IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5195-d865154.html
   My bibliography  Save this article

Electric Vehicles Charging Algorithm with Peak Power Minimization, EVs Charging Power Minimization, Ability to Respond to DR Signals and V2G Functionality

Author

Listed:
  • Grzegorz Benysek

    (Ekoenergetyka Polska S.A., ul. Nowy-Kisielin-Wysockiego 8, 66-002 Zielona Góra, Poland)

  • Bartosz Waśkowicz

    (Ekoenergetyka Polska S.A., ul. Nowy-Kisielin-Wysockiego 8, 66-002 Zielona Góra, Poland)

  • Robert Dylewski

    (Institute of Mathematics, University of Zielona Góra, ul. Licealna 9, 65-417 Zielona Góra, Poland)

  • Marcin Jarnut

    (Ekoenergetyka Polska S.A., ul. Nowy-Kisielin-Wysockiego 8, 66-002 Zielona Góra, Poland)

Abstract

The number of electric vehicles (EV) on the roads, as well as the share of EVs in use, will inevitably increase in coming decades. This creates a number of problems. A large EV fleet is a significant additional load in the power system that is impossible to accurately predict. Another related problem is the limited distribution network capacity, which is not ready for the additional load from the widespread EV infrastructure. There is a need for an EV charging coordination algorithm capable of fulfilling the charging EV needs, while using as low demanded power as possible and using the lowest power values in each EV charging profile. We propose an EV coordinating algorithm that is capable of ensuring that all connected EVs in the considered parking lot will be charged at the user-defined departure time. The algorithm also controls the charging/discharging power of every connected EV in such a way that the parking lot as a whole will use minimal possible peak power while minimizing the charging power of every EV. The proposed algorithm is also capable of responding to demand response (DR) signals. The paper also includes the results of simulation with a statistical summary of the proposed algorithm effectiveness.

Suggested Citation

  • Grzegorz Benysek & Bartosz Waśkowicz & Robert Dylewski & Marcin Jarnut, 2022. "Electric Vehicles Charging Algorithm with Peak Power Minimization, EVs Charging Power Minimization, Ability to Respond to DR Signals and V2G Functionality," Energies, MDPI, vol. 15(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5195-:d:865154
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5195/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/14/5195/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    2. Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
    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. Bibi Ibrahim & Luis Rabelo & Alfonso T. Sarmiento & Edgar Gutierrez-Franco, 2023. "A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics," Energies, MDPI, vol. 16(13), pages 1-29, 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. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Poria Astero & Bong Jun Choi & Hao Liang & Lennart Söder, 2017. "Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs," Energies, MDPI, vol. 10(10), pages 1-18, October.
    3. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).
    4. Liu, Hui & Huang, Kai & Wang, Ni & Qi, Junjian & Wu, Qiuwei & Ma, Shicong & Li, Canbing, 2019. "Optimal dispatch for participation of electric vehicles in frequency regulation based on area control error and area regulation requirement," Applied Energy, Elsevier, vol. 240(C), pages 46-55.
    5. Muhammad Anique Aslam & Syed Abdul Rahman Kashif & Muhammad Majid Gulzar & Mohammed Alqahtani & Muhammad Khalid, 2023. "A Novel Multi Level Dynamic Decomposition Based Coordinated Control of Electric Vehicles in Multimicrogrids," Sustainability, MDPI, vol. 15(16), pages 1-29, August.
    6. 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).
    7. Wang, Xuewei & Wang, Jing & Wang, Lin & Yuan, Ruiming, 2019. "Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Bünning, Felix & Sangi, Roozbeh & Müller, Dirk, 2017. "A Modelica library for the agent-based control of building energy systems," Applied Energy, Elsevier, vol. 193(C), pages 52-59.
    9. Tobias Rodemann & Tom Eckhardt & René Unger & Torsten Schwan, 2019. "Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems," Energies, MDPI, vol. 12(15), pages 1-16, July.
    10. Jeon, Deok Hwan & Cho, Jae Yong & Jhun, Jeong Pil & Ahn, Jung Hwan & Jeong, Sinwoo & Jeong, Se Yeong & Kumar, Anuruddh & Ryu, Chul Hee & Hwang, Wonseop & Park, Hansun & Chang, Cheulho & Lee, Hyoungjin, 2021. "A lever-type piezoelectric energy harvester with deformation-guiding mechanism for electric vehicle charging station on smart road," Energy, Elsevier, vol. 218(C).
    11. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2017. "When do you charge your electric vehicle? A stated adaptation approach," Energy Policy, Elsevier, vol. 108(C), pages 565-573.
    12. Khaki, Behnam & Chu, Chicheng & Gadh, Rajit, 2019. "Hierarchical distributed framework for EV charging scheduling using exchange problem," Applied Energy, Elsevier, vol. 241(C), pages 461-471.
    13. Mohammadpour Shotorbani, Amin & Zeinal-Kheiri, Sevda & Chhipi-Shrestha, Gyan & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Enhanced real-time scheduling algorithm for energy management in a renewable-integrated microgrid," Applied Energy, Elsevier, vol. 304(C).
    14. Su, Jun & Lie, T.T. & Zamora, Ramon, 2020. "A rolling horizon scheduling of aggregated electric vehicles charging under the electricity exchange market," Applied Energy, Elsevier, vol. 275(C).
    15. Charilaos Latinopoulos & Aruna Sivakumar & John W. Polak, 2021. "Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models," Energies, MDPI, vol. 14(4), pages 1-27, February.
    16. Nkounga, Willy Magloire & Ndiaye, Mouhamadou Falilou & Cisse, Oumar & Grandvaux, Françoise & Tabourot, Laurent & Ndiaye, Mamadou Lamine, 2022. "Automatic control and dispatching of charging currents to a charging station for power-assisted bikes," Energy, Elsevier, vol. 246(C).
    17. Zhou, Kaile & Cheng, Lexin & Lu, Xinhui & Wen, Lulu, 2020. "Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices," Applied Energy, Elsevier, vol. 276(C).
    18. Liang, Yanni & Zhang, Xingping, 2018. "Battery swap pricing and charging strategy for electric taxis in China," Energy, Elsevier, vol. 147(C), pages 561-577.
    19. Zhu, Lijing & Wang, Peize & Zhang, Qi, 2019. "Indirect network effects in China’s electric vehicle diffusion under phasing out subsidies," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    20. Lin, Haiyang & Liu, Yiling & Sun, Qie & Xiong, Rui & Li, Hailong & Wennersten, Ronald, 2018. "The impact of electric vehicle penetration and charging patterns on the management of energy hub – A multi-agent system simulation," Applied Energy, Elsevier, vol. 230(C), pages 189-206.

    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:15:y:2022:i:14:p:5195-:d:865154. 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.