Hybrid Multi-Parametric Optimization for Mobility-Aware Scheduling of Electric Vehicles Charging Station

In the past few years, the significant increase of Electric Vehicles (EVs) in the global market has shed light upon the issue of their long charging time. One key challenge in this field is the EVs' charging scheduling in a station, for the purpose of maximizing the profitability of charging stations while minimizing the number of rejected vehicles due to in-frastructure constraints; thus considering customer satisfaction. Due to the permutation nature of the scheduling problem, non-permutation-based algorithms are incapable of solely solving it. Moreover, the literature mostly overlooks the charging priority of each EV based on its individual condition. To address this gap, an algorithm hybridizing a priority-based scheduling algorithm (PBA) with arithmetic meta-heuristic techniques; i.e. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is proposed. These algorithms; namely Genetic Multi-Parametric Optimization (GMPO) and Particle Swarm Multi-Parametric Optimization (PSMPO) respectively, are compared against permutation GA for performance evaluation. A case study is conducted on an EV charging station equipped with Photovoltaic (PV) panels and an Energy Storage System (ESS); where this equipment significantly enhanced the station's prof-itability. The results indicate the effectiveness of the proposed approach. Furthermore, GMPO mostly outperformed both PSMPO and GA in finding better solutions. Despite the higher time complexity of the proposed algorithms compared to GA, they reach convergence faster. This time delay is acceptable relative to the problem scale and the resulting accuracy.