PubMed  Google Scholar 
Cacovich, S. et al. Imaging and quantifying non-radiative losses at 23% efficient inverted perovskite solar cells interfaces. Nat. Commun. 13, 2868 (2022).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Degani, M. et al. 23.7% efficient inverted perovskite solar cells by dual interfacial modification. Sci. Adv. 7, eabj7930 (2021).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Duan, J. et al. Structure and device-operando photostability of quasi-2D Ruddlesden–Popper Perovskites: engineering the spacer cation matters. ACS Energy Lett. 11, 1714–1723 (2026).
Article  CAS  Google Scholar 
Yu, B. et al. Heterogeneous 2D/3D tin-halides perovskite solar cells with certified conversion efficiency breaking 14%. Adv. Mater. 33, 2102055 (2021).
Article  CAS  Google Scholar 
Xu, Y. et al. Unraveling the formation mechanisms of highly oriented tin perovskite with a 3D-over-2D heterostructure. ACS Energy Lett. 9, 4734–4745 (2024).
Article  CAS  Google Scholar 
Liu, T. et al. Suppression of tin oxidation via Sn→B bonding interactions for high-resolution lead-free perovskite neuromorphic imaging sensors. Adv. Mater. 37, 2502015 (2025).
Article  CAS  Google Scholar 
Berryman, O. B., Bryantsev, V. S., Stay, D. P., Johnson, D. W. & Hay, B. P. Structural criteria for the design of anion receptors: the interaction of halides with electron-deficient arenes. J. Am. Chem. Soc. 129, 48–58 (2007).
Article  CAS  PubMed  Google Scholar 
Chifotides, H. T. & Dunbar, K. R. Anion–π interactions in supramolecular architectures. Acc. Chem. Res. 46, 894–906 (2013).
Article  CAS  PubMed  Google Scholar 
Janiak, C. A critical account on π–π stacking in metal complexes with aromatic nitrogen-containing ligands. J. Chem. Soc., Dalton Trans. 29, 3896–3885 (2000).
Google Scholar 
Tong, J. et al. Carrier control in Sn–Pb perovskites via 2D cation engineering for all-perovskite tandem solar cells with improved efficiency and stability. Nat. Energy 7, 642–651 (2022).
Article  CAS  Google Scholar 
Chen, H. et al. Quantum-size-tuned heterostructures enable efficient and stable inverted perovskite solar cells. Nat. Photonics 16, 352–358 (2022).
Article  CAS  Google Scholar 
Teale, S., Degani, M., Chen, B., Sargent, E. H. & Grancini, G. Molecular cation and low-dimensional perovskite surface passivation in perovskite solar cells. Nat. Energy 9, 779–792 (2024).
Article  CAS  Google Scholar 
Zhang, Y. et al. Synchronized crystallization in tin-lead perovskite solar cells. Nat. Commun 15, 6887 (2024).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Gao, Y. et al. Highly stable lead-free perovskite field-effect transistors incorporating linear π-conjugated organic ligands. J. Am. Chem. Soc. 141, 15577–15585 (2019).
Article  CAS  PubMed  Google Scholar 
Harvey, S. P., Messinger, J., Zhu, K., Luther, J. M. & Berry, J. J. Investigating the effects of chemical gradients on performance and reliability within perovskite solar cells with TOF-SIMS. Adv. Energy Mater. 10, 1903674 (2020).
Article  CAS  Google Scholar 
Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).
Article  CAS  Google Scholar 
Blochl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).
Article  CAS  Google Scholar 
Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865 (1996).
Article  CAS  PubMed  Google Scholar 
Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu. J. Chem. Phys. 132, 154104 (2010).
Article  PubMed  Google Scholar 
Henkelman, G., Uberuaga, B. P. & Jónsson, H. Climbing image nudged elastic band method for finding saddle points and minimum energy paths. J. Chem. Phys. 113, 9901–9904 (2000).
Article  CAS  Google Scholar 
Download references
Work at the University of Wisconsin-Madison is supported by the Division of Materials Sciences and Engineering, Office of Basic Energy Sciences, Department of Energy (DOE), under Award DE-SC0002162. C.T.T. also acknowledges support from the National Science Foundation Graduate Research Fellowship Program under grant number DGE-2137424 and the Graduate School and the Office of the Vice Chancellor for Research at the University of Wisconsin–Madison with funding from the Wisconsin Alumni Research Foundation. The study was authored in part by the National Laboratory of the Rockies (NLR) for the US DOE under contract number DE-AC36-08GO28308. Work at the NLR, University of Colorado (CU) Boulder and University of Toledo was primarily supported as part of the Center for Hybrid Organic–Inorganic Semiconductors for Energy (CHOISE), an Energy Frontier Research Center funded by the Office of Basic Energy Sciences, Office of Science, DOE. The DFT calculations were performed using computational resources sponsored by the DOE Office of Critical Minerals and Energy Innovation and located at the NLR, and the resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under contract number DE-AC02-05CH11231 using NERSC award BES-ERCAP0032847. GIWAXS was performed at the Colorado Shared Instrumentation in Nanofabrication and Characterization (COSINC-CHR), CU Boulder (Resource Research Identifier RRID: SCR_018985). General device fabrication and characterization was supported by the Perovskite Enabled Tandems programme, funded by the Integrated Energy Systems Office, Office of Critical Minerals and Energy Innovation, DOE, award number 52776. We also thank S. Marder for consultation on the NMR analysis. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the US DOE or the US government.
These authors contributed equally: Christopher T. Triggs, Lei Chen, Jiahao Xie.
Department of Chemistry, University of Wisconsin–Madison, Madison, WI, USA
Christopher T. Triggs & Song Jin
Chemistry and Nanoscience Center, National Laboratory of the Rockies, Golden, CO, USA
Lei Chen, Jiselle Y. Ye, Ross A. Kerner, Margherita Taddei, Fengjiu Yang, Bennett Addison, Tianran Liu, Steven P. Harvey, Matthew C. Beard & Kai Zhu
Department of Physics and Astronomy, University of Toledo, Toledo, OH, USA
Jiahao Xie, Xiaoming Wang & Yanfa Yan
Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
Nicholas J. Weadock & Michael F. Toney
Materials Science Program, University of Colorado Boulder, Boulder, CO, USA
Nicholas J. Weadock & Michael F. Toney
Department of Physics, University of Colorado Boulder, Boulder, CO, USA
Zihan Zhang
Materials Science Program, Department of Physics, Colorado School of Mines, Golden, CO, USA
Jiselle Y. Ye
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
C.T.T., L.C., K.Z. and S.J. conceived the idea and designed the experiments. C.T.T. synthesized the 2D tin perovskites and performed the powder and single-crystal XRD studies. C.T.T. and F.Y. characterized the surface morphologies of the films. L.C., C.T.T. and F.Y. fabricated the tin PSC devices. L.C. conducted the EQE, J–V and long-term device stability measurements. J.X. performed the DFT calculations and analysis, under the guidance of X.W. and Y.Y., which provided theoretical insights. N.J.W. and Z.Z. conducted the GIWAXS measurements and analysis under the guidance of M.F.T. M.T. performed the absorbance characterizations. T.L. and L.C. performed the UV–vis measurements. S.P.H. performed the TOF-SIMS measurements. J.Y.Y. performed the XPS measurements and analysis. J.Y.Y., R.A.K., B.A. and C.T.T. performed the NMR analyses. S.J., K.Z., Y.Y. and M.C.B. supervised the project. C.T.T., L.C., J.X., Y.Y., K.Z. and S.J. wrote the paper. All authors discussed the results and contributed to the revisions of the paper.
Correspondence to Yanfa Yan, Kai Zhu or Song Jin.
C.T.T., L.C., K.Z. and S.J. have filed a patent with the US Patent and Trademark Office (application # US64/048,398) based on this work. The other authors declare no competing interests.
Nature Materials thanks Zhubing He, Hairen Tan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Notes 1–3, Figs. 1–23, Tables 1–8 and References.
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
Triggs, C.T., Chen, L., Xie, J. et al. A chlorinated organic cation enables stable 2D/3D tin iodide perovskite photovoltaics. Nat. Mater. (2026). https://doi.org/10.1038/s41563-026-02726-z
Download citation
Received: 02 November 2025
Accepted: 29 July 2026
Published: 01 September 2026
Version of record: 01 September 2026
DOI: https://doi.org/10.1038/s41563-026-02726-z
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Nature Materials (Nat. Mater.)
ISSN 1476-4660 (online)
ISSN 1476-1122 (print)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source

Waaree Energies has received a letter of award (LOA) from Solar Energy Corp. of India (SECI) to develop a 700 MW solar power project paired with a 700 MW/2,800 MWh energy storage system (ESS) in Solapur, Maharashtra. The project will supply power under a 25-year power purchase agreement (PPA).
Separately, the company’s board of directors has approved the consolidation of its manufacturing facilities in India through the relocation of plant and machinery from its 1 GW Tumb facility and 1.11 GW Nandigram facility to its existing manufacturing plant in Chikhli, Gujarat.
The board also approved approximately $37 million in capital expenditure by Waaree Solar Americas Inc. (WSA), a wholly owned subsidiary of Waaree Energies, to revamp its module manufacturing facility in Arizona, United States.
The revamp will involve replacing the existing module manufacturing lines with new high-efficiency production lines, increasing WSA’s manufacturing capacity in Arizona from 1 GW to 1.6 GW.
Following the expansion, Waaree Energies’ total manufacturing capacity in the United States will increase to 4.8 GW, comprising 3.2 GW in Texas and 1.6 GW in Arizona.
The company said the Arizona capital expenditure will be funded through a combination of debt and internal accruals.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

Please enable JavaScript to proceed.

source

Meteorologist and Reporter

HONOLULU (Island News) — After Hurricane Lala, power outages harmed many island businesses, but Hawaii solar installation companies have seen a boom to their business, with battery packs and battery generators becoming hot commodities.
The phones have been ringing off the hook at Hawaii Energy Connection in Halawa Valley.
“Business has probably quintupled, right? It’s huge. Even for me, I just can’t keep up right now,” said Eddie Im, a Senior Consultant with Hawaii Energy Connection.
After Hurricane Lala, power outages harmed many island businesses, but Hawaii solar installation companies have seen a boom to their business, with battery packs and battery generators becoming hot commodities.
After the extended outages, that lasted days for some people, customers are calling. He says some have solar but don’t have a battery backup, or residents look to add renewable energy to their home — that can also store power.
“People who didn’t even think about batteries before or didn’t want to even spend money on it, now there is a huge rush on,” said Im.
It has been so busy, he expects to be soon sold out of products for the rest of the year.
There is also other bad news for those looking for immediate protection for the rest of the hurricane season.
“We have a process to go through. HECO to get approvals. We have to pull building permits. If you live in a townhouse, if you live in a flood zone, those permits take a little bit longer. So there’s no way we can guarantee those people that, ‘Hey, can we get it in soon?’,” added Im.
“If people are looking for a product that can immediately help them, then we have a portable battery solution, which is pretty robust<‘ said Eric Carlson, with Revolusun.
At Revoluson, in addition to adding home battery setups to solar systems, they also offer portable batteries that don’t need any permits – so you can plug right into the power.
The smallest one costs $300, but systems that can connect to more power banks and power ore appliances can cost thousands.
“People take them home, they charge them up, and and put them away, and when the power goes out, they can wheel them out and and plug in whatever appliances that they need,” said Carlson.
He says they are already 80% sold out of those portable battery packs. But adds they will fly in more deliveries, if demand stays up.
That will largely depend on just how busy our hurricane season turns out to be.
Do you have a story idea? Email news tips to news@kitv.com
Meteorologist and Reporter

{{description}}
Email notifications are only sent once a day, and only if there are new matching items.
Your browser is out of date and potentially vulnerable to security risks.
We recommend switching to one of the following browsers:

source

US-based SolarWindow Technologies has announced the commercial launch of ElectroFlex, an ultra-thin, flexible solar product designed to generate electricity on flat and curved surfaces.
The product targets applications where conventional PV modules face limitations due to weight, rigidity, or mounting requirements.
The company describes ElectroFlex as a “peel-and-stick” solution that can be applied directly to various surfaces without frames, rigid glass, or conventional support structures. Its size and color can be customized to meet customer specifications.
The new product consists of a five-layer composite laminate measuring 0.85 mm in total thickness. The stack comprises a 0.10 mm front encapsulant, 0.16 mm high-efficiency solar cells, a 0.04 mm copper cell backing, a 0.30 mm composite laminate and a 0.25 mm rear substrate.
ElectroFlex is 0.85 mm thick and integrates interdigitated back-contact (IBC) cells with a power conversion efficiency of 24.4%, according to the manufacturer. It has a power-to-weight ratio of approximately 73 W/kg.
SolarWindow said it also supplies wiring, harnesses, electrical systems, and power-balancing components needed to integrate the product and deliver the electricity it generates.
The company is targeting applications in the transportation, marine, aerospace, architecture, agriculture, infrastructure, utility, and specialty vehicle sectors.
Potential applications include data centers and buildings, roofs of commercial trucks and fleet vehicles, curved train surfaces, and drone and aircraft structures, according to the manufacturer.
ElectroFlex is initially available to Tier 1 original equipment manufacturers (OEMs) in the United States. SolarWindow said it plans to expand sales to selected markets in North America and Asia over the coming quarters.
The company is also developing LiquidElectricity, a transparent coating designed to turn glass and plastic surfaces into electricity-generating elements. SolarWindow claims the technology, which remains under development, could generate electricity from natural and artificial light, as well as low-intensity, shaded, and reflected light.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

A new program in Delaware is set to help residents and business owners gain access to solar panels. NBC10 Delaware Bureau reporter Tim Furlong explains. 
Delaware is launching a major effort to put more solar panels on homes and businesses, with state officials saying the initiative could help residents cut their electric bills while easing strain on the power grid.
Gov. Matt Meyer announced the Delaware Shines program, which he described as an aggressive effort to make solar power and battery storage accessible to more homeowners and small businesses.
Stream Philadelphia News for free, 24/7, wherever you are with NBC10.
The program is designed as a one-stop location where Delawareans can learn about solar energy, apply for available programs and rebates and find licensed solar installers.
“At its core this is about putting the power literally and financially back in the hands of Delawareans so you can save money and control your own energy,” Meyer said.
For homeowners, the program will offer more money through longer-term, low-interest loans. State officials say that could eliminate upfront costs for some homeowners while providing smaller monthly payments.
Drew Slater of Energize Delaware said the financing can also cover more than solar panels alone.
“And importantly what makes this unique is that we can do solar, have storage and we can have roof replacement all in the same loan and there’s very few if any in the country that allows that,” Slater said.
Breaking news and the stories that matter to your neighborhood.
The Delaware Department of Natural Resources and Environmental Control is also adding grants of up to $100,000 for businesses that want to generate solar power using panels installed over parking lots.
In Smyrna, Dee Babin is already seeing the financial benefits of solar panels at her home.
“I am very satisfied, very,” Babin said.
For Babin, the biggest benefit comes when the electric bill arrives.
“The most important thing for me is I don’t pay any electric bill,” Babin said.
Buying or leasing solar panels, however, can be an intimidating process and upfront costs can be out of reach for some families.
Experiences with the financial benefits can also vary with some solar customers saying the savings have been significant and others saying the cost of buying or leasing panels isn’t much lower than their previous electric bills.
Delaware officials say the new programs are intended to make the economics more attractive while expanding renewable energy production in the state.
While renewable energy can be politically contentious, states including Texas, Florida, Indiana, Arizona and Ohio are growing their solar production at the fastest rate. Delaware is now trying to join them.
This story was originally reported for broadcast by NBC Philadelphia. AI tools helped convert the story to a digital article, and an NBC Philadelphia journalist edited the article for publication.

source

The Australian Renewable Energy Agency (ARENA) announced it would provide University of Sydney researchers with $7.25 million towards a $19.5 million project to develop more durable Silicon (Si)-perovskite tandem solar cells in order to maintain their high efficiencies for commercial use.
The University of Sydney team will partner with Brisbane-based solar panel manufacturing startup Unison Solar Energy and scientists from Singapore’s Nanyang Technological University to take the next-generation technology from research towards commercial-scale production. 
“Our ambition is to pioneer a new era of Australian solar manufacturing and commercialise leading technologies here at home,” Unison Solar Chief Executive Officer Allen Guo said.
Si-perovskite tandem cell technology has demonstrated the potential to overcome the performance limitations of current solar technologies that rely on silicon as the sole semiconductor. Silicon’s conversion rate – the amount of solar energy it converts into electricity – currently peaks at about 25% but the researchers said Si-perovskite tandem cell technology could theoretically deliver conversion efficiencies of about 40%.
Team leader Professor Anita Ho-Baillie, John Hooke Chair of Nanoscience at the University of Sydney Nano Institute and School of Physics, said the researchers’ efforts have focused on stacking perovskites, made from synthesising metal with halogens, on top of silicon to form a tandem solar cell, rather than using silicon as the sole semiconductor. 
“There isn’t much room for silicon to improve because its theoretical limit is only 30%, but for perovskite-silicon tandem, it is about 40%,” she said. 
The research team has already shown the greater efficiency of the Si-perovskite technology, achieving Australia’s first 30% efficient Si-perovskite tandems on small and large areas. The team has also reported tandem cells passing industry standard tests against thermal extremes and moisture.  
Despite the potential of the technology, scaling devices beyond the laboratory and ensuring their stability under real-world conditions has proven challenging. Perovskite materials can break down when exposed to light, heat, moisture and mechanical stress.
Ho-Baillie said the new funding will help the researchers prove the reliability of Si-perovskite cells under a series of industry standards and take tandem-cell technology one step closer to becoming commercially viable. The ultimate goal is to improve the cells’ ability to maintain their conversion rate over the life expectancy of solar panels. 
“This is a fantastic opportunity for us to make research we’ve been doing at the university for the last six years translational,” she said. “Our next round of testing will prove this technology’s ability to cope with UV light and mechanical stresses.”
Unison Solar, which is establishing a solar panel production facility in Brisbane’s outer suburbs with an initial 500 MW manufacturing capacity, will work with the researchers during the commercialisation stage.
Guo, a former chief operating officer at Jinko Solar, said the Queensland-headquartered company will assess manufacturing costs, supply chains, customer needs and pathways to pilot production and scale-up.
“This project marks the beginning of collaboration with leading Australian research institutions for Unison Solar Energy,” he said, with the company aiming to establish gigawatt-scale production of advanced solar products in Australia.
Goa, a former chief operating officer at Jinko Solar, said Unison’s goal is to establish a manufacturing-ready technology platform capable of delivering next-generation tandem solar products with outstanding performance and long-term field reliability.
“Australia has been at the forefront of global solar research for more than 50 years, but local manufacturing remains limited and has not reached the scale our energy transition demands,” he said. “By combining Unison’s capability, technology and vision with ARENA’s support and the University of Sydney’s research expertise, we intend to deliver affordable, high-quality Australian-made solar products to Australian families. This is the start of our exciting journey.”
The project is one of 20 research and development initiatives to secure funded as part of a $105.6 million funding round announced by ARENA.
The funding will support projects spanning improved efficiency, cost and stability across advanced cells and modules, to innovations that can help improve solar farm deployment, operations and maintenance. and reduce the levelised cost of electricity (LCOE).
“Australia has played a leading role in the development of solar technology, and these projects will help ensure we continue to strengthen that position,” ARENA acting CEO Chris Faris said.
“The portfolio brings together a mix of near-term improvements and breakthrough technologies that have the potential to lower costs, improve performance and accelerate the deployment of solar energy both in Australia and around the world.”
“Achieving ultra low-cost solar requires innovation across the entire value chain. From the solar cells and modules themselves through to the way solar farms are built, operated and maintained, these projects will help unlock practical solutions that support a faster, more affordable energy transition.”
The funding is to be delivered over five years, commencing in 2027.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

Researchers at the U.S. Department of Energy’s National Laboratory of the Rockies have assessed light- and elevated-temperature-induced degradation (LeTID) in industrial n-type tunnel oxide passivated contact (TOPCon) silicon solar cells processed with and without laser-enhanced contact optimization (LECO) and have found that LeTID-related degradation in these devices is negligible compared with the levels historically observed in passivated emitter and rear contact (PERC) solar cells.
“The previous generation of silicon PV was dominated by p-PERC cells, based on p-type Czochralski (Cz) Si wafers,” the study’s principal investigator, Paul Stradins, told pv magazine. “These wafers, if doped with boron (B), suffered from both light-induced degradation (LID) and LeTID. Replacing B with gallium (Ga) as the base dopant in the second p-PERC generation eliminated LID, but LeTID was still present. Transitioning to the current TOPCon cell generation was thought to eliminate these bulk degradation modes altogether. However, relatively recent research from the University of Konstanz found LeTID in n-Cz wafers as well, which raises potential long-term reliability concerns for TOPCon cells.”
“This potentially novel reliability concern motivated our work,” he continued. “We established that the LeTID effect is present in n-Cz wafers and TOPCon cell precursors, irrespective of dopant type. Nevertheless, the extent of degradation is relatively low due to the asymmetrical carrier capture of a LeTID defect, making it an effective recombination center in p-type Si, but less effective in n-type Si. When accelerated LeTID degradation was applied to the TOPCon solar cells, the degradation and subsequent recovery were still present. However, the effect was practically negligible when the cells were fired with sufficiently low cooling rates.”
For the tests, the scientists used n-type wafers doped with phosphorus (P), antimony (Sb), and arsenic (As). The wafers were symmetrically passivated with hydrogenated silicon nitride (SiNₓ:H), fired at 800 C, subjected to recovery treatment at 20 C under 2-sun illumination, and subsequently exposed to LeTID conditions at 120 C to 130 C under 1-sun illumination.
The researchers found that, although LeTID degradation and regeneration kinetics were similar across all three dopants, As-doped silicon exhibited approximately twice the maximum defect density of P- and Sb-doped material. They attributed this difference primarily to variations in grown-in defects and processing conditions rather than to the dopant species itself.
Overall, the team found that n-type Cz Si exhibited substantially less LeTID degradation than previously reported for p-type Ga-doped Cz Si, indicating greater intrinsic resilience to LeTID. Injection-dependent lifetime measurements further indicated that recovery is primarily associated with the disappearance or transformation of metastable bulk defects.
The analysis also revealed that regenerated samples achieved higher bulk carrier lifetimes than recovered samples, suggesting that the regeneration process can improve bulk material quality beyond the state reached after the initial recovery treatment.
The researchers also investigated recovery pre-treatment and LeTID in unmetallized and metallized TOPCon cells sourced from industrial production lines. They found that both phenomena remain observable at the cell level and can affect device performance if appropriate mitigation measures are not implemented. Their impact, however, can be substantially reduced by optimizing the firing profile and applying suitable post-processing treatments.
A direct comparison of TOPCon cells subjected to identical firing conditions, with and without LECO, showed that the process moderately suppressed changes in open-circuit voltage associated with recovery and subsequent LeTID. The researchers also found that an industrially manufactured TOPCon cell incorporating LECO experienced only limited LeTID-induced efficiency degradation, with the change remaining within acceptable warranty tolerances.
The research is described in the paper “Bulk degradation of P-, Sb- and As-doped Cz Si wafers and TOPCon cells due to light and elevated temperature,” published in Solar Energy Materials and Solar Cells.
“We believe our findings provide some metrics and guidelines for producing LeTID-stable TOPCon cells,” Stradins concluded.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Monday, October 26, 2026
10:30 am – 11:30 am CEST, Berlin, Paris, Madrid
Thursday, September 10, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Tuesday, September 15, 2026
5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
pv magazine USA hosts its multi-day virtual event on U.S. solar and energy storage, covering domestic manufacturing, distributed energy and the growing role of solar-plus-storage in meeting AI-driven power demand.

You have no items in your basket.

source

Mail
myAT&T
AT&T deals
Games
Ver en español
…
Gov. Mikie Sherrill signed a law Tuesday that will allow renters in New Jersey to use portable solar energy to reduce their energy bills.
Known as the Garden State Balcony Solar Act, S2368, the legislation allows renters to use small, plug-in solar panels that can fit on a balcony, patio, porch or other space. The law exempts qualifying plug-in solar systems from certain permitting and utility approval requirements.
The measure also limits the ability of landlords and homeowners associations to stop renters from using the devices.
An estimated 36% of New Jersey residents rent their homes, with the largest concentration in the northern part of the state that borders New York City.
Sherrill said the new law — which overwhelmingly passed the state Legislature earlier this year — will decrease energy bills for consumers.
“Some in politics act like we have to choose between affordability and sustainability,” the Democratic governor said. “As if lower electric bills have to come at the cost of our green spaces or the climate. But that couldn’t be further from the truth. We can protect families’ budgets and the environment at the same time.”
The state has been dealing with soaring energy bills. When Sherrill took office, she also signed an executive order declaring a state of emergency and freezing utility rate hikes.
Sherrill has also made other moves on energy policy. She recently announced New Jersey would solicit bids for new nuclear power projects. The governor also signed legislation last week requiring monitoring of how much power artificial intelligence data centers use.
Sherrill, who signed the solar bill outside the Statehouse in Trenton, stood beside a portable solar panel and plugged it in.
“Just like that, you can soak up the sun, save some money,” she said.
The bill also comes as the Trump administration has rolled back support for some solar and wind energy initiatives.
State Sen. John McKeon, D-Essex, one of the primary sponsors of the solar panel bill, said the legislation was intended to move New Jersey in the opposite direction by embracing the energy source even further.
“Today we’ll continue on that mission,” he said.
State Assemblyman Robert Karabinchak, D-Middlesex, a sponsor of the Assembly version of the bill, said more energy legislation aimed at reducing costs is on the horizon.
“This is just one more step that we’re doing, and more to come where the residents could save on their energy tax bills every single month,” he said.
Traditional solar panels often must be installed facing a specific direction and can be limited by tree cover, said Doug O’Malley, state director for Environment New Jersey. Most renters can’t install rooftop solar panels because they don’t own the property.
“Plug-in solar is designed for everybody,” he said.
Read the original article on NJ.com. Add NJ.com as a Preferred Source by clicking here.

source

Floating solar emerges as India’s next frontier in the clean energy transition  IntelliNews
source

Image Credit: Ikea
Last Thursday, plug-in solar became legal in Great Britain. The week before that, New Zealand announced it would do the same within 12 months. Germany passed a million installed units years ago and is now well past three million. 
In Australia you can buy a plug-in kit today, but you cannot legally plug it in.
I sit on the two Standards Australia committees this technology runs into. EL-005 writes the battery rules. EL-042 writes the inverter and solar installation standards. I have been in those rooms for fifteen years, so I want to be precise about what the obstacle actually is, because it is not what most people assume.
The obstacle is not that plug-in solar is unsafe. The obstacle is that nobody in this country has the authority to say yes.
Here is the tangle. 
Standard AS/NZS 4777.1 says a grid-connected inverter is part of the fixed wiring of the installation, on its own circuit, connected by a licensed electrician. 
Standard AS/NZS 5033 says the same about the panels. 
Standard AS/NZS 3000 treats a socket outlet as a place where you take power out, not a place where you put power in. 
The Clean Energy Council approved product list, which is what networks and rebates actually rely on, has no category for a plug-connected inverter, so a compliant product cannot even be listed. Above all that sit eight state and territory electrical safety regulators, each with its own Act, and every distribution network with its own connection rules.
None of those bodies can move first. Standards Australia writes standards, it does not legalise anything. The CEC cannot list a product that no standard describes. A state regulator will not approve something the standards do not permit.
The networks say, reasonably enough, that they will connect anything that complies with AS/NZS 4777 and is CEC listed, which is a door that cannot currently be opened. Everybody is waiting for somebody else. 
The solution to this red tape quagmire – our energy ministers need to hand the job to the regulators with a deadline, which is exactly what the UK did.
Solar Citizens is leading a consumer push for Plug-in Solar regulation to be on the agenda for the next Climate and Energy Ministerial Council, when all the state and territory energy ministers meet on September 11.
The technical questions are real but small, and other people have already answered them.
The first is the socket itself. When you unplug a generator, the pins are live for as long as the inverter keeps producing. The fix is a product requirement that the inverter shut down in well under a second on loss of the socket, and the plug design that goes with it. This is testable and it is tested.
The second is the circuit. A final subcircuit is protected at 16 or 20 amps and the breaker only sees current coming from the switchboard end. Inject power halfway along and in theory you could load the cable past its rating without the breaker knowing. Germany and now the UK solved this by capping the output at 800 watts, about 3.5 amps, which is small enough that it cannot happen on any normal circuit. One kit per household, and that is the end of the problem.
The third is anti-islanding, and it is the easiest. Australian inverters have done this for 20 years and we have a well-proven test regime for it.
The fourth is metering, and this one is genuinely ours. Plenty of apartment blocks still run old accumulation meters and embedded networks that will either spin backwards or bill exports as consumption. That is a metering conversation, not a safety one, and it should not hold up the rest.
Then there is strata and tenancy law, which will block plenty of people even after the electrical rules change. Germany fixed that too, by saying landlords and bodies corporate cannot unreasonably refuse.
Meanwhile the plug-in solar vacuum is doing its own damage. Kits are being sold here right now and people are quietly plugging them in, with no product specification, no output cap, no testing and no notification to anyone. Doing nothing is not the safe option. It is the option that leaves uncertified hardware on Australian circuits with no rules at all.
The case for acting is straightforward. More than a third of Australians rent or live in apartments, and for most of them this is the only form of solar they will ever own.
An 800 watt system on a balcony or in a backyard makes around 1,000 kilowatt-hours a year in most of the country. It runs the fridge, the router, the standby loads and whatever else is on during the day.
At current tariffs that is $300 to $400 off the bill. A kit that costs about $1,000 pays for itself in three years and keeps working for 20. When you move house, it comes with you.
Australia has the highest rooftop solar penetration on earth because we wrote sensible rules early and let people get on with it. We know how to do this for plug-in solar. We are just not doing it.
Glen Morris is director of Smart Energy Lab and represents the Smart Energy Council on Standards Australia committees EL-005 and EL-042.
In "Electrification"
In "Electrification"
In "Multimedia"

Australia’s carbon credit scheme is working, according to a review, but some of the nation’s…
New report finds half of energy industry employers say they can’t fill job vacancies –…
Perth-based home battery maker named as one of three businesses awarded funding to help grow…
The former PM – once one-eyed in his support of pumped hydro storage – is…
State tips $500,000 into local family-owned electric motor specialist to support delivery of a state-of-the-art…
Energy market operator reinforces need for more flexibility, storage and transmission as electricity demand is…

source

Power outages boost island solar and battery businesses  kitv.com
source

India’s solar PV manufacturing capacity hits 100 GW, but upstream import dependence remains high: NITI Aayog  downtoearth.org.in
source

Ask your smart speaker to
play K Y W Newsradio on Audacy
dt_original: None
show_updated: None
—
SOUTH JERSEY (KYW Newsradio) — Homeowners and renters in New Jersey now have a new way to generate some of their own electricity, after Gov. Mikie Sherrill signed “balcony solar” legislation into law on Tuesday.
Balcony solar is a simple solar panel users can hang outside and plug directly into an outlet at home, offsetting some energy usage during peak sunny hours. Before signing the bill into law, Sherrill said these can shave up to $50 a month off your bill.
“You can pick up a unit like the one we have here online or at your local home improvement store. All you need to install it is a place to hang the panel, like a balcony railing or a fence post,” she said.
Sherrill said balcony solar has been quite popular in European countries for a while, and there are at least 1 million of them in use in Germany.
“There hasn’t been a single safety issue with balcony solar, and we have strong electrical standards in place to keep it that way,” she said.
A basic unit starts at about $250 and more for larger units, so it could take a couple of years to recoup the upfront costs. New Jersey law allows for up to 1200 watts, with each panel typically generating about 400 watts.

source

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Scientific Reports volume 16, Article number: 24495 (2026) Cite this article
1135 Accesses
Metrics details
The prediction and fault diagnosis of photovoltaic power generation are crucial for the efficient utilization of clean energy, but traditional methods have limitations such as low efficiency and strong subjectivity. To this end, an innovative hybrid model combining bidirectional long short-term memory network and Transformer is proposed, integrating the improved Golden Jackal optimization algorithm, time convolutional neural network, and grey wolf algorithm optimized variational mode decomposition to achieve signal adaptive purification, bidirectional temporal dependency capture, and global attention focusing. The experimental results show that the research model has achieved high-precision prediction, with an average accuracy of 95.03% in all seasons, an average absolute error of 4.86 kW, a relative root mean square error of 8.46%, and no significant cross seasonal differences, verifying the stability of the model. In terms of fault diagnosis, the waveform difference of the fault signal is up to 35.2 mm, and the accuracy of classifying five types of faults is 96.87%. SHAP analysis shows that instantaneous irradiance and historical power have the highest contribution, and the component string current is negatively correlated, which conforms to the physical laws of photovoltaics. In terms of deployment, the complete model has a parameter count of 8.77 M and an accuracy rate of 97.08%, making it suitable for high-precision offline and cloud scenarios. These results indicate that the proposed model improves both fault recognition efficiency and prediction accuracy. This study provides a valuable reference for building accurate photovoltaic power prediction and fault diagnosis models under complex environments, thus promoting the development of clean energy.
With the intensifying global energy crisis and environmental pollution, solar energy has gained great attention as a clean and widely distributed resource¹. Photovoltaic power generation, as the main form of solar energy utilization, has the characteristics of cleanliness, environmental protection, flexible application, safety and efficiency². At present, photovoltaic energy has become an important support for the stable operation of hydrogen energy storage based renewable systems and active distribution network renewable integrated energy systems^3,4. To meet the rapidly growing electricity demand in the future, enhancing demand response management in power grids is crucial^5,6. Power prediction and fault diagnosis of photovoltaic systems ensure safe and efficient operation of photovoltaic stations. Improving the accuracy of prediction and diagnosis significantly enhances grid stability, reduces economic loss, improves reliability, and promotes the development of clean energy^7,8. However, this field still faces multiple challenges and significant research gaps. Firstly, the new system architecture of off grid multi energy microgrid scheduling and decentralized coordination, which includes mobile hydrogen storage and refueling stations, requires photovoltaic power prediction to maintain low error in response to sudden changes in irradiance at a minute level update frequency. Otherwise, it will directly lead to an imbalance between hydrogen supply and demand^9,10. At the same time, most existing photovoltaic data analysis methods are limited to physical models and traditional statistical techniques, which are difficult to adapt to the needs of complex scenarios such as improving the resilience of distribution networks under extreme weather conditions and coordinating hydrogen and electricity management^11,12,13. Moreover, traditional methods increase the data processing time of a single prediction task by about 60–80%, and the differences between different operators can reach more than 30%, which has limitations such as low efficiency and strong subjectivity¹⁴. Although deep learning has been introduced, most studies still have shortcomings such as simple stacking of model structures, dependence on manual trial and error for hyperparameters, and separation between preprocessing and feature extraction. These defects result in insufficient generalization performance and weak interpretability of the model. A hybrid model combining Bidirectional Long Short-Term Memory Network (BiLSTM) and Transformer is proposed to address the aforementioned gaps. By using Improved Golden Jackal Optimization (IGJO) to achieve hyperparameter self-tuning, enhancing local features with Time Convolutional Network (TCN), and adaptively denoising with Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD), the accuracy and robustness of prediction and diagnosis are systematically improved. The research expects the proposed model to effectively overcome the three key technical bottlenecks that have long existed in this field, namely insufficient prediction accuracy under multi seasonal fluctuations, poor robustness under extreme weather conditions, and severe dependence on artificial feature engineering and empirical tuning. Through innovative multi module collaborative mechanisms, the model will significantly improve the overall performance and generalization ability of photovoltaic prediction and fault diagnosis, providing reliable and interpretable theoretical support and technical solutions for intelligent operation and maintenance of clean energy systems in complex environments.
With rapid advances in computer technology, performance evaluation of photovoltaic power generation based on intelligent algorithms has become a research hotspot. Intelligent algorithms provide advantages such as high efficiency, objectivity, and reduced manual dependence. Among them, the Long Short-Term Memory (LSTM) network is a specialized recurrent neural network designed to address gradient vanishing and exploding issues, primarily used for time series analysis. Transformer is a deep learning architecture based on the self-attention mechanism, which transforms feature data and improves parallel computing¹⁵. For example, Wu et al. team proposed a prediction model combining Convolutional Neural Network (CNN) and LSTM, which constructs leading related indicators as a sequence array, extracts feature vectors from the sequence data through a convolutional framework, and introduces them into LSTM for analysis, thereby achieving accurate prediction of stock prices and trends¹⁶. Li et al. put forward a dynamic prediction model to address the difficulty of landslide displacement prediction. They decomposed cumulative displacement, used a least square quintic polynomial function to fit the trend, and combined the results with LSTM, which effectively predicted landslide displacement¹⁷. Ozcan et al. focused on phishing prevention. They extracted features through character embedding and natural language processing techniques, then fused them and trained a model using LSTM and deep neural networks to build an accurate phishing detection model¹⁸. Pradani et al. raised an automatic scoring model based on Transformer for essay evaluation. They extracted features with information retrieval and text mining weighting techniques, calculated semantic similarity using cosine similarity, and achieved accurate scoring¹⁹. Zhao et al. raised a multimodal image fusion algorithm based on Transformer. They combined Transformer and Convolutional Neural Network encoders to enhance both global and local information fusion, and results showed good performance²⁰.
Accurate evaluation of photovoltaic power generation effectively promotes the development of clean energy. At present, theories and applications of power prediction and fault diagnosis are relatively mature, and many scholars have carried out in-depth research. Nelega et al. constructed a photovoltaic prediction model using LSTM network to solve output power prediction. They used recursive and non-recursive strategies to calculate input and output variables, and results showed good prediction performance²¹. Salman et al. transformed the data into time series and extracted features with a combination of Convolutional Neural Networks and LSTM. They then optimized the model architecture using Transformer and ultimately developed a hybrid deep learning approach for photovoltaic power prediction²². Said and Alanazi raised a prediction model based on LSTM to address low prediction accuracy. They extracted temporal features using LSTM, further extracted spatial features using an autoencoder, and fused them to achieve high prediction accuracy²³. R. Nelega et al. constructed a photovoltaic power generation prediction model using LSTM and Recurrent Neural Network (RNN) to address issues such as output power prediction in photovoltaic power generation. The input and output quantities were calculated using both recursive and non recursive strategies, and the results showed that the research model had good predictive performance²⁴. Shaban et al. improved the performance and reliability of photovoltaic fault diagnosis by combining preprocessing, feature extraction, selection, and classification modules. They enhanced image quality through preprocessing, filtered the most significant features, and achieved accurate classification of fault types²⁵. X. Chen et al. proposed a cost oriented renewable energy and reserve demand forecasting method to address the issue of insufficient economy in power system unit combination optimization. The predictor was trained using a two-layer mixed integer programming model to induce more economical scheduling plans²⁶. H. Hou et al. proposed an interpretable deep learning model that integrates physical laws, combined with Beluga Whale Optimization (BWO), LSTM, and physical memory units to construct a prediction model. The Shapley value was used to explain the influence of features, and the results showed that the proposed model can effectively predict the ice thickness of overhead transmission lines²⁷. M. The Tan team proposed a soft shared multi task deep learning method to address the problem of difficult extraction of spatiotemporal coupling features in multi node load prediction in power systems. By integrating Gated Temporal Convolutional Network (GTCN) and Gated Recurrent Unit (GRU), a multimodal feature module was constructed, and the soft sharing mechanism was used to collaboratively optimize the prediction tasks of each node²⁸. The summary of the above references is shown in Table 1.
According to Table 1, the current research has the following gaps:
Existing research actively adopts hybrid models, but mainly combines CNN and LSTM, or uses Transformer alone. Most literature does not endogenously and deeply integrate the core mechanisms of LSTM and Transformer to construct a specialized model that combines the advantages of both.
The existing research on photovoltaic power generation prediction and fault diagnosis technology based on intelligent algorithms can collect real-time data according to environmental changes and analyze the characteristics of data changes, but the analysis of seasonal changes is not comprehensive. Most literature focuses on fault diagnosis at specific times, which can easily lead to weak generalization ability, high false alarm rate, and certain limitations.
To address the limitation of incomplete feature extraction in existing photovoltaic (PV) data analysis methods, this study designs a novel hybrid architecture that deeply integrates the BiLSTM network with the Transformer model. The specific design framework for the study is shown in Fig. 1.
Research design framework.
The main contributions of the research are as follows:
Propose a temporal feature collaborative extraction architecture based on deep fusion of BiLSTM and Transformer. To address the shortcomings of incomplete feature extraction in existing methods, the bidirectional long-term dependency capture capability of BiLSTM and the multi head global attention mechanism of Transformer are jointly designed at the mathematical level for the first time. This architecture can simultaneously mine local dynamic patterns and global contextual correlations in photovoltaic time series, improving the completeness of feature expression under complex operating conditions.
Introduce the IGJO algorithm to achieve global optimization of BiLSTM hyperparameters. In response to the problem of traditional gradient optimization easily falling into local optima and weak adaptability, the improved Golden Jackal optimization algorithm is integrated into the BiLSTM framework. By intelligently reconstructing the parameter space, the global search efficiency and convergence speed of the model in high noise and non-stationary photovoltaic data have been significantly improved, endowing the model with stronger deep feature mining capabilities.
Design TCNT algorithm to enhance the local temporal modeling capability of Transformer. In response to the lack of sensitivity of standard Transformers to local fluctuation patterns, an innovative fusion of time convolutional networks, extended causal convolutions, and residual connections is proposed to form the TCNT algorithm. This improvement significantly enhances the modeling accuracy and generalization performance of the model under strong fluctuations in photovoltaic data, ensuring the reliability of predictions in complex scenarios such as extreme weather.
Build an intelligent operation and maintenance model that integrates GWO-VMD with multiple modules. Optimizing VMD parameters through GWO to achieve adaptive decomposition of non-stationary signals. A unified photovoltaic power generation prediction and fault diagnosis model was constructed by integrating signal purification, deep feature collaborative extraction, and multi task joint optimization. This model provides a high-precision and robust solution for the intelligent operation and maintenance of photovoltaic power plants, effectively promoting the reliability and economic benefits of clean energy systems.
This paper is organized into four parts. The first part introduces the background and related literature, analyzing the current research status of photovoltaic power generation. The second part describes in detail the operation process of BiLSTM improved by IGJO, the superiority and operation of TCNT improved Transformer, and the fusion of the two. It also explains how GWO-VMD improves the model and its application to photovoltaic power prediction and fault diagnosis. The third part verifies the performance of the improved model through comparative experiments and evaluates its effect in practical applications. The fourth part summarizes the experimental data and results. The fifth part explores future research directions.
With the rapid development of artificial intelligence, computer intelligent algorithms have provided new technological paths for photovoltaic power expansion, power generation prediction, and fault diagnosis^29,30,31. Traditional prediction and diagnosis methods have defects such as insufficient feature extraction ability, low efficiency, and weak adaptability to different scenarios, which limit their application^32,33. BiLSTM adds a backward LSTM layer. Through the bidirectional structure, it captures information from both directions of the sequence, which effectively improves feature extraction ability, computing efficiency, and accuracy^34,35. At the same time, the IGJO algorithm effectively improves the optimization efficiency and global optimization ability of model parameters through an improved search strategy, and is widely used in the fields of parameter optimization and fault diagnosis of complex systems. Therefore, this paper raises a BiLSTM-based method for photovoltaic power prediction and fault diagnosis. On this basis, the IGJO algorithm is introduced to improve the global search ability of the algorithm under complex environments and to achieve deep mining of data. The operation process of BiLSTM is shown in Fig. 2.
BiLSTM operation process.
As shown in Fig. 2, BiLSTM consists of two forward and backward LSTM structures. The forward LSTM layer handles the sequence in chronological order, capturing historical information at each time step. The backward LSTM layer processes the sequence in reverse, extracting features related to future states. Both LSTM layers calculate the current input and the hidden states of the previous and next moments. They update their states through the input gate, forget gate, and output gate. Finally, the forward and backward hidden states at the same time are fused to enhance the overall representation ability of sequence features. The mathematical expression of information transmission in BiLSTM is shown in Eq. (1)³⁶.
In Eq. (1), ({x_t}) represents input data. ({W_1}) and ({W_2}) represent weight parameters, and ({C_t}) represents the current state. After integrating the forward and backward LSTM layers, the mathematical expression of the obtained information is shown in Eq. (2)
In Eq. (2), ({h_n}^{{[p]}}) represents p layer features. ({W_3}) and ({W_4}) represent weight coefficients, and ({h_a}) and ({h_b}) represent forward and backward information transmission. Traditional BiLSTM still has defects such as slow computing speed and weak global search ability^37,38. The GJO algorithm can track and capture missing features and improve data mining ability. Therefore, this paper introduces the GJO algorithm and modifies it with a cosine factor to escape from local optima, forming the IGJO hybrid algorithm. Its process is shown in Fig. 3.
Operation process of IGJO hybrid algorithm (Icon source from: www.iconfont.cn).
As shown in Fig. 3, the IGJO algorithm initializes by randomly generating a group of solutions and building an initial matrix. The cosine factor helps the algorithm escape from local optima. The algorithm then tracks and captures escape energy, records the global optimal solution, and calculates the fitness value. Position information is updated based on the fitness value. After computing the escape energy, the algorithm adopts hunting and attacking modes on the escaping prey, and then updates the latest position. If the stopping condition is not met, the process returns to recompute the escape energy. If the condition is met, the algorithm outputs the optimal fitness value. IGJO realizes global search in complex spaces. Before iteration, it randomly generates an initial population, whose mathematical expression is shown in Eq. (3).
In Eq. (3), ({X_0}) represents the initial position, (rand) represents a random value in [0,1], ({X_{hbox{max} }}) and ({X_{hbox{min} }}) represent the upper and lower bound of the variable. By calculating escape energy, the IGJO algorithm tracks and captures escaping prey. The mathematical expression of escape energy is shown in Eq. (4).
In Eq. (4), ({c_1}) represents a constant, t is the current iteration, ({E_0}) represents the initial energy value. A cosine control factor is introduced to adjust the algorithm and help the population escape from the local optimal solution. The mathematical expression with cosine factor is shown in Eq. (5).
In Eq. (5), ({X_{0′}}) represents the modified initial population, and (cos) represents the cosine factor. By integrating IGJO with BiLSTM, the IBiLSTM hybrid algorithm is obtained. Its process is shown in Fig. 4.
IBiLSTM hybrid algorithm operation process (Icon source from: www.iconfont.cn).
As shown in Fig. 4, the IGJO algorithm initializes a data matrix, introduces a cosine factor to track and capture escape energy, records the global optimal solution, and updates position information based on fitness value. Then the hunting and attacking modes are applied to escaping prey, and the latest position is obtained until the termination condition is met and the optimal fitness value is output. The optimized data from IGJO is then fed into BiLSTM. The forward and backward LSTM layers process the sequence, update their states through calculation, and fuse hidden states from both directions. This process captures global context information and obtains comprehensive feature data.
The implementation steps of the IBiLSTM algorithm are as follows: Firstly, the photovoltaic timing data denoised by GWO-VMD is divided into 24-hour windows, standardized using Z-score, and input into the network. Build a bidirectional LSTM using the PyTorch framework, with the forward layer processing the sequence in chronological order and the backward layer processing it in reverse order. The number of hidden units is 128, with 2 layers and a dropout rate of 0.3. Each LSTM unit updates its state through input gates, forget gates, output gates, and memory units. Concatenate the forward and backward hidden states at the same time into a 256 dimensional bidirectional context vector. Adopting an improved golden jackal optimization algorithm to automatically search for initial learning rate, L2 coefficient, and number of hidden units. The training uses the Adam optimizer, with a loss function of mean square error, batch size of 64, maximum of 500 rounds, and early stop. Finally, the bidirectional features are mapped into feature data through a fully connected layer.
IBiLSTM enhances data utilization and realizes comprehensive feature search. However, it still faces problems such as the serial structure, which limits full computation, and it may affect the accuracy of power prediction and fault diagnosis of PV systems. It also has some limitations in capturing global long-term dependencies. Transformer enhances data vector representation, reduces gradient vanishing or explosion, and establishes weight association efficiently to realize information transfer^39,40. Therefore, this study introduces Transformer and combines it with the multi-head attention mechanism to further enhance performance and generalization. The Transformer architecture is depicted in Fig. 5.
Structure diagram of the Transformer algorithm (Icon source from: www.iconfont.cn).
As shown in Fig. 5, Transformer adopts an encoder-decoder structure. The encoder and decoder are composed of multi-head attention and feedforward neural networks. Each layer introduces residual structures and layer normalization. The multi-head attention captures information from different perspectives. The feedforward neural network connects feature layers and transforms input features, establishing the relationship between input and output. Residual structures concatenate data, propagate gradients, and enhance stability. Layer normalization ensures consistency during training. The self-attention mechanism evaluates weights of different positions and dynamically analyzes key features. Its mathematical expression is shown in Eq. (6)⁴¹.
In Eq. (6), (sqrt {{d_k}}) represents the scaling factor. Self-attention is calculated independently in each subspace, and multi-head attention is obtained. Its expression is shown in Eq. (7).
In Eq. (7), (concat) represents the connection. The data obtained by multi-head attention are input into the feedforward neural network for feature transformation, capturing relationships among data. The calculation is shown in Eq. (8).
In Eq. (8), (sigma) represents a nonlinear function, ({H^l}) is the output of layer l, ({W^l}) is the weight, and ({b^l}) represents the bias. Traditional Transformer has weak ability in capturing temporal sequences and cannot realize feature extraction in spatial dimensions^42,43. Therefore, this study introduces TCN and integrates dilated causal convolution and residual networks, and finally fuses Transformer to obtain TCNT. The process is shown in Fig. 6.
Schematic diagram of the process of TCN fusion Transformer (Icon source from: www.iconfont.cn).
As shown in Fig. 6, TCNT first inputs feature sequences and processes them through three TCN residual blocks. The TCN residual block expands the receptive field through dilated causal convolution and improves parallel computing. Normalization recalculates weights to speed up convergence. Relu and dropout are applied to fit and filter data. Then the flatten layer and fully connected layer extract features and reduce gradient problems. Finally, the processed data are input into Transformer. Multi-head attention and feedforward networks output the final data. The convolution operation is shown in Eq. (9).
In Eq. (9), ({x_i}^{l}) represents the i-th feature of layer l, ({W_i}^{l}) represents the i-th weight matrix of layer l, f represents the activation function, X represents the output, and ({b_i}^{l}) represents the bias. By replacing standard convolution with causal convolution and expanding the receptive field, the dilated causal convolution is expressed as Eq. (10).
In Eq. (10), (omega) represents the convolution kernel, ({*_d}) represents dilated convolution, d represents sampling distance, and ({x_{t – dn}}) represents data sampled at interval d. The residual block is then added to simplify training. The expression is shown in Eq. (11)
In Eq. (11), x represents input data and (F(x)) represents residual value.
The specific implementation steps of the TCNT algorithm are as follows: the photovoltaic temporal feature sequence is sequentially passed through three TCN residual blocks, each residual block containing dilated causal convolution, weight normalization, ReLU activation, and spatial dropout. The expansion rates are set to 1, 2, and 4, the convolution kernel size is 3, and the number of output channels is 64. Extended causal convolution extracts multi-scale temporal features in parallel by exponentially expanding receptive fields. Set the dropout rate to 0.2 to prevent overfitting. Subsequently, the feature maps output by the three residual blocks are transformed into 1536 dimensional vectors through a flattening layer, followed by dimensionality reduction and abstraction of high-level features through a fully connected layer. Afterwards, input into the Transformer encoder, with 8 heads and 128 attention dimensions in the multi head attention layer, and 512 dimensions and 2 layers in the feedforward network. Finally, the power and fault characteristics are obtained through the linear output layer.
IBiLSTM improves global search ability in complex environments. It updates its state with forward and backward LSTM layers and captures global context information for deep feature mining. Meanwhile, TCNT expands the receptive field through dilated causal convolution, improves parallel ability, and builds relationships between vectors to enhance stability. Therefore, to solve the problems of low efficiency, weak adaptability, and insufficient search ability in traditional PV power prediction and fault diagnosis, this study fuses IBiLSTM and TCNT to form IBiLSTM-TCNT. The process is shown in Fig. 7.
IBiLSTM-TCNT hybrid algorithm operation process (Icon source from: www.iconfont.cn).
As shown in Fig. 7, IBiLSTM-TCNT initializes and introduces the cosine factor to track and capture escape energy. It adopts hunting and attacking modes to update position and outputs the optimal fitness. The optimized data are input into BiLSTM. Forward and backward hidden states are calculated and fused to obtain complete features. Then three TCN residual blocks process sequences. Dilated causal convolution and normalization recalculate weights. Relu and dropout handle the data. The flatten layer and fully connected layer extract features. Finally, the output is processed by Transformer through multi-head attention and feedforward networks. The calculation of the forget gate in convolution is expressed as Eq. (12).
In Eq. (12), (sigma) represents the growth curve function, (omega) represents the weight matrix, ({h_{t – 1}}) represents the neuron output, and ({x_t}) represents the input at time t. The input gate controls information flow and filters new input. It regulates the output of neurons and cell states. The calculation of the output gate ({y_t}) is shown in Eq. (13).
In Eq. (13), (tanh) is the hyperbolic tangent function, and ({C_t}) is the cell state. In order to solve the problems of signal acquisition noise interference in research algorithms, VMD is proposed to remove noise. However, traditional VMD requires pre-set based on experience, resulting in unstable decomposition effects. The GWO algorithm can simulate the social hierarchy and hunting behavior of gray wolves for swarm intelligence optimization, efficiently and globally searching for the optimal solution to the problem. Therefore, the study utilized the GWO algorithm to optimize VMD and obtained the GWO-VMD algorithm. This study combines GWO-VMD with IBiLSTM-TCNT and finally builds a PV power prediction and fault diagnosis model (GV-IBiLSTM-TCNT). The process of GWO-VMD is shown in Fig. 8.
Operation process of GWO-VMD algorithm (Icon source from: www.iconfont.cn).
As shown in Fig. 8, GWO-VMD first processes input signals by GWO, calculates the optimal fitness, and finds optimal parameters. VMD decomposes signals and calculates intrinsic mode functions. Then the components are input into the function to calculate power. A threshold is set. Components greater than the threshold are regarded as noise, and noise amplitudes are set to zero. Components greater than the threshold are kept as effective signals. After filtering, the data are reconstructed to obtain enhanced signals. The normalization of the module is shown in Eq. (14)
In Eq. (14), ({P_j}) represents normalized values, (a(j)) represents the envelope signal, and j represents (left{ {1,2,3,…,N} right}). Normalization reduces noise effectively. Envelope entropy evaluates VMD performance. The expression is shown in Eq. (15).
In Eq. (15), ({E_p}) represents envelope entropy. The final PV power prediction and fault diagnosis model is shown in Fig. 9.
Photovoltaic power generation prediction and fault diagnosis model operation process (Icon source from: www.iconfont.cn).
As shown in Fig. 9, the model first calculates the optimal fitness and finds optimal parameters. VMD decomposes signals, calculates power, and sets a threshold. After filtering effective components, the data are reconstructed to obtain enhanced signals. Then the data are initialized, and the cosine factor is introduced to track and capture escape energy. The hunting and attacking modes update position. The optimized data are input into BiLSTM to obtain complete features. Three TCN residual blocks process sequences. Dilated causal convolution and normalization recalculate weights. The flatten layer and fully connected layer extract data. Multi-head attention and feedforward networks produce the final optimized data. Under sunny, cloudy, and rainy conditions, power prediction is performed and accuracy is evaluated. At the same time, PV faults are classified and identified.
The detailed implementation steps of the model are as follows: Firstly, GWO is used to adaptively search for the optimal parameters of VMD, determine the number of modes K = 5 and the penalty factor α = 2150, and decompose the original photovoltaic signal to obtain 5 intrinsic mode components. Calculate the correlation coefficients between each component and the original signal, and set a threshold of 0.3. Then input the signal into IGJO, population 30, iterate 100 times, and output the optimal solution of BiLSTM parameters. Construct a BiLSTM network based on optimized parameters, extract bidirectional temporal features, and concatenate them into a 256 dimensional vector. This vector sequentially outputs temporal features through three TCN residual blocks. Finally, input the Transformer encoder and output the predicted power value or the probability of five types of faults.
To verify the performance of the IBiLSTM-TCNT algorithm, it was compared with Gorilla Troops Optimizer (GTO), Particle Swarm Optimization-Back Propagation (PSO-BP), and Multi-Marker Similarity Assessment (MMSim). The experiments used Windows 11 Ubuntu 18.04 as the operating system, Intel Core i7-5400 as the processor, 16 GB of memory, Python 3.9 as the programming language, and PyTorch as the deep learning framework. The learning rate was set to 0.0001, and the number of iterations was set within 500. The experimental dataset is selected from the Global Energy Forecasting Competition (GEFCom) dataset, which includes photovoltaic power plant data from different climate zones with a time resolution of 1 h. The dataset is randomly divided into training, validation, and testing sets in an 8:1:1 ratio. Statistical analysis was conducted using SPSS 26.1. All data underwent normality tests and homogeneity of variance tests. Independent and matched t-tests were applied to data conforming to a normal distribution. The significance level was set at p < 0.05. Prior to the significance test, the study assumed that there were no significant differences in the performance of all models. The p-value was calculated to determine whether to reject this assumption. In order to analyze the consistency of cross regional performance of data, independent photovoltaic power station data from sites A, B, C, D, and E with different climate types were collected. Each site came from different regions, and the prediction accuracy of the five sites was cross site verified. Based on site A, the consistency analysis results are shown in Table 2.
As shown in Table 2, the accuracy of Site A is 95.38%. The accuracy results of Sites B, C, D and E in different climates show no statistically significant differences compared to the benchmark Site A (p > 0.05). The above results demonstrate that the model has excellent performance consistency in different geographical and climatic environments, and the learned operating rules are universal. The Generalized Rastrigin and Generalized Rosenbrock functions were applied to evaluate the optimal fitness of IBiLSTM-TCNT, GTO, PSO-BP, and MMSim. The results are shown in Fig. 10.
Optimal fitness evaluation results of the algorithm.
As shown in Fig. 10a, in the Generalized Rastrigin function, the IBiLSTM TCNT algorithm achieves a rapid decrease in fitness value within the first 50 iterations, indicating its strong global exploration ability. The subsequent iterations converge smoothly to the optimal value of 1.05, proving its stability. As shown in Fig. 10b, under the validation of the Generaliad Rosenbrock function, the fitness curve of the IBiLSTM TCNT algorithm rapidly decreases in 0–20 iterations, slows down in 20–130 iterations, then stabilizes, and finally converges with a fitness value of 0.51. Compared with GTO, PSO-BP, and MMSim, the fitness curve of IBiLSTM-TCNT was significantly better and converged faster. This was mainly because the TCN residual block expanded the receptive field through dilated causal convolution and improved parallel ability, which accelerated convergence. The results proved that IBiLSTM-TCNT found the optimal fitness value faster and had excellent optimization ability. To further test prediction performance, the accuracy and loss of IBiLSTM-TCNT, GTO, PSO-BP, and MMSim were evaluated. The results are shown in Fig. 11.
Prediction accuracy and loss value test results.
As shown in Fig. 11a, The accuracy of the IBiLSTM TCNT algorithm sharply rises within the range of 0–50 iterations to 96.18%. After reaching 96.18%, the accuracy curve enters a small oscillation upward phase, and the accuracy eventually converges to a high level of 97.38% and remains stable. As shown in Fig. 11b, the research algorithm rapidly decreases after 50 iterations, with a loss value of 0.27. Within the range of 100–500, the loss value stabilizes at 0.04. The accuracy and loss value data performance of the research algorithm are superior to the other three algorithms, because it indicates that the IGJO algorithm, through its global exploration strategy, enables the algorithm parameters to quickly cross the flat area of the loss surface and efficiently locate the global optimal solution. At the same time, the algorithm tracks and captures escape energy through cosine factors, significantly reducing data loss and further improving the prediction accuracy of the algorithm. The results proved that the algorithm had good prediction performance. To further analyze fitting performance, the residual results of IBiLSTM-TCNT, GTO, PSO-BP, and MMSim were compared, as shown in Fig. 12.
Comparison of fitting performance test results.
As shown in Fig. 12a, the proposed algorithm showed a significant normal distribution, with a fitting value of 0.9314. In Fig. 12b–d, the residual curves of GTO, PSO-BP, and MMSim showed different degrees of deviation. The residual curve of MMSim presented obvious asymmetry, with a fitting value of 0.4254. Although GTO and PSO-BP showed normal distribution, the trends were not obvious, with fitting values of 0.3021 and 0.4287. Compared with MMSim, GTO, and PSO-BP, the fitting performance of the proposed algorithm improved significantly. This was mainly because the multi-head attention captured data information from different perspectives, extended the operating width, and enhanced reliability. In conclusion, the results showed that the proposed algorithm had good fitting performance and superior reliability.
After verifying the performance of IBiLSTM-TCNT, the final GV-IBiLSTM-TCNT model was further evaluated for PV power prediction and compared with GTO, PSO-BP, and MMSim. The convolution kernel number was set to 64, the kernel size to 3, the batch size to 32, and the iteration number to 500. The learning rate was set to 0.0002, and the LSTM node numbers were set to 30 and 15. The dataset used was the GEFCom dataset. Statistical analysis was conducted using SPSS 26.1. All data underwent normality tests and homogeneity of variance tests. Independent and matched t-tests were applied to data conforming to a normal distribution. The significance level was set at p < 0.05. Prior to conducting the significance tests, the study hypothesized that there were no significant differences in the performance of all models. The p-value was calculated to determine whether to reject this hypothesis. The GWO algorithm was used to obtain four optimal parameters as input to VMD for decomposition and denoising. The optimization processes of penalty factor α, modal number K, decomposition level, and denoising threshold were evaluated, and the results are shown in Fig. 13.
Experimental results of the optimization process for multiple indicators.
As shown in Fig. 13a, parameter α rose rapidly with evolution, reached the maximum at the 2nd generation, dropped sharply at the 3rd generation, fluctuated slightly between the 5th and 10th generations, and finally converged to the optimal value of 275.5639. As shown in Fig. 13b, parameter K reached the maximum at the 2nd generation, stabilized between the 4th and 10th generations, and finally converged to the optimal value of 4. As shown in Fig. 13c, the decomposition level increased rapidly between the 1st and 2nd generations, then dropped, and finally stabilized between the 4th and 10th generations, with an optimal value of 2. As shown in Fig. 13d, the denoising threshold increased and decreased sharply between the 1st and 3rd generations, then stabilized near 0, and finally converged to 0.02. Combining these four parameters, the signal-to-noise ratio was 19.3414, which indicated that the model had good denoising performance. In order to further validate the predictive performance of the photovoltaic power generation model, the test set was divided into spring, summer, autumn, and winter groups, and the prediction accuracy of each group was calculated separately. One way analysis of variance was used to test whether the differences between groups were significant. The study tested the predicted values of the model in four seasons: spring, summer, autumn, and winter, and the comparative results are shown in Fig. 14.
Comparison of predicted values and actual values in different seasons.
Figure 14a–d show the power prediction results of the model in spring, summer, autumn, and winter. In spring and autumn, the prediction fluctuations were small, and the results were consistent with the actual values. The prediction accuracies were 95.21% and 96.54%. In summer and winter, the fluctuations were larger, which indicated that seasonal changes affected PV power output. The prediction results were consistent with the actual values, but the accuracies were slightly lower than those in spring and autumn, at 94.25% and 94.11%. The average prediction accuracy of the four seasons was 95.03%. These results showed that the model achieved high accuracy in PV power prediction. This was because the model reduced interference through signal denoising and improved prediction accuracy. The GV-IBiLSTM-TCNT, GTO, PSO-BP, and MMSim models were compared using Mean Absolute Error (MAE) and Relative Root Mean Square Error (rRMSE). The results are shown in Table 3.
As shown in Table 3, the GV BiLSTM TCNT model had MAE and rRMSE of 4.58 kW and 8.44% in spring, respectively. The autumn data was similar to spring, but the errors increased in summer and winter. MAE was 5.06 kW and 5.14 kW, and rRMSE was 8.24% and 8.97%, respectively. Seasonal changes have a certain impact on the model, but the difference compared to the average value is not significant (p > 0.05), indicating that the research model has stability in different climates. However, the other three models showed significant fluctuations in error under different climates, especially in summer, with significant differences (p < 0.05) compared to the mean. The average values of the research models MAE and rRMSE are 4.86 kW and 8.46%, respectively, which were better than GTO, PSO-BP, and MMSim. The reason was that the model mined the influence weights of key meteorological factors through multi-head attention and achieved more accurate prediction results through weighted fusion. In conclusion, the experimental results showed that the model had excellent prediction accuracy.
After verifying the power prediction performance of the GV-IBiLSTM-TCNT model, the study further evaluated its fault diagnosis performance for photovoltaic power generation and compared it with GTO, PSO-BP, and MMSim models. The sampling frequency of the vibration signal was 1.2 kHz, with a sampling number of 12,000 and a duration of 1 µs. The experiment set the number of convolution kernels to 64, the kernel size to 3, the batch size to 32, and the number of iterations to 500. The learning rate was set to 0.0002, and the number of LSTM nodes was set to 30 and 15. The dataset was chosen from GEFCom. The study used sensors to evaluate the fault vibration signals of the GV-IBiLSTM-TCNT, GTO, PSO-BP, and MMSim models. The results were shown in Fig. 15.
Comparison of fault vibration signal experimental results.
As shown in Fig. 15a, the difference between the normal and fault signal waveforms of the research model is most significant, with an average difference of 35.2 mm in vibration signals. In contrast, the waveform discrimination of the GTO, PSO-BP, and MMSim comparison models in Fig. 15b–d gradually weakens, with average signal differences of 23.6 mm, 19.8 mm, and 21.0 mm, respectively. The research results intuitively indicate that the research model has a more sensitive detection and discrimination ability for fault states. This is mainly attributed to the GWO-VMD adaptive signal processing module integrated in the front-end of the model. This module accurately removes the interference components coupled with environmental noise and mechanical background vibration from the original signal through optimization algorithms, and outputs high fidelity fault characteristic signals. To further investigate the fault diagnosis accuracy of the model, select five types of faults: short circuit, obstruction, dust accumulation, hot spot, and aging, with 30 samples for each type, the study compared the classification results of the proposed model with those of the GTO, PSO-BP, and MMSim models. The test results were shown in Fig. 16.
Comparison of classification results of different fault signals.
As shown in Fig. 16a, the proposed model accurately classified the faults into five categories: short circuit, shading, dust accumulation, hotspot, and aging, with a classification accuracy of 96.87%. In Fig. 16b–d, although the GTO, PSO-BP, and MMSim models could classify faults, the number of misclassified samples was significantly higher than that of the proposed model. Their classification accuracies were 89.54%, 91.60%, and 90.37%, respectively. The classification accuracy of the proposed model was significantly higher than that of the comparison models. The reason is that the GWO-VMD layer is effectively used for denoising and enhancing the expression of fault feature data. The IBiLSTM layer captures bidirectional temporal patterns, while the multi head attention of the TCNT layer can adaptively focus on the most discriminative key feature segments of various types of faults, thereby improving classification accuracy. In summary, the proposed model accurately classified photovoltaic faults. To verify the importance of evaluation metrics for the predictive performance of the GV-IBiLSTM-TCNT model, the study selected instantaneous irradiance (W1) and module backplane temperature (W2) as indicators in the meteorological feature dimension, short-term historical power sequence (X1) and first-order difference (X2) in the historical time series dimension, month (Y1) and intra-day time period (Y2) in the time period dimension, and module string current (Z1) and DC bus voltage (Z2) as indicators in the photovoltaic state dimension. The specific contributions of each feature were obtained through Shapley Additive exPlanations (SHAP) analysis and testing. Specifically, the DeepSHAP algorithm is used to approximate based on 1000 randomly sampled background samples. The results are shown in Fig. 17.
SHAP results of research model.
As shown in Fig. 17a, the three indicators with the highest contribution rates are instantaneous irradiance and short-term historical power sequence, while component string current, first-order difference, and DC bus voltage are secondary indicators, with the remaining indicators having relatively smaller contributions. As shown in Fig. 17b, the red part of instantaneous irradiance is concentrated in the positive region, indicating that higher instantaneous irradiance enhances the predictive decision-making of fault diagnosis. The red part of the short-term historical power sequence is more distributed in the positive region, indicating that historical power information has a positive reference value for model prediction. The blue distribution of component string current is in the negative region, indicating that smaller component string current increases the output probability of fault categories. In summary, the GV-IBiLSTM-TCNT model can clearly explain the impact of each feature indicator on fault prediction and diagnosis. In order to further explore the effectiveness of the combination of modules in the GV-IBiLSTM-TCNT model, the fault classification accuracy, recall rate, and F1 value of each module were evaluated, as shown in Table 4.
As shown in Table 4, the classification accuracy of the complete GV-IBiLSTM-TCNT model is 97.08%. With the ablation of the module, the accuracy of the model shows a significant decrease, dropping to 85.02% in the BiLSTM model. At the same time, the recall rate decreased from 96.08% to 87.20%, and F1 decreased to 84.12%. In summary, the ablation experiment results fully validated the effectiveness and necessity of each core module in the GV-IBiLSTM-TCNT model. In order to further analyze the computational complexity of the research model, a comparison was made between the model size and single sample inference time of GV-IBiLSTM-TCNT, GTO, PSO-BP, and MMSim models. The specific results are shown in Fig. 18.
Calculation complexity results of each model.
As shown in Fig. 18a, the median model size of the GV-IBiLSTM-TCNT model is 31.87 MB, which is higher than other comparison models, mainly due to the increased computational overhead caused by the multi module fusion architecture of GV-IBiLSTM-TCNT. According to Fig. 18b, the longest single sample inference time of the GV-IBiLSTM-TCNT model is 2.60ms, which is higher than the comparison model, but the difference is relatively small. The GV-IBiLSTM-TCNT model has paid a moderate computational cost to achieve fault diagnosis performance, and both storage requirements and inference latency can meet the real-time and resource constraints of current mainstream edge devices. The research results reveal the trade-off between model performance and computational complexity, providing clear decision-making basis for practical application deployment.
To address challenges such as low recognition efficiency, complex signal processing, and high parameter computation in PV systems, this study developed a model integrating BiLSTM and Transformer algorithms. The proposed model used IGJO to optimize BiLSTM, which improved the global search ability of the algorithm in complex environments. In addition, the Transformer with a multi-head attention mechanism was introduced to further enhance algorithm performance and generalization ability. The optimized BiLSTM and Transformer were integrated to form the IBiLSTM-TCNT algorithm. Based on this, the study combined the GWO-VMD algorithm to address the problem of noise interference and finally built the GV-IBiLSTM-TCNT model for photovoltaic power prediction and fault diagnosis. To address the issue of noise interference, a photovoltaic power prediction and fault diagnosis model based on GV BiLSTM TCNT was ultimately constructed. The experimental results demonstrate that the prediction accuracy of the core algorithm IBiLSTM TCNT reaches 97.38%, which is 7.84, 5.78, and 7.01% points higher than the three comparison algorithms GTO, PSO-BP, and MMSim, respectively. This significant improvement stems from the global optimization ability of IGJO algorithm for Bilstm super parameters. Unlike the traditional gradient descent method, it is easy to fall into local optimization. IGJO dynamically tracks and escapes energy through cosine factor, enabling the model to quickly converge to the global optimal solution when the irradiance suddenly changes, reflecting the progressiveness of meta heuristic optimization in photovoltaic time series prediction. The complete prediction model has an average power prediction accuracy of 95.03% in all four seasons, with an average absolute error of 4.86 kW and a relative root mean square error of 8.71%, significantly better than all comparison models. The fundamental reason is that the deep fusion architecture of BiLSTM and Transformer breaks through the limitation of the traditional single model feature extraction dimension, while the expansion causal convolution of TCN extracts multi-scale features in parallel, greatly improving the modeling accuracy of the fluctuation period. The classification accuracy of five typical faults reaches 96.87%, which is 5.27% points higher than the optimal comparison model. This result indicates that GWO-VMD achieves adaptive optimal decomposition of signals, removes environmental interference from the source, and overcomes the disadvantage of difficulty in setting manual filtering parameters. In addition, SHAP analysis quantitatively revealed that the model decision conforms to the laws of photovoltaic physics, and ablation experiments verified the effectiveness of the collaborative contribution of each module. In summary, the GV-IBiLSTM-TCNT model efficiently captured feature information, accurately predicted photovoltaic power, and provided fine-grained classification of fault types, thereby improving fault recognition accuracy. This study only included fault detection of simple damage and did not involve research on complex fault types.
The research system constructed and validated the GV BiLSTM TCNT hybrid model, which achieved significant results in high-precision prediction of photovoltaic module power and accurate detection and classification of typical faults, fully demonstrating the excellent ability of the proposed model to extract clear and strong discriminative features from complex data. However, the operation and maintenance environment of actual photovoltaic power plants is much more complex than laboratory conditions, and faults often exist in a composite form of multiple types of mutual coupling, and their evolution is a dynamic process. Therefore, further exploration and verification are needed for the generalization of the model in highly coupled fault detection, which indicates the direction for the performance boundary and next evolutionary direction of the proposed model. Future research will focus on building an intelligent diagnostic framework that can decouple concurrent fault features, further enhancing the decision-making depth and practical value of the model in real complex scenarios.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Pedraza, J. M. The role of renewable energy in the transition to green, low-carbon power generation in Asia. GLCE 1(2), 68–84. https://doi.org/10.47852/bonviewGLCE3202761 (2023).
Article  Google Scholar 
Navesi, R. B., Naghibi, A. F., Zafarani, H., Tahami, H. & Pirouzi, S. Reliable operation of reconfigurable smart distribution network with real-time pricing-based demand response. Electr. Power Syst. Res. 241, 111341. https://doi.org/10.1016/j.epsr.2024.111341 (2025).
Article  Google Scholar 
Wang, R., Akbari, E., Bagherzadeh, L. & Pirouzi, S. Stochastic economic sizing of hydrogen storage-based renewable off-grid system with smart charge of electric vehicles according to combined hydrogen and power model. J. Energy Storage 108, 115171. https://doi.org/10.1016/j.est.2024.115171 (2025).
Article  CAS  Google Scholar 
Yao, M., Moradi, Z., Pirouzi, S., Marzband, M. & Baziar, A. Stochastic economic operation of coupling unit of flexi-renewable virtual power plant and electric spring in the smart distribution network. IEEE. Access 11, 75979–75992. https://doi.org/10.1109/ACCESS.2023.3296254 (2023).
Article  Google Scholar 
Emdadi, K. & Pirouzi, S. Benders decomposition‐based power network expansion planning according to Eco‐sizing of high‐voltage direct‐current system, power transmission cables and renewable/non‐renewable generation units. IET Renew. Power Gener. 19(1), e70025. https://doi.org/10.1049/rpg2.70025 (2025).
Article  Google Scholar 
Stonier, A. A., Harish, R., Srinivasan, M. & Sarathkumar, D. An extensive critique on fault-tolerant systems and diagnostic techniques intended for solar photovoltaic power generation. Energy Sources A 45(1), 1856–1873. https://doi.org/10.1080/15567036.2023.2183282 (2023).
Article  Google Scholar 
Kumar, P. M., Saravanakumar, R., Karthick, A. & Mohanavel, V. Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system. Environ. Sci. Pollut. Res. 29(7), 10173–10182. https://doi.org/10.1007/s11356-021-16398-6 (2022).
Article  Google Scholar 
Kim, J. M., Yum, S. G., Adhikari, M. D. & Bae, J. A LSTM algorithm-driven deep learning approach to estimating repair and maintenance costs of apartment buildings. Eng. Constr. Archit. Ma 31(13), 369–389. https://doi.org/10.1108/ECAM-11-2023-1194 (2024).
Article  Google Scholar 
Rezaei, N., Pezhmani, Y. & Jordehi, A. R. A two-layer hybrid robust-stochastic model for energy management of isolated multi-energy microgrids with mobile storage systems and hydrogen refueling stations. J. Energy Storage 90, 111905. https://doi.org/10.1016/j.est.2024.111905 (2024).
Article  CAS  Google Scholar 
Mansouri, S. A. et al. A robust ADMM-enabled optimization framework for decentralized coordination of microgrids. IEEE Trans. Ind. Inform. 21(2), 1479–1488. https://doi.org/10.1109/TII.2024.3478274 (2025).
Article  Google Scholar 
Zhang, J., Wu, H., Akbari, E., Bagherzadeh, L. & Pirouzi, S. Eco-power management system with operation and voltage security objectives of distribution system operator considering networked virtual power plants with electric vehicles parking lot and price-based demand response. Comput. Electr. Eng. 121, 109895. https://doi.org/10.1016/j.compeleceng.2024.109895 (2025).
Article  Google Scholar 
Zadehbagheri, M., Dehghan, M., Kiani, M. & Pirouzi, S. Resiliency-constrained placement and sizing of virtual power plants in the distribution network considering extreme weather events. Electr. Eng. 107(2), 2089–2105. https://doi.org/10.1007/s00202-024-02583-6 (2025).
Article  Google Scholar 
Naghibi, A. F., Akbari, E., Shahmoradi, S., Pirouzi, S. & Shahbazi, A. Stochastic economic sizing and placement of renewable integrated energy system with combined hydrogen and power technology in the active distribution network. Sci. Rep. 14(1), 28354. https://doi.org/10.1038/s41598-024-79222-4 (2024).
Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 
Ge, W. & Wang, X. PSO–LSTM–Markov coupled photovoltaic power prediction based on sunny, cloudy and rainy weather. J. Electr. Eng. Technol. 20(2), 935–945. https://doi.org/10.1007/s42835-024-02051-y (2025).
Article  Google Scholar 
Alipour, M. A. & Askarzadeh, A. An efficient optimization framework for distribution network planning by simultaneous allocation of photovoltaic distributed generations and transformers. IET Renew. Power Gen. 18(2), 153–168. https://doi.org/10.1049/rpg2.12910 (2024).
Article  Google Scholar 
Wu, J. M. T., Li, Z., Herencsar, N., Vo, B. & Lin, J. C. W. A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Syst. 29(3), 1751–1770. https://doi.org/10.1007/s00530-021-00758-w (2023).
Article  Google Scholar 
Li, L. M., Wang, C. Y., Wen, Z. Z., Gao, J. & Xia, M. F. Landslide displacement prediction based on the ICEEMDAN, ApEn and the CNN-LSTM models. J. Mt. Sci. 20(5), 1220–1231. https://doi.org/10.1007/s11629-022-7606-0 (2023).
Article  Google Scholar 
Ozcan, A., Catal, C., Donmez, E. & Senturk, B. A hybrid DNN–LSTM model for detecting phishing URLs. Neural Comput. Appl. 35(7), 4957–4973. https://doi.org/10.1007/s00521-021-06401-z (2023).
Article  PubMed  Google Scholar 
Pradani, K. A. & Suadaa, L. H. Automated essay scoring Menggunakan semantic textual similarity Berbasis transformer Untuk Penilaian Ujian Esai. JTIIK 10(6), 1177–1184. https://doi.org/10.25126/jtiik.2023107338 (2023).
Article  Google Scholar 
Zhao, Y., Zheng, Q., Zhu, P., Zhang, X. & Ma, W. TUFusion: A transformer-based universal fusion algorithm for multimodal images. IEEE Trans. Circuits Syst. Video Technol. 34(3), 1712–1725. https://doi.org/10.1109/TCSVT.2023.3296745 (2023).
Article  Google Scholar 
Nelega, R. et al. Prediction of power generation of a photovoltaic power plant based on neural networks. IEEE Access 11(1), 20713–20724. https://doi.org/10.1109/ACCESS.2023.3249484 (2023).
Article  Google Scholar 
Salman, D., Direkoglu, C., Kusaf, M. & Fahrioglu, M. Hybrid deep learning models for time series forecasting of solar power. Neural Comput. Appl. 36(16), 9095–9112. https://doi.org/10.1007/s00521-024-09558-5 (2024).
Article  Google Scholar 
Said, Y. & Alanazi, A. AI-based solar energy forecasting for smart grid integration. Neural Comput. Appl. 35(11), 8625–8634. https://doi.org/10.1007/s00521-022-08160-x (2023).
Article  Google Scholar 
de Arruda Bruzadin, J. R. Filho., Tiferes, R. R. & Di Santo, S. G. Photovoltaic module temperature estimation and sensor malfunction detection algorithm based on Kalman filter. IEEE J. Photovolt. 13(6), 929–937. https://doi.org/10.1109/JPHOTOV.2023.3317968 (2023).
Article  Google Scholar 
Shaban, W. M. Detection and classification of photovoltaic module defects based on artificial intelligence. Neural Comput. Appl. 36(27), 16769–16796. https://doi.org/10.1007/s00521-024-10000-z (2024).
Article  Google Scholar 
Chen, X., Liu, Y. & Wu, L. Towards improving unit commitment economics: An add-on tailor for renewable energy and reserve predictions. IEEE Trans. Sustain. Energy 15(4), 2547–2566. https://doi.org/10.1109/TSTE.2024.3426337 (2024).
Article  ADS  Google Scholar 
Hou, H. et al. Explainable physical deep-learning model for overhead transmission line icing-thickness prediction. IEEE Trans. Ind. Appl. https://doi.org/10.1109/TIA.2025.3587191 (2026).
Article  Google Scholar 
Tan, M., Hu, C., Chen, J., Wang, L. & Li, Z. Multi-node load forecasting based on multi-task learning with modal feature extraction. Eng. Appl. Artif. Intell. 112, 104856. https://doi.org/10.1016/j.engappai.2022.104856 (2022).
Article  Google Scholar 
Gupta, A. K. & Singh, R. K. A review of the state of the art in solar photovoltaic output power forecasting using data-driven models. Electr. Eng. 107(4), 4727–4770. https://doi.org/10.1007/s00202-024-02759-0 (2025).
Article  Google Scholar 
AlKandari, M. & Ahmad, I. Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. AJMS 20(3/4), 231–250. https://doi.org/10.1016/j.aci.2019.11.002 (2024).
Article  Google Scholar 
Oboudi, M. H., Hamidpour, H., Zadehbagheri, M., Safaee, S. & Pirouzi, S. Reliability-constrained transmission expansion planning based on simultaneous forecasting method of loads and renewable generations. Electr. Eng. 107(1), 1141–1161. https://doi.org/10.1007/s00202-024-02556-9 (2025).
Article  Google Scholar 
Liu, Y., Liu, X., Li, X., Yuan, H. & Xue, Y. Model predictive control-based dual-mode operation of an energy-stored quasi-Z-source photovoltaic power system. IEEE T Ind Electron. 70(9), 9169–9180. https://doi.org/10.1109/TIE.2022.3215451 (2022).
Article  Google Scholar 
Bouzaher, A., Terki, A. & Bouzaher, M. T. Photovoltaic panel faults diagnosis: Based on the fill factor analysis and use of artificial intelligence techniques. Arab. J. Sci. Eng. 48(5), 6471–6487. https://doi.org/10.1007/s13369-022-07409-w (2023).
Article  Google Scholar 
Presekal, A., Ştefanov, A., Semertzis, I. & Palensky, P. Spatio-temporal advanced persistent threat detection and correlation for cyber-physical power systems using enhanced GC-LSTM. IEEE Trans. Smart Grid. 16(2), 1654–1666. https://doi.org/10.1109/TSG.2024.3474039 (2025).
Article  Google Scholar 
Tehrani, A., Yadollahzadeh-Tabari, M., Zehtab-Salmasi, A. & Enayatifar, R. Wearable sensor-based human activity recognition system employing bi-LSTM algorithm. Comput. J. 67(3), 961–975. https://doi.org/10.1093/comjnl/bxad035 (2024).
Article  Google Scholar 
Patro, B. S. & Bartakke, P. P. Daily rainfall prediction using long short-term memory (LSTM) algorithm. J. Agrometeorol. 26(4), 509–511. https://doi.org/10.54386/jam.v26i4.2745 (2024).
Article  Google Scholar 
Jhong, Y. D., Chen, C. S., Jhong, B. C., Tsai, C. H. & Yang, S. Y. Optimization of LSTM parameters for flash flood forecasting using genetic algorithm. Water Resour. Manag. 38(3), 1141–1164. https://doi.org/10.1007/s11269-023-03713-8 (2024).
Article  Google Scholar 
Dong, X., Shi, P., Liang, T. & Yang, A. CTAFFNet: CNN–transformer adaptive feature fusion object detection algorithm for complex traffic scenarios. Transp. Res. Rec. J. Transp. Res. Board. 2679(1), 1947–1965. https://doi.org/10.1177/03611981241258753 (2025).
Article  Google Scholar 
Pal, S., Roy, A., Palaiahnakote, S. & Pal, U. Adapting a Swin Transformer for license plate number and text detection in drone images. AIA 1(3), 145–154. https://doi.org/10.47852/bonviewAIA3202549 (2023).
Article  Google Scholar 
Zhang, J. et al. Eatformer: Improving vision transformer inspired by evolutionary algorithm. Int. J. Comput. Vis. 132(9), 3509–3536. https://doi.org/10.1007/s11263-024-02034-6 (2024).
Article  Google Scholar 
Kadhim, Q. & Al-Jawher, W. A. M. A new multiple-chaos image encryption algorithm based on block compressive sensing, Swin Transformer, and wild horse optimization. J. Multidiscip. Sci. 7(1), 2025012–2025012. https://doi.org/10.31893/multiscience.2025012 (2025).
Article  Google Scholar 
Papa, L., Russo, P., Amerini, I. & Zhou, L. A survey on efficient vision transformers: Algorithms, techniques, and performance benchmarking. IEEE T Pattern Anal. 46(12), 7682–7700. https://doi.org/10.1109/TPAMI.2024.3392941 (2024).
Article  Google Scholar 
Heo, Y. J., Yeo, W. H. & Kim, B. G. Deepfake detection algorithm based on improved vision transformer. Appl. Intell. 53(7), 7512–7527. https://doi.org/10.1007/s10489-022-03867-9 (2023).
Article  Google Scholar 
Download references
School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China
Yuchen Wang & Han Su
School of Mathematics and Physics, North China Electric Power University, Beijing, 102206, China
Zhongyan Li
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Search author on:PubMed Google Scholar
Y.C.W. processed the numerical attribute linear programming of communication big data, and the mutual information feature quantity of communication big data numerical attribute was extracted by the cloud extended distributed feature fitting method. Z.Y.L. and H.S. Combined with fuzzy C-means clustering and linear regression analysis, the statistical analysis of big data numerical attribute feature information was carried out, and the associated attribute sample set of communication big data numerical attribute cloud grid distribution was constructed. Y.C.W. and Z.Y.L. did the experiments, recorded data, and created manuscripts. All authors read and approved the final manuscript.
Correspondence to Zhongyan Li.
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-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
Wang, Y., Li, Z. & Su, H. Photovoltaic power prediction and fault diagnosis method based on LSTM and transformer. Sci Rep 16, 24495 (2026). https://doi.org/10.1038/s41598-026-54547-4
Download citation
Received: 17 October 2025
Accepted: 20 May 2026
Published: 28 May 2026
Version of record: 07 August 2026
DOI: https://doi.org/10.1038/s41598-026-54547-4
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.

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

source

A new program in Delaware is set to help residents and business owners gain access to solar panels. NBC10 Delaware Bureau reporter Tim Furlong explains. 
Delaware is launching a major effort to put more solar panels on homes and businesses, with state officials saying the initiative could help residents cut their electric bills while easing strain on the power grid.
Gov. Matt Meyer announced the Delaware Shines program, which he described as an aggressive effort to make solar power and battery storage accessible to more homeowners and small businesses.
Stream Philadelphia News for free, 24/7, wherever you are with NBC10.
The program is designed as a one-stop location where Delawareans can learn about solar energy, apply for available programs and rebates and find licensed solar installers.
“At its core this is about putting the power literally and financially back in the hands of Delawareans so you can save money and control your own energy,” Meyer said.
For homeowners, the program will offer more money through longer-term, low-interest loans. State officials say that could eliminate upfront costs for some homeowners while providing smaller monthly payments.
Drew Slater of Energize Delaware said the financing can also cover more than solar panels alone.
“And importantly what makes this unique is that we can do solar, have storage and we can have roof replacement all in the same loan and there’s very few if any in the country that allows that,” Slater said.
Breaking news and the stories that matter to your neighborhood.
The Delaware Department of Natural Resources and Environmental Control is also adding grants of up to $100,000 for businesses that want to generate solar power using panels installed over parking lots.
In Smyrna, Dee Babin is already seeing the financial benefits of solar panels at her home.
“I am very satisfied, very,” Babin said.
For Babin, the biggest benefit comes when the electric bill arrives.
“The most important thing for me is I don’t pay any electric bill,” Babin said.
Buying or leasing solar panels, however, can be an intimidating process and upfront costs can be out of reach for some families.
Experiences with the financial benefits can also vary with some solar customers saying the savings have been significant and others saying the cost of buying or leasing panels isn’t much lower than their previous electric bills.
Delaware officials say the new programs are intended to make the economics more attractive while expanding renewable energy production in the state.
While renewable energy can be politically contentious, states including Texas, Florida, Indiana, Arizona and Ohio are growing their solar production at the fastest rate. Delaware is now trying to join them.
This story was originally reported for broadcast by NBC Philadelphia. AI tools helped convert the story to a digital article, and an NBC Philadelphia journalist edited the article for publication.

source

Staff members of State Grid Huzhou power supply company conduct inspection on the photovoltaic lines and equipment at a photovoltaic power station in Changxing County of Huzhou City, east China’s Zhejiang Province, May 27, 2026. In recent years, State Grid Huzhou power supply company has been constantly promoting the upgrading of the county-level power grids, advancing the integration of renewable energy sources into power grids, and endeavoring to construct a safer, greener and smarter power grid. (Photo: Xinhua)
A series of heat waves has swept across the world, drawing increased attention to the challenge of meeting …
China’s first industrial-use nuclear energy steam supply project, Heqi No.1, was officially put into operation on Wednesday in …
China’s energy demand continues to rise as cold waves are affecting many parts of the country. Experts said …

source

Swedish thin film solar manufacturer Midsummer is putting plans for a new solar cell manufacturing factory in the southeastern municipality of Flen on hold.
Plans for the factory, intended to produce copper indium gallium selenide (CIGS) solar cells, were first announced in 2023.
Midsummer says the financing of the factory was negatively impacted after the company was rejected for an expected investment grant from the Swedish Energy Agency’s Industriklivet program.
“We would be happy to establish a new factory in Sweden in the future when commercial and financial conditions allow it, but it will not happen within the framework of the specific project planned for Flen for which we have been awarded time-limited EU support,” commented Midsummer CEO Eric Jaremalm.
The company confirmed it will forgo EU funding it has been awarded for the factory.
“Even with the EU contribution, self-financing a completely new factory of this size in Flen would have cost us several hundred million kronor and we do not find it responsible to take out such large loans or ask our shareholders for such amounts when we have found other ways to finance our expansion,” Jaremalm added.
Midsummer’s statement adds that “new attractive and significantly less capital-intensive” opportunities for establishing factories outside in Sweden have emerged.
The company explained it is shifting its long-term production strategy to an asset-light model based on collaboration with major industrial partners which will see Midsummer supply machinery and raw materials to enable local production of solar cells on different continents, rather than owning and operating all the factories itself.
Last year, Midsummer was commissioned to supply machinery and equipment with operational responsibility to a solar cell factory in Colombia. The company’s latest update says it has received machinery orders worth SEK 380 million ($39.6 million) for this establishment.
Midsummer adds it is also expanding production in its own factory in Bari, Italy, towards an annual production of 50 MW. Earlier this year, pv magazine profiled the facility.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Martedì, 22 Settembre 2026
11:00 – 12:00 CEST, Roma
Monday, October 26, 2026
10:30 am – 11:30 am CEST, Berlin, Paris, Madrid
Thursday, September 10, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Tuesday, September 15, 2026
5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid
Our special edition for Intersolar South America 2026 is here!
Discover the latest insights into the Brazilian solar market – in Portuguese.
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Saudi Arabia is accelerating its clean energy transition—join the SunRise Arabia Clean Energy Conference 2026 in Riyadh to explore how solar PV and energy storage are powering its digital economy.
pv magazine USA hosts its multi-day virtual event on U.S. solar and energy storage, covering domestic manufacturing, distributed energy and the growing role of solar-plus-storage in meeting AI-driven power demand.
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

Looking for more news? Visit our sister site, OK Politics Today, and subscribe here today!
Kansas City officials are blaming the Trump administration for a delay in a proposed 3,100-acre solar project at the Kansas City International Airport they claim would have powered thousands of homes.
The delay is indefinite because of a cut in federal funding, according to Mayor Quinton Lucas who said the project, set to become one of the largest solar farms at a U.S. airport, will no longer move forward.
“Without these funding sources, the KCI solar project has a funding gap that is difficult to overcome,” Lucas wrote in the announcement. The project was slated to be built on 3,100 acres of undeveloped land near the airport and was expected to eventually produce 500 megawatts of electricity — enough to power 70,000 homes, according to a 2022 city feasibility study.
In his announcement Friday, Mayor Lucas said the newfound funding gap was due to a shift in priorities by the current federal administration, but that clean energy remains a top priority in Kansas City.
“Leaders come and go, but the value of investment in cleaner and more sustainable energy will never go away in a world confronting higher energy prices, high utility costs, and extreme weather events and conditions here and everywhere,” Lucas said.
The Kansas City Star reported that the 2022 study recommended completing the project in phases, the first of which required a $9-15 million investment to install panels on 136 acres, producing 35 megawatts, which would power around 4,500 homes.
At the time, the study recommended completing the project in phases, the first of which required a $9-15 million investment to install panels on 136 acres, producing 35 megawatts, which would power around 4,500 homes.
However, construction was multiple years behind schedule and never began. Jackson Overstreet, a spokesperson for the city, said the airport won’t experience any operational impacts because of the project’s pause.
Jerry Bohnen is the founder and creator of OK Energy Today, which began in 2012. He is an Edward R. Murrow Investigative award winner and has been recognized by other national, regional and state institutions during his 50 years as a broadcast journalist. Contact Jerry at editor@okenergytoday.com
OK Energy Today is a news media publication committed to timely, accurate reporting on Oklahoma energy.
Have a story idea? Send it to us at editor@okenergytoday.com

source

Former NIU student on pretrial release following arrest for creating AI generated child sexual abuse material
Ears what’s coming to DeKalb’s 49th Corn Fest
Food Truck Wednesdays are back
NIU student facing attempted murder charge out on pretrial release, cleared by judge to attend classes
Health Technology Center’s design complete, completion aims for 2029
NIU has spent around $12 million on six new arrays of solar panels being installed across campus, which are expected to supply about 5% of NIU’s daily energy use. 
NIU’s collaboration with Trane Technologies is being implemented as part of the school’s Sustainability and Climate Action Plan. The initiative will result in 50 buildings receiving significant upgrades to energy, water and lighting-efficiency while reducing carbon emissions. The whole plan will cost NIU $56 million. 
Two out of the six planned solar arrays have already been installed, which are located on the rooftops of the Stevens Building and DuSable Hall.
The third solar array, located in the Visitor Pay Lot, is expected to be completed in October. 
According to Sunrun, the number of panels in a solar array determines how much electricity that array can produce. 
Belinda Roller, director of Architectural and Engineering Services, said the remaining solar panels will be installed and fully operational this calendar year.
“Two are already open operationally and have been running and generating power since last November. And then the remaining four, we don’t have the exact dates that they will be turned on, but they will all be fully installed this calendar year,” Roller said. 
The visitor lot was selected as one of the spaces for a solar array because the university wanted the panels to be visible on the central campus, according to Roller
“We’re trying to find a place on central campus that was visible, so people could kind of know about it. Because oftentimes you have solar arrays on roofs, people don’t even know that they’re there,” Roller said.
Roller also said the elevated solar panels provide shade and shelter for those who park in the area.
“The advantage here is they’re on the elevated structures. They’re providing shade for the parking spaces in the visitor lot,” Roller said. “So when it’s 90 degrees outside and you’re parking your car in the visitor lot, you’ll have a little bit of shade, and then also shelter from wind and rain.”
NIU spends approximately $7 to $8 million dollars on electricity annually. The solar panels are set to lower the cost of this bill. According to Trane, the entire Sustainability and Climate Action Plan is set to save $5 million annually and cut down campus energy usage by 26%.
The solar panels have a max generating capacity of 3.2 megawatts, which is 3,200 kilowatts generated at any given point. According to Constellation, the average household uses 30 kilowatt-hours per day. 
If a solar panel was at max generating capacity for two hours, they would produce 6400 kilowatt-hours. This is enough to power 213 average households.
Temperature, clouds, the sun’s position in the sky and many other factors limit the solar panel’s generating capacity.
“On a daily basis, when we are buying power, we anticipate it will buy power for 5% on a daily basis,” Roller said. “The interesting thing to keep in mind is we will be generating energy obviously during the day, right? When it’s sunny outside and that is when electricity costs the most.”
The solar panels will limit the spending on electricity by powering buildings throughout the times that energy is most expensive.
The sustainability plan will have a large impact on the reduction of NIU’s carbon footprint by reducing carbon emissions by 11% while also limiting the University’s spending on electricity. The project is set to be completed by this calendar year.
Already have an account? Sign In
Please enter your username or email address, you will receive a link to create a new password via email.
Cancel reply
Your email address will not be published. Required fields are marked *
Comment *
Name *
Email *

source

View or submit an obituary
Videos, Podcasts, & Gems
Stories, scores, & more
Support local journalism
The latest local listings
Engagements, weddings, anniversaries
Shirts, Mugs, & More
What can we help you find?

The past several months have shown the struggle between neighborhood concerns and the permittable uses of land
QUINCY–Adlerman Eric Entrup (R-Ward 1) announced a plan to avoid the neighborhood squabbles that have bubbled up recently over solar projects being shoehorned into properties with a special use permit.
It’s actually amending the existing city code, Chapter 162.030, to establish a special use permit with conditions for commercial solar energy properties in a RU-1 or rural zoning district.
He made a motion to send the amendment to the city’s plan commission, which was seconded and approved by the full council during Monday night’s weekly meeting.
Entrup has been working with city staff and fellow aldermen, Kelly Mays (R-Ward 4) and Laura McReynolds (R-Ward 5), for a couple of weeks on requirements to head off anticipated neighbor concerns.
“One was the distance between a solar-paneled occupied building,” Entrup said. “The other was increased landscaping. And then proximity to schools, which was a great idea that (Community Development Planner) David Adam came up with first. This is a mile radius around each school. This gives us a better way to plan out.”
Entrup says the protection around schools is especially important considering the taxpayer investment over the last several years into building new schools, and could encourage residential development, as opposed to industrial projects in those areas.
Earlier in the meeting, council members were split but ultimately approved ordinances for two solar projects proposed by Arena Cavern 1 Solar, LLC.
One is immediately north of 928 Nieders Lane, which is outside city limits, but near Ward 6. It had been zoned RU1, or rural.
The second is located immediately north of 2734 South 12th Street, at 2640 South 12th Street, and immediately west of 2640 South 12th Street. Again, outside city limits, but near Ward 6 and zoned as rural.
Alderman Richie Reis (D-Ward 6) moved for the following conditions, including:
The council approved the amendments, but the votes on the ordinance were as follows:
First Solar ordinance
An Ordinance Granting A Special Use Permit For A Planned Development. (Construct and operate a 5.0 MWac Commercial Solar Energy Facility located immediately north of 928 Nieders Lane (Zoned RU1); immediately north of 2734 South 12th Street (Zoned RU1); at 2640 South 12th Street (Zoned RU1) and immediately west of 2640 South 12th.)
No votes:
Dave Bauer (D-Ward 2) abstained. Anthony Sassen (R-Ward 4) recused. Mike Adkins (R-Ward3) was absent.
The motion carried.
Second solar ordinance
An Ordinance Granting A Special Use Permit For A Planned Development (Construct and operate a 3.0 MWac Commercial Solar Energy Facility located immediately north of 928 Nieders Lane.) 
According to City Planning Director Jason Parrott, he did not hear as many complaints about this portion of the project.
No votes:
Bauer abstained. Sassen recused. Adkins was absent.
The motion carried.
Ward 5 Solar Situation
Alderman McReynolds wanted to set the record straight, given the phone calls she has received and social media chatter.
The solar farm special-use permit request at 36th and Payson is back on the plan commission agenda for September 22.
This, after the city’s corporation counsel has received inquiries from lawyers representing the developer on clarification of the grounds by which the proposal was initially refused.
McReynolds also addressed concerns about the possibility of a solar farm at 36th and Harrison near Denman Elementary School. The company held an open house at a nearby park a couple of weeks ago, but Parrott said that no application has been submitted so far.
“If an application were to be submitted, it would through the standard plan commission process,” Parrott said. “This board would refer it to the plan commission, staff would review. At this point, we don’t even have an application.”
McReynolds, who attended the open house, had the impression that they are serious about the project and have had a lease agreement in place with the land owner dating back to 2023.

Click the Purchase Story button below to order a print of this story. We will print it for you on matte photo paper to keep forever.
Sunny. High 88F. Winds NW at 5 to 10 mph.
Do you want to receive the Muddy River News newsletter in your inbox every morning at 8:00 AM?
Enter your email address, click “Subscribe,” and don’t forget to check your email to confirm your subscription.
Muddy River News LLC
535 Maine, Suite 4A
Quincy, IL 62301
Copyright © 2026 • Muddy River News • All Rights Reserved • Privacy and Use Policy

source

Researchers at the U.S. Department of Energy’s National Laboratory of the Rockies have assessed light- and elevated-temperature-induced degradation (LeTID) in industrial n-type tunnel oxide passivated contact (TOPCon) silicon solar cells processed with and without laser-enhanced contact optimization (LECO) and have found that LeTID-related degradation in these devices is negligible compared with the levels historically observed in passivated emitter and rear contact (PERC) solar cells.
“The previous generation of silicon PV was dominated by p-PERC cells, based on p-type Czochralski (Cz) Si wafers,” the study’s principal investigator, Paul Stradins, told pv magazine. “These wafers, if doped with boron (B), suffered from both light-induced degradation (LID) and LeTID. Replacing B with gallium (Ga) as the base dopant in the second p-PERC generation eliminated LID, but LeTID was still present. Transitioning to the current TOPCon cell generation was thought to eliminate these bulk degradation modes altogether. However, relatively recent research from the University of Konstanz found LeTID in n-Cz wafers as well, which raises potential long-term reliability concerns for TOPCon cells.”
“This potentially novel reliability concern motivated our work,” he continued. “We established that the LeTID effect is present in n-Cz wafers and TOPCon cell precursors, irrespective of dopant type. Nevertheless, the extent of degradation is relatively low due to the asymmetrical carrier capture of a LeTID defect, making it an effective recombination center in p-type Si, but less effective in n-type Si. When accelerated LeTID degradation was applied to the TOPCon solar cells, the degradation and subsequent recovery were still present. However, the effect was practically negligible when the cells were fired with sufficiently low cooling rates.”
For the tests, the scientists used n-type wafers doped with phosphorus (P), antimony (Sb), and arsenic (As). The wafers were symmetrically passivated with hydrogenated silicon nitride (SiNₓ:H), fired at 800 C, subjected to recovery treatment at 20 C under 2-sun illumination, and subsequently exposed to LeTID conditions at 120 C to 130 C under 1-sun illumination.
The researchers found that, although LeTID degradation and regeneration kinetics were similar across all three dopants, As-doped silicon exhibited approximately twice the maximum defect density of P- and Sb-doped material. They attributed this difference primarily to variations in grown-in defects and processing conditions rather than to the dopant species itself.
Overall, the team found that n-type Cz Si exhibited substantially less LeTID degradation than previously reported for p-type Ga-doped Cz Si, indicating greater intrinsic resilience to LeTID. Injection-dependent lifetime measurements further indicated that recovery is primarily associated with the disappearance or transformation of metastable bulk defects.
The analysis also revealed that regenerated samples achieved higher bulk carrier lifetimes than recovered samples, suggesting that the regeneration process can improve bulk material quality beyond the state reached after the initial recovery treatment.
The researchers also investigated recovery pre-treatment and LeTID in unmetallized and metallized TOPCon cells sourced from industrial production lines. They found that both phenomena remain observable at the cell level and can affect device performance if appropriate mitigation measures are not implemented. Their impact, however, can be substantially reduced by optimizing the firing profile and applying suitable post-processing treatments.
A direct comparison of TOPCon cells subjected to identical firing conditions, with and without LECO, showed that the process moderately suppressed changes in open-circuit voltage associated with recovery and subsequent LeTID. The researchers also found that an industrially manufactured TOPCon cell incorporating LECO experienced only limited LeTID-induced efficiency degradation, with the change remaining within acceptable warranty tolerances.
The research is described in the paper “Bulk degradation of P-, Sb- and As-doped Cz Si wafers and TOPCon cells due to light and elevated temperature,” published in Solar Energy Materials and Solar Cells.
“We believe our findings provide some metrics and guidelines for producing LeTID-stable TOPCon cells,” Stradins concluded.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Monday, October 26, 2026
10:30 am – 11:30 am CEST, Berlin, Paris, Madrid
Thursday, September 10, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Tuesday, September 15, 2026
5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
pv magazine USA hosts its multi-day virtual event on U.S. solar and energy storage, covering domestic manufacturing, distributed energy and the growing role of solar-plus-storage in meeting AI-driven power demand.

You have no items in your basket.

source

The Group Captive Solar Project will help supply clean electricity to Tata group companies and reduce carbon emissions.
Tata Power Renewable Energy (TPREL) has announced the commissioning of its new 100MW Group Captive Solar Project in Kayathar, Tamil Nadu, India, taking its total operational capacity past 7GW.
The energy company, a subsidiary of Tata Power, now reports a utility portfolio of 12.3GW.
The gold standard of business intelligence.
Find out more
Combine business intelligence and editorial excellence to reach engaged professionals across 36 leading media platforms.
Find out more
Located across the Vellalankottai and Nalandhula villages, the new site is expected to generate 240.63 million units of electricity annually and is projected to offset around 150,000t of carbon dioxide emissions each year.
Power generated from the project will be supplied to TP Solar (40.63MW), Tata Electronics (53.13MW) and Tata Realty and Infrastructure (6.25MW).
According to TPREL, the Kayathar plant marks the company’s first use of Flexible Terrain Compatible Single Axis Tracker technology on a project in India.
The plant is equipped with 261,660 Mono PERC bifacial solar modules to enhance generation efficiency across variable terrain.
Electricity from the site is transmitted through the Kayathar 400kV Grid Substation.
The company stated that its operational capacity consists of more than 5.7GW of solar and 1.3GW of wind assets.
TPREL also reported an additional pipeline totalling 5.3GW, comprising 3.1GW of wind projects and 2.2GW of solar projects. It is under various stages of development and scheduled for phased commissioning in the next six to 24 months.
It is among several companies aiming to contribute to India’s national target of 500GW of non-fossil fuel capacity by 2030.
Last month, TPREL commissioned a 190.5MW solar project in Kalasar, Rajasthan, under tranche one of the SJVN Firm and Dispatchable Renewable Energy (FDRE) initiative. The plant forms part of a broader 460MW FDRE development.
The gold standard of business intelligence.
Find out more
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
Find out more
Give your business an edge with our leading industry insights.
Give your business an edge with our leading industry insights.



Visit our Privacy Policy for more information about our services, how we may use, process and share your personal data, including information on your rights in respect of your personal data and how you can unsubscribe from future marketing communications. Our services are intended for corporate subscribers and you warrant that the email address submitted is your corporate email address.
View all newsletters from across the GlobalData Media network.
Power industry news, data and in-depth articles on the global trends driving power generation, renewables and innovation
Powered by
© GlobalData Plc 2026

source

Gov. Sherrill signs bill aimed at expanding balcony solar  New Jersey Monitor
source

The DRC Commissions 233 MWp Solar-Storage Plant With CrossBoundary Energy  energynews.pro
source

Sep 01, 2026 16:06
Read More On:
See More >
See More »
Stocks
Mutual Funds
Investment Tracking
Home
Market News
Latest News
International Markets
Economy
Industries
Mutual Fund News
IPO News
Search News
My Portfolio
My Watchlist
Gainers
Losers
Sectors
Indices
Forex
Mutual Funds
Feedback

source

The Australian Renewable Energy Agency (ARENA) announced it would provide University of Sydney researchers with $7.25 million towards a $19.5 million project to develop more durable Silicon (Si)-perovskite tandem solar cells in order to maintain their high efficiencies for commercial use.
The University of Sydney team will partner with Brisbane-based solar panel manufacturing startup Unison Solar Energy and scientists from Singapore’s Nanyang Technological University to take the next-generation technology from research towards commercial-scale production. 
“Our ambition is to pioneer a new era of Australian solar manufacturing and commercialise leading technologies here at home,” Unison Solar Chief Executive Officer Allen Guo said.
Si-perovskite tandem cell technology has demonstrated the potential to overcome the performance limitations of current solar technologies that rely on silicon as the sole semiconductor. Silicon’s conversion rate – the amount of solar energy it converts into electricity – currently peaks at about 25% but the researchers said Si-perovskite tandem cell technology could theoretically deliver conversion efficiencies of about 40%.
Team leader Professor Anita Ho-Baillie, John Hooke Chair of Nanoscience at the University of Sydney Nano Institute and School of Physics, said the researchers’ efforts have focused on stacking perovskites, made from synthesising metal with halogens, on top of silicon to form a tandem solar cell, rather than using silicon as the sole semiconductor. 
“There isn’t much room for silicon to improve because its theoretical limit is only 30%, but for perovskite-silicon tandem, it is about 40%,” she said. 
The research team has already shown the greater efficiency of the Si-perovskite technology, achieving Australia’s first 30% efficient Si-perovskite tandems on small and large areas. The team has also reported tandem cells passing industry standard tests against thermal extremes and moisture.  
Despite the potential of the technology, scaling devices beyond the laboratory and ensuring their stability under real-world conditions has proven challenging. Perovskite materials can break down when exposed to light, heat, moisture and mechanical stress.
Ho-Baillie said the new funding will help the researchers prove the reliability of Si-perovskite cells under a series of industry standards and take tandem-cell technology one step closer to becoming commercially viable. The ultimate goal is to improve the cells’ ability to maintain their conversion rate over the life expectancy of solar panels. 
“This is a fantastic opportunity for us to make research we’ve been doing at the university for the last six years translational,” she said. “Our next round of testing will prove this technology’s ability to cope with UV light and mechanical stresses.”
Unison Solar, which is establishing a solar panel production facility in Brisbane’s outer suburbs with an initial 500 MW manufacturing capacity, will work with the researchers during the commercialisation stage.
Guo, a former chief operating officer at Jinko Solar, said the Queensland-headquartered company will assess manufacturing costs, supply chains, customer needs and pathways to pilot production and scale-up.
“This project marks the beginning of collaboration with leading Australian research institutions for Unison Solar Energy,” he said, with the company aiming to establish gigawatt-scale production of advanced solar products in Australia.
Goa, a former chief operating officer at Jinko Solar, said Unison’s goal is to establish a manufacturing-ready technology platform capable of delivering next-generation tandem solar products with outstanding performance and long-term field reliability.
“Australia has been at the forefront of global solar research for more than 50 years, but local manufacturing remains limited and has not reached the scale our energy transition demands,” he said. “By combining Unison’s capability, technology and vision with ARENA’s support and the University of Sydney’s research expertise, we intend to deliver affordable, high-quality Australian-made solar products to Australian families. This is the start of our exciting journey.”
The project is one of 20 research and development initiatives to secure funded as part of a $105.6 million funding round announced by ARENA.
The funding will support projects spanning improved efficiency, cost and stability across advanced cells and modules, to innovations that can help improve solar farm deployment, operations and maintenance. and reduce the levelised cost of electricity (LCOE).
“Australia has played a leading role in the development of solar technology, and these projects will help ensure we continue to strengthen that position,” ARENA acting CEO Chris Faris said.
“The portfolio brings together a mix of near-term improvements and breakthrough technologies that have the potential to lower costs, improve performance and accelerate the deployment of solar energy both in Australia and around the world.”
“Achieving ultra low-cost solar requires innovation across the entire value chain. From the solar cells and modules themselves through to the way solar farms are built, operated and maintained, these projects will help unlock practical solutions that support a faster, more affordable energy transition.”
The funding is to be delivered over five years, commencing in 2027.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid

You have no items in your basket.

source

Please refresh the page or navigate to another page on the site to be automatically logged inPlease refresh your browser to be logged in
The Independent’s journalism is supported by our readers. When you purchase through links on our site, we may earn commission. Why trust us?
Small solar kits may be marketed as ‘plug and play’ or ‘balcony solar’, but that doesn’t necessarily mean you can connect them to a household socket under Britain’s new rules
Removed from bookmarks
From reproductive rights to climate change to Big Tech, The Independent is on the ground when the story is developing. Whether it’s investigating the financials of Elon Musk’s pro-Trump PAC or producing our latest documentary, ‘The A Word’, which shines a light on the American women fighting for reproductive rights, we know how important it is to parse out the facts from the messaging.
At such a critical moment in US history, we need reporters on the ground. Your donation allows us to keep sending journalists to speak to both sides of the story.
The Independent is trusted by Americans across the entire political spectrum. And unlike many other quality news outlets, we choose not to lock Americans out of our reporting and analysis with paywalls. We believe quality journalism should be available to everyone, paid for by those who can afford it.
Plug-in solar panels are now legal in Great Britain, opening the door to a new generation of compact solar systems that can be connected to a suitable household socket. But that doesn’t mean every small solar kit suddenly qualifies as “plug-in solar” under the new rules.
It’s important to understand the distinction because shoppers are already likely to come across products described as “balcony solar”, “plug and play” or “DIY solar” on retailer websites. These may all be perfectly legitimate solar products, but still not meet the definition of plug-in solar under the regulations that came into force on 27 August.
In other words, a product being small, modular or sold for domestic use does not automatically mean it can legally be plugged into a standard three-pin socket under the new system.
Read more: Best plug-in solar panels so far
The government’s new framework applies to a much narrower category of product. Under the new rules, a compliant plug-in solar device must be a complete approved system, typically including at least one solar panel, a grid-following microinverter, the manufacturer-supplied cable and plug, and a mounting system. It must also meet the government’s Interim Product Specification and have a maximum apparent output of 800VA, commonly referred to as 800W.
Just as importantly, the product must be assessed and listed through the Energy Networks Association’s Connect Direct system. That means consumers should be checking the exact make and model, rather than assuming a product is compliant just because it looks similar to one that is.
A good example of this distinction is EcoFlow’s stream solar system. EcoFlow sells the system in the UK and markets it as a compact solar product aimed at balconies and other small domestic spaces. At first glance, that might sound like the sort of product covered by the new plug-in rules.
But EcoFlow’s own UK guidance says that, in Britain, the stream microinverter must currently be connected to the home’s distribution board by a professional installer or electrician. In other words, it is not currently being presented as a three-pin-plug solar product that can be simply plugged into a standard socket under the new regime.
That doesn’t mean the EcoFlow system is banned or improper. Not at all. It’s an excellent product. It just means it falls into a different category. A hard-wired solar kit can still be legally sold and installed in Britain, but it doesn’t benefit from the simplified socket-connected route that was just introduced for compliant plug-in solar panels.
This is an important point for shoppers to understand because the language used by retailers and manufacturers can sometimes blur the lines. Terms such as “plug and play” or “balcony solar” may be catchy marketing shorthand, but they aren’t proof that a system has been approved under Britain’s new plug-in solar framework.
The safest approach is to verify the product before buying. Consumers should check that the exact system appears on the Energy Networks Association’s accredited plug-in solar register and follow the manufacturer’s instructions carefully. If a product requires connection to the distribution board, or doesn’t appear on the register as compliant, it shouldn’t be treated as one of the new plug-in solar devices covered by the 27 August rule change.
As plug-in solar becomes more widely available, this is likely to become one of the biggest areas of confusion. The key thing to remember is that not every compact solar kit is ‘plug-in solar’. Some are approved socket-connected systems under the new rules, while others are still conventional solar products that need a different installation route.
Read more: Best solar panels, compared
Please refresh the page or navigate to another page on the site to be automatically logged inPlease refresh your browser to be logged in

source

N.J. just made it easier for renters to slash their energy bills with plug-in solar panels  Currently.com
source

The US Department of Energy (DOE) has launched a US$12 million research and development (R&D) fund that will support projects that will lower the cost, and expand domestic manufacturing, of solar panels for use in space.
Led by the DOE’s Integrated Energy Systems Office (IESO), the Space Photovoltaics Research and Development Partnership Intermediary Agreement (PIA) is open to university and industry research laboratories developing “advanced” space-based solar PV projects, initiatives that focus on “PV characterisation and stress testing” or “near-commercial pilot-scale space PV solutions”.
Get Premium Subscription
The PIA is split into two “topic areas”. The first, dubbed ‘Next-generation Cell Innovation’, will focus on the advancement of manufacturing methods and improvements in performance of durability of solar cells, while ‘Rapid Production and Demonstration’ will focus on manufacturing processes capable of scaling to “high-volume production” of module prototypes for space or near-space environments. Individual applicants can win up to US$1.5 million for projects in the first area and up to US$2 million for projects in the second area.
Applications are open immediately, and will close on 8 October. The DOE will also host a webinar in collaboration with TECHWERX, a hub that aims to connect researchers, industry and energy leaders, on 15 September to provide more information about the fund. The organisers expect to select winning applicants in December, and complete negotiations for fund awards between January and February next year.
While direct federal support for a relatively early stage clean energy sector like space-based solar power might be something of a surprise from the second Trump administration, the government has made it clear that investing in energy security, regardless of the generation technology used, has been a key priority. Assistant secretary of energy Audrey Robertson said, upon the launch of the funding, that “bolstering our national security” was a goal of the funding.
“The next frontier for solar PV power generation is in space,” said Robertson. “As demand for space-grade PV skyrockets, this investment will establish American leadership in next-generation, space-based PV, bolster our national security and enhance our economic competitiveness.”
Last month, PV Tech Premium heard from Hasan Nazar, head of policy at Crux, about how many of the current policy initiatives align under the priority of improving US energy security, and reducing reliance on parts and components made overseas.
Indeed, a report from Clean Tomorrow, published last year, found that the DOE would need to invest US$25 billion across a number of energy sectors in order to deliver greater energy security for the US, and while the US$12 million for the PIA is a small part of this total, it is nonetheless part of the government’s spending to strengthen energy security.
Space-based solar power has also attracted interest from the private sector this year. In April, tech giant Meta signed an agreement with space-based solar power startup Overview Energy to gain “early access” to a 1GW fleet of space-based solar panels that Overview plans to launch in 2030.
Space-based solar power will be a topic of conversation at this year’s PV CellTech USA conference. Hosted by PV Tech publisher Solar Media in San Francisco, US, on 13-14 October, the final day of the conference will include a presentation from Timothy Siegler, technology manager at the IESO, about how space-based solar power is driving the next generation of PV innovation. Read the full event agenda on the official website.

source

The Delhi government announced an ambitious plan on Tuesday to raise the state subsidy for residential rooftop solar installations in the national capital to ₹78,000, a move that would slash the upfront cost of setting up the clean energy system.
Chief minister Rekha Gupta said households with a monthly consumption of 400 units of electricity or less would effectively be eligible for a fully funded 3-kilowatt rooftop solar system under the revised Delhi Solar Policy.

Under the changes made to the 2023 policy, the Delhi government will offer subsidies of up to ₹78,000 for a 3-kw solar panel system. Paired with equal funding from the central government’s PM Surya Ghar initiative, the total incentives will effectively cover the setup cost for standard 2- and 3-kw units.
Gupta said the government has targeted installing rooftop solar systems in 230,000 households across Delhi by March 2027.
Also Read: The supply chain behind solar panels
Also Read: The supply chain behind solar panels
“The government not only wants to reduce people’s electricity bills but also to support the installation cost of solar panels. The government will provide free rooftop solar panels to all households with monthly electricity consumption of 400 units,” Gupta said at a press conference.
Apart from the state subsidy of ₹78,000 for a 3-kW system, the policy also proposes an additional state top-up of ₹19,000 for consumers using up to 400 units of electricity a month. This would cover the installation cost of ₹1.75 lakh for a 3-kw system.
Also read | ₹35/kg: Rekha Gupta”>Delhi receives 1,000 tonnes of onions from Centre for sale at ₹35/kg: Rekha Gupta
Households that consume more than 400 units would not receive the additional top-up of ₹19,000.

For households consuming 0-200 units a month, the proposed support will vary according to the size of the solar system. A 2-kW system, estimated to cost around ₹1.30 lakh, will receive ₹60,000 as central subsidy, ₹52,000 as Delhi government subsidy and an additional ₹18,000 top-up. The total support of ₹1.30 lakh would effectively cover the listed cost of the system.
The policy also takes into account the savings and income generated from rooftop solar systems.
For consumers in the 0-200 unit category installing a 2-kW system, the government estimates electricity savings of around 200 units a month, translating into a benefit of about ₹300 per month at ₹3 per unit.
For a 3-kW system, consumers are expected to save around 200 units a month and export another 100 units to the grid. Payment for the exported electricity is estimated at ₹650 a month, taking the average additional monthly benefit to around ₹950.
For households consuming 201-400 units a month, the upfront subsidy structure will remain the same — ₹60,000 central subsidy, ₹52,000 Delhi subsidy and ₹18,000 top-up for a 2-kW system, and ₹78,000 each from the Centre and Delhi, along with a ₹19,000 top-up for a 3-kW system. The estimated average additional monthly benefit for this category is ₹1,659.
For households consuming more than 400 units a month, a 3-kW system costing ₹1.75 lakh will receive ₹78,000 each from the Centre and Delhi, while there will be no additional top-up. The proposed consumer contribution will be ₹19,000. The average additional monthly benefit is estimated at ₹2,143, with surplus electricity eligible for payment at ₹6.50 per unit.
Group housing societies
The revised framework provides for support to group housing societies for installing solar systems in common areas. A proposed 100-kW system, estimated to cost ₹45 lakh, will receive ₹18 lakh as central subsidy and ₹11 lakh as Delhi government subsidy, leaving a consumer contribution of ₹16 lakh. The estimated average additional benefit is ₹81,247 per month.
Gupta said the rooftop solar programme is part of the government’s “Green Delhi” initiative. At present, around 10,000 buildings in Delhi have rooftop solar panels installed and most of them government buildings, she said.
…Read MoreSaloni Bhatia is a journalist with over 15 years of experience in reporting and storytelling, with a strong focus on the Delhi government and political developments in the Capital. Over the years, she has closely tracked policy decisions, governance issues, and political shifts. She started off as an entertainment journalist but then moved to covering beats like crime and education. Her experience on the crime beat helped her develop an eye for detail and accuracy, while education reporting allowed her to explore policy impact on students, teachers and institutions. Outside the newsroom, she enjoys reading both fiction and non-fiction. She also has a keen interest in watching Bollywood films. Read Less

source

New Jersey Governor Mikie Sherrill has signed the the Garden State Balcony Solar Act (S2368/A4836) into law, enabling New Jerseyites to install and use portable solar generation devices of up to 1,200 watts without the need to apply for an installation permit or obtain their utility’s approval.
The law, which was passed by the state’s two legislative bodies on unanimous votes in late June, would require the portable solar devices to comply with provisions of the most recent versions of the National Electrical Code (NEC) and the State Uniform Construction Code, in addition to becoming listed or certified under the UL 3700 Outline of Investigation for Interactive Plug-In PV (PIPV) Equipment and Systems.
The bill creates an exemption for devices with power output of 400 watts from the need to obtain the UL listing or comply with the NEC and state code.
“From day one, I’ve been laser-focused on driving down energy costs through an all-of-the-above approach, and that includes putting clean, affordable solar power that you can simply plug in directly into the hands of New Jerseyans,” said Governor Sherrill in a statement. “Balcony solar is a practical, easy-to-use tool that can help families save money while allowing more people to participate in our clean energy future. This bill cuts unnecessary red tape, expands access to affordable solar power, and proves that affordability and sustainability can go hand in hand.”
Notably, the bill also contains provisions that restrict landlords and homeowners’ associations (HOAs) from prohibiting the use of portable solar generation devices, so long as renters (or homeowners subject to HOA oversight) abide by “reasonable restrictions concerning the size, placement, or manner of placement of a portable solar generation device on the exterior of a unit owner’s or tenant’s premises.”
News of the law was celebrated widely among advocates and industry representatives. “By making solar more accessible, New Jersey is building a fairer, more affordable energy system where everyone can share in the benefits of clean power,” said Elowyn Corby, Senior Regional Director for the Mid-Atlantic, Vote Solar Action Fund. “We are grateful Governor Sherrill has stood with New Jersey families and taken a major step toward a clean energy future that delivers greater energy affordability and access to solar.”
“By signing this law, Governor Sherrill and legislative leaders have taken another big step in making solar energy more affordable and accessible for New Jerseyans,” said Stephan Scherer, CEO and co-founder of CraftStrom, a company that sells balcony solar equipment. “As the most densely populated state in the nation, New Jersey is built for plug-in solar: it takes just an hour to install, fits on apartment and condominium balconies, and cuts utility bills immediately. New Jersey is sending a clear signal that the future of solar is portable, affordable, and consumer-led.”
Plug-in solar bills in other states (such as the recently-passed California Plug and Play Solar Act) do not contain similar protections for renters and HOA members. 
With Sherrill’s signature, New Jersey becomes the ninth state in the nation to enact a plug-in solar law. Laws in two other states — New York’s SUNNY Act and the aforementioned California legislation — await action from governors in those states.
The Garden State Balcony Solar Act bill will take effect on March 1, 2027, giving the state Board of Public Utilities time to take action necessary to implement the law’s provisions.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: [email protected].
Comments
Please login to comment
Monday, October 26, 2026
10:30 am – 11:30 am CEST, Berlin, Paris, Madrid
Thursday, September 10, 2026
2:00 pm – 3:00 pm CEST, Berlin, Paris, Madrid
Tuesday, September 15, 2026
5:00 pm – 6:00 pm CEST, Berlin, Paris, Madrid
A two-day conference in Austin, Texas, bringing together leaders in US solar manufacturing, equipment specification, and factory execution.
Thursday, October 7, 2026
11:00 am – 12:30 pm CEST, Berlin, Paris, Madrid
pv magazine USA hosts its multi-day virtual event on U.S. solar and energy storage, covering domestic manufacturing, distributed energy and the growing role of solar-plus-storage in meeting AI-driven power demand.

You have no items in your basket.

source

0
Powered by :
The Australian Renewable Energy Agency (ARENA), an Australia-based renewable energy agency, has announced new funding for solar research and development. ARENA will provide up to AUD 105.6 million (~$74.98 million) for 20 projects focused on advancing ultra low-cost solar. This represents the agency’s largest single investment in PV research and development to date. The selected projects are divided between research on cells and modules and work covering BOS, O&M. Across the two categories, each covering three focus areas, the program will address cell and module efficiency, cost and stability, alongside deployment costs, O&M expenses and solar yield. ARENA initially allocated AUD 60 million (~$42.60 million) before increasing the total funding pool to AUD 105.6 million (~$74.98 million). The program supports ARENA’s ambition to reduce installed solar costs to 30 cents per watt by 2030.
SOLARbytes brings you the latest news in solar world in bite size; essentially a gist of important solar news in few sentences.
Subscribe to our Newsletter!

Quick Links
© 2026 SOLARbytes | SOLARbytes is a publication of Wat(t) Bytes Media Pvt. Ltd. All rights reserved.

source

0
Powered by :
Swiss solar manufacturer Megasol has upgraded its LEVEL Up roof-integrated photovoltaic system to feature its low-glare ZRM+ (Zero Reflect Matt+) microstructured glass as standard. Designed to mimic the low reflectivity of conventional clay tiles, the surface keeps reflection levels between 3,000 and 18,000 cd/m²—well under the 20,000 cd/m² regulatory threshold confirmed in tests by Bern University of Applied Sciences. The frameless glass-on-glass module delivers over 200 Wp/m² with Class 5 hail resistance and CEN/TR 15601 rain tightness, immediately replacing earlier product lines. LEVEL Up with ZRM+ replaces all previous product variants. It is also available immediately for residential, commercial and sensitive planning zones.
SOLARbytes brings you the latest news in solar world in bite size; essentially a gist of important solar news in few sentences.
Subscribe to our Newsletter!

Quick Links
© 2026 SOLARbytes | SOLARbytes is a publication of Wat(t) Bytes Media Pvt. Ltd. All rights reserved.

source

 
ZAGREB, 1 September 2026 – Croatian households will be able to apply for a share of €38 million in government-backed renewable energy subsidies from Wednesday, with battery storage included in the scheme for the first time. 
Applications open at 9am on 2 September through the electronic system of the Environmental Protection and Energy Efficiency Fund (FZOEU).
The programme supports the installation of heat pumps, photovoltaic systems for household consumption and battery storage systems.
Households can receive up to 50% of eligible investment costs, while households at risk of energy poverty can receive up to 70%.
Depending on the investment, subsidies can reach up to €6,250 for a heat pump, €6,000 for a photovoltaic system and €5,600 for a battery storage system. For households at risk of energy poverty, the maximum combined support can be considerably higher.
The introduction of battery subsidies is one of the main changes this year. Batteries can be financed only together with a photovoltaic installation and are intended to allow households to store excess electricity for later use.
The Fund says applications will be accepted electronically and has urged potential applicants to prepare their documentation and ensure they have the required NIAS electronic identification credentials.
The scheme is part of Croatia’s wider efforts to increase household energy independence and renewable energy use.

source