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Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach

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  • Zheng, Yanchong
  • Wang, Yubin
  • Yang, Qiang

Abstract

The uncertainties induced by electric vehicle (EV) demand and market operations pose huge challenges to the optimal bidding decision of the EV aggregator (EVA) in the day-ahead (DA) market. Note that a risk-neutral bidding solution with the expected cost minimization may make the EVA suffer a high financial loss in the market. As such, in this study, a risk-averse bidding strategy is developed to support the EVA to participate in the market via the conditional value-at-risk (CVaR) to handle market price volatility. Specifically, the strategy minimizes the CVaR metric over a collection of real-time (RT) clearing scenarios to reduce the energy transaction risk of the EVA in the market. Moreover, the model is reformulated as a linear programming (LP) problem that is mathematically tractable and can be efficiently solved. The proposed solution is extensively assessed through experiments based on the PJM market against the risk-neutral bidding strategy as a comparison benchmark. The numerical results reveal that the proposed risk-averse bidding strategy outperforms the risk-neutral one in terms of risk control, which enables the EVA to avoid suffering a huge financial loss incurred by RT clearing prices. In addition, the transformed LP model is superior to the original model in computational efficiency.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s036054422302532x
    DOI: 10.1016/j.energy.2023.129138
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    References listed on IDEAS

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    Cited by:

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    2. La Fata, Alice & Brignone, Massimo & Procopio, Renato & Bracco, Stefano & Delfino, Federico & Barbero, Giulia & Barilli, Riccardo, 2024. "An energy management system to schedule the optimal participation to electricity markets and a statistical analysis of the bidding strategies over long time horizons," Renewable Energy, Elsevier, vol. 228(C).

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