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precalculate_assumptions.py
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precalculate_assumptions.py
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import solve
import hashlib, json
import pandas as pd
from server import sanitise_assumptions, compute_weather_hash, compute_results_hash
countries = "country:" + pd.Index(solve.get_country_multipolygons().keys())
regions = "region:" + pd.Index(solve.get_region_multipolygons().keys())
country_names = solve.get_country_names()
locations = countries.append(regions)
years = range(2011,2013)
techs = ["solar","onwind"]
cf_exponents = [2.,1.,0.]
assumption_years = [2020,2030,2050]
base_assumptions = {"wind" : True,
"solar" : True,
"battery" : True,
"hydrogen" : True,
"dispatchable1": False,
"dispatchable2": False,
"co2_limit": False,
"dispatchable1_cost" : 400,
"dispatchable1_marginal_cost" : 50,
"dispatchable1_emissions" : 500,
"dispatchable1_discount" : 10,
"dispatchable2_cost" : 6000,
"dispatchable2_marginal_cost" : 10,
"dispatchable2_emissions" : 0,
"dispatchable2_discount" : 10,
"co2_emissions" : 0,
"load" : 100.0,
"hydrogen_load" : 0.0,
"frequency" : 3,
"discount_rate" : 5.0,
"version" : solve.current_version}
#copied from static/solver.js
tech_assumptions = {"2020" : {"wind_cost" : 1120,
"solar_cost" : 420,
"battery_energy_cost" : 232,
"battery_power_cost" : 270,
"hydrogen_energy_cost" : 0.7,
"hydrogen_electrolyser_cost" : 1100,
"hydrogen_electrolyser_efficiency" : 58,
"hydrogen_turbine_cost" : 880,
"hydrogen_turbine_efficiency" : 56,
},
"2030" : {"wind_cost" : 1040,
"solar_cost" : 300,
"battery_energy_cost" : 142,
"battery_power_cost" : 160,
"hydrogen_energy_cost" : 0.7,
"hydrogen_electrolyser_cost" : 600,
"hydrogen_electrolyser_efficiency" : 62,
"hydrogen_turbine_cost" : 830,
"hydrogen_turbine_efficiency" : 58,
},
"2050" : {"wind_cost" : 960,
"solar_cost" : 240,
"battery_energy_cost" : 75,
"battery_power_cost" : 60,
"hydrogen_energy_cost" : 0.7,
"hydrogen_electrolyser_cost" : 400,
"hydrogen_electrolyser_efficiency" : 67,
"hydrogen_turbine_cost" : 800,
"hydrogen_turbine_efficiency" : 60,
}
}
assumptions = base_assumptions.copy()
for location in locations:
assumptions["location"] = location
if location[:len("country:")] == "country:":
assumptions["location_name"] = country_names[location[len("country:"):]]
elif location[:len("region:")] == "region:":
assumptions["location_name"] = location[len("region:"):]
else:
assumptions["location_name"] = "None"
print(assumptions["location"],assumptions["location_name"])
for cf_exponent in cf_exponents:
for year in years:
assumptions["cf_exponent"] = cf_exponent
assumptions["year"] = year
for assumption_year in assumption_years:
assumptions.update(tech_assumptions[str(assumption_year)])
error_message, assumptions = sanitise_assumptions(assumptions)
if error_message is not None:
print(error_message)
sys.exit()
assumptions["weather_hex"] = compute_weather_hash(assumptions)
if assumption_year == 2030:
assumptions["job_type"] = "weather"
with open('data/weather-assumptions-{}.json'.format(assumptions['weather_hex']), 'w') as fp:
json.dump(assumptions,fp)
assumptions["results_hex"] = compute_results_hash(assumptions)
assumptions["job_type"] = "solve"
with open('data/results-assumptions-{}.json'.format(assumptions['results_hex']), 'w') as fp:
json.dump(assumptions,fp)