reV.SAM.generation.AbstractSamWind

class AbstractSamWind(*args, **kwargs)[source]

Bases: AbstractSamGeneration, PowerCurveLossesMixin, ABC

Wind generation from SAM.

See the PySAM Windpower documentation for the configuration keys required in the sam_sys_inputs config. You may also include the following reV-specific keys:

reV_power_curve_losses : A dictionary that can be used to initialize PowerCurveLossesInput. For example:
reV_power_curve_losses = {
    'target_losses_percent': 9.8,
    'transformation': 'exponential_stretching'
}
See the description of the class mentioned above or the reV losses demo notebook for detailed instructions on how to specify this input.
reV_outages : Specification for reV-scheduled stochastic outage losses. For example:
outage_info = [
    {
        'count': 6,
        'duration': 24,
        'percentage_of_capacity_lost': 100,
        'allowed_months': ['January', 'March'],
        'allow_outage_overlap': True
    },
    {
        'count': 10,
        'duration': 1,
        'percentage_of_capacity_lost': 10,
        'allowed_months': ['January'],
        'allow_outage_overlap': False
    },
    ...
]
See the description of add_scheduled_losses() or the reV losses demo notebook for detailed instructions on how to specify this input.
reV_outages_seed : Integer value used to seed the RNG used to compute stochastic outage losses.

time_index_step : Integer representing the step size used to sample the time_index in the resource data. This can be used to reduce temporal resolution (i.e. for 30 minute input data, time_index_step=1 yields the full 30 minute time series as output, while time_index_step=2 yields hourly output, and so forth).

Note

The reduced data shape (i.e. after applying a step size of time_index_step) must still be an integer multiple of 8760, or the execution will fail.

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_power_curve_losses`()	Adjust power curve in SAM config file to account for losses.
`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.
`wind_resource_from_input`()	Collect wind resource and weights from inputs.

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.
`POWER_CURVE_CONFIG_KEY`	Specify power curve loss target 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.
`input_power_curve`	Original power curve for site.
`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.

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.

POWER_CURVE_CONFIG_KEY = 'reV_power_curve_losses': Specify power curve loss target 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_power_curve_losses()

Adjust power curve in SAM config file to account for losses.

This function reads the information in the reV_power_curve_losses key of the sam_sys_inputs dictionary and computes a new power curve that accounts for the loss percentage specified from that input. If no power curve loss info is specified in sam_sys_inputs, the power curve will not be adjusted.