(714f) Joint Probability and Correlation Analysis of Wind and Solar Power Forecast Errors in the Western Interconnection

Wind and solar power generation differ from conventional power generation because of the variable and uncertain nature of their power output. This has significant impact on grid operations. Short-term forecasting of wind and solar generation is uniquely helpful for balancing supply and demand in the electric power system because it allows for a reduction in the uncertainty. This paper investigates the correlation between wind and solar power forecast errors as well as insightful metrics. The forecast and actual data used were obtained from the Western Wind and Solar Integration Study. Multiple spatial and temporal scales (e.g., day-ahead and 4-hour-ahead) of forecast errors for the Western Interconnection in the United States were analyzed. A joint probability distribution of wind and solar power forecast errors was estimated using kernel density estimation. The Pearson’s correlation coefficient and mutual information between wind and solar power forecast errors were also evaluated. The results showed that wind and solar power forecast errors were correlated, and the correlation between wind and solar power forecast errors became stronger as the size of the analyzed region increased. The forecast errors are less correlated at the day-ahead timescale, which influences economic operations more than reliability, and more correlated at the short-term timescale, where reliability is more impacted by the forecasts. In addition, interesting results were found through cluster and seasonal variation analyses of wind and solar power forecast errors, and they are uniquely useful to operators who maintain the reliability of the electric power system.