Distributed Multi-Agent Reinforcement Learning for Autonomus Management of Renewable Energy Microgrids

This study explores the application of Multi-Agent Reinforcement Learning (MARL) in the autonomous management of renewable energy microgrids, assessing its impact on key performance metrics including grid reliability, energy efficiency, cost reduction, and scalability. Using a series of experiments comparing the MARL framework with traditional rule-based methods, we observed significant improvements across multiple dimensions.The MARL framework demonstrated a 75% reduction in blackout rates and a 60% reduction in recovery time after blackouts, enhancing grid reliability. In terms of energy efficiency, the renewable energy utilization ratio increased by 7% to 87%, while energy losses decreased by 25%. Operational efficiency also saw a notable improvement of 10 percentage points, indicating a more efficient energy management process.Regarding cost reduction, the MARL framework achieved a 33% reduction in energy cost per kWh, leading to an annual savings of approximately $7,300 for an average production rate of 500 kWh per day. Maintenance costs were reduced by 30%, and system downtime was shortened by 60%, resulting in additional annual savings. The total cost reduction with the MARL framework was estimated at$10,000 per year, underscoring the financial benefits of this approach.The scalability and adaptability of the MARL framework further highlight its potential for broader applications in the energy sector. Given the demonstrated improvements in reliability, energy efficiency, and cost reduction, this study suggests that MARL-based management can contribute to more sustainable and resilient renewable energy microgrids.