reV.SAM.generation.AbstractSamGeneration

class AbstractSamGeneration(resource, meta, sam_sys_inputs, site_sys_inputs=None, output_request=None, drop_leap=False)[source]

Bases: RevPySam, ScheduledLossesMixin, ABC

Base class for SAM generation simulations.

Initialize a SAM generation object.

Parameters:

resource (pd.DataFrame) – Timeseries solar or wind resource data for a single location with a pandas DatetimeIndex. There must be columns for all the required variables to run the respective SAM simulation. Remapping will be done to convert typical NSRDB/WTK names into SAM names (e.g. DNI -> dn and wind_speed -> windspeed)
meta (pd.DataFrame | pd.Series) – Meta data corresponding to the resource input for the single location. Should include values for latitude, longitude, elevation, and timezone.
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) – Requested SAM outputs (e.g., ‘cf_mean’, ‘annual_energy’, ‘cf_profile’, ‘gen_profile’, ‘energy_yield’, ‘ppa_price’, ‘lcoe_fcr’).
drop_leap (bool) – Drops February 29th from the resource data. If False, December 31st is dropped from leap years.

Methods

`add_scheduled_losses`([resource])	Add stochastically scheduled losses to SAM config file.
`annual_energy`()	Get annual energy generation value in kWh from SAM.
`assign_inputs`()	Assign the self.sam_sys_inputs attribute to the PySAM object.
`cf_mean`()	Get mean capacity factor (fractional) from SAM.
`cf_profile`()	Get hourly capacity factor (frac) profile in local timezone.
`check_resource_data`(resource)	Check resource dataframe for NaN values
`collect_outputs`([output_lookup])	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.
`energy_yield`()	Get annual energy yield value in kwh/kw from SAM.
`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.
`gen_profile`()	Get power generation profile (local timezone) in kW.
`get_sam_res`(args, *kwargs)	Get the SAM resource iterator object (single year, single file).
`get_time_interval`(time_index)	Get the time interval.
`make_datetime`(series)	Ensure that pd series is a datetime series with dt accessor
`outputs_to_utc_arr`()	Convert array-like SAM outputs to UTC np.ndarrays
`reV_run`(points_control, res_file, site_df[, ...])	Execute SAM generation based on a reV points control instance.
`run`()	Run a reV-SAM generation object by assigning inputs, executing the SAM simulation, collecting outputs, and converting all arrays to UTC.
`run_gen_and_econ`()	Run SAM generation with possibility for follow on econ analysis.
`set_resource_data`(resource, meta)	Placeholder for resource data setting (nsrdb or wtk)
`tz_elev_check`(sam_sys_inputs, ...)	Check timezone+elevation input and use json config timezone+elevation if not in resource meta.

Attributes

`DIR`
`IGNORE_ATTRS`
`MODULE`
`OUTAGE_CONFIG_KEY`	Specify outage information in the config file using this key.
`OUTAGE_SEED_CONFIG_KEY`	Specify a randomizer seed in the config file using this key.
`attr_dict`	Get the heirarchical PySAM object attribute dictionary.
`has_timezone`	Returns true if instance has a timezone set
`input_list`	Get the list of lowest level input attribute/variable names.
`meta`	Get meta data property.
`module`	Get module property.
`outage_seed`	A value to use as the seed for the outage losses.
`pysam`	Get the pysam object.
`site`	Get the site number for this SAM simulation.

check_resource_data(resource)[source]

Check resource dataframe for NaN values

Parameters:: resource (pd.DataFrame) – Timeseries solar or wind resource data for a single location with a pandas DatetimeIndex. There must be columns for all the required variables to run the respective SAM simulation. Remapping will be done to convert typical NSRDB/WTK names into SAM names (e.g. DNI -> dn and wind_speed -> windspeed)

abstract set_resource_data(resource, meta)[source]: Placeholder for resource data setting (nsrdb or wtk)

static tz_elev_check(sam_sys_inputs, site_sys_inputs, meta)[source]

Check timezone+elevation input and use json config timezone+elevation if not in resource meta.

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.
meta (pd.DataFrame | pd.Series) – Meta data corresponding to the resource input for the single location. Should include values for latitude, longitude, elevation, and timezone.

Returns:

meta (pd.DataFrame | pd.Series) – Dataframe or series for a single site. Will include “timezone” and “elevation” from the sam and site system inputs if found.

property has_timezone: Returns true if instance has a timezone set

cf_mean()[source]

Get mean capacity factor (fractional) from SAM.

Returns:: output (float) – Mean capacity factor (fractional).

cf_profile()[source]

Get hourly capacity factor (frac) profile in local timezone. See self.outputs attribute for collected output data in UTC.

Returns:: cf_profile (np.ndarray) – 1D numpy array of capacity factor profile. Datatype is float32 and array length is 8760*time_interval.

annual_energy()[source]

Get annual energy generation value in kWh from SAM.

Returns:: output (float) – Annual energy generation (kWh).

energy_yield()[source]

Get annual energy yield value in kwh/kw from SAM.

Returns:: output (float) – Annual energy yield (kwh/kw).

gen_profile()[source]

Get power generation profile (local timezone) in kW. See self.outputs attribute for collected output data in UTC.

Returns:: output (np.ndarray) – 1D array of hourly power generation in kW. Datatype is float32 and array length is 8760*time_interval.

collect_outputs(output_lookup=None)[source]

Collect SAM output_request, convert timeseries outputs to UTC, and save outputs to self.outputs property.

Parameters:: output_lookup (dict | None) – Lookup dictionary mapping output keys to special output methods. None defaults to generation default outputs.

run_gen_and_econ()[source]: Run SAM generation with possibility for follow on econ analysis.

run()[source]: Run a reV-SAM generation object by assigning inputs, executing the SAM simulation, collecting outputs, and converting all arrays to UTC.

classmethod reV_run(points_control, res_file, site_df, lr_res_file=None, output_request=('cf_mean',), drop_leap=False, gid_map=None, nn_map=None, bias_correct=None)[source]

Execute SAM generation based on a reV points control instance.

Parameters:

points_control (config.PointsControl) – PointsControl instance containing project points site and SAM config info.
res_file (str) – Resource file with full path.
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.
lr_res_file (str | None) – Optional low resolution resource file that will be dynamically mapped+interpolated to the nominal-resolution res_file. This needs to be of the same format as resource_file, e.g. they both need to be handled by the same rex Resource handler such as WindResource
output_request (list | tuple) – Outputs to retrieve from SAM.
drop_leap (bool) – Drops February 29th from the resource data. If False, December 31st is dropped from leap years.
gid_map (None | dict) – Mapping of unique integer generation gids (keys) to single integer resource gids (values). This enables the user to input unique generation gids in the project points that map to non-unique resource gids. This can be None or a pre-extracted dict.
nn_map (np.ndarray) – Optional 1D array of nearest neighbor mappings associated with the res_file to lr_res_file spatial mapping. For details on this argument, see the rex.MultiResolutionResource docstring.
bias_correct (None | pd.DataFrame) – Optional DataFrame or CSV filepath to a wind or solar resource bias correction table. This has columns:
- gid: GID of site (can be index name of dataframe)
- method: function name from rex.bias_correction module
The gid field should match the true resource gid regardless of the optional gid_map input. Only windspeed or GHI + DNI + DHI are corrected, depending on the technology (wind for the former, PV or CSP for the latter). See the functions in the rex.bias_correction module for available inputs for method. Any additional kwargs required for the requested method can be input as additional columns in the bias_correct table e.g., for linear bias correction functions you can include scalar and adder inputs as columns in the bias_correct table on a site-by-site basis. If None, no corrections are applied. By default, None.

Returns:

out (dict) – Nested dictionaries where the top level key is the site index, the second level key is the variable name, second level value is the output variable value.

OUTAGE_CONFIG_KEY = 'reV_outages': Specify outage information in the config file using this key.

OUTAGE_SEED_CONFIG_KEY = 'reV_outages_seed': Specify a randomizer seed in the config file using this key.

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'>

add_scheduled_losses(resource=None)

Add stochastically scheduled losses to SAM config file.

This function reads the information in the reV_outages key of the sam_sys_inputs dictionary and computes stochastically scheduled losses from that input. If the value for reV_outages is a string, it must have been generated by calling json.dumps() on the list of dictionaries containing outage specifications. Otherwise, the outage information is expected to be a list of dictionaries containing outage specifications. See Outage for a description of the specifications allowed for each outage. The scheduled losses are passed to SAM via the hourly key to signify which hourly capacity factors should be adjusted with outage losses. If no outage info is specified in sam_sys_inputs, no scheduled losses are added.

Parameters:: resource (pd.DataFrame, optional) – Time series resource data for a single location with a pandas DatetimeIndex. The year value of the index will be used to seed the stochastically scheduled losses. If None, no yearly seed will be used.