Stochastic bidding strategy of electric vehicles and energy storage systems in uncertain reserve market

Shaofeng Lu, Bing Han*, Fei Xue, Lin Jiang, Xue Feng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


This paper proposes an Electric Vehicle (EV) aggregator bidding strategy in the reserve market. The EV aggregator determines the charging/discharging operations of EVs in providing reserve service for profits maximization. In the Day-Ahead Market (DAM), the EV aggregator submits a bidding plan to the Independent Systems Operator (ISO) including base-load and reserve up/down capacities plans. In the Real-Time Market (RTM), the EV aggregator should deploy reserve based on the ISO's requirements, and the EV aggregator could receive income by deploying reserve or penalty for reserve shortage. The stochastic programming method is applied to address the uncertain reserve deployment requirements in RTM. In addition, Energy Storage Systems (ESS) are utilized by the EV aggregator to enhance the ability in providing reserve service. The aggregator–owner contract is designed to guarantee EV owners' economic benefits. Case studies show the expected profits of the EV aggregator are maximized and the risk of the reserve shortage is well managed, i.e., penalty is minimized. With the utilization of ESS, the performance of the EV aggregator in making response to the ISO's requirements is improved. That is, the required reserve percentage increases from 5.68% to 7.85%, and the deployed reserve percentage increases from 69.71% to 88.47%.

Original languageEnglish
Pages (from-to)3653-3661
Number of pages9
JournalIET Renewable Power Generation
Issue number18
Publication statusPublished - 21 Dec 2020


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