Characterizing local high-frequency solar variability and its impact to distribution studies. (August 2015)

• Record Type: Journal Article
• Authors: Lave, Matthew; Reno, Matthew J.; Broderick, Robert J.
• Is Part Of: Solar energy. Volume 118(2015)
• Page Start: 327
• Page End: 337
• Publication Date: 2015-08
• Subjects: Solar variability -- Distribution grid integration -- Voltage regulator -- Tap changes; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2015.05.028
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: High-frequency (sub 1-min) solar variability is compared at 10 locations. A new metric for quantifying solar variability is proposed. Distribution grid simulations run for each locational variability sample. Up to 3× difference in voltage regulator tap change operations between locations. Up to −70% error when using low-frequency (15-min) solar variability. Abstract: Accurately representing the local solar variability at timescales relevant to distribution grid operations (30-s and shorter) is essential to modeling the impact of solar photovoltaics (PV) on distribution feeders. Due to a lack of available high-frequency solar data, some distribution grid studies have used synthetically-created PV variability or measured PV variability from a different location than their study location. In this work, we show the importance of using accurate solar PV variability inputs in distribution studies. Using high-frequency solar irradiance data from 10 locations in the United States, we compare the ramp rate distributions at the different locations, use a quantitative metric to describe the solar variability at each location, and run distribution simulations using representative 1-week samples from each location to demonstrate the impact of locational solar variability on the number of voltage regulator tap change operations. Results show more than a factor of 3 difference in the number of tap change operations between different PV power variability samples based on irradiance from the different locations. Errors in simulated number of tap changes of up to −70% were found when using low-frequency (e.g., 15-min) solar variability.