Decision-dependent distributionally robust integrated generation, transmission, and storage expansion planning: An enhanced Benders decomposition approach

Abstract

Integrated generation, transmission, and storage expansion planning (IGT&SP) is the cornerstone to realize low-carbon transition considering security constraints in the long run. A novel IGT&SP planning scheme is proposed to balance the planning cost, i.e. renewable energy sources (RESs) and energy storage systems, based on the distributionally ambiguity sets. A novel decision-dependent ambiguity set is proposed to capture the relation between the uncertainties of RES output and long-term planning. A two-stage risk-averse distributionally robust optimization is formulated, where the RESs, energy storage systems, and transmission line expansion are optimized in the first stage and a unit commitment problem is proposed in the second-stage optimization to assess the performance of the expanded system. This problem is reformulated into a two-stage optimization problem with complete mixed-integer recourse, where the state variable is binary. A novel enhanced Benders decomposition algorithm is proposed to solve the IGT&SEP efficiently, where the cutting planes are generated by a primal-dual relaxation of the recourse problem. Simulations are conducted on the modified IEEE-30 test system and modified IEEE-118 test system. Compared with adjustable robust optimization and L1-norm Wasserstein distance-based distributionally robust optimization, numerical results verify the effectiveness of the proposed IGT&SP, together with the solution algorithm.