Abstract
This paper proposes a two-stage, reliability-driven multi-objective planning framework for fast electric vehicle charging stations (FCSs) integrated with solar photovoltaic (PV) generation and battery energy storage systems (BESS) in coupled power–transportation networks. The framework simultaneously addresses electrical network constraints, transportation-driven charging demand, and techno-economic trade-offs in high EV penetration scenarios. A benchmark IEEE 69-bus radial distribution system is co-simulated with a 25-node transportation network to realistically capture spatial and temporal interactions between EV mobility and grid operation. To quantify the combined impacts of voltage stability, service continuity, and charging uncertainty, a novel average voltage deviation reliability index (AVDRI) is introduced. Spatially and temporally varying EV charging demand is modeled using a hybrid approach that integrates queuing theory with gravity-based traffic interaction models, enabling a realistic representation of stochastic arrival patterns and route-dependent charging behavior. In the first stage, a multi-objective optimization problem is formulated to determine the optimal locations and charging capacities of FCSs, minimizing system power losses, reliability degradation, and total system cost while maximizing EV serviceability. Multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimization (MOGWO), and a proposed hybrid GWOPSO algorithm are comparatively evaluated. In the second stage, a bisection-based sizing strategy is employed to determine the optimal PV and BESS capacities required to mitigate solar intermittency and peak power generation mismatches. Results demonstrate that the proposed hybrid GWOPSO based framework achieves superior convergence characteristics and delivers significant improvements in voltage profile, reliability indices, power loss reduction, and overall techno-economic performance compared to conventional approaches.