Real-Time Optimal Charging Strategy for Battery Swapping Stations Under Time-of-Use Pricing

被引:2
作者
Yan, Huanyu [1 ,2 ,3 ]
Sun, Chenxi [2 ]
Liao, Huanxin [1 ,2 ,3 ]
Tang, Xiaoying [1 ,2 ,3 ]
机构
[1] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China
[2] Shenzhen Inst Artificial Intelligence & Robot Soc, Shenzhen 518129, Peoples R China
[3] Shenzhen Key Lab Crowd Intelligence Empowered Lowc, Shenzhen 518172, Peoples R China
来源
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING | 2025年 / 12卷 / 03期
关键词
Batteries; Pricing; Real-time systems; Power grids; Games; Electricity; Costs; Nash equilibrium; Electric vehicles; Charging stations; Game theory; charging scheduling; electric vehicle; battery swapping station; DEMAND-SIDE MANAGEMENT;
D O I
10.1109/TNSE.2025.3543449
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Battery Swapping Stations (BSSs), the emerging infrastructure for electric vehicles (EVs), are swiftly proliferating facilities bridging energy and transportation networks. As the power grid's demand-side-management approach evolves, the optimal charging strategy for competitive BSSs needs further investigation. This paper proposes a real-time optimal charging strategy for each non-cooperative BSS operating under a unified power grid that implements Time-of-use (TOU) pricing. We construct a non-cooperative game model to encapsulate the competition among BSSs under the EV reservation mechanism. To resolve the game, we prove the existence of a unique Nash Equilibrium under any number of players and swapping prices, and design an algorithm to solve the equilibrium. Additionally, we suggest strategies for EVs without reservations. Specifically, we demonstrate the conditions under which the BSS profit diminishes when serving directly drive-in EVs. We also establish that the potential cost arising from no-show reserved EVs is limited by a constant. Simulations validate that our proposed battery charging strategy significantly enhances the profits of a 12-station BSS system. Moreover, the real-time optimal charging strategy also accomplishes peak-shaving over multiple time periods.
引用
收藏
页码:2043 / 2056
页数:14
相关论文
共 34 条
[1]  
Abdulla Khalid, 2017, 2017 IEEE Power & Energy Society General Meeting, DOI [10.1109/TSG.2016.2606490, 10.1109/PESGM.2017.8273930]
[2]   A Cost-Efficient Energy Management System for Battery Swapping Station [J].
Ahmad, Furkan ;
Alam, Mohammad Saad ;
Shariff, Samir M. .
IEEE SYSTEMS JOURNAL, 2019, 13 (04) :4355-4364
[3]   Software Defined Networking Assisted Electric Vehicle Charging: Towards Smart Charge Scheduling and Management [J].
Arikumar, K. S. ;
Prathiba, Sahaya Beni ;
Moorthy, Rajalakshmi Shenbaga ;
Srivastava, Gautam ;
Gadekallu, Thippa Reddy .
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2024, 11 (01) :163-173
[4]  
Basar T., 1998, Dynamic Noncooperative Game Theory
[5]  
emcsg, 2022, Singapore power grid half-hourly load data
[6]  
Engel H., 2018, CHARGING AHEAD ELECT, P1
[7]   Learning in games [J].
Fudenberg, D ;
Levine, D .
EUROPEAN ECONOMIC REVIEW, 1998, 42 (3-5) :631-639
[8]  
Fudenberg D., 1991, Game Theory
[9]   Demand side management in smart grid: A review and proposals for future direction [J].
Gelazanskas, Linas ;
Gamage, Kelum A. A. .
SUSTAINABLE CITIES AND SOCIETY, 2014, 11 :22-30
[10]  
Guo JL, 2019, 2019 IEEE MILAN POWERTECH