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05f5408
add marion's adjustment
williamhobbs Oct 7, 2025
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Update pvlib/pvsystem.py
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more suggested changes
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Update pvlib/pvsystem.py
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4488e3c
fix typo in error eqn
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add more detail on `cap_adjustment`
williamhobbs Oct 9, 2025
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return same object type that was input
williamhobbs Oct 16, 2025
17f7213
fix issue with scalars
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another fix
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Merge branch 'add_marion' of https://github.com/williamhobbs/pvlib-py…
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unit formatting
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call keyword arguments as keyword arguments
williamhobbs Oct 20, 2025
5777613
quick fix
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simplify with_k_and_cap_adjustmen
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cwhanse Nov 7, 2025
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74 changes: 62 additions & 12 deletions pvlib/pvsystem.py
Original file line number Diff line number Diff line change
Expand Up @@ -2878,19 +2878,10 @@

@renamed_kwarg_warning(
"0.13.0", "g_poa_effective", "effective_irradiance")
def pvwatts_dc(effective_irradiance, temp_cell, pdc0, gamma_pdc, temp_ref=25.):
def pvwatts_dc(effective_irradiance, temp_cell, pdc0, gamma_pdc, temp_ref=25.,
k=None, cap_adjustment=False):
r"""
Implements NREL's PVWatts DC power model. The PVWatts DC model [1]_ is:
.. math::
P_{dc} = \frac{G_{poa eff}}{1000} P_{dc0} ( 1 + \gamma_{pdc} (T_{cell} - T_{ref}))
Note that ``pdc0`` is also used as a symbol in
:py:func:`pvlib.inverter.pvwatts`. ``pdc0`` in this function refers to the DC
power of the modules at reference conditions. ``pdc0`` in
:py:func:`pvlib.inverter.pvwatts` refers to the DC power input limit of
the inverter.
Implement NREL's PVWatts (Version 5) DC power model.

Check failure on line 2884 in pvlib/pvsystem.py

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W291 trailing whitespace
Parameters
----------
Expand All @@ -2909,22 +2900,81 @@
temp_ref: numeric, default 25.0
Cell reference temperature. PVWatts defines it to be 25 C and
is included here for flexibility. [C]
k: numeric, optional
Irradiance correction factor, defined in [2]_. [unitless]
cap_adjustment: Boolean, default False
If True, apply the optional adjustment at and below 1000 W/m^2.
Returns
-------
pdc: numeric
DC power. [W]
Notes
-----
The PVWatts Version 5 DC model [1]_ is:
.. math::
P_{dc} = \frac{G_{poa eff}}{1000} P_{dc0} ( 1 + \gamma_{pdc} (T_{cell} - T_{ref}))
This model has also been referred to as the power temperature coefficient
model.
This function accepts an optional irradiance adjustment factor, `k`, based
on [2]_. This applies a piece-wise adjustment to power based on irradiance,
where `k` is the reduction in actual power at 200 Wm⁻² relative to power
calculated at 200 Wm-2 as 0.2*`pdc0`. For example, a 500 W module that
produces 95 W at 200 Wm-2 (a 5% relative reduction in efficiency) would
have a value of `k` = 0.01.
.. math::
k=\frac{0.2P_{dc0}-P_{200}}{P_{dc0}}
This adjustment increases relative efficiency for irradiance above 1000
Wm⁻², which may not be desired. An optional input, `capped_adjustment`,
modifies the adjustment from [2]_ to only apply below 1000 Wm⁻².
Note that ``pdc0`` is also used as a symbol in
:py:func:`pvlib.inverter.pvwatts`. ``pdc0`` in this function refers to the DC
power of the modules at reference conditions. ``pdc0`` in
:py:func:`pvlib.inverter.pvwatts` refers to the DC power input limit of
the inverter.
References
----------
.. [1] A. P. Dobos, "PVWatts Version 5 Manual"
http://pvwatts.nrel.gov/downloads/pvwattsv5.pdf
(2014).
.. [2] B. Marion, "Comparison of Predictive Models for
Photovoltaic Module Performance,"
:doi:`10.1109/PVSC.2008.4922586`,
https://docs.nrel.gov/docs/fy08osti/42511.pdf
(2008).
""" # noqa: E501

pdc = (effective_irradiance * 0.001 * pdc0 *
(1 + gamma_pdc * (temp_cell - temp_ref)))

# apply Marion's correction if k is anything but zero
if k is not None:
err_1 = (k * (1 - (1 - effective_irradiance / 200)**4) /
(effective_irradiance / 1000))
err_2 = (k * (1000 - effective_irradiance) / (1000 - 200))

pdc_marion = np.where(effective_irradiance <= 200,
pdc * (1 - err_1),
pdc * (1 - err_2))

# "cap" Marion's correction at 1000 W/m^2
if cap_adjustment is True:
pdc_marion = np.where(effective_irradiance >= 1000,
pdc,
pdc_marion)

pdc = pdc_marion

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@williamhobbs have you started / do you intend to write tests? The codecov check fails since this section is not covered by tests. Happy to help with that if you need.

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Haven't started, but figured I would need to. Do you have suggestions on tests to add? I have pretty limited experience with the pvlib testing structure/best practices.

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I'd hand-calculate half a dozen points, using k. Test for correct output with input of three types: float, array, Series. Then test with cap_adjustment=True.

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Be creative, test whatever comes to mind! Generally speaking, test a range of intended and extreme conditions.

Intended:
For the if statements, check whether the indented block is executed if the condition is met (for example, if k is not None, use some example values to check whether the correction is applied).
Whether the block is executed correctly should also be checked--- if k is not None, then check example irradiance values >200 and <=200 (is the intended behaviour executed correctly in these cases?)

Extreme:
What if the user enters non-physical values, how should these be handled and are they handled in this way? e.g. negative or NaN irradiance values

Someone else might be able to offer a clearer/more succinct explanation 😅

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I'd hand-calculate half a dozen points, using k. Test for correct output with input of three types: float, array, Series. Then test with cap_adjustment=True.

Good point, check whether different data types are handled appropriately too

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@williamhobbs you do not have to test with k=None, that is covered by existing tests. Any new tests would be better in their own function, e.g., test_pvwatts_dc_with_k.

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@williamhobbs you do not have to test with k=None, that is covered by existing tests.

Correct, my bad. I was trying to make a general point but overlooked that in the example.
See the codecov report

Any new tests would be better in their own function, e.g., test_pvwatts_dc_with_k.

Add to test_pvsystem.py (examples also visible there)

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@cwhanse and @RDaxini, I added some tests. I'm not sure if it's what you had in mind, so let me know if I should remove or add anything.

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@williamhobbs these look good to me! I see tests covering different dtypes, various combinations of k and cap_adjustment, etc. Get a second opinion from @cwhanse in case I am missing something.

One other case to add would be the scalar case when cap_adjustment=True and effective_irradiance>=1000
That should cover the else branch highlighted as missing in the codecov report. Something simple like:

def test_pvwatts_dc_cap_adjustment_scalar_above_1000():
    irrad = 1200
    temp_cell = 25
    k = 0.01
    cap_adjustment = True

    out = pvsystem.pvwatts_dc(irrad, temp_cell, 100, -0.003, 25, k, cap_adjustment)
    expected = 120.0

    assert_allclose(out, expected)

So in this case, the unadjusted pdc value is returned. Right?

return pdc


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