def run_test_example():
date_time_index = pd.date_range('1/1/2012', periods=5, freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
bgas = solph.Bus(label="natural_gas")
bel = solph.Bus(label="electricity")
solph.Sink(label='excess_bel', inputs={bel: solph.Flow()})
solph.Source(label='rgas', outputs={bgas: solph.Flow()})
solph.Sink(label='demand',
inputs={
bel:
solph.Flow(actual_value=[10, 20, 30, 40, 50],
fixed=True,
nominal_value=1)
})
solph.LinearTransformer(
label="pp_gas",
inputs={bgas: solph.Flow()},
outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=50)},
conversion_factors={bel: 0.58})
om = solph.OperationalModel(energysystem)
# check solvers
solver = dict()
for s in ['cbc', 'glpk', 'gurobi', 'cplex']:
try:
om.solve(solver=s)
solver[s] = "working"
except Exception:
solver[s] = "not working"
print("*********")
print('Solver installed with oemof:')
for s, t in solver.items():
print("{0}: {1}".format(s, t))
print("*********")
print("oemof successfully installed.")
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 heating_systems(esystem, dfull, add_elec, p):
power_plants = prepare_data.chp_berlin(p)
time_index = esystem.time_idx
temperature_path = '/home/uwe/rli-lokal/git_home/demandlib/examples'
temperature_file = temperature_path + '/example_data.csv'
temperature = pd.read_csv(temperature_file)['temperature']
sli = pd.Series(list(temperature.loc[:23]), index=list(range(8760, 8784)))
temperature = temperature.append(sli)
temperature = temperature.iloc[0:len(time_index)]
heatbus = dict()
hd = dict()
auxiliary_energy = 0
print(dfull)
for h in dfull.keys():
hd.setdefault(h, 0)
lsink = 'demand_{0}'.format(h)
lbus = 'bus_{0}'.format(h)
ltransf = '{0}'.format(h)
lres_bus = 'bus_{0}'.format(p.heating2resource[h])
for b in dfull[h].keys():
if b.upper() in p.bdew_types:
bc = 0
if b.upper() in ['EFH', 'MFH']:
bc = 1
hd[h] += bdew.HeatBuilding(time_index,
temperature=temperature,
shlp_type=b,
building_class=bc,
wind_class=1,
annual_heat_demand=dfull[h][b],
name=h).get_bdew_profile()
if b.upper() in ['EFH', 'MFH']:
print(h, 'in')
auxiliary_energy += bdew.HeatBuilding(
time_index,
temperature=temperature,
shlp_type=b,
building_class=bc,
wind_class=1,
annual_heat_demand=add_elec[h][b],
name=h).get_bdew_profile()
elif b in [
'i',
]:
hd[h] += pprofiles.IndustrialLoadProfile(
time_index).simple_profile(annual_demand=dfull[h][b])
else:
logging.error('Demandlib typ "{0}" not found.'.format(b))
heatbus[h] = solph.Bus(label=lbus)
solph.Sink(label=lsink,
inputs={
heatbus[h]:
solph.Flow(actual_value=hd[h].div(hd[h].max()),
fixed=True,
nominal_value=hd[h].max())
})
if 'district' not in h:
if lres_bus not in esystem.groups:
solph.Bus(label=lres_bus)
solph.LinearTransformer(
label=ltransf,
inputs={esystem.groups[lres_bus]: solph.Flow()},
outputs={
heatbus[h]:
solph.Flow(nominal_value=hd[h].max(), variable_costs=0)
},
conversion_factors={heatbus[h]: 1})
else:
for pp in power_plants[h].index:
lres_bus = 'bus_' + pp
if lres_bus not in esystem.groups:
solph.Bus(label=lres_bus)
solph.LinearTransformer(
label='pp_chp_{0}_{1}'.format(h, pp),
inputs={esystem.groups[lres_bus]: solph.Flow()},
outputs={
esystem.groups['bus_el']:
solph.Flow(
nominal_value=power_plants[h]['power_el'][pp]),
heatbus[h]:
solph.Flow(
nominal_value=power_plants[h]['power_th'][pp])
},
conversion_factors={
esystem.groups['bus_el']: 0.3,
heatbus[h]: 0.4
})
from matplotlib import pyplot as plt
hd_df = pd.DataFrame(hd)
print(hd_df.sum().sum())
print('z_max:', hd_df['district_z'].max())
print('dz_max:', hd_df['district_dz'].max())
print('z_sum:', hd_df['district_z'].sum())
print('dz_sum:', hd_df['district_dz'].sum())
hd_df.plot(colormap='Spectral')
hd_df.to_csv('/home/uwe/hd.csv')
plt.show()
solph.Sink(label='auxiliary_energy',
inputs={
esystem.groups['bus_el']:
solph.Flow(actual_value=auxiliary_energy.div(
auxiliary_energy.max()),
fixed=True,
nominal_value=auxiliary_energy.max())
})
# Create sinks
# Get normalise demand and maximum value of electricity usage
electricity_usage = electricity.DemandElec(time_index)
normalised_demand, max_demand = electricity_usage.solph_sink(
resample='H', reduce=auxiliary_energy)
sum_demand = normalised_demand.sum() * max_demand
print("delec:", "{:.2E}".format(sum_demand))
solph.Sink(label='elec_demand',
inputs={
esystem.groups['bus_el']:
solph.Flow(actual_value=normalised_demand,
fixed=True,
nominal_value=max_demand)
})
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 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('1/1/2012', 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 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 gas resource
solph.Source(label='rgas', outputs={bgas: solph.Flow(
nominal_value=194397000 * number_timesteps / 8760, summed_max=1)})
# create fixed source object for wind
solph.Source(label='wind', outputs={bel: solph.Flow(
actual_value=data['wind'], nominal_value=1000000, fixed=True,
fixed_costs=20)})
# create fixed source object for pv
solph.Source(label='pv', outputs={bel: solph.Flow(
actual_value=data['pv'], nominal_value=582000, fixed=True,
fixed_costs=15)})
# create simple sink object for demand
solph.Sink(label='demand', inputs={bel: solph.Flow(
actual_value=data['demand_el'], fixed=True, nominal_value=1)})
# create simple transformer object for gas powerplant
solph.LinearTransformer(
label="pp_gas",
inputs={bgas: solph.Flow()},
outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=50)},
conversion_factors={bel: 0.58})
# 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=1/6,
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')
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