Research on The Optimization Reserve Capacity of Integrated Source Grid Load Storage System Based on Chance Constrained Planning

This paper addresses the optimization problem of system reserve capacity in the source grid load storage integration system due to the uncertainty of renewable energy output. Firstly, the paper conducts a reduction analysis on the renewable energy generation scenarios. By using a sampling discretization method, the continuous probability function is discretized into several scenarios. A K-means-based scenario clustering method is used to reduce the large number of generated scenarios, and the silhouette coefficient is used to evaluate the clustering effect and determine the optimal number of clusters. Based on the scenario clustering results and the prediction error of wind and photovoltaic output, a chance constrained planning based source grid load storage integration system reserve capacity optimization model is established. A genetic algorithm based on Monte Carlo random simulation is used to solve the established opportunity-constrained model, and the feasibility and effectiveness of the method are verified through empirical research.