6-parameter Single Diode Model Solver #195
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| iv_diff_cols = ['d_Isc', 'd_Imp', 'd_Vmp', 'd_Pmp'] | ||
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| # 6par_solve.h L 423 | ||
| def current_at_voltage_cec(Vmodule, IL_ref, IO_ref, RS, A_ref, RSH_ref, I_mp_ref): |
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wondering if pvlib.pvsystem.i_from_v() would be suitable here. This solver iterates using a Newton's method, risking numerical overflow from the exponential term at high voltages, and exiting at rather low precision. The default solver behind pvlib's i_from_v should return 10 digits of precision and handles overflow.
And i_from_v is vectorized, if that doesn't conflict with what pyomo needs.
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Hi Cliff, thanks for the suggestion. This current_at_voltage_cec function is only for plotting purposes and isn't used in the actual solving. I'm looking at i_from_v() and will run a comparison
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I've updated the doc string on the function:
Solve for the current at the voltage Vmodule, given the single diode parameters which could have been modified for non-STC condition
Used for plotting only, so numerical precision is only 1e-4. Adapted from ssc/shared/6par_solve.h L 423
For better stability and precision, use pvlib.pvsystem.i_from_v(v, IL_oper, IO_oper, Rs, Rsh_oper, A_oper)
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| def cec_model_params_at_condition(model, Irr, T_cell_K): | ||
| """ | ||
| Get the single diode parameters at a given condition |
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The pvlib equivalent is pvlib.pvsystem.calcparams_cec. I'm guessing that casting to pyomo objects is necessary, though.
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I'll take a look, but this function is again just for plotting purposes. Everything for the solve is via pyomo objects in create_model
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There are two differences I noticed:
- The calculation of IL in SAM has a modification using Adjust to the alpha coefficient that's not present in pvlib.
# pvlib L 1695
IL = effective_irradiance / irrad_ref * (I_L_ref + alpha_sc * (Tcell_K - Tref_K))
# sam
IL_oper = Irr/I_ref *( Il + alpha * (1-Adj/100)*(T_cell_K-Tc_ref) )
- The calculation of IO. SAM's plotting function had simplified the coefficients (probably to deal with numerical issues in LK), so I switched it back to the version using the Boltzmann coefficient in both the plotting function and in pyomo. The numerical impact was small, but in the precision requirement for pyomo, potentially impactful.
# b.Io_Tc = pyo.Expression(expr=m.par.Io* ( b.Tc/m.Tref)**3 * pyo.exp( 11600 * (m.Egref/m.Tref - b.Eg_Tc/b.Tc)))
b.Io_Tc = pyo.Expression(expr=m.par.Io* ( b.Tc/m.Tref)**3 * pyo.exp(b.Eg_Tc / (m.k*(m.Tref)) - (m.Egref / (m.k*(b.Tc)))))
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calcparams_cec calls calcparams_desoto with the modification to alpha_sc in the passed parameters: alpha_sc*(1.0 - Adjust/100)
The CEC and De Soto models only differ by the Adjust parameter so we re-use code here.
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Ah, I'd missed that! Thanks for pointing it out. Makes sense now
dguittet
assigned
cpaulgilman and
mjprilliman and unassigned
cpaulgilman and
mjprilliman
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@cwhanse Thanks for the review! I've addressed your comments, please let me know if you have other feedback |
Curious what solver is being used? Its been some time since I looked at pyomo but I recall that it isn't distributed with a solver. |
It's using IPOPT: |
Run time for Mac w/ 8 processes
To test, install the dependencies listed in tests/requirements.txt and also
conda install -c conda-forge ipoptto install the solver