In a move that could lead to more rooftop solar, Sandia National Laboratories and partners have developed software that shows how solar panels at a specific location would interact with the distribution system during the year.
The new software comes amid a surge in rooftop solar around the United States. U.S. solar installations, including utility-scale solar, are expected to jump 25 percent this year to more than 13,000 megawatts, according to a mid-June report from the Solar Energy Industries Association and Wood Mackenzie Power & Renewables, a consulting firm.
Previously, detailed, second-by-second simulation, called quasi-static time series analysis, wasn’t practical outside a research lab, according to Sandia engineer Matthew Reno. Instead, utilities typically study how rooftop solar will affect the grid by doing a snapshot, power-flow simulation.
“Doing a snapshot of one instance in time is conservative because of the uncertainty about impacts that happen at various times on solar panels, such as the weather,” Reno said. “This can lead to increased connection costs or homeowners living in parts of the city having unnecessarily low limits for adding solar panels, especially in sunny states.”
With $4.1 million in funding from the Department of Energy’s Solar Technologies Energy Office, Sandia, the project’s lead organization, spent three years figuring out how to speed up quasi-static time series analysis, according to the research lab.
Partners on the project included CYME International, a software vendor for utilities, the Electric Power Research Institute, Georgia Tech University and the National Renewable Energy Laboratory.
Reno said the software can take a simulation that used to take 36 hours and finish it in less than five minutes on a desktop computer.
The group was able to speed up the processing time for running simulations by focusing on four areas.
First, the software moves through a simulated year at various speeds. For example, the software jumps more quickly through nighttime when things are stable while devoting more computational effort to the more complex daylight hours, making the program more efficient, according to Reno.
Second, the group updated formulas used to calculate power flows.
Third, the group was able to reduce the complexity of the power-grid model the software uses while maintaining its accuracy, according to Sandia. “The smaller, more efficient model helps the software solve problems faster by focusing its analysis on critical parts of the grid,” Sandia said.
Finally, the software uses all of a computer’s processors instead of using a single core to run the simulation. The software separates parts of the year or parts of the grid, assigns them to the available computing cores and runs them in parallel, Sandia said.
The software can be used to evaluate other technologies such as smart grid controls, according to Sandia.
“Utility companies are going to have continued need to use time-series analysis to see how new electric car charging will impact neighborhoods, investigate the best energy storage controls and applications or determine how smart home controls, like thermostats and lights, can benefit their grids,” Reno said. “In order to understand the new benefits and controllability of the smart grid, these companies will have to be able to simulate it first.”