An Optimal Energy Management to Electricity Distribution System for The Electric Vehicle Charging Stations Using Hybrid Chicken Swarm Genetic Algorithm

This paper proposes an optimal energy management approach for electricity distribution systems integrating electric vehicle (EV) charging stations and distributed generation sources such as wind and solar power. A two-layer dispatch model is established to maximize the revenue of EV charging stations while ensuring safe and economical operation of the distribution network. The upper layer employs a time-of-use electricity pricing strategy and the Lagrangian relaxation method to achieve orderly charging dispatch of multiple EV types. The lower layer formulates an optimal dispatching model considering constraints like power flow balance, node voltage limits, and line transmission capacity. The objective is to minimize operational costs, including network losses, violation penalties, distributed generation output adjustment costs, and electricity purchase costs, while maximizing revenue from EV charging. The Hybrid Chicken Swarm Genetic Algorithm (HCSGA) approach, integrating chicken swarm optimization and genetic algorithms, is proposed to solve the dispatch model efficiently. Simulation results on an IEEE 33-node test system validate the effectiveness of the proposed method in improving voltage profiles, reducing losses, and ensuring grid stability under high EV charging demand and renewable generation uncertainties. The developed strategy facilitates the large-scale integration of EVs and distributed energy resources into distribution networks while maintaining reliable and economical operation.