Energy management supported on genetic algorithms for the equalization of battery energy storage systems in microgrid systems

Energy management plays a fundamental role in ensuring the optimal operation of a microgrid (MG). Since MGs rely heavily on renewable energy sources, having batteries provides a reliable backup. Equalization is important when using batteries in an MG, ensuring their health and prolonging their lifetime. Due to the use of batteries in electric vehicles, they can be considered mobile sources that support energy management in an MG. Two methodologies are presented in this paper for the process of equalization and energy management that seeks to minimize grid usage. The first one prioritizes equalization, performing it quickly but slightly sacrificing grid usage. The second methodology focuses on slow equalization while prioritizing energy management, thereby reducing overall costs. The optimization was performed using a genetic algorithm that evaluates the MG parameters and as a result, provides the optimal current that each battery in the MG must deliver. Two case studies in MATLAB and Simulink are presented to demonstrate the effectiveness of the proposed optimizations. The results showed that both methods allow for achieving energy management and equalization in an MG without compromising its optimal operation. With the first method, equalization was achieved in 29 s with a consumption of 47 kW*s from the grid, while with the second method, equalization was obtained in 110 s with no energy consumption from the grid.