Optimal energy management of distributed generation resources in a microgrid under various load and solar irradiance conditions using the artificial bee colony algorithm

Optimal energy management of distributed generation and storage systems in microgrids plays a critical role in minimizing operational costs, reducing environmental emissions, improving power quality, and enhancing system reliability. Achieving these objectives requires comprehensive modeling of all microgrid components, including load profiles, generation sources, and the network structure. In recent years, metaheuristic optimization techniques have gained significant traction due to their flexibility and robustness in handling complex, nonlinear, and multi-objective problems without the need for initial estimations. This study proposes the artificial bee colony algorithm as an effective tool for the optimal energy management of a hybrid microgrid system comprising photovoltaic panels, wind turbines, fuel cells, microturbines, and battery energy storage systems. The algorithm's performance is evaluated under varying solar irradiance conditions across four distinct operational scenarios. The results demonstrate that the proposed algorithm consistently achieves superior performance in minimizing the total operation cost compared to traditional bio-inspired optimization techniques such as genetic algorithms, particle swarm optimization, and a modified bat algorithm. The findings confirm our method's potential as a robust and efficient approach for real-time microgrid energy management under dynamic operating conditions.