Pvsandiainv¶
Wrapper for SAM Simulation Core model: cmod_pvsandiainv.cpp
Creating an Instance¶
There are three methods to create a new instance of a PySAM module. Using default populates the newclass’ attributes with default values specific to a config. Each technology-financialconfiguration corresponds to a SAM GUI configuration. Using new creates an instance with empty attributes. The wrap function allows compatibility with PySSC, for details, refer to PySSC.
Pvsandiainv model description
Pvsandiainv
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PySAM.Pvsandiainv.default(config) → Pvsandiainv¶ Use financial config-specific default attributes
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PySAM.Pvsandiainv.from_existing(data, optional config) → Pvsandiainv¶ Share underlying data with an existing PySAM class. If config provided, default attributes are loaded otherwise.
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PySAM.Pvsandiainv.new() → Pvsandiainv¶
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PySAM.Pvsandiainv.wrap(ssc_data_t) → Pvsandiainv¶ Use existing PySSC data
Warning
Do not call PySSC.data_free on the ssc_data_t provided to
wrap
Functions¶
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class
PySAM.Pvsandiainv.Pvsandiainv¶ This class contains all the variable information for running a simulation. Variables are grouped together in the subclasses as properties. If property assignments are the wrong type, an error is thrown.
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assign(dict) → None¶ Assign attributes from nested dictionary, except for Outputs
nested_dict = { 'Sandia Inverter Model': { var: val, ...}, ...}
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execute(int verbosity) → None¶ Execute simulation with verbosity level 0 (default) or 1
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export() → dict¶ Export attributes into nested dictionary
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value(name, optional value) → Union[None, float, dict, sequence, str]¶ Get or set by name a value in any of the variable groups.
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SandiaInverterModel Group¶
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class
PySAM.Pvsandiainv.Pvsandiainv.SandiaInverterModel¶ -
assign() → None¶ Assign attributes from dictionary
SandiaInverterModel_vals = { var: val, ...}
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export() → dict¶ Export attributes into dictionary
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c0¶ Defines parabolic curvature of relationship between ac power and dc power at reference conditions [1/W]
Required: True
Type: float Type: C0
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c1¶ Parameter allowing Pdco to vary linearly with dc voltage input [1/V]
Required: True
Type: float Type: C1
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c2¶ Parameter allowing Pso to vary linearly with dc voltage input [1/V]
Required: True
Type: float Type: C2
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c3¶ Parameter allowing C0 to vary linearly with dc voltage input [1/V]
Required: True
Type: float Type: C3
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dc¶ DC power input to inverter [Watt]
Required: True
Type: sequence
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dc_voltage¶ DC voltage input to inverter [Volt]
Constraints: LENGTH_EQUAL=dc
Required: True
Type: sequence
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paco¶ Max AC power rating [Wac]
Required: True
Type: float
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pdco¶ DC power level at which Paco is achieved [Wdc]
Required: True
Type: float
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pntare¶ Parasitic AC consumption [Wac]
Required: True
Type: float
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pso¶ DC power level required to start inversion [Wdc]
Required: True
Type: float
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vdco¶ DV voltage level at which Paco is achieved [Volt]
Required: True
Type: float
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Outputs Group¶
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class
PySAM.Pvsandiainv.Pvsandiainv.Outputs¶ -
assign() → None¶ Assign attributes from dictionary
Outputs_vals = { var: val, ...}
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export() → dict¶ Export attributes into dictionary
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ac¶ AC power output [Wac]
Type: sequence
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acpar¶ AC parasitic power [Wac]
Type: sequence
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cliploss¶ Power loss due to clipping (Wac) [Wac]
Type: sequence
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eff_inv¶ Conversion efficiency [0..1]
Type: sequence
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ntloss¶ Power loss due to night time tare loss (Wac) [Wac]
Type: sequence
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plr¶ Part load ratio [0..1]
Type: sequence
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soloss¶ Power loss due to operating power consumption (Wac) [Wac]
Type: sequence
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