  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) planning
model coordinated with the EV's responses to the incentive is formulated to
determine the days to activate the aggregation program and the optimal
incentives that are broadcasted to the EV owners in the first stage. Given the
determined incentives and EV's responses, an real-time (RT) optimal bidding
algorithm complying with EVs' energy demand in the second stage is designed to
maximize the aggregator's profits in the markets. The proposed models are
tested using the data collected from PJM's energy and regulation markets. The
results show the incentive-based aggregator can benefit both the EV owners and
the aggregator from the credits obtained from the markets. Meanwhile, the
results also indicate the proposed optimal bidding algorithm is capable of
handling the uncertainty of regulation signals and following the signals with
high precision.
