def run_add_constraints_example(solver='cbc'):
# ##### creating an oemof solph optimization model, nothing special here ###
# create an energy system object for the oemof solph nodes
es = EnergySystem(timeindex=pd.date_range('1/1/2012', periods=4, freq='H'))
# add some nodes
boil = Bus(label="oil", balanced=False)
blig = Bus(label="lignite", balanced=False)
b_el = Bus(label="b_el")
Sink(label="Sink",
inputs={b_el: Flow(nominal_value=40,
actual_value=[0.5, 0.4, 0.3, 1],
fixed=True)})
pp_oil = LinearTransformer(label='pp_oil',
inputs={boil: Flow()},
outputs={b_el: Flow(nominal_value=50,
variable_costs=25)},
conversion_factors={b_el: 0.39})
LinearTransformer(label='pp_lig',
inputs={blig: Flow()},
outputs={b_el: Flow(nominal_value=50,
variable_costs=10)},
conversion_factors={b_el: 0.41})
# create the model
om = OperationalModel(es=es)
# add specific emission values to flow objects if source is a commidty bus
for s,t in om.flows.keys():
if s is boil:
om.flows[s,t].emission_factor = 0.27 # t/MWh
if s is blig:
om.flows[s,t].emission_factor = 0.39 # t/MWh
emission_limit = 60e3
# add the outflow share
om.flows[(boil, pp_oil)].outflow_share = [1, 0.5, 0, 0.3]
# Now we are going to add a 'sub-model' and add a user specific constraint
# first we add ad pyomo Block() instance that we can use to add our
# constraints. Then, we add this Block to our previous defined
# OperationalModel instance and add the constraints.
myblock = po.Block()
# create a pyomo set with the flows (i.e. list of tuples),
# there will of course be only one flow inside this set, the one we used to
# add outflow_share
myblock.MYFLOWS = po.Set(initialize=[k for (k, v) in om.flows.items()
if hasattr(v, 'outflow_share')])
# pyomo does not need a po.Set, we can use a simple list as well
myblock.COMMODITYFLOWS = [k for (k,v) in om.flows.items()
if hasattr(v, 'emission_factor')]
# add the submodel to the oemof OperationalModel instance
om.add_component('MyBlock', myblock)
# pyomo rule definition
# here we can use all objects from the block or the om object, in this case
# we don't need anything from the block except the newly defined set MYFLOWS
def _inflow_share_rule(m, s, e, t):
"""
"""
expr = (om.flow[s, e, t] >= om.flows[s, e].outflow_share[t] *
sum(om.flow[i, o, t] for (i, o) in om.FLOWS if o==e))
return expr
myblock.inflow_share = po.Constraint(myblock.MYFLOWS, om.TIMESTEPS,
rule=_inflow_share_rule)
# add emission constraint
myblock.emission_constr = po.Constraint(expr=(
sum(om.flow[i, o, t] for (i,o) in myblock.COMMODITYFLOWS
for t in om.TIMESTEPS) <= emission_limit))
# solve and write results to dictionary
# you may print the model with om.pprint()
om.solve()
def run_add_constraints_example(solver='cbc', nologg=False):
if not nologg:
logging.basicConfig(level=logging.INFO)
# ### creating an oemof solph optimization model, nothing special here ###
# create an energy system object for the oemof solph nodes
es = EnergySystem(timeindex=pd.date_range('1/1/2012', periods=4, freq='H'))
# add some nodes
boil = Bus(label="oil", balanced=False)
blig = Bus(label="lignite", balanced=False)
b_el = Bus(label="b_el")
Sink(label="Sink",
inputs={
b_el:
Flow(nominal_value=40,
actual_value=[0.5, 0.4, 0.3, 1],
fixed=True)
})
pp_oil = LinearTransformer(
label='pp_oil',
inputs={boil: Flow()},
outputs={b_el: Flow(nominal_value=50, variable_costs=25)},
conversion_factors={b_el: 0.39})
LinearTransformer(
label='pp_lig',
inputs={blig: Flow()},
outputs={b_el: Flow(nominal_value=50, variable_costs=10)},
conversion_factors={b_el: 0.41})
# create the model
om = OperationalModel(es=es)
# add specific emission values to flow objects if source is a commodity bus
for s, t in om.flows.keys():
if s is boil:
om.flows[s, t].emission_factor = 0.27 # t/MWh
if s is blig:
om.flows[s, t].emission_factor = 0.39 # t/MWh
emission_limit = 60e3
# add the outflow share
om.flows[(boil, pp_oil)].outflow_share = [1, 0.5, 0, 0.3]
# Now we are going to add a 'sub-model' and add a user specific constraint
# first we add ad pyomo Block() instance that we can use to add our
# constraints. Then, we add this Block to our previous defined
# OperationalModel instance and add the constraints.
myblock = po.Block()
# create a pyomo set with the flows (i.e. list of tuples),
# there will of course be only one flow inside this set, the one we used to
# add outflow_share
myblock.MYFLOWS = po.Set(initialize=[
k for (k, v) in om.flows.items() if hasattr(v, 'outflow_share')
])
# pyomo does not need a po.Set, we can use a simple list as well
myblock.COMMODITYFLOWS = [
k for (k, v) in om.flows.items() if hasattr(v, 'emission_factor')
]
# add the sub-model to the oemof OperationalModel instance
om.add_component('MyBlock', myblock)
def _inflow_share_rule(m, s, e, t):
"""pyomo rule definition: Here we can use all objects from the block or
the om object, in this case we don't need anything from the block
except the newly defined set MYFLOWS.
"""
expr = (om.flow[s, e, t] >= om.flows[s, e].outflow_share[t] *
sum(om.flow[i, o, t] for (i, o) in om.FLOWS if o == e))
return expr
myblock.inflow_share = po.Constraint(myblock.MYFLOWS,
om.TIMESTEPS,
rule=_inflow_share_rule)
# add emission constraint
myblock.emission_constr = po.Constraint(
expr=(sum(om.flow[i, o, t] for (i, o) in myblock.COMMODITYFLOWS
for t in om.TIMESTEPS) <= emission_limit))
# solve and write results to dictionary
# you may print the model with om.pprint()
om.solve(solver=solver)
logging.info("Successfully finished.")
