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An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game

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  • Xiangchu Xu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071000, China)

  • Zewei Zhan

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071000, China)

  • Zengqiang Mi

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071000, China)

  • Ling Ji

    (Guodian Nanjing Automation Co., Nanjing 210003, China)

Abstract

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system’s supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA’s participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO’s revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO’s revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA’s reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets.

Suggested Citation

  • Xiangchu Xu & Zewei Zhan & Zengqiang Mi & Ling Ji, 2023. "An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game," Sustainability, MDPI, vol. 15(20), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:15127-:d:1264645
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    References listed on IDEAS

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    1. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    2. de la Torre, S. & Aguado, J.A. & Sauma, E., 2023. "Optimal scheduling of ancillary services provided by an electric vehicle aggregator," Energy, Elsevier, vol. 265(C).
    3. Faddel, Samy & Aldeek, A. & Al-Awami, Ali T. & Sortomme, Eric & Al-Hamouz, Zakariya, 2018. "Ancillary Services Bidding for Uncertain Bidirectional V2G Using Fuzzy Linear Programming," Energy, Elsevier, vol. 160(C), pages 986-995.
    4. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    5. Olivier Bahn, Mario Samano, and Paul Sarkis, 2021. "Market Power and Renewables: The Effects of Ownership Transfers," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    6. 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).
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    1. Jianhong Hao & Ting Huang & Qiuming Xu & Yi Sun, 2023. "Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game," Sustainability, MDPI, vol. 15(24), pages 1-15, December.

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