Optimizing electric vehicle charging in smart parking lots using particle swarm optimization: A comparative study in Morocco, France, and Tunisia

Abstract

Electric vehicles (EVs) are becoming a basis of sustainable mobility, requiring efficient charging management to minimize costs, balance grid demand, and optimize renewable energy utilization. In workplace parking lots, integrating solar energy and vehicle-to-grid (V2G) technology offers new opportunities for smart energy management. This paper presents an optimization-based charging strategy using Particle Swarm Optimization (PSO) to minimize total energy costs while reducing peak power drawn from the grid, maximizing the use of photovoltaic (PV) energy and ensure that all vehicles reach their target State of Charge (SOC) before leaving the parking lot. Additionally, The proposed approach leverages advantage of V2G technology, enabling EVs to return energy to the grid during peak demand hours, which enhances grid stability and reducing overall energy expenses. A key contribution of this work is the comparative analysis of EV charging management in three different geographical contexts: Morocco, France, and Tunisia. Each country provides distinct energy cost structures, solar availability. A dynamic electricity pricing model is incorporated to adapt the charging strategy based on daily and seasonal tariff variations. The optimization strategy considers multiple constraints like EV arriving and leaving periods, initial and target SOC, PV energy production, and dynamic electricity pricing. Results from simulations indicate that the suggested PSO-based charging strategy achieves significant cost savings can reach up to 65% compared to a conventional unmanaged scenario, reduces peak power coming from the grid, and maximize PV power utilization via self-consumption. Additionally, the findings highlight the benefits of multi-objective optimization in smart parking energy management.