Decentralized Smart Grid Stability Modeling with Machine Learning

Abstract

Predicting the stability of a Decentralized Smart Grid is key to the control of such systems. One of the key aspects that is necessary when observing the control of DSG systems is the need for rapid control. Due to this, the application of AI-based machine learning (ML) algorithms may be key to achieving a quick and precise stability prediction. In this paper, the authors utilize four algorithms—a multilayer perceptron (MLP), extreme gradient boosting (XGB), support vector machines (SVMs), and genetic programming (GP). A public dataset containing 30,000 points was used, with inputs consisting of τ—the time needed for a grid participant to adjust consumption/generation, p—generated power, and γ—the price elasticity coefficient for four grid elements; and outputs consisting of stab—the eigenvalue of stability and stabf, the categorical stability of the system. The system was modeled using the aforementioned methods as a regression model (targeting stab) and a classification model (targeting stabf). Modeling was performed with and without the τ values due to their low correlation. The best results were achieved with the XGB algorithm for classification, with and without the τ values as inputs—indicating them as being unnecessary.