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WISDEM

Wind Plant Integrated System Design and Engineering Model.
https://github.com/wisdem/wisdem

Category: Renewable Energy
Sub Category: Wind Energy

Keywords

openmdao systems-engineering wind wisdem

Keywords from Contributors

wind-energy wind-turbine aeroelasticity wind-farm wind-power energy-system-model

Last synced: about 9 hours ago
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Wind Plant Integrated System Design and Engineering Model

README.md

WISDEM®

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The Wind-Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website.

Author: NREL WISDEM Team

Documentation

See local documentation in the docs-directory or access the online version at https://wisdem.readthedocs.io/en/master/

Packages

WISDEM® is a family of modules. The core modules are:

  • CommonSE includes several libraries shared among modules
  • FloatingSE works with the floating platforms
  • DrivetrainSE sizes the drivetrain and generator systems (formerly DriveSE and GeneratorSE)
  • TowerSE is a tool for tower (and monopile) design
  • RotorSE is a tool for rotor design
  • NREL CSM is the regression-based turbine mass, cost, and performance model
  • ORBIT is the process-based balance of systems cost model for offshore plants
  • LandBOSSE is the process-based balance of systems cost model for land-based plants
  • Plant_FinanceSE runs the financial analysis of a wind plant

The core modules draw upon some utility packages, which are typically compiled code with python wrappers:

  • Airfoil Preppy is a tool to handle airfoil polar data
  • CCBlade is the BEM module of WISDEM
  • pyFrame3DD brings libraries to handle various coordinate transformations
  • MoorPy is a quasi-static mooring line model
  • pyOptSparse provides some additional optimization algorithms to OpenMDAO

Installation

Installation with Anaconda is the recommended approach because of the ability to create self-contained environments suitable for testing and analysis. WISDEM® requires Anaconda 64-bit. However, the conda command has begun to show its age and we now recommend the one-for-one replacement with the Miniforge3 distribution, which is much more lightweight and more easily solves for the WISDEM package dependencies.

Installation as a "library"

To use WISDEM's modules as a library for incorporation into other scripts or tools, WISDEM is available via conda install wisdem or pip install wisdem, assuming that you have already setup your python environment. Note that on Windows platforms, we suggest using conda exclusively.

Installation for direct use

These instructions are for interaction with WISDEM directly, the use of its examples, and the direct inspection of its source code.

The installation instructions below use the environment name, "wisdem-env," but any name is acceptable. For those working behind company firewalls, you may have to change the conda authentication with conda config --set ssl_verify no. Proxy servers can also be set with conda config --set proxy_servers.http http://id:pw@address:port and conda config --set proxy_servers.https https://id:pw@address:port. To setup an environment based on a different Github branch of WISDEM, simply substitute the branch name for master in the setup line.

  1. Setup and activate the Anaconda environment from a prompt (Anaconda3 Power Shell on Windows or Terminal.app on Mac)

    conda config --add channels conda-forge
conda install git
git clone https://github.com/WISDEM/WISDEM.git
cd WISDEM
conda env create --name wisdem-env -f environment.yml
conda activate wisdem-env

  2. In order to directly use the examples in the repository and peek at the code when necessary, we recommend all users install WISDEM in developer / editable mode using the instructions here. If you really just want to use WISDEM as a library and lean on the documentation, you can always do conda install wisdem and be done. Note the differences between Windows and Mac/Linux build systems. For Linux, we recommend using the native compilers (for example, gcc and gfortran in the default GNU suite).

    conda install -y petsc4py=3.22.2 mpi4py                 # (Mac / Linux only)
conda install -y gfortran                        # (Mac only without Homebrew or Macports compilers)
conda install -y m2w64-toolchain libpython       # (Windows only)
pip install --no-deps -e . -v

NOTE: To use WISDEM again after installation is complete, you will always need to activate the conda environment first with conda activate wisdem-env

For Windows users, we recommend installing git and the m2w64 packages in separate environments as some of the libraries appear to conflict such that WISDEM cannot be successfully built from source. The git package is best installed in the base environment.

Run Unit Tests

Each package has its own set of unit tests. These can be run in batch with the test_all.py script located in the top level test-directory.

Feedback

For software issues please use https://github.com/WISDEM/WISDEM/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.

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Last synced: 3 days ago

Total Commits: 3,486
Total Committers: 46
Avg Commits per committer: 75.783
Development Distribution Score (DDS): 0.528

Commits in past year: 295
Committers in past year: 7
Avg Commits per committer in past year: 42.143
Development Distribution Score (DDS) in past year: 0.447

Name Email Commits
Garrett Barter g****r@n****v 1646
pibo p****i@g****m 773
John Jasa j****1@g****m 352
Alicia Key a****7 178
Nikhar Abbas n****s@c****u 120
Evan Gaertner e****r@g****m 89
Katherine Dykes k****s@n****v 73
yqliaohk y****o@u****u 48
Andrew Ning a****g@n****v 43
dzalkind d****d@n****v 32
kilojoules j****8@h****u 23
unknown k****s@k****v 17
Cory Frontin c****n@n****v 13
faisal-bhuiyan f****n@g****m 8
Taylor Parsons t****s@n****v 6
George Scott g****t@n****v 5
Peter p****f@n****v 5
kevybear k****u@b****u 5
Jake Nunemaker j****r@g****m 4
minusfivethirds r****g@g****m 3
Liao y****o@y****v 3
Pietro Bortolotti p****o@e****v 3
Frederik Zahle f****a@l****l 3
Bortolotti p****o@n****v 3
badeshiben b****n@g****m 2
Evan Gaertner e****e@e****v 2
Bortolotti p****o@p****v 2
pre-commit-ci[bot] 6****] 2
mmoniot m****t@v****u 2
dakotaramos r****y@g****m 2
and 16 more...

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Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 199
Total pull requests: 455
Average time to close issues: 7 months
Average time to close pull requests: 3 days
Total issue authors: 84
Total pull request authors: 27
Average comments per issue: 3.5
Average comments per pull request: 0.51
Merged pull request: 423
Bot issues: 0
Bot pull requests: 1

Past year issues: 37
Past year pull requests: 78
Past year average time to close issues: about 2 months
Past year average time to close pull requests: 3 days
Past year issue authors: 19
Past year pull request authors: 7
Past year average comments per issue: 5.14
Past year average comments per pull request: 0.76
Past year merged pull request: 72
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/wisdem/wisdem

Top Issue Authors

  • SeongwooCheon (29)
  • dykesk (25)
  • SamAct (9)
  • WANG-KUI-TJU (6)
  • hishamtariq19 (6)
  • pollinico (6)
  • Ardemia1 (5)
  • akey7 (5)
  • TheMercer (5)
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  • taylor-parsons (3)

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  • ptrbortolotti (141)
  • johnjasa (38)
  • nikhar-abbas (18)
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  • cfrontin (8)
  • yqliaohk (8)
  • evan-gaertner (6)
  • fzahle (4)
  • kevybear (2)
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  • ccoulombe (1)
  • faisal-bhuiyan (1)
  • dakotaramos (1)

Top Issue Labels

  • alicia (5)
  • LandBOSSE Integration (5)
  • enhancement (4)
  • ideas (3)

Top Pull Request Labels

  • bug (1)

Package metadata

proxy.golang.org: github.com/wisdem/wisdem

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/wisdem/wisdem#section-documentation
  • Licenses: apache-2.0
  • Latest release: v3.21.0+incompatible (published 11 days ago)
  • Last Synced: 2025-05-11T21:34:13.221Z (1 day ago)
  • Versions: 50
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent packages count: 6.999%
    • Average: 8.173%
    • Dependent repos count: 9.346%
proxy.golang.org: github.com/WISDEM/WISDEM

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/WISDEM/WISDEM#section-documentation
  • Licenses:
  • Latest release: v3.21.0+incompatible (published 11 days ago)
  • Last Synced: 2025-05-11T21:34:14.426Z (1 day ago)
  • Versions: 50
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent packages count: 6.999%
    • Average: 8.173%
    • Dependent repos count: 9.346%
pypi.org: wisdem

Wind-Plant Integrated System Design & Engineering Model

  • Homepage: https://github.com/WISDEM/WISDEM
  • Documentation: https://wisdem.readthedocs.io
  • Licenses: Apache-2.0
  • Latest release: 3.21.0 (published 10 days ago)
  • Last Synced: 2025-05-11T21:34:13.677Z (1 day ago)
  • Versions: 21
  • Dependent Packages: 2
  • Dependent Repositories: 0
  • Downloads: 7,915 Last month
  • Rankings:
    • Forks count: 5.025%
    • Stargazers count: 6.81%
    • Dependent packages count: 7.133%
    • Average: 12.748%
    • Dependent repos count: 32.023%
  • Maintainers (1)
conda-forge.org: wisdem

The Wind-Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate coe and other wind plant system attributes. WISDEM is developed by the National Renewable Energy Lab, on top of NASA's OpenMDAO library.

  • Homepage: https://wisdem.readthedocs.io
  • Licenses: Apache-2.0
  • Latest release: 3.6.1 (published almost 3 years ago)
  • Last Synced: 2025-05-11T21:34:22.813Z (1 day ago)
  • Versions: 17
  • Dependent Packages: 1
  • Dependent Repositories: 6
  • Rankings:
    • Dependent repos count: 13.953%
    • Forks count: 21.308%
    • Average: 24.318%
    • Dependent packages count: 28.988%
    • Stargazers count: 33.021%

Dependencies

setup.py pypi
  • jsonschema *
  • marmot-agents >=0.2.5
  • nlopt *
  • numpy *
  • openmdao >=3.18
  • openpyxl *
  • pandas *
  • pytest *
  • python-benedict *
  • pyyaml *
  • scipy *
  • simpy *
  • sortedcontainers *
  • statsmodels *
.github/workflows/CI_WISDEM.yml actions
  • actions/checkout v2 composite
  • conda-incubator/setup-miniconda v2 composite
.github/workflows/Publish_WISDEM.yml actions
  • actions/checkout v3 composite
  • actions/download-artifact v3 composite
  • actions/upload-artifact v3 composite
  • awvwgk/setup-fortran main composite
  • pypa/cibuildwheel v2.12.3 composite
  • pypa/gh-action-pypi-publish v1.8.5 composite
pyproject.toml pypi
  • dearpygui *
  • jsonschema *
  • marmot-agents >=0.2.5
  • nlopt *
  • numpy *
  • openmdao >=3.18
  • openpyxl *
  • pandas *
  • pydoe2 *
  • python-benedict *
  • pyyaml *
  • ruamel.yaml *
  • scipy *
  • simpy *
  • sortedcontainers *
  • statsmodels *
environment.yml pypi
  • dearpygui *
  • marmot-agents *

Score: 17.906285423058776