Power System Studies Using Open-Access Software

TL;DR

This paper presents a flexible simulation environment using R/RStudio to integrate open-access power system tools like ATP and OpenDSS, enabling enhanced steady-state and transient studies, data exchange, and machine learning applications.

Contribution

It introduces a novel R/RStudio-based framework that combines multiple open-access power system simulation tools for expanded capabilities and easier data management.

Findings

Successful integration of ATP and OpenDSS with RStudio for power system simulations.

Demonstration of bidirectional data exchange between RStudio and simulation tools.

Potential for applying machine learning to predict power system performance.

Abstract

The use of open-access software is an option that can be considered by those interested in power system studies. In addition, the combination of two or more of these tools can expand the capabilities and the fields of application of each tool. This paper proposes the implementation of a flexible and powerful simulation environment based on R/Rstudio for carrying out power system studies. Several simple case studies are presented aimed at showing how the combination of either EMTP/ATP or OpenDSS with R/RStudio can expand the capabilities of each of these tools for performing either steady-state or transient power system studies. Basically, the proposed environment uses RStudio as control center from which each simulation tool (e.g., R, ATP, OpenDSS) can be run. Some procedures for generating information that must be exchanged between RStudio and ATP or RStudio and OpenDSS have been implemented. Such exchanges are bidirectional: ATP and OpenDSS produce simulation results that can be read by RStudio (text files in the case of ATP, comma separated value (CSV) and text files in the case of OpenDSS), while RStudio capabilities are used to generate files that are embedded into the input file to be read by either ATP or OpenDSS. This late option can be used to change either the configuration or some parameters of the test system under study. Finally, one very interesting option illustrated in this paper is the possibility of using machine learning algorithms to predict the performance of the test system.