Optimal Scheduling of Vehicle-network Interaction Based on Interval Stackelberg Game of Virtual Power Plant

doi:10.16182/j.issn1004731x.joss.23-0503

Abstract

Abstract:

To better exploit the regulation potential of electric vehicles (EVs), resolve the conflicts of interest among the stakeholders in vehicle-to-grid (V2G) interactions, and overcome the uncertainty of distributed energy sources and load, this paper proposes a two-level optimization scheduling model for V2G interactions based on the interval Stackelberg game of a virtual power plant (VPP). The VPP aggregator is considered as the upper level, and the EV users as the lower level. The upper level model uses interval numbers to describe the uncertainty of sources and loads, with the aim of minimizing the operating cost of the VPP aggregator, updating the electricity price information and transmitting it to the lower level model. The lower level model aims to maximize user satisfaction and minimize costs by solving the charging and discharging behavior of EV users and returning the results to the upper level model. The improved particle swarm optimization algorithm with integrated interval possibility degree is used to obtain the optimal scheduling results of the Stackelberg game. Simulation results demonstrate that the proposed model can effectively shave peak and fill the valley, coordinate the bilateral interests of the aggregator and EV users, and has good robustness.

Key words: electric vehicles, vehicle-network interaction, virtual power plant, optimal scheduling, Stackelberg game, interval number

CLC Number:

TP391.9

Liu Weiliang, Yan Qianwen, Zhang Qiliang, Liu Shuai, Liu Changliang, Kang Jiayao, Wang Xin. Optimal Scheduling of Vehicle-network Interaction Based on Interval Stackelberg Game of Virtual Power Plant[J]. Journal of System Simulation, 2024, 36(7): 1559-1572.

Figures/Tables 20

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Table 1

Table 2

Equipment related parameters

设备	参数	功率/kW	波动系数
光伏发电	1 800 kW	—	β=0.1
风力发电	2 500 kW	—	β=0.1
储能系统	750 kW·h	[-80,80]	—
电动汽车	50 kW·h	[-5,5]	θ=0.2
转换效率 $α 、 η$	0.9	—	—
期望电量系数 $λ$	0.9	—	—

Table 2

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Table 3

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参数	取值	参数	取值
学习因子	2	迭代次数	40
惯性权重	0.8	种群个数	不同区域各200
维度D	24	速度范围	[-0.2,0.2]

策略	负荷峰谷差/kW		用户平均成本(元)
策略	住宅区	商业区	住宅区	商业区
无序充电	2 086.8	1 504.6	13.47	15.22
有序充电	1 159.8	1 459.8	6.01	10.27
有序充放电	915.9	1 359.8	3.36	8.61

场景	负荷峰谷差/kW	用户平均成本(元)	聚合商成本(元)
Ⅰ	2 086.8	13.47	191 898
Ⅱ	1 239.8	3.52	111 920
Ⅲ	799.3	3.36	106 041
Ⅳ	915.9	3.23	104 204

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