HELICS

[lmramirea1]

Name

HELICS

Screenshots

Focus Topic

Co-simulation of multi-energy systems

Primary Purpose

Flexible and scalable open-source co-simulation framework is designed to integrate simulators designed for separate energy domains to simulate regional and interconnection-scale power system behaviors at unprecedented levels of detail and speed. Co-simulation enables multiple existing simulators to act like one large simulation by coordinating time and exchanging data at every timestep.

Description

Flexible and scalable open-source co-simulation framework is designed to integrate simulators designed for separate energy system domains--such as transmission, distribution, communications, natural gas, buildings, transportation, natural gas, water, control schemes, etc. Together these simulations can co-simulate a wide range of grid-related applications ranging in spatial scale from a single controller managing an electric vehicle charging station up to regional and interconnection-scale power system behaviors at unprecedented levels of detail and speed. For example, HELICS can link multi-faceted large-scale (20,000+ node) bulk power simulations (markets, powerflow, dynamics) with thousands of distribution simulations (each with thousands of nodes), in combination with hundreds of thousands of controllers, communication simulation with hundreds of thousands of communication points, and more.

This comprehensive multi-energy simulation tool is fundamental for investment decision-making by industry. It's also important to help quantify the impact of the ever-increasing high penetration variable generation, DERs, control schemes, and other changes on the power grid reliability and resiliency; and to simulate possible solutions at scale in silico.

Mathematical Description

No response

Website

https://helics.org/

Documentation

https://docs.helics.org/en/latest/user-guide/index.html

Source

https://github.com/GMLC-TDC/HELICS

Year

2018 (First release)

Institution

NREL, PNNL, LLNL

Funding Source

DOE GMLC

Publications

More than 20

Publication List

1. Palmintier, Bryan, Dheepak Krishnamurthy, Philip Top, Steve Smith, Jeff Daily, and Jason Fuller. 2017. “Design of the HELICS High-Performance Transmission-Distribution-Communication-Market Co-Simulation Framework.” In Proc. of the 2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems. Pittsburgh, PA. https://doi.org/10.1109/MSCPES.2017.8064542.
2. Panossian, Nadia, Tarek Elgindy, Bryan Palmintier, and Diana Wallison. 2021. “Synthetic, Realistic Transmission and Distribution Co-Simulation for Voltage Control Benchmarking.” In 2021 IEEE Texas Power and Energy Conference (TPEC), 1–5. https://doi.org/10.1109/TPEC51183.2021.9384935.
3. Panossian, Nadia V., Haitam Laarabi, Keith Moffat, Heather Chang, Bryan Palmintier, Andrew Meintz, Timothy E. Lipman, and Rashid A. Waraich. 2023. “Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area.” Energies 16 (5): 2189. https://doi.org/10.3390/en16052189.
4. Wang, Jing, Jeff Simpson, Rui Yang, Bryan Palmintier, Soumya Tiwari, and Yingchen Zhang. 2021. “Hardware-in-the-Loop Evaluation of an Advanced Distributed Energy Resource Management Algorithm.” In 2021 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), 1–5. https://doi.org/10.1109/ISGT49243.2021.9372182.
5. Wang, Jing, Jeff Simpson, Rui Yang, Bryan Palmintier, Soumya Tiwari, and Yingchen Zhang. 2021. “Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm.” In 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).
6. Barbierato, Luca, Pietro Rando Mazzarino, Marco Montarolo, Alberto Macii, Edoardo Patti, and Lorenzo Bottaccioli. 2022. “A Comparison Study of Co-Simulation Frameworks for Multi-Energy Systems: The Scalability Problem.” Energy Informatics 5 (4): 53. https://doi.org/10.1186/s42162-022-00231-6.
7. Sergi, Brian, and Kwabena Pambour. 2022. “An Evaluation of Co-Simulation for Modeling Coupled Natural Gas and Electricity Networks.” Energies 15 (14): 5277. https://doi.org/10.3390/en15145277.
8. Yang, Rui, Michael Ingram, and Mengmeng Cai. Situational Awareness of Grid Anomalies (SAGA). No. NREL/PR-5D00-84465. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2022.
9. Jain, Himanshu. HELICS-HLA. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2018. Palmintier, Bryan S. HELICS for Integrated Transmission, Distribution, Communication, & Control (TDC+ C) Modeling. No. NREL/PR-5D00-73977. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2019.
10. Cutler, Dylan, et al. Zero-Export Feeder Through Transactive Markets. No. NREL/PR-7A40-80140. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2021.
11. Palmintier, Bryan. How Use Cases Drove the Design of the HELICS Co-simulation Framework. No. NREL/PR-5D00-71160. National Renewable Energy Lab.(NREL), Golden, CO (United States); Pacific Northwest National Lab.(PNNL), Richland, WA (United States); Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2018.
12. Fang, Xin, Mengmeng Cai, and Anthony Florita. Cyber-Physical Events Emulation Based Transmission and Distribution Co-Simulation for Situation Awareness and Grid Anomaly (SAGA) Detection. No. NREL/CP-5D00-78189. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2021.
13. Muratori, Matteo, et al. Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC). No. NREL/PR-5400-76718. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2020.
14. Wang, Wenbo, et al. Impact of Open Communication Networks on Load Frequency Control with Plug-In Electric Vehicles by Cyber-Physical Dynamic Co-Simulation. No. NREL/CP-6A40-82369. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2022.
15. Sergi, Brian. Modeling Integrated Electricity and Natural Gas Systems Using Co-Simulation. No. NREL/PR-6A40-84354. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2022.
16. Meintz, Andrew, et al. Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC). No. NREL/PR-5400-79963. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2022.
17. Baggu, Murali M., and Annabelle Pratt. NREL's Advanced Distribution Management System (ADMS) Test Bed. No. NREL/PR-5D00-75936. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2020.
18. Huang, Henry, Liang Min, Jason Fuller, Bryan Palmintier, Philip Top, and Shrirang Abhaynkar. 2018. “GMLC 1.4.15: Integrated Transmission, Distribution, and Communication (TDC) System Model: HELICS.” Presented at the Workshop on Co-Simulation Platforms for the Power Grid, Berkeley, CA, May 21.
19. Palmintier, Bryan. 2019. “Integrated Transmission-Distribution-Communication/Control Modeling with HELICS.” Presented at the InnovationXLab: Grid Modernization Technology Showcase, Seattle, WA, January 25.
20. Palmintier, Bryan. 2019. “HELICS for Integrated Transmission, Distribution, Communication, & Control (TDC+C) Modeling.” Presented at the SETO Challenges for Distribution Planning, Operational and Real-time Planning Analytics Workshop, Washington, DC, May 17.

Use Cases

1. Panossian, Nadia, Tarek Elgindy, Bryan Palmintier, and Diana Wallison. 2021. “Synthetic, Realistic Transmission and Distribution Co-Simulation for Voltage Control Benchmarking.” In 2021 IEEE Texas Power and Energy Conference (TPEC), 1–5. https://doi.org/10.1109/TPEC51183.2021.9384935.
2. Panossian, Nadia V., Haitam Laarabi, Keith Moffat, Heather Chang, Bryan Palmintier, Andrew Meintz, Timothy E. Lipman, and Rashid A. Waraich. 2023. “Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area.” Energies 16 (5): 2189. https://doi.org/10.3390/en16052189.
3. Wang, Jing, Jeff Simpson, Rui Yang, Bryan Palmintier, Soumya Tiwari, and Yingchen Zhang. 2021. “Hardware-in-the-Loop Evaluation of an Advanced Distributed Energy Resource Management Algorithm.” In 2021 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), 1–5. https://doi.org/10.1109/ISGT49243.2021.9372182.
4. Wang, Jing, Jeff Simpson, Rui Yang, Bryan Palmintier, Soumya Tiwari, and Yingchen Zhang. 2021. “Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm.” In 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).
5. Barbierato, Luca, Pietro Rando Mazzarino, Marco Montarolo, Alberto Macii, Edoardo Patti, and Lorenzo Bottaccioli. 2022. “A Comparison Study of Co-Simulation Frameworks for Multi-Energy Systems: The Scalability Problem.” Energy Informatics 5 (4): 53. https://doi.org/10.1186/s42162-022-00231-6.
6. Sergi, Brian, and Kwabena Pambour. 2022. “An Evaluation of Co-Simulation for Modeling Coupled Natural Gas and Electricity Networks.” Energies 15 (14): 5277. https://doi.org/10.3390/en15145277.
7. Yang, Rui, Michael Ingram, and Mengmeng Cai. Situational Awareness of Grid Anomalies (SAGA). No. NREL/PR-5D00-84465. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2022.
8. Fang, Xin, Mengmeng Cai, and Anthony Florita. Cyber-Physical Events Emulation Based Transmission and Distribution Co-Simulation for Situation Awareness and Grid Anomaly (SAGA) Detection. No. NREL/CP-5D00-78189. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2021.
9. Muratori, Matteo, et al. Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC). No. NREL/PR-5400-76718. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2020.
10. Wang, Wenbo, et al. Impact of Open Communication Networks on Load Frequency Control with Plug-In Electric Vehicles by Cyber-Physical Dynamic Co-Simulation. No. NREL/CP-6A40-82369. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2022.
11. Sergi, Brian. Modeling Integrated Electricity and Natural Gas Systems Using Co-Simulation. No. NREL/PR-6A40-84354. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2022.
12. Baggu, Murali M., and Annabelle Pratt. NREL's Advanced Distribution Management System (ADMS) Test Bed. No. NREL/PR-5D00-75936. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2020.

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
• Time series Simulation

Capabilities

-Co-simulation platform that has been designed to allow integration of these simulators across a variety of computation platforms and languages.
-Scalable: 2-1,000,000+ federates
-Modular: mix and match existing tools.
-Differentiates physical values exchange from communication/control messages
-Supports co-iteration to converge physical values between federates before advancing time (aka tight coupling)
-Built-in support for communication message "Filters" to add delays, drop packets, change data, reroute to different simulators, etc.
-Built-in unit management
-Tools for managing large runs
-Existing interfaces for: PSS/E, GridDyn, PowerWorld, SIIP, MATPOWER, CYME, PyDSS, OpenDSS, GridLAB-D, NS-3, SAInt, NG-Fast, NG-Transient, BEAM, Polaris, Opal-RT, and others
-Supports multiple simulation types including: Time series, dynamics, discrete event
-Interfaces for FMI

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

Only other open-source libraries

What is the software tool's license?

BSD-3

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?

1 ms to 1 hr depending on application and which other tools are connected

What is the largest temporal scope supported by the tool?

Arbitrary, 1 year common max

What is the typical temporal scope supported by the tool?

1-hour (e.g. dynamic response to grid outage) to 1-year (e.g. integrated T&D production cost) depending on application and which other tools are connectedNone

What is the highest spatial resolution supported by the tool?

Component

What is the typical spatial resolution supported by the tool?

Varies by use case, component common,

What is the largest spatial scope supported by the tool?

Grid intersection (e.g. Western US, Europe, etc.)

What is the typical spatial scope supported by the tool?

Varies by application, data, and which other tools are connected

Input Data Format

Varies by tools integrated.

Input Data Description

Interconnected tools generally use their own native input formats. HELICS provides built-in "player" file support (CSV) to substitute for missing simulators during development or to provide known inputs to subsets of a federation

Output Data Format

Varies by tools integrated

Output Data Description

Interconnected tools generally use their own native output formats. In addition, HELICS provides built-in "recorder" file support (CSV) to capture data exchanged as desired

Contact Details

helicsteam@helics.org

Interface, Integration, and Linkage

No response

[bpalmintier]

Updates/Additions for HELICS:

Name: HELICS, the Hierarchical Engine for Large-scale Infrastructure Co-Simulation

Focus Topic

Co-simulation of multi-energy systems

Primary Purpose

Flexible and scalable open-source co-simulation framework is designed to integrate simulators designed for separate energy domains to simulate regional and interconnection-scale power system behaviors at unprecedented levels of detail and speed. Co-simulation enables multiple existing simulators to act like one large simulation by coordinating time and exchanging data at every timestep.

Description

Flexible and scalable open-source co-simulation framework is designed to integrate simulators designed for separate energy system domains--such as transmission, distribution, communications, natural gas, buildings, transportation, natural gas, water, control schemes, etc. Together these simulations can co-simulate a wide range of grid-related applications ranging in spatial scale from a single controller managing an electric vehicle charging station up to regional and interconnection-scale power system behaviors at unprecedented levels of detail and speed. For example, HELICS can link multi-faceted large-scale (20,000+ node) bulk power simulations (markets, powerflow, dynamics) with thousands of distribution simulations (each with thousands of nodes), in combination with hundreds of thousands of controllers, communication simulation with hundreds of thousands of communication points, and more.

This comprehensive multi-energy simulation tool is fundamental for investment decision-making by industry. It's also important to help quantify the impact of the ever-increasing high penetration variable generation, DERs, control schemes, and other changes on the power grid reliability and resiliency; and to simulate possible solutions at scale in silico.

Documentation

https://docs.helics.org/en/latest/index.html

Source

https://github.com/GMLC-TDC/HELICS

Year

2018 (First release)

Institution

NREL, PNNL, LLNL

Funding Source

DOE GMLC

Publications

Lots, please add the following, plus some extras coming from PNNL and other locations and then update the sum

_(Additional) Publication List

Note: the current list is mostly presentations, not sure if you would want to include all of these or are focussed more on papers.

1. (should list first, current standard HELICS reference, although we are working on an updated one expected mid-2023) Palmintier, Bryan, Dheepak Krishnamurthy, Philip Top, Steve Smith, Jeff Daily, and Jason Fuller. 2017. “Design of the HELICS High-Performance Transmission-Distribution-Communication-Market Co-Simulation Framework.” In Proc. of the 2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems. Pittsburgh, PA. https://doi.org/10.1109/MSCPES.2017.8064542.
2. Panossian, Nadia, Tarek Elgindy, Bryan Palmintier, and Diana Wallison. 2021. “Synthetic, Realistic Transmission and Distribution Co-Simulation for Voltage Control Benchmarking.” In 2021 IEEE Texas Power and Energy Conference (TPEC), 1–5. https://doi.org/10.1109/TPEC51183.2021.9384935.
3. Panossian, Nadia V., Haitam Laarabi, Keith Moffat, Heather Chang, Bryan Palmintier, Andrew Meintz, Timothy E. Lipman, and Rashid A. Waraich. 2023. “Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area.” Energies 16 (5): 2189. https://doi.org/10.3390/en16052189.
4. Wang, Jing, Jeff Simpson, Rui Yang, Bryan Palmintier, Soumya Tiwari, and Yingchen Zhang. 2021. “Hardware-in-the-Loop Evaluation of an Advanced Distributed Energy Resource Management Algorithm.” In 2021 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), 1–5. https://doi.org/10.1109/ISGT49243.2021.9372182.
5. Wang, Jing, Jeff Simpson, Rui Yang, Bryan Palmintier, Soumya Tiwari, and Yingchen Zhang. 2021. “Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm.” In 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).
6. Barbierato, Luca, Pietro Rando Mazzarino, Marco Montarolo, Alberto Macii, Edoardo Patti, and Lorenzo Bottaccioli. 2022. “A Comparison Study of Co-Simulation Frameworks for Multi-Energy Systems: The Scalability Problem.” Energy Informatics 5 (4): 53. https://doi.org/10.1186/s42162-022-00231-6.
7. Sergi, Brian, and Kwabena Pambour. 2022. “An Evaluation of Co-Simulation for Modeling Coupled Natural Gas and Electricity Networks.” Energies 15 (14): 5277. https://doi.org/10.3390/en15145277.

If you do want more Presentations:
8. Huang, Henry, Liang Min, Jason Fuller, Bryan Palmintier, Philip Top, and Shrirang Abhaynkar. 2018. “GMLC 1.4.15: Integrated Transmission, Distribution, and Communication (TDC) System Model: HELICS.” Presented at the Workshop on Co-Simulation Platforms for the Power Grid, Berkeley, CA, May 21.
9. Palmintier, Bryan. 2019. “Integrated Transmission-Distribution-Communication/Control Modeling with HELICS.” Presented at the InnovationXLab: Grid Modernization Technology Showcase, Seattle, WA, January 25.
10. Palmintier, Bryan. 2019. “HELICS for Integrated Transmission, Distribution, Communication, & Control (TDC+C) Modeling.” Presented at the SETO Challenges for Distribution Planning, Operational and Real-time Planning Analytics Workshop, Washington, DC, May 17.

Use Cases

I'll point @lmramirea1 to some slides from various workshops showcasing past use cases

What Infrastructure Sector does this software tool model?

Note: only included relevant ones

Buildings
Communications
Electric
District heating
Hydrogen
Liquid fuels
Natural Gas
Transportation
Water

Represented Behavior

What is the represented behavior that the tool is modeling?

Built Infrastructure

Modeling Paradigm

What modeling Paradigm does this software tool follow?

Discrete Simulation
Dynamic Simulation (assumed to focus on msec grid dynamics, term might be confusing in entry form)
Engineering/Design
Time series Simulation (should be added to the list)

Capabilities

In addition to the summary in original issue entry

1. Scalable: 2-1,000,000+ federates
2. Modular: mix and match existing tools.
3. Differentiates physical values exchange from communication/control messages
4. Supports co-iteration to converge physical values between federates before advancing time (aka tight coupling)
5. Built-in support for communication message "Filters" to add delays, drop packets, change data, reroute to different simulators, etc.
6. Built-in unit management
7. Tools for managing large runs
8. Existing interfaces for: PSS/E, GridDyn, PowerWorld, SIIP, MATPOWER, CYME, PyDSS, OpenDSS, GridLAB-D, NS-3, SAInt, NG-Fast, NG-Transient, BEAM, Polaris, Opal-RT, and others
9. Supports multiple simulation types including: Time series, dynamics, discrete event
10. Interfaces for FMI

Programming Language

C – ISO/IEC 9899
(written in) C++ (C plus plus) – ISO/IEC 14882
C# (C sharp) – ISO/IEC 23270
Java
Julia
MATLAB
Octave
Python

Required Dependencies

Only other open-source libraries

What is the software tool's license?

BSD-3

Operating System Support

Windows
Mac OSX
Linux

User Interface

What user interfaces are supported by the software tool?

Programmatic
Command line
Web based

Parallel Computing Paradigm

Multi-threaded computing
Multi-core computing
Distributed computing
Cluster computing
Massively parallel computing

What is the highest temporal resolution supported by the tool?

Instant

What is the typical temporal resolution supported by the tool?

1 ms to 1 hr depending on application and which other tools are connected

What is the largest temporal scope supported by the tool?

Arbitrary, 1 year common max

What is the typical temporal scope supported by the tool?

1-hour (e.g. dynamic response to grid outage) to 1-year (e.g. integrated T&D production cost) depending on application and which other tools are connected

What is the highest spatial resolution supported by the tool?

Component

What is the typical spatial resolution supported by the tool?

Varies by use case, component common,

What is the largest spatial scope supported by the tool?

Grid intercection (e.g. Western US, Europe, etc.)

What is the typical spatial scope supported by the tool?

Varies by application, data, and which other tools are connected

Input Data Format

Varies by tools integrated.

Input Data Description

Interconnected tools generally use their own native input formats. HELICS provides built-in "player" file support (CSV) to substitute for missing simulators during development or to provide known inputs to subsets of a federation

Output Data Format

Varies by tools integrated

Output Data Description

Interconnected tools generally use their own native output formats. In addition, HELICS provides built-in "recorder" file support (CSV) to capture data exchanged as desired

Interface, Integration, and Linkage

See list above

[lmramirea1]

Thank you @bpalmintier . I included all your comments except the interface, integration and linkage list because I couldn't see it.