def set_cf_max(str_hy=None, reset=False, excl_pp=[]):
''' Select maximum capacity factors for selected year. '''
if str_hy == None:
reset = True
str_hy = ''
slct_col = 'cf_max' + (str_hy if not reset else '')
msg = ('Resetting cf_max to base year values.' if reset else
'Setting cf_max to values' + str_hy.replace('_', ' '))
if 'cf_max' in self.ml.m.dict_monthly_factors.keys():
df_mt = self.ml.m.dict_monthly_factors['cf_max']
df = cols2tuplelist(self.ml.m.df_plant_encar[[
'pp_id', 'ca_id'
]].loc[self.ml.m.df_plant_encar.pp_id.isin(
self.ml.m.setlst['pp'])],
df_mt['mt_id'],
return_df=True)
df = df.join(self.ml.m.df_plant_encar.set_index(
['pp_id', 'ca_id'])[slct_col],
on=['pp_id', 'ca_id']).fillna(0)
df = df.join(df_mt.set_index(
['mt_id', 'ca_id',
'pp_id'])['mt_fact' + (str_hy if not reset else '')],
on=['mt_id', 'pp_id', 'ca_id']).fillna(1)
df['value'] = df[slct_col] * df['mt_fact' +
(str_hy if not reset else '')]
dct_cf_max = df.set_index(['mt_id', 'pp_id',
'ca_id'])['value'].to_dict()
else:
dct_cf_max = (
self.ml.m.df_plant_encar.
loc[-self.ml.m.df_plant_encar.pp_id.isin(excl_pp)].loc[
self.ml.m.df_plant_encar.pp_id.isin(
self.ml.m.setlst['pp'])].set_index(
['pp_id', 'ca_id'])[slct_col].to_dict())
print(msg)
for kk, vv in dct_cf_max.items():
self.ml.m.cf_max[kk] = vv
def set_co2_price(str_hy=None, reset=False):
''' Select CO2 price for selected year. '''
if str_hy == None:
reset = True
str_hy = ''
slct_col = 'price_co2' + (str_hy if not reset else '')
msg = ('Resetting price_co2 to base year values.' if reset else
'Setting price_co2 to values' + str_hy.replace('_', ' '))
if 'price_co2' in self.ml.m.dict_monthly_factors.keys():
df_mt = self.ml.m.dict_monthly_factors['price_co2']
df = cols2tuplelist(self.ml.m.df_def_node[[
'nd_id'
]].loc[self.ml.m.df_def_node.nd_id.isin(
self.ml.m.slct_node_id)],
df_mt['mt_id'],
return_df=True)
df = df.join(self.ml.m.df_def_node.set_index(['nd_id'
])[slct_col],
on=['nd_id']).fillna(0)
df = df.join(df_mt.set_index(
['mt_id',
'nd_id'])['mt_fact' + (str_hy if not reset else '')],
on=['mt_id', 'nd_id']).fillna(1)
df['value'] = df[slct_col] * df['mt_fact' +
(str_hy if not reset else '')]
dct_price_co2 = df.set_index(['mt_id',
'nd_id'])['value'].to_dict()
else:
dct_price_co2 = (self.ml.m.df_def_node.set_index(
['nd_id'])[slct_col].to_dict())
print(msg)
for kk, vv in dct_price_co2.items():
self.ml.m.price_co2[kk] = vv
def set_fuel_prices(str_hy=None, reset=False):
''' Select fuel price values for selected year. '''
if str_hy == None:
reset = True
str_hy = ''
slct_col = 'vc_fl' + (str_hy if not reset else '')
msg = ('Resetting vc_fl to base year values' if reset else
'Setting vc_fl to values' + str_hy.replace('_', ' '))
msg += ' from column {}'.format(slct_col)
if 'vc_fl' in self.ml.m.dict_monthly_factors.keys():
df_mt = self.ml.m.dict_monthly_factors['vc_fl']
df = cols2tuplelist(
self.ml.m.df_fuel_node_encar[['fl_id', 'nd_id']],
df_mt['mt_id'],
return_df=True)
df = df.join(self.ml.m.df_fuel_node_encar.set_index(
['fl_id', 'nd_id'])[slct_col],
on=['fl_id', 'nd_id']).fillna(0)
df = df.join(df_mt.set_index(
['mt_id', 'fl_id',
'nd_id'])['mt_fact' + (str_hy if not reset else '')],
on=['mt_id', 'fl_id', 'nd_id']).fillna(1)
df['value'] = df[slct_col] * df['mt_fact' +
(str_hy if not reset else '')]
dct_prices = df.set_index(['mt_id', 'fl_id',
'nd_id'])['value'].to_dict()
else:
dct_prices = (self.ml.m.df_fuel_node_encar.set_index(
['fl_id', 'nd_id'])[slct_col].to_dict())
print(msg)
for kk, vv in dct_prices.items():
self.ml.m.vc_fl[kk] = vv
def _init_tmsy_sets(self):
'''
The plant ids and the time slots are connected
through the node-specific time resolution.
'''
self.tm = po.Set(initialize=self.df_tm_soy.tm_id.unique(),
ordered=True)
list_tmsy = cols2tuplelist(self.df_tm_soy[['tm_id', 'sy']])
self.tmsy = po.Set(within=self.tm * self.sy,
initialize=list_tmsy,
ordered=True)
# only constructed if self.mt exists
self.tmsy_mt = (po.Set(within=self.tmsy * self.mt,
initialize=cols2tuplelist(self.df_tm_soy[[
'tm_id', 'sy', 'mt_id'
]])) if not self.mt is None else None)
df = pd.merge(self.df_def_node,
self.df_node_encar,
on='nd_id',
how='outer')[['nd_id', 'ca_id']]
df = df.loc[~df.ca_id.isna()].drop_duplicates()
df['tm_id'] = df.nd_id.replace(self.dict_nd_tm_id)
cols = ['sy', 'nd_id', 'ca_id']
list_syndca = pd.merge(self.df_tm_soy[['tm_id', 'sy']],
df,
on='tm_id',
how='outer')[cols]
self.sy_ndca = po.Set(within=self.sy * self.ndca,
ordered=True,
initialize=cols2tuplelist(list_syndca))
mask_pp = self.df_plant_encar.pp_id.isin(self.setlst['ppall'])
df = self.df_plant_encar.loc[mask_pp, ['pp_id', 'ca_id']].copy()
df['tm_id'] = (df.pp_id.replace(self.mps.dict_plant_2_node_id).replace(
self.dict_nd_tm_id))
cols = ['sy', 'pp_id', 'ca_id']
list_syppca = pd.merge(self.df_tm_soy[['sy', 'tm_id']],
df,
on='tm_id',
how='outer')[cols]
list_syppca = list_syppca.loc[~(list_syppca.pp_id.isna()
| list_syppca.ca_id.isna())]
list_syppca = cols2tuplelist(list_syppca)
for slct_set in [
'ppall', 'rp', 'st', 'hyrs', 'pr', 'pp', 'chp', 'ror', 'lin'
]:
set_name = 'sy_%s_ca' % slct_set
within = self.sy * getattr(self, slct_set) * self.ca
if slct_set in self.setlst:
logger.info('Defining set ' + set_name)
set_pp = set(self.setlst[slct_set])
setattr(
self, set_name,
po.Set(within=within,
ordered=True,
initialize=[
row for row in list_syppca if row[1] in set_pp
]))
else:
setattr(self, set_name, po.Set(within=within, initialize=[]))
def define_sets(self):
r'''
Add all required sets to the model.
Adds sets as defined by
* the ``setlst`` dictionary initialized in the :func:`get_setlst`
method
* the DataFrame attributes of the :class:`ModelBase` class for more
complex derived sets
%s
'''
self.nd = po.Set(initialize=self.setlst['nd'])
self.ca = po.Set(initialize=self.setlst['ca'])
self.fl = po.Set(initialize=self.setlst['fl'])
self.pf = po.Set(initialize=self.setlst['pf'])
df_ndca = self.df_def_plant[['pp_id', 'nd_id']].set_index('pp_id')
df_ndca = self.df_plant_encar[['pp_id', 'ca_id']].join(df_ndca,
on='pp_id')
df_ndca = df_ndca[['pp_id', 'nd_id', 'ca_id']]
slct_cols = ['pp_id', 'ca_id']
for iset in self.slct_sets:
logger.info('Defining basic sets for {}'.format(iset))
''' SUB SETS PP'''
setattr(
self, iset,
po.Set(within=(None if iset == 'ppall' else self.ppall),
initialize=self.setlst[iset])
if iset in self.setlst.keys() else po.Set(within=self.ppall,
initialize=[]))
''' SETS PP x ENCAR '''
_df = self.df_plant_encar.copy()
_df = _df.loc[_df['pp_id'].isin(getattr(self, iset))]
setattr(
self, iset + '_ca',
po.Set(within=getattr(self, iset) * self.ca,
initialize=cols2tuplelist(_df[slct_cols])))
''' SETS PP x ND x ENCAR '''
_df = df_ndca.copy()
_df = _df.loc[df_ndca['pp_id'].isin(self.setlst[iset] if iset in
self.setlst.keys() else [])]
setattr(
self, iset + '_ndca',
po.Set(within=getattr(self, iset) * self.nd * self.ca,
initialize=cols2tuplelist(_df)))
# no scf fuels in the _cafl and _ndcafl
# These are used to calculate fuel costs, that's why we don't want
# the generated fuels in there.
df_0 = self.df_def_plant[['pp_id', 'nd_id', 'fl_id']]
df_0 = df_0.set_index('pp_id')
df_0 = self.df_plant_encar[['pp_id', 'ca_id']].join(df_0, on='pp_id')
df_0 = df_0.loc[df_0.fl_id.isin(self.setlst['fl'])]
list_sets = ['ppall', 'hyrs', 'pp', 'chp', 'ror', 'st', 'lin']
# list_sets = [st for st in list_sets if st in self.setlst.keys()]
for iset in list_sets:
if iset in self.setlst:
cols_ppcafl = ['pp_id', 'ca_id', 'fl_id']
df = df_0.loc[df_0['pp_id'].isin(self.setlst[iset]),
cols_ppcafl]
new_set = po.Set(within=getattr(self, iset) * self.ca *
self.fl,
initialize=cols2tuplelist(df))
setattr(self, iset + '_cafl', new_set)
slct_cols_ppndcafl = ['pp_id', 'nd_id', 'ca_id', 'fl_id']
df = df_0.loc[df_0.pp_id.isin(self.setlst[iset]),
slct_cols_ppndcafl]
setattr(
self, iset + '_ndcafl',
po.Set(within=(getattr(self, iset) * self.nd * self.ca *
self.fl),
initialize=cols2tuplelist(df)))
else:
new_set_cafl = po.Set(within=getattr(self, iset) * self.ca *
self.fl,
initialize=[])
setattr(self, iset + '_cafl', new_set_cafl)
new_set_ndcafl = po.Set(within=getattr(self, iset) * self.nd *
self.ca * self.fl,
initialize=[])
setattr(self, iset + '_ndcafl', new_set_ndcafl)
# plants selling fuels ... only ppall, therefore outside the loop
lst = cols2tuplelist(df.loc[df.pp_id.isin(
self.setlst['sll'] if 'sll' in self.setlst else [])])
setattr(self, 'pp_ndcafl_sll',
po.Set(within=self.pp_ndcafl, initialize=lst))
# temporal
self.sy = po.Set(initialize=list(self.df_tm_soy.sy.unique()),
ordered=True)
self.sy_hydbc = (po.Set(within=self.sy,
initialize=set(self.df_plant_month.sy))
if not self.df_plant_month is None else None)
self.mt = (po.Set(initialize=list(self.df_def_month['mt_id']))
if not self.df_def_month is None else None)
self.wk = (po.Set(initialize=list(self.df_def_week['wk_id']))
if not self.df_def_week is None else None)
# pp_cacafcl; used to account for conversions of ca in the supply rule
if 'fl_id' in self.df_def_encar:
df_cafl = self.df_def_encar.set_index('fl_id')['ca_id']
df_cafl = df_cafl.rename('ca_fl_id')
df_ppca = self.df_plant_encar.set_index('pp_id')['ca_id']
df = (self.df_def_plant.join(df_ppca, on='pp_id').join(df_cafl,
on='fl_id'))
df = df.loc[-df.ca_fl_id.isnull()
& -df.ca_id.isnull(),
['pp_id', 'nd_id', 'ca_id', 'ca_fl_id']]
self.pp_ndcaca = po.Set(within=self.pp_ndca * self.ca,
initialize=cols2tuplelist(df))
else:
self.pp_ndcaca = None
# inter-node connections
if not self.df_node_connect is None and not self.df_node_connect.empty:
df = self.df_node_connect[['nd_id', 'nd_2_id', 'ca_id']]
self.ndcnn = po.Set(within=self.nd * self.nd * self.ca,
initialize=cols2tuplelist(df),
ordered=True)
df = self.df_symin_ndcnn[['symin', 'nd_id', 'nd_2_id', 'ca_id']]
self.symin_ndcnn = po.Set(within=self.sy * self.nd * self.nd *
self.ca,
initialize=cols2tuplelist(df),
ordered=True)
else:
self.ndcnn = po.Set(within=self.nd * self.nd * self.ca)
self.symin_ndcnn = po.Set(within=self.sy * self.nd * self.nd *
self.ca)
# ndca for electricity only; mainly used for flexible demand;
# then again: why would only EL have flexible demand?
df = pd.concat([
pd.Series(self.slct_node_id, name='nd_id'),
pd.Series(
np.ones(len(self.slct_node_id)) * self.mps.dict_ca_id['EL'],
name='ca_id')
],
axis=1)
self.ndca_EL = po.Set(within=self.nd * self.ca,
initialize=cols2tuplelist(df),
ordered=True)
# general ndca
df = self.df_node_encar[['nd_id', 'ca_id']].drop_duplicates()
self.ndca = po.Set(within=self.nd * self.ca,
initialize=cols2tuplelist(df),
ordered=True)
# general ndcafl
if not self.df_fuel_node_encar is None:
df = self.df_fuel_node_encar[['nd_id', 'ca_id', 'fl_id']]
self.ndcafl = po.Set(within=self.nd * self.ca * self.fl,
initialize=cols2tuplelist(df),
ordered=True)
else:
self.ndcafl = None
# fuels with energy constraints
if 'is_constrained' in self.df_def_fuel:
lst = self.df_def_fuel.loc[self.df_def_fuel.is_constrained == 1,
'fl_id'].tolist()
self.fl_erg = po.Set(within=self.fl, initialize=lst, ordered=True)
else:
self.fl_erg = po.Set(within=self.fl, initialize=[])
# set pf_id for profiles
for pf_set in ['dmnd_pf', 'supply_pf', 'pricesll_pf', 'pricebuy_pf']:
setattr(
self, pf_set,
po.Set(within=self.pf,
initialize=self.setlst[pf_set],
ordered=True))
self._init_tmsy_sets()