IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v294y2024ics0360544224007394.html

Energy optimal scheduling strategy considering V2G characteristics of electric vehicle

Author

Listed:
  • Yin, Wanjun
  • Jia, Leilei
  • Ji, Jianbo

Abstract

in recent years, large-scale electric vehicles connected to the power grid has a significant impact on demand-side resources. In addition, the vehicle-to-grid technology of electric vehicles introduces more uncertainties, which brings great challenges to the optimal scheduling of integrated energy systems. To solve this problem, this paper fully considers the uncertainty and fluctuation of wind power. Firstly, the objective function of joint configuration is constructed to maximize the utilization of wind power and minimize the planned generation cost of the system. Secondly, the linearized power flow equations are used to represent the relations among the state variables, and the second-order cone relaxation method is applied to deal with the branch power flow constraints, which is transformed into a solvable mixed-integer second-order cone programming model. Finally, the validity of the model is verified by an example. The system achieves obvious peak-shaving and valley-filling effect and improves the economic benefit of the system.

Suggested Citation

  • Yin, Wanjun & Jia, Leilei & Ji, Jianbo, 2024. "Energy optimal scheduling strategy considering V2G characteristics of electric vehicle," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224007394
    DOI: 10.1016/j.energy.2024.130967
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224007394
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130967?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Yongxing Wang & Jun Bi & Chaoru Lu & Cong Ding, 2020. "Route Guidance Strategies for Electric Vehicles by Considering Stochastic Charging Demands in a Time-Varying Road Network," Energies, MDPI, vol. 13(9), pages 1-24, May.
    2. Erdogan, Nuh & Kucuksari, Sadik & Murphy, Jimmy, 2022. "A multi-objective optimization model for EVSE deployment at workplaces with smart charging strategies and scheduling policies," Energy, Elsevier, vol. 254(PA).
    3. Yang, Jun & He, Lifu & Fu, Siyao, 2014. "An improved PSO-based charging strategy of electric vehicles in electrical distribution grid," Applied Energy, Elsevier, vol. 128(C), pages 82-92.
    4. Liu, Wenxia & Huang, Yuchen & Li, Zhengzhou & Yang, Yue & Yi, Fang, 2020. "Optimal allocation for coupling device in an integrated energy system considering complex uncertainties of demand response," Energy, Elsevier, vol. 198(C).
    5. Wu, Ji & Su, Hao & Meng, Jinhao & Lin, Mingqiang, 2023. "Electric vehicle charging scheduling considering infrastructure constraints," Energy, Elsevier, vol. 278(PA).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lin, Hao & Liu, Shilin & Liao, Shiwu & Wang, Shinong, 2025. "A two-stage robust optimal capacity configuration method for charging station integrated with photovoltaic and energy storage system considering vehicle-to-grid and uncertainty," Energy, Elsevier, vol. 319(C).
    2. Zhang, Wei & Cao, Xiangli & Li, Chengjiang & Qin, Quande & Yang, Jing & Li, Wenbo, 2025. "Study on V2G potential of electric taxis based on map-matching multi-objective optimization," Transport Policy, Elsevier, vol. 172(C).
    3. Aoli, Huang & Suhua, Lou & Gang, Lu & Wenrui, Zhang, 2025. "Real-time nested scheduling model with embedded adaptive charging–discharging strategy considering EV uncertainty," Energy, Elsevier, vol. 328(C).
    4. Lee, Wonjong & Koo, Yoonmo & Kim, Yong-gun, 2024. "Environmental time-of-use scheme: Strategic leveraging of financial and environmental incentives for greener electric vehicle charging," Energy, Elsevier, vol. 309(C).
    5. Rehman, Anis Ur & Lu, Junwei & Du, Bo & Bai, Feifei & Sanjari, Mohammad J. & Hossain, Md Alamgir, 2026. "Vehicle-to-grid technology for load balancing and energy management: A comprehensive review of technical, economic and environmental perspectives," Applied Energy, Elsevier, vol. 402(PB).
    6. Tang, Zhongyi & Zhang, Zhirong & Shen, Xianxian & Zhong, Anjie & Nazir, Muhammad Shahzad & Peng, Tian & Zhang, Chu, 2024. "Evolutionary hybrid deep learning based on feature engineering and deep projection encoded echo-state network for lithium batteries state of health estimation," Energy, Elsevier, vol. 313(C).
    7. Song, Tianhua & Teh, Jiashen & Alharbi, Bader, 2024. "Reliability impact of dynamic thermal line rating and electric vehicles on wind power integrated networks," Energy, Elsevier, vol. 313(C).
    8. Wu, Hongbin & Lan, Xinjie & He, Ye & Wu, Andrew Y. & Ding, Ming, 2025. "Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation," Applied Energy, Elsevier, vol. 378(PA).
    9. Zhao, Xudong & Wang, Yibo & Liu, Chuang & Cai, Guowei & Ge, Weichun & Wang, Bowen & Wang, Dongzhe & Shang, Jingru & Zhao, Yiru, 2024. "Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition," Energy, Elsevier, vol. 302(C).
    10. Chen, Jiawen & Zou, Yuan & Zhang, Jun & Zhang, Xudong, 2025. "Optimal charging and discharging strategy for workplace electric vehicle charging stations with renewable energy and power network constraints," Energy, Elsevier, vol. 336(C).
    11. Wang, Weijun & Li, Chen & He, Yan & Bai, Haining & Jia, Kaiqing & Kong, Zhe, 2024. "Enhancement of household photovoltaic consumption potential in village microgrid considering electric vehicles scheduling and energy storage system configuration," Energy, Elsevier, vol. 311(C).
    12. Mei, Haozhou & Wu, Qiong & Ren, Hongbo & Li, Qifen & Gao, Weijun, 2025. "Research status and prospects of regional distribution grid resilience enhancement methods taking into account electrified transportation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 223(C).
    13. Mei, Haozhou & Wu, Qiong & Ren, Hongbo & Zhang, Jinli & Li, Qifen, 2025. "Optimization of electric vehicle charging station layout considering the improvement of distribution network resilience under extreme disasters," Energy, Elsevier, vol. 323(C).
    14. Wan, Yuyang & Wang, Ning & Du, Ershun & Liu, Xueshan & Wang, Yanbo & Chen, Zhe & Kang, Chongqing, 2025. "Coordinated operation of alternative fuel vehicle-integrated microgrid in a coupled power-transportation network: a Stackelberg–Nash game framework," Applied Energy, Elsevier, vol. 401(PC).
    15. Zhang, Yuanshi & Zou, Bokang & Jin, Xu & Luo, Yifu & Song, Meng & Ye, Yujian & Hu, Qinran & Chen, Qirui & Zambroni, Antonio Carlos, 2025. "Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks," Applied Energy, Elsevier, vol. 377(PD).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yizhe Chen & Yifan Gao & Ruifeng Zhao & Jiangang Lu & Ming Li & Chengzhi Wei & Junhao Li, 2025. "Orderly Charging and Discharging Strategy for Electric Vehicles with Integrated Consideration of User and Distribution Grid Benefits," Energies, MDPI, vol. 18(9), pages 1-18, April.
    2. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    3. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    4. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.
    5. An, Sihai & Qiu, Jing & Lin, Jiafeng & Yao, Zongyu & Liang, Qijun & Lu, Xin, 2025. "Planning of a multi-agent mobile robot-based adaptive charging network for enhancing power system resilience under extreme conditions," Applied Energy, Elsevier, vol. 395(C).
    6. Ghosh, Sanchita & Roy, Tanushree, 2025. "Detection and isolation of battery charging cyberattacks via Koopman operator," Applied Energy, Elsevier, vol. 401(PB).
    7. Benoliel, Peter & Taylor, Margaret & Coburn, Timothy & Desai, Ranjit R. & Schey, Stephen & Gerdes, Mindy & Peng, Peng, 2025. "Soft costs and EVSE – Knowledge gaps as a barrier to successful projects," Applied Energy, Elsevier, vol. 389(C).
    8. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    9. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    10. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    11. Myers, Jeff & Hanna, Ryan & Garg, Teevrat & Kavanagh, Josh & Kleissl, Jan & Tebbe, Sebastian & Washom, Byron & Victor, David G., 2025. "Design of workplace and destination-based EV charging networks considering driver behavior, habits, and preferences," Renewable Energy, Elsevier, vol. 242(C).
    12. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    13. Kayhan Alamatsaz & Sadam Hussain & Chunyan Lai & Ursula Eicker, 2022. "Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review," Energies, MDPI, vol. 15(21), pages 1-39, October.
    14. Yin, WanJun & Qin, Xuan, 2022. "Cooperative optimization strategy for large-scale electric vehicle charging and discharging," Energy, Elsevier, vol. 258(C).
    15. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2016. "Low carbon technologies as providers of operational flexibility in future power systems," Applied Energy, Elsevier, vol. 168(C), pages 724-738.
    16. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    17. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    18. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    19. Zeng, Zhiqiang & Hong, Mengna & Li, Jigeng & Man, Yi & Liu, Huanbin & Li, Zeeman & Zhang, Huanhuan, 2018. "Integrating process optimization with energy-efficiency scheduling to save energy for paper mills," Applied Energy, Elsevier, vol. 225(C), pages 542-558.
    20. Zhang, Qiang & Ogren, Ryan M. & Kong, Song-Charng, 2016. "A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA," Applied Energy, Elsevier, vol. 165(C), pages 676-684.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224007394. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.