Hierarchical Deep Reinforcement Learning and Model Predictive Control for Voltage-Aware Electric Vehicle Charging Coordination in Distribution Network

The integration of electric vehicles (EVs) into distribution networks poses challenges for peak demand management and voltage regulation. This paper presents a hierarchical framework combining deep reinforcement learning (DRL) and model predictive control (MPC) for voltage-aware EV charging coordination. A Proximal Policy Optimization (PPO) agent learns strategic power allocation policies from historical data, while a lower-level MPC layer enforces network constraints using linearized power flow (LinDistFlow) equations. Validation on 873,286 smart meter measurements from 25 Pecan Street households over a full calendar year demonstrates that the proposed method achieves a 6.8% additional peak demand reduction (95% CI: [5.2%, 8.4%]) compared to standalone MPC, while maintaining zero voltage violations under LinDistFlow-enforced constraints, within the validated operating regime. PV self-consumption reaches 72.1% versus 61.2% for MPC-only (p < 0.001, Cohen’s d = 2.00). Comparison against six baselines confirms consistent improvements, though gains are incremental rather than transformative. Computational analysis demonstrates within-interval computational feasibility (211±50 ms) and scalability up to 50 EVs. The results suggest that hierarchical learning–optimization architectures offer modest but statistically significant benefits for network-constrained EV charging coordination.