Stochastic Multi‐Objective Planning Framework for Optimal Placement and Sizing of Distributed Generators and Electric Vehicle Charging Stations in Active Distribution Systems

ABSTRACT

This paper proposes a hybrid optimization framework for optimal placement and sizing of distributed generators (DGs) and electric vehicle charging stations (EVCSs) in active distribution systems under stochastic operating conditions. The increasing penetration of renewable energy resources and electric vehicles (EVs) introduces significant operational uncertainties due to intermittent DG generation and stochastic EV charging demand, which lead to complex multi‐objective planning problems. A hybrid grey wolf optimizer–local search (GWO–LS) framework is proposed to tackle these issues and minimize network power loss, operating cost, voltage deviation as well as the deterioration of voltage stability while meeting operational constraints. The proposed framework combines the global search capability of GWO with the neighbourhood refinement capability of local search to enhance convergence accuracy and optimization robustness. The methodology is tested on the IEEE 33‐bus distribution system considering different renewable‐generation availability levels and EV charging scenarios. The proposed framework is validated using weight‐sensitivity analysis with dominance‐based comparison, and comparison with GWO, LS, particle swarm optimization (PSO), and Aquila Optimizer (AO). The results show that the proposed hybrid GWO–LS framework achieves better techno‐economic and voltage stability performance than existing optimization approaches. Under the 100% renewable‐generation availability condition, daily energy loss is reduced from 4871.31 kWh to 2138.37 kWh in the distribution network, with improved voltage stability. The results demonstrate that the proposed framework provides an effective and practicable solution for coordinated DG and EVCS planning in active distribution systems.