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

NOTICE The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC (Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for US Government purposes. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. 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.