This study proposes a novel approach to optimizing the sizing of battery energy storage systems (BESS) tailored for university campus applications, employing Particle Swarm Optimization (PSO). The primary aim is to minimize electricity consumption charges while ensuring that the BESS capacity is optimized to avoid unnecessary costs. Through PSO, the solution space is effectively explored to identify the optimal BESS size, striking a balance between cost reduction and capacity requirements. Results demonstrate significant cost savings while maintaining sufficient energy storage capacity. Our research highlights the practical utility of PSO in real-world energy management, enhancing decision-making processes and resource allocation. Future research directions include investigating scalability for larger-scale energy systems, integrating dynamic factors such as fluctuating energy demand and renewable energy sources, and exploring additional applications for optimized battery systems. Overall, this study underscores the potential of PSO in designing tailored BESS solutions for diverse applications, addressing complex energy optimization challenges with precision and effectiveness.