Day‐ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market. Issue 12 (17th May 2019) - Han, Bing; Lu, Shaofeng; Xue, Fei; Jiang, Lin
Abstract : Electric vehicle (EV) as dynamic energy storage systems could provide ancillary services to the grids. The aggregator could coordinate the charging/discharging of EV fleets to attend the electricity market to get profits. However, the aggregator profits are threatened by the uncertainty of the electricity market. In this study, an EV aggregator bidding strategy in the day‐ahead market (DAM) is proposed, both reserve capacity and reserve deployment are considered. The novelty of this study is that: (i) The uncertainty of the reserve developments is addressed in terms of both time and amount. (ii) Scenario‐based stochastic programming method is used to maximise the average aggregator profits based on one‐year data. The proposed method, jointly considers the reserve capacity in the DAM and the reserve deployment requirements in the real‐time market (RTM). (iii) The risk of the deployed reserve shortage is addressed by introducing a penalty factor in the model. (iv) An owner‐aggregator contract is designed, which is used to mitigate the economic inconsistency issue between the EV owners and the aggregator. Results verify the performance of the proposed strategy, that is the average aggregator profits are guaranteed by maximising reserve deployment payments and mitigating the penalties in RTM and thus the reserve deployment requirements uncertainty is well managed.