reV.bespoke.bespoke.BespokeSinglePlant

class BespokeSinglePlant(gid, excl, res, tm_dset, sam_sys_inputs, objective_function, capital_cost_function, fixed_operating_cost_function, variable_operating_cost_function, min_spacing='5x', wake_loss_multiplier=1, ga_kwargs=None, output_request=('system_capacity', 'cf_mean'), ws_bins=(0.0, 20.0, 5.0), wd_bins=(0.0, 360.0, 45.0), excl_dict=None, inclusion_mask=None, data_layers=None, resolution=64, excl_area=None, exclusion_shape=None, eos_mult_baseline_cap_mw=200, prior_meta=None, gid_map=None, bias_correct=None, pre_loaded_data=None, close=True)

Bases: object

Framework for analyzing and optimized a wind plant layout specific to the local wind resource and exclusions for a single reV supply curve point.

Parameters:

• gid (int) – gid for supply curve point to analyze.

• excl (str | ExclusionMask) – Filepath to exclusions h5 or ExclusionMask file handler.

• res (str | Resource) – Filepath to .h5 wind resource file or pre-initialized Resource handler

• tm_dset (str) – Dataset name in the exclusions file containing the exclusions-to-resource mapping data.

• sam_sys_inputs (dict) – SAM windpower compute module system inputs not including the wind resource data.

• objective_function (str) – The objective function of the optimization as a string, should return the objective to be minimized during layout optimization. Variables available are:

  • n_turbines: the number of turbines

  • system_capacity: wind plant capacity

  • aep: annual energy production

  • fixed_charge_rate: user input fixed_charge_rate if included as part of the sam system config.

  • self.wind_plant: the SAM wind plant object, through which all SAM variables can be accessed

  • capital_cost: plant capital cost as evaluated by capital_cost_function

  • fixed_operating_cost: plant fixed annual operating cost as evaluated by fixed_operating_cost_function

  • variable_operating_cost: plant variable annual operating cost as evaluated by variable_operating_cost_function

• capital_cost_function (str) – The plant capital cost function as a string, must return the total capital cost in $. Has access to the same variables as the objective_function.

• fixed_operating_cost_function (str) – The plant annual fixed operating cost function as a string, must return the fixed operating cost in $/year. Has access to the same variables as the objective_function.

• variable_operating_cost_function (str) – The plant annual variable operating cost function as a string, must return the variable operating cost in $/kWh. Has access to the same variables as the objective_function.

• min_spacing (float | int | str) – Minimum spacing between turbines in meters. Can also be a string like “5x” (default) which is interpreted as 5 times the turbine rotor diameter.

• wake_loss_multiplier (float, optional) – A multiplier used to scale the annual energy lost due to wake losses. .. WARNING:: This multiplier will ONLY be applied during the optimization process and will NOT be come through in output values such as the hourly profiles, aep, any of the cost functions, or even the output objective.

• ga_kwargs (dict | None) – Dictionary of keyword arguments to pass to GA initialization. If None, default initialization values are used. See GeneticAlgorithm for a description of the allowed keyword arguments.

• output_request (list | tuple) – Outputs requested from the SAM windpower simulation after the bespoke plant layout optimization. Can also request resource means like ws_mean, windspeed_mean, temperature_mean, pressure_mean.

• ws_bins (tuple) – 3-entry tuple with (start, stop, step) for the windspeed binning of the wind joint probability distribution. The stop value is inclusive, so ws_bins=(0, 20, 5) would result in four bins with bin edges (0, 5, 10, 15, 20).

• wd_bins (tuple) – 3-entry tuple with (start, stop, step) for the winddirection binning of the wind joint probability distribution. The stop value is inclusive, so ws_bins=(0, 360, 90) would result in four bins with bin edges (0, 90, 180, 270, 360).

• excl_dict (dict | None) – Dictionary of exclusion keyword arugments of the format {layer_dset_name: {kwarg: value}} where layer_dset_name is a dataset in the exclusion h5 file and kwarg is a keyword argument to the reV.supply_curve.exclusions.LayerMask class. None if excl input is pre-initialized.

• inclusion_mask (np.ndarray) – 2D array pre-extracted inclusion mask where 1 is included and 0 is excluded. The shape of this will be checked against the input resolution.

• data_layers (None | dict) – Aggregation data layers. Must be a dictionary keyed by data label name. Each value must be another dictionary with “dset”, “method”, and “fpath”.

• resolution (int) – Number of exclusion points per SC point along an axis. This number**2 is the total number of exclusion points per SC point.

• excl_area (float | None, optional) – Area of an exclusion pixel in km2. None will try to infer the area from the profile transform attribute in excl_fpath, by default None

• exclusion_shape (tuple) – Shape of the full exclusions extent (rows, cols). Inputing this will speed things up considerably.

• eos_mult_baseline_cap_mw (int | float, optional) – Baseline plant capacity (MW) used to calculate economies of scale (EOS) multiplier from the capital_cost_function. EOS multiplier is calculated as the $-per-kW of the wind plant divided by the $-per-kW of a plant with this baseline capacity. By default, 200 (MW), which aligns the baseline with ATB assumptions. See here: https://tinyurl.com/y85hnu6h.

• prior_meta (pd.DataFrame | None) – Optional meta dataframe belonging to a prior run. This will only run the timeseries power generation step and assume that all of the wind plant layouts are fixed given the prior run. The meta data needs columns “capacity”, “turbine_x_coords”, and “turbine_y_coords”.

• gid_map (None | str | dict) – Mapping of unique integer generation gids (keys) to single integer resource gids (values). This can be None, a pre-extracted dict, or a filepath to json or csv. If this is a csv, it must have the columns “gid” (which matches the techmap) and “gid_map” (gids to extract from the resource input). This is useful if you’re running forecasted resource data (e.g., ECMWF) to complement historical meteorology (e.g., WTK).

• bias_correct (str | pd.DataFrame, optional) – 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.

• pre_loaded_data (BespokeSinglePlantData, optional) – A pre-loaded BespokeSinglePlantData object, or None. Can be useful to speed up execution on file systems with slow parallel reads.

• close (bool) – Flag to close object file handlers on exit.

Methods

agg_data_layers()	Aggregate optional data layers if requested and save to self.meta
bias_correct_ws(ws, dset, h5_gids)	Bias correct windspeed data if the bias_correct input was provided.
check_dependencies()	Check special dependencies for bespoke
close()	Close any open file handlers via the sc point attribute.
get_lcoe_kwargs()	Get a namespace of arguments for calculating LCOE based on the bespoke optimized wind plant capacity
get_weighted_res_dir()	Special method for calculating the exclusion-weighted mean wind direction for the BespokeSinglePlant
get_weighted_res_ts(dset)	Special method for calculating the exclusion-weighted mean resource timeseries data for the BespokeSinglePlant.
get_wind_handler(res)	Get a wind resource handler for a resource filepath.
initialize_wind_plant_ts()	Initialize the annual wind plant timeseries analysis object(s) using the annual resource data and the sam system inputs from the optimized plant.
recalc_lcoe()	Recalculate the multi-year mean LCOE based on the multi-year mean annual energy production (AEP)
run(*args, **kwargs)	Run the bespoke optimization for a single wind plant.
run_plant_optimization()	Run the wind plant layout optimization and export outputs to outputs property.
run_wind_plant_ts()	Run the wind plant multi-year timeseries analysis and export output requests to outputs property.

Attributes

DEPENDENCIES
OUT_ATTRS
annual_time_indexes	Get an ordered list of single-year time index objects that matches the profile outputs from the wind_plant_ts object.
gid	SC point gid for this bespoke plant.
hub_height	Get the integer SAM system config turbine hub height (meters)
include_mask	Get the supply curve point 2D inclusion mask (included is 1, excluded is 0)
meta	Get the basic supply curve point meta data
original_sam_sys_inputs	Get the original (pre-optimized) SAM windpower system inputs.
outputs	Saved outputs for the single wind plant bespoke optimization.
pixel_side_length	Get the length of a single exclusion pixel side (meters)
plant_optimizer	Bespoke plant turbine placement optimizer object.
res_df	Get the reV compliant wind resource dataframe representing the aggregated and included wind resource in the current reV supply curve point at the turbine hub height.
sam_sys_inputs	Get the SAM windpower system inputs.
sc_point	Get the reV supply curve point object.
wind_dist	Get the wind joint probability distribution and corresonding bin edges
wind_plant_pd	reV WindPowerPD compute object for plant layout optimization based on wind joint probability distribution
wind_plant_ts	reV WindPower compute object(s) based on wind resource timeseries data keyed by year
years	Get the sorted list of analysis years.

close()

Close any open file handlers via the sc point attribute. If this class was initialized with close=False, this will not close any handlers.

bias_correct_ws(ws, dset, h5_gids)

Bias correct windspeed data if the bias_correct input was provided.

Parameters:

• ws (np.ndarray) – Windspeed data in shape (time, space)

• dset (str) – Resource dataset name e.g., “windspeed_100m”, “temperature_100m”, “pressure_100m”, or something similar

• h5_gids (list | np.ndarray) – Array of integer gids (spatial indices) from the source h5 file. This is used to get the correct bias correction parameters from bias_correct table based on its gid column

Returns:

ws (np.ndarray) – Bias corrected windspeed data in same shape as input

get_weighted_res_ts(dset)

Special method for calculating the exclusion-weighted mean resource timeseries data for the BespokeSinglePlant.

Parameters:

dset (str) – Resource dataset name e.g., “windspeed_100m”, “temperature_100m”, “pressure_100m”, or something similar

Returns:

data (np.ndarray) – Timeseries data of shape (n_time,) for the wind plant weighted by the plant inclusions mask.

get_weighted_res_dir()

Special method for calculating the exclusion-weighted mean wind direction for the BespokeSinglePlant

Returns:

mean_wind_dirs (np.ndarray) – Timeseries array of winddirection data in shape (n_time,) in units of degrees from north.

property gid

SC point gid for this bespoke plant.

Returns:

int

property include_mask

Get the supply curve point 2D inclusion mask (included is 1, excluded is 0)

Returns:

np.ndarray

property pixel_side_length

Get the length of a single exclusion pixel side (meters)

Returns:

float

property original_sam_sys_inputs

Get the original (pre-optimized) SAM windpower system inputs.

Returns:

dict

property sam_sys_inputs

Get the SAM windpower system inputs. If the wind plant has not yet been optimized, this returns the initial SAM config. If the wind plant has been optimized using the wind_plant_pd object, this returns the final optimized SAM plant config.

Returns:

dict

property sc_point

Get the reV supply curve point object.

Returns:

AggSCPoint

property meta

Get the basic supply curve point meta data

Returns:

pd.DataFrame

property hub_height

Get the integer SAM system config turbine hub height (meters)

Returns:

int

property res_df

Get the reV compliant wind resource dataframe representing the aggregated and included wind resource in the current reV supply curve point at the turbine hub height. Includes a DatetimeIndex and columns for temperature, pressure, windspeed, and winddirection.

Returns:

pd.DataFrame

property years

Get the sorted list of analysis years.

Returns:

list

property annual_time_indexes

Get an ordered list of single-year time index objects that matches the profile outputs from the wind_plant_ts object.

Returns:

list

property wind_dist

Get the wind joint probability distribution and corresonding bin edges

Returns:

• wind_dist (np.ndarray) – 2D array probability distribution of (windspeed, winddirection) normalized so the sum of all values = 1.

• ws_edges (np.ndarray) – 1D array of windspeed (m/s) values that set the bin edges for the wind probability distribution. Same len as wind_dist.shape[0] + 1

• wd_edges (np.ndarray) – 1D array of winddirections (deg) values that set the bin edges for the wind probability dist. Same len as wind_dist.shape[1] + 1

initialize_wind_plant_ts()

Initialize the annual wind plant timeseries analysis object(s) using the annual resource data and the sam system inputs from the optimized plant.

Returns:

wind_plant_ts (dict) – Annual reV.SAM.generation.WindPower object(s) keyed by year.

property wind_plant_pd

reV WindPowerPD compute object for plant layout optimization based on wind joint probability distribution

Returns:

reV.SAM.generation.WindPowerPD

property wind_plant_ts

reV WindPower compute object(s) based on wind resource timeseries data keyed by year

Returns:

dict

property plant_optimizer

Bespoke plant turbine placement optimizer object.

Returns:

PlaceTurbines

recalc_lcoe()

Recalculate the multi-year mean LCOE based on the multi-year mean annual energy production (AEP)

get_lcoe_kwargs()

Get a namespace of arguments for calculating LCOE based on the bespoke optimized wind plant capacity

Returns:

lcoe_kwargs (dict) – kwargs for the SAM lcoe model. These are based on the original sam_sys_inputs, normalized to the original system_capacity, and updated based on the bespoke optimized system_capacity, includes fixed_charge_rate, system_capacity (kW), capital_cost ($), fixed_operating_cos ($), variable_operating_cost ($/kWh) Data source priority: outputs, plant_optimizer, original_sam_sys_inputs, meta

static get_wind_handler(res)

Get a wind resource handler for a resource filepath.

Parameters:

res (str) – Resource filepath to wtk .h5 file. Can include * wildcards for multi year resource.

Returns:

handler (WindResource | MultiYearWindResource) – Wind resource handler or multi year handler

classmethod check_dependencies()

Check special dependencies for bespoke

run_wind_plant_ts()

Run the wind plant multi-year timeseries analysis and export output requests to outputs property.

Returns:

outputs (dict) – Output dictionary for the full BespokeSinglePlant object. The multi-year timeseries data is also exported to the BespokeSinglePlant.outputs property.

run_plant_optimization()

Run the wind plant layout optimization and export outputs to outputs property.

Returns:

outputs (dict) – Output dictionary for the full BespokeSinglePlant object. The layout optimization output data is also exported to the BespokeSinglePlant.outputs property.

agg_data_layers()

Aggregate optional data layers if requested and save to self.meta

property outputs

Saved outputs for the single wind plant bespoke optimization.

Returns:

dict

classmethod run(*args, **kwargs)

Run the bespoke optimization for a single wind plant.

Parameters:

See the class initialization parameters.

Returns:

bsp (dict) – Bespoke single plant outputs namespace keyed by dataset name including a dataset “meta” for the BespokeSinglePlant meta data.