Joint Optimization of Energy Storage Sharing and Demand Response in Microgrid Considering Multiple Uncertainties

Abstract

Energy storage (ES) is playing an increasingly important role in reducing the spatial and temporal power imbalance of supply and demand caused by the uncertainty and periodicity of renewable energy in the microgrid. The utilization efficiency of distributed ES belonging to different entities can be improved through sharing, and considerable flexibility resources can be provided to the microgrid through the coordination of ES sharing and demand response, but its reliability is affected by multiple uncertainties from different sources. In this study, a two-stage ES sharing mechanism is proposed, in which the idle ES capacity is aggregated on the previous day to provide reliable resources for real-time optimization. Then, a two-layer semi-coupled optimization strategy based on a deep deterministic policy gradient is proposed to solve the asynchronous decision problems of day-ahead sharing and intra-day optimization. To deal with the impact of multiple uncertainties, Monte Carlo sampling is applied to ensure that the shared ES capacity is sufficient in any circumstances. Simulation verifies that the local consumption rate of renewable energy is effectively increased by 12.9%, and both microgrid operator and prosumers can improve their revenue through the joint optimization of ES sharing and demand response.