A Review on the Application of Machine Learning Algorithms on Smart Grid Optimization

Abstract

With the increasing challenge of distributed and renewable energy sources, maintaining the stability of the power grid is becoming increasingly difficult. By incorporating information and communication technologies, along with machine intelligence, the conventional power grid has the potential to evolve into a smart grid. The integration of machine learning equips the smart grid to make its decisions and efficiently handle generation, power outages, transmission line failures, unforeseen shifts in customer demands, overall fluctuations in renewable energy production, or any unexpected catastrophic events. These diverse machine-learning algorithms play a crucial role in enhancing the functionality of smart grids, contributing to their optimization. This article examines various machine learning algorithms and approaches designed to optimize the responsiveness of each facet of smart grid optimization.