Robust Scheduling of Fast-Charging EVs With Exogenous and Endogenous Uncertainties for Urban Power Congestion Relief

As fast-charging electric vehicles (FCEVs) become one of the primary modes of urban transportation, the randomness and uncertainty of users’ charging behaviors pose significant congestion risks to the grid's scheduling. In this paper, the urban power system guides FCEVs to orderly charge through appropriate pricing strategies. To do this, a novel demand response (DR) model is established to simulate the spatial flexibility of FCEVs under different charging prices. In order to realistically reflect the power deviations when FCEVs participate in DR, a hybrid exogenous and endogenous uncertainty set is proposed to capture the effects of environmental and user psychological factors. Furthermore, a two-stage robust model is developed to relieve congestion. Specifically, a bi-level model is employed to optimize locational marginal electricity prices, aiming to achieve collaboration between transmission and distribution networks in the first stage. The two-stage robust model is then transformed into a tractable form and solved using the modified parametric column-and-constraint generation (CCG) algorithm proposed in this paper. Numerical simulations verify the proposed model and algorithm's effectiveness and practicability using both modified IEEE standard test cases and real-world cases from Puyang's urban power system in China.