A hierarchical stochastic coordination strategy for residential electric vehicle charging

With the rapid growth of electric vehicle (EV) adoption, time-of-use (ToU) tariffs have been widely implemented to guide residential charging behavior. In China, residential consumers are served by the grid company, which typically operates as local monopoly as many states in the US. Current ToU tariffs fail to effectively shift EV charging loads for peak shaving due to identical pricing for both EV and non-EV loads. Recent policies mandate differentiated pricing for EV charging. In addition, even after responding to ToU, some EVs may still have some flexibility in charging power due to later departure time, which is especially beneficial for a residential community to avoid overloading distribution lines and defer upgrades. This paper proposes a hierarchical stochastic coordination strategy to reduce the peak caused by EV load. In the first layer, a bi-level optimization model designs EV-specific ToU tariffs, where the grid company optimizes peak reduction while consumers minimize costs. In the second layer, the residential community charging operator coordinates EV charging using a moving horizon stochastic approach within scheduled charging windows by the consumers after reacting to the ToU tariff. Simulations based on real-world data demonstrate the strategy’s effectiveness in managing peak demand, and incentivizing residential consumers to provide their flexibility for a lower bill. The proposed strategy is also generalizable to liberalized power systems in which the utility operates as a regulated local monopoly.