The United States has 37 gigawatts of utility-scale solar power – enough to power more than 4,070,000,000 LED lights – with an astounding 112 gigawatts of additional capacity currently under development.
With so much solar energy already on a large scale, current trends in power systems clearly indicate that renewables and battery energy storage systems are major players in the power grids of the future. But these new technologies bring with them additional complexities and challenges. Given the obstacles, how can we understand the behavior of modern networks and the ways in which power system operators and policy makers can ensure their continued reliability at scale? NREL analysts, along with colleagues at the University of California, Berkeley (UCB), have published a new open source computational analysis method in IEEE Electrification The article that helps unlock the answer.
“Current commercial software tools used in modeling have been successful in power system analysis for decades. However, we are in a phase of rapid power system changes that are imposing new demands on modeling needs,” said Clayton Barrows, senior researcher at NREL and contributing author. from the article. “In order to keep pace with these emerging technologies, we need transparent software that is easy to modify. Updated and flexible software tools will allow the research community to address computational questions and understand the impacts of new technologies before they reach the market.”
Understanding Low Inertia Power Systems
The introduction of renewable energy sources and battery energy storage systems, as well as a move away from traditional rotary generators, has led to uncommon power systems with low levels of physical inertia. Power systems in the past were dominated by synchronous machines in which one of the critical sources of network stability was physical rotation behaving according to the laws of physics. However, modern power systems have renewable energy sources as well as inverter-based generation where stability is maintained not through mechanical processes but through logical and electronic controls.
All of this has fundamentally changed our understanding of network stability and behavior – and presented new obstacles to studying and predicting these systems. The new modeling approach developed by NREL and UCB addresses inefficiencies resulting from emerging grid variable power systems.
Bridging the modeling gap with scientific computing
The tools and computer simulations are uniquely suited to handle the complexity and scale of power system analysis. Scientific computing allows researchers to identify and understand energy systems that contain widespread renewable energy sources and battery energy storage systems. Computer-aided simulations are repeatable, with results that can be validated, and computation models can be scaled to reflect the real-world ratios of our modern networks.
Scalability and flexibility were previously the biggest obstacles facing researchers in this field. Large-scale experiments have required proprietary models and algorithms that are expensive and time-consuming to set up, and it is difficult – if not impossible – to fully represent emerging technologies. This lack of access ultimately hampers research and innovation in the energy systems community, hampering the deployment of modern grid systems.
NREL and UCB analysts saw this need and came up with a suite of open source simulation tools and a computational approach that could bridge the access gap.
Choose a common language
The development of any simulation tool begins with choosing a programming language. The NREL analysts behind the latest article argue that Julia – a dynamically typed programming language developed by Besanson et al. 2017 – is the best answer for large-scale power system modeling.
Julia is designed to make high performance computing more accessible by bridging the gap between scripting languages and high performance computing languages. Julia makes it easy to write and maintain highly reliable, well-performing software. Programs that are easy to write are also easy to read and reproduce. These capabilities, NREL analysts determined, make Julia an excellent match for meeting the challenges of scientific computing in the energy systems community.
Create a scalable integrated infrastructure planning framework
With the definition of a programming language, the NREL team set out to develop fully accessible programming tools that would meet the research needs of modern, ever-evolving power systems. The result is the Scalable Integrated Infrastructure Planning Framework (SIIP) – a first-of-its-kind flexible modeling framework that includes new solution algorithms, advanced data analytics, and scalable high-performance computing.
Julia’s features and capabilities are used extensively in SIIP to provide open source tools that provide consistent, high-performance data models for utility-scale power systems. SIIP includes three integrated modeling packages:
- PowerSystems.jl provides a reusable and customizable data model that is general to the details of mathematical model implementation and applicable to multiple simulation strategies. It also provides scalability by design that facilitates integration into other initiatives.
- PowerSimulations.jl enables steady-state power system modeling activities, including production cost modeling, unit commitment, economic dispatch, spontaneous generation control simulation, optimum power flow, and more.
- PowerSimulationsDynamics.jl allows simulation of power system dynamics by providing an extensive model library and access to various Julia numerical integrations and the latest low inertial modeling methods.
The software suites included with SIIP are now freely available to the energy systems research community. By addressing the shortcomings of previous modeling platforms, SIIP helps take one step closer to breaking down barriers to the development and deployment of modern, renewable energy-based energy systems.
“The goal of the SIIP is to create a common platform for electrical engineers to represent new technologies, computational scientists to develop algorithms, and analysts to conduct applied studies. Ultimately, we hope that SIIP will help enhance the nation’s ability to test and analyze our future networks,” Barrows said. “This approach provides a useful and accessible way to overcome challenges in studying low inertia systems, and we are excited to see these tools applied to investigate a wide range of future renewable network models.”
Access open source SIIP software suites and learn more about the SIIP Modeling Framework developed by NREL Energy Analysts.
Julia programming language that deals with the challenges of differential equations
Rodrigo Henriquez-Auba et al, Transient simulation with significant penetration of the transient generation: challenges and opportunities for scientific computing, IEEE Electrification Magazine (2021). DOI: 10.1109 / MELE.2021.3070939
Presented by the National Renewable Energy Laboratory
the quote: New Scientific Computing Method for Studying Utility-Scale Renewable Energy Systems (2021, July 21) Retrieved on July 21, 2021 from https://techxplore.com/news/2021-07-scientific-method-utility-scale-renewable-power . programming language
This document is subject to copyright. Notwithstanding any fair dealing for the purpose of private study or research, no part may be reproduced without written permission. The content is provided for informational purposes only.