Two-Stage robust optimal operation of photovoltaic-energy storage-fast charging stations in power-transportation coupled systems with user bounded rationality biases

With the continuous advancement in electric vehicle charging capabilities, load surges induced by large-scale deployment of fast-charging infrastructure coupled with spatiotemporal stochasticity in user charging behavior present substantial challenges to the secure and economically efficient operation of power distribution networks. To address the optimal operation uncertainty problem of integrated photovoltaic-energy storage-fast charging stations in power-transportation coupled systems (PTCS), a two-stage robust optimization method for power distribution networks is proposed in the paper that incorporates the bounded rationality biases of transportation network users. First, the stochastic user equilibrium (SUE) model characterizes electric vehicle users’ route-choice and charging behaviors under varying perception biases, deriving spatiotemporal distributions of charging load uncertainty under these behavioral deviations. Subsequently, incorporating multiple uncertainties in photovoltaic generation and charging loads, a distribution network two-stage robust optimization model is constructed using second-order cone relaxation and duality theory. Then, the column-and-constraint generation (C&CG) algorithm with nested outer approximation (OA) for efficient model solving is proposed to dynamically optimizes decision variables through master-subproblem iterations, significantly boosting computational efficiency for nonlinear objectives. Finally, numerical case studies on a power-transportation coupled system validate the effectiveness of the proposed strategy.