Improved multi-objective differential evolution algorithm and its application in the capacity configuration of urban rail photovoltaic hybrid energy storage systems

Abstract

With the rapid expansion of urban rail transit, energy demand is continuously increasing. Integrating photovoltaic (PV) systems into hybrid energy storage systems (HESS) to form a rail transit PV hybrid energy storage system (RTPHESS) is an effective energy-saving and emission reduction measure. However, the integration changes the energy composition of the rail power supply system, making traditional HESS capacity configuration schemes inadequate to meet the stability requirement. In light of this, an RTPHESS model was established aiming at suppressing traction network voltage fluctuations and minimizing the total life-cycle cost of HESS. The model introduces traction network voltage fluctuation rate and HESS total life-cycle cost as evaluation objectives. Based on this, an improved multi-objective differential evolution algorithm (IMODE) was proposed to optimize HESS capacity. Optimal point set theory was used for population initialization to enhance the uniformity and diversity of the initial population, thereby improving global optimization capabilities. During the mutation phase, a hybrid Gaussian-Cauchy mutation strategy was introduced to improve both global and local search capabilities. The proposed algorithm's effectiveness was evaluated using IGD, SP, MS, and ER metrics on eight benchmark test functions with different characteristics, and compared with SaMODE_LS, MODE, MOPSO, MOALO, and MOMGA. Finally, IMODE was applied to the capacity configuration of RTPHESS. The results demonstrate that IMODE outperforms traditional algorithms in terms of accuracy and stability, reducing traction network voltage fluctuations by approximately 4.1 % and lowering the total life-cycle cost to 3,201,100 yuan.