def main(mode=None, resume_year=None, endyear=None, ReEDS_inputs=None):
"""
Compute the economic adoption of distributed generation resources on an agent-level basis.
Model output is saved to a `/runs` file within the dGen directory.
"""
try:
# =====================================================================
# SET UP THE MODEL TO RUN
# =====================================================================
# initialize Model Settings object
# (this controls settings that apply to all scenarios to be executed)
model_settings = settings.init_model_settings()
prerun_test.check_dependencies()
# make output directory
# create the logger and stamp with git hash
logger = utilfunc.get_logger(
os.path.join(model_settings.out_dir, 'dg_model.log'))
logger.info("Model version is git commit {:}".format(
model_settings.git_hash))
# =====================================================================
# LOOP OVER SCENARIOS
# =====================================================================
out_subfolders = {'solar': []}
for i, scenario_file in enumerate(model_settings.input_scenarios):
logger.info('============================================')
logger.info('============================================')
logger.info("Running Scenario {i} of {n}".format(
i=i + 1, n=len(model_settings.input_scenarios)))
# initialize ScenarioSettings object
# (this controls settings that apply only to this specific scenario)
scenario_settings = settings.init_scenario_settings(
scenario_file, model_settings)
# log summary high level secenario settings
logger.info('Scenario Settings:')
logger.info('\tScenario Name: %s' %
scenario_settings.scenario_name)
logger.info('\tSectors: %s' %
list(scenario_settings.sector_data.keys()))
logger.info('\tTechnologies: %s' % scenario_settings.techs)
logger.info(
'\tYears: %s - %s' %
(scenario_settings.start_year, scenario_settings.end_year))
logger.info('Results Path: %s' % (scenario_settings.out_scen_path))
#==========================================================================================================
# CREATE AGENTS
#==========================================================================================================
logger.info("-------------- Agent Preparation ---------------")
if scenario_settings.generate_agents:
logger.info('\tCreating Agents')
solar_agents = Agents(
agent_mutation.init_solar_agents(scenario_settings))
logger.info('....{} agents in input csv'.format(
len(solar_agents)))
# Write base agents to disk
solar_agents.df.to_pickle(scenario_settings.out_scen_path +
'/agent_df_base.pkl')
else:
logger.info('Loading %s' % scenario_settings.agents_file_name)
with open(scenario_settings.agents_file_name, "r") as f:
solar_agents = Agents(pickle.load(f))
# Get set of columns that define agent's immutable attributes
cols_base = list(solar_agents.df.columns.values)
#==============================================================================
# TECHNOLOGY DEPLOYMENT
#==============================================================================
logger.info("-------------- Yearly Analysis ---------------")
complete_df = pd.DataFrame()
if scenario_settings.techs == ['solar']:
solar_agents.df['tech'] = 'solar'
for i, year in enumerate(scenario_settings.model_years):
is_first_year = year == model_settings.start_year
logger.info('\tWorking on %s' % year)
# determine any non-base columns and drop them
cols = list(solar_agents.df.columns.values)
cols_to_drop = [x for x in cols if x not in cols_base]
if len(cols_to_drop) != 0:
solar_agents.df.drop(cols_to_drop,
axis=1,
inplace=True)
# copy the core agent object and set their year
solar_agents.df['year'] = year
# get and apply load growth
load_growth_yearly = scenario_settings.get_load_growth(
year)
solar_agents.on_frame(
agent_mutation.elec.apply_load_growth,
(load_growth_yearly))
# Normalize the hourly load profile to updated total load which includes load growth multiplier
solar_agents.on_frame(agent_mutation.elec.
apply_scale_normalized_load_profiles)
# Get and apply net metering parameters
net_metering_yearly = scenario_settings.get_nem_settings(
year)
solar_agents.on_frame(
agent_mutation.elec.apply_export_tariff_params,
(net_metering_yearly))
# Apply each agent's electricity price change and assumption about increases
solar_agents.on_frame(
agent_mutation.elec.
apply_elec_price_multiplier_and_escalator,
[year, scenario_settings.get_rate_escalations()])
# Apply PV Specs
solar_agents.on_frame(agent_mutation.elec.apply_pv_specs,
scenario_settings.get_pv_specs())
solar_agents.on_frame(
agent_mutation.elec.apply_storage_specs, [
scenario_settings.get_batt_price_trajectories(),
year, scenario_settings
])
# Apply financial terms
solar_agents.on_frame(
agent_mutation.elec.apply_financial_params, [
scenario_settings.get_financing_terms(),
scenario_settings.
financial_options['annual_inflation_pct']
])
# Apply wholesale electricity prices
solar_agents.on_frame(
agent_mutation.elec.apply_wholesale_elec_prices,
scenario_settings.get_wholesale_elec_prices())
# Size S+S system and calculate electric bills
if 'ix' not in os.name:
cores = None
else:
cores = model_settings.local_cores
solar_agents.on_row(
fFuncs.calc_system_size_and_financial_performance,
cores=cores)
solar_agents.df['agent_id'] = solar_agents.df.index.values
# Calculate the financial performance of the S+S systems
solar_agents.on_frame(
financial_functions.calc_financial_performance)
# Calculate Maximum Market Share
solar_agents.on_frame(
financial_functions.calc_max_market_share,
scenario_settings.get_max_market_share())
# determine "developable" population
solar_agents.on_frame(
agent_mutation.elec.
calculate_developable_customers_and_load)
# Apply market_last_year
if is_first_year:
solar_agents.on_frame(
agent_mutation.elec.estimate_initial_market_shares)
market_last_year_df = None
else:
solar_agents.on_frame(
agent_mutation.elec.apply_market_last_year,
market_last_year_df)
# Calculate diffusion based on economics and bass diffusion
solar_agents.df, market_last_year_df = diffusion_functions.calc_diffusion_solar(
solar_agents.df, is_first_year,
scenario_settings.get_bass_params())
# Estimate total generation
solar_agents.on_frame(
agent_mutation.elec.estimate_total_generation)
# Aggregate results
scenario_settings.output_batt_dispatch_profiles = False
if is_first_year == True:
interyear_results_aggregations = agent_mutation.elec.aggregate_outputs_solar(
solar_agents.df, year, is_first_year,
scenario_settings)
else:
interyear_results_aggregations = agent_mutation.elec.aggregate_outputs_solar(
solar_agents.df, year, is_first_year,
scenario_settings, interyear_results_aggregations)
# --- Check to ensure that agent_df isn't growing (i.e. merges are failing silently) ---
df_print = solar_agents.df.copy()
df_print = df_print.loc[df_print['year'] == year]
df_print = df_print.groupby(['sector_abbr'
])['pv_kw_cum'].sum()
#==========================================================================================================
# WRITE AGENT DF AS PICKLES FOR POST-PROCESSING
#==========================================================================================================
# Write Outputs to the database
drop_fields = [
'consumption_hourly_initial', 'bill_savings',
'consumption_hourly', 'solar_cf_profile',
'tariff_dict', 'deprec_sch', 'batt_dispatch_profile'
] #dropping because are arrays or json
df_write = solar_agents.df.drop(drop_fields, axis=1)
write_annual = False
if write_annual:
df_write.to_pickle(scenario_settings.out_scen_path +
'/agent_df_%s.pkl' % year)
if i == 0:
complete_df = df_write
else:
complete_df = pd.concat([complete_df, df_write],
sort=False)
#==============================================================================
# Outputs & Visualization
#==============================================================================
logger.info("---------Saving Model Results---------")
complete_df.to_csv(scenario_settings.out_scen_path +
'/agent_outputs.csv')
logger.info("-------------Model Run Complete-------------")
logger.info('Completed in: %.1f seconds' %
(time.time() - model_settings.model_init))
except Exception as e:
if 'logger' in locals():
logger.error(e.__str__(), exc_info=True)
logger.info('Error on line {}'.format(
sys.exc_info()[-1].tb_lineno))
logger.info('Type of error {}'.format(type(e)))
logger.info('Error Text: {}'.format(e))
if 'logger' not in locals():
raise
finally:
if 'logger' in locals():
utilfunc.shutdown_log(logger)
utilfunc.code_profiler(model_settings.out_dir)
def import_agent_file(scenario_settings, con, cur, engine, model_settings,
agent_file_status, input_name):
"""
Generates new agents or uses pre-generated agents from provided .pkl file
Parameters
----------
**scenario_settings** : 'SQL schema'
Schema of the scenario settings
**con** : 'SQL connection'
SQL connection to connect to database
**cur** : 'SQL cursor'
Cursor
**engine** : 'SQL engine'
SQL engine to intepret SQL query
**model_settings** : 'object'
Model settings that apply to all scenarios
**agent_file_status** : 'attribute'
Attribute that describes whether to use pre-generated agent file or create new
**input_name** : 'string'
.Pkl file name substring of pre-generated agent table
Returns
-------
**solar_agents** : 'Class'
Instance of Agents class with either user pre-generated or new data
"""
schema = scenario_settings.schema
input_agent_dir = model_settings.input_agent_dir
state_to_model = scenario_settings.state_to_model
ISO_List = ['ERCOT', 'NEISO', 'NYISO', 'CAISO', 'PJM', 'MISO', 'SPP']
if agent_file_status == 'Use pre-generated Agents':
userdefined_table_name = "input_" + input_name + "_user_defined"
scenario_userdefined_name = get_userdefined_scenario_settings(
schema, userdefined_table_name, con)
scenario_userdefined_value = scenario_userdefined_name['val'].values[0]
solar_agents_df = pd.read_pickle(
os.path.join(input_agent_dir, scenario_userdefined_value + ".pkl"))
if scenario_settings.region in ISO_List:
solar_agents_df = pd.read_pickle(
os.path.join(input_agent_dir,
scenario_userdefined_value + ".pkl"))
else:
solar_agents_df = solar_agents_df[
solar_agents_df['state_abbr'].isin(state_to_model)]
if solar_agents_df.empty:
raise ValueError(
'Region not present within pre-generated agent file - Edit Inputsheet'
)
solar_agents = Agents(solar_agents_df)
solar_agents.on_frame(agent_mutation.elec.reassign_agent_tariffs, con)
else:
raise ValueError(
'Generating agents is not supported at this time. Please select "Use pre-generated Agents" in the input sheet'
)
return solar_agents
def import_agent_file(scenario_settings, con, cur, engine, model_settings,
agent_file_status, input_name):
"""
Generates new agents or uses pre-generated agents from provided .pkl file
Parameters
----------
**scenario_settings** : 'SQL schema'
Schema of the scenario settings
**con** : 'SQL connection'
SQL connection to connect to database
**cur** : 'SQL cursor'
Cursor
**engine** : 'SQL engine'
SQL engine to intepret SQL query
**model_settings** : 'object'
Model settings that apply to all scenarios
**agent_file_status** : 'attribute'
Attribute that describes whether to use pre-generated agent file or create new
**input_name** : 'string'
.Pkl file name substring of pre-generated agent table
Returns
-------
**solar_agents** : 'Class'
Instance of Agents class with either user pre-generated or new data
"""
schema = scenario_settings.schema
input_agent_dir = model_settings.input_agent_dir
state_to_model = scenario_settings.state_to_model
if agent_file_status == 'Use pre-generated Agents':
userdefined_table_name = 'input_' + input_name + '_user_defined'
scenario_userdefined_name = get_userdefined_scenario_settings(
schema, userdefined_table_name, con)
scenario_userdefined_value = scenario_userdefined_name['val'].values[0]
agents_df = pd.read_pickle(
os.path.join(input_agent_dir, scenario_userdefined_value + '.pkl'))
agents_df = agents_df[agents_df['state_abbr'].isin(state_to_model)]
if agents_df.empty:
raise ValueError(
'Region not present within pre-generated agent file - Edit Inputsheet'
)
# Convert dtypes of specific columns to floats
cols = [
'customers_in_bin_initial', 'load_kwh_per_customer_in_bin_initial',
'load_kwh_in_bin_initial', 'max_demand_kw', 'avg_monthly_kwh',
'cap_cost_multiplier', 'developable_roof_sqft',
'pct_of_bldgs_developable'
]
agents_df[cols] = agents_df[cols].astype(float)
agents = Agents(agents_df)
# Re-assign tariffs to agents with incompatible rate units
agents.on_frame(agent_mutation.elec.reassign_agent_tariffs, con)
# For wind: Ingest agent core attributes to database from pickle file
if scenario_settings.techs in [['wind']]:
ingest_agent_core_attributes(agents.df, con, cur, engine, schema,
model_settings.role,
scenario_settings.sectors,
scenario_settings.techs)
else:
raise ValueError(
'Generating agents is not supported at this time. Please select "Use pre-generated Agents" in the input sheet'
)
return agents