Network-Aware Online Charge Control with Reinforcement Learning

The rising market of electric vehicles leads to an increased number of chargers needed to be connected to the power grid. It sometimes leads to their installation being postponed due to the capacity limits violations, which can be eased by implementing a smart charging control. In this paper the smart charging control is proposed from the point of view of grid loading as a primary indicator. The reinforcement learning agent controls the charger's power of consumption to optimize expenses and prevent lines and transformers from being overloaded. The simulations were carried out in the IEEE 13 bus test feeder with the load profile data coming from the residential area. To simulate the real availability of data, an agent is trained with only the transformer current and the local charger's state, like state of the charge and timestamp. Several algorithms are tested to select the best one to utilize in the stochastic environment and low frequency of data streaming.