def test_get_curtailment_time_series(self):
arg = [(scenario, "Washington"), (scenario, "Bay Area"),
(scenario, "all")]
expected_return = [
pd.DataFrame(
{
"solar_curtailment":
mock_curtailment["solar"].sum(axis=1).values,
},
index=mock_solar.index,
),
pd.DataFrame(
{
"wind_curtailment":
mock_curtailment["wind"].sum(axis=1).values,
"wind_offshore_curtailment":
mock_curtailment["wind_offshore"].sum(axis=1).values,
},
index=mock_wind.index,
),
pd.DataFrame(
{
"solar_curtailment":
mock_curtailment["solar"].sum(axis=1).values,
"wind_curtailment":
mock_curtailment["wind"].sum(axis=1).values,
"wind_offshore_curtailment":
mock_curtailment["wind_offshore"].sum(axis=1).values,
},
index=mock_pg.index,
),
]
for a, e in zip(arg, expected_return):
check_dataframe_matches(get_curtailment_time_series(*a), e)
def test_get_generation_time_series_by_resources():
arg = [(scenario, "Washington", "wind"), (scenario, "Oregon", "hydro")]
expected = [
pd.DataFrame({"wind": mock_pg["B"]}),
pd.DataFrame({"hydro": mock_pg[["C", "D"]].sum(axis=1)}),
]
for a, e in zip(arg, expected):
check_dataframe_matches(get_generation_time_series_by_resources(*a), e)
def test_sum_capacity_by_type_zone():
expected_df = pd.DataFrame(
{
201: [9000, 5000],
202: [0, 4000]
},
index=["solar", "wind"],
)
check_dataframe_matches(expected_df, sum_capacity_by_type_zone(scenario))
def test_sum_generation(self):
expected_return = pd.DataFrame({
"type": ["hydro", "solar", "wind"],
1: [0, 10, 15],
2: [23, 0, 0]
})
expected_return.set_index("type", inplace=True)
summed_generation = sum_generation_by_type_zone(self.scenario)
check_dataframe_matches(summed_generation, expected_return)
def test_sum_generation_by_state_values_scaled(sim_gen_result):
expected_return = pd.DataFrame(
{
"hydro": [0.023, 0, 0, 0.023, 0.023],
"solar": [0, 0, 0.01, 0.01, 0.01],
"wind": [0, 0.015, 0, 0.015, 0.015],
},
index=["California", "Oregon", "Washington", "Western", "all"],
)
check_dataframe_matches(sim_gen_result, expected_return)