Energy and Capacity Management of Hybrid Energy Storage System Applied to Urban Rail Transit by Nondominated Sorting Genetic Algorithm-II

In recent years, the introduction of Energy Storage System (ESS) into rail transit has increased the ratio of regenerative energy recovery. However, the investment of energy storage devices and ratio of energy saving varies due to different types of ESS. To overcome the problem, hybrid energy storage system (HESS) is an effective solution to balance cost, output power, and capacity. Besides, to optimize two objectives, minimization of investment and energy consumption, multi-objective programming is of great significance. This paper proposes to apply HESS to railway systems by combining superconducting magnetic energy storage (SMES) with batteries. SMES holds high power density, low energy density. On the contrary, battery has low power density, high energy density. These two ESSs can take advantage of their own strength, cover each other's shortcomings by combination, and achieve energy balance in railway transit. In this paper, energy management strategy(EMS) and capacity management is proposed taking characteristics of SMES, battery and DCDC converter into account. Furthermore, SMES, battery, DCDC converter, train and substation are modeled and programmed regarding a timetable. Finally, Nondominated Sorting Genetic Algorithm-II(NSGA-II) is introduced to balance energy consumption and cost achieving multi objective optimization.