Sample-Based Optimal Dispatch of Shared Energy Storage in Community Microgrids Considering Uncertainty

Shared energy storage (SES) in communities equipped with renewable energy sources (RESs) can effectively maintain power supply reliability. However, the dispatch of SES is highly influenced by the uncertainty of RES output. Traditional optimization approaches, such as chance-constrained optimization (CC) and robust optimization (RO), have limitations. The former relies heavily on distribution information, while the latter tends to be overly conservative. The above problems are prominent when the size of available samples is limited. To address this, we introduce the concept of statistical feasibility and propose a sample-based robust optimization approach. This approach constructs the uncertainty set through shape learning and size calibration based solely on the sample set and further reconstructs it by introducing constraint information. Our numerical studies show that the proposed approach can obtain feasible optimal results with a 10.82% cost increase compared to deterministic optimization, and the reconstruction of the uncertainty set can increase the level of utilization of the stability requirement to around 0.05. Comparisons with several traditional optimization approaches demonstrate the effectiveness of the proposed approach.