Spatiotemporal decomposed dispatch of integrated electricity-gas system via stochastic dual dynamic programming-based value function approximation

This paper proposes a novel stochastic dual dynamic programming-based value function approximation approach for the spatiotemporal decomposed dispatch of integrated electricity-gas systems with uncertainties. Stochastic dual dynamic programming is employed to decompose the optimal dispatch problem into several subproblems in both spatial and temporal dimensions. Then, Benders cuts are used in the real-time dispatch process to describe the interaction among these subproblems, according to which each subsystem can make decentralized and real-time decisions. In this way, both the information privacy and decision independence of each subsystem can be guaranteed, while the future variability of wind power and loads can be firmed. More-over, the historical information can be used to obtain the Benders cuts offline, which helps to omit the time-consuming iterations in the real-time decision stage, while the near-optimal solutions can still be obtained. The effectiveness of the proposed approach is verified by case studies on a 4-bus-4-node system and a 118-bus-20-node system. Numerical results demonstrate that the proposed method reduces the average error of the operation cost by 1 order of magnitude compared with the traditional real-time methods. Moreover, the computational time is significantly reduced by up to 4 orders of magnitude compared with the traditional decentralized algorithms.