Authors:
Aimen Khiar
1
;
2
;
Mohamed-el-Amine Brahmia
1
;
Ammar Oulamara
3
and
Lhassane Idoumghar
2
Affiliations:
1
CESI Lineact UR 7527, Strasbourg, France
;
2
IRIMAS UR 7499, University of Haute-Alsace, Mulhouse, France
;
3
LORIA UMR 7503, University of Lorraine, Nancy, France
Keyword(s):
Electric Vehicle, Charging Station Allocation, Multi-Objective Optimization, Scheduling, Metaheuristics, NSGA-II, MOCS.
Abstract:
The transition to electric mobility offers substantial environmental benefits but also introduces significant challenges, particularly in managing the high demand for electric vehicle (EV) charging. This demand creates the need for intelligent scheduling to optimize charging station resources and maintain grid stability. In order to address this purpose, we propose a multi-objective scheduling model designed to both minimize peak energy consumption and maximize user satisfaction by reducing waiting times at charging stations. Our model accurately represents real-world scenarios, including sequential charger usage, vehicle-to-charger compatibility, and limited availability of various charger types, each providing a constant power output. Given the complexity of the problem, we adapt and evaluate two metaheuristic algorithms: the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and the Multi-Objective Cuckoo Search (MOCS), to approximate optimal solutions. The results show that the
proposed MOCS adaptation surpasses that of NSGA-II in terms of dominance and achieving a well-distributed Pareto front approximation in a reasonable time frame. The proposed approach thus provides a powerful framework for efficient EV charging management, balancing user needs with grid stability and highlighting its strong potential for adoption in large-scale charging infrastructures.
(More)