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The National Renewable Energy Laboratory Wind Analysis Libray (NRWAL)

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Welcome to NRWAL!

https://codecov.io/gh/nrel/NRWAL/branch/main/graph/badge.svg?token=NB29X039VU

The National Renewable Energy Laboratory Wind Analysis Library (NRWAL):

  1. A library of offshore wind cost equations (plus new energy technologies like marine hydro!)
  2. Easy equation manipulation without editing source code
  3. Full continental-scale integration with the NREL Renewable Energy Potential Model (reV)
  4. Ready-to-use configs for basic users
  5. Dynamic python tools for intuitive equation handling
  6. One seriously badass sea unicorn

To get started with NRWAL, check out the NRWAL Config documentation or the NRWAL example notebook. You can also launch the notebook in an interactive jupyter shell right in your browser without any downloads or software using binder.

Ready to build a model with NRWAL but don't want to contribute to the library? No problem! Check out the example getting started project here.

Here is the important stuff:

Installing NRWAL

Option 1: Install from PIP or Conda (recommended for analysts):

  1. Create a new environment:
    conda create --name nrwal
  2. Activate directory:
    conda activate nrwal
  3. Install reVX:
    1. pip install NREL-NRWAL or
    2. conda install nrel-nrwal --channel=nrel

Option 2: Clone repo (recommended for developers)

  1. from home dir, git clone https://github.com/NREL/NRWAL.git
    1. enter github username
    2. enter github password
  2. Create NRWAL environment and install package
    1. Create a conda env: conda create -n nrwal
    2. Run the command: conda activate nrwal
    3. cd into the repo cloned in 1.
    4. prior to running pip below, make sure the branch is correct (install from master!)
    5. Install NRWAL and its dependencies by running: pip install . (or pip install -e . if running a dev branch or working on the source code)

NRWAL Variables for Offshore Wind (OSW)

NRWAL Inputs
Variable Name Long Name Source Units
aeff Array Efficiency array_efficiency input layer, computed from ORBIT %
capex_multi CAPEX Multiplier Supplied by user unit-less
depth Water depth (positive values) bathymetry input layer m
dist_a_to_s Distance from assembly area to site Computed from assembly_area input layer km
dist_op_to_s Distance from operating port to site ports_operations input layer km
dist_p_to_a Distance from port (construction no-limit) to assembly area assembly_area input layer km
dist_p_to_s Distance from construction port to site ports_construction input layer km
dist_p_to_s_nolimit Distance from no-limit construction port to site ports_construction_nolimit input layer km
dist_s_to_l Distance site to nearest land dist_to_coast input layer km
fixed_downtime Average weather downtime for fixed structure turbines weather_downtime_fixed_bottom input layer fraction
floating_downtime Average weather downtime for floating structure turbines weather_downtime_floating input layer fraction
gcf Gross capacity factor Computed by reV / SAM with losses == 0 unit-less
hs_average Significant wave height to determine weather downtime weather_downtime_mean_wave_height_buoy input layer m
num_turbines Number of turbines in array Supplied by user unit-less
transmission_multi Tranmission cost multiplier Supplied by user unit-less
turbine_capacity Capacity of each turbine in the array Supplied by user MW

Recommended Citation

If using the NRWAL software (replace with current version and DOI):

If using the Offshore Wind (OSW) cost equations:

  • Beiter, Philipp, Walter Musial, Aaron Smith, Levi Kilcher, Rick Damiani, Michael Maness, Senu Sirnivas, Tyler Stehly, Vahan Gevorgian, Meghan Mooney, and George Scott. “A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015–2030.” National Renewable Energy Lab. (NREL), Golden, CO (United States), September 1, 2016. https://doi.org/10.2172/1324526. https://www.nrel.gov/docs/fy16osti/66579.pdf.

If using the marine energy reference model (RM) cost models: