Energy-Optimized Intelligent Distributed Energy Resources in a Microgrid

Abstract

The modern smart grid replaces old power networks with networked microgrids with a high penetration rate of energy-storing technology and renewable energy sources. The control strategy is one of the most crucial elements in operating a microgrid power system. Although different control methods have been examined to control hybrid microgrids with interlinking converters, further research is required. A distributed energy system is built on integrating battery energy storage systems (BESS) and renewable energy sources like wind, solar and small hydro systems. The charging facilities for electric cars are also included in this scheme. This work proposed a novel Zebra-based Deep Belief Neural Mechanism (ZbDBNM) with a robust control mechanism. Using a Zebra-based fitness function, this novel approach predicts and optimizes energy cost, Total Harmonic distortion (THD) and power loss to match established norms. An evaluation of the proposed control approach's effectiveness and efficiency against established techniques is provided through comparison.