Optimization of Renewable Energy Integration in Microgrid Systems Using Artificial Intelligence

Abstract

The integration of renewable energy resources has brought new challenges to the stability, reliability and efficiency of modern power systems. The combination of renewable energy sources, like solar and wind, with a localized energy network has been addressed by the microgrid system. But with the intermittent and unpredictable nature of renewable energy generation, advanced optimization becomes necessary to optimize energy use. This research examines how AI can be used to improve the integration of renewable energy in microgrid systems. For renewable energy forecasting, load prediction, battery management, and intelligent energy scheduling, various AI techniques such as Machine Learning, Artificial Neural Networks, Deep Learning, and Reinforcement Learning were employed. The simulation results showed the effectiveness of the AI optimization in enhancing the accuracy of forecasts, renewable energy utilization, grid stability, battery performance, and minimizing operational costs and energy losses. Moreover, the study showed that AI-based energy management systems proved to be more effective than traditional control systems in managing dynamic operating scenarios and maintaining power quality. The research concludes the AI-based optimization frameworks are highly efficient for the development of sustainable, intelligent and resilient microgrid systems for future smart grid applications.