def add_emission_policies(transmission_df, settings, DistrZones=None):
"""Add emission policies to the transmission dataframe
Parameters
----------
transmission_df : DataFrame
Zone to zone transmission constraints
settings : dict
User-defined parameters from a settings file. Should have keys of `input_folder`
(a Path object of where to find user-supplied data) and
`emission_policies_fn` (the file to load).
DistrZones : [type], optional
Placeholder setting, by default None
Returns
-------
DataFrame
The emission policies provided by user next to the transmission constraints.
"""
model_year = settings["model_year"]
case_id = settings["case_id"]
policies = load_policy_scenarios(settings)
year_case_policy = policies.loc[(case_id, model_year), :]
# Bug where multiple regions for a case will return this as a df, even if the policy
# for this case applies to all regions (code below expects a Series)
ycp_shape = year_case_policy.shape
if ycp_shape[0] == 1 and len(ycp_shape) > 1:
year_case_policy = year_case_policy.squeeze() # convert to series
zones = settings["model_regions"]
zone_num_map = {
zone: f"z{number + 1}"
for zone, number in zip(zones, range(len(zones)))
}
zone_cols = ["Region description", "Network_zones", "DistrZones"] + list(
policies.columns)
zone_df = pd.DataFrame(columns=zone_cols)
zone_df["Region description"] = zones
zone_df["Network_zones"] = zone_df["Region description"].map(zone_num_map)
if DistrZones is None:
zone_df["DistrZones"] = 0
# Add code here to make DistrZones something else!
# If there is only one region, assume that the policy is applied across all regions.
if isinstance(year_case_policy, pd.Series):
logger.info(
"Only one zone was found in the emissions policy file."
" The same emission policies are being applied to all zones.")
for col, value in year_case_policy.iteritems():
if col == "CO_2_Max_Mtons":
zone_df.loc[:, col] = 0
if value > 0:
zone_df.loc[0, col] = value
else:
zone_df.loc[:, col] = value
else:
for region, col in product(year_case_policy["region"].unique(),
year_case_policy.columns):
zone_df.loc[zone_df["Region description"] == region,
col] = year_case_policy.loc[year_case_policy.region ==
region, col].values[0]
zone_df = zone_df.drop(columns="region")
network_df = pd.concat([zone_df, transmission_df], axis=1)
return network_df
def make_genx_settings_file(pudl_engine, settings, calculated_ces=None):
"""Make a copy of the GenX settings file for a specific case.
This function tries to make some intellegent choices about parameter values like
the RPS/CES type and can also read values from a file.
There should be a base-level GenX settings file with parameters like the solver and
solver-specific settings that stay constant across all cases.
Parameters
----------
pudl_engine : sqlalchemy.Engine
A sqlalchemy connection for use by pandas to access IPM load profiles. These
load profiles are needed when DG is calculated as a fraction of load.
settings : dict
User-defined parameters from a settings file. Should have keys of `model_year`
`case_id`, 'case_name', `input_folder` (a Path object of where to find
user-supplied data), `emission_policies_fn`, 'distributed_gen_profiles_fn'
(the files to load in other functions), and 'genx_settings_fn'.
Returns
-------
dict
Dictionary of settings for a GenX run
"""
model_year = settings["model_year"]
case_id = settings["case_id"]
case_name = settings["case_name"]
genx_settings = load_settings(settings["genx_settings_fn"])
policies = load_policy_scenarios(settings)
year_case_policy = policies.loc[(case_id, model_year), :]
# Bug where multiple regions for a case will return this as a df, even if the policy
# for this case applies to all regions (code below expects a Series)
ycp_shape = year_case_policy.shape
if ycp_shape[0] == 1 and len(ycp_shape) > 1:
year_case_policy = year_case_policy.squeeze() # convert to series
if settings.get("distributed_gen_profiles_fn"):
dg_generation = make_distributed_gen_profiles(pudl_engine, settings)
total_dg_gen = dg_generation.sum().sum()
else:
total_dg_gen = 0
if isinstance(year_case_policy, pd.DataFrame):
year_case_policy = year_case_policy.sum()
# If a value isn't supplied to the function use value from file
if calculated_ces is None:
CES = year_case_policy["CES"]
else:
CES = calculated_ces
RPS = year_case_policy["RPS"]
# THIS WILL NEED TO BE MORE FLEXIBLE FOR OTHER SCENARIOS
if float(year_case_policy["CO_2_Max_Mtons"]) >= 0:
genx_settings["CO2Cap"] = 2
else:
genx_settings["CO2Cap"] = 0
if float(year_case_policy["RPS"]) > 0:
# print(total_dg_gen)
# print(year_case_policy["RPS"])
if policies.loc[(case_id, model_year), "region"].all() == "all":
genx_settings["RPS"] = 3
genx_settings["RPS_Adjustment"] = float((1 - RPS) * total_dg_gen)
else:
genx_settings["RPS"] = 2
genx_settings["RPS_Adjustment"] = 0
else:
genx_settings["RPS"] = 0
genx_settings["RPS_Adjustment"] = 0
if float(year_case_policy["CES"]) > 0:
if policies.loc[(case_id, model_year), "region"].all() == "all":
genx_settings["CES"] = 3
# This is a little confusing but for partial CES
if settings.get("partial_ces"):
genx_settings["CES_Adjustment"] = 0
else:
genx_settings["CES_Adjustment"] = float(
(1 - CES) * total_dg_gen)
else:
genx_settings["CES"] = 2
genx_settings["CES_Adjustment"] = 0
else:
genx_settings["CES"] = 0
genx_settings["CES_Adjustment"] = 0
# Don't wrap when time domain isn't reduced
if not settings.get("reduce_time_domain"):
genx_settings["OperationWrapping"] = 0
genx_settings["case_id"] = case_id
genx_settings["case_name"] = case_name
genx_settings["year"] = str(model_year)
# This is a new setting, will need to have a way to change.
genx_settings["CapacityReserveMargin"] = 0
genx_settings["LDS"] = 0
# Load user defined values for the genx settigns file. This overrides the
# complicated logic above.
if settings.get("case_genx_settings_fn"):
user_genx_settings = load_user_genx_settings(settings)
user_case_settings = user_genx_settings.loc[(case_id, model_year), :]
for key, value in user_case_settings.items():
if not pd.isna(value):
genx_settings[key] = value
return genx_settings