Abstract:

To address the security challenges posed by the integration of Electric Vehicles (EVs) into distribution networks and the limitations of existing assessment methods in spatiotemporal dynamics and multi-constraint coordination, a multi-dimensional carrying capacity assessment method for EVs considering spatiotemporal coupling and operational constraints is proposed. The method constructs a spatiotemporal probabilistic model for EV charging demand based on trip chain-geographic information, using a bimodal Weibull mixture distribution and a hierarchical Poisson point process to achieve spatiotemporal coupling. An assessment framework integrating voltage security, line thermal stability, and protection coordination is established, employing an improved sequential Monte Carlo simulation for dynamic verification. A three-dimensional index system encompassing technical, economic, and resilience aspects is built, and the entropy-weight-TOPSIS algorithm is introduced to generate a comprehensive carrying capacity index. Simulations show that with unupdated protection settings, the comprehensive carrying capacity is 37.2%. After optimizing protection settings, the physical limits of grid equipment become the core limiting factor. Implementing an orderly charging strategy can increase the carrying capacity to 41.8%. The research provides a theoretical basis for the precise planning and operation of distribution networks with high-penetration EV integration.

Key words: spatiotemporal coupling, electric vehicles (EVs), distribution network, carrying capacity, assessment