PowerSystems.jl

[claytonpbarrows]

Name

PowerSystems.jl

Screenshots

Focus Topic

quasi-static and dynamic system data specifications

Primary Purpose

Rigorous data specification for optimal scheduling, power flow, contingency, and dynamic analysis

Description

PowerSystems.jl is a package to organize and manipulate data for the study of energy systems with diverse modeling requirements. This software serves two main purposes: to reduce the burden of large power system data set development, and to promote reproducible research and simulation. PowerSystems.jl implements an abstract hierarchy to represent and customize power systems data and includes data containers for quasi-static and dynamic simulation applications.

Mathematical Description

basic power flow

Website

https://www.nrel.gov/analysis/siip.html

Documentation

https://nrel-siip.github.io/PowerSystems.jl/stable/

Source

https://github.com/NREL-SIIP/PowerSystems.jl

Year

2017

Institution

NREL

Funding Source

DOE

Publications

8

Publication List

1. J. D. Lara, C. Barrows, D. Thom, D. Krishnamurthy, and D. Callaway, “PowerSystems.jl — A power system data management package for large scale modeling,” SoftwareX, vol. 15, p. 100747, Jul. 2021, doi: 10.1016/j.softx.2021.100747.
2. B. Bush, Topology-based machine-learning for modeling power-system responses to contingencies American Statistical Association 2020 Joint Statistical Meetings (2020).
3. J. D. Lara, J. T. Lee, D. S. Callaway, B.-M. Hodge, Computational experiment design for operations model simulation, Electric Power Systems Research 189 (2020) 106680. doi: https://doi.org/10.1016/j.epsr.2020.106680. URL http://www.sciencedirect.com/science/article/pii/ S0378779620304831
4. J. D. Lara, R. Henriquez-Auba, B.-M. Hodge, D. S. Callaway, AGC simulation model for large renewable energy penetration studies, in: (to appear) 2020 North American Power Symposium (NAPS), 2020, pp. 1–6.
5. R. Henriquez-Auba, J. D. Lara, D. S. Callaway, and C. Barrows, “Transient Simulations With a Large Penetration of Converter-Interfaced Generation: Scientific Computing Challenges And Opportunities,” IEEE Electrification Magazine, vol. 9, no. 2, pp. 72–82, Jun. 2021, doi: 10.1109/MELE.2021.3070939.
6. B. Bush, Y. Chen, O.-B. D., Y. Gei, Topological machine learning methods for power system responses to contingencies, The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence, As- sociation for the Advancement of Artificial Intelligence (2020).
7. Henriquez-Auba, Rodrigo, Jose D. Lara, Ciaran Roberts, and Duncan S. Callaway. "Grid Forming Inverter Small Signal Stability: Examining Role of Line and Voltage Dynamics." In IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, pp. 4063-4068. IEEE, 2020.
8. Roberts, C., Lara, J.D., Henriquez-Auba, R., Poolla, B.K. and Callaway, D.S., 2020, November. Grid-Coupled Dynamic Response of Battery-Driven Voltage Source Converters. In 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.

Use Cases

PowerSystems.jl enables a range of modeling activities by providing rigorous data structure, parsing capabilities, and basic power system data transformations (e.g. Ybus calculation)

Infrastructure Sector

• Atmospheric dispersion
• Agriculture
• Biomass
• Buildings
• Communications
• Cooling
• Ecosystems
• Electric
• District heating
• Forestry
• Health
• Hydrogen
• Individual heating
• Land use
• Liquid fuels
• Natural Gas
• Transportation
• Water

Represented Behavior

• Earth Systems
• Employment
• Built Infrastructure
• Financial
• Macro-economy
• Micro-economy
• Policy
• Social

Modeling Paradigm

• Analytics
• Data
• Discrete Simulation
• Dynamic Simulation
• Equilibrium
• Engineering/Design
• Optimization
• Visualization

Capabilities

1. Supported data formats: MATPOWER, PSSE v33 .raw and .dyr, tabular/csv data
2. Generic time series data support
3. Basic power flow

Programming Language

• C – ISO/IEC 9899
• C++ (C plus plus) – ISO/IEC 14882
• C# (C sharp) – ISO/IEC 23270
• Delphi
• GAMS (General Algebraic Modeling System)
• Go
• Haskell
• Java
• JavaScript(Scripting language)
• Julia
• Kotlin
• LabVIEW
• Lua
• MATLAB
• Modelica
• Nim
• Object Pascal
• Octave
• Pascal Script
• Python
• R
• Rust
• Simulink
• Swift (Apple programming language)
• WebAssembly
• Zig

Required Dependencies

No response

What is the software tool's license?

3-clause BSD License (BSD-3-Clause)

Operating System Support

• Windows
• Mac OSX
• Linux
• iOS
• Android

User Interface

• Programmatic
• Command line
• Web based
• Graphical user
• Menu driven
• Form based
• Natural language

Parallel Computing Paradigm

• Multi-threaded computing
• Multi-core computing
• Distributed computing
• Cluster computing
• Massively parallel computing
• Grid computing
• Reconfigurable computing with field-programmable gate arrays (FPGA)
• General-purpose computing on graphics processing units
• Application-specific integrated circuits
• Vector processors

What is the highest temporal resolution supported by the tool?

Instant

What is the typical temporal resolution supported by the tool?

Hours

What is the largest temporal scope supported by the tool?

Years

What is the typical temporal scope supported by the tool?

Years

What is the highest spatial resolution supported by the tool?

Component

What is the typical spatial resolution supported by the tool?

Device

What is the largest spatial scope supported by the tool?

Continent

What is the typical spatial scope supported by the tool?

Region

Input Data Format

JSON, CSV, MATPOWER, PSSE .raw .dyr

Input Data Description

Load flow case, O&M costs, time series

Output Data Format

JSON

Output Data Description

No response

Contact Details

clayton.barrows@nrel.gov

Interface, Integration, and Linkage

No response