Akademik Veri Yönetim Sistemi
13th IEEE International Conference on Smart Grid, icSmartGrid 2025, Glasgow, İngiltere, 27 - 29 Mayıs 2025, ss.542-549, (Tam Metin Bildiri)
Energy Storage Systems (ESS) play a vital role in both distributed and large-scale power systems by reducing operational costs, emissions, power losses, and enhancing voltage stability. This study utilizes the Enhanced Coronavirus Disease Optimization Algorithm (ENHCOVIDOA) to tackle the Multi-Objective Optimal Power Flow (MO-OPF) problem, incorporating renewable energy sources (REs) such as wind and solar and Battery Energy Storage Systems (BESS), within an uncertain operating environment of wind and solar. The objectives are to minimize fuel cost, emissions, voltage deviation, and power losses. Uncertainties in renewable generation are handled using Monte Carlo Simulation (MCS) and the Two-Point Estimation Method (TPEM) alongside nonlinear power flow equations. System performance is evaluated by comparing total operational cost under deterministic and stochastic conditions. MCS yields a 0.0752% error with only renewables and 0.03875% with BESS. TPEM shows a 0.0508% error with only renewables, reduced to 0.01471% with BESS. The results confirm that the size and placement of BESS have a significant impact on both cost and accuracy. The proposed method enhances cost efficiency and improves uncertainty management, which is crucial for operating a modern power grid. Case studies on the IEEE 30-bus system validate the method’s effectiveness and emphasize the value of optimal BESS allocation and the higher accuracy of TPEM in uncertainty modeling.