Multi-Stage Statistical Approach to Wind Power Forecast Errors Evaluation: A Southern Sulawesi Case Study

Abstract

Wind Power Plant (WPP) is part of renewable energy sources, with rapid expansion worldwide. It has the advantages of clean and green energy, but its uncertainty leads to an additional grid integration cost. The uncertainty of wind power output is much dependent on the accuracy of the wind power forecast (WPF) result. Since there is no perfect wind power forecast, understanding the current system's forecast accuracy characteristics is essential in expecting typical errors faced in the future. This paper proposed a new algorithm of the statistical approach method to evaluate characteristics of wind power forecast errors (WPFE) from an observed power system with high-penetration WPP. This method combined the approach of scatter diagram, statistical distribution, standard error performance, and score weighting in a multi-stage algorithm. It consists of serial and parallel processes to check the consistency of the results. In this study, a comprehensive analysis was made of various scenarios based on location and timescale. This proposed algorithm has been successfully tested on statistical data of Sidrap WPP and Jeneponto WPP in the Southern Sulawesi power system. The result showed that the scenario with the aggregation of both WPPs in hour-ahead timescale has the most accurate and consistent performance among all scenarios. It demonstrated specific characteristics of WPFE in the observed power system that can be used as an essential starting point in conducting future wind integration expansion studies.