Cooperative optimization of capacity and energy management of hybrid energy storage system in rail transit system based on non-dominated sorting genetic algorithm-II and mixed-integer linear programming

This paper addresses the challenge of optimizing the capacity and energy management of energy storage system (ESS) in rail transit systems, with a focus on the utilization of regenerative braking energy (RBE) generated by train braking. The system utilizes a hybrid energy storage system (HESS) consisting of superconducting magnetic energy storage (SMES) and battery to exploit the advantages of both ESS. Traditional methods have limitations in solving capacity redundancy problem. To address this issue, a cooperative optimization approach based on non-dominated sorting genetic algorithm-II (NSGAII) and mixed-integer linear programming (MILP) is proposed in this paper. By optimizing the capacity of the HESS via NSGA-II, the dual-objective optimization targeting expenditure and RBE utilization rate is achieved. MILP optimizes the input and output power of the HESS to meet various energy balance requirements. Simulation results demonstrate that the proposed approach effectively balanced system operating expenditure and RBE utilization, exploring the optimal configuration of HESS and ensuring power system stability and reliability at the same time.