def solve(self, with_duals=False, tee=True, logfile=None, solver=None):
if solver is None:
solver_name = cfg.get('general', 'solver')
else:
solver_name = solver
logging.info("Optimising using {0}.".format(solver_name))
if with_duals:
self.model.receive_duals()
if self.debug:
filename = os.path.join(helpers.extend_basic_path('lp_files'),
'reegis.lp')
logging.info('Store lp-file in {0}.'.format(filename))
self.model.write(filename,
io_options={'symbolic_solver_labels': True})
self.model.solve(solver=solver_name,
solve_kwargs={
'tee': tee,
'logfile': logfile
})
self.es.results['main'] = outputlib.processing.results(self.model)
self.es.results['meta'] = outputlib.processing.meta_results(self.model)
self.es.results['param'] = outputlib.processing.parameter_as_dict(
self.es)
self.es.results['scenario'] = self.scenario_info(solver_name)
self.es.results['meta']['in_location'] = self.location
self.es.results['meta']['file_date'] = datetime.datetime.fromtimestamp(
os.path.getmtime(self.location))
self.es.results['meta']['oemof_version'] = logger.get_version()
self.results = self.es.results['main']
def solve(self, with_duals=False, tee=True, logfile=None, solver=None):
logging.info("Optimising using {0}.".format(solver))
if with_duals:
self.model.receive_duals()
if self.debug:
filename = os.path.join(helpers.extend_basic_path("lp_files"),
"reegis.lp")
logging.info("Store lp-file in {0}.".format(filename))
self.model.write(filename,
io_options={"symbolic_solver_labels": True})
self.model.solve(solver=solver,
solve_kwargs={
"tee": tee,
"logfile": logfile
})
self.es.results["main"] = processing.results(self.model)
self.es.results["meta"] = processing.meta_results(self.model)
self.es.results["param"] = processing.parameter_as_dict(self.es)
self.es.results["meta"]["scenario"] = self.scenario_info(solver)
self.es.results["meta"]["in_location"] = self.location
self.es.results["meta"]["file_date"] = datetime.datetime.fromtimestamp(
os.path.getmtime(self.location))
self.es.results["meta"]["oemof_version"] = logger.get_version()
self.results = self.es.results["main"]
def setup_class(self):
self.objective_pattern = re.compile("^objective.*(?=s\.t\.)",
re.DOTALL | re.MULTILINE)
self.date_time_index = pd.date_range('1/1/2012', periods=3, freq='H')
self.tmppath = helpers.extend_basic_path('tmp')
logging.info(self.tmppath)
def setup_class(cls):
cls.objective_pattern = re.compile(r'^objective.*(?=s\.t\.)',
re.DOTALL | re.MULTILINE)
cls.date_time_index = pd.date_range('1/1/2012', periods=3, freq='H')
cls.tmpdir = helpers.extend_basic_path('tmp')
logging.info(cls.tmpdir)
def setup_class(self):
self.objective_pattern = re.compile("^objective.*(?=s\.t\.)",
re.DOTALL|re.MULTILINE)
self.date_time_index = pd.date_range('1/1/2012', periods=3, freq='H')
self.tmppath = helpers.extend_basic_path('tmp')
logging.info(self.tmppath)
def define_logging(inifile='logging.ini', basicpath=None, subdir='log_files'):
r"""Initialise the logger using the logging.conf file in the local path.
Several sentences providing an extended description. Refer to
variables using back-ticks, e.g. `var`.
Parameters
----------
inifile : string, optional (default: logging.ini)
Name of the configuration file to define the logger. If no ini-file
exist a default ini-file will be downloaded from
'http://vernetzen.uni-flensburg.de/~git/logging_default.ini' and used.
basicpath : string, optional (default: '.oemof' in HOME)
The basicpath for different oemof related informations. By default
a ".oemof' folder is created in your home directory.
subdir : string, optional (default: 'log_files')
The name of the subfolder of the basicpath where the log-files are
stored.
Notes
-----
By default the INFO level is printed on the screen and the debug level
in a file, but you can easily configure the ini-file.
Every module that wants to create logging messages has to import the
logging module. The oemof logger module has to be imported once to
initialise it.
Examples
--------
To define the default logge you have to import the python logging library
and this function. The first logging message should be the path where the
log file is saved to.
>>> import logging
>>> from oemof.tools import logger
>>> logger.define_logging() # doctest: +SKIP
17:56:51-INFO-Path for logging: /HOME/.oemof/log_files
...
>>> logging.debug("Hallo")
"""
url = 'http://vernetzen.uni-flensburg.de/~git/logging_default.ini'
if basicpath is None:
basicpath = helpers.get_basic_path()
logpath = helpers.extend_basic_path(subdir)
log_filename = os.path.join(basicpath, 'logging.ini')
if not os.path.isfile(log_filename):
helpers.download_file(log_filename, url)
logging.config.fileConfig(os.path.join(basicpath, 'logging.ini'))
logger = logging.getLogger('simpleExample')
logger.debug('*********************************************************')
logging.info('Path for logging: %s' % logpath)
try:
check_git_branch()
except:
check_version()
def setUpClass(self):
self.objective_pattern = re.compile("^objective.*(?=s\.t\.)",
re.DOTALL|re.MULTILINE)
self.time_index = pd.date_range('1/1/2012', periods=3, freq='H')
self.sim = es.Simulation(
timesteps=range(len(self.time_index)), solver='glpk',
objective_options={
'function': predefined_objectives.minimize_cost})
self.tmppath = helpers.extend_basic_path('tmp')
logging.info(self.tmppath)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
# initialise the operational model
model = solph.Model(energysystem)
# This is for debugging only. It is not(!) necessary to solve the problem and
# should be set to True to save time and disc space in normal use. For
# debugging the timesteps should be set to 3, to increase the readability of
# the lp-file.
if debug:
filename = os.path.join(
helpers.extend_basic_path('lp_files'), r'C:\Users\Winfried\Oemof\results\lp\TI8784O.lp')
logging.info('Store lp-file in {0}.'.format(filename))
model.write(filename, io_options={'symbolic_solver_labels': True})
# if tee_switch is true solver messages will be displayed
logging.info('Solve the optimization problem')
model.solve(solver=solver, solve_kwargs={'tee': solver_verbose})
logging.info('Store the energy system with the results.')
# The processing module of the outputlib can be used to extract the results
# from the model transfer them into a homogeneous structured dictionary.
# add results to the energy system to make it possible to store them.
energysystem.results['main'] = outputlib.processing.results(model)
energysystem.results['meta'] = outputlib.processing.meta_results(model)
inputs={bel: solph.Flow(nominal_value=10)},
outputs={bel: solph.Flow(nominal_value=10, variable_costs=0.001)},
capacity_loss=0.00, initial_capacity=None,
inflow_conversion_factor=1, outflow_conversion_factor=1,)
energysystem.add(storage)
logging.info('Optimise the energy system')
model = solph.Model(energysystem)
# nur für debug. kann ignoriert werden
if debug:
filename = os.path.join(
helpers.extend_basic_path('lp_files'), 'basic_example.lp')
logging.info('Store lp-file in {0}.'.format(filename))
model.write(filename, io_options={'symbolic_solver_labels': True})
logging.info('Solve the optimization problem')
model.solve(solver=solver, solve_kwargs={'tee': solver_verbose})
logging.info('Store the energy system with the results.')
# add results to the energy system to make it possible to store them.
energysystem.results['main'] = outputlib.processing.results(model)
energysystem.results['meta'] = outputlib.processing.meta_results(model)
def optimise_storage_size(energysystem, filename="variable_chp.csv",
solver='cbc', debug=True, tee_switch=True):
# Read data file with heat and electrical demand (192 hours)
full_filename = os.path.join(os.path.dirname(__file__), filename)
data = pd.read_csv(full_filename, sep=",")
##########################################################################
# Create oemof.solph objects
##########################################################################
logging.info('Create oemof.solph objects')
# create natural gas bus
bgas = solph.Bus(label="natural_gas")
# create commodity object for gas resource
solph.Source(label='rgas', outputs={bgas: solph.Flow(variable_costs=50)})
# create two electricity buses and two heat buses
bel = solph.Bus(label="electricity")
bel2 = solph.Bus(label="electricity_2")
bth = solph.Bus(label="heat")
bth2 = solph.Bus(label="heat_2")
# create excess components for the elec/heat bus to allow overproduction
solph.Sink(label='excess_bth_2', inputs={bth2: solph.Flow()})
solph.Sink(label='excess_therm', inputs={bth: solph.Flow()})
solph.Sink(label='excess_bel_2', inputs={bel2: solph.Flow()})
solph.Sink(label='excess_elec', inputs={bel: solph.Flow()})
# create simple sink object for electrical demand for each electrical bus
solph.Sink(label='demand_elec', inputs={bel: solph.Flow(
actual_value=data['demand_el'], fixed=True, nominal_value=1)})
solph.Sink(label='demand_el_2', inputs={bel2: solph.Flow(
actual_value=data['demand_el'], fixed=True, nominal_value=1)})
# create simple sink object for heat demand for each thermal bus
solph.Sink(label='demand_therm', inputs={bth: solph.Flow(
actual_value=data['demand_th'], fixed=True, nominal_value=741000)})
solph.Sink(label='demand_th_2', inputs={bth2: solph.Flow(
actual_value=data['demand_th'], fixed=True, nominal_value=741000)})
# This is just a dummy transformer with a nominal input of zero
solph.LinearTransformer(
label='fixed_chp_gas',
inputs={bgas: solph.Flow(nominal_value=0)},
outputs={bel: solph.Flow(), bth: solph.Flow()},
conversion_factors={bel: 0.3, bth: 0.5})
# create a fixed transformer to distribute to the heat_2 and elec_2 buses
solph.LinearTransformer(
label='fixed_chp_gas_2',
inputs={bgas: solph.Flow(nominal_value=10e10)},
outputs={bel2: solph.Flow(), bth2: solph.Flow()},
conversion_factors={bel2: 0.3, bth2: 0.5})
# create a fixed transformer to distribute to the heat and elec buses
solph.VariableFractionTransformer(
label='variable_chp_gas',
inputs={bgas: solph.Flow(nominal_value=10e10)},
outputs={bel: solph.Flow(), bth: solph.Flow()},
conversion_factors={bel: 0.3, bth: 0.5},
conversion_factor_single_flow={bel: 0.5}
)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
om = solph.OperationalModel(energysystem)
if debug:
filename = os.path.join(
helpers.extend_basic_path('lp_files'), 'variable_chp.lp')
logging.info('Store lp-file in {0}.'.format(filename))
om.write(filename, io_options={'symbolic_solver_labels': True})
logging.info('Solve the optimization problem')
om.solve(solver=solver, solve_kwargs={'tee': tee_switch})
return energysystem
initial_storage_level=param_value['init_capacity_storage_el'],
inflow_conversion_factor=param_value[
'inflow_conv_factor_storage_el'],
outflow_conversion_factor=param_value[
'outflow_conv_factor_storage_el']))
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
model = solph.Model(energysystem)
if cfg['debug']:
filename = os.path.join(helpers.extend_basic_path('lp_files'), 'model.lp')
logging.info('Store lp-file in {0}.'.format(filename))
model.write(filename, io_options={'symbolic_solver_labels': True})
# if tee_switch is true solver messages will be displayed
logging.info('Solve the optimization problem')
model.solve(solver=cfg['solver'], solve_kwargs={'tee': cfg['solver_verbose']})
logging.info('Store the energy system with the results.')
energysystem.results['main'] = outputlib.processing.results(model)
energysystem.results['meta'] = outputlib.processing.meta_results(model)
energysystem.dump(dpath=abs_path + "/results/optimisation_results/dumps",
filename="model.oemof")
def test_helpers():
ok_(os.path.isdir(os.path.join(os.path.expanduser('~'), '.oemof')))
new_dir = helpers.extend_basic_path('test_xf67456_dir')
ok_(os.path.isdir(new_dir))
os.rmdir(new_dir)
ok_(not os.path.isdir(new_dir))
def run_model_flexchp(config_path, scenario_nr):
with open(config_path, 'r') as ymlfile:
cfg = yaml.load(ymlfile)
if cfg['debug']:
number_of_time_steps = 3
else:
number_of_time_steps = 8760
solver = cfg['solver']
debug = cfg['debug']
periods = number_of_time_steps
solver_verbose = cfg['solver_verbose']
abs_path = os.path.dirname(os.path.abspath(os.path.join(__file__, '..')))
logger.define_logging(logpath=abs_path +
'/results/optimisation_results/log/',
logfile=cfg['filename_logfile'] +
'_scenario_{0}.log'.format(scenario_nr),
screen_level=logging.INFO,
file_level=logging.DEBUG)
logging.info('Use parameters for scenario {0}'.format(scenario_nr))
logging.info('Initialize the energy system')
date_time_index = pd.date_range(cfg['start_date'],
periods=number_of_time_steps,
freq=cfg['frequency'])
energysystem = solph.EnergySystem(timeindex=date_time_index)
##########################################################################
# Read time series and parameter values from data files
##########################################################################
file_path_demand_ts = abs_path + cfg['demand_time_series']
data = pd.read_csv(file_path_demand_ts)
file_path_param_01 = abs_path + cfg['parameters_energy_system'][scenario_nr
- 1]
file_path_param_02 = abs_path + cfg['parameters_all_energy_systems']
param_df_01 = pd.read_csv(file_path_param_01, index_col=1)
param_df_02 = pd.read_csv(file_path_param_02, index_col=1)
param_df = pd.concat([param_df_01, param_df_02], sort=True)
param_value = param_df['value']
##########################################################################
# Create oemof object
##########################################################################
logging.info('Create oemof objects')
bgas = solph.Bus(label="natural_gas")
bel = solph.Bus(label="electricity")
bth = solph.Bus(label='heat')
energysystem.add(bgas, bel, bth)
# Sources and sinks
energysystem.add(
solph.Sink(
label='excess_bel',
inputs={
bel:
solph.Flow(variable_costs=param_value['var_costs_excess_bel'])
}))
energysystem.add(
solph.Sink(
label='excess_bth',
inputs={
bth:
solph.Flow(variable_costs=param_value['var_costs_excess_bth'])
}))
energysystem.add(
solph.Source(
label='shortage_bel',
outputs={
bel:
solph.Flow(
variable_costs=param_value['var_costs_shortage_bel'])
}))
energysystem.add(
solph.Source(
label='shortage_bth',
outputs={
bth:
solph.Flow(
variable_costs=param_value['var_costs_shortage_bth'])
}))
energysystem.add(
solph.Source(label='rgas',
outputs={
bgas:
solph.Flow(
nominal_value=param_value['nom_val_gas'],
summed_max=param_value['sum_max_gas'],
variable_costs=param_value['var_costs_gas'])
}))
energysystem.add(
solph.Source(
label='P2H',
outputs={
bth:
solph.Flow(actual_value=data['neg_residual_el'],
nominal_value=param_value['nom_val_neg_residual'] *
param_value['conversion_factor_p2h'],
fixed=True)
}))
energysystem.add(
solph.Sink(label='demand_el',
inputs={
bel:
solph.Flow(
actual_value=data['demand_el'],
nominal_value=param_value['nom_val_demand_el'],
fixed=True)
}))
energysystem.add(
solph.Sink(label='demand_th',
inputs={
bth:
solph.Flow(
actual_value=data['demand_th'],
nominal_value=param_value['nom_val_demand_th'],
fixed=True)
}))
energysystem.add(
solph.components.GenericCHP(
label='CHP_01',
fuel_input={
bgas:
solph.Flow(H_L_FG_share_max=[
param_value['H_L_FG_share_max'] for p in range(0, periods)
])
},
electrical_output={
bel:
solph.Flow(P_max_woDH=[
param_value['P_max_woDH'] for p in range(0, periods)
],
P_min_woDH=[
param_value['P_min_woDH']
for p in range(0, periods)
],
Eta_el_max_woDH=[
param_value['Eta_el_max_woDH']
for p in range(0, periods)
],
Eta_el_min_woDH=[
param_value['Eta_el_min_woDH']
for p in range(0, periods)
])
},
heat_output={
bth:
solph.Flow(Q_CW_min=[
param_value['Q_CW_min_chp'] for p in range(0, periods)
])
},
Beta=[param_value['Beta_chp'] for p in range(0, periods)],
back_pressure=False))
energysystem.add(
solph.Transformer(
label='boiler',
inputs={bgas: solph.Flow()},
outputs={
bth:
solph.Flow(nominal_value=param_value['nom_val_out_boiler'],
variable_costs=param_value['var_costs_boiler'])
},
conversion_factors={bth: param_value['conversion_factor_boiler']}))
if param_value['nom_capacity_storage_th'] > 0:
storage_th = solph.components.GenericStorage(
nominal_capacity=param_value['nom_capacity_storage_th'],
label='storage_th',
inputs={
bth:
solph.Flow(
nominal_value=param_value['nom_val_input_bth_storage_th'],
variable_costs=param_value[
'var_costs_input_bth_storage_th'])
},
outputs={
bth:
solph.Flow(
nominal_value=param_value['nom_val_output_bth_storage_th'],
variable_costs=param_value[
'var_costs_output_bth_storage_th'])
},
capacity_loss=param_value['capacity_loss_storage_th'],
initial_capacity=param_value['init_capacity_storage_th'],
inflow_conversion_factor=param_value[
'inflow_conv_factor_storage_th'],
outflow_conversion_factor=param_value[
'outflow_conv_factor_storage_th'])
energysystem.add(storage_th)
if param_value['nom_capacity_storage_el'] > 0:
storage_el = solph.components.GenericStorage(
nominal_capacity=param_value['nom_capacity_storage_el'],
label='storage_el',
inputs={
bel:
solph.Flow(
nominal_value=param_value['nom_val_input_bel_storage_el'],
variable_costs=param_value[
'var_costs_input_bel_storage_el'])
},
outputs={
bel:
solph.Flow(
nominal_value=param_value['nom_val_output_bel_storage_el'],
variable_costs=param_value[
'var_costs_output_bel_storage_el'])
},
capacity_loss=param_value['capacity_loss_storage_el'],
initial_capacity=param_value['init_capacity_storage_el'],
inflow_conversion_factor=param_value[
'inflow_conv_factor_storage_el'],
outflow_conversion_factor=param_value[
'outflow_conv_factor_storage_el'])
energysystem.add(storage_el)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
model = solph.Model(energysystem)
if debug:
lpfile_name = 'flexCHP_scenario_{0}.lp'.format(scenario_nr)
filename = os.path.join(helpers.extend_basic_path('lp_files'),
lpfile_name)
logging.info('Store lp-file in {0}.'.format(filename))
model.write(filename, io_options={'symbolic_solver_labels': True})
logging.info('Solve the optimization problem')
model.solve(solver=solver, solve_kwargs={'tee': solver_verbose})
logging.info('Store the energy system with the results.')
energysystem.results['main'] = outputlib.processing.results(model)
energysystem.results['meta'] = outputlib.processing.meta_results(model)
energysystem.dump(dpath=abs_path + "/results/optimisation_results/dumps",
filename=cfg['filename_dumb'] +
'_scenario_{0}.oemof'.format(scenario_nr))
def define_logging(inifile='logging.ini',
basicpath=None,
subdir='log_files',
log_version=True):
r"""Initialise the logger using the logging.conf file in the local path.
Several sentences providing an extended description. Refer to
variables using back-ticks, e.g. `var`.
Parameters
----------
inifile : string, optional (default: logging.ini)
Name of the configuration file to define the logger. If no ini-file
exist a default ini-file will be copied from 'default_files' and
used.
basicpath : string, optional (default: '.oemof' in HOME)
The basicpath for different oemof related information. By default
a ".oemof' folder is created in your home directory.
subdir : string, optional (default: 'log_files')
The name of the subfolder of the basicpath where the log-files are
stored.
log_version : boolean
If True the actual version or commit is logged while initialising the
logger.
Notes
-----
By default the INFO level is printed on the screen and the debug level
in a file, but you can easily configure the ini-file.
Every module that wants to create logging messages has to import the
logging module. The oemof logger module has to be imported once to
initialise it.
Examples
--------
To define the default logger you have to import the python logging
library and this function. The first logging message should be the
path where the log file is saved to.
>>> import logging
>>> from oemof.tools import logger
>>> logger.define_logging() # doctest: +SKIP
17:56:51-INFO-Path for logging: /HOME/.oemof/log_files
...
>>> logging.debug("Hallo")
"""
if basicpath is None:
basicpath = helpers.get_basic_path()
logpath = helpers.extend_basic_path(subdir)
log_filename = os.path.join(basicpath, inifile)
default_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'default_files', 'logging_default.ini')
if not os.path.isfile(log_filename):
shutil.copyfile(default_file, log_filename)
logging.config.fileConfig(os.path.join(basicpath, inifile))
logger = logging.getLogger('simpleExample')
logger.debug('*********************************************************')
logging.info('Path for logging: %s' % logpath)
if log_version:
try:
check_git_branch()
except FileNotFoundError:
check_version()
def main():
# ****************************************************************************
# ********** PART 1 - Define and optimise the energy system ******************
# ****************************************************************************
###############################################################################
# imports
###############################################################################
# Default logger of oemof
from oemof.tools import logger
from oemof.tools import helpers
import oemof.solph as solph
import oemof.outputlib as outputlib
import logging
import os
import pandas as pd
import pprint as pp
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
solver = 'cbc' # 'glpk', 'gurobi',....
debug = False # Set number_of_timesteps to 3 to get a readable lp-file.
number_of_time_steps = 24 * 7 * 8
solver_verbose = False # show/hide solver output
# initiate the logger (see the API docs for more information)
logger.define_logging(logfile='oemof_example.log',
screen_level=logging.INFO,
file_level=logging.DEBUG)
logging.info('Initialize the energy system')
date_time_index = pd.date_range('1/1/2012',
periods=number_of_time_steps,
freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
# Read data file
filename = os.path.join(os.path.dirname(__file__), 'basic_example.csv')
data = pd.read_csv(filename)
##########################################################################
# Create oemof object
##########################################################################
logging.info('Create oemof objects')
# The bus objects were assigned to variables which makes it easier to connect
# components to these buses (see below).
# create natural gas bus
bgas = solph.Bus(label="natural_gas")
# create electricity bus
bel = solph.Bus(label="electricity")
# adding the buses to the energy system
energysystem.add(bgas, bel)
# create excess component for the electricity bus to allow overproduction
energysystem.add(solph.Sink(label='excess_bel', inputs={bel:
solph.Flow()}))
# create source object representing the natural gas commodity (annual limit)
energysystem.add(
solph.Source(
label='rgas',
outputs={bgas: solph.Flow(nominal_value=29825293, summed_max=1)}))
# create fixed source object representing wind power plants
energysystem.add(
solph.Source(label='wind',
outputs={
bel:
solph.Flow(actual_value=data['wind'],
nominal_value=1000000,
fixed=True)
}))
# create fixed source object representing pv power plants
energysystem.add(
solph.Source(label='pv',
outputs={
bel:
solph.Flow(actual_value=data['pv'],
nominal_value=582000,
fixed=True)
}))
# create simple sink object representing the electrical demand
energysystem.add(
solph.Sink(label='demand',
inputs={
bel:
solph.Flow(actual_value=data['demand_el'],
fixed=True,
nominal_value=1)
}))
# create simple transformer object representing a gas power plant
energysystem.add(
solph.Transformer(
label="pp_gas",
inputs={bgas: solph.Flow()},
outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=50)},
conversion_factors={bel: 0.58}))
# create storage object representing a battery
storage = solph.components.GenericStorage(
nominal_capacity=10077997,
label='storage',
inputs={bel: solph.Flow()},
outputs={bel: solph.Flow(variable_costs=0.001)},
capacity_loss=0.00,
initial_capacity=None,
nominal_input_capacity_ratio=1 / 6,
nominal_output_capacity_ratio=1 / 6,
inflow_conversion_factor=1,
outflow_conversion_factor=0.8,
)
energysystem.add(storage)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
# initialise the operational model
model = solph.Model(energysystem)
# This is for debugging only. It is not(!) necessary to solve the problem and
# should be set to False to save time and disc space in normal use. For
# debugging the timesteps should be set to 3, to increase the readability of
# the lp-file.
if debug:
filename = os.path.join(helpers.extend_basic_path('lp_files'),
'storage_invest.lp')
logging.info('Store lp-file in {0}.'.format(filename))
model.write(filename, io_options={'symbolic_solver_labels': True})
# if tee_switch is true solver messages will be displayed
logging.info('Solve the optimization problem')
model.solve(solver=solver, solve_kwargs={'tee': solver_verbose})
logging.info('Store the energy system with the results.')
# The processing module of the outputlib can be used to extract the results
# from the model transfer them into a homogeneous structured dictionary.
# add results to the energy system to make it possible to store them.
energysystem.results['main'] = outputlib.processing.results(model)
energysystem.results['meta'] = outputlib.processing.meta_results(model)
# The default path is the '.oemof' folder in your $HOME directory.
# The default filename is 'es_dump.oemof'.
# You can omit the attributes (as None is the default value) for testing cases.
# You should use unique names/folders for valuable results to avoid
# overwriting.
# store energy system with results
energysystem.dump(dpath=None, filename=None)
# ****************************************************************************
# ********** PART 2 - Processing the results *********************************
# ****************************************************************************
logging.info('**** The script can be divided into two parts here.')
logging.info('Restore the energy system and the results.')
energysystem = solph.EnergySystem()
energysystem.restore(dpath=None, filename=None)
# define an alias for shorter calls below (optional)
results = energysystem.results['main']
storage = energysystem.groups['storage']
# print a time slice of the state of charge
print('')
print('********* State of Charge (slice) *********')
print(results[(
storage,
None)]['sequences']['2012-02-25 08:00:00':'2012-02-26 15:00:00'])
print('')
# get all variables of a specific component/bus
custom_storage = outputlib.views.node(results, 'storage')
electricity_bus = outputlib.views.node(results, 'electricity')
# plot the time series (sequences) of a specific component/bus
if plt is not None:
custom_storage['sequences'].plot(kind='line', drawstyle='steps-post')
plt.show()
electricity_bus['sequences'].plot(kind='line', drawstyle='steps-post')
plt.show()
# print the solver results
print('********* Meta results *********')
pp.pprint(energysystem.results['meta'])
print('')
# print the sums of the flows around the electricity bus
print('********* Main results *********')
print(electricity_bus['sequences'].sum(axis=0))
def optimise_storage_size(energysystem,
filename="variable_chp.csv",
solver='cbc',
debug=True,
tee_switch=True):
# Read data file with heat and electrical demand (192 hours)
full_filename = os.path.join(os.path.dirname(__file__), filename)
data = pd.read_csv(full_filename, sep=",")
##########################################################################
# Create oemof.solph objects
##########################################################################
logging.info('Create oemof.solph objects')
# create natural gas bus
bgas = solph.Bus(label="natural_gas")
# create commodity object for gas resource
solph.Source(label='rgas', outputs={bgas: solph.Flow(variable_costs=50)})
# create two electricity buses and two heat buses
bel = solph.Bus(label="electricity")
bel2 = solph.Bus(label="electricity_2")
bth = solph.Bus(label="heat")
bth2 = solph.Bus(label="heat_2")
# create excess components for the elec/heat bus to allow overproduction
solph.Sink(label='excess_bth_2', inputs={bth2: solph.Flow()})
solph.Sink(label='excess_therm', inputs={bth: solph.Flow()})
solph.Sink(label='excess_bel_2', inputs={bel2: solph.Flow()})
solph.Sink(label='excess_elec', inputs={bel: solph.Flow()})
# create simple sink object for electrical demand for each electrical bus
solph.Sink(label='demand_elec',
inputs={
bel:
solph.Flow(actual_value=data['demand_el'],
fixed=True,
nominal_value=1)
})
solph.Sink(label='demand_el_2',
inputs={
bel2:
solph.Flow(actual_value=data['demand_el'],
fixed=True,
nominal_value=1)
})
# create simple sink object for heat demand for each thermal bus
solph.Sink(label='demand_therm',
inputs={
bth:
solph.Flow(actual_value=data['demand_th'],
fixed=True,
nominal_value=741000)
})
solph.Sink(label='demand_th_2',
inputs={
bth2:
solph.Flow(actual_value=data['demand_th'],
fixed=True,
nominal_value=741000)
})
# This is just a dummy transformer with a nominal input of zero
solph.LinearTransformer(label='fixed_chp_gas',
inputs={bgas: solph.Flow(nominal_value=0)},
outputs={
bel: solph.Flow(),
bth: solph.Flow()
},
conversion_factors={
bel: 0.3,
bth: 0.5
})
# create a fixed transformer to distribute to the heat_2 and elec_2 buses
solph.LinearTransformer(label='fixed_chp_gas_2',
inputs={bgas: solph.Flow(nominal_value=10e10)},
outputs={
bel2: solph.Flow(),
bth2: solph.Flow()
},
conversion_factors={
bel2: 0.3,
bth2: 0.5
})
# create a fixed transformer to distribute to the heat and elec buses
solph.VariableFractionTransformer(
label='variable_chp_gas',
inputs={bgas: solph.Flow(nominal_value=10e10)},
outputs={
bel: solph.Flow(),
bth: solph.Flow()
},
conversion_factors={
bel: 0.3,
bth: 0.5
},
conversion_factor_single_flow={bel: 0.5})
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
om = solph.OperationalModel(energysystem)
if debug:
filename = os.path.join(helpers.extend_basic_path('lp_files'),
'variable_chp.lp')
logging.info('Store lp-file in {0}.'.format(filename))
om.write(filename, io_options={'symbolic_solver_labels': True})
logging.info('Solve the optimization problem')
om.solve(solver=solver, solve_kwargs={'tee': tee_switch})
return energysystem
def define_logging(inifile='logging.ini', basicpath=None,
subdir='log_files', log_version=True):
r"""Initialise the logger using the logging.conf file in the local path.
Several sentences providing an extended description. Refer to
variables using back-ticks, e.g. `var`.
Parameters
----------
inifile : string, optional (default: logging.ini)
Name of the configuration file to define the logger. If no ini-file
exist a default ini-file will be copied from 'default_files' and
used.
basicpath : string, optional (default: '.oemof' in HOME)
The basicpath for different oemof related information. By default
a ".oemof' folder is created in your home directory.
subdir : string, optional (default: 'log_files')
The name of the subfolder of the basicpath where the log-files are
stored.
log_version : boolean
If True the actual version or commit is logged while initialising the
logger.
Returns
-------
str : Place where the log file is stored.
Notes
-----
By default the INFO level is printed on the screen and the debug level
in a file, but you can easily configure the ini-file.
Every module that wants to create logging messages has to import the
logging module. The oemof logger module has to be imported once to
initialise it.
Examples
--------
To define the default logger you have to import the python logging
library and this function. The first logging message should be the
path where the log file is saved to.
>>> import logging
>>> from oemof.tools import logger
>>> logger.define_logging() # doctest: +SKIP
17:56:51-INFO-Path for logging: /HOME/.oemof/log_files
...
>>> logging.debug("Hallo")
"""
if basicpath is None:
basicpath = helpers.get_basic_path()
logpath = helpers.extend_basic_path(subdir)
log_filename = os.path.join(basicpath, inifile)
default_file = os.path.join(os.path.dirname(
os.path.realpath(__file__)), 'default_files', 'logging_default.ini')
if not os.path.isfile(log_filename):
shutil.copyfile(default_file, log_filename)
logging.config.fileConfig(os.path.join(basicpath, inifile))
try:
returnpath = logging.getLoggerClass().root.handlers[1].baseFilename
except AttributeError:
returnpath = None
logger = logging.getLogger('simpleExample')
logger.debug('*********************************************************')
logging.info('Path for logging: %s' % logpath)
if log_version:
try:
check_git_branch()
except FileNotFoundError:
check_version()
return returnpath
def run_model(config_path, team_number):
with open(config_path, 'r') as ymlfile:
cfg = yaml.load(ymlfile, Loader=yaml.CLoader)
if cfg['debug']:
number_of_time_steps = 3
else:
number_of_time_steps = 8760
solver = cfg['solver']
debug = cfg['debug']
periods = number_of_time_steps
solver_verbose = cfg['solver_verbose'] # show/hide solver output
# initiate the logger (see the API docs for more information)
logger.define_logging(logfile='model_team_{0}.log'.format(team_number + 1),
screen_level=logging.INFO,
file_level=logging.DEBUG)
logging.info('Initialize the energy system')
date_time_index = pd.date_range('1/1/2030',
periods=number_of_time_steps,
freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
##########################################################################
# Read time series and parameter values from data files
##########################################################################
abs_path = os.path.dirname(os.path.abspath(os.path.join(__file__, '..')))
file_path_ts = abs_path + '/data/' + cfg['time_series_file_name']
data = pd.read_csv(file_path_ts)
# file_path_weather_ts = abs_path + '/data_preprocessed/' + cfg[
# 'weather_time_series']
# weather_data = pd.read_csv(file_path_weather_ts)
file_name_param_01 = cfg['design_parameters_file_name'][team_number]
file_name_param_02 = cfg['parameters_file_name']
file_path_param_01 = (abs_path + '/data/' + file_name_param_01)
file_path_param_02 = (abs_path + '/data/' + file_name_param_02)
param_df_01 = pd.read_csv(file_path_param_01, index_col=1)
param_df_02 = pd.read_csv(file_path_param_02, index_col=1)
param_df = pd.concat([param_df_01, param_df_02], sort=True)
param_value = param_df['value']
##########################################################################
# Create oemof object
##########################################################################
logging.info('Create oemof objects')
bgas = solph.Bus(label="natural_gas")
bel = solph.Bus(label="electricity")
bth = solph.Bus(label='heat')
energysystem.add(bgas, bel, bth)
# Sources and sinks
energysystem.add(
solph.Sink(
label='excess_bel',
inputs={
bel:
solph.Flow(variable_costs=param_value['var_costs_excess_bel'])
}))
energysystem.add(
solph.Sink(
label='excess_bth',
inputs={
bth:
solph.Flow(variable_costs=param_value['var_costs_excess_bth'])
}))
energysystem.add(
solph.Source(
label='shortage_bel',
outputs={
bel:
solph.Flow(
variable_costs=param_value['var_costs_shortage_bel'])
}))
energysystem.add(
solph.Source(
label='shortage_bth',
outputs={
bth:
solph.Flow(
variable_costs=param_value['var_costs_shortage_bth'])
}))
energysystem.add(
solph.Source(label='rgas',
outputs={
bgas:
solph.Flow(
nominal_value=param_value['nom_val_gas'],
summed_max=param_value['sum_max_gas'],
variable_costs=param_value['var_costs_gas'])
}))
if param_value['number_of_windturbines'] > 0:
energysystem.add(
solph.Source(
label='wind_turbine',
outputs={
bel:
solph.Flow(
actual_value=(data['Wind_power [kW/unit]'] *
param_value['number_of_windturbines'] *
0.001), # [MWh]
nominal_value=1,
fixed=True)
}))
energysystem.add(
solph.Sink(
label='demand_el',
inputs={
bel:
solph.Flow(
actual_value=data['Demand_el [MWh]'], # [MWh]
nominal_value=1,
fixed=True)
}))
energysystem.add(
solph.Sink(
label='demand_th',
inputs={
bth:
solph.Flow(
actual_value=data['Demand_th [MWh]'], # [MWh]
nominal_value=1,
fixed=True)
}))
# Open-field photovoltaic power plant
if param_value['number_of_PV_pp'] > 0:
energysystem.add(
solph.Source(
label='PV_pp',
outputs={
bel:
solph.Flow(
actual_value=(data['Sol_irradiation [Wh/sqm]'] *
0.000001 *
param_value['eta_PV']), # [MWh/m²]
nominal_value=param_value['PV_pp_surface_area'] *
10000, # [m²]
fixed=True)
}))
# Rooftop photovoltaic
if param_value['area_PV'] > 0:
energysystem.add(
solph.Source(
label='PV',
outputs={
bel:
solph.Flow(
actual_value=(data['Sol_irradiation [Wh/sqm]'] *
0.000001 *
param_value['eta_PV']), # [MWh/m²]
nominal_value=param_value['area_PV'] * 10000, # [m²]
fixed=True)
}))
# Rooftop solar thermal
if param_value['area_solar_th'] > 0:
energysystem.add(
solph.Source(
label='solar_thermal',
outputs={
bth:
solph.Flow(
actual_value=(data['Sol_irradiation [Wh/sqm]'] *
0.000001 *
param_value['eta_solar_th']), # [MWh/m²]
nominal_value=param_value['area_solar_th'] *
10000, # [m²]
fixed=True)
}))
if param_value['number_of_chps'] > 0:
energysystem.add(
solph.Transformer(
label='chp',
inputs={bgas: solph.Flow()},
outputs={
bth:
solph.Flow(nominal_value=param_value['number_of_chps'] *
0.5), # [MW]
bel:
solph.Flow()
},
conversion_factors={
bth: param_value['conversion_factor_bth_chp'],
bel: param_value['conversion_factor_bel_chp']
}))
if param_value['number_of_heat_pumps'] > 0:
energysystem.add(
solph.Transformer(
label='heat_pump',
inputs={bel: solph.Flow()},
outputs={
bth:
solph.Flow(
nominal_value=(param_value['number_of_heat_pumps'] *
param_value['COP_heat_pump']))
}, # [MW]
conversion_factors={bth: param_value['COP_heat_pump']}))
if param_value['number_of_boilers'] > 0:
energysystem.add(
solph.Transformer(
label='boiler',
inputs={bgas: solph.Flow()},
outputs={
bth:
solph.Flow(nominal_value=param_value['number_of_boilers'] *
3)
}, # [MW]
conversion_factors={
bth: param_value['conversion_factor_boiler']
}))
if param_value['capacity_thermal_storage'] > 0:
storage_th = solph.components.GenericStorage(
nominal_storage_capacity=(param_value['capacity_thermal_storage'] *
param_value['daily_demand_th']),
label='storage_th',
inputs={
bth:
solph.Flow(
nominal_value=(param_value['capacity_thermal_storage'] *
param_value['daily_demand_th'] /
param_value['charge_time_storage_th']))
},
outputs={
bth:
solph.Flow(
nominal_value=(param_value['capacity_thermal_storage'] *
param_value['daily_demand_th'] /
param_value['charge_time_storage_th']))
},
loss_rate=param_value['capacity_loss_storage_th'],
initial_storage_level=param_value['init_capacity_storage_th'],
inflow_conversion_factor=param_value[
'inflow_conv_factor_storage_th'],
outflow_conversion_factor=param_value[
'outflow_conv_factor_storage_th'])
energysystem.add(storage_th)
if param_value['capacity_electr_storage'] > 0:
storage_el = solph.components.GenericStorage(
nominal_storage_capacity=(param_value['capacity_electr_storage'] *
param_value['daily_demand_el']),
label='storage_el',
inputs={
bel:
solph.Flow(
nominal_value=(param_value['capacity_electr_storage'] *
param_value['daily_demand_el'] /
param_value['charge_time_storage_el']))
},
outputs={
bel:
solph.Flow(
nominal_value=(param_value['capacity_electr_storage'] *
param_value['daily_demand_el'] /
param_value['charge_time_storage_el']))
},
loss_rate=param_value['capacity_loss_storage_el'],
initial_storage_level=param_value['init_capacity_storage_el'],
inflow_conversion_factor=param_value[
'inflow_conv_factor_storage_el'],
outflow_conversion_factor=param_value[
'outflow_conv_factor_storage_el'])
energysystem.add(storage_el)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
model = solph.Model(energysystem)
if debug:
filename = os.path.join(helpers.extend_basic_path('lp_files'),
'model_team_{0}.lp'.format(team_number + 1))
logging.info('Store lp-file in {0}.'.format(filename))
model.write(filename, io_options={'symbolic_solver_labels': True})
# if tee_switch is true solver messages will be displayed
logging.info(
'Solve the optimization problem of team {0}'.format(team_number + 1))
model.solve(solver=solver, solve_kwargs={'tee': solver_verbose})
logging.info('Store the energy system with the results.')
energysystem.results['main'] = outputlib.processing.results(model)
energysystem.results['meta'] = outputlib.processing.meta_results(model)
energysystem.dump(dpath=abs_path + "/results/optimisation_results/dumps",
filename="model_team_{0}.oemof".format(team_number + 1))
def optimise_storage_size(filename="storage_investment.csv", solver='cbc',
debug=True, number_timesteps=8760, tee_switch=True):
logging.info('Initialize the energy system')
date_time_index = pd.date_range('1/1/2012', periods=number_timesteps,
freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
# Read data file
full_filename = os.path.join(os.path.dirname(__file__), filename)
data = pd.read_csv(full_filename, sep=",")
##########################################################################
# Create oemof object
##########################################################################
logging.info('Create oemof objects')
# create natural gas bus
bgas = solph.Bus(label="natural_gas")
# create electricity bus
bel = solph.Bus(label="electricity")
# create excess component for the electricity bus to allow overproduction
solph.Sink(label='excess_bel', inputs={bel: solph.Flow()})
# create source object representing the natural gas commodity (annual limit)
solph.Source(label='rgas', outputs={bgas: solph.Flow(
nominal_value=194397000 * number_timesteps / 8760, summed_max=1)})
# create fixed source object representing wind power plants
solph.Source(label='wind', outputs={bel: solph.Flow(
actual_value=data['wind'], nominal_value=1000000, fixed=True,
fixed_costs=20)})
# create fixed source object representing pv power plants
solph.Source(label='pv', outputs={bel: solph.Flow(
actual_value=data['pv'], nominal_value=582000, fixed=True,
fixed_costs=15)})
# create simple sink object representing the electrical demand
solph.Sink(label='demand', inputs={bel: solph.Flow(
actual_value=data['demand_el'], fixed=True, nominal_value=1)})
# create simple transformer object representing a gas power plant
solph.LinearTransformer(
label="pp_gas",
inputs={bgas: solph.Flow()},
outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=50)},
conversion_factors={bel: 0.58})
# If the period is one year the equivalent periodical costs (epc) of an
# investment are equal to the annuity. Use oemof's economic tools.
epc = economics.annuity(capex=1000, n=20, wacc=0.05)
# create storage object representing a battery
solph.Storage(
label='storage',
inputs={bel: solph.Flow(variable_costs=10e10)},
outputs={bel: solph.Flow(variable_costs=10e10)},
capacity_loss=0.00, initial_capacity=0,
nominal_input_capacity_ratio=1/6,
nominal_output_capacity_ratio=1/6,
inflow_conversion_factor=1, outflow_conversion_factor=0.8,
fixed_costs=35,
investment=solph.Investment(ep_costs=epc),
)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
# initialise the operational model
om = solph.OperationalModel(energysystem)
# if debug is true an lp-file will be written
if debug:
filename = os.path.join(
helpers.extend_basic_path('lp_files'), 'storage_invest.lp')
logging.info('Store lp-file in {0}.'.format(filename))
om.write(filename, io_options={'symbolic_solver_labels': True})
# if tee_switch is true solver messages will be displayed
logging.info('Solve the optimization problem')
om.solve(solver=solver, solve_kwargs={'tee': tee_switch})
return energysystem
def run_model_flexchp(config_path, variation_nr):
with open(config_path, 'r') as ymlfile:
cfg = yaml.load(ymlfile)
if cfg['debug']:
number_of_time_steps = 3
else:
number_of_time_steps = 8760
solver = cfg['solver']
debug = cfg['debug']
solver_verbose = cfg['solver_verbose'] # show/hide solver output
abs_path = os.path.dirname(os.path.abspath(os.path.join(__file__, '..')))
# initiate the logger (see the API docs for more information)
logger.define_logging(logpath=(abs_path
+ '/results/optimisation_results/log/'),
logfile=(cfg['filename_logfile']+'_scenario_{0}.log'.
format(variation_nr)),
screen_level=logging.INFO,
file_level=logging.DEBUG)
logging.info('Use parameters for scenario {0}'.format(variation_nr))
logging.info('Initialize the energy system')
date_time_index = pd.date_range(cfg['start_date'],
periods=number_of_time_steps,
freq=cfg['frequency'])
energysystem = solph.EnergySystem(timeindex=date_time_index)
##########################################################################
# Read time series and parameter values from data files
##########################################################################
file_path_demand_ts = abs_path + cfg['demand_time_series']
data = pd.read_csv(file_path_demand_ts)
file_path_param_01 = abs_path + cfg['parameters_energy_system']
file_path_param_02 = abs_path + cfg['parameter_variation'][variation_nr]
param_df_01 = pd.read_csv(file_path_param_01, index_col=1)
param_df_02 = pd.read_csv(file_path_param_02, index_col=1)
param_df = pd.concat([param_df_01, param_df_02], sort=True)
param_value = param_df['value']
##########################################################################
# Create oemof object
##########################################################################
logging.info('Create oemof objects')
bgas = solph.Bus(label="natural_gas")
bel = solph.Bus(label="electricity")
bel_residual = solph.Bus(label="residual")
bth = solph.Bus(label='heat')
energysystem.add(bgas, bel, bel_residual, bth)
# Sources and sinks
energysystem.add(solph.Sink(
label='excess_bel',
inputs={bel: solph.Flow(
variable_costs=param_value['var_costs_excess_bel'])}))
energysystem.add(solph.Sink(
label='excess_bth',
inputs={bth: solph.Flow(
variable_costs=param_value['var_costs_excess_bth'])}))
energysystem.add(solph.Source(
label='shortage_bel',
outputs={bel: solph.Flow(
variable_costs=param_value['var_costs_shortage_bel'])}))
energysystem.add(solph.Source(
label='shortage_bth',
outputs={bth: solph.Flow(
variable_costs=param_value['var_costs_shortage_bth'])}))
energysystem.add(solph.Source(
label='rgas',
outputs={bgas: solph.Flow(
nominal_value=param_value['nom_val_gas'],
summed_max=param_value['sum_max_gas'],
variable_costs=(param_value['var_costs_gas']
* param_value['gas_price_variation']))}))
energysystem.add(solph.Source(
label='residual_el',
outputs={bel_residual: solph.Flow(
actual_value=data['neg_residual_el'],
nominal_value=param_value['nom_val_neg_residual'],
fixed=True)}))
if cfg['price_el_quadratic'] == True:
energysystem.add(solph.Sink(
label='demand_el',
inputs={bel: solph.Flow(
variable_costs=(param_value['el_price']
* param_value['price_factor_sqr']
* param_value['el_price_variation']
* data['demand_el']**2),
nominal_value=8000)}))
if cfg['price_el_quadratic'] == False:
energysystem.add(solph.Sink(
label='demand_el',
inputs={bel: solph.Flow(
variable_costs=(param_value['el_price']
* param_value['el_price_variation']
* data['demand_el']),
nominal_value=8000)}))
energysystem.add(solph.Sink(
label='demand_th',
inputs={bth: solph.Flow(
actual_value=data['demand_th'],
nominal_value=param_value['nom_val_demand_th'],
fixed=True,
variable_costs=(- param_value['var_costs_gas']
/ param_value['conversion_factor_boiler']))}))
# Auxiliary component to prevent CHP electricity being used in P2H (not
# representing a physical component!)
energysystem.add(solph.Transformer(
label='oneway',
inputs={bel_residual: solph.Flow(
nominal_value=param_value['nom_val_neg_residual'])},
outputs={bel: solph.Flow()},
conversion_factors={bel: 1}))
# Combined Heat and Power Plant (CHP)
# Calculate annuity per installed unit of power [€/MW]
ep_costs_CHP = economics.annuity(
capex=param_value['capex_CHP'],
n=param_value['lifetime_CHP'],
wacc=param_value['wacc_CHP'])
# Add CHP with its technical specifications to the energy system
energysystem.add(ExtractionTurbineCHP(
label='CHP_01',
inputs={bgas: solph.Flow(
investment=solph.Investment(
ep_costs=(ep_costs_CHP*param_value['conv_factor_full_cond']),
maximum=1667))},
outputs={
bel: solph.Flow(),
bth: solph.Flow()},
conversion_factors={
bel: param_value['conv_factor_bel_CHP'],
bth: param_value['conv_factor_bth_CHP']},
conversion_factor_full_condensation={
bel: param_value['conv_factor_full_cond']}))
# Peak load gas boiler
# Calculate annuity per installed unit of power [€/MW]
ep_costs_boiler = economics.annuity(
capex=param_value['capex_boiler'],
n=param_value['lifetime_boiler'],
wacc=param_value['wacc_boiler'])
# Add boiler with its technical specifications to the energy system
energysystem.add(solph.Transformer(
label='boiler',
inputs={bgas: solph.Flow()},
outputs={bth: solph.Flow(investment=solph.Investment(
ep_costs=ep_costs_boiler))},
conversion_factors={bth: param_value['conversion_factor_boiler']}))
ep_costs_p2h = economics.annuity(
capex=param_value['capex_p2h'],
n=param_value['lifetime_p2h'],
wacc=param_value['wacc_p2h'])
energysystem.add(solph.Transformer(
label='P2H',
inputs={bel_residual: solph.Flow()},
outputs={bth: solph.Flow(investment=solph.Investment(
ep_costs=ep_costs_p2h))},
conversion_factors={bth: param_value['conversion_factor_p2h']}))
ep_costs_TES = economics.annuity(
capex=(param_value['capex_TES']*param_value['TES_capex_variation']),
n=param_value['lifetime_TES'],
wacc=param_value['wacc_TES'])
storage_th = solph.components.GenericStorage(
label='storage_th',
inputs={bth: solph.Flow()},
outputs={bth: solph.Flow()},
capacity_loss=param_value['capacity_loss_storage_th'],
initial_capacity=param_value['init_capacity_storage_th'],
inflow_conversion_factor=param_value['inflow_conv_factor_storage_th'],
outflow_conversion_factor=param_value[
'outflow_conv_factor_storage_th'],
invest_relation_input_capacity=1/param_value[
'charging_time_storage_th'],
invest_relation_output_capacity=1/param_value[
'charging_time_storage_th'],
investment=solph.Investment(ep_costs=ep_costs_TES))
energysystem.add(storage_th)
ep_costs_EES = economics.annuity(
capex=param_value['capex_EES']*param_value['EES_capex_variation'],
n=param_value['lifetime_EES'],
wacc=param_value['wacc_EES'])
storage_el = solph.components.GenericStorage(
label='storage_el',
inputs={bel: solph.Flow()},
outputs={bel: solph.Flow()},
capacity_loss=param_value['capacity_loss_storage_el'],
initial_capacity=param_value['init_capacity_storage_el'],
inflow_conversion_factor=param_value['inflow_conv_factor_storage_el'],
outflow_conversion_factor=param_value[
'outflow_conv_factor_storage_el'],
invest_relation_input_capacity=1 / param_value[
'charging_time_storage_el'],
invest_relation_output_capacity=1 / param_value[
'charging_time_storage_el'],
investment=solph.Investment(ep_costs=ep_costs_EES))
energysystem.add(storage_el)
##########################################################################
# Optimise the energy system and store the results
##########################################################################
logging.info('Optimise the energy system')
model = solph.Model(energysystem)
if debug:
lpfile_name = 'flexCHP_scenario_{0}.lp'.format(variation_nr)
filename = os.path.join(
helpers.extend_basic_path('lp_files'), lpfile_name)
logging.info('Store lp-file in {0}.'.format(filename))
model.write(filename, io_options={'symbolic_solver_labels': True})
logging.info('Solve the optimization problem')
model.solve(solver=solver, solve_kwargs={'tee': solver_verbose})
logging.info('Store the energy system with the results.')
energysystem.results['main'] = outputlib.processing.results(model)
energysystem.results['meta'] = outputlib.processing.meta_results(model)
if cfg['price_el_quadratic']:
energysystem.dump(
dpath=(abs_path + "/results/optimisation_results/dumps/"
"quadratic_price_relationship"),
filename=(cfg['filename_dumb']+'_scenario_{0}.oemof'.
format(variation_nr)))
if cfg['price_el_quadratic'] == False:
energysystem.dump(
dpath=(abs_path + "/results/optimisation_results/dumps/"
"linear_price_relationship"),
filename=(cfg['filename_dumb']+'_scenario_{0}.oemof'.format(
variation_nr)))
def write_lp_file():
filename = os.path.join(
helpers.extend_basic_path('lp_files'), 'variable_chp.lp')
logging.info('Store lp-file in {0}.'.format(filename))
om.write(filename, io_options={'symbolic_solver_labels': True})
def berlin_model(berlin_e_system):
"""
Parameters
----------
berlin_e_system : solph.EnergySystem
Returns
-------
"""
time_index = berlin_e_system.time_idx
p = preferences.Basic()
d = preferences.Data()
logging.info("Adding objects to the energy system...")
# Electricity
solph.Bus(label='bus_el')
heat_demand = heat.DemandHeat(time_index)
heating_systems = [s for s in heat_demand.get().columns if "frac_" in s]
remove_string = 'frac_'
heat_demand.demand_by('total_loss_pres',
heating_systems,
d.bt_dict,
remove_string,
percentage=True)
heat_demand.df = heat_demand.dissolve('bezirk', 'demand_by', index=True)
heat_demand.df = heat_demand.df.rename(
columns={
k: k.replace('frac_', '')
for k in heat_demand.df.columns.get_level_values(1)
})
for t in p.types:
heat_demand.df[t] = (
heat_demand.df[t].multiply(
d.sanierungsanteil[t] * d.sanierungsreduktion[t]) +
heat_demand.df[t].multiply(1 - d.sanierungsanteil[t])) * 1000
ep = dict()
ep['basic'] = pd.read_csv('/home/uwe/e_p.csv', ';')
ep['stromanteil'] = pd.read_csv('/home/uwe/stromanteil_e_p.csv', ';')
fraction_heating_system_saniert = {
'off-peak_electricity_heating': 0,
'district_heating': 0.6,
'natural_gas_heating': 0.4,
'oil_heating': 0.4,
'coal_stove': 0,
'wp': 1,
}
fraction_electrical_dhw = {
'off-peak_electricity_heating': 1,
'district_heating': 0.11,
'natural_gas_heating': 0.09,
'oil_heating': 0.58,
'coal_stove': 1,
'wp': 0,
}
ep_mix = dict()
for e in ep.keys():
ep_mix[e] = dict()
for b in p.types:
ep_mix[e][b] = dict()
cols = [x.replace('_int_DHW', '') for x in ep[e].columns]
cols = set([x.replace('_el_DHW', '') for x in cols])
cols.remove('gtype')
cols.remove('building')
cols.remove('heating_system')
for h in cols:
tmp = dict()
for bs in ['saniert', 'unsaniert']:
tmp[bs] = dict()
for hs in ['saniert', 'unsaniert']:
qu = 'gtype=="{0}"'.format(b.upper())
qu += ' and building=="{0}"'.format(bs)
qu += ' and heating_system=="{0}"'.format(hs)
# Mix internal DHW and electrical DHW
ep[e].ix[(ep[e].gtype == b.upper()) &
(ep[e].building == bs) &
(ep[e].heating_system == hs),
h] = (ep[e].query(qu)[h + '_int_DHW'] *
(1 - fraction_electrical_dhw[h]) +
ep[e].query(qu)[h + '_el_DHW'] *
fraction_electrical_dhw[h])
tmp[bs][hs] = ep[e].query(qu)[h]
ep_mix[e][b][h] = (
float(tmp['saniert']['saniert']) * d.sanierungsanteil[b] *
fraction_heating_system_saniert[h] +
float(tmp['saniert']['unsaniert']) *
d.sanierungsanteil[b] *
(1 - fraction_heating_system_saniert[h]) +
float(tmp['unsaniert']['saniert']) *
(1 - d.sanierungsanteil[b]) *
fraction_heating_system_saniert[h] +
float(tmp['unsaniert']['unsaniert']) *
(1 - d.sanierungsanteil[b]) *
(1 - fraction_heating_system_saniert[h]))
add_dict = {
'district_dz': 'district_heating',
'district_z': 'district_heating',
'bhkw': 'district_heating',
}
for e in ep_mix.keys():
for b in ep_mix[e].keys():
for new_key in add_dict.keys():
ep_mix[e][b][new_key] = ep_mix[e][b][add_dict[new_key]]
# Add heating systems
sum_wp = 6.42e+10 # Bei 2000 Volllaststunden
sum_bhkw = 6.75e+11 # Bei 2000 Volllaststunden
# sum_wp = 50e+9
# sum_bhkw = 50e+9
sum_existing = heat_demand.df.sum().sum()
reduction = (sum_existing - (sum_wp + sum_bhkw)) / sum_existing
frac_mfh = heat_demand.df.mfh.sum().sum() / heat_demand.df.sum().sum()
new = {
'efh': {
'wp': sum_wp * (1 - frac_mfh),
'bhkw': sum_bhkw * (1 - frac_mfh)
},
'mfh': {
'wp': sum_wp * frac_mfh,
'bhkw': sum_bhkw * frac_mfh
}
}
heat_demand.df *= reduction
# Join some categories
ol = d.other_demand.pop('oil_light')
oh = d.other_demand.pop('oil_heavy')
oo = d.other_demand.pop('oil_other')
for c in ['ghd', 'i']:
d.other_demand['oil_heating'][c] = ol[c] + oh[c] + oo[c]
d.other_demand['natural_gas_heating'][c] += d.other_demand[
'liquid_gas'][c]
d.other_demand.pop('liquid_gas')
heat_demand.df.sortlevel(axis='columns', inplace=True)
# noinspection PyTypeChecker
district_z = heat_demand.df.loc[:, (slice(None),
'district_heating')].multiply(
d.fw_verteilung, axis=0).sum()
# noinspection PyTypeChecker
district_dz = heat_demand.df.loc[:,
(slice(None),
'district_heating')].multiply(
(1 - d.fw_verteilung), axis=0).sum()
dsum = heat_demand.df.sum()
for b in ['efh', 'mfh']:
dsum[b, 'district_dz'] = district_dz[b]['district_heating']
dsum[b, 'district_z'] = district_z[b]['district_heating']
dsum[b, 'bhkw'] = new[b]['bhkw']
dsum[b, 'wp'] = new[b]['wp']
dsum.drop('district_heating', 0, 'second', True)
dsum.sort_index(inplace=True)
ew = pd.read_csv('/home/uwe/chiba/RLI/data/stadtnutzung_erweitert.csv')[[
'ew', 'schluessel_planungsraum'
]]
grp = ew.schluessel_planungsraum.astype(str).str[:-8]
grp = grp.apply(lambda x: '{0:0>2}'.format(x))
ew = ew.groupby(grp).sum().drop('schluessel_planungsraum', 1)
# dhw_profile = pd.read_csv('/home/uwe/dhw_demand.csv')
# *ew.sum() * 657
dhw = ew.sum() * 657000 # 657000 Wh pro EW
dhw_factor = (dsum.sum().sum() + float(dhw)) / dsum.sum().sum()
dsum *= dhw_factor
dfull = d.other_demand
aux_elec = dict()
sum_aux = 0
print(ep_mix)
for b in dsum.keys().levels[0]:
for h in dsum[b].keys():
dfull.setdefault(h, dict())
aux_elec.setdefault(h, dict())
aux_elec[h][b] = (dsum[b][h] * ep_mix['basic'][b][h] *
ep_mix['stromanteil'][b][h] / 100)
print("{:.2E}".format(aux_elec[h][b]))
sum_aux += aux_elec[h][b]
dfull[h][b] = dsum[b][h] * ep_mix['basic'][b][h] - aux_elec[h][b]
# print(dfull)
# e = 0
# for a in dfull.keys():
# for b in dfull[a].keys():
# if b in ['i']:
# print(a, b, dfull[a][b])
# e += dfull[a][b]
# print(e)
# exit(0)
create_objects.heating_systems(berlin_e_system, dfull, aux_elec, p)
mylist = list(berlin_e_system.groups.items())
# Add excess and slack for every BUS
for k, g in mylist:
if isinstance(g, solph.Bus):
solph.Sink(label='excess_{0}'.format(k),
inputs={g: solph.Flow(variable_costs=9000)})
solph.Source(label='slack_{0}'.format(k),
outputs={g: solph.Flow(variable_costs=9000)})
# slacks = ['bus_el', 'bus_district_z', 'bus_district_dz']
# for s in slacks:
# obj = berlin_e_system.groups[s]
# solph.Source(label='slack_{0}'.format(s),
# outputs={obj: solph.Flow(variable_costs=9000)})
# sources
source_costs = {
'lignite': 90,
'natural_gas': 120,
'fuel_bio': 150,
'solar_thermal': 0,
'biomass': 140,
'oil': 130,
'coal': 100
}
for src in source_costs:
if 'bus_' + src in berlin_e_system.groups:
solph.Source(label=src,
outputs={
berlin_e_system.groups['bus_' + src]:
solph.Flow(variable_costs=source_costs[src])
})
else:
logging.warning("No need for a {0} source.".format(src))
# import pprint as pp
# pp.pprint(berlin_e_system.groups)
logging.info('Optimise the energy system')
om = solph.OperationalModel(berlin_e_system)
filename = os.path.join(helpers.extend_basic_path('lp_files'),
'storage_invest.lp')
logging.info('Store lp-file in {0}.'.format(filename))
om.write(filename, io_options={'symbolic_solver_labels': True})
logging.info('Solve the optimization problem')
om.solve(solver='gurobi', solve_kwargs={'tee': True})
berlin_e_system.dump('/home/uwe/')
return berlin_e_system
def run_model(config_path, team_number):
with open(config_path, 'r') as ymlfile:
cfg = yaml.load(ymlfile, Loader=yaml.CLoader)
if cfg['debug']:
number_of_time_steps = 3
else:
number_of_time_steps = 8760
solver = cfg['solver']
debug = cfg['debug']
periods = number_of_time_steps
solver_verbose = cfg['solver_verbose'] # show/hide solver output
logger.define_logging(logfile='model_team_{0}.log'.format(team_number + 1),
screen_level=logging.INFO,
file_level=logging.DEBUG)
logging.info('Initialize the energy system')
date_time_index = pd.date_range('1/1/2019',
periods=number_of_time_steps,
freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
# timestr = time.strftime("%Y%m%d")
##########################################################################
# Einlesen der Zeitreihen und Variablen aus den Dateien
##########################################################################
abs_path = os.path.dirname(os.path.abspath(os.path.join(__file__, '..')))
file_path_ts = abs_path + '/data_raw/' + cfg['time_series_file_name']
data = pd.read_csv(file_path_ts, sep=';')
file_name_param_01 = cfg['design_parameters_file_name'][team_number]
file_path_param_01 = (abs_path + '/data/' + file_name_param_01)
param_df_01 = pd.read_csv(file_path_param_01,
index_col=1,
sep=';',
encoding='unicode_escape')
param_value = param_df_01['value']
# periodische Kosten
epc_PV = economics.annuity(capex=param_value['capex_PV'],
n=param_value['n_PV'],
wacc=param_value['wacc'])
epc_Solarthermie = economics.annuity(capex=param_value['capex_Sol'],
n=param_value['n_Sol'],
wacc=param_value['wacc'])
epc_gas_boiler = economics.annuity(capex=param_value['capex_Gaskessel'],
n=param_value['n_Gaskessel'],
wacc=param_value['wacc'])
epc_BHKW = economics.annuity(capex=param_value['capex_BHKW'],
n=param_value['n_BHKW'],
wacc=param_value['wacc'])
epc_heat_pump = economics.annuity(capex=param_value['capex_Waermepumpe'],
n=param_value['n_Waermepumpe'],
wacc=param_value['wacc'])
epc_el_storage = economics.annuity(
capex=param_value['capex_Stromspeicher'],
n=param_value['n_Stromspeicher'],
wacc=param_value['wacc'])
epc_th_storage = economics.annuity(
capex=param_value['capex_Waermespeicher'],
n=param_value['n_Waermespeicher'],
wacc=param_value['wacc'])
##########################################################################
# Erstellung der Oemof-Komponenten
##########################################################################
logging.info('Create oemof objects')
### Definition der bus'es #################################################
# Erdgasbus
b_gas = solph.Bus(label="Erdgas")
# Strombus
b_el = solph.Bus(label="Strom")
# Waermebus
b_th = solph.Bus(label="Waerme")
# Hinzufügen zum Energiesystem
energysystem.add(b_gas, b_el, b_th)
### Definition des Ueberschusses #########################################
# Senke fuer Stromueberschusss
energysystem.add(
solph.Sink(label='excess_bel', inputs={b_el: solph.Flow()}))
# Senke fuer Waermeueberschuss
energysystem.add(
solph.Sink(label='excess_bth', inputs={b_th: solph.Flow()}))
### Definition Quellen #################################################
# Quelle: Erdgasnetz
energysystem.add(
solph.Source(label='Gasnetz',
outputs={
b_gas:
solph.Flow(variable_costs=param_value['vc_gas'] +
param_value['vc_CO2'])
})) #[€/kWh]
# Quelle: Stromnetz
energysystem.add(
solph.Source(label='Strombezug',
outputs={
b_el: solph.Flow(variable_costs=param_value['vc_el'])
})) #[€/kWh]
# Quelle: Waermenetz/Fernwaerme
energysystem.add(
solph.Source(label='Waermebezug',
outputs={
b_th: solph.Flow(variable_costs=param_value['vc_th'])
})) #[€/kWh]
# Quelle: Solaranlage
if param_value['A_Kollektor_gesamt'] > 0:
energysystem.add(
solph.Source(
label='PV',
outputs={
b_el:
solph.Flow(
actual_value=(data['Sol_irradiation [Wh/sqm]'] *
0.001 * param_value['cf_PV']), #[kWh/m²]
fixed=True,
investment=solph.Investment(
ep_costs=epc_PV,
minimum=param_value['A_min_PV'] * 1 *
param_value['cf_PV']))
}))
# Quelle: Solarthermieanlage
if param_value['A_Kollektor_gesamt'] > 0:
energysystem.add(
solph.Source(
label='Solarthermie',
outputs={
b_th:
solph.Flow(
actual_value=(data['Sol_irradiation [Wh/sqm]'] *
0.001 *
param_value['cf_Sol']), #[kWh/m²]
fixed=True,
investment=solph.Investment(
ep_costs=epc_Solarthermie,
minimum=param_value['A_min_Sol'] * 1 *
param_value['cf_Sol']))
}))
### Definition Bedarf ################################################
# Strombedarf
energysystem.add(
solph.Sink(
label='Strombedarf',
inputs={
b_el:
solph.Flow(
actual_value=data['P*'],
nominal_value=param_value['W_el'], #[kWh]
fixed=True)
}))
# Waermebedarf
energysystem.add(
solph.Sink(
label='Waermebedarf',
inputs={
b_th:
solph.Flow(
actual_value=data['Q*'],
nominal_value=param_value['W_th'], #[kWh]
fixed=True)
}))
### Definition Systemkomponenten #########################################
# Transformer: Gaskessel
if param_value['max_Gaskessel'] > 0:
energysystem.add(
solph.Transformer(
label="Gaskessel",
inputs={b_gas: solph.Flow()},
outputs={
b_th:
solph.Flow(investment=solph.Investment(
ep_costs=epc_gas_boiler,
minimum=param_value['min_Gaskessel'], #[kW]
maximum=param_value['max_Gaskessel']))
}, #[kW]
conversion_factors={b_th: param_value['cf_Gaskessel']}))
# Transformer: BHKW
if param_value['max_BHKW'] > 0:
energysystem.add(
solph.Transformer(
label="BHKW",
inputs={b_gas: solph.Flow()},
outputs={
b_el:
solph.Flow(),
b_th:
solph.Flow(investment=solph.Investment(
ep_costs=epc_BHKW,
minimum=param_value['min_BHKW'], #[kW]
maximum=param_value['max_BHKW']))
}, #[kW]
conversion_factors={
b_el: param_value['cf_BHKW_el'],
b_th: 0.85 - param_value['cf_BHKW_el']
}))
# Transformer: Waermepumpe
if param_value['max_Waermepumpe'] > 0:
energysystem.add(
solph.Transformer(
label="Waermepumpe",
inputs={b_el: solph.Flow()},
outputs={
b_th:
solph.Flow(investment=solph.Investment(
ep_costs=epc_heat_pump,
minimum=param_value['min_Waermepumpe'], #[kW]
maximum=param_value['max_Waermepumpe']))
}, #[kW]
conversion_factors={b_th: param_value['COP_Waermepumpe']}))
# Speicher: Stromspeicher
if param_value['max_Stromspeicher'] > 0:
Stromspeicher = solph.components.GenericStorage(
label='Stromspeicher',
inputs={b_el: solph.Flow()},
outputs={b_el: solph.Flow()},
loss_rate=param_value['lr_Stromspeicher'],
initial_storage_level=param_value['isl_Stromspeicher'],
inflow_conversion_factor=param_value['cf_Stromspeicher_ein'],
outflow_conversion_factor=param_value['cf_Stromspeicher_aus'],
investment=solph.Investment(
ep_costs=epc_el_storage,
minimum=param_value['min_Stromspeicher'], #[kWh]
maximum=param_value['max_Stromspeicher'])) #[kWh]
energysystem.add(Stromspeicher)
# Speicher: Waermespeicher
if param_value['max_Waermespeicher'] > 0:
Waermespeicher = solph.components.GenericStorage(
label='Waermespeicher',
inputs={b_th: solph.Flow()},
outputs={b_th: solph.Flow()},
loss_rate=param_value['lr_Waermespeicher'],
initial_storage_level=param_value['isl_Waermespeicher'],
inflow_conversion_factor=param_value['cf_Waermespeicher_ein'],
outflow_conversion_factor=param_value['cf_Waermespeicher_aus'],
investment=solph.Investment(
ep_costs=epc_th_storage,
minimum=param_value['min_Waermespeicher'], #[kWh]
maximum=param_value['max_Waermespeicher'])) #[kWh]
energysystem.add(Waermespeicher)
logging.info('Optimise the energy system')
# Initialisierung des Modells
model = solph.Model(energysystem)
##########################################################################
# Constraint fuer Kollektorgesamtflaeche
##########################################################################
PV_installed = energysystem.groups['PV']
Sol_installed = energysystem.groups['Solarthermie']
myconstrains = po.Block()
model.add_component('MyBlock', myconstrains)
myconstrains.collector_area = po.Constraint(expr=(
(((model.InvestmentFlow.invest[PV_installed, b_el]) /
(1 * param_value['cf_PV'])) +
((model.InvestmentFlow.invest[Sol_installed, b_th]) /
(1 * param_value['cf_Sol']))) <= param_value['A_Kollektor_gesamt']))
##########################################################################
# Optimierung des Systems und Speichern des Ergebnisses
##########################################################################
if debug:
filename = os.path.join(helpers.extend_basic_path('lp_files'),
'model_team_{0}.lp'.format(team_number + 1))
logging.info('Store lp-file in {0}.'.format(filename))
model.write(filename, io_options={'symbolic_solver_labels': True})
# if tee_switch is true solver messages will be displayed
logging.info(
'Solve the optimization problem of team {0}'.format(team_number + 1))
model.solve(solver=solver, solve_kwargs={'tee': solver_verbose})
logging.info('Store the energy system with the results.')
# add results to the energy system to make it possible to store them.
energysystem.results['main'] = outputlib.processing.results(model)
energysystem.results['meta'] = outputlib.processing.meta_results(model)
results = energysystem.results['main']
energysystem.dump(dpath=abs_path + "/results",
filename="model_team_{0}.oemof".format(team_number + 1))
def optimise_storage_size(filename="storage_invest.csv",
solvername='cbc',
debug=True,
number_timesteps=8760,
tee_switch=True):
logging.info('Initialize the energy system')
date_time_index = pd.date_range('1/1/' + str(year),
periods=number_timesteps,
freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
# Read data file
full_filename = os.path.join(os.path.dirname(__file__), filename)
data = pd.read_csv(full_filename, sep=",")
data_demand = data['demand_el'] / data['demand_el'].sum()
##########################################################################
# Create oemof object
##########################################################################
consumption = 5165 * 1e6
wind_installed = 1516 * 1e3
pv_installed = 1491 * 1e3
grid_share = 0.75
logging.info('Create oemof objects')
# create gas bus
bgas = solph.Bus(label="natural_gas")
# create electricity bus
bel = solph.Bus(label="electricity")
# create excess component for the electricity bus to allow overproduction
solph.Sink(label='excess_bel', inputs={bel: solph.Flow()})
# Create commodity object for import electricity resource
solph.Source(label='gridsource',
outputs={
bel:
solph.Flow(nominal_value=consumption * grid_share *
number_timesteps / 8760,
summed_max=1)
})
# create fixed source object for wind
solph.Source(label='wind',
outputs={
bel:
solph.Flow(actual_value=wind_feedin,
nominal_value=wind_installed,
fixed=True,
fixed_costs=20)
})
# create fixed source object for pv
solph.Source(label='pv',
outputs={
bel:
solph.Flow(actual_value=pv_feedin,
nominal_value=pv_installed,
fixed=True,
fixed_costs=15)
})
# create simple sink object for demand
solph.Sink(label='demand',
inputs={
bel:
solph.Flow(actual_value=data_demand,
fixed=True,
nominal_value=consumption)
})
# Calculate ep_costs from capex to compare with old solph
capex = 1000
lifetime = 20
wacc = 0.05
epc = capex * (wacc * (1 + wacc)**lifetime) / ((1 + wacc)**lifetime - 1)
# create storage transformer object for storage
solph.Storage(
label='storage',
inputs={bel: solph.Flow(variable_costs=10e10)},
outputs={bel: solph.Flow(variable_costs=10e10)},
capacity_loss=0.00,
initial_capacity=0,
nominal_input_capacity_ratio=1,
nominal_output_capacity_ratio=1,
inflow_conversion_factor=1,
outflow_conversion_factor=0.8,
fixed_costs=35,
investment=solph.Investment(ep_costs=epc),
)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
om = solph.OperationalModel(energysystem)
if debug:
filename = os.path.join(helpers.extend_basic_path('lp_files'),
'storage_invest.lp')
logging.info('Store lp-file in {0}.'.format(filename))
om.write(filename, io_options={'symbolic_solver_labels': True})
logging.info('Solve the optimization problem')
om.solve(solver=solvername, solve_kwargs={'tee': tee_switch})
return energysystem
def optimise_storage_size(filename="storage_investment.csv",
solver='cbc',
debug=True,
number_timesteps=8760,
tee_switch=True):
logging.info('Initialize the energy system')
date_time_index = pd.date_range('1/1/2012',
periods=number_timesteps,
freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
# Read data file
full_filename = os.path.join(os.path.dirname(__file__), filename)
data = pd.read_csv(full_filename, sep=",")
##########################################################################
# Create oemof object
##########################################################################
logging.info('Create oemof objects')
# create natural gas bus
bgas = solph.Bus(label="natural_gas")
# create electricity bus
bel = solph.Bus(label="electricity")
# create excess component for the electricity bus to allow overproduction
solph.Sink(label='excess_bel', inputs={bel: solph.Flow()})
# create source object representing the natural gas commodity (annual limit)
solph.Source(label='rgas',
outputs={
bgas:
solph.Flow(nominal_value=194397000 * number_timesteps /
8760,
summed_max=1)
})
# create fixed source object representing wind power plants
solph.Source(label='wind',
outputs={
bel:
solph.Flow(actual_value=data['wind'],
nominal_value=1000000,
fixed=True,
fixed_costs=20)
})
# create fixed source object representing pv power plants
solph.Source(label='pv',
outputs={
bel:
solph.Flow(actual_value=data['pv'],
nominal_value=582000,
fixed=True,
fixed_costs=15)
})
# create simple sink object representing the electrical demand
solph.Sink(label='demand',
inputs={
bel:
solph.Flow(actual_value=data['demand_el'],
fixed=True,
nominal_value=1)
})
# create simple transformer object representing a gas power plant
solph.LinearTransformer(
label="pp_gas",
inputs={bgas: solph.Flow()},
outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=50)},
conversion_factors={bel: 0.58})
# If the period is one year the equivalent periodical costs (epc) of an
# investment are equal to the annuity. Use oemof's economic tools.
epc = economics.annuity(capex=1000, n=20, wacc=0.05)
# create storage object representing a battery
solph.Storage(
label='storage',
inputs={bel: solph.Flow(variable_costs=10e10)},
outputs={bel: solph.Flow(variable_costs=10e10)},
capacity_loss=0.00,
initial_capacity=0,
nominal_input_capacity_ratio=1 / 6,
nominal_output_capacity_ratio=1 / 6,
inflow_conversion_factor=1,
outflow_conversion_factor=0.8,
fixed_costs=35,
investment=solph.Investment(ep_costs=epc),
)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
# initialise the operational model
om = solph.OperationalModel(energysystem)
# if debug is true an lp-file will be written
if debug:
filename = os.path.join(helpers.extend_basic_path('lp_files'),
'storage_invest.lp')
logging.info('Store lp-file in {0}.'.format(filename))
om.write(filename, io_options={'symbolic_solver_labels': True})
# if tee_switch is true solver messages will be displayed
logging.info('Solve the optimization problem')
om.solve(solver=solver, solve_kwargs={'tee': tee_switch})
return energysystem
def define_logging(logpath=None,
logfile='oemof.log',
file_format=None,
screen_format=None,
file_datefmt=None,
screen_datefmt=None,
screen_level=logging.INFO,
file_level=logging.DEBUG,
log_version=True,
log_path=True,
timed_rotating=None):
r"""Initialise customisable logger.
Parameters
----------
logfile : str
Name of the log file, default: oemof.log
logpath : str
The path for log files. By default a ".oemof' folder is created in your
home directory with subfolder called 'log_files'.
file_format : str
Format of the file output.
Default: "%(asctime)s - %(levelname)s - %(module)s - %(message)s"
screen_format : str
Format of the screen output.
Default: "%(asctime)s-%(levelname)s-%(message)s"
file_datefmt : str
Format of the datetime in the file output. Default: None
screen_datefmt : str
Format of the datetime in the screen output. Default: "%H:%M:%S"
screen_level : int
Level of logging to stdout. Default: 20 (logging.INFO)
file_level : int
Level of logging to file. Default: 10 (logging.DEBUG)
log_version : boolean
If True the actual version or commit is logged while initialising the
logger.
log_path : boolean
If True the used file path is logged while initialising the logger.
timed_rotating : dict
Option to pass parameters to the TimedRotatingFileHandler.
Returns
-------
str : Place where the log file is stored.
Notes
-----
By default the INFO level is printed on the screen and the DEBUG level
in a file, but you can easily configure the logger.
Every module that wants to create logging messages has to import the
logging module. The oemof logger module has to be imported once to
initialise it.
Examples
--------
To define the default logger you have to import the python logging
library and this function. The first logging message should be the
path where the log file is saved to.
>>> import logging
>>> from oemof.tools import logger
>>> mypath = logger.define_logging(
... log_path=True, log_version=True, timed_rotating={'backupCount': 4},
... screen_level=logging.ERROR, screen_datefmt = "no_date")
>>> mypath[-9:]
'oemof.log'
>>> logging.debug("Hallo")
"""
if logpath is None:
logpath = helpers.extend_basic_path('log_files')
file = os.path.join(logpath, logfile)
log = logging.getLogger('')
# Remove existing handlers to avoid interference.
log.handlers = []
log.setLevel(logging.DEBUG)
if file_format is None:
file_format = (
"%(asctime)s - %(levelname)s - %(module)s - %(message)s")
file_formatter = logging.Formatter(file_format, file_datefmt)
if screen_format is None:
screen_format = "%(asctime)s-%(levelname)s-%(message)s"
if screen_datefmt is None:
screen_datefmt = "%H:%M:%S"
screen_formatter = logging.Formatter(screen_format, screen_datefmt)
tmp_formatter = logging.Formatter("%(message)s")
ch = logging.StreamHandler(sys.stdout)
ch.setFormatter(screen_formatter)
ch.setLevel(screen_level)
log.addHandler(ch)
timed_rotating_p = {'when': 'midnight', 'backupCount': 10}
if timed_rotating is not None:
timed_rotating_p.update(timed_rotating)
fh = handlers.TimedRotatingFileHandler(file, **timed_rotating_p)
fh.setFormatter(tmp_formatter)
fh.setLevel(file_level)
log.addHandler(fh)
logging.debug("******************************************************")
fh.setFormatter(file_formatter)
if log_path:
logging.info("Path for logging: {0}".format(file))
if log_version:
logging.info("Used oemof version: {0}".format(get_version()))
return file
def optimise_storage_size(filename="storage_invest.csv", solvername='cbc',
debug=True, number_timesteps=8760, tee_switch=True):
logging.info('Initialize the energy system')
date_time_index = pd.date_range('1/1/' + str(year), periods=number_timesteps,
freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
# Read data file
full_filename = os.path.join(os.path.dirname(__file__), filename)
data = pd.read_csv(full_filename, sep=",")
data_demand = data['demand_el']/data['demand_el'].sum()
##########################################################################
# Create oemof object
##########################################################################
consumption = 2255 * 1e6
wind_installed = 1000 * 1e3
pv_installed = 582 * 1e3
grid_share = 0.05
logging.info('Create oemof objects')
# create gas bus
bgas = solph.Bus(label="natural_gas")
# create electricity bus
bel = solph.Bus(label="electricity")
# create excess component for the electricity bus to allow overproduction
solph.Sink(label='excess_bel', inputs={bel: solph.Flow()})
# Create commodity object for import electricity resource
solph.Source(label='gridsource', outputs={bel: solph.Flow(
nominal_value=consumption * grid_share * number_timesteps / 8760,
summed_max=1)})
# create fixed source object for wind
solph.Source(label='wind', outputs={bel: solph.Flow(
actual_value=wind_feedin, nominal_value=wind_installed, fixed=True,
fixed_costs=20)})
# create fixed source object for pv
solph.Source(label='pv', outputs={bel: solph.Flow(
actual_value=pv_feedin, nominal_value=pv_installed, fixed=True,
fixed_costs=15)})
# create simple sink object for demand
solph.Sink(label='demand', inputs={bel: solph.Flow(
actual_value=data_demand, fixed=True, nominal_value=consumption)})
# Calculate ep_costs from capex to compare with old solph
capex = 1000
lifetime = 20
wacc = 0.05
epc = capex * (wacc * (1 + wacc) ** lifetime) / ((1 + wacc) ** lifetime - 1)
# create storage transformer object for storage
solph.Storage(
label='storage',
inputs={bel: solph.Flow(variable_costs=10e10)},
outputs={bel: solph.Flow(variable_costs=10e10)},
capacity_loss=0.00, initial_capacity=0,
nominal_input_capacity_ratio=1,
nominal_output_capacity_ratio=1,
inflow_conversion_factor=1, outflow_conversion_factor=0.8,
fixed_costs=35,
investment=solph.Investment(ep_costs=epc),
)
##########################################################################
# Optimise the energy system and plot the results
##########################################################################
logging.info('Optimise the energy system')
om = solph.OperationalModel(energysystem)
if debug:
filename = os.path.join(
helpers.extend_basic_path('lp_files'), 'storage_invest.lp')
logging.info('Store lp-file in {0}.'.format(filename))
om.write(filename, io_options={'symbolic_solver_labels': True})
logging.info('Solve the optimization problem')
om.solve(solver=solvername, solve_kwargs={'tee': tee_switch})
return energysystem