powersimdata.design.investment package

Subpackages

• powersimdata.design.investment.tests package
  • Submodules
  • powersimdata.design.investment.tests.test_investment_costs module
    • mock_grid()
    • test_calculate_ac_inv_costs()
    • test_calculate_ac_inv_costs_lines_only()
    • test_calculate_ac_inv_costs_not_summed()
    • test_calculate_ac_inv_costs_transformers_only()
    • test_calculate_dc_inv_costs()
    • test_calculate_dc_inv_costs_not_summed()
    • test_calculate_gen_inv_costs_2030()
    • test_calculate_gen_inv_costs_not_summed()
  • Module contents

Submodules

powersimdata.design.investment.const module

powersimdata.design.investment.create_mapping_files module

powersimdata.design.investment.create_mapping_files.bus_to_neem_reg(df)

Map bus to NEEM regions.

Parameters:

df (pandas.DataFrame) – bus data frame.

Returns:

(pandas.DataFrame) – index: bus id, columns: lat, lon, name_abbr (NEEM region)

Note

the shapefile used for mapping is pulled from the Energy Zones Mapping tool. This map is overly detailed, so the shapes are simplified using 1 km distance (Douglas-Peucker) method in QGIS.

powersimdata.design.investment.create_mapping_files.bus_to_reeds_reg(df)

Map bus to ReEDS regions.

Parameters:

df (pandas.DataFrame) – bus data frame.

Returns:

(pandas.DataFrame) – index: bus id, columns rs (wind resource region) and rb (BA region).

powersimdata.design.investment.create_mapping_files.points_to_polys(df, name, shpfile, search_dist=0.04)

Map node to closest region.

Parameters:

• df (pandas.DataFrame) – data frame with node id as index and ‘lat’ and ‘lon’ as columns.

• name (str) – name of node, e.g., bus, plant, substation, etc.

• shpfile (str) – shapefile enclosing Polygon/Multipolygon with region id.

• search_dist (float/int) – radius around point to detect polygons.

Raises:

ValueError – if some points are dropped because too far away from polygons.

Returns:

(geopandas.GeoDataFrame) – columns: id name, (point) geometry, region and properties of region.

powersimdata.design.investment.create_mapping_files.sjoin_nearest(left_df, right_df, search_dist=0.06)

Perform a spatial join between two input layers.

Parameters:

• left_df (geopandas.GeoDataFrame) – A dataframe of Points.

• right_df (geopandas.GeoDataFrame) – A dataframe of Polygons/Multipolygons.

• search_dist (float/int) – radius (in map units) around point to detect polygons.

Returns:

(geopandas.GeoDataFrame) – data frame of Points mapped to each Polygon.

Note

data from nearest Polygon/Multipolygon will be used as a result if a Point falls outside all available Polygon/Multipolygons.

powersimdata.design.investment.create_mapping_files.write_bus_neem_map(base_grid)

Write bus location to NEEM region mapping to file.

Parameters:

base_grid (powersimdata.input.grid.Grid) – a Grid instance.

Raises:

TypeError – if base_grid is not a Grid instance.

powersimdata.design.investment.create_mapping_files.write_bus_reeds_map(base_grid)

Write bus location to ReEDS region mapping to file.

Parameters:

base_grid (powersimdata.input.grid.Grid) – a Grid instance.

Raises:

TypeError – if base_grid is not a Grid instance.

powersimdata.design.investment.create_mapping_files.write_poly_shapefile()

Convert ReEDS wind resource csv-format file to a shapefile.

Note

gis_rs.csv is from ReEDS open-source: /bokehpivot/in/gis_rs.csv, hierarchy.csv is from: /bokehpivot/in/reeds2/hierarchy.csv.

powersimdata.design.investment.inflation module

powersimdata.design.investment.inflation.calculate_inflation(start_year, end_year=None)

Calculate the overall inflation between two years.

Parameters:

• start_year (int) – Year to start calculating inflation from.

• end_year (int/None) – Year to calculate inflation to. Calculates using the rates from [start_year, end_year), since we calculate _to_ end_year, not _through_ end_year. If None, inflates to as recent as possible.

Returns:

(float) – Inflation factor.

powersimdata.design.investment.investment_costs module

powersimdata.design.investment.investment_costs.append_keep_index_name(df1, df2, *args, **kwargs)

Append df2 to df1, preserving the index name of the first data frame.

Parameters:

• df1 (pandas.DataFrame) – first data frame.

• df2 (pandas.DataFrame) – second data frame.

• *args – arbitrary positional arguments passed to pandas concat().

• **kwargs – arbitrary keyword arguments passed to pandas concat().

Returns:

(pandas.DataFrame) – df2 appended to df1 with index name set to name of df1 index.

powersimdata.design.investment.investment_costs.calculate_ac_inv_costs(scenario, sum_results=True, exclude_branches=None, base_grid=None)

Calculate cost of upgrading AC lines and/or transformers in a scenario. NEEM regions are used to find regional multipliers.

Parameters:

• scenario (powersimdata.scenario.scenario.Scenario) – scenario instance.

• sum_results (bool) – whether to sum data frame for each branch type. Defaults to True.

• base_grid (powersimdata.input.grid.Grid) – a Grid to compare against. If None, the grid model and interconnect from the scenario are used to instantiate a corresponding unmodified Grid.

Returns:

(dict) – keys are {‘line_cost’, ‘transformer_cost’}, values are either float if sum_results, or pandas Series indexed by branch ID. Whether summed or not, values are $USD, inflation-adjusted to today.

powersimdata.design.investment.investment_costs.calculate_dc_inv_costs(scenario, sum_results=True, base_grid=None)

Calculate cost of upgrading HVDC lines in a scenario.

Parameters:

• scenario (powersimdata.scenario.scenario.Scenario) – scenario instance.

• sum_results (bool) – whether to sum series to return total cost. Defaults to True.

• base_grid (powersimdata.input.grid.Grid) – a Grid to compare against. If None, the grid model and interconnect from the scenario are used to instantiate a corresponding unmodified Grid.

Returns:

(pandas.Series/float) – cost of upgrading HVDC lines, in $USD, inflation-adjusted to today. If sum_results, a float is returned, otherwise a Series.

powersimdata.design.investment.investment_costs.calculate_gen_inv_costs(scenario, year, cost_case, sum_results=True, base_grid=None)

Calculate cost of upgrading generators in a scenario. ReEDS regions are used to find regional multipliers.

Parameters:

• scenario (powersimdata.scenario.scenario.Scenario) – scenario instance.

• year (int/str) – building year.

• cost_case (str) – ATB cost case of data. ‘Moderate’: mid cost case, ‘Conservative’: generally higher costs, ‘Advanced’: generally lower costs

• sum_results (bool) – whether to sum data frame for plant costs. Defaults to True.

• base_grid (powersimdata.input.grid.Grid) – a Grid to compare against. If None, the grid model and interconnect from the scenario are used to instantiate a corresponding unmodified Grid.

Returns:

(pandas.Series) – Overnight generation investment cost. If sum_results, indices are technologies and values are total cost. Otherwise, indices are IDs of plants (including storage, which is given pseudo-plant-IDs), and values are individual generator costs. Whether summed or not, values are $USD, inflation-adjusted to today.

Todo

it currently uses one (arbitrary) sub-technology. The rest of the costs are dropped. Wind and solar will need to be fixed based on the resource supply curves.

powersimdata.design.investment.investment_costs.merge_keep_index(df1, df2, **kwargs)

Merge df1 and df2, preserving the index of the first dataframe.

Parameters:

• df1 (pandas.DataFrame) – first data frame, to call pandas merge() from.

• df2 (pandas.DataFrame) – second data frame, argument to pandas merge().

• **kwargs – arbitrary keyword arguments passed to pandas merge().

Returns:

(pandas.DataFrame) – df1 merged with df2 with indices preserved.

Module contents