Day‐ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market. Issue 12 (17th May 2019)

• Record Type: Journal Article
• Authors: Han, Bing; Lu, Shaofeng; Xue, Fei; Jiang, Lin
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 12(2019)
• Page Start: 2517
• Page End: 2525
• Publication Date: 2019-05-17
• Subjects: power markets -- energy storage -- stochastic programming -- electric vehicles; day‐ahead electric vehicle aggregator -- bidding strategy -- uncertain reserve market -- dynamic energy storage systems -- ancillary services -- charging/discharging -- electricity market -- EV aggregator -- day‐ahead market -- DAM -- reserve capacity -- reserve developments -- Scenario‐based stochastic programming method -- average aggregator profits -- real‐time market -- deployed reserve shortage -- owner‐aggregator contract -- EV owners -- reserve deployment payments -- reserve deployment requirements uncertainty; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6951
• Languages: English
• ISSNs: 1751-8687

Abstract

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.