Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques

Abstract

The management of renewable energy is one of the most important areas in the ability to use resources efficiently and ensure stability in the production of energy and the grid. This paper focuses on a networked microgrid (MG) system which is composed of multiple energy sources including biomass, photovoltaic (PV) solar panels, wind turbines (WTs), battery energy storage systems (BESS), and pumped hydro storage. The study analyzes an energy management method based on hierarchical deep learning (HDL) through several scenarios. These include normal operation, peak load, changes in renewable energy generation finding faults and odd events extreme weather, cost-effective energy distribution, and long-term planning. The HDL approach uses predictive analysis real-time data, and layered control algorithms to improve energy distribution strategies, make operations more flexible, and help provide grid support services. This provides a complete outlook on how efficient it is in different operating environments. Finally, the study employs the MATLAB/Simulink environment to validate the efficacy and accuracy of the proposed energy management systems (EMSs) strategy.

Keywords

• Energy management
• Distributed generation
• Renewable energy integration
• Networked microgrids
• Hierarchical deep learning
• Predictive analytics techniques