计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 265-270.doi: 10.11896/jsjkx.230800051

• 人工智能 • 上一篇    下一篇

碳达峰约束下电动汽车在线充电调度算法

曹永胜1,2, 刘洋1,2, 王永全1, 夏天3   

  1. 1 华东政法大学智能科学与信息法学系 上海201620
    2 上海交通大学智慧法院研究院 上海200240
    3 上海第二工业大学计算机与信息工程学院 上海201209
  • 收稿日期:2023-08-08 修回日期:2024-01-24 出版日期:2024-03-15 发布日期:2024-03-13
  • 通讯作者: 曹永胜(yongshengcao@ecupl.edu.cn)
  • 基金资助:
    上海市科技创新行动计划启明星项目(扬帆专项)(22YF1411900);第四批中国博士后科学基金特别资助项目(2022TQ0210);国家社会科学基金(20&ZD199);教育部人文社会科学研究项目(20YJC820030);国家社科基金重大项目(21&ZD200);国家重点研发计划重点专项课题(2023YFC3306103,2023YFC3306105)

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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摘要： 随着电动汽车数量的增加,电动汽车充电对社区电网总负荷和碳排放量产生了很大的影响,导致社区电网不太稳定,降低了电能质量。文中基于碳达峰的约束条件,研究了未提前知晓电动汽车到达时间、出发时间和充电需求等情况下的电动汽车充电碳排放问题。首先,建立了电动汽车充电碳排放问题,并在未来信息未知的情况下进行了研究。针对电动汽车充电行为的不确定性,提出了一种改进型演员-评论家的智能充电碳排放算法。该算法采用的是连续碳排放动作的电动汽车充电碳排放策略,而不是离散近似碳排放动作。仿真结果表明,相比OA和AEM两个基准算法,所提算法能够降低电动汽车预期成本约24.03%和21.49%。

关键词: 碳达峰, 电动汽车, 不确定性, 在线充电, 能量管理

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

中图分类号: 

  • TP311
[1]YANG F,ZHANG J J.Current Status and Prospect of Low-carbon Development in China's Power Industry under the Carbon Peak and Carbon Neutrality Goals[J].Environmental Protection,2021,49(17): 720-746.
[2]TAN Q L,DAI M,MEI S F.Research on power system dispatch considering carbon quota and demand response of electric vehicles [J].Power System and Clean Energy,2021,37(7): 79-86.
[3]CAO Y,WANG H,LI D,et al.Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning[J].IEEE Internet of Things Journal,2022,9(1): 684-694.
[4]SHU Y B,ZHANG L Y,ZHANG Y Z,et al.Research on China'sPath to Carbon Peak and Carbon Neutrality in the Power Sector[J].Engineering Sciences in China,2021,23(6):1-14.
[5]HU Y,CHEN C,HE J,et al.IoT-based proactiveenergy supply control for connected electric vehicles[J],IEEE Internet of Things Journal,2019,6(5):7395-7405.
[6]FU Q,DU W,WANG H,et al.Stability Analysis of DC Distribution System Considering Stochastic State of Electric Vehicle Charging Stations[J].IEEE Transactions on Power Systems,2022,37(3):1893-1903.
[7]QI X,WU G,BORIBOONSOMSIN K,et al.Barth.Development andevaluation of an evolutionary algorithm-based online energy management system for plug-in hybrid electric vehicles[J].IEEE Transactions Intell.Transp.Syst.,2017,18(8): 2181-2191.
[8]LI Y,WANG X,KANG Q,et al.An MCTS-Based Solution Approach to Solve Large-Scale Airline Crew Pairing Problems[J].IEEE Transactions on Intelligent Transportation Systems,2023,24(5):5477-5488.
[9]LI P,WANG H,ZHANG B.A distributed online pricing strategy fordemand response programs[J].IEEE Trans.Smart Grid,2019,10(1): 350-360.
[10]QUDDUS M A,SHAHVARI O,MARUFUZZAMAN M,et al.A collaborative energy sharing optimization model amongelectric vehicle charging stations,commercial buildings,and powergrid[J].Appl.Energy,2018,229:841-857.
[11]NEMATKHAH F,BAHRAMI S,AMINIFAR F,et al.Exploiting the Potentials of HVAC Systems in Transactive Energy Markets[J].IEEE Transactions on Smart Grid,2021,12(5):4039-4048.
[12]DUAN J,XU H,LIU W.Q-learning-based damping control of widearea power systems under cyber uncertainties[J].IEEE Trans.Smart Grid,2018,9(6):6408-6418.
[13]RUELENSF,CLAESSENS B J,VANDAEL S,et al.Residential demand response of thermostatically controlled loads using batch reinforcement learning[J].IEEE Trans.SmartGrid,2017,8(5):2149-2159.
[14]HU H X,WEN C,WEN G.A Distributed Lyapunov-Based Redesign Approach for Heterogeneous Uncertain Agents With Cooperation-Competition Interactions[J].IEEE Transactions on Neural Networks and Learning Systems,2022,33(11):6946-6960.
[15]SUTTON R S,BARTO A G.Reinforcement learning: An introduction[J].IEEE Trans.Neural Netw.,1998,9(5):1054.
[16]ZHANG Z J,LIU F Z,LIU T,et al.A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning[J].IEEE Transactions on Circuits and Systems I: Regular Papers,2023,70(8):3305-3315.
[17]WANG X,DING D,DONG H,et al.Neural-network-based control for discrete-time nonlinear systems with input saturation understochastic communication protocol[J].IEEE/CAA Journal Automatica Sinica,2021,8(4):766-778.
[18]CHEN Z,LU Y,XING Q,et al.Analysis of Power SystemScheduling Considering Carbon Quota of Electric Vehicles[J].Automation of Electric Power Systems,2019,43(16): 44-51.
[19]CAO Y,WANG H,LI D,et al.Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning[J].IEEE Internet of Things Journal,2022,9(1):684-694.
[20]MA Z,CALLAWAY D S,HISKENS I A.Decentralized Char-ging Control of Large Populations of Plug-in Electric Vehicles[J].IEEE Transactions on Control Systems Technology,2013,21(1):67-78.
[21]KIM B,PAIK M,KIM Y,et al.Distributed Electric VehicleCharging Mechanism: A Game-Theoretical Approach[J].IEEE Transactions on Vehicular Technology,2022,71(8):8309-8317.
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