Deep reinforcement learning–driven electric vehicle charging coordination for renewable-integrated intelligent energy districts

Coordinated electric-vehicle charging in renewable-integrated intelligent energy districts must respond within quarter-hourly operating windows while balancing uncertain photovoltaic generation, tariffs, mobility demand, and distribution-feeder limits. This study develops and evaluates a closed-loop hybrid framework in which a four-head Generalized Value Function Network constructs predictive states, asynchronous IMPALA coordinates ten district controllers, and an SOCP–DistFlow projection screens the aggregate dispatch against relaxed-model voltage and branch-loading constraints. The framework was tested on the IEEE 69-bus feeder using two years of operational records for ten districts and 70 electric vehicles. Within the reported episode histories, IMPALA reached a sustained 90%-of-peak reward threshold by episode 50, retained 99.6% of its best 100-episode reward, and exhibited a 3.0% post-convergence coefficient of variation. These statistics support method selection within the observed histories and do not establish between-seed superiority. The integrated pipeline completed a quarter-hourly schedule in approximately 4.8 min, compared with 26.9 min for the pure-optimization benchmark, representing an 82% reduction in runtime with an approximately 3% cost deviation. Sensitivity and joint-uncertainty analyses indicated stable mean performance over the tested envelopes, while severe joint conditions increased voltage, branch-loading, and upper-tail cost exposure. The results show that predictive-state learning, asynchronous coordination, and feeder-level conic screening can produce deadline-compatible schedules with a transparent cost–latency trade-off for the evaluated simulation. The evidence remains limited to the relaxed SOCP feeder model and reported histories; nonlinear AC replay, repeated seeded trials, hardware-in-the-loop testing, and controlled utility pilots remain necessary before field deployment.