Multi-objective Advanced Grey Wolf optimization Framework for Smart Charging Scheduling of EVs in Distribution Grids

This paper proposes a multi-objective optimization technique for scheduling the charging of electric vehicles (EVs) in electrical distribution systems (DSs). A multi-objective advanced grey wolf optimization algorithm (MOAGWO) is developed to find the Pareto optimal solutions that minimize the DS’s operational costs, energy losses costs, voltage violations, and the energy not supplied to EV users using several scenarios. A 449-node system with 63% penetration of EVs is used to demonstrate the efficiency of the proposed method. The quality of the non-dominated optimal solutions found by MOAGWO are validated via a comparison analysis with other well-known methods such as the multi-objective grey wolf optimizer (MOGWO) and the multi-objective particle swarm optimization (MOPSO) algorithm, based on domination rate, spacing index, hypervolume index, and computational cost measurements. The Pareto solutions indicate that the smart charging coordination found by MOAGWO makes the techno-economic operation of the DS possible while satisfying energy-based goals of the EV users.