| Name | NREL-reV JSON |
| Version |
0.9.6
JSON |
| download |
| home_page | https://nrel.github.io/reV/ |
| Summary | National Renewable Energy Laboratory's (NREL's) Renewable Energy Potential(V) Model: reV |
| upload_time | 2024-10-23 19:50:52 |
| maintainer | None |
| docs_url | None |
| author | Galen Maclaurin |
| requires_python | >=3.8 |
| license | BSD 3-Clause |
| keywords |
rev
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
|Docs| |Tests| |Linter| |PythonV| |Pypi| |Codecov| |Zenodo| |Binder|
.. |Docs| image:: https://github.com/NREL/reV/workflows/Documentation/badge.svg
:target: https://nrel.github.io/reV/
.. |Tests| image:: https://github.com/NREL/reV/workflows/Pytests/badge.svg
:target: https://github.com/NREL/reV/actions?query=workflow%3A%22Pytests%22
.. |Linter| image:: https://github.com/NREL/reV/workflows/Lint%20Code%20Base/badge.svg
:target: https://github.com/NREL/reV/actions?query=workflow%3A%22Lint+Code+Base%22
.. |PythonV| image:: https://img.shields.io/pypi/pyversions/NREL-reV.svg
:target: https://pypi.org/project/NREL-reV/
.. |Pypi| image:: https://badge.fury.io/py/NREL-reV.svg
:target: https://badge.fury.io/py/NREL-reV
.. |Codecov| image:: https://codecov.io/gh/nrel/reV/branch/main/graph/badge.svg?token=U4ZU9F0K0Z
:target: https://codecov.io/gh/nrel/reV
.. |Zenodo| image:: https://zenodo.org/badge/201343076.svg
:target: https://zenodo.org/badge/latestdoi/201343076
.. |Binder| image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/nrel/reV/HEAD
|
.. inclusion-intro
**reV** (the Renewable Energy Potential model)
is an open-source geospatial techno-economic tool that
estimates renewable energy technical potential (capacity and generation),
system cost, and supply curves for solar photovoltaics (PV),
concentrating solar power (CSP), geothermal, and wind energy.
reV allows researchers to include exhaustive spatial representation
of the built and natural environment into the generation and cost estimates
that it computes.
reV is highly dynamic, allowing analysts to assess potential at varying levels
of detail — from a single site up to an entire continent at temporal resolutions
ranging from five minutes to hourly, spanning a single year or multiple decades.
The reV model can (and has been used to) provide broad coverage across large spatial
extents, including North America, South and Central Asia, the Middle East, South America,
and South Africa to inform national and international-scale analyses. Still, reV is
equally well-suited for regional infrastructure and deployment planning and analysis.
For a detailed description of reV capabilities and functionality, see the
`NREL reV technical report <https://www.nrel.gov/docs/fy19osti/73067.pdf>`_.
How does reV work?
==================
reV is a set of `Python classes and functions <https://nrel.github.io/reV/_autosummary/reV.html>`_
that can be executed on HPC systems using `CLI commands <https://nrel.github.io/reV/_cli/cli.html>`_.
A full reV execution consists of one or more compute modules
(each consisting of their own Python class/CLI command)
strung together using a `pipeline framework <https://nrel.github.io/reV/_cli/reV%20pipeline.html>`_,
or configured using `batch <https://nrel.github.io/reV/_cli/reV%20batch.html>`_.
A typical reV workflow begins with input wind/solar/geothermal resource data
(following the `rex data format <https://nrel.github.io/rex/misc/examples.nsrdb.html#data-format>`_)
that is passed through the generation module. This output is then collected across space and time
(if executed on the HPC), before being sent off to be aggregated under user-specified land exclusion scenarios.
Exclusion data is typically provided via a collection of high-resolution spatial data layers stored in an HDF5 file.
This file must be readable by reV's
`ExclusionLayers <https://nrel.github.io/reV/_autosummary/reV.handlers.exclusions.ExclusionLayers.html#reV.handlers.exclusions.ExclusionLayers>`_
class. See the `reVX Setbacks utility <https://nrel.github.io/reVX/misc/examples.setbacks.html>`_
for instructions on generating setback exclusions for use in reV.
Next, transmission costs are computed for each aggregated
"supply-curve point" using user-provided transmission cost tables.
See the `reVX transmission cost calculator utility <https://github.com/NREL/reVX/tree/main/reVX/least_cost_xmission/>`_
for instructions on generating transmission cost tables.
Finally, the supply curves and initial generation data can be used to
extract representative generation profiles for each supply curve point.
.. inclusion-flowchart
|
.. inclusion-get-started
To get up and running with reV, first head over to the `installation page <https://nrel.github.io/reV/misc/installation.html>`_,
then check out some of the `Examples <https://nrel.github.io/reV/misc/examples.html>`_ or
go straight to the `CLI Documentation <https://nrel.github.io/reV/_cli/cli.html>`_!
You can also check out the `guide on running GAPs models <https://nrel.github.io/gaps/misc/examples.users.html>`_.
.. inclusion-install
Installing reV
==============
NOTE: The installation instruction below assume that you have python installed
on your machine and are using `conda <https://docs.conda.io/en/latest/index.html>`_
as your package/environment manager.
Option 1: Install from PIP (recommended for analysts):
1. Create a new environment:
``conda create --name rev python=3.9``
2. Activate directory:
``conda activate rev``
3. Install reV:
1) ``pip install NREL-reV`` or
- NOTE: If you install using conda and want to use `HSDS <https://github.com/NREL/hsds-examples>`_
you will also need to install h5pyd manually: ``pip install h5pyd``
Option 2: Clone repo (recommended for developers)
1. from home dir, ``git clone git@github.com:NREL/reV.git``
2. Create ``reV`` environment and install package
1) Create a conda env: ``conda create -n rev``
2) Run the command: ``conda activate rev``
3) cd into the repo cloned in 1.
4) prior to running ``pip`` below, make sure the branch is correct (install
from main!)
5) Install ``reV`` and its dependencies by running:
``pip install .`` (or ``pip install -e .`` if running a dev branch
or working on the source code)
3. Check that ``reV`` was installed successfully
1) From any directory, run the following commands. This should return the
help pages for the CLI's.
- ``reV``
reV command line tools
======================
- `reV <https://nrel.github.io/reV/_cli/reV.html#reV>`_
- `reV template-configs <https://nrel.github.io/reV/_cli/reV%20template-configs.html>`_
- `reV batch <https://nrel.github.io/reV/_cli/reV%20batch.html>`_
- `reV pipeline <https://nrel.github.io/reV/_cli/reV%20pipeline.html>`_
- `reV project-points <https://nrel.github.io/reV/_cli/reV%20project-points.html>`_
- `reV bespoke <https://nrel.github.io/reV/_cli/reV%20bespoke.html>`_
- `reV generation <https://nrel.github.io/reV/_cli/reV%20generation.html>`_
- `reV econ <https://nrel.github.io/reV/_cli/reV%20econ.html>`_
- `reV collect <https://nrel.github.io/reV/_cli/reV%20collect.html>`_
- `reV multiyear <https://nrel.github.io/reV/_cli/reV%20multiyear.html>`_
- `reV supply-curve-aggregation <https://nrel.github.io/reV/_cli/reV%20supply-curve-aggregation.html>`_
- `reV supply-curve <https://nrel.github.io/reV/_cli/reV%20supply-curve.html>`_
- `reV rep-profiles <https://nrel.github.io/reV/_cli/reV%20rep-profiles.html>`_
- `reV hybrids <https://nrel.github.io/reV/_cli/reV%20hybrids.html>`_
- `reV nrwal <https://nrel.github.io/reV/_cli/reV%20nrwal.html>`_
- `reV qa-qc <https://nrel.github.io/reV/_cli/reV%20qa-qc.html>`_
- `reV script <https://nrel.github.io/reV/_cli/reV%20script.html>`_
- `reV status <https://nrel.github.io/reV/_cli/reV%20status.html>`_
- `reV reset-status <https://nrel.github.io/reV/_cli/reV%20reset-status.html>`_
Launching a run
Tips
- Only use a screen session if running the pipeline module: `screen -S rev`
- `Full pipeline execution <https://nrel.github.io/reV/misc/examples.full_pipeline_execution.html>`_
.. code-block:: bash
reV pipeline -c "/scratch/user/rev/config_pipeline.json"
- Running simply generation or econ can just be done from the console:
.. code-block:: bash
reV generation -c "/scratch/user/rev/config_gen.json"
General Run times and Node configuration on Eagle
- WTK Conus: 10-20 nodes per year walltime 1-4 hours
- NSRDB Conus: 5 nodes walltime 2 hours
`Eagle node requests <https://nrel.github.io/reV/misc/examples.eagle_node_requests.html>`_
.. inclusion-citation
Recommended Citation
====================
Please cite both the technical paper and the software with the version and
DOI you used:
Maclaurin, Galen J., Nicholas W. Grue, Anthony J. Lopez, Donna M. Heimiller,
Michael Rossol, Grant Buster, and Travis Williams. 2019. “The Renewable Energy
Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply
Curve Modeling.” Golden, Colorado, United States: National Renewable Energy
Laboratory. NREL/TP-6A20-73067. https://doi.org/10.2172/1563140.
Grant Buster, Michael Rossol, Paul Pinchuk, Brandon N Benton, Robert Spencer,
Mike Bannister, & Travis Williams. (2023).
NREL/reV: reV 0.8.0 (v0.8.0). Zenodo. https://doi.org/10.5281/zenodo.8247528
Raw data
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"description": "\n \n\n\n|Docs| |Tests| |Linter| |PythonV| |Pypi| |Codecov| |Zenodo| |Binder|\n\n.. |Docs| image:: https://github.com/NREL/reV/workflows/Documentation/badge.svg\n :target: https://nrel.github.io/reV/\n\n.. |Tests| image:: https://github.com/NREL/reV/workflows/Pytests/badge.svg\n :target: https://github.com/NREL/reV/actions?query=workflow%3A%22Pytests%22\n\n.. |Linter| image:: https://github.com/NREL/reV/workflows/Lint%20Code%20Base/badge.svg\n :target: https://github.com/NREL/reV/actions?query=workflow%3A%22Lint+Code+Base%22\n\n.. |PythonV| image:: https://img.shields.io/pypi/pyversions/NREL-reV.svg\n :target: https://pypi.org/project/NREL-reV/\n\n.. |Pypi| image:: https://badge.fury.io/py/NREL-reV.svg\n :target: https://badge.fury.io/py/NREL-reV\n\n.. |Codecov| image:: https://codecov.io/gh/nrel/reV/branch/main/graph/badge.svg?token=U4ZU9F0K0Z\n :target: https://codecov.io/gh/nrel/reV\n\n.. |Zenodo| image:: https://zenodo.org/badge/201343076.svg\n :target: https://zenodo.org/badge/latestdoi/201343076\n\n.. |Binder| image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/nrel/reV/HEAD\n\n|\n\n.. inclusion-intro\n\n**reV** (the Renewable Energy Potential model)\nis an open-source geospatial techno-economic tool that\nestimates renewable energy technical potential (capacity and generation),\nsystem cost, and supply curves for solar photovoltaics (PV),\nconcentrating solar power (CSP), geothermal, and wind energy.\nreV allows researchers to include exhaustive spatial representation\nof the built and natural environment into the generation and cost estimates\nthat it computes.\n\nreV is highly dynamic, allowing analysts to assess potential at varying levels\nof detail \u2014 from a single site up to an entire continent at temporal resolutions\nranging from five minutes to hourly, spanning a single year or multiple decades.\nThe reV model can (and has been used to) provide broad coverage across large spatial\nextents, including North America, South and Central Asia, the Middle East, South America,\nand South Africa to inform national and international-scale analyses. Still, reV is\nequally well-suited for regional infrastructure and deployment planning and analysis.\n\n\nFor a detailed description of reV capabilities and functionality, see the\n`NREL reV technical report <https://www.nrel.gov/docs/fy19osti/73067.pdf>`_.\n\nHow does reV work?\n==================\nreV is a set of `Python classes and functions <https://nrel.github.io/reV/_autosummary/reV.html>`_\nthat can be executed on HPC systems using `CLI commands <https://nrel.github.io/reV/_cli/cli.html>`_.\nA full reV execution consists of one or more compute modules\n(each consisting of their own Python class/CLI command)\nstrung together using a `pipeline framework <https://nrel.github.io/reV/_cli/reV%20pipeline.html>`_,\nor configured using `batch <https://nrel.github.io/reV/_cli/reV%20batch.html>`_.\n\nA typical reV workflow begins with input wind/solar/geothermal resource data\n(following the `rex data format <https://nrel.github.io/rex/misc/examples.nsrdb.html#data-format>`_)\nthat is passed through the generation module. This output is then collected across space and time\n(if executed on the HPC), before being sent off to be aggregated under user-specified land exclusion scenarios.\nExclusion data is typically provided via a collection of high-resolution spatial data layers stored in an HDF5 file.\nThis file must be readable by reV's\n`ExclusionLayers <https://nrel.github.io/reV/_autosummary/reV.handlers.exclusions.ExclusionLayers.html#reV.handlers.exclusions.ExclusionLayers>`_\nclass. See the `reVX Setbacks utility <https://nrel.github.io/reVX/misc/examples.setbacks.html>`_\nfor instructions on generating setback exclusions for use in reV.\nNext, transmission costs are computed for each aggregated\n\"supply-curve point\" using user-provided transmission cost tables.\nSee the `reVX transmission cost calculator utility <https://github.com/NREL/reVX/tree/main/reVX/least_cost_xmission/>`_\nfor instructions on generating transmission cost tables.\nFinally, the supply curves and initial generation data can be used to\nextract representative generation profiles for each supply curve point.\n\n\n\n\n.. inclusion-flowchart\n\n\n\n|\n\n.. inclusion-get-started\n\nTo get up and running with reV, first head over to the `installation page <https://nrel.github.io/reV/misc/installation.html>`_,\nthen check out some of the `Examples <https://nrel.github.io/reV/misc/examples.html>`_ or\ngo straight to the `CLI Documentation <https://nrel.github.io/reV/_cli/cli.html>`_!\nYou can also check out the `guide on running GAPs models <https://nrel.github.io/gaps/misc/examples.users.html>`_.\n\n.. inclusion-install\n\n\nInstalling reV\n==============\n\nNOTE: The installation instruction below assume that you have python installed\non your machine and are using `conda <https://docs.conda.io/en/latest/index.html>`_\nas your package/environment manager.\n\nOption 1: Install from PIP (recommended for analysts):\n\n1. Create a new environment:\n ``conda create --name rev python=3.9``\n\n2. Activate directory:\n ``conda activate rev``\n\n3. Install reV:\n 1) ``pip install NREL-reV`` or\n\n - NOTE: If you install using conda and want to use `HSDS <https://github.com/NREL/hsds-examples>`_\n you will also need to install h5pyd manually: ``pip install h5pyd``\n\nOption 2: Clone repo (recommended for developers)\n\n1. from home dir, ``git clone git@github.com:NREL/reV.git``\n\n2. Create ``reV`` environment and install package\n 1) Create a conda env: ``conda create -n rev``\n 2) Run the command: ``conda activate rev``\n 3) cd into the repo cloned in 1.\n 4) prior to running ``pip`` below, make sure the branch is correct (install\n from main!)\n 5) Install ``reV`` and its dependencies by running:\n ``pip install .`` (or ``pip install -e .`` if running a dev branch\n or working on the source code)\n\n3. Check that ``reV`` was installed successfully\n 1) From any directory, run the following commands. This should return the\n help pages for the CLI's.\n\n - ``reV``\n\n\nreV command line tools\n======================\n\n- `reV <https://nrel.github.io/reV/_cli/reV.html#reV>`_\n- `reV template-configs <https://nrel.github.io/reV/_cli/reV%20template-configs.html>`_\n- `reV batch <https://nrel.github.io/reV/_cli/reV%20batch.html>`_\n- `reV pipeline <https://nrel.github.io/reV/_cli/reV%20pipeline.html>`_\n- `reV project-points <https://nrel.github.io/reV/_cli/reV%20project-points.html>`_\n- `reV bespoke <https://nrel.github.io/reV/_cli/reV%20bespoke.html>`_\n- `reV generation <https://nrel.github.io/reV/_cli/reV%20generation.html>`_\n- `reV econ <https://nrel.github.io/reV/_cli/reV%20econ.html>`_\n- `reV collect <https://nrel.github.io/reV/_cli/reV%20collect.html>`_\n- `reV multiyear <https://nrel.github.io/reV/_cli/reV%20multiyear.html>`_\n- `reV supply-curve-aggregation <https://nrel.github.io/reV/_cli/reV%20supply-curve-aggregation.html>`_\n- `reV supply-curve <https://nrel.github.io/reV/_cli/reV%20supply-curve.html>`_\n- `reV rep-profiles <https://nrel.github.io/reV/_cli/reV%20rep-profiles.html>`_\n- `reV hybrids <https://nrel.github.io/reV/_cli/reV%20hybrids.html>`_\n- `reV nrwal <https://nrel.github.io/reV/_cli/reV%20nrwal.html>`_\n- `reV qa-qc <https://nrel.github.io/reV/_cli/reV%20qa-qc.html>`_\n- `reV script <https://nrel.github.io/reV/_cli/reV%20script.html>`_\n- `reV status <https://nrel.github.io/reV/_cli/reV%20status.html>`_\n- `reV reset-status <https://nrel.github.io/reV/_cli/reV%20reset-status.html>`_\n\n\nLaunching a run\n\nTips\n\n- Only use a screen session if running the pipeline module: `screen -S rev`\n- `Full pipeline execution <https://nrel.github.io/reV/misc/examples.full_pipeline_execution.html>`_\n\n.. code-block:: bash\n\n reV pipeline -c \"/scratch/user/rev/config_pipeline.json\"\n\n- Running simply generation or econ can just be done from the console:\n\n.. code-block:: bash\n\n reV generation -c \"/scratch/user/rev/config_gen.json\"\n\nGeneral Run times and Node configuration on Eagle\n\n- WTK Conus: 10-20 nodes per year walltime 1-4 hours\n- NSRDB Conus: 5 nodes walltime 2 hours\n\n`Eagle node requests <https://nrel.github.io/reV/misc/examples.eagle_node_requests.html>`_\n\n\n.. inclusion-citation\n\n\nRecommended Citation\n====================\n\nPlease cite both the technical paper and the software with the version and\nDOI you used:\n\nMaclaurin, Galen J., Nicholas W. Grue, Anthony J. Lopez, Donna M. Heimiller,\nMichael Rossol, Grant Buster, and Travis Williams. 2019. \u201cThe Renewable Energy\nPotential (reV) Model: A Geospatial Platform for Technical Potential and Supply\nCurve Modeling.\u201d Golden, Colorado, United States: National Renewable Energy\nLaboratory. NREL/TP-6A20-73067. https://doi.org/10.2172/1563140.\n\nGrant Buster, Michael Rossol, Paul Pinchuk, Brandon N Benton, Robert Spencer,\nMike Bannister, & Travis Williams. (2023).\nNREL/reV: reV 0.8.0 (v0.8.0). Zenodo. https://doi.org/10.5281/zenodo.8247528\n\n\n",
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