BCS-Tool: An open-source web application for capacity sizing of battery energy storage systems

Distributed battery energy storage systems (BESS) have become increasingly popular to reduce utility costs, to mitigate the mismatch between uncontrollable renewable generation and electricity demand, and therefore to improve grid stability. However, determining the optimal BESS capacity remains a complex challenge, especially for large-scale industrial parks with distributed renewable energy systems. Existing approaches typically rely on Mixed-Integer Linear Programming approaches and nonlinear economic battery models, which create significant bottlenecks for computational efficiency and financial evaluation. To tackle these problems, this paper proposed a computationally efficient Linear Programming optimization framework and developed an off-the-shelf software tool for optimal BESS sizing in large-scale industrial parks. A key contribution of this work is the development of a linearized battery life-cycle cost model that embeds self-degradation rates directly into the Capital Recovery Factor, avoiding the computational burden of non-linear degradation models while ensuring economic accuracy. A comprehensive case study is conducted at a real-world industrial park located in Changsha, China, to verify the performance of the developed software and investigate the impact of multiple factors on the optimal BESS capacity. The results demonstrate that the developed Linear Programming framework identifies the maximum profitability bound for BESS more accurately than rule-based control framework and operates with significantly higher computational efficiency than Mixed-Integer Linear Programming models. Furthermore, the synergistic effect between the BESS and photovoltaic systems is also identified, revealing that their combined deployment yields a payback period of 4.56 years, thereby providing valuable quantitative insights for the optimal design of distributed energy systems.