Day-ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market

Abstract

Electric vehicle as dynamic energy storage systems could provide ancillary services to the grids. The aggregator could coordinate the charging/discharging of electric vehicle fleets to attend the electricity market to get profits. However, the aggregator profits is threaten by the uncertainty of the electricity market. In this paper, an electric vehicle aggregator bidding strategy the day-ahead market is proposed, both reserve capacity and reserve deployment are considered. The novelty of this paper is that, (1) The uncertainty of the reserve developments are address in terms of both time and amount. (2) Scenario-based stochastic programming method is used to maximize the average aggregator profits based on one-year data. The proposed method jointly consider the reserve capacity in the day-ahead market and the reserve deployment requirements in the real-time market. (3) The risk of the deployed reserve shortage is addressed by introducing a penalty factor in the model. (4) An owner-aggregator contract is designed, which is used to mitigate the economic inconsistency issue between EV owners and the aggregator. Results verify the performance of the proposed strategy, that is the average aggregator profits are guaranteed by maximizing reserve deployment payments and mitigating the penalties in RTM and thus the reserve deployment requirements uncertainty is well managed. Nomenclature Indices n Number of EVs from 1 to N t Time from 1 to M m Any time between 1 and M ω Number of scenarios form 1 to Ω q Number of days from 1 to Q Parameters M The total time intervals