Multi-Objective Distributionally Robust Optimal Scheduling of Park-Level Integrated Energy System

In order to reduce operational costs and improve energy utilization efficiency of the park-level integrated energy system (IES), a multi-objective distributionally robust scheduling optimization approach is proposed. The objective functions aim to minimize operating costs and maximize comprehensive energy efficiency. To address uncertainties related to wind and photovoltaic power generation, the Wasserstein metric-based distributionally robust optimization method is employed. The strong duality theory and reformulation-linearization technique are utilized to linearize the non-convex model of distributionally robust optimization. To obtain the Pareto frontier set effectively, the NNC (normalized normal constraint) method is employed to transform the multi-objective optimization problem into a single-objective optimization problem. The compromise solution within the Pareto solution set is then determined using the fuzzy membership function. The case study analysis demonstrates that the obtained Pareto frontier set is well-distributed, and compared with robust optimization and stochastic optimization approaches, the proposed approach can effectively balance optimism and conservativeness.