An Evolutionary Game Approach to Incentive Mechanism of Vehicle-to-Grid

doi:10.16183/j.cnki.jsjtu.2023.603

Abstract

Abstract:

Electric vehicles (EVs) can provide significant support for the flexible operation of power systems, in which vehicle-to-grid (V2G) mode is an important way for EVs to participate in the frequency and voltage regulation of power grids. However, the commercialization of V2G has experienced slow progress to date, and the lack of an effective market operation mechanism makes it difficult for large-scale EVs to participate in the ancillary services of the grid. Therefore, a novel evolutionary game model is proposed with the participation of the electricity regulatory commission, power grid company, and EVs and the impact of the strategic choices of the three parties on the operation of the V2G market is explored to identify the subsidy and pricing mechanisms for the government to facilitate the long-term evolution of the V2G. First, replicator dynamic equations for the game are established to investigate the stability of multiple strategy equilibrium points in the three-party evolutionary game. Then, the Lyapunov stability theory is employed to analyze the stability of these equilibrium points and to determine the subsidy amount to promote V2G development. Next, a simulation analysis is conducted on the actual electricity price data from Shanghai in China, which quantitatively identified the government subsidy coefficient range and electricity price range to incentivize EV participation in the V2G model. The simulation results provide theoretical support for the electricity regulatory commission and power grid company in formulating subsidy and pricing strategies.

Key words: evolutionary game, electric vehicles (EVs), vehicle-to-grid (V2G), evolutionary stable strategy

CLC Number:

TM732

PAN Yi, WANG Mingshen, MIAO Huiyu, YUAN Xiaodong, HAN Huachun. An Evolutionary Game Approach to Incentive Mechanism of Vehicle-to-Grid[J]. Journal of Shanghai Jiao Tong University, 2025, 59(11): 1637-1646.

Figures/Tables 8

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策略配置方式	ERC	PG	EV
(SP, IM, PM)	γ_g(δ_gE_c+E_d$\sum _{i=1}^{n}$δ_iη_i)+φ_pE_c-φ_cE_d	μ_cE_c+E_dE_G-μ_dE_d-φ_pE_c	μ_dE_d-μ_cE_c+φ_cE_d
(SP, IM, NPM)	γ_gδ_gE_c+φ_pE_c	μ_cE_c-φ_pE_c	-μ_cE_c
(SP, NIM, PM)	γ_gδ_gE_c+φ_pE_c	μ_cE_c-φ_pE_c	-μ_cE_c
(SP, NIM, NPM)	γ_gδ_gE_c+φ_pE_c	μ_cE_c-φ_pE_c	-μ_cE_c
(MP, IM, NPM)	γ_g$\left({\delta }_{g}{E}_{c}+{E}_{d}\sum _{i=1}^{n}{\delta }_{i}{\eta }_{i}\right)$	μ_cE_c+E_dE_G-μ_dE_d	μ_dE_d-μ_cE_c
(MP, IM, NPM)	γ_gδ_gE_c	μ_cE_c	-μ_cE_c
(MP, NIM, PM)	γ_gδ_gE_c	μ_cE_c	-μ_cE_c
(MP, NIM, NPM)	γ_gδ_gE_c	μ_cE_c	-μ_cE_c

均衡点	λ₁, λ₂, λ₃	实部范围	稳定性结论
(0, 0, 0)	φ_pE_c, 0, 0	(+, 0, 0)	不稳定
(0, 0, 1)	φ_pE_c, E_dE_G-μ_dE_d, 0	(+, ×, 0)	不稳定
(0, 1, 0)	φ_pE_c, 0, μ_dE_d	(+, 0, +)	不稳定
(0, 1, 1)	φ_pE_c-φ_cE_d, μ_dE_d-E_dE_G, -μ_dE_d	(×, ×, ×)	不确定
(1, 0, 0)	-φ_pE_c, 0, 0	(-, 0, 0)	不确定
(1, 1, 0)	-φ_pE_c, 0, φ_cE_d+μ_dE_d	(-, 0, +)	不稳定
(1, 0, 1)	-φ_pE_c, μ_dE_d-E_dE_G, 0	(-, ×, 0)	不确定
(1, 1, 1)	φ_cE_d-φ_pE_c, μ_dE_d-E_dE_G, -μ_dE_d-φ_cE_d	(×, ×, ×)	不确定