Pvsandiainv

Wrapper for SAM Simulation Core model: cmod_pvsandiainv.cpp

Input Consistency Warning

As described in Possible Problems, some input parameters are interdependent but the equations that enforce consistency are not available in this PySAM module. Therefore, the onus is on the PySAM user to check that interdependencies are correctly handled. The variables which may require additional logic include:

Provided for each of these inputs is a list of other inputs that are potentially interdependent.

Creating an Instance

Refer to the Initializing a Model page for details on the different ways to create an instance of a PySAM class.

Pvsandiainv model description

Pvsandiainv

PySAM.Pvsandiainv.default(config) → Pvsandiainv

Use default attributes None

PySAM.Pvsandiainv.from_existing(data, optional config) → Pvsandiainv

Share underlying data with an existing PySAM class. If config provided, default attributes are loaded otherwise.

PySAM.Pvsandiainv.new() → Pvsandiainv
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

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.

assign(dict) → None

Assign attributes from nested dictionary, except for Outputs

nested_dict = { 'Sandia Inverter Model': { var: val, ...}, ...}

execute(int verbosity) → None

Execute simulation with verbosity level 0 (default) or 1

export() → dict

Export attributes into nested dictionary

replace(dict) → None

Replace attributes from nested dictionary, except for Outputs. Unassigns all values in each Group then assigns from the input dict.

nested_dict = { 'Sandia Inverter Model': { var: val, ...}, ...}

unassign(name) → None

Unassign a value in any of the variable groups.

value(name, optional value) → Union[None, float, dict, sequence, str]

Get or set by name a value in any of the variable groups.

SandiaInverterModel Group

class PySAM.Pvsandiainv.Pvsandiainv.SandiaInverterModel
assign(dict) → None

Assign attributes from dictionary, overwriting but not removing values

SandiaInverterModel_vals = { var: val, ...}

export() → dict

Export attributes into dictionary

replace(dict) → None

Replace attributes from dictionary, unassigning values not present in input dict

SandiaInverterModel_vals = { var: val, ...}

c0

Defines parabolic curvature of relationship between ac power and dc power at reference conditions [1/W]

Required: True

Type:float
Type:C0
c1

Parameter allowing Pdco to vary linearly with dc voltage input [1/V]

Required: True

Type:float
Type:C1
c2

Parameter allowing Pso to vary linearly with dc voltage input [1/V]

Required: True

Type:float
Type:C2
c3

Parameter allowing C0 to vary linearly with dc voltage input [1/V]

Required: True

Type:float
Type:C3
dc

DC power input to inverter [Watt]

Required: True

Type:sequence
dc_voltage

DC voltage input to inverter [Volt]

Constraints: LENGTH_EQUAL=dc

Required: True

Type:sequence
paco

Max AC power rating [Wac]

Required: True

Type:float
pdco

DC power level at which Paco is achieved [Wdc]

Required: True

Type:float
pntare

Parasitic AC consumption [Wac]

Required: True

Type:float
pso

DC power level required to start inversion [Wdc]

Required: True

Type:float
vdco

DV voltage level at which Paco is achieved [Volt]

Required: True

Type:float

Outputs Group

class PySAM.Pvsandiainv.Pvsandiainv.Outputs
assign(dict) → None

Assign attributes from dictionary, overwriting but not removing values

Outputs_vals = { var: val, ...}

export() → dict

Export attributes into dictionary

replace(dict) → None

Replace attributes from dictionary, unassigning values not present in input dict

Outputs_vals = { var: val, ...}

ac

AC power output [Wac]

Type:sequence
acpar

AC parasitic power [Wac]

Type:sequence
cliploss

Power loss due to clipping (Wac) [Wac]

Type:sequence
eff_inv

Conversion efficiency [0..1]

Type:sequence
ntloss

Power loss due to night time tare loss (Wac) [Wac]

Type:sequence
plr

Part load ratio [0..1]

Type:sequence
soloss

Power loss due to operating power consumption (Wac) [Wac]

Type:sequence