# add 2017
dict_cap_compl = {
('IT0', 'WIN_ONS', 2017): 9383.933906 + 359.2, # +359.2 MW http://www.qualenergia.it/articoli/20180213-eolico-i-dati-2017-sull-installato-italia-europa-e-nel-mondo-
('IT0', 'SOL_PHO', 2017): 19283.173 + 409, # + 409 http://www.solareb2b.it/nel-2017-italia-installati-409-mw-nuovi-impianti-fv-11/
('CH0', 'SOL_PHO', 2017): 1660.21 + 260, # +260 MW https://www.energie-cluster.ch/admin/data/files/file/file/2090/180112_mm_markt17.pdf?lm=1516007459
('CH0', 'WIN_ONS', 2017): 74.9, # no change http://www.suisse-eole.ch/de/windenergie/statistik/
('FR0', 'SOL_PHO', 2017): 7647, # https://www.rte-france.com/sites/default/files/panorama-31mars18.pdf
('FR0', 'WIN_ONS', 2017): 13539, # https://www.rte-france.com/sites/default/files/panorama-31mars18.pdf
('AT0', 'SOL_PHO', 2017): 1089.529000 + 153, # http://www.iea-pvps.org/fileadmin/dam/public/report/statistics/IEA-PVPS_-_A_Snapshot_of_Global_PV_-_1992-2017.pdf
}
mps = maps.Maps('lp_input_calibration_years', 'storage2')
cols = ['pp_id'] + [c for c in aql.get_sql_cols('plant_encar', 'lp_input_calibration_years', 'storage2').keys()
if 'cap_pwr_leg' in c]
dfcap = aql.read_sql('storage2', 'lp_input_calibration_years', 'plant_encar')[cols]
dfcap = dfcap.rename(columns={'cap_pwr_leg': 'cap_pwr_leg_yr2015'})
dfcap['pp_id'].replace(mps.dict_pp, inplace=True)
dfcap = dfcap.loc[dfcap.pp_id.str.contains('WIN|SOL')]
dfcap['nd_id'] = dfcap['pp_id'].apply(lambda x: x[:2] + '0')
dfcap['pt_id'] = dfcap['pp_id'].apply(lambda x: x[3:])
# add missing values 2017:
dfcap = dfcap.set_index(['nd_id', 'pt_id'])
for kk, vv in dict_cap_compl.items():
print(kk, vv)
dfcap.loc[kk[:2], 'cap_pwr_leg_yr2017'] = vv
do_add.get_index_lists()
return do_add
if __name__ == '__main__':
# %%
import grimsel.auxiliary.maps as maps
sc_out = 'out_levels'
slct_nd = 'DE0'
db = 'storage2'
mps = maps.Maps(sc_out, db)
ind_pltx = ['sta_mod']
ind_plty = ['pwrerg_cat']
ind_axx = ['sy']
values = ['value_posneg']
series = ['bool_out', 'fl']
table = sc_out + '.analysis_time_series'
stats_data = {'DE0': '%agora%', 'FR0': '%eco2%', 'CH0': '%entsoe%'}
filt = [
('nd', [slct_nd]),
('swfy_vl', ['yr2015', 'nan'], ' LIKE '),
# ('fl', ['%nuclear%'], ' LIKE '),
sqa = SqlAnalysis(sc_out='out_dev', db='flexible_fuels')
print(sqa.build_tables_plant_run(list_timescale=['']))
print(sqa.build_table_plant_run_tot_balance())
slct_run_id = [-1]
sqa = SqlAnalysis(sc_out='out_dev',
db='flexible_fuels',
slct_run_id=slct_run_id)
print(sqa.generate_analysis_time_series(False))
print(sqa.cost_disaggregation_high_level())
# %% PLOT TIME SERIES
mps = maps.Maps('out_dev', 'flexible_fuels')
reload(pltpg)
ind_pltx = ['ca', 'nd']
ind_plty = ['pwrerg_cat']
ind_axx = ['sy']
values = ['value_posneg']
series = ['bool_out', 'pt']
table = 'out_dev' + '.analysis_time_series'
filt = [('run_id', [-1])]
post_filt = []
series_order = []
def __init__(self, sc_out, db, **kwargs):
self.mps = maps.Maps(sc_out, db)
super().__init__(sc_out, db, **kwargs)
import PROFILE_READER.sketch_agora as ag
import PROFILE_READER.hydro_level as hy
import PROFILE_READER.timemap as tm
import PROFILE_READER.monthly_production as mp
import PROFILE_READER.entsoe_cross_border as xb
import PROFILE_READER.config as conf
# make sure we are in the right folder if the BASE_DIR is specified as relative path
os.chdir(os.path.dirname(os.path.realpath(__file__)))
base_dir = conf.BASE_DIR
sc_maps = 'lp_input_replace'
db = 'storage2'
mps = maps.Maps(sc_maps, db)
sys.exit()
# %%
#conf.
tm.build_timestamp_template(db, 'profiles_raw', 2005, 2020)
kw_dict = dict(dict_sql=dict(db=db),
base_dir=base_dir,
tm_filt={'year': range(2005, 2018)},
col_filt=[],
exclude_substrings=[],
ext='xlsx')
op = pr.WeeklyRORReader(kw_dict)
op.read_all(skip_sql=True)
def set_calibration_variations(self, dict_cl=None):
if dict_cl is None:
dict_cl = {
4: 'double_ramping_cost',
1: 'triple_ramping_cost',
2: 'inflexible_hydro',
3: 'fr_nuclear_reduction',
0: 'default'
}
slct_cl = self.ml.dct_step['swcl']
str_cl = dict_cl[slct_cl]
# reset discharge duration
for kk, vv in self.ml.m.df_plant_encar.join(
self.ml.m.df_def_plant.set_index('pp_id')['pp'],
on='pp_id').loc[
self.ml.m.df_plant_encar.discharge_duration > 0].set_index(
['pp_id',
'ca_id'])['discharge_duration'].to_dict().items():
self.ml.m.discharge_duration[kk] = vv
self.ml.m.hy_erg_min.activate()
self.ml.m.hy_month_min.activate()
self.ml.m.hy_reservoir_boundary_conditions.activate()
self.ml.m.monthly_totals.activate()
if str_cl == 'inflexible_hydro':
self.ml.m.hy_erg_min.deactivate()
self.ml.m.hy_month_min.deactivate()
self.ml.m.hy_reservoir_boundary_conditions.deactivate()
self.ml.m.monthly_totals.deactivate()
for kk in self.ml.m.discharge_duration:
self.ml.m.discharge_duration[kk] = 0
self.ml.m.cap_pwr_leg[kk] = 100000
# reset ramping
for kk, vv in self.ml.m.df_plant_encar.set_index(
['pp_id', 'ca_id'])['vc_ramp'].to_dict().items():
if kk in [k for k in self.ml.m.vc_ramp]:
self.ml.m.vc_ramp[kk].value = vv
if str_cl == 'double_ramping_cost':
for kk in self.ml.m.vc_ramp:
self.ml.m.vc_ramp[kk].value = self.ml.m.vc_ramp[kk].value * 2
if str_cl == 'triple_ramping_cost':
for kk in self.ml.m.vc_ramp:
self.ml.m.vc_ramp[kk].value = self.ml.m.vc_ramp[kk].value * 3
if str_cl == 'fr_nuclear_reduction':
# Power capacity reduced by approximate NTC of not included neighboring countries
mps = maps.Maps(self.ml.io.sc_inp, self.ml.io.db)
dict_cap_pwr_leg = {
kk: self.ml.m.cap_pwr_leg[kk].value - 7000
for kk in self.ml.m.cap_pwr_leg.keys()
if kk[0] in mps.dict_pp.keys()
and mps.dict_pp[kk[0]] == 'FR_NUC_ELC'
}
for kk, vv in dict_cap_pwr_leg.items():
self.ml.m.cap_pwr_leg[kk] = vv
self.ml.dct_vl['swcl_vl'] = str_cl