A novel planning strategy for DER-integrated EV charging stations in stochastic unbalanced distribution network

Abstract

The increasing penetration of electric vehicles (EVs) and distributed energy resources (DERs), including photovoltaic systems, wind turbines, battery energy storage systems, and hydrogen fuel cells, is reforming modern distribution networks. However, their stochastic behavior presents additional operational challenges, particularly in unbalanced radial feeders. This paper proposes an uncertainty-aware multi-objective planning framework for the optimal placement and coordinated operation of EV charging stations together with DERs, aiming to balance technical performance and economic cost. Uncertainties in load demand, renewable generation, and EV charging demand are represented using probabilistic models. Monte Carlo simulation generates hourly scenarios over a 24-h horizon, which are reduced using K-means clustering to retain scenario diversity while reducing computational burden. Energy loss, the voltage deviation index (VDI), and operating cost are optimized instantaneously, while the proposed approach is examined on the IEEE 37-bus unbalanced distribution feeder through MATLAB–OpenDSS co-simulation. Multi-objective particle swarm optimization (MOPSO), multi-objective genetic algorithm, and second-order cone programming (SOCP) are employed to obtain Pareto-optimal solutions. Results show that MOPSO delivers the best-compromise solution with 3100.42 kWh energy loss, VDI of 190.16 p.u., and 1463.27 USD/day operating cost, while SOCP provides competitive benchmark performance. A 20-year life cycle cost analysis further supports the economic feasibility of the coordinated planning solution. Overall, the coordinated EV charging stations (EVCS)–DER planning strategy strengthens feeder performance under uncertainty by improving operating conditions and maintaining more stable network behavior.