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build_input_data.py
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build_input_data.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Build input database schema.
All tables are copied to csv files at the very end.
'''
import pandas as pd
import sys, os
from xlrd import open_workbook
from importlib import reload
import numpy as np
from grimsel.auxiliary.aux_general import print_full
from grimsel.auxiliary.aux_general import expand_rows
from grimsel.auxiliary.aux_general import read_xlsx_table
from grimsel.auxiliary.aux_general import translate_id
from grimsel.auxiliary.aux_m_func import cols2tuplelist
import grimsel.auxiliary.sqlutils.aux_sql_func as aql
from grimsel.auxiliary.timemap import TimeMap
import grimsel.config as config
reload(config)
# %%
db = config.DATABASE
sc = config.SCHEMA
fn = config.FN_XLSX
data_path = config.PATH_CSV
wb = open_workbook(fn)
wb_fy = open_workbook(os.path.join(os.path.dirname(fn),
'future_capacity.xlsx'))
sqlc = aql.sql_connector(db)
reload(aql)
def init_table(*args, **kwargs):
return aql.init_table(*args, **kwargs, con_cur=sqlc.get_pg_con_cur())
sys.exit()
# %%
exec_str = ('''
DROP SCHEMA IF EXISTS {sc} CASCADE;
CREATE SCHEMA IF NOT EXISTS {sc};
''').format(sc=sc, )
aql.exec_sql(exec_str, db=db)
def yr_getter(par, data_type=False, rnge=range(2015, 2050 + 1, 5)):
return [par + i if not data_type else (par + i, data_type)
for i in [''] + ['_yr' + str(ii) for ii
in rnge if not ii == 2015]]
# <codecell>
tb_name = 'def_pp_type'
cols = [('pt_id',' SMALLINT'),
('pt',' varchar(20)'),
('pp_broad_cat', 'varchar(100)'),
('color', 'VARCHAR(7)')]
pk = ['pt_id']
unique = ['pt']
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'def_fuel'
cols = [('fl_id', 'SMALLINT'), ('fl', 'varchar(20)'),
('co2_int', 'DOUBLE PRECISION'),
('is_ca', 'SMALLINT'),
('is_constrained', 'SMALLINT'),
('color', 'VARCHAR(7)')]
pk = ['fl_id']
unique = ['fl']
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'def_node'
cols = [('nd_id', 'SMALLINT'), ('nd', 'VARCHAR(3)'),
('color', 'VARCHAR(7)')] + yr_getter('price_co2', 'DOUBLE PRECISION')
pk = ['nd_id']
unique = ['nd']
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'node_encar'
cols = [('nd_id', 'SMALLINT'),
('ca_id', 'SMALLINT'),
('grid_losses', 'DOUBLE PRECISION'),
('grid_losses_absolute', 'DOUBLE PRECISION'),
] + yr_getter('dmnd_sum', 'DOUBLE PRECISION')
pk = ['nd_id', 'ca_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'def_encar'
cols = [('ca_id', 'SMALLINT'),
('fl_id', 'SMALLINT'),
('ca', 'VARCHAR(2)')]
pk = ['ca_id']
unique = ['ca']
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'def_month'
cols = [('mt_id',' SMALLINT'),
('month_min_hoy',' SMALLINT'),
('month_weight',' SMALLINT'),
('mt',' VARCHAR(3)')]
pk = ['mt_id']
unique = ['name']
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'def_week'
cols = [('wk_id',' SMALLINT'),
('wk',' SMALLINT'),
('week_weight', 'SMALLINT')]
pk = ['wk_id']
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'def_plant'
cols = [('pp_id',' SMALLINT'), ('pp',' VARCHAR(20)'),
('nd_id',' SMALLINT', sc + '.def_node(nd_id)'),
('fl_id',' SMALLINT', sc + '.def_fuel(fl_id)'),
('pt_id',' SMALLINT', sc + '.def_pp_type(pt_id)'),
('set_def_pr',' SMALLINT'),
('set_def_cain',' SMALLINT'),
('set_def_ror',' SMALLINT'),
('set_def_pp',' SMALLINT'), ('set_def_st',' SMALLINT'),
('set_def_hyrs',' SMALLINT'),
('set_def_chp',' SMALLINT'),
('set_def_add',' SMALLINT'),
('set_def_rem',' SMALLINT'),
('set_def_sll',' SMALLINT'),
('set_def_curt',' SMALLINT'),
('set_def_lin',' SMALLINT'),
('set_def_scen',' SMALLINT'),
('set_def_winsol',' SMALLINT'),
('set_def_tr', 'SMALLINT'),
('set_def_peak', 'SMALLINT')]
pk = ['pp_id']
unique = ['pp']
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'plant_month'
cols = [('mt_id',' SMALLINT', sc + '.def_month(mt_id)'),
('pp_id',' SMALLINT', sc + '.def_plant(pp_id)'),
('hyd_erg_bc','DOUBLE PRECISION')]
pk = ['mt_id', 'pp_id']
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'plant_encar'
cols = [('pp_id',' SMALLINT', sc + '.def_plant(pp_id)'),
('ca_id',' SMALLINT', sc + '.def_encar(ca_id)'),
('pp_eff','DOUBLE PRECISION'),
('erg_max','DOUBLE PRECISION'),
('discharge_duration','DOUBLE PRECISION'),
('st_lss_rt','DOUBLE PRECISION'),
('st_lss_hr','DOUBLE PRECISION'),
('factor_lin_0', 'DOUBLE PRECISION'),
('factor_lin_1','DOUBLE PRECISION'),
('vc_ramp','DOUBLE PRECISION'),
('vc_om','DOUBLE PRECISION'),
] + (yr_getter('cap_pwr_leg', 'DOUBLE PRECISION')
+ yr_getter('erg_chp', 'DOUBLE PRECISION'))
pk = ['pp_id', 'ca_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'plant_encar_scenarios'
cols = [('pp_id',' SMALLINT', sc + '.def_plant(pp_id)'),
('ca_id',' SMALLINT', sc + '.def_encar(ca_id)'),
('scenario', 'SMALLINT'),
] + (yr_getter('cap_pwr_leg', 'DOUBLE PRECISION'))
pk = ['pp_id', 'ca_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'imex_comp'
cols = [('nd_id', 'SMALLINT', sc + '.def_node(nd_id)'),
('nd_2_id', 'SMALLINT', sc + '.def_node(nd_id)'),
] + yr_getter('erg_trm', 'DOUBLE PRECISION', [2015])
pk = ['nd_id', 'nd_2_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'profdmnd'
cols = [('nd_id', 'SMALLINT', sc + '.def_node(nd_id)'),
('ca_id', 'SMALLINT', sc + '.def_encar(ca_id)'),
('hy', 'SMALLINT')] + yr_getter('value', 'NUMERIC(18,9)', [2015])
pk = ['hy', 'ca_id', 'nd_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'profchp'
cols = [('nd_id', 'SMALLINT', sc + '.def_node(nd_id)'),
('ca_id', 'SMALLINT', sc + '.def_encar(ca_id)'),
('hy', 'SMALLINT'), ('value', 'DOUBLE PRECISION')]
pk = ['hy', 'nd_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'profinflow'
cols = [('pp_id', 'SMALLINT', sc + '.def_plant(pp_id)'),
('ca_id', 'SMALLINT', sc + '.def_encar(ca_id)'),
('hy', 'SMALLINT'), ('value', 'DOUBLE PRECISION')]
pk = ['hy', 'pp_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'profprice'
cols = [('hy', 'SMALLINT'),
('nd_id', 'SMALLINT', sc + '.def_node(nd_id)'),
('fl_id', 'SMALLINT', sc + '.def_fuel(fl_id)'),
] + yr_getter('value', 'DOUBLE PRECISION', [2015])
pk = ['hy', 'nd_id', 'fl_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
#
tb_name = 'fuel_node_encar'
cols = ([('fl_id', 'SMALLINT', sc + '.def_fuel(fl_id)'),
('nd_id', 'SMALLINT', sc + '.def_node(nd_id)'),
('ca_id', 'SMALLINT', sc + '.def_encar(ca_id)'),
('has_profile', 'SMALLINT'),
('is_chp', 'SMALLINT'),
] + yr_getter('erg_inp', 'DOUBLE PRECISION')
+ yr_getter('vc_fl', 'DOUBLE PRECISION'))
pk = ['fl_id', 'nd_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
#
tb_name = 'fuel_node_encar_scenarios'
cols = ([('fl_id', 'SMALLINT', sc + '.def_fuel(fl_id)'),
('nd_id', 'SMALLINT', sc + '.def_node(nd_id)'),
('ca_id', 'SMALLINT', sc + '.def_encar(ca_id)'),
] + yr_getter('erg_inp', 'DOUBLE PRECISION'))
pk = ['fl_id', 'nd_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
# table with monthly parameter modifiers
tb_name = 'parameter_month'
cols = ([('set_1_name', 'VARCHAR'), # from {'nd_id', 'fl_id', 'pp_id'}
('set_2_name', 'VARCHAR'), # from {'nd_id', 'fl_id', 'pp_id'}
('set_1_id', 'SMALLINT'),
('set_2_id', 'SMALLINT'),
('mt_id',' SMALLINT', sc + '.def_month(mt_id)'),
('parameter', 'VARCHAR'), # the parameter this applies to
('mt_fact', 'NUMERIC(10,9)'),
('mt_fact_others', 'NUMERIC(10,9)'),
])
pk = ['parameter', 'set_1_id', 'set_2_id', 'mt_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'node_connect'
cols = [('nd_id', 'SMALLINT', sc + '.def_node (nd_id)'),
('nd_2_id', 'SMALLINT', sc + '.def_node (nd_id)'),
('ca_id', 'SMALLINT', sc + '.def_encar (ca_id)'),
('mt_id', 'SMALLINT', sc + '.def_month(mt_id)'),
('cap_trme_leg', 'DOUBLE PRECISION'),
('cap_trmi_leg', 'DOUBLE PRECISION'),
]
pk = ['nd_id', 'nd_2_id', 'mt_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
tb_name = 'hydro'
cols = [('pp_id',' SMALLINT', sc + '.def_plant(pp_id)'),
('min_erg_mt_out_share', 'DOUBLE PRECISION'),
('max_erg_mt_in_share', 'DOUBLE PRECISION'),
('min_erg_share', 'DOUBLE PRECISION')]
pk = ['pp_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
# %%
ppca_cols = ['pp_id', 'ca_id', 'pp_eff', 'discharge_duration',
'st_lss_rt', 'vc_ramp', 'vc_om',
'factor_lin_0', 'factor_lin_1',
] + ['cap_pwr_leg']
df_plant_encar = read_xlsx_table(wb, ['PLANT_ENCAR'], columns=ppca_cols)
df_plant_encar = df_plant_encar.rename(columns={'cap_pwr_leg': 'cap_pwr_leg_old'})
ppca_cap_cols = ['pp_id'] + yr_getter('cap_pwr_leg') + yr_getter('erg_chp')
df_plant_encar_capacities = read_xlsx_table(wb_fy, ['PLANT_ENCAR_CAP'], columns=ppca_cap_cols)
df_plant_encar = (df_plant_encar.join(df_plant_encar_capacities.set_index('pp_id')[yr_getter('cap_pwr_leg')], on='pp_id')
.fillna(1))
df_plant_encar = (df_plant_encar.join(df_plant_encar_capacities.set_index('pp_id')[yr_getter('erg_chp')], on='pp_id'))
df_plant_encar.erg_chp
df_plant_encar.set_index('pp_id')[['cap_pwr_leg_old', 'cap_pwr_leg']].plot.bar()
df_plant_encar_scenarios = read_xlsx_table(wb_fy, ['PLANT_ENCAR_CAP'],
columns=['pp_id'] + yr_getter('cap_pwr_leg') + ['scenario'],
sub_table='SCENARIOS')
df_plant_encar_scenarios['ca_id'] = 'EL'
lst_set = ['pr', 'cain', 'ror', 'pp', 'st', 'hyrs', 'chp', 'add',
'rem', 'scen', 'sll', 'curt', 'lin', 'tr', 'peak']
dfpp_cols = ['pp_id', 'pp', 'nd_id', 'fl_id', 'pt_id',
] + ['set_def_' + i for i in lst_set]
df_def_plant = read_xlsx_table(wb, ['DEF_PLANT'], dfpp_cols)
df_def_encar = read_xlsx_table(wb, ['DEF_ENCAR'], ['ca_id', 'fl_id', 'ca'])
dfpt_cols = ['pt_id', 'pt', 'pp_broad_cat', 'color']
df_def_pp_type = read_xlsx_table(wb, ['DEF_PP_TYPE'], dfpt_cols)
nd_cols = ['nd_id', 'nd', 'color'] + yr_getter('price_co2')
df_def_node = read_xlsx_table(wb, ['DEF_NODE'], nd_cols)
ndca_cols = ['nd_id', 'ca_id', 'grid_losses',
'grid_losses_absolute', 'vc_dmnd_flex'] + yr_getter('dmnd_sum')
df_node_encar = read_xlsx_table(wb, ['NODE_ENCAR'], ndca_cols)
ndca_dmnd_cols = ['nd_id', 'ca_id', 'dmnd', 'dmnd_2020', 'dmnd_2025', 'dmnd_2030',
'dmnd_2035', 'dmnd_2040', 'dmnd_2045', 'dmnd_2050']
df_node_encar_dmnd = read_xlsx_table(wb_fy, ['NODE_ENCAR_DMND'], ndca_dmnd_cols)
df_def_fuel = read_xlsx_table(wb, ['DEF_FUEL'],
['fl_id', 'fl', 'co2_int', 'is_ca',
'is_constrained', 'color'])
df_fuel_node_encar = read_xlsx_table(wb_fy, ['FUEL_NODE_ENCAR'],
(['fl_id', 'nd_id', 'ca_id', 'is_chp']
+ yr_getter('erg_inp')
+ yr_getter('vc_fl')))
df_fuel_node_encar_scenarios = read_xlsx_table(wb_fy, ['FUEL_NODE_ENCAR'],
(['fl_id', 'nd_id', 'ca_id']
+ yr_getter('erg_inp') + ['scenario']),
sub_table='SCENARIOS')
ndcn_cols = ['nd_id', 'nd_2_id', 'ca_id', 'mt_id', 'cap_trme_leg',
'cap_trmi_leg']
df_node_connect = read_xlsx_table(wb, ['NODE_CONNECT'], ndcn_cols)
###############################################################################
###############################################################################
# For replacement of efficiency in df_plant_encar
# specific hydro parameters
h_c = ['pp_id', 'min_erg_mt_out_share', 'max_erg_mt_in_share', 'min_erg_share']
df_hydro = read_xlsx_table(wb, ['HYDRO'], h_c, sub_table='HYDRO')
hm_c = ['pp_id', 'parameter', 'mt_id', 'value']
df_plant_month = read_xlsx_table(wb, ['HYDRO'], hm_c, None, 'PLANT_MONTH')
# import/export stats
imex_stats_cols = ['nd_id', 'nd_2_id'] + yr_getter('erg_trm')
df_imex_comp = read_xlsx_table(wb, ['IMEX_STATS'], imex_stats_cols)
df_imex_comp = df_imex_comp[[c for c in df_imex_comp.columns if not 'yr20' in c]]
tm = TimeMap()
tm.gen_hoy_timemap()
df_tm = tm.df_time_map
df_def_month = df_tm.pivot_table(index=['mt_id', 'mt'],
values=['hy'],
aggfunc=[min, len])
df_def_month.columns = (df_def_month.columns.droplevel(level=1))
new_cols = {'month': 'mt', 'min': 'month_min_hoy',
'len': 'month_weight'}
df_def_month = (df_def_month.reset_index().rename(columns=new_cols))
df_def_week = df_tm.pivot_table(index=['wk_id', 'wk'], values='hy',
aggfunc=[len])
df_def_week.columns = (df_def_week.columns.droplevel(level=1))
new_cols = {'week': 'wk', 'len': 'week_weight'}
df_def_week = (df_def_week.reset_index().rename(columns=new_cols))
# %%
########### PROFINFLOW: HYD PROFILES TO HOURLY TABLE ###########
df_tm = df_tm[['hy', 'doy', 'mt']].rename(columns={'mt': 'mt_id'})
# monthly inflow reservoirs
dfres = df_plant_month.loc[df_plant_month['parameter'] == 'monthly_share_inflow']
dfres = pd.merge(df_tm, dfres, on='mt_id')
dfres = dfres[['hy', 'pp_id', 'value']]
# concatenate
df_profinflow = dfres
df_profinflow['ca_id'] = 'EL'
# normalize each profile
df_profinflow['value'] = (df_profinflow.groupby(['pp_id'])['value']
.transform(lambda x: x/x.sum()))
########### REPLACE INFLOW IN FRANCE WITH EXTRA DATA ###########
exec_strg = '''
WITH tb_lvl AS (SELECT * FROM profiles_raw.hydro_level_rte_fr)
, tb_hyd AS (
SELECT EXTRACT(week FROM "DateTime")::SMALLINT - 1 AS wk_id, *
FROM profiles_raw.rte_production_eco2mix
WHERE fl_id = 'reservoirs'
), tb_prd_wk AS (
SELECT year, wk_id, sum(value) AS hydro_production_week FROM tb_hyd
GROUP BY year, wk_id
ORDER BY year, wk_id
), tb_wk AS (
SELECT year, slot AS hy, EXTRACT(week FROM datetime)::SMALLINT - 1 AS wk_id
FROM profiles_raw.timestamp_template
WHERE year IN (SELECT DISTINCT year FROM tb_lvl)
), tb_wk_weight AS (
SELECT year, wk_id, COUNT(*) AS wk_weight FROM tb_wk
GROUP BY year, wk_id
)
SELECT hy, 'FR_HYD_RES'::VARCHAR AS pp_id,
/* CALCULATING AVERAGE HOURLY WEEKLY INFLOW HERE */
(hydro_production_week - start_level_diff) / wk_weight AS value,
'EL'::VARCHAR AS ca_id
FROM tb_wk
LEFT JOIN tb_wk_weight USING (year, wk_id)
LEFT JOIN tb_prd_wk USING (year, wk_id)
LEFT JOIN tb_lvl USING (year, wk_id)
WHERE year = 2015
'''
df_profinflow_FR = pd.DataFrame(aql.exec_sql(exec_strg, db=db),
columns=['hy', 'pp_id', 'value', 'ca_id'])
df_profinflow_FR['value'] = (df_profinflow_FR.value
/ df_profinflow_FR.value.sum())
df_profinflow = pd.concat([df_profinflow.loc[df_profinflow.pp_id != 'FR_HYD_RES'],
df_profinflow_FR])
df_profinflow.pivot_table(index='hy', columns='pp_id', values='value').plot()
############## PLANT_MONTH: HYDRO RESERVOIR FILLING LEVEL BCs #################
bc_mask = df_plant_month['parameter'] =='energy_boundary_condition'
df_plant_month = (df_plant_month.loc[bc_mask, ['mt_id', 'pp_id', 'value']]
.rename(columns={'value':'hyd_erg_bc'}))
################ NODE_CONNECT #################################################
# expand non-swiss node_connect to all months
df_node_connect = expand_rows(df_node_connect, ['mt_id'],
[df_node_connect.loc[
df_node_connect.nd_id.isin(['CH0']),
'mt_id'].unique().tolist()])
'''
id columns to int
'''
df_def_encar['ca_id'] = df_def_encar['ca_id'].apply(int)
df_def_plant['pp_id'] = df_def_plant['pp_id'].apply(int)
df_def_node['nd_id'] = df_def_node['nd_id'].apply(int)
df_def_fuel['fl_id'] = df_def_fuel['fl_id'].apply(int)
df_def_encar, _ = translate_id(df_def_encar, df_def_fuel, 'fl')
df_plant_encar, dict_encar_id = translate_id(df_plant_encar, df_def_encar, 'ca')
df_plant_encar, dict_plant_id = translate_id(df_plant_encar, df_def_plant, 'pp')
df_plant_encar_scenarios, _ = translate_id(df_plant_encar_scenarios, df_def_encar, 'ca')
df_plant_encar_scenarios, _ = translate_id(df_plant_encar_scenarios, df_def_plant, 'pp')
df_def_plant, dict_fuel_id = translate_id(df_def_plant, df_def_fuel, 'fl')
df_def_plant, dict_node_id = translate_id(df_def_plant, df_def_node, 'nd')
df_fuel_node_encar, _ = translate_id(df_fuel_node_encar, df_def_fuel, 'fl')
df_fuel_node_encar, _ = translate_id(df_fuel_node_encar, df_def_encar, 'ca')
df_fuel_node_encar, _ = translate_id(df_fuel_node_encar, df_def_node, 'nd')
df_fuel_node_encar_scenarios, _ = translate_id(df_fuel_node_encar_scenarios, df_def_fuel, 'fl')
df_fuel_node_encar_scenarios, _ = translate_id(df_fuel_node_encar_scenarios, df_def_encar, 'ca')
df_fuel_node_encar_scenarios, _ = translate_id(df_fuel_node_encar_scenarios, df_def_node, 'nd')
df_def_plant, _ = translate_id(df_def_plant, df_def_pp_type, 'pt')
df_node_encar, _ = translate_id(df_node_encar, df_def_encar, 'ca')
df_node_encar, _ = translate_id(df_node_encar, df_def_node, 'nd')
df_node_connect, _ = translate_id(df_node_connect, df_def_node, 'nd')
df_node_connect, _ = translate_id(df_node_connect, df_def_node, ['nd', 'nd_2'])
df_node_connect, _ = translate_id(df_node_connect, df_def_encar, 'ca')
df_profinflow, _ = translate_id(df_profinflow, df_def_encar, 'ca')
df_profinflow, _ = translate_id(df_profinflow, df_def_plant, 'pp')
df_hydro, _ = translate_id(df_hydro, df_def_plant, 'pp')
df_plant_month, _ = translate_id(df_plant_month, df_def_month, 'mt')
df_plant_month, _ = translate_id(df_plant_month, df_def_plant, 'pp')
df_imex_comp, _ = translate_id(df_imex_comp, df_def_node, 'nd')
df_imex_comp, _ = translate_id(df_imex_comp, df_def_node, ['nd', 'nd_2'])
'''
add various set definition columns to df_def_plant
'''
print('Write all output')
write_dfs = [
(df_def_node, 'def_node'),
(df_def_encar, 'def_encar'),
(df_def_pp_type, 'def_pp_type'),
(df_def_fuel, 'def_fuel'),
(df_def_plant, 'def_plant'),
(df_def_month, 'def_month'),
(df_def_week, 'def_week'),
(df_plant_encar, 'plant_encar'),
# (df_profchp, 'profchp'),
# (df_profdmnd, 'profdmnd'),
# (df_parmt, 'parameter_month'),
# (df_profsupply, 'profsupply'),
(df_profinflow, 'profinflow'),
(df_hydro, 'hydro'),
(df_node_encar, 'node_encar'),
(df_plant_month, 'plant_month'),
# (df_plant_week, 'plant_week'),
(df_node_connect, 'node_connect'),
(df_fuel_node_encar, 'fuel_node_encar'),
(df_imex_comp, 'imex_comp'),
# (df_profprice, 'profprice_comp'),
]
for idf in write_dfs:
print('Writing ', idf[1])
aql.write_sql(idf[0], db, sc, idf[1], 'append')
################ PROF ROR #####################################################
exec_strg = '''
INSERT INTO {sc}.profinflow (pp_id, ca_id, hy, value)
SELECT dfpp.pp_id, 0::SMALLINT AS ca_id, hy, value AS value
FROM profiles_raw.weekly_ror_data AS ror
LEFT JOIN (SELECT nd_id, nd FROM {sc}.def_node) AS dfnd ON dfnd.nd = ror.nd_id
LEFT JOIN (SELECT pp_id, nd_id
FROM {sc}.def_plant WHERE pp LIKE '%ROR%') AS dfpp ON dfpp.nd_id = dfnd.nd_id
'''.format(sc=sc)
aql.exec_sql(exec_strg, db=db)
################ PROFCHP ######################################################
exec_strg = '''
INSERT INTO {sc}.profchp (hy, nd_id, ca_id, value)
SELECT hy, dfnd.nd_id, 0::SMALLINT AS ca_id, value AS value
FROM profiles_raw.chp_profiles AS chp
LEFT JOIN (SELECT nd_id, nd FROM {sc}.def_node) AS dfnd
ON dfnd.nd = chp.nd_id;
'''.format(sc=sc)
aql.exec_sql(exec_strg, db=db)
################ PROFDMND #####################################################
# %%
df_profdmnd_0 = aql.read_sql(db, 'profiles_raw', 'load_complete',
keep=['nd_id', 'value', 'hy', 'year'],
filt=[('nd_id', ['DE0'], ' NOT LIKE '),
('year', [2015])])
df_profdmnd_de = pd.DataFrame(aql.exec_sql('''
SELECT nd_id, value, how, wk_id, mt_id, hy, year
FROM (SELECT how, wk_id, mt_id, slot AS hy,
datetime AS "DateTime"
FROM profiles_raw.timestamp_template
WHERE year = 2015) AS ts
LEFT JOIN (SELECT * FROM profiles_raw.agora_profiles
WHERE year = 2015 AND fl_id = 'dmnd') AS ag
ON ts."DateTime" = ag."DateTime"
''', db=db), columns=['nd_id', 'value', 'how', 'wk_id', 'mt_id', 'hy', 'year'])
df_profdmnd_de_oct = df_profdmnd_de.loc[df_profdmnd_de.mt_id.isin([10, 9])].pivot_table(index='how', columns='wk_id', values='value').loc[:, 40:44]
# linear interpolation of the differences between target and source column
targt_start = df_profdmnd_de_oct.loc[117, 42]
targt_end = df_profdmnd_de_oct.loc[167, 42]
source_start = df_profdmnd_de_oct.loc[117, 41]
source_end = df_profdmnd_de_oct.loc[167, 41]
diff_start = targt_start - source_start
diff_end = targt_end - source_end
idx_nan = df_profdmnd_de_oct.loc[df_profdmnd_de_oct[42].isnull()].index.values
idx_nan = np.concatenate((np.array([idx_nan.min() - 1]),
idx_nan,
np.array([idx_nan.max() + 1])))
shifts = (diff_end - diff_start) / (idx_nan[-1] - idx_nan[0]) * (idx_nan - idx_nan[0]) + diff_start
df_profdmnd_de_oct_new = pd.DataFrame(np.array([idx_nan, shifts]).T,
columns=['how', 'new'])
df_profdmnd_de_oct_new = df_profdmnd_de_oct_new.set_index('how')
df_profdmnd_de_oct = df_profdmnd_de_oct.join(df_profdmnd_de_oct_new, on='how')
df_profdmnd_de_oct['new'] += df_profdmnd_de_oct[41]
df_profdmnd_de_oct[42].fillna(df_profdmnd_de_oct['new'], inplace=True)
df_profdmnd_de_oct.columns = df_profdmnd_de_oct.columns.rename('wk_id')
df_profdmnd_de_oct = df_profdmnd_de_oct[[42]].stack().rename('new')
df_profdmnd_de = pd.merge(df_profdmnd_de, df_profdmnd_de_oct.reset_index(), on=['wk_id', 'how'], how='left')
df_profdmnd_de['value'].fillna(df_profdmnd_de.new, inplace=True)
df_profdmnd_de = df_profdmnd_de.drop(['new', 'wk_id', 'mt_id', 'how'], axis=1)
df_profdmnd_de = df_profdmnd_de.sort_values('hy').reset_index(drop=True)
df_profdmnd_de['nd_id'] = 'DE0'
df_profdmnd_de['year'] = 2015
df_profdmnd_de.set_index('hy')['value'].plot()
df_profdmnd_0 = pd.concat([df_profdmnd_0, df_profdmnd_de], axis=0)
df_profdmnd_0 = df_profdmnd_0.reset_index(drop=True)
df_profdmnd_0['ca_id'] = 'EL'
df_profdmnd_0, _ = translate_id(df_profdmnd_0, df_def_node, 'nd')
df_profdmnd_0, _ = translate_id(df_profdmnd_0, df_def_encar, 'ca')
df_profdmnd_0.pivot_table(index=['nd_id', 'ca_id', 'year', 'hy'], aggfunc=len).max()
# just scaling profile for now
# 1. normalize
df_profdmnd_0['value_norm'] = \
(df_profdmnd_0.groupby(['nd_id', 'ca_id', 'year'])['value']
.apply(lambda x: x/x.sum()))
df_dmnd = df_node_encar[[c for c in df_node_encar.columns if 'dmnd_sum' in c] + ['nd_id', 'ca_id']].copy()
#df_dmnd['dmnd_sum_yr2017'].fillna(df_dmnd['dmnd_sum_yr2016'], inplace=True)
df_dmnd.columns = [(c if not 'dmnd' in c else (int(c.replace('dmnd_sum_yr', ''))) if 'yr' in c else 2015)
for c in df_dmnd.columns]
df_dmnd = df_dmnd.set_index(['nd_id', 'ca_id']).stack().reset_index().rename(columns={'level_2': 'year', 0: 'dmnd'})
df_dmnd = df_dmnd.set_index(['nd_id', 'ca_id', 'year'])
df_profdmnd_0['value_scaled'] = df_profdmnd_0.groupby(['nd_id', 'ca_id', 'year'])['value_norm'].apply(lambda x: x * df_dmnd.loc[x.name].values[0])
df_profdmnd = df_profdmnd_0.pivot_table(values='value_scaled',
index=['nd_id', 'ca_id', 'hy'],
columns=['year']).reset_index()
df_profdmnd.columns = [(c if not type(c) is int
else ('value_yr' + str(c)
if not c == 2015 else 'value'))
for c in df_profdmnd.columns]
dmnd_scale = {'FR0': 1.072368,
'DE0': 1.06832,
'IT0': 0.982416,
'AT0': 0.91,
'CH0': 1
}
dmnd_scale = {dict_node_id[kk]: vv for kk, vv in dmnd_scale.items()}
df_profdmnd['scale'] = df_profdmnd.nd_id.replace(dmnd_scale)
df_profdmnd['value'] *= df_profdmnd.scale
df_profdmnd = df_profdmnd.drop('scale', axis=1)
# %%
'''
Comparison price profiles
'''
df_profprice = aql.read_sql(db, 'profiles_raw', 'epex_price_volumes',
filt=[('quantity', ['price_eur_mwh']),
('year', [2015], ' = ')],
keep=['hy', 'nd_id', 'year', 'value'])
df_profprice = df_profprice.pivot_table(index=['hy', 'nd_id'],
values='value', columns='year')
df_profprice.columns = ['value' + ('_yr%d'%c if not c == 2015 else '')
for c in df_profprice.columns]
df_profprice = df_profprice[[c for c in yr_getter('value')
if c in df_profprice.columns]]
df_profprice = df_profprice.reset_index()
df_profprice['fl_id'] = 'electricity'
df_profprice, _ = translate_id(df_profprice, df_def_node, 'nd')
df_profprice, _ = translate_id(df_profprice, df_def_fuel, 'fl')
print('Write all output')
write_dfs = [
(df_profdmnd, 'profdmnd'),
(df_profprice, 'profprice'),
]
for idf in write_dfs:
print('Writing ', idf[1])
aql.write_sql(idf[0], db, sc, idf[1], 'append')
# %%
######## PROFSUPPLY FROM PROFILES_RAW.NINJA DATA STRAIGHT TO LP_INPUT #########
cf_data_type = 'NUMERIC(9,8)'
# Copy capacity scale adjusted base year profiles to profsupply table
exec_strg = '''
DROP TABLE IF EXISTS {sc}.profsupply CASCADE;
SELECT hy, pp_id AS pp, 0::SMALLINT AS ca_id, value_mod AS value
INTO {sc}.profsupply
FROM profiles_raw.ninja_mod
WHERE year = 2015;
ALTER TABLE {sc}.profsupply
ALTER value TYPE {cf_data_type}
'''.format(sc=sc, cf_data_type=cf_data_type)
aql.exec_sql(exec_strg, db=db)
#
## only 2015 !!
#for iyr in list(set([yr[0] for yr in
# aql.exec_sql('SELECT DISTINCT year FROM profiles_raw.ninja_mod;',
# db=db) if not 2015 in yr])):
# exec_strg = '''
# ALTER TABLE {sc}.profsupply
# ADD COLUMN IF NOT EXISTS value_yr{yr} {cf_data_type};
#
# UPDATE {sc}.profsupply AS prf
# SET value_yr{yr} = rw.value_mod
# FROM profiles_raw.ninja_mod AS rw
# WHERE rw.year = {yr}
# AND prf.hy = rw.hy
# AND prf.pp = rw.pp_id;
# '''.format(yr=str(iyr), sc=sc, cf_data_type=cf_data_type)
# aql.exec_sql(exec_strg, db=db)
exec_strg = '''
ALTER TABLE {sc}.profsupply
ADD COLUMN pp_id SMALLINT;
UPDATE {sc}.profsupply AS prfsp
SET pp_id = dfpp.pp_id
FROM (SELECT pp, pp_id FROM {sc}.def_plant) AS dfpp
WHERE dfpp.pp = prfsp.pp;
DELETE FROM {sc}.profsupply
WHERE pp_id IS NULL;
ALTER TABLE {sc}.profsupply
DROP COLUMN IF EXISTS pp,
DROP CONSTRAINT IF EXISTS pk_profsupply,
ADD CONSTRAINT pk_profsupply
PRIMARY KEY (pp_id, hy, ca_id),
ADD CONSTRAINT fk_profsupply_pp_id
FOREIGN KEY (pp_id) REFERENCES {sc}.def_plant(pp_id),
ADD CONSTRAINT fk_profsupply_ca_id
FOREIGN KEY (ca_id) REFERENCES {sc}.def_encar(ca_id);
'''.format(sc=sc)
aql.exec_sql(exec_strg, db=db)
slct_pp_id = [vv for kk, vv in dict_plant_id.items() if 'FR_WIN_OFF' in kk or 'DE_WIN_OFF' in kk]
df_slct = aql.read_sql(db, sc, 'profsupply',
filt=[('pp_id', slct_pp_id)]).set_index('hy')
df_slct['value'] = df_slct.value.astype(float)
df_slct.sort_index()[[c for c in df_slct.columns if 'value' in c]].plot(marker='.')
df_slct.reset_index().pivot_table(index='hy', columns=['pp_id'], values='value').plot()
# %% ADDING BIOMASS PRODUCTION PROILE AS VRE
df = aql.read_sql(db, 'profiles_raw', 'entsoe_generation', filt=[('fl_id', ['bio_all'])])
df_CH = df.loc[df.nd_id == 'AT0']
df_CH['nd_id'] = 'CH0'
df = pd.concat([df, df_CH])
dfpv = df.loc[df.DateTime.dt.year == 2016].pivot_table(index=['DateTime'], columns='nd_id', values='value')
dfpv[dfpv == 0] = np.nan
dfpv[[c for c in dfpv.columns]] = dfpv.groupby(pd.TimeGrouper('M'))[[c for c in dfpv.columns]].transform(lambda x: x.mean())
dftm = df.loc[df.nd_id.isin(['DE0']) & df.year.isin([2016])].set_index('DateTime')['hy']
dfbio = dfpv.stack().reset_index().rename(columns={0: 'value'}).join(dftm, on='DateTime').copy()
dfbio['value'] = dfbio.groupby('nd_id')['value'].transform(lambda x: x / x.sum())
dfbio, _ = translate_id(dfbio, df_def_node, 'nd')
dfbio = dfbio.join(df_def_plant.loc[df_def_plant.pp.str.contains('BAL')].set_index('nd_id')['pp_id'], on='nd_id')
dfbio['ca_id'] = 0
'''
Copy data to additional years... doesn't do anything since profsupply is 2015
only!
'''
dfbio = dfbio.loc[:, list(aql.get_sql_cols('profsupply', sc, db).keys())]
fill_list = [c for c in dfbio.columns if 'yr' in c]
dfbio[fill_list] = value=dfbio[['value'] * len(fill_list)]
list_years = [c.replace('value', '') for c in dfbio.columns if 'value' in c]
for iyr in list_years:
cap_col = 'cap_pwr_leg' + iyr
erg_col = 'erg_inp' + iyr
val_col = 'value' + iyr
df_cap = df_plant_encar.loc[df_plant_encar.pp_id.replace({vv: kk for kk, vv in dict_plant_id.items()}).str.contains('BAL'), ['pp_id', cap_col]]
df_erg = df_fuel_node_encar.loc[df_fuel_node_encar.fl_id.replace({vv: kk for kk, vv in dict_fuel_id.items()}).str.contains('bio'), ['fl_id', 'nd_id', erg_col]]
df_erg = df_erg.join(df_def_plant.loc[df_def_plant.pp.str.contains('BAL')].set_index(['nd_id', 'fl_id'])['pp_id'], on=['nd_id', 'fl_id'])
dfbio = (dfbio.join(df_erg.set_index(['pp_id'])[erg_col], on='pp_id')
.join(df_cap.set_index(['pp_id'])[cap_col], on='pp_id'))
dfbio[val_col] *= dfbio[erg_col] / dfbio[cap_col]
dfbio = dfbio.drop([cap_col, erg_col], axis=1)
aql.write_sql(dfbio, db, sc, 'profsupply', 'append')
# %%
tb_name = 'profprice_comp'
cols = [('nd_id', 'SMALLINT', sc + '.def_node(nd_id)'),
('ca_id', 'SMALLINT', sc + '.def_encar(ca_id)'),
('swhy_vl', 'VARCHAR(6)'),
('hy', 'SMALLINT'),
('price_eur_mwh', 'DOUBLE PRECISION'),
('volume_mwh', 'DOUBLE PRECISION')]
pk = ['hy', 'nd_id', 'swhy_vl', 'ca_id']
unique = []
init_table(tb_name=tb_name, cols=cols, schema=sc, ref_schema=sc,
pk=pk, unique=unique, db=db)
df_profprice = aql.read_sql(db, 'profiles_raw', 'epex_price_volumes',
filt=[('year', [2015])])
df_profprice = df_profprice.pivot_table(index=['year', 'nd_id', 'hy'],
values='value',
columns='quantity').reset_index()
df_profprice = df_profprice.rename(columns={'year': 'swhy_vl'})
df_profprice['swhy_vl'] = 'yr' + df_profprice['swhy_vl'].astype(str)
df_profprice['ca_id'] = 'EL'
df_profprice, _ = translate_id(df_profprice, df_def_node, 'nd')
df_profprice, _ = translate_id(df_profprice, df_def_encar, 'ca')
write_dfs = [(df_profprice, 'profprice_comp')]
for idf in write_dfs:
print('Writing ', idf[1])
aql.write_sql(idf[0], db, sc, idf[1], 'append')
# %% PARAMETER MONTHLY ADJUSTMENTS
# capacity availability for lignite and nuclear from monthly production and capacities
exec_strg = '''
WITH nhours AS (
SELECT mt_id, COUNT(datetime) FROM profiles_raw.timestamp_template
WHERE year = 2015
GROUP BY mt_id
), ppca AS (
SELECT nd, fl, pp, cap_pwr_leg FROM {sc}.plant_encar
NATURAL LEFT JOIN (SELECT pp, pp_id, fl_id, nd_id FROM {sc}.def_plant) AS dfpp
NATURAL LEFT JOIN (SELECT fl, fl_id FROM {sc}.def_fuel) AS dffl
NATURAL LEFT JOIN (SELECT nd, nd_id FROM {sc}.def_node) AS dfnd
), all_cap_fl AS (
SELECT fl, nd, SUM(cap_pwr_leg) AS cap_pwr_leg FROM ppca
GROUP BY fl, nd
), tb_fr AS (
SELECT fl_id AS fl, year, nd_id AS nd, mt_id, SUM(value) AS erg FROM profiles_raw.rte_production_eco2mix
WHERE fl_id IN ('nuclear_fuel') --AND year IN (2015,
GROUP BY fl_id, year, nd_id, mt_id
), tb_de AS (
SELECT fl_id AS fl, year, nd_id AS nd, mt_id, 1000 * SUM(value) AS erg
FROM (SELECT *, EXTRACT(month FROM "DateTime") - 1 AS mt_id
FROM profiles_raw.agora_profiles) AS tbag
WHERE fl_id IN ('nuclear_fuel', 'lignite') AND year <> 2013
GROUP BY fl_id, year, nd_id, mt_id
), tb_ch AS (
SELECT fl, year, nd, mt_id, erg FROM profiles_raw.monthly_production
WHERE fl IN ('nuclear_fuel') And nd = 'CH0'
), tb_all AS (
SELECT * FROM tb_fr
UNION ALL
SELECT * FROM tb_de
UNION ALL
SELECT * FROM tb_ch
), tb_final AS (
SELECT tb_all.*, erg / count AS cap_from_erg, erg / count / cap_pwr_leg AS cap_avlb
FROM tb_all
LEFT JOIN nhours ON nhours.mt_id = tb_all.mt_id
NATURAL LEFT JOIN (SELECT nd, fl, cap_pwr_leg FROM all_cap_fl) AS ppca
)
SELECT tb_final.fl, tb_final.nd, pp, year, tb_final.mt_id, 'EL'::VARCHAR AS ca,
CASE WHEN cap_avlb > 1 THEN 1 ELSE cap_avlb END AS cap_avlb
FROM tb_final
/* EXPAND TO PLANTS */
FULL OUTER JOIN (SELECT nd, fl, pp FROM ppca WHERE fl IN (SELECT fl FROM tb_final)) AS ppca
ON ppca.fl = tb_final.fl AND ppca.nd = tb_final.nd;
'''.format(sc=sc)
df_parmt_cap_avlb = pd.DataFrame(aql.exec_sql(exec_strg, db=db),
columns=['fl_id', 'nd_id', 'pp_id', 'year',
'mt_id', 'ca_id', 'cap_avlb'])
df_parmt_cap_avlb[['year', 'fl_id', 'nd_id']].drop_duplicates()
# keep 2015 and median of all years
df_2015 = df_parmt_cap_avlb.loc[df_parmt_cap_avlb.year == 2015].assign(year='')
df_medi = df_parmt_cap_avlb.pivot_table(index=['nd_id', 'ca_id', 'pp_id', 'fl_id', 'mt_id'],
aggfunc=np.median,
values='cap_avlb').reset_index().assign(year='median')
df_parmt_cap_avlb = pd.concat([df_2015, df_medi], axis=0, sort=True)
# %
df_ch = aql.read_sql('storage2', 'profiles_raw', 'monthly_production', filt=[('year', [2007], ' > ')])
ax = df_ch.loc[df_ch.fl.isin(['nuclear_fuel']) &
df_ch.nd.isin(['CH0'])].pivot_table(index=['mt_id'],
columns=['year'], values='erg').plot()
df_ch.loc[df_ch.fl.isin(['nuclear_fuel']) &
df_ch.nd.isin(['CH0']) &
df_ch.year.isin([2015])].pivot_table(index=['mt_id'],
columns=['year'], values='erg').plot(marker='o', ax=ax)
ax.set_ylim(bottom=0)
df_parmt_cap_avlb = df_parmt_cap_avlb.pivot_table(index=['pp_id', 'ca_id', 'mt_id'], columns='year', values='cap_avlb')
df_parmt_cap_avlb = df_parmt_cap_avlb.reset_index().rename(columns={'': 'mt_fact',
'median': 'mt_fact_others' ,
'pp_id': 'set_1_id',
'ca_id': 'set_2_id'})
df_parmt_cap_avlb['set_1_name'] = 'pp_id'
df_parmt_cap_avlb['set_2_name'] = 'ca_id'
df_parmt_cap_avlb['parameter'] = 'cap_avlb'
df_parmt_cap_avlb['set_3_name'] = np.nan
df_parmt_cap_avlb['set_3_id'] = -1
###############################################################################
## expanding monthly factors for vc_fl to all countries #######################
parmt_cols = ['mt_id', 'set_1_name', 'set_2_name', 'set_3_name',
'set_1_id', 'set_2_id', 'set_3_id', 'parameter'] + yr_getter('mt_fact')
df_parmt_fl_co2 = read_xlsx_table(wb, ['MONTHLY_FL_CO2'], columns=parmt_cols)
df_parmt_fl_co2 = df_parmt_fl_co2[[c for c in df_parmt_fl_co2.columns if not 'yr20' in c]]
mask_vc_fl = df_parmt_fl_co2.parameter.isin(['vc_fl'])
df_parmt_fl_co2 = pd.concat([df_parmt_fl_co2.loc[-mask_vc_fl],
expand_rows(df_parmt_fl_co2.loc[mask_vc_fl], ['set_2_id'],
[df_def_node.nd.tolist()])], axis=0)
df_parmt_fl_co2['mt_fact_others'] = 1
###############################################################################
df_parmt = pd.concat([df_parmt_fl_co2, df_parmt_cap_avlb], axis=0, sort=True)
df_parmt = df_parmt.drop(['set_3_id', 'set_3_name'], axis=1)
for idict in [dict_node_id, dict_encar_id, dict_fuel_id, dict_plant_id]:
for icol in ['set_1_id', 'set_2_id']: