reV.SAM.econ.Economic

class Economic(sam_sys_inputs, site_sys_inputs=None, output_request='lcoe_fcr')[source]

Bases: RevPySam

Base class for SAM economic models.

Initialize a SAM economic model object.

Parameters:

sam_sys_inputs (dict) – Site-agnostic SAM system model inputs arguments.
site_sys_inputs (dict) – Optional set of site-specific SAM system inputs to complement the site-agnostic inputs.
output_request (list | tuple | str) – Requested SAM output(s) (e.g., ‘ppa_price’, ‘lcoe_fcr’).

Methods

`assign_inputs`()	Assign the self.sam_sys_inputs attribute to the PySAM object.
`collect_outputs`()	Collect SAM output_request, convert timeseries outputs to UTC, and save outputs to self.outputs property.
`default`()	Get the executed default pysam object.
`drop_leap`(resource)	Drop Feb 29th from resource df with time index.
`ensure_res_len`(arr, time_index)	Ensure time_index has a constant time-step and only covers 365 days (no leap days).
`execute`()	Call the PySAM execute method.
`flip_actual_irr`()	Get actual IRR (from PPA/SingleOwner model).
`get_sam_res`(args, *kwargs)	Get the SAM resource iterator object (single year, single file).
`get_time_interval`(time_index)	Get the time interval.
`gross_revenue`()	Get cash flow total revenue (from PPA/SingleOwner model).
`lcoe_fcr`()	Get LCOE ($/MWh).
`lcoe_nom`()	Get nominal LCOE ($/MWh) (from PPA/SingleOwner model).
`lcoe_real`()	Get real LCOE ($/MWh) (from PPA/SingleOwner model).
`make_datetime`(series)	Ensure that pd series is a datetime series with dt accessor
`npv`()	Get net present value (NPV) ($).
`outputs_to_utc_arr`()	Convert array-like SAM outputs to UTC np.ndarrays
`ppa_price`()	Get PPA price ($/MWh).
`reV_run`(site, site_df, inputs, output_request)	Run the SAM econ model for a single site.

Attributes

`DIR`
`IGNORE_ATTRS`
`MODULE`
`attr_dict`	Get the heirarchical PySAM object attribute dictionary.
`input_list`	Get the list of lowest level input attribute/variable names.
`meta`	Get meta data property.
`module`	Get module property.
`pysam`	Get the pysam object.
`site`	Get the site number for this SAM simulation.

ppa_price()[source]

Get PPA price ($/MWh).

Native units are cents/kWh, mult by 10 for $/MWh.

npv()[source]

Get net present value (NPV) ($).

Native units are dollars.

lcoe_fcr()[source]

Get LCOE ($/MWh).

Native units are $/kWh, mult by 1000 for $/MWh.

lcoe_nom()[source]

Get nominal LCOE ($/MWh) (from PPA/SingleOwner model).

Native units are cents/kWh, mult by 10 for $/MWh.

lcoe_real()[source]

Get real LCOE ($/MWh) (from PPA/SingleOwner model).

Native units are cents/kWh, mult by 10 for $/MWh.

flip_actual_irr()[source]

Get actual IRR (from PPA/SingleOwner model).

Native units are %.

gross_revenue()[source]

Get cash flow total revenue (from PPA/SingleOwner model).

Native units are $.

collect_outputs()[source]: Collect SAM output_request, convert timeseries outputs to UTC, and save outputs to self.outputs property.

classmethod reV_run(site, site_df, inputs, output_request)[source]

Run the SAM econ model for a single site.

Parameters:

site (int) – Site gid.
site_df (pd.DataFrame) – Dataframe of site-specific input variables. Row index corresponds to site number/gid (via df.loc not df.iloc), column labels are the variable keys that will be passed forward as SAM parameters.
inputs (dict) – Dictionary of SAM system input parameters.
output_request (list | tuple | str) – Requested SAM output(s) (e.g., ‘ppa_price’, ‘lcoe_fcr’).

Returns:

sim.outputs (dict) – Dictionary keyed by SAM variable names with SAM numerical results.

PYSAM = <module 'PySAM.GenericSystem' from '/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/PySAM/GenericSystem.cpython-38-x86_64-linux-gnu.so'>

assign_inputs(): Assign the self.sam_sys_inputs attribute to the PySAM object.

property attr_dict

Get the heirarchical PySAM object attribute dictionary.

Returns:

_attr_dict (dict) –

Dictionary with:: keys: variable groups values: lowest level attribute/variable names

classmethod default()

Get the executed default pysam object.

Returns:: PySAM.GenericSystem

static drop_leap(resource)

Drop Feb 29th from resource df with time index.

Parameters:: resource (pd.DataFrame) – Resource dataframe with an index containing a pandas time index object with month and day attributes.
Returns:: resource (pd.DataFrame) – Resource dataframe with all February 29th timesteps removed.

static ensure_res_len(arr, time_index)

Ensure time_index has a constant time-step and only covers 365 days (no leap days). If not remove last day

Parameters:

arr (ndarray) – Array to truncate if time_index has a leap day
time_index (pandas.DatatimeIndex) – Time index associated with arr, used to check time-series frequency and number of days

Returns:

arr (ndarray) – Truncated array of data such that there are 365 days

execute(): Call the PySAM execute method. Raise SAMExecutionError if error. Include the site index if available.

static get_sam_res(*args, **kwargs): Get the SAM resource iterator object (single year, single file).

classmethod get_time_interval(time_index)

Get the time interval.

Parameters:: time_index (pd.series) – Datetime series. Must have a dt attribute to access datetime properties (added using make_datetime method).
Returns:: time_interval (int:) – This value is the number of indices over which an hour is counted. So if the timestep is 0.5 hours, time_interval is 2.

property input_list

Get the list of lowest level input attribute/variable names.

Returns:: _inputs (list) – List of lowest level input attributes.

static make_datetime(series): Ensure that pd series is a datetime series with dt accessor

property meta: Get meta data property.

property module: Get module property.

outputs_to_utc_arr(): Convert array-like SAM outputs to UTC np.ndarrays

property pysam: Get the pysam object.

property site: Get the site number for this SAM simulation.