Two-Stage Distributionally Robust Optimal Scheduling of Distribution Network Taking into Account Demand-Side Flexibility Resources

With the increasing integration of distributed renewable energy into distribution networks, challenges arise due to the variability and uncertainty associated with renewable energy sources, such as power flow reversals and voltage violations. Consequently, enhanced flexibility is required in the distribution network. This paper proposes a distributionally robust optimization (DRO) scheduling model that considers demand-side flexibility resources, such as controllable distributed generation (DG), energy storage systems (ESS), soft open points (SOP), smart loads (SL), and electric vehicles (EV). The model utilizes probabilistic scenario reduction to generate typical scenarios for wind generation, solar generation, and loads. It formulates a two-stage three-layer DRO model, optimizing day-ahead scheduling and worst-case scenario probability distribution to minimize intra-day adjustment costs. The proposed model is validated using an enhanced IEEE 33-bus distribution system. The results show that the proposed model can ensure a balanced supply-demand relationship and effectively cope with scenarios of insufficient flexibility.