Migrate NREL references to NLR (National Laboratory of the Rockies)

.github/workflows/pytest-remote-data.yml

@@ -96,7 +96,7 @@ jobs:
         shell: bash -l {0}  # necessary for conda env to be active
         env:
           # copy GitHub Secrets into environment variables for the tests to access
-          NREL_API_KEY: ${{ secrets.NRELAPIKEY }}
+          NLR_API_KEY: ${{ secrets.NRELAPIKEY }}
           SOLARANYWHERE_API_KEY: ${{ secrets.SOLARANYWHERE_API_KEY }}
           BSRN_FTP_USERNAME: ${{ secrets.BSRN_FTP_USERNAME }}
           BSRN_FTP_PASSWORD: ${{ secrets.BSRN_FTP_PASSWORD }}

.gitignore

@@ -99,3 +99,8 @@ coverage.xml
 env
 results
 
+
+# Gas Town runtime
+.beads/
+.claude/
+.runtime/

docs/examples/bifacial/plot_irradiance_nonuniformity_loss.py

@@ -47,7 +47,7 @@
 # described in Figure 1 (A), [1]_. We will cover this case for educational
 # purposes, although it can be achieved with the packages
 # `solarfactors <https://github.com/pvlib/solarfactors/>`_ and
-# `bifacial_radiance <https://github.com/NREL/bifacial_radiance>`_.
+# `bifacial_radiance <https://github.com/NatLabRockies/bifacial_radiance>`_.
 #
 # Here we set and plot the global irradiance level of each cell.
 

docs/examples/irradiance-decomposition/plot_diffuse_fraction.py

@@ -52,7 +52,7 @@
 # ----
 #
 # DISC :py:func:`~pvlib.irradiance.disc` is an empirical correlation developed
-# at SERI (now NREL) in 1987. The direct normal irradiance (DNI) is related to
+# at SERI (now NLR) in 1987. The direct normal irradiance (DNI) is related to
 # clearness index (kt) by two polynomials split at kt = 0.6, then combined with
 # an exponential relation with airmass.
 
@@ -216,5 +216,5 @@
 # correlations, which include additional variables such as airmass. These
 # methods seem to reduce DNI spikes over 1000 [W/m^2].
 #
-# .. _TMY3: https://www.nrel.gov/docs/fy08osti/43156.pdf
-# .. _NSRDB: https://www.nrel.gov/docs/fy12osti/54824.pdf
+# .. _TMY3: https://doi.org/10.2172/928611
+# .. _NSRDB: https://doi.org/10.2172/1054832

docs/examples/spectrum/average_photon_energy.py

@@ -34,7 +34,7 @@
 from scipy.integrate import trapezoid
 from pvlib import spectrum, solarposition, irradiance, atmosphere
 
-lat, lon = 39.742, -105.18  # NREL SRRL location
+lat, lon = 39.742, -105.18  # SRRL location
 surface_tilt = 25
 surface_azimuth = 180  # south-facing system
 pressure = 81190  # at 1828 metres AMSL, roughly
@@ -194,5 +194,5 @@
 #        for the solar spectral influence on photovoltaic device performance."
 #        Energy 286 :doi:`10.1016/j.energy.2023.129461`
 # .. [4] Bird Simple Spectral Model: spectrl2_2.c
-#        https://www.nrel.gov/grid/solar-resource/spectral.html
-#        (Last accessed: 18/09/2024)
+#        https://www.nlr.gov/grid/solar-resource/spectral
+#        (Last accessed: 03/03/2026)

docs/sphinx/source/reference/iotools.rst

@@ -179,7 +179,7 @@ A solar radiation network in the USA, run by NOAA.
 MIDC
 ^^^^
 
-A solar radiation network in the USA, run by NREL.
+A solar radiation network in the USA, run by NLR (known as NREL prior to December 2025).
 
 .. autosummary::
    :toctree: generated/

docs/sphinx/source/user_guide/extras/faq.rst

@@ -126,7 +126,7 @@ The CEC table doesn't include my module or inverter, what should I do?
 ----------------------------------------------------------------------
 
 The CEC tables for module and inverter parameters included in pvlib are periodically
-copied from `SAM <https://github.com/NREL/SAM/tree/develop/deploy/libraries>`_,
+copied from `SAM <https://github.com/NatLabRockies/SAM/tree/develop/deploy/libraries>`_,
 so you can check the tables there for more up-to-date tables.
 
 For modules, if even the SAM files don't include the module you're looking for

docs/sphinx/source/user_guide/getting_started/installation.rst

@@ -242,15 +242,15 @@ pvlib-python is distributed with several validated, high-precision, and
 high-performance solar position calculators. We strongly recommend using
 the built-in solar position calculators.
 
-pvlib-python also includes unsupported wrappers for the official NREL
-SPA algorithm. NREL's license does not allow redistribution of the
+pvlib-python also includes unsupported wrappers for the official NLR
+implementation of NREL SPA. NLR's license does not allow redistribution of the
 source code, so you must jump through some hoops to use it with pvlib.
 You will need a C compiler to use this code.
 
 To install the NREL SPA algorithm for use with pvlib:
 
 #. Download the pvlib repository (as described in :ref:`obtainsource`)
-#. Download the `SPA files from NREL <http://www.nrel.gov/midc/spa/>`_
+#. Download the `SPA files from NLR <http://www.nlr.gov/midc/spa/>`_
 #. Copy the SPA files into ``pvlib-python/pvlib/spa_c_files``
 #. From the ``pvlib-python`` directory, run ``pip uninstall pvlib``
    followed by ``pip install .``

docs/sphinx/source/whatsnew/v0.15.1.rst

@@ -20,11 +20,14 @@ Bug fixes
 * Fix a division-by-zero condition in
   :py:func:`pvlib.transformer.simple_efficiency` when ``load_loss = 0``.
   (:issue:`2645`, :pull:`2646`)
+* Update URLs in :py:mod:`pvlib.iotools.psm4` to use nlr.gov instead of
+  nrel.gov. (:issue:`2701`, :pull:`2705`)
 * Fix a bug in :py:func:`pvlib.iotools.era5._m_to_cm` where meters were
   incorrectly divided by 100 instead of multiplied, causing precipitations to be
   underestimated by a factor of 10,000 when ``map_variables=True``.
   (:issue:`2724`, :pull:`2725`)
 
+
 Enhancements
 ~~~~~~~~~~~~
 * Use ``k`` and ``cap_adjustment`` from :py:func:`pvlib.pvsystem.Array.module_parameters` in :py:func:`pvlib.pvsystem.PVSystem.pvwatts_dc`
@@ -71,6 +74,10 @@ Requirements
 
 Maintenance
 ~~~~~~~~~~~
+* Update all NREL references to NLR (National Laboratory of the Rockies)
+  following the laboratory rename and domain migration from ``nrel.gov``
+  to ``nlr.gov``. Rename ``NREL_API_KEY`` environment variable to
+  ``NLR_API_KEY``. (:issue:`2701`, :pull:`2705`)
 
 
 Contributors
@@ -81,6 +88,7 @@ Contributors
 * Cliff Hansen (:ghuser:`cwhanse`)
 * Anton Driesse (:ghuser:`adriesse`)
 * Kevin Anderson (:ghuser:`kandersolar`)
+* Jason Curtis (:ghuser:`jason-curtis`)
 * Rohan Saxena (:ghuser:`r0hansaxena`)
 * Marco Fumagalli (:ghuser:`fuma900`)
 * Jean-Baptiste Pasquier (:ghuser:`pasquierjb`)

docs/tutorials/tmy_to_power.ipynb

@@ -70,11 +70,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "pvlib comes with a couple of TMY files, and we'll use one of them for simplicity. You could also load a file from disk, or specify a url. See this NREL website for a list of TMY files:\n",
-    "\n",
-    "http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/by_state_and_city.html"
-   ]
+   "source": "pvlib comes with a couple of TMY files, and we'll use one of them for simplicity. You could also load a file from disk, or specify a url. See this NLR website for a list of TMY files:\n\nhttps://nsrdb.nlr.gov/data-sets/archives"
   },
   {
    "cell_type": "code",
@@ -1515,13 +1511,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "Next, we will assume that the SAPM model is representative of the real world performance so that we can use scipy's optimization routine to derive simulated PVUSA coefficients. You will need to install scipy to run these functions.\n",
-    "\n",
-    "Here's one PVUSA reference:\n",
-    "\n",
-    "http://www.nrel.gov/docs/fy09osti/45376.pdf\n"
-   ]
+   "source": "Next, we will assume that the SAPM model is representative of the real world performance so that we can use scipy's optimization routine to derive simulated PVUSA coefficients. You will need to install scipy to run these functions.\n\nHere's one PVUSA reference:\n\nhttps://www.osti.gov/biblio/951223\n"
   },
   {
    "cell_type": "code",

pvlib/atmosphere.py

@@ -448,7 +448,7 @@ def bird_hulstrom80_aod_bb(aod380, aod500):
     References
     ----------
     .. [1] Bird and Hulstrom, "Direct Insolation Models" (1980)
-       `SERI/TR-335-344 <http://www.nrel.gov/docs/legosti/old/344.pdf>`_
+       `SERI/TR-335-344 <http://www.nlr.gov/docs/legosti/old/344.pdf>`_
 
     .. [2] R. E. Bird and R. L. Hulstrom, "Review, Evaluation, and Improvement
        of Direct Irradiance Models", Journal of Solar Energy Engineering

pvlib/bifacial/infinite_sheds.py

@@ -166,7 +166,7 @@ def _shaded_fraction(solar_zenith, solar_azimuth, surface_tilt,
        :doi:`10.1109/PVSC40753.2019.8980572`.
     .. [2] Kevin Anderson and Mark Mikofski, "Slope-Aware Backtracking for
        Single-Axis Trackers", Technical Report NREL/TP-5K00-76626, July 2020.
-       https://www.nrel.gov/docs/fy20osti/76626.pdf
+       :doi:`10.2172/1660126`
     """
     tan_phi = utils._solar_projection_tangent(
         solar_zenith, solar_azimuth, surface_azimuth)

pvlib/bifacial/loss_models.py

@@ -135,7 +135,7 @@ def power_mismatch_deline(
     --------
     `solarfactors <https://github.com/pvlib/solarfactors/>`_
         Calculate the irradiance at different points of the module.
-    `bifacial_radiance <https://github.com/NREL/bifacial_radiance>`_
+    `bifacial_radiance <https://github.com/NatLabRockies/bifacial_radiance>`_
         Calculate the irradiance at different points of the module.
 
     References

pvlib/clearsky.py

@@ -942,7 +942,7 @@ def bird(zenith, airmass_relative, aod380, aod500, precipitable_water,
     """
     Bird Simple Clear Sky Broadband Solar Radiation Model
 
-    Based on NREL Excel implementation by Daryl R. Myers [1, 2].
+    Based on NLR Excel implementation by Daryl R. Myers [1, 2].
 
     Bird and Hulstrom define the zenith as the "angle between a line to
     the sun and the local zenith". There is no distinction in the paper
@@ -953,7 +953,7 @@ def bird(zenith, airmass_relative, aod380, aod500, precipitable_water,
     was to compare existing clear sky models with "rigorous radiative
     transfer models" (RTM) it is possible that apparent zenith was
     obtained as output from the RTM. However, the implementation presented
-    in PVLIB is tested against the NREL Excel implementation by Daryl
+    in PVLIB is tested against the NLR Excel implementation by Daryl
     Myers which uses an analytical expression for solar zenith instead
     of apparent zenith.
 
@@ -1001,13 +1001,13 @@ def bird(zenith, airmass_relative, aod380, aod500, precipitable_water,
     .. [2] Daryl R. Myers, "Solar Radiation: Practical Modeling for Renewable
        Energy Applications", pp. 46-51 CRC Press (2013)
 
-    .. [3] `NREL Bird Clear Sky Model <http://rredc.nrel.gov/solar/models/
-       clearsky/>`_
+    .. [3] `Bird Clear Sky Model <http://www.nlr.gov/grid/solar-resource/
+       clearsky.html>`_
 
-    .. [4] `SERI/TR-642-761 <https://www.nrel.gov/docs/legosti/old/761.pdf>`_
+    .. [4] SERI/TR-642-761 :doi:`10.2172/6510849`
 
-    .. [5] `Error Reports <http://rredc.nrel.gov/solar/models/clearsky/
-       error_reports.html>`_
+    .. [5] `Error Reports <http://www.nlr.gov/grid/solar-resource/
+       clearsky-error-reports.html>`_
     """
     etr = dni_extra  # extraradiation
     ze_rad = np.deg2rad(zenith)  # zenith in radians

pvlib/inverter.py

@@ -117,7 +117,7 @@ def sandia(v_dc, p_dc, inverter):
        for Grid-Connected Photovoltaic Inverters", Sandia National
        Laboratories, Albuquerque, N.M., USA, SAND2007-5036, Sept. 2007.
        :doi:`10.2172/920449`
-    .. [2] System Advisor Model web page. https://sam.nrel.gov.
+    .. [2] System Advisor Model web page. https://sam.nlr.gov.
 
     See also
     --------
@@ -335,7 +335,7 @@ def adr(v_dc, p_dc, inverter, vtol=0.10):
 
 def pvwatts(pdc, pdc0, eta_inv_nom=0.96, eta_inv_ref=0.9637):
     r"""
-    NREL's PVWatts inverter model.
+    NLR's PVWatts inverter model.
 
     The PVWatts inverter model [1]_ calculates inverter efficiency :math:`\eta`
     as a function of input DC power :math:`P_{dc}`
@@ -414,7 +414,7 @@ def pvwatts(pdc, pdc0, eta_inv_nom=0.96, eta_inv_ref=0.9637):
 
 def pvwatts_multi(pdc, pdc0, eta_inv_nom=0.96, eta_inv_ref=0.9637):
     r"""
-    Extend NREL's PVWatts inverter model for multiple MPP inputs.
+    Extend NLR's PVWatts inverter model for multiple MPP inputs.
 
     DC input power is summed over MPP inputs to obtain the DC power
     input to the PVWatts inverter model. See :py:func:`pvlib.inverter.pvwatts`

pvlib/iotools/midc.py

@@ -1,4 +1,4 @@
-"""Functions to read NREL MIDC data.
+"""Functions to read NLR MIDC data.
 """
 import io
 
@@ -15,7 +15,7 @@
 #
 # In particular, these mappings coincide with the raw ddata files.
 # All site's field list can be found at:
-#     https://midcdmz.nrel.gov/apps/daily.pl?site=<SITE ID>&live=1
+#     https://midcdmz.nlr.gov/apps/daily.pl?site=<SITE ID>&live=1
 # Where id is the key found in this dictionary
 MIDC_VARIABLE_MAP = {
     'BMS': {
@@ -158,7 +158,7 @@ def _format_index_raw(data):
 
 
 def read_midc(filename, variable_map={}, raw_data=False, **kwargs):
-    """Read in National Renewable Energy Laboratory Measurement and
+    """Read in National Laboratory of the Rockies Measurement and
     Instrumentation Data Center weather data.  The MIDC is described in [1]_.
 
     Parameters
@@ -196,12 +196,12 @@ def read_midc(filename, variable_map={}, raw_data=False, **kwargs):
     :ref:`nomenclature`.
 
     Be sure to check the units for the variables you will use on the
-    `MIDC site <https://midcdmz.nrel.gov/>`_.
+    `MIDC site <https://midcdmz.nlr.gov/>`_.
 
     References
     ----------
-    .. [1] NREL: Measurement and Instrumentation Data Center
-        `https://midcdmz.nrel.gov/ <https://midcdmz.nrel.gov/>`_
+    .. [1] NLR: Measurement and Instrumentation Data Center
+        `https://midcdmz.nlr.gov/ <https://midcdmz.nlr.gov/>`_
     """
     data = pd.read_csv(filename, **kwargs)
     if raw_data:
@@ -248,13 +248,13 @@ def read_midc_raw_data_from_nrel(site, start, end, variable_map={},
     -----
     Requests spanning an instrumentation change will yield an error. See the
     MIDC raw data api page
-    `here <https://midcdmz.nrel.gov/apps/data_api_doc.pl?_idtextlist>`_
+    `here <https://midcdmz.nlr.gov/apps/data_api_doc.pl?_idtextlist>`_
     for more details and considerations.
     """
     args = {'site': site,
             'begin': pd.to_datetime(start).strftime('%Y%m%d'),
             'end': pd.to_datetime(end).strftime('%Y%m%d')}
-    url = 'https://midcdmz.nrel.gov/apps/data_api.pl'
+    url = 'https://midcdmz.nlr.gov/apps/data_api.pl'
     # NOTE: just use requests.get(url, params=args) to build querystring
     # number of header columns and data columns do not always match,
     # so first parse the header to determine the number of data columns