Optimizing Energy Storage System Operations and Configuration through a Whale Optimization Algorithm Enhanced with Chaotic Mapping and IoT Data: Enhancing Efficiency and Longevity of Energy Storage Stations

To enhance the charging and discharging strategy of the energy storage system (ESS) and optimize its economic efficiency, this paper proposes a novel approach based on the enhanced whale algorithm. Recognizing that the standard whale algorithm can sometimes suffer from local optima in high-dimensional multiobjective optimization, this study introduces chaotic mapping and individual information exchange mechanisms to address this challenge. The proposed algorithm explores optimal configurations for different energy device placements and capacities through encircling and bubble searches, evaluating various multiobjective functions for optimization. In addition, the algorithm refines both the system operation model and the ESS configuration model, with the objective function being the analysis of the average annual revenue of the ESS. Model testing results demonstrate that this algorithm yields more moderate energy storage (ES) capacity decay, extending operational time to 3, 124 days and achieving a full-life cycle benefit of the ESS as high as 1, 821, 623.68 yuan. Also, our algorithm demonstrates high efficiency, with minimal test time (68.36 seconds) and quick optimization (0.031 seconds per cycle), regardless of problem complexity.