Energy Management System of a Microgrid Using Deep Learning

The increasing adoption of microgrids with renewable energy systems, driven by environmental and socioeconomic factors, faces challenges such as renewable energy variability and dynamic load fluctuations, leading to increased grid consumption. This study addresses these challenges by proposing an advanced Energy Management System (EMS) integrated with a Deep Learning model for load forecasting. The objective is to enhance the efficiency and cost-effectiveness of microgrids by dynamically adjusting to forecasted load demands. The EMS utilizes Long Short-Term Memory (LSTM) networks to predict the load demand of a commercial building, allowing for optimized battery scheduling and reduced reliance on the utility grid. The study conducted a month-long simulation using real historical load and solar power data, comparing the proposed EMS with a standard EMS. Key findings indicate that the proposed EMS significantly reduces grid consumption, resulting in a 9.3% reduction in monthly electricity bills. Integrating deep learning in EMS demonstrates substantial improvements in handling dynamic conditions and optimizing energy usage. These findings imply that deep learning-based EMS can lead to significant cost savings and more efficient microgrid energy management, promoting the broader adoption of renewable energy solutions.