Estimating the Distributed Generation Unit Sizing and Its Effects on the Distribution System by Using Machine Learning Methods

Abstract

Many approaches about the planning and operation of power systems, such as network reconfiguration and distributed generation (DG), have been proposed to overcome the challenges caused by the increase in electricity consumption. Besides the positive effects on the grid, contributions on environmental pollution and other advantages, the rapid developments in renewable energy technologies have made the DG resources an important issue, however, improper DG allocation may result in network damages. A lot of studies have been practised with analytical and heuristic methods based on load flow for optimal DG integration to the network. This novel method based on estimation is proposed to determine the size of DG and its effects on the network to get rid of the coercive and time-consuming load flow techniques. Machine learning algorithms, such as Linear Regression, Artificial Neural Network, Support Vector Regression, K-Nearest Neighbor, and Decision Tree, have been used for the estimations and have been applied to well-known test systems, such as IEEE 12-bus, 33-bus, and 69-bus distribution systems. The accuracy of the proposed estimation methods has been verified with R-squared and mean absolute percentage error. Results show that the proposed DG allocation method is effective, applicable, and flexible.