Distributionally robust day-ahead scheduling of park-level integrated energy system considering generalized energy storages

Highlights: • A distributionally robust day-ahead scheduling model of PIES is proposed. • Wasserstein metric-based DRO is introduced to deal with the uncertainty problem. • Strong duality theory is utilized to linearize the proposed non-convex model. • The model of generalized energy storages is proposed. The optimal scheduling of park-level integrated energy system can improve the efficiency of energy utilization and promote the consumption level of renewable energy. However, the uncertainty of renewable energy sources' output power may lead to negative impacts on the scheduling of park-level integrated energy system. Therefore, a distributionally robust day-ahead scheduling model of PIES considering generalized energy storages is proposed in this paper, aiming to reduce the operating cost, renewable energy curtailment, and carbon emission of park-level integrated energy system. In the proposed model, the actual multi-energy storage devices, integrated demand response and pipeline energy storages are synergistically modeled as generalized energy storages to improve the operating flexibility of park-level integrated energy system; the Wasserstein metric-based distributionally robust optimization method is utilized to handle the uncertainty problems in the scheduling of park-level integrated energy system, which can obtain the expected operating costs of park-level integrated energy system under the worst-case probability distribution restricted in an ambiguity set; the strong duality theory and reformulation–linearization technique are utilized to linearize the proposed non-convex model and make it easier to be solved by the commercial solver. Case studies are performed on a park-level integrated energy system that consists of an IEEE 33-bus distribution network, a 44-node district heating network and a 20-node natural gas network for verifying the effectiveness and advantages of the proposed model.