Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection

Abstract

Wind and solar power generation differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar power generation is uniquely helpful for balancing supply and demand in an electric power system. This paper investigates the correlation between wind and solar power forecast errors. The forecast and the actual data were obtained from the Western Wind and Solar Integration Study. Both the day-ahead and 4-hour-ahead forecast errors for the Western Interconnection of the United States were analyzed. A joint distribution of wind and solar power forecast errors was estimated using a kernel density estimation method; the Pearson’s correlation coefficient between wind and solar forecast errors was also evaluated. The results showed that wind and solar power forecast errors were weakly correlated. The absolute Pearson’s correlation coefficient between wind and solar power forecast errors increased with the size of the analyzed region. The study is also useful for assessing the ability of balancing areas to integrate wind and solar power generation.