AI-Based Optimization Framework of Microgrid Energy Flows for Carbon Reduction

As the global demand for sustainable energy solutions intensifies, microgrids have emerged as a pivotal component in decentralized energy systems, enabling the integration of renewable energy sources and enhancing grid resilience. However, managing energy flows within microgrids remains a complex challenge, particularly when balancing renewable variability, storage dynamics, and fossil fuel backup with the imperative of reducing carbon emissions. This study proposes an AI-based optimization framework to intelligently manage energy dispatch within a hybrid microgrid comprising solar, wind, battery storage, and diesel generation. The proposed system combines machine learning for short-term forecasting of energy demand and renewable generation with reinforcement learning and evolutionary algorithms for optimal scheduling of distributed energy resources. The objective is to minimize operational costs and carbon emissions while maintaining power reliability. A case study simulation using real-world weather and load data demonstrates that the AI-enhanced controller achieves a significant reduction in carbon emissions, up to 35%, compared to traditional rule-based dispatch methods. This research underscores the potential of artificial intelligence to facilitate carbon-aware decision-making in microgrid operations, contributing to global efforts toward net-zero emissions and advancing the development of intelligent, low-carbon energy systems.