An Overview of Machine Learning Algorithms on Microgrids

Abstract

The concept of microgrid (MG) is based on the notion of small-scale power systems that can operate independently or in conjunction with the larger power grid. MGs are generally made up of renewable energy resources, such as solar panels, wind turbines, and energy storage devices (batteries). Overuse of non-renewable resources causes depletion of the ozone layer and eventually leads to global warming. The classical techniques are not sufficient to solve the problem and require modern solutions like machine learning (ML) algorithms—a subset of artificial intelligence, and deep learning -a subset of ML algorithms. Though MGs have many advantages, they also have issues like high costs, complex management, and the need for better energy storage. ML can predict energy demand, optimize power flow to save money, improve energy storage management, enhances cybersecurity, and protects MGs from hackers. The chapter presented here provides a review of different ML techniques that can be implemented on MGs, their existing problems, and some improvised solutions to overcome the grid issues.