Optimal Bidding of Plug-in Electric Vehicles in a Market-Based Control Setup

This paper presents a market-based control approach to minimize the charging costs of plug-in electric vehicles (PEVs) without impacting their end-use. In the proposed framework, vehicles are modeled as agents actively placing bids to purchase electricity in the spot market. Their bids depend on status variables which represent the urgency to charge. To ensure scalability, the bids of large numbers of PEVs are aggregated. Then, they are cleared with the remaining market supply and demand bids. In this paper we focus on determining the optimal bidding strategies of individual PEVs, taking into account the uncertainty related to market bids and to driving behavior. For this purpose, we formulate a learning process based on Q-learning, where each vehicle adapts its bidding strategy over time according to the market outcomes. We perform simulations with historical market bid data, and realistic vehicle driving patterns from an agent-based transport simulation. Results show that the costs of charging can be significantly reduced compared with an uncontrolled charging approach. Moreover, we compare the results with those of a centralized aggregator-based approach, where an aggregator directly manages charging and purchases electricity on behalf of PEVs on the spot market. We show that the results of the decentralized market-based control approach are just slightly higher than those of the centralized approach.