Whether you agree or not, the funding machine for solar and wind energy is in motion and it is hard to stop - look at the debate over ending ethanol subsidies.

One way to make an informed policy decision is to truly know just how much solar energy will be provided in a 'smart' grid scenario, beyond optimistic projections by lobbyists.   U.C. San Diego Professor Jan Kleissl and Matthew Lave, a Ph.D. student in the Department of Mechanical and Aerospace Engineering at the Jacobs School, say they can do it. They developed a software program that allows power grid managers to predict fluctuations in the solar grid caused by changes in the cloud cover - and even discovered a solar variability 'law'.

Solar power, even in California, is too risky, so utilities are limited to 15 percent even on sunny days.   Mandating targets doesn't help, though Gov. Jerry Brown signed a bill in April 2011 requiring all electricity retailers in the state to generate 33 percent of their power sales from renewable energy sources by 2020. 

The variability in the output of photovoltaic power systems has long been a source of great concern for utility operators worldwide, but Kleissl and Lave say that variability for large photovoltaic systems is much smaller than previously thought. It also can be modeled accurately, they say, based on measurements from just a single weather station. 

His findings are based on analysis of one year's worth of data from the UC San Diego solar grid—the most monitored grid in the nation, with 16 weather stations and 5,900 solar panels totaling 1.2 megawatts in output. Lave looked at variations in the amount of solar radiation the weather stations were receiving for intervals as short as a second. The amount of radiation correlates with the amount of power the panels produce.

Based on these observations, he found that when the distance between weather stations is divided by the time frame for the change in power output, a solar variability law ensues. This operation was inspired by a presentation by Clean Power Research, a Napa-based company, at the Department of Energy – California Public Utility Commission High Penetration Solar forum hosted by UC San Diego in March 2011.

"For any pair of stations at any time horizon, this variability law is applicable" says Lave. In other words, the law can be applied to any configuration of photovoltaic systems on an electric grid to quantify the system's variability for any given time frame.

Lave developed an interface in MATLAB that simulates the variability of photovoltaic systems. Data can be input as a text file, but the interface also allows users to simply draw a polygon around each system on a satellite Google Map. Based on solar radiation measurements at a single sensor on a given day, the model calculates the variability in total output across all systems.

"It is as easy as painting by numbers," said Kleissl. "In Google Maps, photovoltaics show up as dark rectangles on rooftops. Draw some polygons around them, push the button, and out comes the total variability."

Kleissl said he anticipates this tool will be useful to figure out whether problems in voltage fluctuation may occur in power feeder systems with a large amount of photovoltaic arrays. At this point, the solar installations on almost all feeders are still far below the capacity that would cause any major issues. But as the United States moves to affordable solar systems producing energy at lower costs through the Department of Energy's SunShot initiative and continued robust growth in installations, this will change. That's when the tool developed by Lave and Kleissl could become key.

While the tool is being prepared for public release, the authors would be happy to consider requests by third parties that can provide PV system location and size data to run the tool.
 
Kleissl presented the paper, titled 'Modeling Solar Variability Effects on Power Plants,' this week at the National Renewable Energy Laboratory in Golden, Colo.   The model development was sponsored by DOE's High PV Penetration Program grant 10DE-EE002055. Further information is available at https://solarhighpen.energy.gov/project/university_of_california_san_diego and http://solar.ucsd.edu