Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets

Abstract

With a potential future large-scale introduction of plug-in electric vehicles (PEVs), the introduction of a new entity, the PEV fleet aggregator, is envisaged. Among other tasks, the aggregator would be responsible for managing charging and for purchasing electricity on behalf of the vehicles. PEV load can be considered flexible since vehicles are typically used only intermittently and, therefore, their demand can be shifted in time. In this paper we consider the problem of an aggregator bidding into the day-ahead electricity market with the objective to minimize charging costs while satisfying PEVs' flexible demand. The available charging flexibility depends on vehicle driving patterns, which determine the arrival and departure times and trip energy consumption. To take driver end-use constraints into account the fleet is modeled as a virtual storage resource with power and energy characteristics that depend on vehicle behavior. The bidding strategy of the aggregator is modeled as a bilevel problem. The upper-level problem represents the charging cost minimization of the aggregator subject to the power and energy constraints of the fleet. The lower-level problem represents the market clearing where the bids of other market participants are not known ex ante. Mathematically this problem can be described as a mathematical problem with equilibrium constraints (MPEC), which is implemented in the form of a mixed-integer linear program. Results show that with flexible charging, costs can be significantly reduced compared to inflexible charging. Moreover, even with a simple mechanism to guess the bids of other market participants, results close to a perfect information benchmark can be achieved.