def test_generic_invest_limit(self):
"""
"""
bus = solph.Bus(label='bus_1')
solph.Source(
label='source_0',
outputs={
bus:
solph.Flow(investment=solph.Investment(ep_costs=50, space=4))
})
solph.Source(
label='source_1',
outputs={
bus:
solph.Flow(investment=solph.Investment(ep_costs=100, space=1))
})
solph.Source(label='source_2',
outputs={
bus:
solph.Flow(investment=solph.Investment(ep_costs=75))
})
om = self.get_om()
om = solph.constraints.additional_investment_flow_limit(om,
"space",
limit=20)
self.compare_lp_files('generic_invest_limit.lp', my_om=om)
def source_dispatchable_fix(model, dict_asset, **kwargs):
r"""
Defines a dispatchable source with a fixed capacity.
See :py:func:`~.source` for more information, including parameters.
Notes
-----
Tested with:
- test_source_dispatchable_fix_normalized_timeseries()
- test_source_dispatchable_fix_timeseries_not_normalized_timeseries()
Returns
-------
Indirectly updated `model` and dict of asset in `kwargs` with the source object.
"""
if TIMESERIES_NORMALIZED in dict_asset:
outputs = {
kwargs[OEMOF_BUSSES][dict_asset[OUTFLOW_DIRECTION]]:
solph.Flow(
label=dict_asset[LABEL],
max=dict_asset[TIMESERIES_NORMALIZED],
existing=dict_asset[INSTALLED_CAP][VALUE],
variable_costs=dict_asset[DISPATCH_PRICE][VALUE],
# add emission_factor for emission contraint
emission_factor=dict_asset[EMISSION_FACTOR][VALUE],
)
}
source_dispatchable = solph.Source(
label=dict_asset[LABEL],
outputs=outputs,
)
else:
if TIMESERIES in dict_asset:
logging.info(
f"Asset {dict_asset[LABEL]} is introduced as a dispatchable source with an availability schedule."
)
logging.debug(
f"The availability schedule is solely introduced because the key {TIMESERIES_NORMALIZED} was not in the asset´s dictionary. \n"
f"It should only be applied to DSO sources. "
f"If the asset should not have this behaviour, please create an issue.",
)
outputs = {
kwargs[OEMOF_BUSSES][dict_asset[OUTFLOW_DIRECTION]]:
solph.Flow(
label=dict_asset[LABEL],
existing=dict_asset[INSTALLED_CAP][VALUE],
variable_costs=dict_asset[DISPATCH_PRICE][VALUE],
)
}
source_dispatchable = solph.Source(
label=dict_asset[LABEL],
outputs=outputs,
)
model.add(source_dispatchable)
kwargs[OEMOF_SOURCE].update({dict_asset[LABEL]: source_dispatchable})
logging.debug(
f"Added: Dispatchable source {dict_asset[LABEL]} (fixed capacity) to bus {dict_asset[OUTFLOW_DIRECTION]}."
)
def define_emission_limit():
bel = solph.Bus(label='electricityBus')
solph.Source(label='source1', outputs={bel: solph.Flow(
nominal_value=100, emission=0.8)})
solph.Source(label='source2', outputs={bel: solph.Flow(
nominal_value=100)})
om = self.get_om()
solph.constraints.emission_limit(om, om.flows, limit=777)
def test_flow_without_emission_for_emission_constraint_no_error(self):
"""
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='source1', outputs={bel: solph.Flow(
nominal_value=100, emission=0.8)})
solph.Source(label='source2', outputs={bel: solph.Flow(
nominal_value=100)})
om = self.get_om()
solph.constraints.emission_limit(om, limit=777)
def must_run_source(param, data, busses):
r"""
Get must run source for Generic Model energy system.
Parameters
----------
param : dict
JSON parameter file of user defined constants.
data : pandas.DataFrame
csv file of user defined time dependent parameters.
busses : dict of solph.Bus
Busses of the energy system.
Note
----
Must run source uses the following parameters:
- 'active' is a binary parameter wether is used or not
- 'type' defines wether it is used constant or time dependent
- 'op_cost_var' are the variable operational costs in €/MWh
- 'Q_N' is the constant nominal value in MWh
- 'Q_MR' is the time series of nominal value in MWh
Topology
--------
Input: none
Output: High temperature heat network (wnw)
"""
if param['MR']['active']:
if param['MR']['type'] == 'constant':
must_run_source = solph.Source(
label='Mustrun',
outputs={
busses['wnw']:
solph.Flow(variable_costs=param['MR']['op_cost_var'],
nominal_value=float(param['MR']['Q_N']),
actual_value=1)
})
return must_run_source
elif param['MR']['type'] == 'time series':
must_run_source = solph.Source(
label='Mustrun',
outputs={
busses['wnw']:
solph.Flow(variable_costs=param['MR']['op_cost_var'],
nominal_value=1,
actual_value=data['Q_MR'])
})
return must_run_source
def create_fuel_bus_with_source(nodes, fuel, region, input_data):
"""
Parameters
----------
nodes
fuel
region
input_data
Returns
-------
"""
fuel = fuel.replace("_", " ")
cs_data = input_data["commodity sources"].loc[region, fuel]
bus_label = Label("bus", "commodity", fuel, region)
if bus_label not in nodes:
nodes[bus_label] = solph.Bus(label=bus_label)
cs_label = Label("source", "commodity", fuel, region)
co2_price = float(input_data["general"]["co2 price"])
variable_costs = cs_data["emission"] / 1000 * co2_price + cs_data["costs"]
if cs_data.get("annual limit", float("inf")) != float("inf"):
if cs_label not in nodes:
nodes[cs_label] = solph.Source(
label=cs_label,
outputs={
nodes[bus_label]: solph.Flow(
variable_costs=variable_costs,
emission=cs_data["emission"],
nominal_value=cs_data["annual limit"],
summed_max=1,
)
},
)
else:
if cs_label not in nodes:
nodes[cs_label] = solph.Source(
label=cs_label,
outputs={
nodes[bus_label]: solph.Flow(
variable_costs=variable_costs,
emission=cs_data["emission"],
)
},
)
def test_fixed_source_invest_sink(self):
""" Wrong constraints for fixed source + invest sink w. `summed_max`.
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='wind',
outputs={
bel:
solph.Flow(actual_value=[12, 16, 14],
nominal_value=1000000,
fixed=True)
})
solph.Sink(label='excess',
inputs={
bel:
solph.Flow(summed_max=2.3,
variable_costs=25,
max=0.8,
investment=solph.Investment(ep_costs=500,
maximum=10e5,
existing=50))
})
self.compare_lp_files('fixed_source_invest_sink.lp')
def source_non_dispatchable_fix(model, dict_asset, **kwargs):
r"""
Defines a non dispatchable source with a fixed capacity.
See :py:func:`~.source` for more information, including parameters.
Notes
-----
Tested with:
- test_source_non_dispatchable_fix()
Returns
-------
Indirectly updated `model` and dict of asset in `kwargs` with the source object.
"""
outputs = {
kwargs[OEMOF_BUSSES][dict_asset[OUTFLOW_DIRECTION]]:
solph.Flow(
label=dict_asset[LABEL],
fix=dict_asset[TIMESERIES],
nominal_value=dict_asset[INSTALLED_CAP][VALUE],
variable_costs=dict_asset[DISPATCH_PRICE][VALUE],
emission_factor=dict_asset[EMISSION_FACTOR][VALUE],
)
}
source_non_dispatchable = solph.Source(label=dict_asset[LABEL],
outputs=outputs)
model.add(source_non_dispatchable)
kwargs[OEMOF_SOURCE].update({dict_asset[LABEL]: source_non_dispatchable})
logging.debug(
f"Added: Non-dispatchable source {dict_asset[LABEL]} (fixed capacity) to bus {dict_asset[OUTFLOW_DIRECTION]}.",
)
def gas_source(param, busses):
r"""
Get gas source for Generic Model energy system.
Parameters
----------
param : dict
JSON parameter file of user defined constants.
busses : dict of solph.Bus
Busses of the energy system.
Note
----
Gas source uses the following parameters:
- 'gas_price' is the constant price of gas in €/MWh
- 'co2_price' is the constant price of caused emissions in €/t_co2
- 'ef_gas' is the constant emission factor of gas in t_co2/MWh
Topology
--------
Input: none
Output: Gas network (gnw)
"""
gas_source = solph.Source(
label='Gasquelle',
outputs={
busses['gnw']:
solph.Flow(variable_costs=(
param['param']['gas_price'] +
(param['param']['co2_price'] * param['param']['ef_gas'])))
})
return gas_source
def test_equate_variables_constraint(self):
"""Testing the equate_variables function in the constraint module."""
bus1 = solph.Bus(label='Bus1')
storage = solph.components.GenericStorage(
label='storage_constraint',
invest_relation_input_capacity=0.2,
invest_relation_output_capacity=0.2,
inputs={bus1: solph.Flow()},
outputs={bus1: solph.Flow()},
investment=solph.Investment(ep_costs=145))
sink = solph.Sink(
label='Sink',
inputs={
bus1: solph.Flow(investment=solph.Investment(ep_costs=500))
})
source = solph.Source(
label='Source',
outputs={
bus1: solph.Flow(investment=solph.Investment(ep_costs=123))
})
om = self.get_om()
solph.constraints.equate_variables(
om, om.InvestmentFlow.invest[source, bus1],
om.InvestmentFlow.invest[bus1, sink], 2)
solph.constraints.equate_variables(
om, om.InvestmentFlow.invest[source, bus1],
om.GenericInvestmentStorageBlock.invest[storage])
self.compare_lp_files('connect_investment.lp', my_om=om)
def test_investment_flow_grouping(self):
""" Flows of investment sink should be grouped.
The constraint tests uncovered a spurious error where the flows of an
investment `Sink` where not put into the `InvestmentFlow` group,
although the corresponding grouping was present in the energy system.
The error occured in the case where the investment `Sink` was not
instantiated directly after the `Bus` it is connected to.
This test recreates this error scenario and makes sure that the
`InvestmentFlow` group is not empty.
"""
b = solph.Bus(label='Bus')
solph.Source(label='Source', outputs={b: solph.Flow(
actual_value=[12, 16, 14], nominal_value=1000000,
fixed=True)})
solph.Sink(label='Sink', inputs={b: solph.Flow(
summed_max=2.3, variable_costs=25, max=0.8,
investment=Investment(ep_costs=500, maximum=10e5))})
ok_(self.es.groups.get(IF),
("Expected InvestmentFlow group to be nonempty.\n" +
"Got: {}").format(self.es.groups.get(IF)))
def check_oemof_installation(silent=False):
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.Transformer(
label="pp_gas",
inputs={bgas: solph.Flow()},
outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=50)},
conversion_factors={bel: 0.58})
om = solph.Model(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"
if not silent:
print("*********")
print('Solver installed with oemof:')
for s, t in solver.items():
print("{0}: {1}".format(s, t))
print("*********")
print("oemof successfully installed.")
def add_sources(it, labels, gd, nodes, busd):
for i, cs in it.iterrows():
labels['l_3'] = 'source'
if cs['active']:
labels['l_2'] = cs['label_2']
outflow_args = {}
if cs['cost_series']:
print('error: noch nicht angepasst!')
else:
outflow_args['variable_costs'] = cs['variable costs']
nodes.append(
solph.Source(
label=oh.Label(labels['l_1'], labels['l_2'], labels['l_3'],
labels['l_4']),
outputs={
busd[(labels['l_1'], cs['label_2'], 'bus', labels['l_4'])]:
solph.Flow(**outflow_args)
}))
return nodes, busd
def test_nominal_value_to_zero(self):
"""If the nominal value is set to zero nothing should happen.
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='s1', outputs={bel: solph.Flow(nominal_value=0)})
self.compare_lp_files('nominal_value_to_zero.lp')
def create_oemof_model(self, busses, _):
from_grid = solph.Source(label=self.name,
outputs={
busses[self.bus_out]:
solph.Flow(nominal_value=self.power_max,
variable_costs=self.current_ac)
})
return from_grid
def test_special():
date_time_index = pd.date_range('1/1/2012', periods=5, freq='H')
energysystem = solph.EnergySystem(timeindex=date_time_index)
bel = solph.Bus(label='electricityBus')
flow1 = solph.Flow(nominal_value=100, my_factor=0.8)
flow2 = solph.Flow(nominal_value=50)
src1 = solph.Source(label='source1', outputs={bel: flow1})
src2 = solph.Source(label='source2', outputs={bel: flow2})
energysystem.add(bel, src1, src2)
model = solph.Model(energysystem)
flow_with_keyword = {
(src1, bel): flow1,
}
solph.constraints.generic_integral_limit(model,
"my_factor",
flow_with_keyword,
limit=777)
def test_flow_count_limit(self):
"""
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='source1',
outputs={
bel:
solph.Flow(nonconvex=solph.NonConvex(),
nominal_value=100,
emission_factor=[0.5, -1.0, 2.0])
})
solph.Source(label='source2',
outputs={
bel:
solph.Flow(nonconvex=solph.NonConvex(),
nominal_value=100,
emission_factor=3.5)
})
# Should be ignored because emission_factor is not defined.
solph.Source(label='source3',
outputs={
bel:
solph.Flow(nonconvex=solph.NonConvex(),
nominal_value=100)
})
# Should be ignored because it is not NonConvex.
solph.Source(label='source4',
outputs={
bel:
solph.Flow(emission_factor=1.5,
min=0.3,
nominal_value=100)
})
om = self.get_om()
# one of the two flows has to be active
solph.constraints.limit_active_flow_count_by_keyword(om,
"emission_factor",
lower_limit=1,
upper_limit=2)
self.compare_lp_files('flow_count_limit.lp', my_om=om)
def test_that_the_solph_source_warnings_actually_get_raised():
""" Source doesn't warn about potentially erroneous usage.
"""
look_out = network.Bus()
msg = "`Source` 'solph_sink' constructed without `outputs`."
with warnings.catch_warnings(record=True) as w:
solph.Source(label="solph_sink", inputs={look_out: "A typo!"})
ok_(len(w) == 1)
eq_(msg, str(w[-1].message))
def test_emission_constraints(self):
"""
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='source1', outputs={bel: solph.Flow(
nominal_value=100, emission=0.5)})
solph.Source(label='source2', outputs={bel: solph.Flow(
nominal_value=100, emission=0.8)})
# Should be ignored because the emission attribute is not defined.
solph.Source(label='source3', outputs={bel: solph.Flow(
nominal_value=100)})
om = self.get_om()
solph.constraints.emission_limit(om, limit=777)
self.compare_lp_files('emission_limit.lp', my_om=om)
def create_oemof_model(self, busses, _):
energy_source_from_csv = solph.Source(
label=self.name,
outputs={
busses[self.bus_out]:
solph.Flow(
actual_value=self.data.iloc[self.sim_params.i_interval],
nominal_value=self.nominal_value,
fixed=True)
})
return energy_source_from_csv
def test_gradient(self):
"""
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='powerplant', outputs={bel: solph.Flow(
nominal_value=999, variable_costs=23,
positive_gradient={'ub': 0.03, 'costs': 7},
negative_gradient={'ub': 0.05, 'costs': 8})})
self.compare_lp_files('source_with_gradient.lp')
def run_model(params, wind_invest=False, pv_invest=False, storage_invest=False):
logging.info('Initialize the energy system')
energysystem = solph.EnergySystem(timeindex=date_time_index)
Node.registry = energysystem
logging.info('Create oemof objects')
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(nominal_value=params['rgas_nom_val'],
summed_max=1)})
solph.Source(label='wind', outputs={bel: solph.Flow(
actual_value=data['wind'], nominal_value=params['wind_nom_val'], fixed=True)})
solph.Source(label='pv', outputs={bel: solph.Flow(
actual_value=data['pv'], nominal_value=params['pv_nom_val'], fixed=True)})
solph.Sink(label='demand', inputs={bel: solph.Flow(
actual_value=data['demand_el'], fixed=True, nominal_value=1)})
solph.Transformer(
label="pp_gas",
inputs={bgas: solph.Flow()},
outputs={bel: solph.Flow(nominal_value=10e10, variable_costs=50)},
conversion_factors={bel: 0.58})
logging.info('Optimise the energy system')
om = solph.Model(energysystem)
logging.info('Solve the optimization problem')
om.solve(solver='cbc')
energysystem.results['main'] = processing.convert_keys_to_strings(processing.results(om))
energysystem.results['param'] = processing.convert_keys_to_strings(processing.param_results(energysystem))
return energysystem, om
def test_fixed_source_variable_sink(self):
"""Constraint test with a fixed source and a variable sink.
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='wind', outputs={bel: solph.Flow(
actual_value=[.43, .72, .29], nominal_value=10e5, fixed=True)})
solph.Sink(label='excess', inputs={bel: solph.Flow(variable_costs=40)})
self.compare_lp_files('fixed_source_variable_sink.lp')
def test_max_source_min_sink(self):
"""
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='wind', outputs={
bel: solph.Flow(nominal_value=54, max=(.85, .95, .61))})
solph.Sink(label='minDemand', inputs={bel: solph.Flow(
nominal_value=54, min=(.84, .94, .59), variable_costs=14)})
self.compare_lp_files('max_source_min_sink.lp')
def source_non_dispatchable_optimize(model, dict_asset, **kwargs):
r"""
Defines a non dispatchable source with a capacity to be optimized.
See :py:func:`~.source` for more information, including parameters.
Notes
-----
Tested with:
- test_source_non_dispatchable_optimize()
Returns
-------
Indirectly updated `model` and dict of asset in `kwargs` with the source object.
"""
if MAXIMUM_ADD_CAP_NORMALIZED in dict_asset:
maximum = dict_asset[MAXIMUM_ADD_CAP_NORMALIZED][VALUE]
else:
maximum = dict_asset[MAXIMUM_ADD_CAP][VALUE]
if INSTALLED_CAP_NORMALIZED in dict_asset:
existing = dict_asset[INSTALLED_CAP_NORMALIZED][VALUE]
else:
existing = dict_asset[INSTALLED_CAP][VALUE]
outputs = {
kwargs[OEMOF_BUSSES][dict_asset[OUTFLOW_DIRECTION]]:
solph.Flow(
label=dict_asset[LABEL],
fix=dict_asset[TIMESERIES_NORMALIZED],
investment=solph.Investment(
ep_costs=dict_asset[SIMULATION_ANNUITY][VALUE] /
dict_asset[TIMESERIES_PEAK][VALUE],
maximum=maximum,
existing=existing,
),
# variable_costs are devided by time series peak as normalized time series are used as actual_value
variable_costs=dict_asset[DISPATCH_PRICE][VALUE] /
dict_asset[TIMESERIES_PEAK][VALUE],
# add emission_factor for emission contraint
emission_factor=dict_asset[EMISSION_FACTOR][VALUE],
)
}
source_non_dispatchable = solph.Source(label=dict_asset[LABEL],
outputs=outputs)
model.add(source_non_dispatchable)
kwargs[OEMOF_SOURCE].update({dict_asset[LABEL]: source_non_dispatchable})
logging.debug(
f"Added: Non-dispatchable source {dict_asset[LABEL]} (capacity to be optimized) to bus {dict_asset[OUTFLOW_DIRECTION]}."
)
def test_optimal_solution():
es = solph.EnergySystem(timeindex=[1])
bel = solph.Bus(label='bus')
es.add(bel)
es.add(
solph.Sink(
inputs={
bel: solph.Flow(nominal_value=5, actual_value=[1], fixed=True)
}))
es.add(solph.Source(outputs={bel: solph.Flow(variable_costs=5)}))
m = solph.models.Model(es, timeincrement=1)
m.solve('cbc')
m.results()
outputlib.processing.meta_results(m)
def add_buses(it, labels, nodes, busd):
"""
:param it: pd.Dataframe containing tabular information for the creation of
buses
:param labels: dict of label strings
:return:
"""
for i, b in it.iterrows():
labels['l_3'] = 'bus'
if b['active']:
labels['l_2'] = b['label_2']
l_bus = oh.Label(labels['l_1'], labels['l_2'], labels['l_3'],
labels['l_4'])
# check if bus already exists (due to infrastructure)
if l_bus in busd:
print('bus bereits vorhanden:', l_bus)
else:
bus = solph.Bus(label=l_bus)
nodes.append(bus)
busd[l_bus] = bus
if b['excess']:
labels['l_3'] = 'excess'
nodes.append(
solph.Sink(
label=oh.Label(labels['l_1'], labels['l_2'],
labels['l_3'], labels['l_4']),
inputs={
busd[l_bus]:
solph.Flow(variable_costs=b['excess costs'])
}))
if b['shortage']:
labels['l_3'] = 'shortage'
nodes.append(
solph.Source(
label=oh.Label(labels['l_1'], labels['l_2'],
labels['l_3'], labels['l_4']),
outputs={
busd[l_bus]:
solph.Flow(variable_costs=b['shortage costs'])
}))
return nodes, busd
def test_maximum_shutdowns(self):
"""Testing maximum_shutdowns attribute for nonconvex flows."""
bus_t = solph.Bus(label='Bus_C')
solph.Source(label='cheap_plant_maximum_shutdowns',
outputs={
bus_t:
solph.Flow(
nominal_value=10,
min=0.5,
max=1.0,
variable_costs=10,
nonconvex=solph.NonConvex(maximum_shutdowns=2))
})
self.compare_lp_files('maximum_shutdowns.lp')
def test_invest_source_fixed_sink(self):
"""Constraint test with a fixed sink and a dispatch invest source.
"""
bel = solph.Bus(label='electricityBus')
solph.Source(label='pv', outputs={bel: solph.Flow(
max=[45, 83, 65], variable_costs=13,
investment=solph.Investment(ep_costs=123))})
solph.Sink(label='excess', inputs={bel: solph.Flow(
actual_value=[.5, .8, .3], nominal_value=10e4, fixed=True)})
self.compare_lp_files('invest_source_fixed_sink.lp')
def add_upstream_import_export_nodes(nodes, bus, costs):
logging.info("Add upstream prices from {0}".format(costs["name"]))
exp_label = Label("export", "electricity", "all", bus.label.region)
nodes[exp_label] = solph.Sink(
label=exp_label,
inputs={bus: solph.Flow(variable_costs=costs["export"])},
)
imp_label = Label("import", "electricity", "all", bus.label.region)
nodes[imp_label] = solph.Source(
label=imp_label,
outputs={bus: solph.Flow(variable_costs=costs["import"])},
)
return nodes