An EDA-based Genetic Algorithm for EV Charging Scheduling under Surge Demand

With continually increased Electric Vehicles (EVs), the EVs Charging Scheduling is of great importance to managing multiple charging demands for maximizing user satisfactions and minimizing adverse influences on the grid. However, it is challenging to effectively manage EVs charging schedules when a...

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Vydáno v:Proceedings of the ... IEEE International Conference on Services Computing s. 231 - 238
Hlavní autoři: Li, Tianyang, Li, Xiaolong, He, Ting, Zhang, Yufeng
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2022
Témata:
charging scheduling
Electric vehicle charging
Estimation
Estimation of Distribution Algorithm
EV charging
genetic algorithm
Processor scheduling
Schedules
Scheduling
Service computing
surge demand
Urban areas
ISSN:2474-2473
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Shrnutí:With continually increased Electric Vehicles (EVs), the EVs Charging Scheduling is of great importance to managing multiple charging demands for maximizing user satisfactions and minimizing adverse influences on the grid. However, it is challenging to effectively manage EVs charging schedules when a large number of (on-the-move) EVs are planning to charge at the same time. With this concern, we focus on Charging Station (CS)-selection decision making by the global aggregator that is taken as controller to implement charging management for EVs and CSs. An Estimation of Distribution Algorithm (EDA)-based genetic algorithm is proposed to find constrained charging scheduling plans to maximize the charging efficiency, which may improve user satisfaction and alleviate impacts on the grid. Experimental results under a city scenario with realistic EVs and CSs show the advantage of our proposal, in terms of minimized queuing time and maximized charging performance at both the EV and CS sides. The code and data are available at https://github.com/EV-charging-scheduling-algorithm.
ISSN:2474-2473
DOI:10.1109/SCC55611.2022.00042