Network-Constrained Reinforcement Learning for Optimal EV Charging Control
Abstract
This paper introduces a comprehensive control model that integrates aggregate electric vehicle (EV) charging demand with power grid systems operations, capitalizing on the flexible nature of EV charging. This innovative approach allows us to model and manage electrical loads in a scalable manner. The main contribution is the study of a constrained reinforcement learning (CRL) method for the predictive control of optimal power flow, paired with EV charging control. The CRL-based control method operates with the understanding that future EV arrivals are uncertain, while ensuring the feasibility of control actions. Our case studies, conducted on IEEE standard systems, highlight the superior performance of our approach that dynamically adapts to the evolving EV environment while consistently upholding safety constraints.
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