Risk Optimization for Energy Management in Virtual Power Plants Using Conditional Value-At-Risk

The shift towards renewable energy sources necessitates power systems to possess flexible resources to manage the fluctuations associated with renewable energy generation. This paper presents a risk optimization method for managing energy within a virtual power plant operator-based day-ahead accounting system that aggregates various distributed energy sources. The proposed method takes into consideration extreme events and stochastic generating unit output uncertainty and seeks to minimize the impact of these factors on the virtual power plant's operating revenue. Its main objective is to avoid extreme low-revenue scenarios. To assess the proposed algorithm's efficiency and the model's completeness, the 13-bus distribution networks in Yunnan Province with a high penetration of renewable energy sources and electric vehicles are analyzed. The results indicate the effectiveness of the proposed algorithm as well as the completeness of the model for handling energy management complexities within virtual power plants.