A Stochastic Game Approach for PEV Charging Station Operation in Smart Grid

被引:37
|
作者
Liu, Yuan [1 ]
Deng, Ruilong [1 ]
Liang, Hao [1 ]
机构
[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 1H9, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Charging station; Nash equilibrium; plugin electric vehicle (PEV); real time pricing (RTP); smart grid; stochastic game (SG); DEMAND-SIDE MANAGEMENT; POWER-FLOW; ELECTRICITY; VEHICLES; STRATEGY;
D O I
10.1109/TII.2017.2781226
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the future, smart grid charging stations will be critical infrastructures for plug-in electric vehicle (PEV) to replenish their batteries in a convenient way. Due to the ever-increasing penetration rate of PEVs, how to efficiently manage the loads of PEV charging stations to ensure system efficiency and reliability is amajor challenge faced by the distribution service providers (DSPs) in the smart grid. This challenge is further complicated by the highly dynamic PEV mobility, which results in random PEV arrivals, departures, and charging demands. In order to address this challenge, a stochastic game approach is proposed in this paper to characterize the interactions among DSP, charging stations, and PEV owners, where the randomness in charging decision making processes of PEV owners is modeled by a Markov decision process. Based on the Nash equilibrium solution of the stochastic game, a real time pricing scheme is proposed for the DSP to minimize power distribution losses while ensuring system reliability. The performance of the proposed approach is evaluated via extensive simulations based on the IEEE 123 bus test feeder with real vehicle mobility data fromthe 2009 National Household Travel Survey and the 2010 National Travel Survey.
引用
收藏
页码:969 / 979
页数:11
相关论文
共 50 条
  • [41] Prioritizing Consumers in Smart Grid: A Game Theoretic Approach
    Tushar, Wayes
    Zhang, Jian A.
    Smith, David B.
    Poor, H. Vincent
    Thiebaux, Sylvie
    IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (03) : 1429 - 1438
  • [42] Energy Trading in the Smart Grid: A Game Theoretic Approach
    Yaagoubi, Naouar
    Mouftah, Hussein T.
    2015 IEEE INTERNATIONAL CONFERENCE ON SMART ENERGY GRID ENGINEERING (SEGE 2015), 2015,
  • [43] Sharing Storage in a Smart Grid: A Coalitional Game Approach
    Chakraborty, Pratyush
    Baeyens, Enrique
    Poolla, Kameshwar
    Khargonekar, Pramod P.
    Varaiya, Pravin
    IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (04) : 4379 - 4390
  • [44] A Dynamic Battery Charging Approach for Energy Trading in the Smart Grid
    Sharma, Avinash
    Rathore, Akshay Kumar
    Kumar, Rajesh
    2018 INTERNATIONAL POWER ELECTRONICS CONFERENCE (IPEC-NIIGATA 2018 -ECCE ASIA), 2018, : 2456 - 2461
  • [45] A kind of demand response approach for PHEVs charging in smart grid
    Liu, Shanshan
    Li, Xiaohui
    Ding, Yueming
    Su, Qian
    Liu, Zhenxing
    PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020), 2020, : 2002 - 2007
  • [46] A Game Approach for Charging Station Placement Based on User Preferences and Crowdedness
    Bae, Sangjun
    Jang, Inmo
    Gros, Sebastien
    Kulcsar, Balazs
    Hellgren, Jonas
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (04) : 3654 - 3669
  • [47] Real Time Evaluation and Operation of the Smart Grid Using Game Theory
    Swearingen, Michael
    2011 IEEE RURAL ELECTRIC POWER CONFERENCE (REPC), 2011,
  • [48] The Effect of PEV Uncontrolled and Smart Charging on Distribution System Planning
    Bin Humayd, Abdullah S.
    Lami, Badr
    Bhattacharya, Kankar
    2016 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC), 2016,
  • [49] Task Time Allocation and Reward Scheme for PEV Charging Station Advertising
    Li, Mushu
    Gao, Jie
    Zhao, Lian
    Shen, Xuemin
    ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2019,
  • [50] Optimized Power Trading of a PEV Charging Station with Energy Storage System
    Tehrani, Nima H.
    Shrestha, G. B.
    Wang, Peng
    2012 CONFERENCE ON POWER & ENERGY - IPEC, 2012, : 305 - 310