Planning under Uncertainty for Aggregated Electric Vehicle Charging Using Markov Decision Processes

The increasing penetration of renewable energy sources and electric vehicles raises important challenges related to the operation of electricity grids. For instance, the amount of power generated by wind turbines is time-varying and dependent on the weather, which makes it hard to match flexible electric vehicle demand and uncertain wind power supply. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control charging of multiple electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator while reducing usage of conventionally-generated power and cost of customers.