Singapore-based renewables developer Vena Energy has launched commercial operations of a 125 MW solar project in the Australian state of Queensland. It is backed by a long-term offtake agreement with global tech giant Amazon.
In a new weekly update for pv magazine, OPIS, a Dow Jones company, offers bite-sized analysis on solar PV module supply and price trends.
As the US Inflation Reduction Act (IRA) ramps up clean energy efforts across the United States, the government has outlined a plan to expand transmission lines to accommodate more power.
The National Renewable Energy Laboratory developed a proof of concept for a method to remove polymers from solar panel manufacturing to enable more efficient recycling.
The European Parliament has approved the Net-Zero Industry Act, which now awaits formal adoption by the European Council to become law. The European Solar Manufacturing Council has welcomed the decision, saying that it “gives a green light for procurement of sustainable European-made solar panels.”
The Irish authorities have presented a new plan to allow homeowners to borrow between €5,000 ($5,350) and €75,000 for up to 10 years, with low interest rates. Heat pumps, solar electricity and solar water heater installations are all eligible under the scheme.
Greek renewables developer Elica SA is seeking consultants for two desktop studies related to an electrical interconnection project between Greece and Egypt. The undersea link will transmit green energy to Europe via a 3 GW HDVC connection.
On April 25, 1954, US researchers presented the first prototype of a usable solar module. The efficiency at that time was around 6%. A lot has happened since then.
Burundi, the poorest country on earth, is unable to buy fossil fuels on theinternational market due to a lack of hard currency. pv magazine spoke with the United Nations Development Programme (UNDP) and a PV analyst to assess the true potential of PV in the nation’s current energy crisis.
An international research team has used the convolutional neural network (CNN) deep learning algorithm to identify faults in solar panels. Its work showed the proposed technique has a high degree of accuracy, especially if combined with transfer learning models.
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