Analysis of charging tariffs for residential electric vehicle users based on Stackelberg game

Establishing a rational pricing mechanism for electric vehicles (EVs) can incentivize users to engage in charging activities, foster positive consumption behaviors, and indirectly benefit the grid by increasing load demand. Therefore, this paper conducts a Stackelberg game study between the grid and EV users by utilizing real charging and discharging data of EV users in F city. First, the price elasticity of EV users' electricity consumption behavior is analyzed. Second, the Stackelberg game model is constructed, multiple tariff schemes are designed to calculate the equilibrium tariff of the game, and the benefits of the grid and EV users are analyzed in comparison with the tariff policy that will be implemented in F city. Lastly, different seasonal scenarios are constructed to extend the applicability of the research results. It is found that Scheme 3 effectively incentivizes charging load growth, increases cross-subsidies and transmission and distribution fees, and protects the level of charging and discharging costs for residents; furthermore, lowering charging and discharging tariffs during seasons of high price elasticity for residents to increase demand is an optimal strategy for grid pricing.

Authors

Institutions

Publication Details

Journal
Energy Reports
Published
2024-08-05
DOI
https://doi.org/10.1016/j.egyr.2024.07.054
Citations
7
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
1.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of charging tariffs for residential electric vehicle users based on Stackelberg game

Zhonghai Zheng, Lingyan Xu, Chaofeng Zheng, Zhibin Wei
7 citations
Energy Reports
Electric Vehicles and Infrastructure
1.46
article

Analysis of charging tariffs for residential electric vehicle users based on Stackelberg game

Zhonghai Zheng, Lingyan Xu, Chaofeng Zheng, Zhibin Wei
article en
7 citations

Abstract

Establishing a rational pricing mechanism for electric vehicles (EVs) can incentivize users to engage in charging activities, foster positive consumption behaviors, and indirectly benefit the grid by increasing load demand. Therefore, this paper conducts a Stackelberg game study between the grid and EV users by utilizing real charging and discharging data of EV users in F city. First, the price elasticity of EV users' electricity consumption behavior is analyzed. Second, the Stackelberg game model is constructed, multiple tariff schemes are designed to calculate the equilibrium tariff of the game, and the benefits of the grid and EV users are analyzed in comparison with the tariff policy that will be implemented in F city. Lastly, different seasonal scenarios are constructed to extend the applicability of the research results. It is found that Scheme 3 effectively incentivizes charging load growth, increases cross-subsidies and transmission and distribution fees, and protects the level of charging and discharging costs for residents; furthermore, lowering charging and discharging tariffs during seasons of high price elasticity for residents to increase demand is an optimal strategy for grid pricing.

Energy ReportsVol. 12
State Grid Corporation of China (China) (CN), Fujian Electric Power Survey & Design Institute (CN)
Openalex Percentile: Top 18%
Electric Vehicles and Infrastructure
1.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Analysis of charging tariffs for residential electric vehicle users based on Stackelberg game — Zhonghai Zheng, Lingyan Xu, et al. · Energy Reports (2024) | TGRS Research Map | TGRS