Battery Energy Storage System (BESS) Sizing Analysis of Bess-Assisted Fast-Charge Station Based on Double-Layer optimization Method

Recent years, the increasingly decrease of battery energy storage system (BESS) costs makes BESS-assisted fast-charge station economically feasible. Meanwhile, the implementation of BESS could help distribution network alleviate the strike from massive charging load. To ensure BESS-assisted fast-charging station attaining optimum economic benefit, BESS has to be optimally sized. In this paper, a double-layer optimization method is proposed to Figure out the BESS sizing optimization, and genetic algorithm (GA) with elitist strategy was used for the optimal solution. In the external layer, GA is utilized to seek optimal BESS sizing and its durable years to pass these parameters to the internal layer. In the internal layer, a mixed integer linear programming (MILP) BESS lifetime model, which is based on BESS state of health (SOH) estimation and capacity fading, is established and adapted to optimize fast-charging station operation for typical days, and lower level will return the optimal result back to upper level. The proposed model is validated through a specific charge station in Shanghai. The simulation results validate that: 1) the proposed model is able to determine the optimal BESS size and durable year; 2) the practicality of the BESS lifetime and residual capacity estimation is enhanced; 3) fast-charge station operation strategy of typical days can be used as reference for charge station operators.