;
Researchers from the University of San Diego have discovered a new "solar variability law" that can be used to predict how fluctuations in weather conditions – such as clouds passing over the sun – will affect power output from solar panels.
UC San Diego Professor Jan Kleissl, working with Matthew Lave, a Ph.D. student in the Department of Mechanical and Aerospace Engineering at the Jacobs School, also developed a software program based on the solar variability principle, which could see an increase in solar energy derived electricity flowing into American households.
Currently, because of concerns over variability, the amount of solar power allowed into the US residential grid is capped at 15 percent. Kleissl and Lave’s research could see this limit lifted as utilities apply the solar variability program to photovoltaic systems.
The ability to forecast and plan for ebbs and flows in energy outputs from solar arrays will become essential as the USA begins switching over to smart grid technology, which allows for a decentralised power generation network, accommodating more renewable energy sources.
The researchers have found that in terms of reducing variability from cloud cover, more smaller solar energy systems spread over a large area is better than building a single large solar farm in one area. The distance between panels is key, says Lave, as clouds covering one panel are less likely to cover others.
To establish his solar variability law, Lave used the 1.2 megawatt UC San Diego solar grid with its 16 weather stations and 5,900 solar panels. He analysed a year’s worth of variations in solar radiation relayed from the stations and came up with his equation for solar variability: Distance between weather stations, divided by the time frame for change of power output, equals solar variability.
The findings were presented in a paper entitled, “Modeling Solar Variability Effects on Power Plants,” this week at the National Renewable Energy Laboratory in Colorado.
