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Coordination and payment mechanisms for electric vehicle aggregators

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  • Perez-Diaz, Alvaro
  • Gerding, Enrico
  • McGroarty, Frank

Abstract

Motivated by the high electric vehicle (EV) penetration percentages foreseen for the near future, this paper studies the participation of large fleets of EVs in electricity day-ahead markets. Specifically, we consider a scenario where a number of independent and self-interested EV aggregators participate in the day-ahead market to purchase energy to satisfy their clients’ driving needs. In this scenario, independent bidding can drive prices up unnecessarily, resulting in increased electricity costs for all participants. Inter-aggregator cooperation can mitigate this by producing coordinated bids. However, this is challenging due to the self-interested nature of the aggregators, who may try to manipulate the system in order to obtain personal benefit. In order to overcome this issue, we employ techniques from mechanism design to develop a coordination mechanism which incentivises self-interested EV aggregators to report their energy requirements truthfully to a third-party coordinator. This coordinator is then able to employ a day-ahead bidding algorithm to optimise the global bids on their behalf, extending the benefits of smart bidding to groups of competing EV aggregators. Importantly, the proposed coordination mechanism is straightforward to implement and does not require any additional infrastructure. To ensure scalability and computational tractability, a novel price-maker day-ahead bidding algorithm is proposed, which is formulated in terms of simple energy requirement constraints. The coordination mechanism substantially reduces bidding costs, as shown in a case study which uses real market and driver data from the Iberian Peninsula.

Suggested Citation

  • Perez-Diaz, Alvaro & Gerding, Enrico & McGroarty, Frank, 2018. "Coordination and payment mechanisms for electric vehicle aggregators," Applied Energy, Elsevier, vol. 212(C), pages 185-195.
  • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:185-195
    DOI: 10.1016/j.apenergy.2017.12.036
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    Cited by:

    1. Dapeng Chen & Zhaoxia Jing & Huijuan Tan, 2019. "Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model," Energies, MDPI, vol. 12(7), pages 1-19, April.
    2. Emmanouil S. Rigas & Enrico H. Gerding & Sebastian Stein & Sarvapali D. Ramchurn & Nick Bassiliades, 2022. "Mechanism Design for Efficient Offline and Online Allocation of Electric Vehicles to Charging Stations," Energies, MDPI, vol. 15(5), pages 1-21, February.
    3. Prince Waqas Khan & Yung-Cheol Byun, 2021. "Blockchain-Based Peer-to-Peer Energy Trading and Charging Payment System for Electric Vehicles," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    4. Zheng, Yanchong & Yu, Hang & Shao, Ziyun & Jian, Linni, 2020. "Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets," Applied Energy, Elsevier, vol. 280(C).
    5. Wang, Haibing & Zheng, Tianhang & Sun, Weiqing & Khan, Muhammad Qasim, 2023. "Research on the pricing strategy of park electric vehicle agent considering carbon trading," Applied Energy, Elsevier, vol. 340(C).
    6. Will, Christian & Zimmermann, Florian & Ensslen, Axel & Fraunholz, Christoph & Jochem, Patrick & Keles, Dogan, 2023. "Can electric vehicle charging be carbon neutral? Uniting smart charging and renewables," Working Paper Series in Production and Energy 69, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    7. Shahid Hussain & Mohamed A. Ahmed & Ki-Beom Lee & Young-Chon Kim, 2020. "Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot," Energies, MDPI, vol. 13(12), pages 1-27, June.
    8. 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.
    9. Abbasi, Mohammad Hossein & Taki, Mehrdad & Rajabi, Amin & Li, Li & Zhang, Jiangfeng, 2019. "Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach," Applied Energy, Elsevier, vol. 239(C), pages 1294-1307.
    10. Shuang Gao & Jianzhong Wu & Bin Xu, 2019. "Controllability Evaluation of EV Charging Infrastructure Transformed from Gas Stations in Distribution Networks with Renewables," Energies, MDPI, vol. 12(8), pages 1-20, April.
    11. Fan, Jing-Li & Wang, Jia-Xing & Zhang, Xian, 2020. "An innovative subsidy model for promoting the sharing of Electric Vehicles in China: A pricing decisions analysis," Energy, Elsevier, vol. 201(C).
    12. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.
    13. Hosseinnia, Hamed & Modarresi, Javad & Nazarpour, Daryoush, 2020. "Optimal eco-emission scheduling of distribution network operator and distributed generator owner under employing demand response program," Energy, Elsevier, vol. 191(C).
    14. Morteza Nazari-Heris & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "An Updated Review and Outlook on Electric Vehicle Aggregators in Electric Energy Networks," Sustainability, MDPI, vol. 14(23), pages 1-24, November.
    15. Gustavo E. Coria & Angel M. Sanchez & Ameena S. Al-Sumaiti & Guiseppe A. Rattá & Sergio R. Rivera & Andrés A. Romero, 2019. "A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles," Energies, MDPI, vol. 12(23), pages 1-18, November.

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