Capacity credit assessment of regional renewable generation considering multi-time-scale forecast errors

Abstract

The temporal variability and forecast uncertainty of renewable energy pose great challenges to supply–demand balance in power systems. Long-term forecasting is crucial for improving renewable integration and ensuring safe operation during extreme weather events. However, existing methods for capacity credit (CC) assessment of renewable primarily focus on annual timescale, which may fail to capture the impact of multi-time-scale forecast errors on power supply. This paper proposes a method to characterize multi-time-scale forecast errors using multivariate kernel density estimation based on wavelet packet decomposition, and then establishes a continuous multi-state model of renewable to quantify forecast uncertainty. Subsequently, a CC assessment framework for regional renewable is developed incorporating multi-time-scale forecast errors. The impact of multi-time-scale forecast errors on the CC of renewable is investigated through the RTS-GMLC system and a Chinese provincial system. The results indicate that the proposed method enables accurate assessment of the power supply capability of renewable during cold wave weather events, facilitating effective anticipation of operational risks and supporting the dispatch of power systems with high renewable penetration.