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.

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

“…In [34], the authors have proposed machine learning methods to estimate the DG size and its effects on DS. The proposed methods such as Linear Regression, Artificial Neural Networks, Support Vector Regression, K-Nearest Neighbor and Decision Tree were applied on IEEE 12, 33 and 69-bus standard test systems.…”

“…The seasonal load curve is shown in Appendix Table A-2 and graphically in Fig. 6 [34]. While annual active and reactive energy losses were calculated as 680.8 MWh and 453.8 MVArh, the energy of 20.5 GWh active and 12.7 GVArh reactive were injected from the substation (SS) in a year.…”

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

“…Various machine learning techniques, including linear regression, artificial neural networks, support vector regression, K-nearest neighbours, and decision trees, have been leveraged for these estimations and applied across established test systems. Purlu and Turkay (2021).…”