def plot():
dirs = get_experiment_dirs('all_scenarios')
price_el = pd.read_csv(os.path.join(dirs['raw'],
'price_electricity_spot_2017.csv'),
index_col=0)
price_el.columns = ['electricity']
plot_load_duration(
price_el,
legend=False,
plot_original=True,
title='Electricity spot price',
ylabel='Hourly price [Eur/MWh]',
)
filename = os.path.join(dirs['plots'], 'price_el.pdf')
plt.savefig(filename)
plt.close()
print(f"Saved plot to {filename}")
def plot():
dirs = get_experiment_dirs('all_scenarios')
demand_heat = pd.read_csv(
os.path.join(dirs['raw'], 'demand_heat_2017.csv'),
index_col=0
).sum(1)
demand_heat.name = 'heat-demand'
plot_load_duration(
demand_heat,
legend=False,
plot_original=True,
title='Heat demand',
ylabel='Hourly heat demand [MWh]',
)
filename = os.path.join(dirs['plots'], 'heat_demand.pdf')
plt.savefig(filename)
plt.close()
print(f"Saved plot to {filename}")
def main(**scenario_assumptions):
logging.info('Optimisation')
dirs = get_experiment_dirs(scenario_assumptions['scenario'])
optimize(dirs['preprocessed'],
dirs['optimised'],
debug=scenario_assumptions['debug'])
def get_scenario_paths(scenario_assumptions):
scenario_paths = {}
for scenario in scenario_assumptions['scenario']:
path = get_experiment_dirs(scenario)['postprocessed']
scenario_paths.update({scenario: path})
return scenario_paths
def main(**scenario_assumptions):
print('Postprocessing')
dirs = get_experiment_dirs(scenario_assumptions['scenario'])
subdir = os.path.join(dirs['postprocessed'], 'sequences')
if not os.path.exists(subdir):
os.mkdir(subdir)
# restore EnergySystem with results
es = EnergySystem()
es.restore(dirs['optimised'])
sequences = write_results(es, dirs['postprocessed'])
capacities = get_capacities(es)
capacities = cap_el_to_cap_th(capacities)
capacity_cost = get_capacity_cost(es)
carrier_cost = get_carrier_cost(es)
marginal_cost = get_marginal_cost(es)
yearly_electricity = get_yearly_sum(sequences['electricity'], var_name='yearly_electricity')
heat_sequences = pd.concat([sequences['heat_central'], sequences['heat_decentral']], 1)
yearly_heat = get_yearly_sum(heat_sequences, var_name='yearly_heat')
# full_load_hours = get_flh(capacities, yearly_sum)
share_el_heat = get_share_el_heat(yearly_heat)
spec_cost_of_heat = get_spec_cost_of_heat(yearly_heat, capacity_cost, carrier_cost, marginal_cost)
scalars = pd.concat(
[capacities, yearly_electricity, yearly_heat, capacity_cost, carrier_cost, marginal_cost,
share_el_heat, spec_cost_of_heat], 0)
# some of the rows carry the component classes as index. This maps all index labels to str.
scalars = multi_index_dtype_to_str(scalars)
scalars.sort_values(by=['name', 'var_name', 'type', 'carrier', 'tech'], inplace=True)
scalars.to_csv(os.path.join(dirs['postprocessed'], 'scalars.csv'))
def main(scenario_assumptions):
print("Combining scenario results")
dirs = get_experiment_dirs('all_scenarios')
scenario_paths = get_scenario_paths(scenario_assumptions)
scenario_dfs = get_scenario_dfs(scenario_paths, 'scalars.csv')
all_scalars = combine_scalars(scenario_dfs)
all_scalars.drop('heat-distribution', level='name', inplace=True)
all_scalars.drop('heat-demand', level='name', inplace=True)
all_scalars.drop('heat_decentral-shortage', level='name', inplace=True)
file_path = os.path.join(dirs['postprocessed'], 'scalars.csv')
all_scalars.to_csv(file_path)
print(f"Saved scenario results to {file_path}")
import os
import pandas as pd
from tools.helper import get_experiment_dirs, get_scenario_assumptions
dirs =get_experiment_dirs('all_scenarios')
input_file_path = os.path.join(dirs['postprocessed'], 'scalars.csv')
all_scalars = pd.read_csv(input_file_path)
def filter_and_round(df, filter, scenario_select, decimals):
_df = df.copy()
_df = _df.loc[all_scalars['var_name'] == filter]
_df = _df[['scenario', 'var_value']].set_index('scenario')
_df = _df.loc[scenario_select, :]
_df = _df.round(decimals)
return _df
def filter_assumptions(scenario_assumptions, scenario_select, assumption_select):
assumptions = scenario_assumptions.loc[
scenario_assumptions['scenario'].isin(scenario_select)]
def plot():
dirs = get_experiment_dirs('all_scenarios')
scenarios = {
'Status quo': [
'SQ',
'SQ-CHP',
'SQ-HP-50',
'SQ-HP-50-COP-175',
'SQ-HP-50-COP-200',
'SQ-Std-200',
],
'Transition to FF': [
'FF-50',
'FF-70',
'FF-80',
'FF-90',
],
'FF': [
'FF',
'FF-COP-75',
'FF-Mean-150',
'FF-Std-150',
'FF-Mean-150-Std-150',
],
}
all_scalars = pd.read_csv(
os.path.join(dirs['postprocessed'], 'scalars.csv'),
index_col=[0,1,2,3,4,5]
)
remove_scenario_index_name(all_scalars)
fig, axs = plt.subplots(1, 3, figsize=(12, 6), sharey=True)
legend = False
for i, (title, scenario_bunch) in enumerate(scenarios.items()):
slicing = idx[scenario_bunch, :, :, :, :, ['capacity', 'invest']]
select = all_scalars.loc[slicing, :]
if i == 2:
legend=True
plot_stacked_bar(
select,
scenario_bunch,
title,
'Capacity in MW',
ax=axs[i],
legend=legend,
yticks=np.arange(0, 300, 50),
)
filename = os.path.join(dirs['plots'], 'capacities.pdf')
plt.tight_layout()
plt.savefig(filename)
print(f"Saved plot to {filename}")
def main(**scenario_assumptions):
dirs = get_experiment_dirs(scenario_assumptions['scenario'])
price_el = pd.read_csv(os.path.join(dirs['preprocessed'], 'data',
'sequences',
'carrier_cost_profile.csv'),
index_col=0)
electricity = pd.read_csv(
os.path.join(dirs['postprocessed'], 'sequences', 'electricity.csv'),
index_col=0,
)
heat_central = pd.read_csv(os.path.join(dirs['postprocessed'], 'sequences',
'heat_central.csv'),
index_col=0)
heat_decentral = pd.read_csv(os.path.join(dirs['postprocessed'],
'sequences',
'heat_decentral.csv'),
index_col=0)
timeseries = pd.concat([heat_central, heat_decentral], 1)
timeseries = timeseries.drop('heat-distribution', axis=1)
timeseries = timeseries.drop('heat_decentral-shortage', axis=1)
supply = timeseries.drop('heat-demand', axis=1)
demand = timeseries['heat-demand']
plot_load_duration(
price_el,
legend=False,
plot_original=True,
title='Electricity prices (buying)',
ylabel='Hourly price [Eur/MWh]',
)
plt.savefig(os.path.join(dirs['plots'], 'price_el.pdf'))
plt.close()
plot_load_duration(
demand,
legend=False,
plot_original=True,
title='Heat demand',
ylabel='Hourly heat demand [MWh]',
)
plt.savefig(os.path.join(dirs['plots'], 'heat_demand.pdf'))
plt.close()
plot_load_duration(
supply,
linewidth=10,
)
plt.savefig(os.path.join(dirs['plots'], 'heat_supply.pdf'))
plt.close()
start = '2017-02-01'
end = '2017-02-14'
plot_dispatch(supply[start:end], demand[start:end],
os.path.join(dirs['plots'], 'heat_dispatch.pdf'))
winter_a = '2017-01-10'
winter_b = '2017-01-24'
summer_a = '2017-06-01'
summer_b = '2017-06-14'
electricity_chp = pd.DataFrame(electricity['gas-chp'])
multiplot_dispatch((supply[winter_a:winter_b], supply[summer_a:summer_b]),
(electricity_chp[winter_a:winter_b],
electricity_chp[summer_a:summer_b]),
os.path.join(dirs['plots'], 'heat_el_dispatch.pdf'))
def plot():
dirs = get_experiment_dirs('all_scenarios')
scenarios = {
'Status quo': [
'SQ',
'SQ-CHP',
'SQ-HP-50',
'SQ-HP-50-COP-175',
'SQ-HP-50-COP-200',
'SQ-Std-200',
],
'Transition to FF': [
'FF-50',
'FF-70',
'FF-80',
'FF-90',
],
'FF': [
'FF',
'FF-COP-75',
'FF-Mean-150',
'FF-Std-150',
'FF-Mean-150-Std-150',
],
}
all_scalars = pd.read_csv(os.path.join(dirs['postprocessed'],
'scalars.csv'),
index_col=[0, 1, 2, 3, 4, 5])
fig, axs = plt.subplots(1, 3, figsize=(12, 6), sharey=True)
legend = False
for i, (title, scenario_bunch) in enumerate(scenarios.items()):
slicing = idx[scenario_bunch, :, :, :, :,
['capacity_cost', 'carrier_cost', 'marginal_cost']]
select = all_scalars.loc[slicing, :]
# select = group_varom_cost(select)
remove_scenario_index_name(select)
select = select / 300000 #TODO: Find a better solution than this fix.
if i == 2:
legend = True
plot_stacked_bar(
select,
scenario_bunch,
title,
ax=axs[i],
legend=legend,
yticks=np.arange(-100, 120, 20),
)
axs[i].set_ylim(-90, 100)
filename = os.path.join(dirs['plots'], 'costs.pdf')
axs[0].set_ylabel('Cost of heat in Eur/MWh')
plt.tight_layout()
plt.savefig(filename)
print(f"Saved plot to {filename}")
def main(**scenario_assumptions):
print('Preprocessing')
timeindex = TIMEINDEX
if scenario_assumptions['debug']:
timeindex = timeindex[:3]
dirs = get_experiment_dirs(scenario_assumptions['scenario'])
elements_dir = os.path.join(dirs['preprocessed'], 'data', 'elements')
copy_base_scenario(os.path.join(dirs['raw'], 'base_scenario'),
os.path.join(dirs['preprocessed']))
sequences_dir = os.path.join(dirs['preprocessed'], 'data', 'sequences')
if not os.path.exists(sequences_dir):
os.makedirs(sequences_dir)
gas_price = scenario_assumptions['market_price_gas']\
+ scenario_assumptions['charges_tax_levies_gas']
set_gas_price(gas_price, elements_dir)
constants = get_constants(dirs['raw'])
params = constants.copy()
params.loc[
'electricity-hp',
'overnight_cost'] = scenario_assumptions['overnight_cost_heat_pump']
fix_cost_data = calculate_fix_cost(params)
set_element_params(
elements_dir,
'electricity-hp',
capacity_cost=fix_cost_data.loc['electricity-hp', 'ep_cost'],
efficiency=scenario_assumptions['cop_heat_pump'],
)
set_element_params(elements_dir,
'electricity-respth',
capacity_cost=fix_cost_data.loc['electricity-respth',
'ep_cost'])
set_element_params(elements_dir,
'heat-storage',
storage_capacity_cost=list(fix_cost_data.loc[
['heat_central-storage', 'heat_decentral-storage'],
'ep_cost']))
prepare_heat_demand_profile(os.path.join(dirs['raw'],
'demand_heat_2017.csv'),
os.path.join(dirs['preprocessed'], 'data',
'sequences'),
timeindex=timeindex)
prepare_electricity_price_profiles(
scenario_assumptions['market_price_el'],
scenario_assumptions['charges_tax_levies_el'],
scenario_assumptions['standard_dev_el'],
scenario_assumptions['chp_surcharge'],
os.path.join(dirs['raw'], 'price_electricity_spot_2017.csv'),
os.path.join(dirs['preprocessed'], 'data', 'sequences'),
timeindex=timeindex)
infer_metadata('name', dirs['preprocessed'])