Communication Dans Un Congrès Année : 2024

Enhancing Electric Vehicle Charging Schedules: A Surrogate-Assisted Approach

Résumé

This paper addresses the pressing issue of efficiently scheduling electric vehicle (EV) charging at public stations to alleviate strain on the electrical grid. EV drivers provide their charging needs beforehand, and the scheduler optimizes charger allocation and power distribution to minimize discrepancies in state-of-charge levels at departure. To tackle the complexity of this NP-hard problem, this study proposes a solution framework combining a genetic algorithm with linear programming. Surrogate models are also investigated to expedite problem-solving. Simulation results demonstrate the effectiveness of these approaches in managing the complexities of EV charging scheduling.

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Dates et versions

hal-04956258 , version 1 (19-02-2025)

Identifiants

Citer

Abdennour Azerine, Mahmoud Golabi, Ammar Oulamara, Lhassane Idoumghar. Enhancing Electric Vehicle Charging Schedules: A Surrogate-Assisted Approach. GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion, Feb 2024, Melbourne VIC Australia, Australia. pp.183-186, ⟨10.1145/3638530.3654303⟩. ⟨hal-04956258⟩
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