Skip to main content

National Renewable Energy Laboratory's (NREL's) Renewable Energy Potential(V) Model: reV

Project description

               

https://github.com/NREL/reV/workflows/Documentation/badge.svg https://github.com/NREL/reV/workflows/Pytests/badge.svg https://github.com/NREL/reV/workflows/Lint%20Code%20Base/badge.svg https://img.shields.io/pypi/pyversions/NREL-reV.svg https://badge.fury.io/py/NREL-reV.svg https://codecov.io/gh/nrel/reV/branch/main/graph/badge.svg?token=U4ZU9F0K0Z https://zenodo.org/badge/201343076.svg https://mybinder.org/badge_logo.svg

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.

How does reV work?

reV is a set of Python classes and functions that can be executed on HPC systems using CLI commands. 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, or configured using batch.

A typical reV workflow begins with input wind/solar/geothermal resource data (following the rex 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 class. See the reVX Setbacks utility 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 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.


To get up and running with reV, first head over to the installation page, then check out some of the Examples or go straight to the CLI Documentation!

Installing reV

NOTE: The installation instruction below assume that you have python installed on your machine and are using conda 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 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

Launching a run

Tips

reV -c "/scratch/user/rev/config_pipeline.json" pipeline
  • Running simply generation or econ can just be done from the console:

reV -c "/scratch/user/rev/config_gen.json" generation

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

NREL-reV-0.8.2.tar.gz (256.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

NREL_reV-0.8.2-py3-none-any.whl (552.0 kB view details)

Uploaded Python 3

File details

Details for the file NREL-reV-0.8.2.tar.gz.

File metadata

  • Download URL: NREL-reV-0.8.2.tar.gz
  • Upload date:
  • Size: 256.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for NREL-reV-0.8.2.tar.gz
Algorithm Hash digest
SHA256 ee287de490485936e1fa860e78214aa1aeee85eb867ef4e8c13dce7bb569c0ab
MD5 a8d76fcbb8b04f34b45fcf061f0545c0
BLAKE2b-256 ebd078546f99ffabc03077292697eea8a406d3d823c595decffacf886309fe70

See more details on using hashes here.

File details

Details for the file NREL_reV-0.8.2-py3-none-any.whl.

File metadata

  • Download URL: NREL_reV-0.8.2-py3-none-any.whl
  • Upload date:
  • Size: 552.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for NREL_reV-0.8.2-py3-none-any.whl
Algorithm Hash digest
SHA256 363a35adb3e575cad36e1162685c78e9fd1885f89dd0ca46d379b3e5a8374b51
MD5 59126f43c865df04c42b0ee12dbdb8ea
BLAKE2b-256 43ae337daa73c7d778be449df0fac74a57f76a6172d1d1fb7ccdca15e37bde63

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page