The rapid expansion of electric vehicles (EVs) and renewable energy resources introduces new operational stresses in modern power networks such as peak-load surges, voltage fluctuations, and quality degradation. This work presents a hybrid intelligence-based multi-agent framework that combines Adversarial Reinforcement Learning (ARL) with Dynamic Grey Wolf Optimization (DGWO) to coordinate EV charging, renewable usage, and energy trading. Each entity—EVs, charging stations, renewable units, and the grid operator—acts as an adaptive agent capable of self-learning and cooperative decision-making under uncertainty. The ARL component strengthens learning under variable demand, while DGWO continuously refines control parameters to ensure fast and stable convergence. Simulation studies on a renewable-supported microgrid show a 21% reduction in peak demand, 18% higher renewable energy utilization, 22% less EV waiting time, and 15% greater profitability than conventional GA, PSO, GWO, and RL methods. Voltage deviation stayed within ±3%, power factor exceeded 0.97, and THD remained below 4%, meeting IEEE 519/1547 standards. These results confirm that the proposed ARL–DGWO framework offers a scalable and reliable solution for next-generation EV-grid coordination.
Kotla et al. (2026) studied this question.