reV.supply_curve.points.GenerationSupplyCurvePoint

class GenerationSupplyCurvePoint(gid, excl, gen, tm_dset, gen_index, excl_dict=None, inclusion_mask=None, res_class_dset=None, res_class_bin=None, excl_area=None, power_density=None, cf_dset='cf_mean-means', lcoe_dset='lcoe_fcr-means', h5_dsets=None, resolution=64, exclusion_shape=None, close=False, friction_layer=None, recalc_lcoe=True, apply_exclusions=True)

Bases: AggregationSupplyCurvePoint

Supply curve point summary framework that ties a reV SC point to its respective generation and resource data.

Parameters:

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

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

• gen (str | reV.handlers.Outputs) – Filepath to .h5 reV generation output results or reV Outputs file handler.

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

• gen_index (np.ndarray) – Array of generation gids with array index equal to resource gid. Array value is -1 if the resource index was not used in the generation run.

• 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.

• res_class_dset (str | np.ndarray | None) – Dataset in the generation file dictating resource classes. Can be pre-extracted resource data in np.ndarray. None if no resource classes.

• res_class_bin (list | None) – Two-entry lists dictating the single resource class bin. None if no resource classes.

• 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

• power_density (float | None | pd.DataFrame) – Constant power density float, None, or opened dataframe with (resource) “gid” and “power_density columns”.

• cf_dset (str | np.ndarray) – Dataset name from gen containing capacity factor mean values. Can be pre-extracted generation output data in np.ndarray.

• lcoe_dset (str | np.ndarray) – Dataset name from gen containing LCOE mean values. Can be pre-extracted generation output data in np.ndarray.

• h5_dsets (None | list | dict) – Optional list of dataset names to summarize from the gen/econ h5 files. Can also be pre-extracted data dictionary where keys are the dataset names and values are the arrays of data from the h5 files.

• resolution (int | None) – SC resolution, must be input in combination with gid.

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

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

• friction_layer (None | FrictionMask) – Friction layer with scalar friction values if valid friction inputs were entered. Otherwise, None to not apply friction layer.

• recalc_lcoe (bool) – Flag to re-calculate the LCOE from the multi-year mean capacity factor and annual energy production data. This requires several datasets to be aggregated in the h5_dsets input: system_capacity, fixed_charge_rate, capital_cost, fixed_operating_cost, and variable_operating_cost.

• apply_exclusions (bool) – Flag to apply exclusions to the resource / generation gid’s on initialization.

Methods

agg_data_layers(summary, data_layers)	Perform additional data layer aggregation.
aggregate(arr)	Calc sum (aggregation) of the resource data.
close()	Close all file handlers.
economies_of_scale(cap_cost_scale, summary)	Apply economies of scale to this point summary
exclusion_weighted_mean(flat_arr)	Calc the exclusions-weighted mean value of a flat array of gen data.
get_agg_slices(gid, shape, resolution)	Get the row, col slices of an aggregation gid.
mean_wind_dirs(arr)	Calc the mean wind directions at every time-step
point_summary([args])	Get a summary dictionary of a single supply curve point.
run(gid, excl, agg_h5, tm_dset, *agg_dset[, ...])	Compute exclusions weight mean for the sc point from data
sc_mean(gid, excl, tm_dset, data[, ...])	Compute exclusions weight mean for the sc point from data
sc_sum(gid, excl, tm_dset, data[, ...])	Compute the aggregate (sum) of data for the sc point
summarize(gid, excl_fpath, gen_fpath, ...[, ...])	Get a summary dictionary of a single supply curve point.

Attributes

POWER_DENSITY
area	Get the non-excluded resource area of the supply curve point in the current resource class.
bool_mask	Get a boolean inclusion mask (True if excl point is not excluded).
capacity	Get the estimated capacity in MW of the supply curve point in the current resource class with the applied exclusions.
capacity_ac	Get the AC estimated capacity in MW of the supply curve point in the current resource class with the applied exclusions.
centroid	Get the supply curve point centroid coordinate.
cols	Get the cols of the exclusions layer associated with this SC point.
country	Get the SC point country based on the resource meta data.
county	Get the SC point county based on the resource meta data.
elevation	Get the SC point elevation based on the resource meta data.
exclusions	Get the exclusions object.
friction_data	Get the friction data for the full SC point (no exclusions)
gen	Get the generation output object.
gen_data	Get the generation capacity factor data array.
gen_gid_set	Get list of unique generation gids corresponding to this sc point.
gid	supply curve point gid
gid_counts	Get the number of exclusion pixels in each resource/generation gid corresponding to this sc point.
h5	h5 Resource handler object
h5_dsets_data	Get any additional/supplemental h5 dataset data to summarize.
h5_gid_set	Get list of unique h5 gids corresponding to this sc point.
include_mask	[0, 1] where 1 is included and 0 is excluded).
include_mask_flat	Get the flattened inclusion mask (normalized with expected range: [0, 1] where 1 is included and 0 is excluded).
latitude	Get the SC point latitude
lcoe_data	Get the LCOE data array.
longitude	Get the SC point longitude
mean_cf	Get the mean capacity factor for the non-excluded data.
mean_friction	Get the mean friction scalar for the non-excluded data.
mean_h5_dsets_data	Get the mean supplemental h5 datasets data (optional)
mean_lcoe	Get the mean LCOE for the non-excluded data.
mean_lcoe_friction	Get the mean LCOE for the non-excluded data, multiplied by the mean_friction scalar value.
mean_res	Get the mean resource for the non-excluded data.
n_gids	Get the total number of not fully excluded pixels associated with the available resource/generation gids at the given sc gid.
offshore	Get the SC point offshore flag based on the resource meta data (if offshore column is present).
pixel_area	The area in km2 of a single exclusion pixel.
power_density	Get the estimated power density either from input or infered from generation output meta.
power_density_ac	Get the estimated AC power density either from input or inferred from generation output meta.
res_data	Get the resource data array.
res_gid_set	Get list of unique resource gids corresponding to this sc point.
resolution	Get the supply curve grid aggregation resolution
rows	Get the rows of the exclusions layer associated with this SC point.
sc_point_annual_energy	Get the total annual energy (MWh) for the entire SC point.
sc_point_annual_energy_ac	Get the total AC annual energy (MWh) for the entire SC point.
sc_point_capital_cost	Get the capital cost for the entire SC point.
sc_point_fixed_operating_cost	Get the fixed operating cost for the entire SC point.
sc_point_gid	Supply curve point gid
state	Get the SC point state based on the resource meta data.
summary	Supply curve point's meta data summary
timezone	Get the SC point timezone based on the resource meta data.

exclusion_weighted_mean(flat_arr)

Calc the exclusions-weighted mean value of a flat array of gen data.

Parameters:

flat_arr (np.ndarray) – Flattened array of resource/generation/econ data. Must be index-able with the self._gen_gids array (must be a 1D array with an entry for every site in the generation extent).

Returns:

mean (float) – Mean of flat_arr masked by the binary exclusions then weighted by the non-zero exclusions.

property gen

Get the generation output object.

Returns:

_gen (Resource) – reV generation Resource object

property res_gid_set

Get list of unique resource gids corresponding to this sc point.

Returns:

res_gids (list) – List of resource gids.

property gen_gid_set

Get list of unique generation gids corresponding to this sc point.

Returns:

gen_gids (list) – List of generation gids.

property h5_gid_set

Get list of unique h5 gids corresponding to this sc point. Same as gen_gid_set

Returns:

h5_gids (list) – List of h5 gids.

property gid_counts

Get the number of exclusion pixels in each resource/generation gid corresponding to this sc point.

Returns:

gid_counts (list) – List of exclusion pixels in each resource/generation gid.

property res_data

Get the resource data array.

Returns:

_res_data (np.ndarray) – Multi-year-mean resource data array for all sites in the generation data output file.

property gen_data

Get the generation capacity factor data array.

Returns:

_gen_data (np.ndarray) – Multi-year-mean capacity factor data array for all sites in the generation data output file.

property lcoe_data

Get the LCOE data array.

Returns:

_lcoe_data (np.ndarray) – Multi-year-mean LCOE data array for all sites in the generation data output file.

property mean_cf

Get the mean capacity factor for the non-excluded data. Capacity factor is weighted by the exclusions (usually 0 or 1, but 0.5 exclusions will weight appropriately).

Returns:

mean_cf (float | None) – Mean capacity factor value for the non-excluded data.

property mean_lcoe

Get the mean LCOE for the non-excluded data.

Returns:

mean_lcoe (float | None) – Mean LCOE value for the non-excluded data.

property mean_res

Get the mean resource for the non-excluded data.

Returns:

mean_res (float | None) – Mean resource for the non-excluded data.

property mean_lcoe_friction

Get the mean LCOE for the non-excluded data, multiplied by the mean_friction scalar value.

Returns:

mean_lcoe_friction (float | None) – Mean LCOE value for the non-excluded data multiplied by the mean friction scalar value.

property mean_friction

Get the mean friction scalar for the non-excluded data.

Returns:

friction (None | float) – Mean value of the friction data layer for the non-excluded data. If friction layer is not input to this class, None is returned.

property friction_data

Get the friction data for the full SC point (no exclusions)

Returns:

friction_data (None | np.ndarray) – 2D friction data layer corresponding to the exclusions grid in the SC domain. If friction layer is not input to this class, None is returned.

property power_density

Get the estimated power density either from input or infered from generation output meta.

Returns:

_power_density (float) – Estimated power density in MW/km2

property power_density_ac

Get the estimated AC power density either from input or inferred from generation output meta.

This value is only available for solar runs with a “dc_ac_ratio” dataset in the generation file. If these conditions are not met, this value is None.

Returns:

_power_density_ac (float | None) – Estimated AC power density in MW/km2

property capacity

Get the estimated capacity in MW of the supply curve point in the current resource class with the applied exclusions.

Returns:

capacity (float) – Estimated capacity in MW of the supply curve point in the current resource class with the applied exclusions.

property capacity_ac

Get the AC estimated capacity in MW of the supply curve point in the current resource class with the applied exclusions.

This values is provided only for solar inputs that have the “dc_ac_ratio” dataset in the generation file. If these conditions are not met, this value is None.

Returns:

capacity (float | None) – Estimated AC capacity in MW of the supply curve point in the current resource class with the applied exclusions. Only not None for solar runs with “dc_ac_ratio” dataset in the generation file

property sc_point_capital_cost

Get the capital cost for the entire SC point.

This method scales the capital cost based on the included-area capacity. The calculation requires ‘capital_cost’ and ‘system_capacity’ in the generation file and passed through as h5_dsets, otherwise it returns None.

Returns:

sc_point_capital_cost (float | None) – Total supply curve point capital cost ($).

property sc_point_fixed_operating_cost

Get the fixed operating cost for the entire SC point.

This method scales the fixed operating cost based on the included-area capacity. The calculation requires ‘fixed_operating_cost’ and ‘system_capacity’ in the generation file and passed through as h5_dsets, otherwise it returns None.

Returns:

sc_point_fixed_operating_cost (float | None) – Total supply curve point fixed operating cost ($).

property sc_point_annual_energy

Get the total annual energy (MWh) for the entire SC point.

This value is computed using the capacity of the supply curve point as well as the mean capacity factor. If the mean capacity factor is None, this value will also be None.

Returns:

sc_point_annual_energy (float | None) – Total annual energy (MWh) for the entire SC point.

property sc_point_annual_energy_ac

Get the total AC annual energy (MWh) for the entire SC point.

This value is computed using the AC capacity of the supply curve point as well as the mean capacity factor. If either the mean capacity factor or the AC capacity value is None, this value will also be None.

Returns:

sc_point_annual_energy_ac (float | None) – Total AC annual energy (MWh) for the entire SC point.

property h5_dsets_data

Get any additional/supplemental h5 dataset data to summarize.

Returns:

h5_dsets_data (dict | None)

property mean_h5_dsets_data

Get the mean supplemental h5 datasets data (optional)

Returns:

mean_h5_dsets_data (dict | None) – Mean dataset values for the non-excluded data for the optional h5_dsets input.

point_summary(args=None)

Get a summary dictionary of a single supply curve point.

Parameters:

args (tuple | list | None) – List of summary arguments to include. None defaults to all available args defined in the class attr.

Returns:

summary (dict) – Dictionary of summary outputs for this sc point.

static economies_of_scale(cap_cost_scale, summary)

Apply economies of scale to this point summary

Parameters:

• cap_cost_scale (str) – LCOE scaling equation to implement “economies of scale”. Equation must be in python string format and return a scalar value to multiply the capital cost by. Independent variables in the equation should match the names of the columns in the reV supply curve aggregation table.

• summary (dict) – Dictionary of summary outputs for this sc point.

Returns:

summary (dict) – Dictionary of summary outputs for this sc point.

agg_data_layers(summary, data_layers)

Perform additional data layer aggregation. If there is no valid data in the included area, the data layer will be taken from the full SC point extent (ignoring exclusions). If there is still no valid data, a warning will be raised and the data layer will have a NaN/None value.

Parameters:

• summary (dict) – Dictionary of summary outputs for this sc point.

• 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”.

Returns:

summary (dict) – Dictionary of summary outputs for this sc point. A new entry for each data layer is added.

aggregate(arr)

Calc sum (aggregation) of the resource data.

Parameters:

arr (np.ndarray) – Array of resource data.

Returns:

agg (float) – Sum of arr masked by the binary exclusions

property area

Get the non-excluded resource area of the supply curve point in the current resource class.

Returns:

area (float) – Non-excluded resource/generation area in square km.

property bool_mask

Get a boolean inclusion mask (True if excl point is not excluded).

Returns:

mask (np.ndarray) – Mask with length equal to the flattened exclusion shape

property centroid

Get the supply curve point centroid coordinate.

Returns:

centroid (tuple) – SC point centroid (lat, lon).

close()

Close all file handlers.

property cols

Get the cols of the exclusions layer associated with this SC point.

Returns:

cols (slice) – Column slice to index the high-res layer (exclusions layer) for the gid in the agg layer (supply curve layer).

property country

Get the SC point country based on the resource meta data.

property county

Get the SC point county based on the resource meta data.

property elevation

Get the SC point elevation based on the resource meta data.

property exclusions

Get the exclusions object.

Returns:

_excls (ExclusionMask) – ExclusionMask h5 handler object.

static get_agg_slices(gid, shape, resolution)

Get the row, col slices of an aggregation gid.

Parameters:

• gid (int) – Gid of interest in the aggregated layer.

• shape (tuple) – (row, col) shape tuple of the underlying high-res layer.

• resolution (int) – Resolution of the aggregation: number of pixels in 1D being aggregated.

Returns:

• row_slice (slice) – Row slice to index the high-res layer for the gid in the agg layer.

• col_slice (slice) – Col slice to index the high-res layer for the gid in the agg layer.

property gid

supply curve point gid

property h5

h5 Resource handler object

Returns:

_h5 (Resource) – Resource h5 handler object.

property include_mask

[0, 1] where 1 is included and 0 is excluded).

Returns:

np.ndarray

Type:

Get the 2D inclusion mask (normalized with expected range

property include_mask_flat

Get the flattened inclusion mask (normalized with expected range: [0, 1] where 1 is included and 0 is excluded).

Returns:

np.ndarray

property latitude

Get the SC point latitude

property longitude

Get the SC point longitude

mean_wind_dirs(arr)

Calc the mean wind directions at every time-step

Parameters:

arr (np.ndarray) – Array of wind direction data.

Returns:

mean_wind_dirs (np.ndarray | float) – Mean wind direction of arr masked by the binary exclusions

property n_gids

Get the total number of not fully excluded pixels associated with the available resource/generation gids at the given sc gid.

Returns:

n_gids (list)

property offshore

Get the SC point offshore flag based on the resource meta data (if offshore column is present).

property pixel_area

The area in km2 of a single exclusion pixel. If this value was not provided on initialization, it is determined from the profile of the exclusion file.

Returns:

float

property resolution

Get the supply curve grid aggregation resolution

property rows

Get the rows of the exclusions layer associated with this SC point.

Returns:

rows (slice) – Row slice to index the high-res layer (exclusions layer) for the gid in the agg layer (supply curve layer).

classmethod run(gid, excl, agg_h5, tm_dset, *agg_dset, agg_method='mean', excl_dict=None, inclusion_mask=None, resolution=64, excl_area=None, exclusion_shape=None, close=True, gen_index=None)

Compute exclusions weight mean for the sc point from data

Parameters:

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

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

• agg_h5 (str | Resource) – Filepath to .h5 file to aggregate or Resource handler

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

• agg_dset (str) – Dataset to aggreate, can supply multiple datasets or no datasets. The datasets should be scalar values for each site. This method cannot aggregate timeseries data.

• agg_method (str) – Aggregation method, either mean or sum/aggregate

• 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.

• 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.

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

• gen_index (np.ndarray) – Array of generation gids with array index equal to resource gid. Array value is -1 if the resource index was not used in the generation run.

Returns:

out (dict) – Given datasets and meta data aggregated to supply curve points

classmethod sc_mean(gid, excl, tm_dset, data, excl_dict=None, resolution=64, exclusion_shape=None, close=True)

Compute exclusions weight mean for the sc point from data

Parameters:

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

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

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

• data (ndarray | ResourceDataset) – Array of data or open dataset handler to apply exclusions too

• 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.

• 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.

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

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

Returns:

ndarray – Exclusions weighted means of data for supply curve point

property sc_point_gid

Supply curve point gid

Returns:

int

classmethod sc_sum(gid, excl, tm_dset, data, excl_dict=None, resolution=64, exclusion_shape=None, close=True)

Compute the aggregate (sum) of data for the sc point

Parameters:

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

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

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

• data (ndarray | ResourceDataset) – Array of data or open dataset handler to apply exclusions too

• 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.

• 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.

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

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

Returns:

ndarray – Sum / aggregation of data for supply curve point

property state

Get the SC point state based on the resource meta data.

classmethod summarize(gid, excl_fpath, gen_fpath, tm_dset, gen_index, excl_dict=None, inclusion_mask=None, res_class_dset=None, res_class_bin=None, excl_area=None, power_density=None, cf_dset='cf_mean-means', lcoe_dset='lcoe_fcr-means', h5_dsets=None, resolution=64, exclusion_shape=None, close=False, friction_layer=None, args=None, data_layers=None, cap_cost_scale=None, recalc_lcoe=True)

Get a summary dictionary of a single supply curve point.

Parameters:

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

• excl_fpath (str) – Filepath to exclusions h5.

• gen_fpath (str) – Filepath to .h5 reV generation output results.

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

• gen_index (np.ndarray) – Array of generation gids with array index equal to resource gid. Array value is -1 if the resource index was not used in the generation run.

• 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.

• res_class_dset (str | np.ndarray | None) – Dataset in the generation file dictating resource classes. Can be pre-extracted resource data in np.ndarray. None if no resource classes.

• res_class_bin (list | None) – Two-entry lists dictating the single resource class bin. None if no resource classes.

• 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

• power_density (float | None | pd.DataFrame) – Constant power density float, None, or opened dataframe with (resource) “gid” and “power_density columns”.

• cf_dset (str | np.ndarray) – Dataset name from gen containing capacity factor mean values. Can be pre-extracted generation output data in np.ndarray.

• lcoe_dset (str | np.ndarray) – Dataset name from gen containing LCOE mean values. Can be pre-extracted generation output data in np.ndarray.

• h5_dsets (None | list | dict) – Optional list of dataset names to summarize from the gen/econ h5 files. Can also be pre-extracted data dictionary where keys are the dataset names and values are the arrays of data from the h5 files.

• resolution (int | None) – SC resolution, must be input in combination with gid.

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

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

• friction_layer (None | FrictionMask) – Friction layer with scalar friction values if valid friction inputs were entered. Otherwise, None to not apply friction layer.

• args (tuple | list, optional) – List of summary arguments to include. None defaults to all available args defined in the class attr, by default None

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

• cap_cost_scale (str | None) – Optional LCOE scaling equation to implement “economies of scale”. Equations must be in python string format and return a scalar value to multiply the capital cost by. Independent variables in the equation should match the names of the columns in the reV supply curve aggregation table.

• recalc_lcoe (bool) – Flag to re-calculate the LCOE from the multi-year mean capacity factor and annual energy production data. This requires several datasets to be aggregated in the h5_dsets input: system_capacity, fixed_charge_rate, capital_cost, fixed_operating_cost, and variable_operating_cost.

Returns:

summary (dict) – Dictionary of summary outputs for this sc point.

property summary

Supply curve point’s meta data summary

Returns:

pandas.Series – List of supply curve point’s meta data

property timezone

Get the SC point timezone based on the resource meta data.