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