Real‐Time User‐Centric Routing Leveraging DG–DRM Integration in the Real‐Time Distribution Network

The rapid adoption of electric vehicles (EVs) poses new challenges for both transportation networks and power distribution systems. To address these issues, effective EV routing strategies are essential to minimize grid stress and ensure efficient energy utilization. This research proposes an optimal EV routing by incorporating user‐centric parameters and coordination of demand response management (DRM) with distributed generation (DG), facilitating effective synergy between user preferences and grid operational reliability. A loopless route is formulated considering distance and travel time (TT) to minimize the routing cost using Yen’s algorithm (YA). The optimized route, integrated with DG–DRM, ensures the minimization of power loss (PL) and the maximization of customer benefit (CB) using population‐based incremental learning (PBIL) algorithm. To enable effective coordination between EV routing and DRM in the distribution network, the Monte Carlo sampling method is employed to validate stochastic traffic and load variations. IEEE‐33 bus and the Indian utility power system (IUPS) network comprising 17 busses, are taken as test systems. The proposed methodology is compared with other soft computing techniques, and the findings demonstrate its superiority by achieving a 12.3% reduction in routing cost (RRC), a 16.74% reduction in PL, and a 22.31% increase in CB.