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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Published in:Proceedings of the ... IEEE International Conference on Services Computing pp. 231 - 238
Main Authors: Li, Tianyang, Li, Xiaolong, He, Ting, Zhang, Yufeng
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2022
Subjects:
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
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Li, Tianyang
Zhang, Yufeng
He, Ting
Li, Xiaolong
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  organization: Northeast Electric Power University,School of Computer Science,Jilin,China
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  givenname: Xiaolong
  surname: Li
  fullname: Li, Xiaolong
  email: long1378568805@163.com
  organization: Harbin Branch, Bank of Inner Mongolia Co., Ltd,Harbin,China
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  givenname: Ting
  surname: He
  fullname: He, Ting
  organization: Huaqiao University,College of Computer Science and Technology,Xiamen,China
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  givenname: Yufeng
  surname: Zhang
  fullname: Zhang, Yufeng
  organization: University of Birmingham,Birmingham Business School,Birmingham,UK
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Snippet With continually increased Electric Vehicles (EVs), the EVs Charging Scheduling is of great importance to managing multiple charging demands for maximizing...
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StartPage 231
SubjectTerms charging scheduling
Electric vehicle charging
Estimation
Estimation of Distribution Algorithm
EV charging
genetic algorithm
Processor scheduling
Schedules
Scheduling
Service computing
surge demand
Urban areas
Title An EDA-based Genetic Algorithm for EV Charging Scheduling under Surge Demand
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