# -------------------------------------------------------------------------------------------------------------------- #
# %% initialize GAMS, GAMS workspace and load model data
# -------------------------------------------------------------------------------------------------------------------- #
# import base data from gdx
ws = GamsWorkspace(system_directory=cfg.GMS_SYS_DIR)
db_input = ws.add_database_from_gdx(
os.path.join(cfg.MEDEA_ROOT_DIR, 'projects', PROJECT_NAME, 'opt',
'medea_main_data.gdx'))
# %% read parameters that change in scenarios (and corresponding sets)
# -------------------------------------------------------------------------------------------------------------------- #
# *** read in all sets over which adjusted parameters are defined ***
dict_r = {rec.keys[0] for rec in db_input['n']}
# *** read in all parameters to be adjusted ***
INITIAL_CAP_G = gdx2df(db_input, 'INITIAL_CAP_G', ['z', 'i'], [])
INITIAL_CAP_R = gdx2df(db_input, 'INITIAL_CAP_R', ['z', 'n'], [])
DEMAND = gdx2df(db_input, 'DEMAND', ['z', 't', 'm'], [])
GEN_PROFILE = gdx2df(db_input, 'GEN_PROFILE', ['z', 't', 'n'], [])
# %% generate 'dynamic' parameter variations (modifications that constitute the scenarios, i.e. that change across runs)
# -------------------------------------------------------------------------------------------------------------------- #
# ensure that we are in the correct model directory
os.chdir(os.path.join(cfg.MEDEA_ROOT_DIR, 'projects', PROJECT_NAME, 'opt'))
# create empty scenario parameter in GAMS database so that it can be modified subsequently
CO2_SCENARIO = df2gdx(db_input, pd.DataFrame(data=[0]), 'CO2_SCENARIO', 'par',
0, 'CO2 price scenario')
WIND_ON_LIMIT = df2gdx(db_input, pd.DataFrame(data=[0]), 'WIND_ON_LIMIT',
'par', 0, 'max wind_on capacity addition')
PV_CAPEX = df2gdx(db_input, pd.DataFrame(data=[0]), 'PV_CAPEX', 'par', 0,
for wind_limit in dict_camp['wind_on_cap']:
for price_co2 in dict_camp['carbon_price']:
for budget_co2 in dict_camp['carbon_limit']:
identifier = f'{PROJECT_NAME}_{campaign}_{re_share}_{wind_limit}_{price_co2}_{budget_co2}'
FNAME = os.path.join(cfg.MEDEA_ROOT_DIR, 'projects',
PROJECT_NAME, 'opt',
f'medea_out_{identifier}.gdx')
db_output = ws.add_database_from_gdx(FNAME)
# calculation of air pollution cost
# air_pol_cost_by_fuel = gdx2df(db_output, 'cost_air_pol', ['f'], ['z'])
df_collect = pd.DataFrame()
for symbol, sets in symbols_to_read.items():
df = gdx2df(db_output, symbol, sets[0], sets[1])
if sets[0]:
strix = [f'{symbol}_{ix}' for ix in df.index]
else:
strix = [symbol]
df.index = strix
# collect results
df_collect = df_collect.append(df, ignore_index=False)
df_collect.index = pd.MultiIndex.from_product(
[[campaign], [re_share], [wind_limit], [price_co2],
[budget_co2], df_collect.index],
names=('campaign', 're_share', 'wind_cap',
'carb_price', 'carb_limit', 'variable'))
# add air pollution cost
# df_collect.loc[(campaign, re_share, wind_limit, 'cost_airpol'), :] = air_pol_cost_by_fuel.sum()
# -------------------------------------------------------------------------------------------------------------------- #
# *** read in all sets over which adjusted parameters are defined ***
# general example for reading a GAMS set from the gdx-database to a python dictionary:
# dict_set = {rec.keys[0] for rec in db_input['set_name']}
# ---
# read sets for calibration of power plant efficiencies
dict_prd = {rec.keys[0] for rec in db_input['m']}
dict_fuel = {rec.keys[0] for rec in db_input['f']}
dict_tec = {rec.keys[0] for rec in db_input['i']}
# *** read in all parameters to be adjusted ***
# general example for reading parameter 'PARAMETER_NAME' defined over 'set_name' to pandas DataFrame df
# df = gdx2df(db_input, 'PARAMETER_NAME', ['set_name'], [])
# ---
# read power plant efficiency data
df_eff = gdx2df(db_input, 'EFFICIENCY_G', ['i', 'm', 'f'], [])
# read fuel requirement of CHP plants-data
df_fuelreq = gdx2df(db_input, 'FEASIBLE_INPUT', ['i', 'l', 'f'], [])
# %% generate 'static' parameter variations (modifications that remain the same across all scenarios)
# -------------------------------------------------------------------------------------------------------------------- #
# example: calibrate power plant efficiencies -- note: calibration remains the same across all scenarios!
# efficiency -dictionary needs to be imported from settings_{project_name}.py
idx = pd.IndexSlice
fuel_thermal = ['Biomass', 'Coal', 'Gas', 'Lignite', 'Nuclear', 'Oil']
# modify power plant efficiency
df_eff_mod = df_eff.copy()
for fl in fuel_thermal:
df_eff_mod.loc[idx[:, :, fl], :] = \
TS_FILE = os.path.join(cfg.MEDEA_ROOT_DIR, 'data', 'processed',
'medea_regional_timeseries.csv')
ts = pd.read_csv(TS_FILE)
ts['DateTime'] = pd.to_datetime(ts['DateTime'])
ts.set_index('DateTime', inplace=True)
# constrain data to scenario year
ts = ts.loc[
(pd.Timestamp(cfg.year, 1, 1, 0, 0).tz_localize('UTC') <= ts.index)
& (ts.index <= pd.Timestamp(cfg.year, 12, 31, 23, 0).tz_localize('UTC'))]
# read hourly net imports from gdx output
identifier = f'asparagus_h2stack_30_16_36715'
FNAME = os.path.join('C:/Users', 'Sebastian', 'asparagus_gdx',
f'medea_out_{identifier}.gdx')
db_output = ws.add_database_from_gdx(FNAME)
df = gdx2df(db_output, 'x', ['t'], ['z', 'zz'])
df.index = ts.index
nx_at = pd.DataFrame(data=0.0,
index=df.index,
columns=['X', 'I', 'pv_de', 'wind_at', 'load_at'])
nx_at.loc[df.loc[:, idx['AT', 'DE']] > 0,
'X'] = df.loc[df.loc[:, idx['AT', 'DE']] > 0, idx['AT', 'DE']]
nx_at.loc[df.loc[:, idx['AT', 'DE']] < 0,
'I'] = -df.loc[df.loc[:, idx['AT', 'DE']] < 0, idx['AT', 'DE']]
nx_at.index = ts.index
nx_at.loc[:, 'pv_de'] = ts.loc[:, 'DE-pv-profile']
nx_at.loc[:, 'pv_at'] = ts.loc[:, 'AT-pv-profile']
nx_at.loc[:, 'wind_at'] = ts.loc[:, 'AT-wind_on-profile']
nx_at.loc[:, 'load_at'] = ts.loc[:, 'AT-power-load']
dict_camp.update(dict_campaigns[campaign])
for price_co2 in dict_camp['co2_price']:
for cap_wind in dict_camp['wind_cap']:
for pv_cost in dict_camp['pv_cost']:
identifier = f'{PROJECT_NAME}_{campaign}_{price_co2}_{cap_wind}_{pv_cost}'
FNAME = os.path.join(cfg.MEDEA_ROOT_DIR, 'projects',
PROJECT_NAME, 'opt',
f'medea_out_{identifier}.gdx')
# FNAME = os.path.join('C:/Users', 'Sebastian', 'asparagus_gdx', f'medea_out_{identifier}.gdx')
db_output = ws.add_database_from_gdx(FNAME)
# calculation of air pollution cost
air_pol_cost_by_fuel = gdx2df(db_output, 'cost_air_pol', ['f'],
['z'])
df_collect = pd.DataFrame()
for symbol, sets in symbols_to_read.items():
df = gdx2df(db_output, symbol, sets[0], sets[1])
if sets[0]:
strix = [f'{symbol}_{ix}' for ix in df.index]
else:
strix = [symbol]
df.index = strix
# collect results
df_collect = df_collect.append(df, ignore_index=False)
df_collect.index = pd.MultiIndex.from_product(
[[campaign], [price_co2], [cap_wind], [pv_cost],
df_collect.index],