Networked microgrid energy management: Enhancing system stability and efficiency in high-variability scenarios

This study proposes a hierarchical deep learning (HDL) framework to optimize energy systems under complex operational scenarios, including high penetration of electric vehicles (EVs), distributed energy resources (DERs), grid resiliency challenges, energy storage system (ESS) optimization, and long-term energy planning. The HDL framework leverages advanced machine learning techniques to handle high-dimensional data, capture spatial and temporal dependencies, and provide actionable intelligence for real-time grid operations. The workflow includes data collection and preprocessing, feature extraction, hierarchical representation, and output layer decision-making, supported by a feedback loop for continuous learning and adaptation. The framework is applied to four key scenarios: (1) microgrid operation with high EV penetration, (2) grid resiliency and recovery after cyberattacks, (3) ESS optimization for grid stability, and (4) DER management with high variability. By integrating diverse data sources such as EVs, DERs, weather stations, and grid monitoring systems, HDL ensures accurate load forecasting, anomaly detection, and energy dispatch optimization. The results demonstrate HDL's ability to improve system stability, reduce peak demand by up to 12%, and minimize energy losses by up to 10%. The framework's hierarchical structure and attention mechanisms enable it to outperform traditional methods in accuracy, efficiency, and scalability, providing a comprehensive solution for energy system optimization.