Vehicle to Grid Optimization Strategy from the Perspective of Supply and Demand Game
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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洽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%.
Abstract:With the explosive development of electric vehicles (EV), the contradiction between the impact of charging load and grid support capacity is highlighted. In response to this problem, an EV charging and discharging (vehicle to grid, V2G) optimization strategy was proposed from the perspective of the game between supply and demand. Firstly, a power price sharing mechanism was constructed to make EV charging and discharging mutually appropriate with base load by combining the characteristics of EV charging and discharging behaviors. Then, for the leader-follower game relationship between the aggregator’s pricing of electricity and the EV users’ charging and discharging behavior selection process, an optimization model was established, whose optimization objectives are maximizing the revenue of the aggregator on the leader level and minimizing the cost of electricity for EV users on the follower level. Finally, the seeker optimization algorithm was used to solve the optimization objectives of both sides separately, and the game cycle was carried out until the equilibrium, so that the optimal electricity pricing strategy and EV charging/discharging strategy were obtained. The simulation results show that the proposed charging and discharging strategy can realize the peak-cutting and valley-filling of the base load curve by the EV charging and discharging load. The variance of the base load curve is reduced by 56.6%, and the difference between peak and valley is decreased by 28.0%. Meanwhile, the charging and discharging cost of EV users is lowered by 40.4%, and the revenue of the aggregator is increased by 40.1%.
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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 5 充放电优化与无序充电
Figure 5. Charging and discharging optimization and disorderly charging
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） T1 0.84252 T2 0.63740 T3 0.42228 
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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/
元EVA 收益/
元基础负荷 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
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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
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