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供需双方博弈视角下的V2G优化策略

戴朝华 杨帅 叶圣永 范文礼

戴朝华, 杨帅, 叶圣永, 范文礼. 供需双方博弈视角下的V2G优化策略[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230097
引用本文: 戴朝华, 杨帅, 叶圣永, 范文礼. 供需双方博弈视角下的V2G优化策略[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230097
DAI Chaohua, YANG Shuai, YE Shengyong, FAN Wenli. Vehicle to Grid Optimization Strategy from the Perspective of Supply and Demand Game[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230097
Citation: DAI Chaohua, YANG Shuai, YE Shengyong, FAN Wenli. Vehicle to Grid Optimization Strategy from the Perspective of Supply and Demand Game[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230097

供需双方博弈视角下的V2G优化策略

doi: 10.3969/j.issn.0258-2724.20230097
基金项目: 北京市自然科学基金项目(L221002);国家电网有限公司科技项目(SGSCJY00GHJS1900011)
详细信息
    作者简介:

    戴朝华(1973—),男,副教授,博士生导师,研究方向为能源互联网规划与运行、轨道交通新能源技术等,E-mail:daichaohua@swjtu.edu.cn

  • 中图分类号: TM73;U491.8

Vehicle to Grid Optimization Strategy from the Perspective of Supply and Demand Game

  • 摘要:

    随着电动汽车爆发式发展,充电负荷的冲击性与电网支撑能力的矛盾突出. 为此,提出一种基于供需双方博弈视角的电动汽车充放电(vehicle to grid,V2G)优化策略. 首先,结合用户充放电行为特性,构建使电动汽车充放电与基础负荷互恰的电能价格分享机制;然后,针对聚合商电能定价与电动汽车用户充放电行为选择过程中的领导-追随者博弈关系,建立优化模型,领导者层面以聚合商收益最大化为目标,追随者层面以电动汽车用户用电成本最小化为目标;最后,利用搜寻者优化算法分别求解双方的优化目标,进行博弈循环直到均衡,从而得到最优的电能定价策略和电动汽车充放电策略. 仿真结果表明:所提出的充放电策略能使电动汽车充放电负荷对基础负荷曲线起到削峰填谷作用,使基础负荷曲线方差减小56.6%,峰谷差减少28.0%,同时,电动汽车用户的充放电成本减少40.4%,而聚合商收益增加约40.1%.

     

  • 图 1  基于领导-追随者博弈的V2G调度流程

    Figure 1.  V2G scheduling process based on leader-follower game

    图 2  博弈模型结构

    Figure 2.  Game model structure

    图 3  基于领导-追随者博弈的有序充放电策略流程

    Figure 3.  Flowchart of orderly charging and discharging strategy based on leader-follower game

    图 4  区域节点基础负荷分布

    Figure 4.  Regional node base load distribution

    图 5  充放电优化与无序充电

    Figure 5.  Charging and discharging optimization and disorderly charging

    图 6  各时段电价与总交易电量

    Figure 6.  Tariff and total traded electricity by time period

    图 7  充放电优化时不同参与度的调峰结果

    Figure 7.  Peaking results under charging and discharging optimization at different participation levels

    图 8  不同参与度下EV和EVA收益对比

    Figure 8.  Comparison of EV and EVA revenue at different participation levels

    表  1  四川省某充电站分时电价

    Table  1.   Time-of-use tariff at a charging station in Sichuan Province

    时段电价/(元·(kW·h)−1
    T10.84252
    T20.63740
    T30.42228
    下载: 导出CSV

    表  2  充放电优化与无序充电定量对比

    Table  2.   Quantitative comparison of charging and discharging optimization with disorderly charging

    充电策略 峰谷差/
    kW
    负荷
    方差/kW2
    用户成本/
    EVA收益/
    基础负荷 2344 557359.6
    无序充电 2247 495058.3 1449.8 210.1
    有序充放电 1688 241684.3 864.7 294.3
    下载: 导出CSV

    表  3  不同参与度有序充放电优化效果

    Table  3.   Optimization effect of orderly charging and discharging at different participation levels kW·h

    参与度/% 削峰容量 填谷容量 峰值变化 谷值变化
    100 1560.6 4409.8 −217.6 438.4
    60 710.6 3369.4 −95.2 380.2
    20 0 2424.2 91.8 298.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-03-14
  • 修回日期:  2023-09-22
  • 网络出版日期:  2024-10-14

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