Open AccessPower charging management strategy for electric vehicles based on a Stackelberg game
Author(s) -
Zhaoan Fei,
Baochuan Fu,
Xuefeng Xi,
Zhengtian Wu,
Zhenping Chen,
Xinyin Xu
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0122
Subject(s) - stackelberg competition , key (lock) , grid , game theory , computer science , electric vehicle , process (computing) , power (physics) , electricity , control (management) , automotive engineering , mathematical optimization , simulation , operations research , engineering , electrical engineering , computer security , microeconomics , economics , physics , geometry , mathematics , quantum mechanics , artificial intelligence , operating system
Increasing electric vehicles (EV) charging efficiency and reducing charging cost are important issues of EVs power management and key factor that affect the spread of EVs. Researching the competitive relationship between power grids and EV users is the key to solving this problem. Therefore, the authors propose a charging control strategy that is based on a 1‐N‐type Stackelberg game. In the game, power grid, retailers, and users are all able to do power decision making. Thus, the charging strategy here can flexibly meet different demands of power grid, retailers, and users. The equilibrium of the game model is solved by the inverse induction method. The benefits are compared by simulation, by the disordered charging process, and by charging control methods based on static time‐sharing electricity prices. The influence of the parameters on the charging process in the game model is analysed, and the feasibility of the proposed method is verified. Results show that load peak is reduced by 34.9% while users' charging costs are reduced by 25.1%. The approaches they proposed can effectively solve the competition between the two.