Optimal VPP Pricing Strategy for Large‐Scale EV Integration with V2G and Constrained Charging Facilities

ABSTRACT

This paper proposes an optimal pricing strategy for a virtual power plant (VPP) handling large‐scale electric vehicle (EV) integration, including vehicle‐to‐grid (V2G) services and constrained charging facilities. The framework uses a bi‐level iterative Stackelberg game, with the VPP as the leader setting prices and incentives, and EVs as followers optimising their charging and discharging to minimise costs and maximise V2G revenue. Bayesian optimisation (BO) addresses the computational and coordination challenges of integrating large‐scale EVs into VPP operations. The study incorporates realistic infrastructure constraints and stochastic EV data, proposing two allocation strategies that balance VPP profitability with user satisfaction. Simulation results indicate that V2G significantly reduces EV charging costs and enhances VPP profitability. Fair allocation is more effective when charger availability is limited, whereas the priority‐based strategy achieves comparable profitability when sufficient chargers are available. The sample‐efficient BO algorithm outperforms constrained PSO in achieving higher VPP profitability and more efficient scheduling and coordination of large EV populations.