# dict(inputs_subdir='re_cost_trend',
# wind_capital_cost_escalator=0.011,
# pv_capital_cost_escalator=-0.064),
# dict(inputs_subdir='triple_ph',
# pumped_hydro_projects=[
# args["pumped_hydro_projects"][0], # standard Lake Wilson project
# ['Project_2_(1.2x)', 'Oahu', 1.2*2800*1000+35e6/150, 50, 0.015, 0.77, 0, 100],
# ['Project_3_(1.3x)', 'Oahu', 1.3*2800*1000+35e6/150, 50, 0.015, 0.77, 0, 100],
# ]
# ),
# dict(
# inputs_subdir='rps_2030',
# time_sample = "rps_fast_mini",
# rps_targets = {2020: 0.4, 2025: 0.7, 2030: 1.0, 2035: 1.0},
# ),
]
# annual change in capital cost of new renewable projects:
# solar cost projections: decline by 6.4%/year (based on residential PV systems from 1998 to 2014 in Fig. 7 of "Tracking the Sun VIII: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States," https://emp.lbl.gov/reports (declines have been faster over more recent time period, and faster for non-residential systems).
# wind cost projections:
# increase by 1.1%/year (based on 1998-2014 in 2014 Wind Technologies Market Report, https://emp.lbl.gov/reports)
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)
# rather than assigning a cost per MW-km here.
connect_cost_per_mw_km = 1000,
base_financial_year = 2015,
interest_rate = 0.06,
discount_rate = 0.03,
inflation_rate = 0.025, # used to convert nominal costs in the tables to real costs
)
args.update(
battery_capital_cost_per_mwh_capacity=363636.3636,
battery_n_cycles=4500,
battery_max_discharge=0.9,
battery_min_discharge_time=6,
battery_efficiency=0.75,
)
args.update(
pumped_hydro_capital_cost_per_mw=2800*1000+35e6/150,
pumped_hydro_project_life=50,
pumped_hydro_fixed_om_percent=0.015, # use the low-end O&M, because it always builds the big version
pumped_hydro_efficiency=0.8,
pumped_hydro_inflow_mw=10,
pumped_hydro_max_capacity_mw=1000,
)
if "skip_cf" in sys.argv:
print "Skipping variable capacity factors..."
args["skip_cf"] = True
scenario_data.write_tables(**args)
})
# data definitions for alternative scenarios
alt_args = [
dict(inputs_dir="inputs", time_sample="rps_mini"),
dict(inputs_dir="inputs_tiny", time_sample="tiny"),
dict(inputs_dir="inputs_tiny_tiny",
time_sample="tiny",
exclude_technologies=args["exclude_technologies"] +
('CentralTrackingPV', 'Wind')),
]
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)
if cmd_line_args['write_rhos']:
# calculate and store the per-variable rho values
print "creating rho settings files (takes several minutes)..."
import ReferenceModel
inputs_dirs = set()
inputs_dirs.add(args["inputs_dir"])
for a in alt_args:
inputs_dirs.add(a["inputs_dir"])
for inputs_dir in inputs_dirs:
ReferenceModel.inputs_dir = inputs_dir
ReferenceModel.create_model()
ReferenceModel.load_inputs()
ReferenceModel.save_rho_file()
else:
import sys, os
path_to_core = os.path.abspath(os.path.join(os.path.dirname(__file__), 'switch-hawaii-core'))
sys.path.append(path_to_core)
import scenario_data
###########################
# Scenario Definition
# particular settings chosen for this case
# (these will be passed as arguments when the queries are run)
scenario_data.write_tables(
load_scen_id = "med", # "hist"=pseudo-historical, "med"="Moved by Passion"
fuel_scen_id = 3, # 1=low, 2=high, 3=reference
time_sample = "rps_test", # could be '2007', '2016test', 'rps_test' or 'main'
load_zones = ('Oahu',), # subset of load zones to model
enable_must_run = 0, # should the must_run flag be converted to set minimum commitment for existing plants?
# TODO: integrate the connect length into switch financial calculations,
# rather than assigning a cost per MW-km here.
connect_cost_per_mw_km = 1000000,
base_financial_year = 2015,
interest_rate = 0.06,
discount_rate = 0.03
)
