reVX.least_cost_xmission.trans_cap_costs.TransCapCosts

class TransCapCosts(cost_fpath, sc_point, features, capacity_class, radius=None, xmission_config=None, barrier_mult=100)[source]

Bases: TieLineCosts

Compute total transmission capital cost (least-cost tie-line cost + connection cost) for all features to be connected a single supply curve point

Parameters:

  • cost_fpath (str) – Full path of .h5 file with cost arrays

  • sc_point (gpd.GeoSeries) – Supply Curve Point meta data

  • features (pandas.DataFrame) – Table of transmission features

  • capacity_class (int | str) – Transmission feature capacity_class class

  • radius (int, optional) – Radius around sc_point to clip cost to, by default None

  • xmission_config (str | dict | XmissionConfig, optional) – Path to Xmission config .json, dictionary of Xmission config .jsons, or preloaded XmissionConfig objects, by default None

  • barrier_mult (int, optional) – Multiplier on transmission barrier costs, by default 100

Methods

compute([min_line_length, save_paths, ...])

Compute Transmission capital cost of connecting SC point to transmission features.

compute_connection_costs([features])

Calculate connection costs for tie lines

compute_tie_line_costs([min_line_length, ...])

Compute least cost path and distance between supply curve point and every transmission feature

least_cost_path(end_idx[, save_path])

Find least cost path, its length, and its total (un-barriered) cost

run(cost_fpath, sc_point, features, ...[, ...])

Compute Transmission capital cost of connecting SC point to transmission features.

Attributes

capacity_class

SC point capacity class

clip_mask

Polygon used to clip transmission lines to the clipped raster bounds

clip_shape

Shaped of clipped cost raster

col

Column index inside clipped array

col_offset

Offset to apply to column indices to move into clipped array

cost

Tie line costs array

features

Table of transmission features

mcp

MCP_Geometric instance initialized on mcp_cost array with starting point at sc_point

mcp_cost

Tie line costs array with barrier costs applied for MCP analysis

row

Row index inside clipped array

row_offset

Offset to apply to row indices to move into clipped array

sc_point

Supply curve point data: - gid - lat - lon - idx (row, col)

sc_point_gid

Supply curve point gid

tie_line_voltage

Tie line voltage in kV
property sc_point

Supply curve point data: - gid - lat - lon - idx (row, col)

Returns:

pandas.Series

property sc_point_gid

Supply curve point gid

Returns:

int

property features

Table of transmission features

Returns:

pandas.DataFrame

property clip_mask

Polygon used to clip transmission lines to the clipped raster bounds

Returns:

shapely.Polygon

property tie_line_voltage

Tie line voltage in kV

Returns:

int

compute_tie_line_costs(min_line_length=5.7, save_paths=False)[source]

Compute least cost path and distance between supply curve point and every transmission feature

Parameters:

  • min_line_length (float, optional) – Minimum line length in km, by default 5.7

  • save_paths (bool, optional) – Flag to save least cost path as a multi-line geometry, by default False

Returns:

tie_line_costs (gpd.GeoDataFrame) – Updated table of transmission features with the tie-line cost and distance added

compute_connection_costs(features=None)[source]

Calculate connection costs for tie lines

Returns:

features (pd.DataFrame) – Updated table of transmission features with the connection costs added

compute(min_line_length=5.7, save_paths=False, simplify_geo=None)[source]

Compute Transmission capital cost of connecting SC point to transmission features. trans_cap_cost = tie_line_cost + connection_cost

Parameters:

  • min_line_length (float, optional) – Minimum line length in km, by default 5.7

  • save_paths (bool, optional) – Flag to save least cost path as a multi-line geometry, by default False

  • simplify_geo (float | None, optional) – If float, simplify geometries using this value

Returns:

features (pd.DataFrame | gpd.GeoDataFrame) – Transmission table with tie-line costs and distances and connection costs added. Includes paths if save_paths == True

classmethod run(cost_fpath, sc_point, features, capacity_class, radius=None, xmission_config=None, barrier_mult=100, min_line_length=5.7, save_paths=False, simplify_geo=None)[source]

Compute Transmission capital cost of connecting SC point to transmission features. trans_cap_cost = tie_line_cost + connection_cost

Parameters:

  • cost_fpath (str) – Full path of .h5 file with cost arrays

  • sc_point (gpd.GeoSeries) – Supply Curve Point meta data

  • features (pandas.DataFrame) – Table of transmission features

  • capacity_class (int | str) – Transmission feature capacity_class class

  • radius (int, optional) – Radius around sc_point to clip cost to, by default None

  • xmission_config (str | dict | XmissionConfig, optional) – Path to Xmission config .json, dictionary of Xmission config .jsons, or preloaded XmissionConfig objects, by default None

  • barrier_mult (int, optional) – Multiplier on transmission barrier costs, by default 100

  • min_line_length (float, optional) – Minimum line length in km, by default 5.7

  • save_paths (bool, optional) – Flag to save least cost path as a multi-line geometry, by default False

  • simplify_geo (float | None, optional) – If float, simplify geometries using this value

Returns:

features (pd.DataFrame | gpd.GeoDataFrame | None) – Transmission table with tie-line costs and distances and connection costs added. Will include paths if save_paths == True

property capacity_class

SC point capacity class

Returns:

str

property clip_shape

Shaped of clipped cost raster

Returns:

tuple

property col

Column index inside clipped array

Returns:

int

property col_offset

Offset to apply to column indices to move into clipped array

Returns:

int

property cost

Tie line costs array

Returns:

ndarray

least_cost_path(end_idx, save_path=False)

Find least cost path, its length, and its total (un-barriered) cost

Parameters:

  • end_idx (Tuple[int, int]) – (row, col) index of end point to connect and compute least cost path to.

  • save_path (bool) – Flag to save least cost path as a multi-line geometry. By default, False.

Returns:

  • length (float) – Length of path (km).

  • cost (float) – Cost of path including terrain and land use multipliers, but not barrier costs.

  • poi_lat, poi_lon (numpy.float64) – Latitude and longitude of the end_idx of the least cost path (i.e. the POI/transmission feature that was connected to).

  • path (shapely.geometry.linestring, optional) – Path as a LineString, if save_path was set to True.

property mcp

MCP_Geometric instance initialized on mcp_cost array with starting point at sc_point

Returns:

MCP_Geometric

property mcp_cost

Tie line costs array with barrier costs applied for MCP analysis

Returns:

ndarray

property row

Row index inside clipped array

Returns:

int

property row_offset

Offset to apply to row indices to move into clipped array

Returns:

int