A data-driven dynamic pricing scheme for EV charging stations with price-sensitive customers

The increasing adoption of electric vehicles (EVs) has left power network providers to deal with new challenges in terms of grid stability and electricity market design. On the latter direction, a demanding problem is represented by the development of probabilistic algorithms capable of computing optimal time-varying price profiles for EVs charging stations to induce a desired aggregative behavior. Here, the inclusion of demand elasticity represents a key feature to provide usable schemes for real-world cases. In this paper, we propose an "estimate-then-optimize" framework for optimal dynamic pricing computation in the presence of price-sensitive customers. It consists of an estimation step based on nonparametric kernel methods to infer about the demand elasticity, followed by an optimization step to maximize the expected daily profit. We describe the charging process via a probabilistic framework and we show the benefits of the proposed formulation via extensive numerical experiments.