Day-Ahead Optimal Scheduling for Electric Bus PV-Storage Charging Station Under Uncertainty: An IGDT-Based Approach

Efficient scheduling of electric bus (EB) photovoltaic-storage charging stations (PSCSs) is essential for ensuring the operational economy of public transit and the security of the power grid. Existing scheduling studies generally simplify charging and storage efficiencies as fixed constants, neglecting their dynamic dependence on power levels. Meanwhile, the stochasticity of photovoltaic (PV) generation further complicates scheduling decisions. To address these issues, this paper proposes a day-ahead robust scheduling method for EB PSCSs that incorporates dynamic charging efficiency. First, the dynamic battery efficiency model is reasonably simplified and reformulated, and the big-M method is employed to transform the nonlinear efficiency model into an equivalent set of linear constraints, thereby effectively integrating dynamic efficiency characteristics into the day-ahead optimization framework. Then, information gap decision theory (IGDT) is adopted to model PV output uncertainty, establishing a risk-averse decision optimization model. On this basis, a two-stage solution algorithm integrated with the bisection method is designed to decompose the IGDT optimization problem into a series of linear programming subproblems, balancing solution accuracy and computational efficiency. Case studies validate the effectiveness of the proposed method. The results demonstrate that the dynamic efficiency model significantly improves scheduling accuracy, and the IGDT framework provides a reliable, robust scheduling strategy for PSCSs under limited information conditions.