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Network-constrained optimal bidding strategy of a plug-in electric vehicle aggregator: A stochastic/robust game theoretic approach

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  • Moghaddam, Saeed Zolfaghari
  • Akbari, Tohid

Abstract

This paper presents a strategic bidding model for several price-taker plug-in electric vehicle aggregators sharing the same distribution network that participate in both day-ahead energy and ancillary services (up/down-regulation reserve) markets. Since the strategic feasible space of an aggregator depends on the actions of the other aggregators due to the limited capacity of the existing feeders, the proposed problem forms a generalized Nash equilibrium problem. The aggregators’ objective is considered to be the cost of purchased energy from the day-ahead and real-time market minus the revenue from the day-ahead regulation market. A hybrid stochastic/robust optimization model is employed to deal with different uncertainties an aggregator faces in the bidding strategy problem. These uncertainties include day-ahead energy prices, day-ahead up/down-regulation prices, and real-time energy prices. Day-ahead prices are modeled by different scenarios, while real-time prices are represented by the confidence bounds. Results of a case study are shown to demonstrate the applicability and tractability of the proposed model.

Suggested Citation

  • Moghaddam, Saeed Zolfaghari & Akbari, Tohid, 2018. "Network-constrained optimal bidding strategy of a plug-in electric vehicle aggregator: A stochastic/robust game theoretic approach," Energy, Elsevier, vol. 151(C), pages 478-489.
    • Handle: RePEc:eee:energy:v:151:y:2018:i:c:p:478-489
    DOI: 10.1016/j.energy.2018.03.074
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    7. Hu, Junfeng & Yan, Qingyou & Kahrl, Fredrich & Liu, Xu & Wang, Peng & Lin, Jiang, 2021. "Evaluating the ancillary services market for large-scale renewable energy integration in China's northeastern power grid," Utilities Policy, Elsevier, vol. 69(C).
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    14. Izabela Zoltowska, 2024. "Risk Preferences of EV Fleet Aggregators in Day-Ahead Market Bidding: Mean-CVaR Linear Programming Model," Energies, MDPI, vol. 18(1), pages 1-19, December.
    15. Wang, Qi & Huang, Chunyi & Wang, Chengmin & Li, Kangping & Xie, Ning, 2024. "Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions," Applied Energy, Elsevier, vol. 360(C).
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    17. Lu, Xi & Xia, Shiwei & Gu, Wei & Chan, Ka Wing & Shahidehpour, Mohammad, 2021. "Two-stage robust distribution system operation by coordinating electric vehicle aggregator charging and load curtailments," Energy, Elsevier, vol. 226(C).
    18. Xiao, Xiangsheng & Wang, Jianxiao & Lin, Rui & Hill, David J. & Kang, Chongqing, 2020. "Large-scale aggregation of prosumers toward strategic bidding in joint energy and regulation markets," Applied Energy, Elsevier, vol. 271(C).
    19. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.
    20. Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.

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