Akademik Veri Yönetim Sistemi
JOURNAL OF ENERGY STORAGE, cilt.142, 2026 (SCI-Expanded, Scopus)
This paper presents a novel approach for optimizing the placement and sizing of Battery Energy Storage Systems (BESS) in modern power grids. It accounts for the variability of Renewable Energy Sources (RES), including Wind Turbines (WT) and Photovoltaic (PV) units, as well as load demand. The method integrates planning and operational costs using nonlinear power flow equations. Uncertainties are modeled efficiently with the TwoPoint Estimation Method (TPEM). A two-stage optimization framework is proposed: the first stage uses linear integer optimization to determine the optimal number and locations of BESS units, minimizing planning costs. The second stage employs a Bi-level Enhanced COVIDOA algorithm to optimize operational costs, including fuel consumption, emissions, losses, and voltage deviations, while ensuring reasonable computational time. The Binary Differential Evolution (BDE) algorithm enhances solution searching, reducing computational effort significantly. Numerical experiments on the IEEE 30-bus system demonstrate a 92.97 % reduction in operationstage runtime, along with reductions of 7.27 %, 9.86 %, and 12.48 % in the minimum, mean, and maximum multi-objective operation costs (across 25 runs), respectively, compared to conventional methods. This highlights the method's effectiveness for optimal BESS deployment under uncertain RES and load conditions.