Day ahead scheduling of battery energy storage system operation using growth optimizer within cyber–physical–social systems

Integrating Battery Energy Storage Systems (BESS) into Cyber–Physical–Social Systems (CPSS) is pivotal for reducing energy costs, enhancing grid stability, and extending battery lifespan. However, existing optimization methods often struggle to balance operational cost, battery degradation, and grid reliability, particularly under uncertain demand and supply conditions. This paper introduces the Growth Optimizer (GO), a novel meta-heuristic algorithm specifically designed for day-ahead BESS scheduling in CPSS environments. Unlike traditional methods, GO explicitly incorporates cyber, physical, and social dimensions, capturing the interdependent dynamics among energy consumption behavior, grid operations, and economic incentives. By leveraging adaptive scheduling under varying battery capacities, GO effectively mitigates uncertainties such as demand fluctuations and renewable intermittency. When applied to five Australian states, GO achieves up to a 15% improvement in multi-objective performance metrics, resulting in measurable financial savings, extended battery life, and reduced infrastructure costs. This approach empowers end users to optimize energy use proactively, enhancing both economic efficiency and energy autonomy.