import numpy as np
import pandas as pd
from powersimdata.tests.mock_scenario import MockScenario
from postreise.analyze.transmission import congestion
mock_plant = {
"plant_id": ["A", "B", "C", "D"],
"bus_id": [1, 1, 2, 3],
}
mock_bus = {
"bus_id": [1, 2, 3, 4],
"Pd": [5, 6, 30, 1],
"zone_id": [1, 1, 1, 2],
}
grid_attrs = {"plant": mock_plant, "bus": mock_bus}
def _check_return(expected_return, surplus):
assert isinstance(surplus, pd.Series)
msg = "Time series indices don't match"
np.testing.assert_array_equal(
surplus.index.to_numpy(), expected_return.index.to_numpy(), msg
)
msg = "Values don't match expected"
np.testing.assert_array_equal(surplus.to_numpy(), expected_return.to_numpy(), msg)
[docs]def test_calculate_congestion_surplus_single_time():
"""Congested case from Kirschen & Strbac Section 5.3.2.4"""
bus_demand = pd.DataFrame({"UTC": ["t1"], 1: [50], 2: [60], 3: [300], 4: [0]})
lmp = pd.DataFrame({"UTC": ["t1"], 1: [7.5], 2: [11.25], 3: [10], 4: [0]})
pg = pd.DataFrame({"UTC": ["t1"], "A": [50], "B": [285], "C": [0], "D": [75]})
for df in (bus_demand, lmp, pg):
df.set_index("UTC", inplace=True)
mock_scenario = MockScenario(grid_attrs, bus_demand=bus_demand, lmp=lmp, pg=pg)
expected_return = pd.Series(
data=[787.5],
index=pd.date_range(start="2016-01-01", periods=1, freq="H"),
)
expected_return.rename_axis("UTC")
surplus = congestion.calculate_congestion_surplus(mock_scenario)
_check_return(expected_return, surplus)
[docs]def test_calculate_congestion_surplus_three_times():
"""First: congested. Second: uncongested. Third: uncongested, fuzzy."""
time_indices = ["t1", "t2", "t3"]
bus_demand = pd.DataFrame(
{"UTC": time_indices, 1: [50] * 3, 2: [60] * 3, 3: [300] * 3, 4: [0] * 3}
)
lmp = pd.DataFrame(
{
"UTC": time_indices,
1: [7.5, 7.5, 7.5],
2: [11.25, 7.5, 7.5],
3: [10, 7.5, 7.49],
4: [0, 0, 0],
}
)
pg = pd.DataFrame(
{
"UTC": time_indices,
"A": [50, 125, 125],
"B": [285] * 3,
"C": [0] * 3,
"D": [75, 0, 0],
}
)
for df in (bus_demand, lmp, pg):
df.set_index("UTC", inplace=True)
mock_scenario = MockScenario(grid_attrs, bus_demand=bus_demand, lmp=lmp, pg=pg)
expected_return = pd.Series(
data=[787.5, 0, 0],
index=pd.date_range(start="2016-01-01", periods=3, freq="H"),
)
expected_return.rename_axis("UTC")
surplus = congestion.calculate_congestion_surplus(mock_scenario)
_check_return(expected_return, surplus)
