Optimal Allocation of Battery Energy Storage System in Wind Energy System Using Probabilistic Approach

Abstract

The increasing integration of renewable energy resources into modern electrical power systems has emerged as an essential strategy for reducing greenhouse gas emissions, enhancing energy security, and decreasing dependence on conventional fossil-fuel-based generation. Among the available renewable energy technologies, wind energy has gained considerable attention because of its abundant availability, environmental benefits, and continuously decreasing installation costs. However, the stochastic and intermittent nature of wind speed introduces significant operational challenges in power systems. Variations in wind speed directly affect wind power generation, leading to power fluctuations, voltage instability, frequency deviations, increased reserve requirements, and reduced reliability of grid-connected systems. These uncertainties limit the penetration capability of wind energy and create difficulties in maintaining the balance between generation and demand.

Battery Energy Storage Systems (BESS) have been recognized as an effective solution for addressing the variability associated with wind energy systems. The integration of BESS enables the storage of surplus electrical energy generated during periods of high wind availability and supplies stored energy during periods of low wind generation. Proper allocation of battery storage systems can improve renewable energy utilization, enhance power quality, reduce operational costs, and increase overall system reliability. However, determining the optimal size and placement of battery storage within a wind energy system remains a complex optimization problem due to uncertainty in wind characteristics and varying operating conditions.

This study proposes a probabilistic approach for the optimal allocation of Battery Energy Storage Systems in a wind energy system considering wind speed uncertainty. The proposed methodology employs a Weibull probability distribution function to model stochastic wind behavior and generate representative wind scenarios. A mathematical framework is developed to model wind power generation, battery charging and discharging characteristics, and operational constraints of the integrated system. The optimization problem is formulated to minimize the total annual system cost while improving system reliability and reducing power fluctuations. Various operational constraints, including battery capacity limits, state-of-charge restrictions, and power balance conditions, are incorporated into the optimization framework.

The effectiveness of the proposed approach is evaluated through simulation studies under different wind operating conditions. The obtained results demonstrate that the probabilistic method accurately captures the uncertainty associated with wind speed variations and determines an optimal battery allocation strategy that enhances economic and technical performance. Comparative analysis indicates that the proposed approach achieves significant reductions in annual operating costs and improves system reliability compared with conventional deterministic methods. The findings of this study suggest that integrating probabilistic analysis with optimal battery allocation strategies can substantially enhance the operational efficiency and stability of wind energy systems.