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UC San Diego Researchers Introduce Tool to Assess Solar Power Fluctuations

Professor Jan Kleissl of UC San Diego and Matthew Lave, a Ph.D. student at the Jacobs School, have created a software program that can be utilized by grid managers to understand sudden power fluctuations induced by cloud cover. The software program is based on a solar variability law established by Lave.

The research paper titled 'Modeling Solar Variability Effects on Power Plants,' presented by Kleissl at the Golden, Colorado, located National Renewable Energy Laboratory has established that inconsistency among the large sized photovoltaic systems lesser than previous findings and it can be patterned precisely by utilizing measurements from a single weather station. The research also points out that the variations can also be brought down by erecting smaller sized solar panels at a number of locations instead of having bigger sized solar panels at a single location to avoid cloud cover over all the panels.

The researchers utilized one year data collected from the 1.2 MW UC San Diego solar grid utilizing 5,900 solar panels whose output is monitored by 16 weather stations. They checked out for variations in the quantity of solar radiation received by the weather stations by narrowing down the intervals to seconds and found that the quantity of radiation is linked with quantity of power generated by the panels. By utilizing the observations the researchers established that when the distance between two weather stations is separated by a particular time frame a solar variability law results for the change in power production.

Lave, one of the researchers has developed an easy to deploy interface in MATLAB to enable the operators and grid planners to replicate the variability of PV systems. The interface enabled the users to input their data in the form of a text file or draw a polygon over each solar PV system on satellite Google Map. The developed model utilizes e solar heat measurements of a single sensor on a particular day and computes the changes in total output at all the systems.

This tool is expected to be valuable for the feeder systems that are connected to large number of solar PV systems to figure out the fluctuation problems before hand.

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