The variability in the output of photovoltaic power systems has long been a source of great concern for utility operators worldwide. This is a real concern in tropical countries like India where the monsoon clouds will control the situation for 3 to 4 months. Once we can predict and model it, it will be a fine tool for the Utility Grid operators to control and switch other generators when needed to stabilise the Grid.
Currently, with solar plants accounting for a very small fraction of generation on most electrical grids these changes do not cause any problems for grid operators. However, if solar penetration levels increase in the future, as they likely will, such variability will pose problems for grid operators. A fast change in generation from cloud transients will need to be balanced by other sources of generation to balance load.
The power output fluctuations from solar panels during the cloud cover have to be predicted for addressing this issue. We hope that the latest innovation from the Department of Mechanical and Aerospace Engineering at the Jacobs School, San Diego can solve the problem to a greater extent.UC San Diego Professor Jan Kleissl and Matthew Lave, a Ph.D. student in the Department of Mechanical and Aerospace Engineering at the Jacobs School, have found the answer to these questions. They also have developed a software program that allows power grid managers to easily predict fluctuations in the solar grid caused by changes in the cloud cover. The program uses a solar variability law Lave discovered.
But Kleissl and Lave found that variability for large photovoltaic systems is much smaller than previously thought. It also can be modeled accurately, and easily, based on measurements from just a single weather station.
The finding comes at a time when the Obama administration is pushing for the creation of a smart power grid throughout the nation. The improved grid would allow for better use of renewable power sources, including wind and solar.
Also, more utilities have been increasing the amount of renewable energy sources they use to power homes and businesses. For example, Indian Ministry of New and Renewable Energy have come up with National Solar Energy Mission to add 20000 MW by 2020. Inspired by this announcement many MNCs and Indian companies have come up with proposal for solar farms.
Kleissl and Lave's finding could have a dramatic impact on the amount of solar power allowed to feed into the grid. Right now, because of concerns over variability in power output, the amount of solar power flowing in the grid at residential peak demand times in California, say -- is limited to 15 percent before utilities are required to perform additional studies. As operators are able to better predict a photovoltaic system's variability, they will be able to increase this limit.
Incidentally, Kleissl and Lave's research shows that the amount of solar variability can also be reduced by installing smaller solar panel arrays in multiple locations rather than building bigger arrays in just one spot, since a cloud covering one panel is less likely to cover the other panels, Lave said. "The distance between arrays is key," he said.
Kleissl presented the paper, titled 'Modeling Solar Variability Effects on Power Plants,' this week at the National Renewable Energy Laboratory in Golden, Colo.
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. "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.
But Lave didn't stop there. He developed an easy-to-use interface in MATLAB that allows grid planners and operators to simulate 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. The tool developed by Lave and Kleissl could become key in solar installations in all parts of the world.
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