Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 265-270.doi: 10.11896/jsjkx.230800051

• Artificial Intelligence • Previous Articles     Next Articles

Online Electric Vehicle Charging Algorithm Based on Carbon Peak Constraint

CAO Yongsheng1,2, LIU Yang1,2, WANG Yongquan1, XIA Tian3   

  1. 1 Department of Intelligent Science and Information Law,East China University of Political Science and Law,Shanghai,201620,China
    2 Shanghai Jiao Tong University Intelligent Court Research Institute,Shanghai,200240,China
    3 School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai,201209,China
  • Received:2023-08-08 Revised:2024-01-24 Online:2024-03-15 Published:2024-03-13
  • About author:CAO Yongsheng,born in 1991,Ph.D,lecturer.His main research interests include data security,energy management,electric vehicle,and AI with big data.
  • Supported by:
    Shanghai Science and Technology Innovation Action Plan Star Project(Sail special Project)(22YF1411900),Fourth Special Projects Funded by China Postdoctoral Science Foundation(2022TQ0210),National Social Science Foundation of China(20&ZD199),Humanities and Social Sciences Research Project of Ministry of Education(20YJC820030),Major project of National Social Science Fund(21&ZD200) and Key Research and Development Program of China(2023YFC3306103,2023YFC3306105).

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Abstract: With the increasing number of electric vehicles(EVs),EV charging significantly increases the total load of the community,greatly increases the carbon emissions of the community,brings great instability to the community power grid,and reduces the power quality of the community.This paper studies the problem of scheduling EV charging based on the constraints of carbon peak when the arrival time,departure time,and charging demand of EVs are not known in advance.First,we formulate and study the problem of charging EVs without knowing future information.Aiming to address the uncertainty of EV charging behavior,we propose an algorithm for intelligent charging carbon emissions using the actor-critic approach,which learns the optimal strategy for EV charging through continuous charging instead of using a discrete approximation of carbon emissions.Simulation results demonstrate that compared with the online charging algorithm and the AEM energy management algorithm,the proposed algorithm can reduce the expected cost by 24.03% and 21.49%.

Key words: Carbon peak, Electric vehicles, Uncertainty, Online charging, Energy management

CLC Number: 

  • TP311
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