期刊
ENERGY
卷 283, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2023.129138
关键词
Electric vehicles; Bidding strategy; Risk aversion; Electricity markets; Price uncertainty
This study proposes a risk-averse bidding strategy for electric vehicle aggregators (EVA) to handle the uncertainties in the day-ahead market. By minimizing the conditional value-at-risk, the strategy aims to reduce the energy transaction risk of the EVA. The model is transformed into a linear programming problem for efficient computation.
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.
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