A synthetic, spatially decorrelating solar irradiance generator and application to a LV grid model with high PV penetration

• Published in: Solar energy 2017-05, Vol.147, p.83-98
• Main Authors: Bright, Jamie M., Babacan, Oytun, Kleissl, Jan, Taylor, Peter G., Crook, Rolf
• Format: Article
• Language: English
• Subjects: Atmospheric pressure; Cloud cover; Clouds; Confidence intervals; Confidence limits; Correlation coefficients; Crosswinds; Data processing; Distribution grid; Electric power distribution; Grid impacts; Irradiance; Irradiance generation; Photovoltaic cells; Regression analysis; Sky; Solar cells; Solar energy; Spatial correlation; Spatial distribution; Time series; Wind speed
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2017.03.018

•A 1-min resolution spatio-temporal solar irradiance generator methodology is presented.•This methodology outputs statistically accurate, spatially decorrelating irradiance time series.•Spatially decorrelating 1-min GHI are generated from 1-h observation data.•Using a correlating GHI profile overestimates total tap depth operations.•Using multiple spatially decorrelating GHI profiles improves tap depth analysis. Residential photovoltaic (PV) technology is expected to have mass global deployment. With widespread PV in the electricity distribution grids, the variable nature of the solar resource must be understood to facilitate reliable operation. This research demonstrates that synthetic, 1-min resolution irradiance time series that vary on a spatial dimension can be generated based on the following inputs: mean hourly meteorological observations of okta, wind speed, cloud height and atmospheric pressure. The synthetic time series temporally validate against observed 1-min irradiance data for four locations—Cambourne, UK; Lerwick, UK; San Diego, CA USA; and Oahu, HI USA—when analysing 4 metrics of variability indices, ramp-rate size, irradiance magnitude frequency and clear-sky index frequency. Each metric is calculated for the modelled and observed data at each location and CDF profile correlation compared as well as applying the Kolmogorov-Smirnov (K–S) test with 99% confidence limits. CDF correlation coefficients of each metric are all above R⩾0.908, and a minimum of 90.96% of daily irradiance time series passed the K–S test. A spatial validation was performed comparing the model outputs to real observation data. The spatial correlation coefficient regression with site separation was successfully recreated with MAPE=0.865%, RMSE=0.01 and R=0.955. The spatial instantaneous correlation was shown to behave anisotropically when using fixed cloud direction, with different correlation in along and cross wind directions. Cloud cover states of 40–60% showed the most spatial decorrelation while 0% and 100% had the least. The model outputs are applied to a distribution grid impact model using the IEEE-8500 node test feeder. PV scenarios of 25%,50%, and 75% uptake were modelled across a 1.5×1.5km grid. The magnitude and frequency of severe tap changing events are found to be significantly higher when using a single irradiance time series for all PV systems versus individually assigning spatially decorrelating time series.