Optimal Bidding Strategy of a Plug-In Electric Vehicle Aggregator in Day-Ahead Electricity Markets Under Uncertainty

Abstract

With a large-scale introduction of plug-in electric vehicles (PEVs), a new entity, the PEV fleet aggregator, is expected to be responsible for managing the charging of, and for purchasing electricity for, the vehicles. We approach the problem of an aggregator bidding into the day-ahead electricity market with the objective of minimizing charging costs while satisfying the PEVs' flexible demand. The aggregator places demand bids only (no vehicle-to-grid is considered). The aggregator is assumed to potentially influence market prices, in contrast to what is commonly found in the literature. Specifically, the bidding strategy of the aggregator is formulated as a bilevel problem, which is implemented as a mixed-integer linear program. The upper level problem represents the charging cost minimization of the aggregator, whereas the lower level problem represents the market clearing. Since the bids of other market participants are not known to the aggregator ex ante, a simple strategy is used to estimate them, based on historical data. An aggregated representation of the PEV end-use requirements as a virtual battery, with time varying power and energy constraints, is proposed, derived from individual driving patterns. Since driving patterns cannot be perfectly forecasted, we introduce a probabilistic virtual battery model. We compare the results of the proposed bidding strategy with those of a bidding strategy assuming exogenous prices, uncontrolled charging, and a central dispatch of the fleet. We also explore the impacts of different sources of uncertainty. Results show that with the proposed bidding strategy, costs are significantly lower than under inflexible charging and are lower than assuming exogenous prices. Moreover, the approach also performs well under uncertainty. Results also suggest that the aggregator only has limited market power potential at moderate PEV penetrations.