reV.handlers.transmission.TransmissionCosts

class TransmissionCosts(trans_table, line_tie_in_cost=14000, line_cost=2279, station_tie_in_cost=0, center_tie_in_cost=0, sink_tie_in_cost=1000000000.0, avail_cap_frac=1, line_limited=False)[source]

Bases: TransmissionFeatures

Class to compute supply curve -> transmission feature costs

Parameters:

  • trans_table (str | pandas.DataFrame) – Path to .csv or config file or DataFrame containing supply curve transmission mapping

  • line_tie_in_cost (float, optional) – Cost of connecting to a transmission line in $/MW, by default 14000

  • line_cost (float, optional) – Cost of building transmission line during connection in $/MW-km, by default 2279

  • station_tie_in_cost (float, optional) – Cost of connecting to a substation in $/MW, by default 0

  • center_tie_in_cost (float, optional) – Cost of connecting to a load center in $/MW, by default 0

  • sink_tie_in_cost (float, optional) – Cost of connecting to a synthetic load center (infinite sink) in $/MW, by default 1e9

  • avail_cap_frac (float, optional) – Fraction of capacity that is available for connection, by default 1

  • line_limited (bool, optional) – Substation connection is limited by maximum capacity of the attached lines, legacy method, by default False

Methods

available_capacity(gid)

Get available capacity for given line

check_availability(gid)

Check availablity of feature with given gid

check_feature_dependencies()

Check features for dependencies that are missing and raise error.

connect(gid, capacity[, apply])

Check if you can connect to given feature If apply, update internal dictionary accordingly

cost(gid, distance[, ...])

Compute levelized cost of transmission (LCOT) for connecting to give feature

feature_capacity(trans_table[, avail_cap_frac])

Compute available capacity for all features

feature_costs(trans_table[, capacity, ...])

Compute costs for all connections in given transmission table
available_capacity(gid)[source]

Get available capacity for given line

Parameters:

gid (int) – Unique id of feature of interest

Returns:

avail_cap (float) – Available capacity = capacity * available fraction default = 100%

classmethod feature_costs(trans_table, capacity=None, line_tie_in_cost=14000, line_cost=2279, station_tie_in_cost=0, center_tie_in_cost=0, sink_tie_in_cost=1000000000.0, avail_cap_frac=1, line_limited=False)[source]

Compute costs for all connections in given transmission table

Parameters:

  • trans_table (str | pandas.DataFrame) – Path to .csv or .json containing supply curve transmission mapping

  • capacity (float) – Capacity needed in MW, if None DO NOT check if connection is possible

  • line_tie_in_cost (float, optional) – Cost of connecting to a transmission line in $/MW, by default 14000

  • line_cost (float, optional) – Cost of building transmission line during connection in $/MW-km, by default 2279

  • station_tie_in_cost (float, optional) – Cost of connecting to a substation in $/MW, by default 0

  • center_tie_in_cost (float, optional) – Cost of connecting to a load center in $/MW, by default 0

  • sink_tie_in_cost (float, optional) – Cost of connecting to a synthetic load center (infinite sink) in $/MW, by default 1e9

  • avail_cap_frac (float, optional) – Fraction of capacity that is available for connection, by default 1

  • line_limited (bool, optional) – Substation connection is limited by maximum capacity of the attached lines, legacy method, by default False

Returns:

cost (ndarray) – Cost of transmission in $/MW, if None indicates connection is NOT possible

check_availability(gid)

Check availablity of feature with given gid

Parameters:

gid (int) – Feature gid to check

Returns:

bool – Whether the gid is available or not

check_feature_dependencies()

Check features for dependencies that are missing and raise error.

connect(gid, capacity, apply=True)

Check if you can connect to given feature If apply, update internal dictionary accordingly

Parameters:

  • gid (int) – Unique id of feature of intereset

  • capacity (float) – Capacity needed in MW

  • apply (bool) – Apply capacity to feature with given gid and update internal dictionary

Returns:

connected (bool) – Flag as to whether connection is possible or not

cost(gid, distance, transmission_multiplier=1, capacity=None)

Compute levelized cost of transmission (LCOT) for connecting to give feature

Parameters:

  • gid (int) – Feature gid to connect to

  • distance (float) – Distance to feature in kms

  • line_multiplier (float) – Multiplier for region specific line cost increases

  • capacity (float) – Capacity needed in MW, if None DO NOT check if connection is possible

Returns:

cost (float) – Cost of transmission in $/MW, if None indicates connection is NOT possible

classmethod feature_capacity(trans_table, avail_cap_frac=1)

Compute available capacity for all features

Parameters:

  • trans_table (str | pandas.DataFrame) – Path to .csv or .json containing supply curve transmission mapping

  • avail_cap_frac (float, optional) – Fraction of capacity that is available for connection, by default 1

Returns:

feature_cap (pandas.DataFrame) – Available Capacity for each transmission feature