A Comparative Heuristic Optimization Approach for BESS Sizing in Nanogrids

Abstract

This study focuses on optimizing the capacity of Battery Energy Storage System (BESS) for a nanogrid using three heuristic-based optimization algorithms: Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO). The energy management of the nanogrid is modeled in Python using a rule-based approach to minimize energy imports from the grid, followed by the application of these heuristic algorithms to determine the optimal BESS capacity. Simulations conducted as a part of this study revealed the performance characteristics of each algorithm. The GWO algorithm stabilized at 1.85 kW by the 25th iteration. In contrast, the ABC algorithm achieved a rapid increase, reaching 2.14 kW by the 10th iteration and maintaining this level thereafter. The PSO algorithm exhibited a more stable and consistent trajectory, maintaining a capacity of approximately 1.52 kW. The findings highlight the distinct advantages offered by each algorithm in nanogrid energy management. While GWO and ABC excelled in fast convergence and broad search capabilities, PSO demonstrates a more consistent and stable solution. In addressing the complex energy management challenges posed by BESS capacity optimization, the performance of each algorithm is evaluated and compared to determine the most efficient strategy for managing energy storage systems.