An Enhanced MOEA/D Algorithm for Optimal Charging of Electric Vehicle Populations Considering Traffic and Power Grid Factors

This paper addresses traffic dynamics and power grid constraints by proposing an enhanced Multi-objective Evolutionary Algorithm Based on Decomposition (EMOEA/D) to optimize charging strategies of electric vehicle (EV) populations. Firstly, the proposed algorithm systematically decomposes the addressed multi-objective problem into scalar optimization tasks and evolves them by using MOEA/D. Further, to assist decision-makers in selecting the most suitable solution from the Pareto Optimal Set (POS), a knee-point based method is employed to effectively balance the objectives of minimizing charging time and reducing power grid cost. Extensive simulations on a 48-node traffic system and a modified IEEE 118-bus power system demonstrate the effectiveness of the proposed algorithm, highlighting its contribution to the sustainability and stability of integrated transportation and energy systems.