# data definitions for alternative scenarios
alt_args = [
# base scenario
dict(),
# more detailed sampling
dict(time_sample='PSIP_2016_12_23', inputs_dir='inputs_12x24'),
# 2045-only (for experiments)
dict(time_sample='2045_15', inputs_dir='inputs_2045'),
# lower-cost hydrogen
dict(
inputs_dir='inputs_future_hydrogen',
**future_hydrogen_args
),
# only 2 days, useful for testing and debugging
dict(inputs_dir='inputs_tiny', time_sample='tiny'),
# different rates of EV adoption
dict(inputs_dir='inputs_ev_slow', ev_scenario='Business as Usual'),
dict(inputs_dir='inputs_ev_full', ev_scenario='Full Adoption'),
]
for a in alt_args:
# clone the arguments dictionary and update it with settings from the alt_args entry, if any
active_args = dict(args.items() + a.items())
scenario_data.write_tables(**active_args)
2040: 0.70,
2045: 1.00
})
# data definitions for alternative scenarios
alt_args = [
# base scenario
dict(),
# more detailed sampling
dict(time_sample='PSIP_2016_12_23', inputs_dir='inputs_12x24'),
# 2045-only (for experiments)
dict(time_sample='2045_15', inputs_dir='inputs_2045'),
# lower-cost hydrogen
dict(inputs_dir='inputs_future_hydrogen', **future_hydrogen_args),
# only 2 days, useful for testing and debugging
dict(inputs_dir='inputs_tiny', time_sample='tiny'),
# different rates of EV adoption
dict(inputs_dir='inputs_ev_slow', ev_scenario='Business as Usual'),
dict(inputs_dir='inputs_ev_full', ev_scenario='Full Adoption'),
]
for a in alt_args:
# clone the arguments dictionary and update it with settings from the alt_args entry, if any
active_args = dict(args.items() + a.items())
scenario_data.write_tables(**active_args)
# We assume each data tag is unique, even across data types, so we can use the
# tag as a unique ID for any adjustment to the data. It would be possible to
# have different tag "name spaces" for each type of data (load, ev, prices, rps
# fraction) by making alt_args into a dict, with keys for each data type, but
# then we would need to maintain a list of data types somewhere and iterate
# through that. (That may eventually be a good idea, if it allows collapsing
# all the code into simpler loops.)
alt_args = []
alt_args.extend(
dict(tag=load_name, load_scen_id=load_scen_id)
for load_name, load_scen_id in load_scenarios
)
alt_args.extend(
dict(tag=ev_name, ev_scenario=ev_scenario)
for ev_name, ev_scenario in ev_scenarios
)
alt_args.extend(
dict(tag=price_name, **all_price_scenarios[price_name])
for price_name in price_scenarios
)
# note: the 100% target ends up using the base rps_targets file since it is identical
alt_args.extend(
dict(
tag=t,
rps_targets={k: f * v for k, v in args['rps_targets'].items()}
)
for t, f in rps_fractions
)
scenario_data.write_tables(args, alt_args, scenario_list)
def write_inputs(args, **alt_args):
all_args = args.copy()
all_args.update(alt_args)
scenario_data.write_tables(all_args)