Investigating the impact of electric vehicles on increasing the reliability of the distribution system using the enhanced gray wolf evolutionary algorithm model

This paper proposes a tri‑level optimization framework for the coordinated planning and operation of electric‑vehicle‑integrated distribution systems. The upper level optimizes charging‑station siting and sizing under investment and capacity constraints. The middle level schedules vehicle charging/discharging with detailed models of battery degradation, time‑varying prices, and vehicle‑to‑grid (V2G) participation. The lower level evaluates operational feasibility and reliability under stochastic load scenarios and N‑1 contingencies. The resulting nonconvex problem is solved using an enhanced gray wolf optimization algorithm incorporating chaotic initialization, adaptive mutation, Lévy‑flight exploration, opposition‑based learning, and local search. Applied to a 33‑bus system with 150 EVs, the framework deploys five charging stations (total capacity 1550 kW). Coordinated V2G operation raises minimum voltages by 4.10%, reduces voltage deviation by > 50%, and cuts active‑power losses by 23.02%. Reliability improves significantly: expected energy not served drops from 651.30 to 192.70 MWh/year, and the composite reliability index rises from 0.152 to 0.367. Contingency analysis shows 66-78% reductions in load curtailment. Economically, the solution achieves a net present value of 7.93 M USD, a benefit‑cost ratio of 17.42, an internal rate of return of 287%, and a payback period of 0.21 years. Comparative studies confirm the enhanced algorithm's superiority in solution quality, convergence speed, and constraint satisfaction. The results demonstrate that coordinated EV‑grid integration substantially enhances efficiency, reliability, and economic performance in medium‑voltage distribution networks.