Low-carbon collaborative scheduling method for EV charging stations based on robust optimization and cooperative game theory

Modern electric vehicle charging stations (EVCSs) are usually integrated with renewable energy sources like photovoltaic (PV) units and energy storage systems (ESSs). This can help promote decarbonization and flexible management but also introduces several challenges, including uncertainties in electric vehicle (EV) charging behavior and PV power output, benefit distribution among participants, and the absence of low-carbonization mechanisms. To address this, this paper proposes a coordinated EVCSs scheduling method based on robust optimization and cooperative game theory. First, a multi-energy-flow coupling model of the EV-PV-ESS-Grid system is established, which quantifies the carbon reduction benefits of EVCS clusters by embedding carbon emission flow constraints. After that, a robust optimization model is formed to minimize total operating costs and carbon emission costs. Gaussian mixture modeling (GMM) and a distributionally robust chance constraint (DRCC) framework driven by residual-based PV uncertainty are integrated to handle the uncertainties in EV behavior and PV generation. Furthermore, based on cooperative game theory, a collaborative framework is designed for EVCS clusters, combining Shapley value-based benefit distribution with a carbon trading mechanism to incentivize low-carbon collaboration and ensure equitable benefit distribution. Simulation results of a test system with three interconnected EVCSs demonstrate that the proposed method can reduce total operating and carbon emission costs by 6.71 % and 7.9 % respectively. Additionally, the benefit imbalance issue is effectively resolved with the Shapley value-based benefit distribution method.