Interdependent Variables

Some variables are inputs to a compute module and are not interdependent on other variables. For example, the value of the Pvwattsv8 module input array_type does not depend on any other input variable, and its value does not affect the value of any other input.

An input is an interdependent variable if either its value depends on the value of one or more other inputs, or its value affects the value of one or more other inputs.

For example, the Pvsamv1 module’s system_capacity input depends on subarray1_modules_per_string and subarray1_nstrings among other inputs because the photovoltaic system capacity is calculated from the number of modules in the array. Similarly, several inputs depend on the value of the inv_ds_paco input, including inv_pd_pdco because the inverter’s rated DC power is calculated from its rated AC power and nominal efficiency.

Finding Relationships between Variables

Use the following resources to identify the relationship between interdependent variables:

  • In SAM’s user interface, blue “calculated values” indicate interdependent variables, and SAM’s Help system includes descriptions of many of the relationships with equations.

  • The code for the calculated values in SAM’s user interface is in the runtime/ui folder of the SAM installation on your computer, and also available on GitHub.

  • The SDKtool application that comes with SAM has a Module Browser that you can use to explore SSC compute modules. Its script editor is also useful for testing code. Learn more on the SAM Software Development Kit (SDK) page of the SAM website.

  • The compute module documentation accessible from List of SSC Compute Modules includes lists of interdependent variables: All of a module’s interdependent variables are listed at the top of the module’s page, and the description of each variable includes a list of any interdependent variables.

  • Ask for help from the SAM team: Getting Help.

Calculated Values in SAM

SAM’s user interface displays “calculated values” in blue boxes that you cannot edit. A calculated value may be:

  • A compute module input calculated from other inputs. For example, the Nameplate DC capacity calculated value on the Detailed PV model’s System Design page is the Pvsamv1 input system_capacity, which is calculated from the module nameplate capacity, along with the modules per string in subarray and strings in parallel in subarray for each enabled subarray.

  • A value copied from a different page and shown for reference that may or may not be an input to a compute module. For example, on Detailed PV model’s System Design page, under Electrical Sizing Information, the inverter maximum DC voltage, minimum MPPT voltage, and maximum MPPT voltage are values copied from the Inverter page. These values are shown to help you determine the correct number of strings in parallel for each subarray.

  • A value shown for information that is not an input to a compute module. For example, the Detailed PV model’s System Design page shows the total number of modules in each subarray, which is not an input to any compute module. The value is shown to help you see how large the array is.


Calculated values in SAM may not be SSC inputs. See Identifying Variables for information about how to use SAM to find SSC variables.


For some compute modules, including TroughPhysical and Tscmoltensalt, the equations in the user interface used for calculated values have been migrated to SSC eliminating the problem of interdependent variables for those modules. NREL is working to migrate equations for other modules for future versions of SAM and PySAM.


These examples are for the Pvsamv1 module, but you can use similar techniques for other modules.

Pvsamv1 System Nameplate Capacity

The system_capacity input to the Pvsamv1 module is an example of an interdependent variable. The module documentation shows that its value depends on several variables, including subarray1_nstrings. To explore this interdependence, we created a default Photovoltaic – No Financial case in SAM, and experimented with doubling the size of the system by doubling the values of Strings in parallel in subarray and Number of inverters, noting how the calculated value Nameplate DC capacity changes: Doubling the number of strings doubles the system capacity as we would expect. However, if you try this with PySAM, you will note that system_capacity does not change when you change subarray1_nstrings.

We used the following information to figure out why:

And then, based on what we learned, wrote the following test script:

import os
import PySAM.Pvsamv1 as pv
import PySAM.Grid as gr

# calculate Pvsamv1 system_capacity for array with one subarray
def pv_capacity_calc():
    v_mp = system.value('cec_v_mp_ref')
    i_mp = system.value('cec_i_mp_ref')
    n_modules_per_string = system.value('subarray1_modules_per_string')
    n_strings = system.value('subarray1_nstrings')
    return v_mp * n_modules_per_string * i_mp * n_strings * 0.001

# print inputs and outputs to demonstrate results
def print_results():
    print('subarray1_nstrings = ', system.value('subarray1_nstrings'))
    print('inverter_count = ', system.value('inverter_count'))
    print('system_capacity = ', system.value('system_capacity'))
    print('calculated system_capacity = ', pv_capacity_calc())
    print('Outputs.annual_energy = ', system.Outputs.annual_energy)
    print('Outputs.capacity_factor output = ', system.Outputs.capacity_factor)

system = pv.default('FlatPlatePVNone')
grid = gr.from_existing(system,'FlatPlatePVNone')

# use weather file downloaded from SAM as "denver, co", you can replace this with a path to any valid weather file in the SAM CSV format
filename = os.path.expanduser('~') + '/SAM Downloaded Weather Files/denver_co_39.7385_-104.985_psm3-tmy_60_tmy.csv'
system.SolarResource.solar_resource_file = filename

# default system capacity and annual energy
print('\n** defaults **')

# double system capacity by doubling value of strings in parallel and number of inverters
print('** double number of strings and inverters without recalculating system_capacity **')
print('  capacity_factor doubles because we did not recalculate system_capacity!!\n')

# recalculate and set system_capacity
print('** recalculate system capacity **')

Pvsamv1 Ground Coverage Ratio (GCR)

The ground coverage ratio (GCR) is used in two ways by the Pvsamv1 compute module when it is paired with the Singleowner financial model for the Detailed PV – Single Owner configuration. It used by Pvsamv1 for self-shading calculations, and in user interface equations to calculate the land area for Singleowner land-related installation and operating costs. If your Python code changes the value of Pvsamv1.SystemDesign.gcr to x, and you are including land cost y in $/acre in your analysis, you need code like the following adapted from the equations for SAM’s user interface in runtime/ui/PV System Design.txt to ensure the change is accounted for in Singleowner.SystemCosts.total_installed_cost:

pv.SystemDesign.subarray1_gcr = x
land_area = pv.CECPerformanceModelWithModuleDatabase.cec_area
        * (pv.SystemDesign.subarray1_nstrings
        * pv.SystemDesign.subarray1_modules_per_string) / x * 0.0002471  # m^2 to acres

# total_installed_cost = total_direct_cost + permitting_total + engr_total +
#                       grid_total + landprep_total + sales_tax_total + land_total
# y = land cost in $/acre
so.SystemCosts.total_installed_cost = cost_without_land + y * land_area