def calculate_dc_inv_costs(scenario, sum_results=True, base_grid=None):
"""Calculate cost of upgrading HVDC lines in a scenario.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
:param bool sum_results: whether to sum series to return total cost. Defaults to
True.
:param powersimdata.input.grid.Grid base_grid: a Grid to compare against. If None,
the grid model and interconnect from the ``scenario`` are used to instantiate a
corresponding unmodified Grid.
:return: (*pandas.Series/float*) -- cost of upgrading HVDC lines, in $USD,
inflation-adjusted to today. If ``sum_results``, a float is returned, otherwise
a Series.
"""
grid_differences = scenario.get_grid()
if base_grid is None:
base_grid = scenario.get_base_grid()
else:
_check_grid_models_match(base_grid, grid_differences)
# find upgraded DC lines
capacity_difference = calculate_dcline_difference(base_grid, grid_differences)
grid_differences.dcline = grid_differences.dcline.assign(
Pmax=capacity_difference["diff"].to_numpy()
)
grid_differences.dcline = grid_differences.dcline.query("Pmax != 0.0")
costs = _calculate_dc_inv_costs(grid_differences, sum_results)
return costs
def 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.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
:param int/str year: building year.
:param str cost_case: ATB cost case of data. *'Moderate'*: mid cost case,
*'Conservative'*: generally higher costs, *'Advanced'*: generally lower costs
:param bool sum_results: whether to sum data frame for plant costs. Defaults to
True.
:param powersimdata.input.grid.Grid base_grid: a Grid to compare against. If None,
the grid model and interconnect from the ``scenario`` are used to instantiate a
corresponding unmodified Grid.
:return: (*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.
"""
grid_differences = scenario.get_grid()
if base_grid is None:
base_grid = scenario.get_base_grid()
else:
_check_grid_models_match(base_grid, grid_differences)
# Find change in generation capacity
capacity_difference = calculate_plant_difference(
base_grid.plant, grid_differences.plant
)
grid_differences.plant = grid_differences.plant.assign(
Pmax=capacity_difference["diff"].to_numpy()
)
grid_differences.plant = grid_differences.plant.query("Pmax >= 0.01")
# Find change in storage capacity
# Reindex so that we don't get NaN when calculating upgrades for new storage
base_grid.storage["gen"] = base_grid.storage["gen"].reindex(
grid_differences.storage["gen"].index, fill_value=0
)
grid_differences.storage["gen"].Pmax = (
grid_differences.storage["gen"].Pmax - base_grid.storage["gen"].Pmax
)
grid_differences.storage["gen"]["type"] = "storage"
costs = _calculate_gen_inv_costs(grid_differences, year, cost_case, sum_results)
return costs
def 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.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
:param bool sum_results: whether to sum data frame for each branch type. Defaults to
True.
:param powersimdata.input.grid.Grid base_grid: a Grid to compare against. If None,
the grid model and interconnect from the ``scenario`` are used to instantiate a
corresponding unmodified Grid.
:return: (*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.
"""
grid_differences = scenario.get_grid()
if base_grid is None:
base_grid = scenario.get_base_grid()
else:
_check_grid_models_match(base_grid, grid_differences)
# find upgraded AC lines
capacity_difference = calculate_branch_difference(
base_grid.branch, grid_differences.branch
)
grid_differences.branch = grid_differences.branch.assign(
rateA=capacity_difference["diff"].to_numpy()
)
grid_differences.branch = grid_differences.branch.query("rateA != 0.0")
if exclude_branches is not None:
present_exclude_branches = set(exclude_branches) & set(
grid_differences.branch.index
)
grid_differences.branch.drop(index=present_exclude_branches, inplace=True)
costs = _calculate_ac_inv_costs(grid_differences, sum_results)
return costs
def check_grid_models_match_failure():
grid1 = Grid("Western")
grid2 = Grid("Texas")
grid2.grid_model == "foo"
with pytest.raises(ValueError):
_check_grid_models_match(grid1, grid2)
def check_grid_models_match_success():
_check_grid_models_match(Grid("Western"), Grid("Texas"))