Solidifying Capacity Credit of Renewable Energy Cluster by Forecast Uncertainty Quantification

The volatility and forecast uncertainty of renewable energy present great challenges to achieving supply-demand balance in power systems. Existing approaches for assessing renewable capacity credit (CC) mainly focus on annual timescale at the planning level, which may fail to capture the impact of forecast uncertainty on power supply. This paper introduces a clustering-based multivariate kernel density estimation method to model the forecast errors of renewable clusters and then establishes a continuous multi-state model to quantify forecast uncertainty. Subsequently, a CC assessment framework for renewable cluster is developed considering forecast errors to solidify their power supply capability. The influence of forecast error on the CC of renewable is demonstrated through numerical case studies. The results demonstrate that improving forecast accuracy can effectively enhance the power supply capability of renewable energy.