IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i9p670-d76564.html
   My bibliography  Save this article

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

Author

Listed:
  • Jun Yang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Jiejun Chen

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Lei Chen

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Feng Wang

    (Computer School of Wuhan University, Wuhan 430072, China)

  • Peiyuan Xie

    (State Grid Hunan Power Supply Company, Changsha 410007, China)

  • Cilin Zeng

    (State Grid Hunan Power Supply Company, Changsha 410007, China)

Abstract

With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price.

Suggested Citation

  • Jun Yang & Jiejun Chen & Lei Chen & Feng Wang & Peiyuan Xie & Cilin Zeng, 2016. "A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles," Energies, MDPI, vol. 9(9), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:670-:d:76564
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/9/670/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/9/670/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fernandes, Camila & Frías, Pablo & Latorre, Jesús M., 2012. "Impact of vehicle-to-grid on power system operation costs: The Spanish case study," Applied Energy, Elsevier, vol. 96(C), pages 194-202.
    2. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    3. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.
    4. Foley, Aoife & Tyther, Barry & Calnan, Patrick & Ó Gallachóir, Brian, 2013. "Impacts of Electric Vehicle charging under electricity market operations," Applied Energy, Elsevier, vol. 101(C), pages 93-102.
    5. 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.
    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. Junjie Hu & Hugo Morais & Tiago Sousa & Shi You & Reinhilde D’hulst, 2017. "Integration of Electric Vehicles into the Power Distribution Network with a Modified Capacity Allocation Mechanism," Energies, MDPI, vol. 10(2), pages 1-20, February.
    2. Su Su & Hao Li & David Wenzhong Gao, 2017. "Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits," Energies, MDPI, vol. 10(7), pages 1-15, July.

    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. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.
    2. 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.
    3. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    4. Sovacool, Benjamin K. & Kester, Johannes & Noel, Lance & Zarazua de Rubens, Gerardo, 2020. "Actors, business models, and innovation activity systems for vehicle-to-grid (V2G) technology: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Crossin, Enda & Doherty, Peter J.B., 2016. "The effect of charging time on the comparative environmental performance of different vehicle types," Applied Energy, Elsevier, vol. 179(C), pages 716-726.
    6. 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.
    7. Qun Niu & Kecheng Jiang & Zhile Yang, 2019. "An Improved, Negatively Correlated Search for Solving the Unit Commitment Problem’s Integration with Electric Vehicles," Sustainability, MDPI, vol. 11(24), pages 1-21, December.
    8. Jun Yang & Wanmeng Hao & Lei Chen & Jiejun Chen & Jing Jin & Feng Wang, 2016. "Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles," Energies, MDPI, vol. 9(7), pages 1-20, July.
    9. Mike F. Voss & Steven P. Haveman & Gerrit Maarten Bonnema, 2021. "In-Company Smart Charging: Development of a Simulation Model to Facilitate a Smart EV Charging System," Energies, MDPI, vol. 14(20), pages 1-34, October.
    10. Jian, Linni & Zheng, Yanchong & Shao, Ziyun, 2017. "High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles," Applied Energy, Elsevier, vol. 186(P1), pages 46-55.
    11. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    12. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2017. "Electricity costs for a Level 3 electric vehicle fueling station integrated with a building," Applied Energy, Elsevier, vol. 191(C), pages 367-384.
    13. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    14. Salah, Florian & Ilg, Jens P. & Flath, Christoph M. & Basse, Hauke & Dinther, Clemens van, 2015. "Impact of electric vehicles on distribution substations: A Swiss case study," Applied Energy, Elsevier, vol. 137(C), pages 88-96.
    15. Staudt, Philipp & Schmidt, Marc & Gärttner, Johannes & Weinhardt, Christof, 2018. "A decentralized approach towards resolving transmission grid congestion in Germany using vehicle-to-grid technology," Applied Energy, Elsevier, vol. 230(C), pages 1435-1446.
    16. Raymond Kene & Thomas Olwal & Barend J. van Wyk, 2021. "Sustainable Electric Vehicle Transportation," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    17. Welsch, Manuel & Deane, Paul & Howells, Mark & Ó Gallachóir, Brian & Rogan, Fionn & Bazilian, Morgan & Rogner, Hans-Holger, 2014. "Incorporating flexibility requirements into long-term energy system models – A case study on high levels of renewable electricity penetration in Ireland," Applied Energy, Elsevier, vol. 135(C), pages 600-615.
    18. De Gennaro, Michele & Paffumi, Elena & Scholz, Harald & Martini, Giorgio, 2014. "GIS-driven analysis of e-mobility in urban areas: An evaluation of the impact on the electric energy grid," Applied Energy, Elsevier, vol. 124(C), pages 94-116.
    19. Poghosyan, Anush & Greetham, Danica Vukadinović & Haben, Stephen & Lee, Tamsin, 2015. "Long term individual load forecast under different electrical vehicles uptake scenarios," Applied Energy, Elsevier, vol. 157(C), pages 699-709.
    20. Liping Huang & Haisheng Li & Chun Sing Lai & Ahmed F. Zobaa & Bang Zhong & Zhuoli Zhao & Loi Lei Lai, 2024. "Electric Vehicle Cluster Scheduling Model for Distribution Systems Considering Reactive-Power Compensation of Charging Piles," Energies, MDPI, vol. 17(11), pages 1-24, May.

    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:gam:jeners:v:9:y:2016:i:9:p:670-:d:76564. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.