def test_label_small_hyro(generators_eia860_data, test_settings,
plant_region_map_ipm_data):
region_agg_map = reverse_dict_of_lists(
test_settings.get("region_aggregations", {}))
model_region_map_df = map_agg_region_names(
df=plant_region_map_ipm_data,
region_agg_map=region_agg_map,
original_col_name="region",
new_col_name="model_region",
)
df = pd.merge(generators_eia860_data,
model_region_map_df,
on="plant_id_eia",
how="left")
logger.info(df[["plant_id_eia", "technology_description",
"model_region"]].head())
# df["model_region"] = df["region"].map(reverse_dict_of_lists)
df = label_small_hydro(df,
test_settings,
by=["plant_id_eia", "report_date"])
print(df.query("plant_id_eia==34"))
logger.info(df["technology_description"].unique())
assert "Small Hydroelectric" in df["technology_description"].unique()
assert np.allclose(
df.loc[df.technology_description == "Small Hydroelectric",
"capacity_mw"], 12.1)
def agg_transmission_constraints(
pudl_engine,
settings,
pudl_table="transmission_single_epaipm",
settings_agg_key="region_aggregations",
):
zones = settings["model_regions"]
zone_num_map = {
zone: f"z{number + 1}" for zone, number in zip(zones, range(len(zones)))
}
combos = list(itertools.combinations(zones, 2))
reverse_combos = [(combo[-1], combo[0]) for combo in combos]
logger.info("Loading transmission constraints from PUDL")
transmission_constraints_table = pd.read_sql_table(pudl_table, con=pudl_engine)
# Settings has a dictionary of lists for regional aggregations. Need
# to reverse this to use in a map method.
region_agg_map = reverse_dict_of_lists(settings[settings_agg_key])
# IPM regions to keep. Regions not in this list will be dropped from the
# dataframe
keep_regions = [
x
for x in settings["model_regions"] + list(region_agg_map)
if x not in region_agg_map.values()
]
# Create new column "model_region_from" and "model_region_to" with labels that
# we're using for aggregated regions
transmission_constraints_table = transmission_constraints_table.loc[
(transmission_constraints_table.region_from.isin(keep_regions))
& (transmission_constraints_table.region_to.isin(keep_regions)),
:,
].drop(columns="id")
logger.info("Map and aggregate region names for transmission constraints")
for col in ["region_from", "region_to"]:
model_col = "model_" + col
transmission_constraints_table = map_agg_region_names(
df=transmission_constraints_table,
region_agg_map=region_agg_map,
original_col_name=col,
new_col_name=model_col,
)
transmission_constraints_table.drop(
columns=["firm_ttc_mw", "tariff_mills_kwh"], inplace=True
)
transmission_constraints_table = transmission_constraints_table.groupby(
["model_region_from", "model_region_to"]
).sum()
# Build the final output dataframe
logger.info(
"Build a new transmission constraints dataframe with a single line between "
"regions"
)
tc_joined = pd.DataFrame(
columns=["Network_lines"] + zones + ["Line_Max_Flow_MW", "Line_Min_Flow_MW"],
index=transmission_constraints_table.reindex(combos).dropna().index,
data=0,
)
tc_joined["Network_lines"] = range(1, len(tc_joined) + 1)
tc_joined["Line_Max_Flow_MW"] = transmission_constraints_table.reindex(
combos
).dropna()
reverse_tc = transmission_constraints_table.reindex(reverse_combos).dropna() * -1
reverse_tc.index = tc_joined.index
tc_joined["Line_Min_Flow_MW"] = reverse_tc
for idx, row in tc_joined.iterrows():
tc_joined.loc[idx, idx[0]] = 1
tc_joined.loc[idx, idx[-1]] = -1
tc_joined.rename(columns=zone_num_map, inplace=True)
tc_joined = tc_joined.reset_index()
tc_joined["Transmission Path Name"] = (
tc_joined["model_region_from"] + "_to_" + tc_joined["model_region_to"]
)
tc_joined = tc_joined.set_index("Transmission Path Name")
tc_joined.drop(columns=["model_region_from", "model_region_to"], inplace=True)
return tc_joined