Optimal Configuration of a DC Microgrid Using a Grey Wolf Optimization Algorithm
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
In recent years, a DC-based microgrid has been considered as a better and more feasible solution to meet local loads. The DC microgrid has an advantage in its compatibility with renewable energy source systems, energy storage systems; high efficiency; and reliability. This paper proposes a strategy to optimize the configuration of the DC microgrid. The optimization of the configuration of the DC microgrid becomes important. This is a key factor for ensuring the stable and economic operation of the DC microgrid. The considered configuration of the DC microgrid consists of solar photovoltaic (PV) generation sources, diesel generation sources, battery energy storage systems, supercapacitor energy storage systems, loads, and control systems. The objective function of the optimal configuration is based on the total annualized cost. Then, a grey wolf optimization (GWO) algorithm is proposed to apply and find an optimal solution of the optimal configuration. The GWO algorithm is a population-based meta-heuristic optimization algorithm inspired by the social behaviour and hunting mechanism of grey wolves. It is better than other meta-heuristic algorithms in terms of efficiency, convergence, fewer control parameters, flexibility, as well as parallelism. The GWO algorithm-based achievement is compared with the results by using a genetic algorithm (GA), and a particle swarm optimization (PSO) algorithm to validate the proposal of determining the optimal configuration of the DC microgrid.
Publication Source (Journal or Book title)
Proceedings of 2024 7th International Conference on Green Technology and Sustainable Development, GTSD 2024
First Page
401
Last Page
406
Recommended Citation
Huynh, D., Pham, H., Ho, L., Nguyen, H., Dunnigan, M., & Barbalata, C. (2024). Optimal Configuration of a DC Microgrid Using a Grey Wolf Optimization Algorithm. Proceedings of 2024 7th International Conference on Green Technology and Sustainable Development, GTSD 2024, 401-406. https://doi.org/10.1109/GTSD62346.2024.10674805
