import copy
import pytest
from powersimdata.design.investment.inflation import calculate_inflation
from powersimdata.design.investment.investment_costs import (
_calculate_ac_inv_costs,
_calculate_dc_inv_costs,
_calculate_gen_inv_costs,
)
from powersimdata.tests.mock_grid import MockGrid
# bus_id is the index
mock_bus = {
"bus_id": [2010228, 2021106, 2010319, 2010320, 29409, 30778],
"lat": [47.6146, 37.7849, 47.6408, 47.6408, 30.7252, 30.5581],
"lon": [-122.326, -122.407, -122.339, -122.339, -88.2648, -88.5396],
"baseKV": [100, 346, 230, 800, 345, 345],
}
# branch 10-12 from Seattle (s3, p1, NWPP Coal) to San Francisco (s25, p9, NP15) (~679 miles)
# branch 13-14 are transformers (0 miles)
# branch 15 is in Southern Company territory (testing for no multiplier in data)
mock_branch = {
"branch_id": [10, 11, 12, 13, 14, 15],
"rateA": [0, 10, 1100, 30, 40, 50],
"from_bus_id": [2010228, 2010228, 2010319, 2010319, 2021106, 29409],
"to_bus_id": [2021106, 2021106, 2021106, 2010320, 2021106, 30778],
"branch_device_type": 3 * ["Line"] + 2 * ["Transformer"] + ["Line"],
}
mock_branch["from_lat"] = [
mock_bus["lat"][mock_bus["bus_id"].index(bus)] for bus in mock_branch["from_bus_id"]
]
mock_branch["from_lon"] = [
mock_bus["lon"][mock_bus["bus_id"].index(bus)] for bus in mock_branch["from_bus_id"]
]
mock_branch["to_lat"] = [
mock_bus["lat"][mock_bus["bus_id"].index(bus)] for bus in mock_branch["to_bus_id"]
]
mock_branch["to_lon"] = [
mock_bus["lon"][mock_bus["bus_id"].index(bus)] for bus in mock_branch["to_bus_id"]
]
mock_plant = {
"plant_id": [3, 5, 6, 7, 8, 9, 10, 11],
"bus_id": [2010228, 2010228, 2021106, 2010319, 2010319, 2010319, 2010320, 2021106],
"type": ["solar", "coal", "wind", "solar", "solar", "ng", "wind", "nuclear"],
"Pmax": [15, 30, 10, 12, 8, 20, 15, 1000],
}
mock_plant["lat"] = [
mock_bus["lat"][mock_bus["bus_id"].index(bus)] for bus in mock_plant["bus_id"]
]
mock_plant["lon"] = [
mock_bus["lon"][mock_bus["bus_id"].index(bus)] for bus in mock_plant["bus_id"]
]
mock_dcline = {
"dcline_id": [5, 14],
"Pmax": [10, 200],
"from_bus_id": [2010228, 29409],
"to_bus_id": [2021106, 30778],
}
mock_storage_gen = {
"Pmax": [100, 200],
"bus_id": [2010228, 2021106],
"type": ["storage"] * 2,
}
mock_storage_data = {"UnitIdx": [12, 13]}
grid_attrs = {
"plant": mock_plant,
"bus": mock_bus,
"branch": mock_branch,
"dcline": mock_dcline,
"storage_gen": mock_storage_gen,
"storage_StorageData": mock_storage_data,
}
[docs]@pytest.fixture
def mock_grid():
return MockGrid(grid_attrs)
[docs]def test_calculate_ac_inv_costs(mock_grid):
expected_ac_cost = {
# ((reg_mult1 + reg_mult2) / 2) * sum(basecost * rateA * miles)
"line_cost": (
calculate_inflation(2010)
* (
(
((1 + 2.25) / 2)
* (3666.67 * 10 * 679.179925842 + 1500 * 1100 * 680.986501516)
)
+ ((1 + 1) / 2) * 2333.33 * 50 * 20.003889808
)
),
# for each: rateA * basecost * regional multiplier
"transformer_cost": ((30 * 7670 * 1) + (40 * 8880 * 2.25))
* calculate_inflation(2020),
}
ac_cost = _calculate_ac_inv_costs(mock_grid)
assert ac_cost.keys() == expected_ac_cost.keys()
for k in ac_cost.keys():
assert ac_cost[k] == pytest.approx(expected_ac_cost[k])
[docs]def test_calculate_ac_inv_costs_lines_only(mock_grid):
expected_ac_cost = {
# ((reg_mult1 + reg_mult2) / 2) * sum(basecost * rateA * miles)
"line_cost": (
calculate_inflation(2010)
* (
(
((1 + 2.25) / 2)
* (3666.67 * 10 * 679.179925842 + 1500 * 1100 * 680.986501516)
)
+ ((1 + 1) / 2) * 2333.33 * 50 * 20.003889808
)
),
# for each: rateA * basecost * regional multiplier
"transformer_cost": 0,
}
this_grid = copy.deepcopy(mock_grid)
this_grid.branch = this_grid.branch.query("branch_device_type == 'Line'")
ac_cost = _calculate_ac_inv_costs(this_grid)
assert ac_cost.keys() == expected_ac_cost.keys()
for k in ac_cost.keys():
assert ac_cost[k] == pytest.approx(expected_ac_cost[k])
[docs]def test_calculate_ac_inv_costs_not_summed(mock_grid):
inflation_2010 = calculate_inflation(2010)
inflation_2020 = calculate_inflation(2020)
expected_ac_cost = {
# ((reg_mult1 + reg_mult2) / 2) * sum(basecost * rateA * miles)
"line_cost": {
10: 0, # This branch would normally be dropped by calculate_ac_inv_costs
11: ((1 + 2.25) / 2) * 3666.67 * 10 * 679.179925842 * inflation_2010,
12: ((1 + 2.25) / 2) * 1500 * 1100 * 680.986501516 * inflation_2010,
15: ((1 + 1) / 2) * 2333.33 * 50 * 20.003889808 * inflation_2010,
},
# for each: rateA * basecost * regional multiplier
"transformer_cost": {
13: (30 * 7670 * 1) * inflation_2020,
14: (40 * 8880 * 2.25) * inflation_2020,
},
}
ac_cost = _calculate_ac_inv_costs(mock_grid, sum_results=False)
for branch_type, upgrade_costs in expected_ac_cost.items():
assert set(upgrade_costs.keys()) == set(ac_cost[branch_type].index)
for branch, cost in upgrade_costs.items():
assert cost == pytest.approx(ac_cost[branch_type].loc[branch])
[docs]def test_calculate_dc_inv_costs(mock_grid):
expected_dc_cost = (
# lines
calculate_inflation(2015)
* 457.1428571
* (10 * 679.1799258421203 + 200 * 20.003889808)
# terminals
+ 135e3 * (10 + 200) * 2 * calculate_inflation(2020)
)
dc_cost = _calculate_dc_inv_costs(mock_grid)
assert dc_cost == pytest.approx(expected_dc_cost)
[docs]def test_calculate_dc_inv_costs_not_summed(mock_grid):
expected_dc_cost = {
5: (
457.1428571 * 10 * 679.1799258421203 * calculate_inflation(2015)
+ 135e3 * 10 * 2 * calculate_inflation(2020)
),
14: (
457.1428571 * 200 * 20.003889808 * calculate_inflation(2015)
+ 135e3 * 200 * 2 * calculate_inflation(2020)
),
}
dc_cost = _calculate_dc_inv_costs(mock_grid, sum_results=False)
for dcline_id, expected_cost in expected_dc_cost.items():
assert expected_cost == pytest.approx(dc_cost.loc[dcline_id])
[docs]def test_calculate_gen_inv_costs_2030(mock_grid):
gen_inv_cost = _calculate_gen_inv_costs(mock_grid, 2030, "Moderate").to_dict()
expected_gen_inv_cost = {
# for each: capacity (kW) * regional multiplier * base technology cost
"solar": sum(
[
15e3 * 1.01701 * 836.3842785,
12e3 * 1.01701 * 836.3842785,
8e3 * 1.01701 * 836.3842785,
]
),
"coal": 30e3 * 1.05221 * 4049.047403,
"wind": 10e3 * 1.16979 * 1297.964758 + 15e3 * 1.04348 * 1297.964758,
"ng": 20e3 * 1.050755 * 983.2351768,
"storage": 100e3 * 1.012360 * 817 + 200e3 * 1.043730 * 817,
"nuclear": 1000e3 * 1.07252 * 6727.799801,
}
inflation = calculate_inflation(2018)
expected_gen_inv_cost = {k: v * inflation for k, v in expected_gen_inv_cost.items()}
assert gen_inv_cost.keys() == expected_gen_inv_cost.keys()
for k in gen_inv_cost.keys():
assert gen_inv_cost[k] == pytest.approx(expected_gen_inv_cost[k])
[docs]def test_calculate_gen_inv_costs_not_summed(mock_grid):
gen_inv_cost = _calculate_gen_inv_costs(
mock_grid, 2025, "Advanced", sum_results=False
)
expected_gen_inv_cost = {
# for each: capacity (kW) * regional multiplier * base technology cost
3: 15e3 * 1.01701 * 1013.912846,
5: 30e3 * 1.05221 * 4099.115851,
6: 10e3 * 1.16979 * 1301.120135,
7: 12e3 * 1.01701 * 1013.912846,
8: 8e3 * 1.01701 * 1013.912846,
9: 20e3 * 1.050755 * 1008.001936,
10: 15e3 * 1.04348 * 1301.120135,
11: 1000e3 * 1.07252 * 6928.866991,
12: 100e3 * 1.012360 * 779,
13: 200e3 * 1.043730 * 779,
}
inflation = calculate_inflation(2018)
expected_gen_inv_cost = {k: v * inflation for k, v in expected_gen_inv_cost.items()}
assert set(gen_inv_cost.index) == set(expected_gen_inv_cost.keys())
for k in gen_inv_cost.index:
assert gen_inv_cost.loc[k] == pytest.approx(expected_gen_inv_cost[k])
