Distributed V2G Grid Frequency Regulation Considering EV Owner Participation via Cooperative Integral Reinforcement Learning

With the increasing penetration of renewable energy, power systems are facing stronger frequency fluctuations, which make fast and flexible frequency support increasingly important. Although vehicle-to-grid (V2G) technology provides a promising source of distributed regulation capacity, many existing studies do not explicitly consider EV owners’ participation, which may lead to a mismatch between theoretical regulation potential and practically available V2G support. To address this issue, this paper proposes a distributed Grid–Aggregator–EV frequency-regulation (FR) framework that incorporates EV participation factor into the control design. A three-layer architecture and a dynamic participation-aware model are established to describe the coordination of distributed V2G resources, and a Hamiltonian-based robust control law is developed under V2G power constraints. An integral reinforcement learning scheme is then adopted to realize the optimal regulation policy online, where the controller does not require explicit online knowledge of the system drift matrix, while preserving the physical control structure. In this way, the proposed method explicitly links the EV participation factor, dispatchable V2G regulation capacity, and coordinated FR, thereby improving robustness, adaptability, and practical relevance. Simulation studies on the IEEE 14-bus and IEEE 39-bus systems, together with an evening-period, time-varying participation case, demonstrate that the proposed method provides more effective frequency-deviation suppression, better overall regulation performance, and stable operation under dynamic EV participation.