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Optimal scheduling of ancillary services provided by an electric vehicle aggregator

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  • de la Torre, S.
  • Aguado, J.A.
  • Sauma, E.

Abstract

Massification of Electric vehicles (EVs) is becoming a worldwide reality as a means to combat climate change and local pollution. Considering that most of the time vehicles are in parking places, there is an opportunity for using EVs to provide some valuable services to the power network. In particular, EVs can provide ancillary services in electricity markets through an aggregating agent. To this end, EVs aggregators need to develop decision support tools to optimally allocate energy and regulation resources considering power network constraints. Unlike optimization models for EVs aggregators currently available in the literature, in this paper we propose an optimization approach for EVs aggregators that jointly considers the most important aspects influencing EVs profitability, such as uncertainty, drivers’ patterns, capacity constraints, state of charge constraints, regulation demand constraints, regulation offer constraints, regulation bounds constraints, and power-system security constraints. The optimization problem is formulated as a mixed-integer linear programming problem, thus ensuring global optimality. Results are presented in the form of the hourly allocation for charging/discharging power profiles, distinguishing between day-ahead energy and capacity/energy for regulation, and the profit that can be reached, while accounting for network constraints.

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  • 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).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s036054422203033x
    DOI: 10.1016/j.energy.2022.126147
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    Cited by:

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    2. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Joint chance-constrained program based electric vehicles optimal dispatching strategy considering drivers' response uncertainty," Applied Energy, Elsevier, vol. 356(C).
    3. Genov, Evgenii & Cauwer, Cedric De & Kriekinge, Gilles Van & Coosemans, Thierry & Messagie, Maarten, 2024. "Forecasting flexibility of charging of electric vehicles: Tree and cluster-based methods," Applied Energy, Elsevier, vol. 353(PA).
    4. 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).
    5. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach," Energy, Elsevier, vol. 283(C).
    6. 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.
    7. Li, Jiamei & Ai, Qian & Chen, Minyu, 2023. "Strategic behavior modeling and energy management for electric-thermal-carbon-natural gas integrated energy system considering ancillary service," Energy, Elsevier, vol. 278(C).
    8. Heping Jia & Qianxin Ma & Yun Li & Mingguang Liu & Dunnan Liu, 2023. "Integrating Electric Vehicles to Power Grids: A Review on Modeling, Regulation, and Market Operation," Energies, MDPI, vol. 16(17), pages 1-18, August.

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