Effective Centralized Power Control and Management of Nano-Grid Using DT Based Novel Distributed Framework

Abstract

Nano-grid is an independent hybrid sustainable framework which uses both renewable and non-renewable power sources to continuously supply energy to load. Nano-grid finds its possibilities for the integration of distributed energy sources for realizing versatile and efficient energy management systems for future homes, local communities, and buildings. Nano-grid's energy trading system effectiveness might depend on various factors including core efficient management components such as energy storage systems (ESS) and renewable energy devices. Smart advanced functions in consumer devices and their unpredictable usage patterns result in unpredictable fluctuations in consumption of power. These fluctuations pose significant challenges in stability and quality of the power rid and create complex power imbalance issues which become harder to control and manage. Innovative power control and management models are essential to solve these issues in the nano-grid. In recent times, machine learning algorithms can be used to predict, track the current conditions, and make appropriate adjustments to the quality settings of power. In this research, effective centralized power control and management of the nano-grid using DT-based novel distributed framework is presented. This system utilizes a novel distributed framework based on a decision tree to enhance the agility and stability of complex and large-scale power systems. Performance measures like accuracy, F1-score, and RMSE are used to evaluate the performance of this system. This system will achieve better centralized power control and management.