基于极限学习机的输配一体储能系统选址定容协同优化策略

Aiming at the problems of insufficient resource coordination and complex solution in the traditional model of energy storage location and capacity determination, a collaborative optimization strategy for location and capacity determination of energy storage system considering transmission and distribution integration based on ELM(Extreme Learning Machine) is proposed. Firstly, considering the security operation constraints and economic optimization objectives of the transmission and distribution network, the location and capacity determination models of the transmission and distribution network are established respectively. Then, the nonconvex constraint of the second-order cone relaxation transformation model is introduced to establish the location and capacity optimization model of energy storage system considering transmission and distribution integration based on the second-order cone relaxation. Secondly, considering the coordinated operation mechanism of transmission and distribution grids, the locational marginal price of transmission grid with second-order cone constraint is derived. Then, the state representation model of transmission and distribution grids based on ELM is constructed to realize the fast response of states for transmission and distribution grids. Thirdly, the collaborative optimization algorithm for location and capacity determination of energy storage system considering transmission and distribution integration based on ELM is proposed, so as to obtain the global optimal allocation of energy storage system in transmission and distribution grids. Finally, taking a T6D7D9 system as an example for simulation analysis, simulative results show that the proposed strategy can fully coordinate the resources of transmission and distribution grids, promote the safe consumption of clean energy, improve the operation economy of transmission and distribution grids effectively, and achieve the goal of "mutual benefit".