Stochastic portfolio management of an electric vehicles aggregator under price uncertainty

This paper considers the portfolio management problem of a flexibility aggregator under uncertainty on real-time prices. Solving this stochastic optimal control problem in a reasonable time, considering overall scalability, comfort settings and grid constraints, is a challenging task. This paper tackles these problems by making use of a Three-Step Approach (TSA). Two control approaches are considered in the second step of the TSA: Model Predictive Control (MPC) and Approximate Dynamic Programming (ADP). The performance of both controllers for different temporal autocorrelated price profiles is illustrated for an aggregator with a fleet of 1000 electric vehicles. The simulations show that the TSA extended with a stochastic controller can reduce the cost of the aggregator compared to a certainty equivalent approach. The paper concludes by discussing the strength and weaknesses of MPC and ADP in a smart grid setting.