Electric DQ0 Library Model for Smart Grid Simulation

Abstract

This paper addresses the pressing need for advanced simulation tools in electric phasor modeling and Smart Grid-Power to X systems. The motivation for this study stems from the critical importance of enhancing the balance between performance and the detailed dynamic representation of the system behavior in the simulations. The identified problem lies in the absence of a comprehensive framework that seamlessly integrates electric phasor DQ0 components into a multi-purpose object-oriented environment. The primary objective of this research is to develop and introduce two simulation libraries, centered around the core component, Electric_DQ0. These libraries aim to establish a robust phasor-based framework, incorporating essential electric components such as sources, loads, branches, power converters, and electric machines. The main goal is to enable dynamic frequency and voltage simulations, particularly focusing on transients in alternators and facilitating Voltage and Frequency Rate of Change analysis during power production-demand imbalances. The libraries were developed within a versatile object-oriented environment, employing the Electric_DQ0 components as the foundation. Through ports, these components transmit turning frequencies, supporting the simulation of dynamic frequency and voltage. The libraries are designed to comprehensively support monophasic and triphasic systems, encompassing delta and wye connections, with a flexible neutral configuration under both balanced and unbalanced conditions. A validation case is presented to demonstrate the tool’s ability to accurately reproduce predictions when compared to one of the most widely used electrical modeling tools in the market. A study case is also presented to evaluate the toolkit’s capabilities. The study sets a specific power demand to fulfill, utilizing diverse energy sources. The obtained results showcase the libraries’ effectiveness in addressing the identified problem, providing valuable insights into their performance and applicability in real-world scenarios. The results demonstrate the efficacy of the proposed framework, delivering accurate outcomes within a reduced execution time.