A V2G Capacity-Maximized Dispatch Control Strategy via Nested Consensus Control Learning for Grid Frequency Regulation

Optimizing vehicle-to-grid (V2G)-based frequency regulation (FR) is challenging due to the need to coordinate dispatch control under diverse electric vehicle (EV) owner behaviors while avoiding excessive charge/discharge. This paper proposes an optimized V2G dispatch control strategy for FR using nested consensus reinforcement learning (NCRL) to improve coordination among V2G resources. The strategy applies an integral reinforcement learning framework to optimally dispatch FR signals for EV clusters, while a novel nested consensus mechanism synchronizes energy consumption to prevent premature cluster withdrawal and sustain maximum FR capacity. An innovative exponential metric is developed for consensus-based power dispatch, which, by incorporating energy consumption consensus, preserves the maximum aggregated V2G FR power and enhances performance during critical grid disturbances. Compared with conventional proportional metrics, the proposed approach maximizes the available V2G FR capacity, i.e., the deliverable FR power. Simulations on the IEEE 39-bus system demonstrate that the strategy achieves inter-cluster consensus, maintains maximum FR capacity, and improves V2G FR performance.