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This paper proposes an incentive-based aggregator for electric vehicles (EVs)in workplace parking lots to participate in the energy and regulation markets.With the implementation of seasonal Autoregressive Integrated Moving Average(ARIMA) model to predict the market information, a Day-Ahead (DA) planningmodel coordinated with the EV's responses to the incentive is formulated todetermine the days to activate the aggregation program and the optimalincentives that are broadcasted to the EV owners in the first stage. Given thedetermined incentives and EV's responses, an real-time (RT) optimal biddingalgorithm complying with EVs' energy demand in the second stage is designed tomaximize the aggregator's profits in the markets. The proposed models aretested using the data collected from PJM's energy and regulation markets. Theresults show the incentive-based aggregator can benefit both the EV owners andthe aggregator from the credits obtained from the markets. Meanwhile, theresults also indicate the proposed optimal bidding algorithm is capable ofhandling the uncertainty of regulation signals and following the signals withhigh precision.

A Two-Stage Optimal Bidding Algorithm for Incentive-based Aggregation of Electric Vehicles in Workplace Parking Lots