EMS-ml: A Machine Learning-Driven Optimization Framework for Battery Storage in Renewable Energy Microgrids

This paper mainly focuses on the machine learning integration of battery storage system in renewable energy microgrid. In the architecture of renewable energy microgrid, battery storage system has some challenges, such as weak adaptability to output fluctuation, lagging charging and discharging strategies, and rapid life loss, while the existing regulation methods are deficient in stability and energy efficiency. In view of these situations, an innovative machine learning integrated optimization scheme, namely EMS-ml algorithm, combines multi-model fusion and real-time data-driven technology to optimize the renewable energy consumption and charging and discharging scheduling process to ensure the safe operation of battery storage system. The experimental results show that the scheme effectively improves the response speed of the battery storage system to the fluctuation of wind and solar output, reduces the response delay to 0.2 s, and in the high fluctuation scenario, that is, when the fluctuation of wind and solar output is ± 30%, the absorption rate of renewable energy reaches 92.3%, and also improves the cycle life of the battery. The energy efficiency index reaches 8.72. In the actual microgrid prototype, the charging and discharging accuracy index FCD is 356, which provides a strong support for the stable operation of the renewable energy microgrid.