def calculate_gen_inv_costs(scenario, year, cost_case, sum_results=True):
"""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.
: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.
"""
base_grid = Grid(scenario.info["interconnect"].split("_"))
grid = scenario.state.get_grid()
# Find change in generation capacity
grid_new = cp.deepcopy(grid)
# Reindex so that we don't get NaN when calculating upgrades for new generators
base_grid.plant = base_grid.plant.reindex(grid_new.plant.index).fillna(0)
grid_new.plant.Pmax = grid.plant.Pmax - base_grid.plant.Pmax
# 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_new.storage["gen"].index, fill_value=0
)
grid_new.storage["gen"].Pmax = (
grid.storage["gen"].Pmax - base_grid.storage["gen"].Pmax
)
grid_new.storage["gen"]["type"] = "storage"
# Drop small changes
grid_new.plant = grid_new.plant[grid_new.plant.Pmax > 0.01]
costs = _calculate_gen_inv_costs(grid_new, year, cost_case, sum_results)
return costs
def calculate_gen_inv_costs(scenario, year, cost_case, sum_results=True):
"""Given a Scenario object, calculate the total cost of building that scenario's
upgrades of generation.
Currently only uses one (arbutrary) sub-technology. Drops the rest of the costs.
Will want to fix for wind/solar (based on resource supply curves).
Currently uses ReEDS regions to find regional multipliers.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
:param int/str year: year of builds.
:param str cost_case: the ATB cost case of data:
'Moderate': mid cost case,
'Conservative': generally higher costs,
'Advanced': generally lower costs
:return: (*pandas.DataFrame*) -- Total generation investment cost summed by
technology.
"""
base_grid = Grid(scenario.info["interconnect"].split("_"))
grid = scenario.state.get_grid()
# Find change in generation capacity
grid_new = cp.deepcopy(grid)
# Reindex so that we don't get NaN when calculating upgrades for new generators
base_grid.plant = base_grid.plant.reindex(grid_new.plant.index).fillna(0)
grid_new.plant.Pmax = grid.plant.Pmax - base_grid.plant.Pmax
# 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_new.storage["gen"].index, fill_value=0)
grid_new.storage["gen"].Pmax = (grid.storage["gen"].Pmax -
base_grid.storage["gen"].Pmax)
grid_new.storage["gen"]["type"] = "storage"
# Drop small changes
grid_new.plant = grid_new.plant[grid_new.plant.Pmax > 0.01]
costs = _calculate_gen_inv_costs(grid_new, year, cost_case, sum_results)
return costs
