IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i11p6450-d823764.html
   My bibliography  Save this article

A Novel Fairness-Based Cost Model for Adopting Smart Charging at Fast Charging Stations

Author

Listed:
  • Sami M. Alshareef

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

Abstract

This research introduces a cost model for application at fast charging stations (FCSs) with the aim to adopt smart charging, which can mitigate voltage fluctuation caused by the on/off status of FCS. When the operation of FCSs causes a voltage fluctuation and light flicker, the FCSs may be disconnected, as per the utility general standard practice, which results in financial loss represented by FCS downtime. However, FCS downtime can be avoided by applying the smart charging method referred to in this paper, or by installing mitigation devices that are available on the market. The proposed smart charging method provides three charging powers (or options), namely premium, regular, and economic, which consumers can select according to their needs and/or priority, whether this may be time or cost. Thus, the output power of each type is different as well as the per unit cost. The offered cost of smart charging is reliant on a ‘fairness’ policy that is, from the viewpoint of an FCS operator or investor, characterized by the value of cost for any customer at the FCS being equivalent to the customer’s value of time. For instance, customers A and B require X kwh from the FCS. When arriving at the FCS, if customer A values time the most, the premium power can be selected with the highest $/kwh cost. If the cost is more valuable to customer B, regular or economic power can be selected, but customer B will spend more time than customer A to get the same X kwh. The proposed fairness policy indicates that, to get the required X kwh, the percent of total costs saved by B (by using regular or economic power) in comparison to A is equivalent to the percent of total time saved by A (by using premium power) in comparison to B, for the same X kwh. The annual cost of applying smart charging at the FCS is estimated and compared with the annual cost of the best flicker mitigation device. The comparison reveals that distribution static compensators are considered the cheapest mitigation device, according to the cost per kVAr basis and the total annual equivalent cost. The proposed smart charging method achieves a tremendous reduction in the cost of mitigating the voltage fluctuation and light flicker. The annual cost of the proposed smart charging method is less than the annual cost of distribution static compensators by a minimum of 90% to a maximum of 99%.

Suggested Citation

  • Sami M. Alshareef, 2022. "A Novel Fairness-Based Cost Model for Adopting Smart Charging at Fast Charging Stations," Sustainability, MDPI, vol. 14(11), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6450-:d:823764
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/11/6450/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/11/6450/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sami M. Alshareef, 2022. "A Novel Smart Charging Method to Mitigate Voltage Fluctuation at Fast Charging Stations," Energies, MDPI, vol. 15(5), pages 1-25, February.
    2. van der Kam, Mart & van Sark, Wilfried, 2015. "Smart charging of electric vehicles with photovoltaic power and vehicle-to-grid technology in a microgrid; a case study," Applied Energy, Elsevier, vol. 152(C), pages 20-30.
    3. Seyed Abbas Taher & Seyed Ahmadreza Afsari, 2012. "Optimal Location and Sizing of UPQC in Distribution Networks Using Differential Evolution Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-20, August.
    4. Ardeshiri, Ali & Rashidi, Taha Hossein, 2020. "Willingness to pay for fast charging station for electric vehicles with limited market penetration making," Energy Policy, Elsevier, vol. 147(C).
    5. Xiaowei Ding & Weige Zhang & Shaoyuan Wei & Zhenpo Wang, 2021. "Optimization of an Energy Storage System for Electric Bus Fast-Charging Station," Energies, MDPI, vol. 14(14), pages 1-17, July.
    6. Elma, Onur, 2020. "A dynamic charging strategy with hybrid fast charging station for electric vehicles," Energy, Elsevier, vol. 202(C).
    Full references (including those not matched with items on IDEAS)

    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. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
    2. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Optimal energy management in all-electric residential energy systems with heat and electricity storage," Applied Energy, Elsevier, vol. 254(C).
    3. Vij, Akshay & Souza, Flavio F. & Barrie, Helen & Anilan, V. & Sarmiento, Sergio & Washington, Lynette, 2023. "Employee preferences for working from home in Australia," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 782-800.
    4. Morro-Mello, Igoor & Padilha-Feltrin, Antonio & Melo, Joel D. & Heymann, Fabian, 2021. "Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory," Energy, Elsevier, vol. 235(C).
    5. Luo, Lizi & He, Pinquan & Gu, Wei & Sheng, Wanxing & Liu, Keyan & Bai, Muke, 2022. "Temporal-spatial scheduling of electric vehicles in AC/DC distribution networks," Energy, Elsevier, vol. 255(C).
    6. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    7. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
    8. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    9. Sami M. Alshareef, 2022. "A Novel Smart Charging Method to Mitigate Voltage Fluctuation at Fast Charging Stations," Energies, MDPI, vol. 15(5), pages 1-25, February.
    10. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    11. Alessandro Di Giorgio & Emanuele De Santis & Lucia Frettoni & Stefano Felli & Francesco Liberati, 2023. "Electric Vehicle Fast Charging: A Congestion-Dependent Stochastic Model Predictive Control under Uncertain Reference," Energies, MDPI, vol. 16(3), pages 1-16, January.
    12. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    13. Kuang, Yanqing & Chen, Yang & Hu, Mengqi & Yang, Dong, 2017. "Influence analysis of driver behavior and building category on economic performance of electric vehicle to grid and building integration," Applied Energy, Elsevier, vol. 207(C), pages 427-437.
    14. Varone, Alberto & Heilmann, Zeno & Porruvecchio, Guido & Romanino, Alessandro, 2024. "Solar parking lot management: An IoT platform for smart charging EV fleets, using real-time data and production forecasts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    15. Brinkel, N.B.G. & Schram, W.L. & AlSkaif, T.A. & Lampropoulos, I. & van Sark, W.G.J.H.M., 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits," Applied Energy, Elsevier, vol. 276(C).
    16. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    17. Ruben Garruto & Michela Longo & Wahiba Yaïci & Federica Foiadelli, 2020. "Connecting Parking Facilities to the Electric Grid: A Vehicle-to-Grid Feasibility Study in a Railway Station’s Car Park," Energies, MDPI, vol. 13(12), pages 1-23, June.
    18. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    19. Yap, Kah Yung & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2022. "Solar Energy-Powered Battery Electric Vehicle charging stations: Current development and future prospect review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    20. Huang, Xingjun & Lin, Yun & Lim, Ming K. & Zhou, Fuli & Liu, Feng, 2022. "Electric vehicle charging station diffusion: An agent-based evolutionary game model in complex networks," Energy, Elsevier, vol. 257(C).

    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:jsusta:v:14:y:2022:i:11:p:6450-:d:823764. 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.