def run_scenario(countries,
regions_method,
area_factor,
rooftop_pv,
save_dir,
national=False):
calliope.set_log_verbosity('CRITICAL', include_solver_output=False)
eu_ds = euses.import_dataset('euses_datasets.nc')
# remove power plants not considered
fuels_considered = ['Biomass and biogas', 'Natural gas', 'Solar', 'Wind']
fuels_removed = np.setdiff1d(eu_ds.ds.coords['fuel'].values,
fuels_considered)
eu_ds.ds = eu_ds.ds.drop(fuels_removed, dim='fuel')
tech_not_grouped = ['Solar', 'Wind', 'Wind Offshore']
tech_list = np.setdiff1d(eu_ds.ds.coords['tech'].values, tech_not_grouped)
# group natural gas and biomass/biogas based power_plants in single group
sum_var = eu_ds.ds['power_plants'].loc[:, tech_list, :].sum(axis=1)
tech_list = np.setdiff1d(tech_list, ['Combined cycle'])
eu_ds.ds['power_plants'].loc[:,
['Combined cycle']] = [[g]
for g in sum_var.values
]
eu_ds.ds = eu_ds.ds.drop(tech_list, dim='tech')
# only percentage of rooftop_pv and utility_pv area considered
eu_ds.ds['rooftop_pv'] = eu_ds.ds['rooftop_pv'] * rooftop_pv
eu_ds.ds['utility_pv'] = eu_ds.ds['utility_pv'] * 0.50
filt_ds = eu_ds.filter_countries(countries)
# Build and solve calliope model
filt_ds.create_regions(regions_method, area_factor)
regions_gpd = gpd.GeoDataFrame(
filt_ds.ds_regions['regions'].values,
columns=['id'],
geometry=filt_ds.ds_regions['geometry'].values)
filt_ds.create_calliope_model(op_mode='plan',
sectors=['power', 'heat'],
co2_cap_factor=0.2,
national=national)
model = calliope.Model('calliope_model/model.yaml',
scenario='time_3H',
override_dict={'run.solver': 'gurobi'})
model.run()
model.to_netcdf('calliope_model/results/' + save_dir + '.nc')
return model, regions_gpd
def test_set_log_verbosity(self):
calliope.set_log_verbosity("CRITICAL", include_solver_output=True)
assert logging.getLogger("calliope").getEffectiveLevel() == 50
assert logging.getLogger("py.warnings").getEffectiveLevel() == 50
assert (logging.getLogger(
"calliope.backend.pyomo.model").getEffectiveLevel() == 10)
calliope.set_log_verbosity("CRITICAL", include_solver_output=False)
assert logging.getLogger("calliope").getEffectiveLevel() == 50
assert logging.getLogger("py.warnings").getEffectiveLevel() == 50
assert (logging.getLogger(
"calliope.backend.pyomo.model").getEffectiveLevel() == 50)
calliope.set_log_verbosity()
assert logging.getLogger("calliope").getEffectiveLevel() == 20
assert logging.getLogger("py.warnings").getEffectiveLevel() == 20
assert (logging.getLogger(
"calliope.backend.pyomo.model").getEffectiveLevel() == 10)
def test_set_log_verbosity(self):
calliope.set_log_verbosity('CRITICAL', include_solver_output=True)
assert logging.getLogger('calliope').getEffectiveLevel() == 50
assert logging.getLogger('py.warnings').getEffectiveLevel() == 50
assert logging.getLogger(
'calliope.backend.pyomo.model').getEffectiveLevel() == 10
calliope.set_log_verbosity('CRITICAL', include_solver_output=False)
assert logging.getLogger('calliope').getEffectiveLevel() == 50
assert logging.getLogger('py.warnings').getEffectiveLevel() == 50
assert logging.getLogger(
'calliope.backend.pyomo.model').getEffectiveLevel() == 50
calliope.set_log_verbosity()
assert logging.getLogger('calliope').getEffectiveLevel() == 20
assert logging.getLogger('py.warnings').getEffectiveLevel() == 20
assert logging.getLogger(
'calliope.backend.pyomo.model').getEffectiveLevel() == 10
import calliope
import os
DIR = os.getcwd()
calliope.set_log_verbosity('INFO')
model = calliope.Model(DIR + '/model.yaml', scenario='milp')
model.run()
# model.results
@author: FLomb
"""
import pandas as pd
import numpy as np
import math
import os
import copy
import calliope
from obj_utils import load_obj, save_obj
from calliope_process import netcdf_to_data, p2h_plot, profile_dict_to_matrix
from summary_plots import choropleth_map_nrmse, choropleth_map_energy
calliope.set_log_verbosity(
'ERROR') #sets the level of verbosity of Calliope's operations
#%%
def nrmse(vpp, tot_real):
n = len(vpp.index)
sq_diff_hp_con = 0
sq_diff_tes_soc = 0
for i in range(n):
sq_diff_hp_con += math.pow(
(vpp['en_con_hp'].iloc[i] - tot_real['en_con_hp'].iloc[i]), 2)
sq_diff_tes_soc += math.pow(
(vpp['soc_tes'].iloc[i] - tot_real['soc_tes'].iloc[i]), 2)
