This chapter explores the strategic deployment of battery energy storage systems (BESSs) to enhance the efficiency of distribution grids. It emphasizes the critical factors of BESS placement, sizing, and operation to improve network performance. A well-configured BESS not only addresses peak energy demands but also maximizes the benefits of renewable integration. Additionally, it aids in power quality management and reduces distribution costs. This chapter provides a thorough overview of utilizing artificial intelligence (AI) techniques for optimal BESS allocation and operation. The discussion covers various grid states, performance goals, strategies, and BESS types and evaluates their pros and cons. The model’s effectiveness is demonstrated through its application to the IEEE 33-bus radial distribution system test case, affirming its accuracy. Furthermore, the reactive capability of batteries brings additional benefits, such as improved voltage profiles, reduced reactive power flow, lower network losses, and increased network capacity. This chapter outlines the use of popular AI-based optimization algorithms like genetic algorithms, particle swarm optimization, and simulated annealing for determining the optimal locations and sizing of BESS. This chapter also examines the combined impact of renewable distributed generations from solar and wind sources, along with electric vehicle charging stations in conjunction with BESS on the distribution grid. It concludes with practical recommendations covering requirements, BESS selection, intelligent charging and discharging strategies, optimal sizing and placement, and considerations for power quality. Finally, the study identifies potential research opportunities, including challenges in optimal BESS placement planning, development and implementation considerations, advanced optimization techniques, social implications, and the critical aspect of energy security. This chapter serves as a comprehensive guide for understanding and implementing BESS deployment in the evolving energy landscape. © 2024 Elsevier B.V., All rights reserved.