def convert_prob(self):
self.logger.info("Converting optimization problem")
self.model.con_list = ConstraintList()
# Set of objective functions
self.model.Os = Set(ordered=True, initialize=[o + 2 for o in self.iter_obj2])
# Slack for objectives introduced as constraints
self.model.Slack = Var(self.model.Os, within=NonNegativeReals)
self.model.e = Param(
self.model.Os,
initialize=[np.nan for _ in self.model.Os],
within=Any,
mutable=True,
) # RHS of constraints
# Add p-1 objective functions as constraints
for o in range(1, self.n_obj):
self.model.obj_list[1].expr += self.opts.eps * (
10 ** (-1 * (o - 1)) * self.model.Slack[o + 1] / self.obj_range[o - 1]
)
self.model.con_list.add(
expr=self.model.obj_list[o + 1].expr - self.model.Slack[o + 1]
== self.model.e[o + 1]
)
def _apply_to(self, instance, **kwds):
options = kwds.pop('options', {})
bound = kwds.pop('mpec_bound', 0.0)
#
# Create a mutable parameter that defines the value of the upper bound
# on the constraints
#
bound = options.get('mpec_bound', bound)
instance.mpec_bound = Param(mutable=True, initialize=bound)
#
# Setup transformation data
#
tdata = instance._transformation_data['mpec.simple_nonlinear']
tdata.compl_cuids = []
#
# Iterate over the model finding Complementarity components
#
for complementarity in instance.component_objects(
Complementarity,
active=True,
descend_into=(Block, Disjunct),
sort=SortComponents.deterministic):
block = complementarity.parent_block()
for index in sorted(complementarity.keys()):
_data = complementarity[index]
if not _data.active:
continue
_data.to_standard_form()
#
_type = getattr(_data.c, "_complementarity_type", 0)
if _type == 1:
#
# Constraint expression is bounded below, so we can replace
# constraint c with a constraint that ensures that either
# constraint c is active or variable v is at its lower bound.
#
_data.ccon = Constraint(
expr=(_data.c.body - _data.c.lower) *
_data.v <= instance.mpec_bound)
del _data.c._complementarity_type
elif _type == 3:
#
# Variable v is bounded above and below. We can define
#
_data.ccon_l = Constraint(
expr=(_data.v - _data.v.bounds[0]) *
_data.c.body <= instance.mpec_bound)
_data.ccon_u = Constraint(
expr=(_data.v - _data.v.bounds[1]) *
_data.c.body <= instance.mpec_bound)
del _data.c._complementarity_type
elif _type == 2: #pragma:nocover
raise ValueError(
"to_standard_form does not generate _type 2 expressions"
)
tdata.compl_cuids.append(ComponentUID(complementarity))
block.reclassify_component_type(complementarity, Block)
def beforeChild(self, node, child, child_idx):
if type(child) is IndexTemplate:
return False, child
if type(child) is EXPR.GetItemExpression:
_id = _GetItemIndexer(child)
if _id not in self.templatemap:
self.templatemap[_id] = Param(mutable=True)
self.templatemap[_id].construct()
self.templatemap[_id]._name = "%s[%s]" % (
_id.base.name, ','.join(str(x) for x in _id.args))
return False, self.templatemap[_id]
return super().beforeChild(node, child, child_idx)
def visiting_potential_leaf(self, node):
if type(node) is IndexTemplate:
return True, node
if type(node) is EXPR.GetItemExpression:
_id = _GetItemIndexer(node)
if _id not in self.templatemap:
self.templatemap[_id] = Param(mutable=True)
self.templatemap[_id].construct()
self.templatemap[_id]._name = "%s[%s]" % (
_id.base.name, ','.join(str(x) for x in _id.args))
return True, self.templatemap[_id]
return super(Pyomo2Scipy_Visitor, self).visiting_potential_leaf(node)
def visiting_potential_leaf(self, node):
if type(node) in native_numeric_types or \
not node.is_expression_type() or\
type(node) is IndexTemplate:
return True, node
if type(node) is EXPR.GetItemExpression:
_id = _GetItemIndexer(node)
if _id not in self.templatemap:
self.templatemap[_id] = Param(mutable=True)
self.templatemap[_id].construct()
_args = []
self.templatemap[_id]._name = "%s[%s]" % (
node._base.name, ','.join(str(x) for x in _id._args))
return True, self.templatemap[_id]
return False, None
def make_noisy(self, cov_dict, conf_level=2):
self.d1.name = "Noisy plant (d1)"
k = 0
for x in self.states:
s = getattr(self.d1, x) #: state
xicc = getattr(self.d1, x + "_icc")
xicc.deactivate()
for j in self.state_vars[x]:
self.xp_l.append(s[(1, 0) + j])
self.xp_key[(x, j)] = k
k += 1
self.d1.xS_pnoisy = Set(initialize=[
i for i in range(0, len(self.xp_l))
]) #: Create set of noisy_states
self.d1.w_pnoisy = Var(self.d1.xS_pnoisy,
initialize=0.0) #: Model disturbance
self.d1.Q_pnoisy = Param(self.d1.xS_pnoisy, initialize=1, mutable=True)
self.d1.obj_fun_noisy = Objective(
sense=maximize,
expr=0.5 * sum(self.d1.Q_pnoisy[k] * self.d1.w_pnoisy[k]**2
for k in self.d1.xS_pnoisy))
self.d1.ics_noisy = ConstraintList()
k = 0
for x in self.states:
s = getattr(self.d1, x) #: state
xic = getattr(self.d1, x + "_ic")
for j in self.state_vars[x]:
expr = s[(1, 1) + j] == xic[j] + self.d1.w_pnoisy[k]
self.d1.ics_noisy.add(expr)
k += 1
for key in cov_dict:
vni = key
v_i = self.xp_key[vni]
self.d1.Q_pnoisy[v_i].value = cov_dict[vni]
self.d1.w_pnoisy[v_i].setlb(-conf_level * cov_dict[vni])
self.d1.w_pnoisy[v_i].setub(conf_level * cov_dict[vni])
with open("debug.txt", "w") as f:
self.d1.Q_pnoisy.display(ostream=f)
self.d1.obj_fun_noisy.pprint(ostream=f)
self.d1.ics_noisy.pprint(ostream=f)
self.d1.w_pnoisy.display(ostream=f)
def set_testl(market_instances_list, airport_list, market_data,
segment_travel_time, time_zone_dict, iti_dict, fleet_data,
passeger_type_dict, attr_value, marketpt_attr_sum, solver):
"""
很多输入变量都是字典，就是为了在模型中生成子集的时候有筛选功能 即 if dict【m】 in dict
:param airport_list:
:param market_data:
:param segment_travel_time:
:param iti_dict:itinerary number key ,info value :{0: {'market': "M('SEA', 'LAX')", 'non_stop': ('SEA', 'LAX'), ('SEA', 'LAX'): 1, 'legs': [('SEA', 'LAX')]}
:param fleet_data:
:param passeger_type_dict: passenger_type as key , and it's proportions
:param attr_value:
:param marketpt_attr_sum:
:param solver:
:return: time_table, q_variable for each itinerary,
"""
market_iti = {} #market:itinerary list
for m in market_instances_list:
l = [
i for i, v in iti_dict.items()
if v['market'] == 'M' + str(m.od_pair)
]
market_iti.update({m: l})
model = ConcreteModel()
market = list(market_data.keys()) #['M' + str(m.od_pair) for m in ]
# market_segment={m:[(market_data[m][4],market_data[m][5])] for m in market_data.keys()}
model.M = Set(initialize=market, doc='Player Market_obj')
model.Mi = Set(list(market_iti.keys()),
initialize=market_iti,
doc='Player Market_obj')
model.AP = Set(initialize=airport_list)
model.Segment = Set(initialize=((i, j) for i in model.AP for j in model.AP
if i != j))
# create time spot [1-72] and it's time
a = np.linspace(1, int((1440 - 360) / 15), int((1440 - 360) / 15))
time_list = np.arange(360, 1440, 15)
time_dict = dict(zip(a, time_list))
# reverse_time_dict = {v: k for k, v in time_dict.items()}
model.T = Set(
initialize=a,
ordered=True,
doc='Time period from 300 min to 1440 ,step :15min,number:73')
model.I = Set(initialize=iti_dict.keys(), doc='itinerary _index,size 4334')
model.Im = Set(initialize=((m, i) for m in model.M for i in market_iti[m]),
doc='tuple(m,i),size 4334') # 筛选出只在m的itinerary 的 号码
model.PT = Set(initialize=passeger_type_dict.keys())
model.F = Set(initialize=fleet_data.keys())
d = {} #create a dict as which OD and time get all itinerary_index in it
for ap1, ap2 in model.Segment:
for t in model.T:
v = []
for i, va in iti_dict.items():
if (ap1, ap2) in va['legs'] and t == va[(ap1, ap2)]:
v.append(i)
d[(ap1, ap2, t)] = v
model.St = Set(
list(d.keys()),
initialize=d) # index as (ap1,ap2,time) get [itinerary index list]
def _filter3(model, i, m, ap1, ap2, t):
return i in model.Mi[m].value and i in model.St[(ap1, ap2, t)].value
# def Parameters
demand_pt = {
} # get demand of pt in the market by total demand times its proportion
print("passenger_type_proportion:", passeger_type_dict)
for m, va in market_data.items():
for ty, rato in passeger_type_dict.items():
demand_pt.update({(m, ty):
va[0] * rato}) # market demand times proportions
model.Dem = Param(model.M,
model.PT,
initialize=demand_pt,
doc='Market demand for each type of passenger')
price_dict = {}
for i in model.I:
rato = np.linspace(1.3, 0.7, len(passeger_type_dict))
for index, ty in enumerate(passeger_type_dict):
if iti_dict[i]['non_stop']:
price_dict.update({
(i, ty):
market_data[iti_dict[i]['market']][2] * rato[index]
}) # market_data[m][2] is price
else:
price_dict.update({
(i, ty):
0.8 * market_data[iti_dict[i]['market']][2] * rato[index]
}) # market_data[m][2] is price
model.p = Param(model.I, model.PT, initialize=price_dict)
model.Avail = Param(
model.F,
initialize={fleet: value[0]
for fleet, value in fleet_data.items()})
model.Cap = Param(
model.F,
initialize={fleet: value[1]
for fleet, value in fleet_data.items()})
model.distance = Param(model.Segment,
initialize={(value[-2], value[-1]): value[1]
for value in market_data.values()})
def ope_cost(model, ap1, ap2, f):
if model.distance[ap1, ap2] <= 3106:
return (1.6 * model.distance[ap1, ap2] + 722) * (model.Cap[f] +
104) * 0.019
else:
return (1.6 * model.distance[ap1, ap2] + 2200) * (model.Cap[f] +
211) * 0.0115
model.Ope = Param(model.Segment, model.F, initialize=ope_cost, doc='cost')
freq = {(market_data[m][4], market_data[m][5]): market_data[m][3]
for m in market_data.keys()}
model.Freq = Param(model.Segment, initialize=freq)
model.A = Param(model.I, model.PT, initialize=attr_value)
model.Am = Param(model.M, model.PT, initialize=marketpt_attr_sum)
# Step 2: Define decision variables
model.x = Var(model.Segment, model.F, model.T, within=Binary)
model.y_1 = Var(model.F, model.AP, model.T, within=PositiveIntegers)
model.y_2 = Var(model.F, model.AP, model.T, within=PositiveIntegers)
model.q = Var(model.I, model.PT, within=NonNegativeReals)
model.non_q = Var(model.M, model.PT, within=NonNegativeReals
) # number of pax that choose others airine and no fly.
# Step 3: Define Objective
def obj_rule(model):
return sum(model.q[i, pt] * model.p[i, pt] for i in model.I
for pt in model.PT) - sum(model.Ope[s, f] * model.x[s, f, t]
for s in model.Segment
for f in model.F for t in model.T)
model.obj = Objective(rule=obj_rule, sense=maximize)
def obj_cost(model):
return sum(model.Ope[s, f] * model.x[s, f, t] for s in model.Segment
for f in model.F for t in model.T)
model.obj_cost = Expression(rule=obj_cost)
def obj_revenue(model):
return sum(model.q[i, pt] * model.p[i, pt] for i in model.I
for pt in model.PT)
model.obj_revenue = Expression(rule=obj_revenue)
# add constraint
# Aircraft count:
def aircraft_con(model, f):
return sum(model.y_1[f, ap, model.T[1]]
for ap in model.AP) <= model.Avail[f]
model.count = Constraint(model.F, rule=aircraft_con)
# flow balance cons
def flow_balance_1(model, f, ap):
return model.y_1[f, ap, model.T[1]] == model.y_2[f, ap, model.T[-1]]
model.con_fb_1 = Constraint(model.F, model.AP, rule=flow_balance_1)
def flow_balance_2(model, f, ap, t):
# if t == model.T[-1]:
# return Constraint.Skip
# else:
return model.y_1[f, ap, t + 1] == model.y_2[f, ap, t]
def filter2(model, t):
return t != model.T[-1]
model.Tm = Set(initialize=(i for i in model.T if i != model.T[-1]))
#model.con_fb_2 = Constraint(model.F, model.AP, model.T, rule=flow_balance_2)
model.con_fb_2 = Constraint(model.F,
model.AP,
model.Tm,
rule=flow_balance_2)
# time_zone_dict={('ANC', 'PDX'): 60, ('SEA', 'PDX'): 0, ('SEA', 'ANC'): -60, ('ANC', 'LAX'): 60, ('PDX', 'SEA'): 0, ('LAX', 'PDX'): 0,
# ('LAX', 'SEA'): 0, ('PDX', 'ANC'): -60, ('SEA', 'LAX'): 0, ('ANC', 'SEA'): 60, ('LAX', 'ANC'): -60, ('PDX', 'LAX'): 0}
Travel_time = segment_travel_time
def flow_balance_3(model, f, ap, t):
def D(s, t, turnaround=30):
arrival_time = time_dict[t]
dep_time = arrival_time - (Travel_time[s] +
turnaround) - time_zone_dict[s]
if dep_time >= 360:
t0 = ((dep_time - 360) // 15) + 1
else:
t0 = 72 - (abs(360 - dep_time) // 15)
return t0
return model.y_1[f, ap, t] + sum(
model.x[s, f, D(s, t)]
for s in model.Segment if s[1] == ap) == model.y_2[f, ap, t] + sum(
model.x[s, f, t] for s in model.Segment if s[0] == ap)
model.con_fb_3 = Constraint(model.F,
model.AP,
model.T,
rule=flow_balance_3)
# Demand and capacity constrains:
def attract_con(model, market, i, pt):
return model.Am[market, pt] * (
model.q[i, pt] / model.Dem[market, pt]) <= model.A[i, pt] * (
model.non_q[market, pt] / model.Dem[market, pt])
model.attractiveness = Constraint(model.Im, model.PT, rule=attract_con)
def capacity_con(model, ap1, ap2, t):
return sum(model.q[i, pt] for i in d[(ap1, ap2, t)]
for pt in model.PT) <= sum(
model.Cap[f] * model.x[ap1, ap2, f, t] for f in model.F)
model.con_d1 = Constraint(model.Segment, model.T, rule=capacity_con)
def demand_market_con(model, market, pt):
return sum(model.q[i, pt] for i in model.I if iti_dict[i]['market'] == market) + model.non_q[market, pt] == \
model.Dem[market, pt]
model.con_d3 = Constraint(model.M, model.PT, rule=demand_market_con)
# Itinerary selection constraints:
model.AC = Set(initialize=model.I * model.M * model.Segment * model.T,
filter=_filter3)
def iti_selection(model, i, m, ap1, ap2, t, pt):
# if i in market_iti[m] and i in d[(ap1, ap2, t)]:
return sum(model.x[ap1, ap2, f, t]
for f in model.F) >= model.q[i, pt] / model.Dem[m, pt]
model.con_iti_selection = Constraint(model.AC,
model.PT,
rule=iti_selection)
# Restrictions on fight leg variables:
def flight_leg_con(model, ap1, ap2, t):
return sum(model.x[ap1, ap2, f, t] for f in model.F) <= 1
model.con_leg_1 = Constraint(model.Segment, model.T, rule=flight_leg_con)
def freq_con(model, ap1, ap2):
return sum(model.x[ap1, ap2, f, t] for t in model.T
for f in model.F) == model.Freq[ap1, ap2]
model.con_let_2 = Constraint(model.Segment, rule=freq_con)
print("____" * 5)
# for con in model.component_map(Constraint).itervalues():
# con.pprint()
SOLVER_NAME = solver
TIME_LIMIT = 60 * 60 * 2
results = SolverFactory(SOLVER_NAME)
if SOLVER_NAME == 'cplex':
results.options['timelimit'] = TIME_LIMIT
elif SOLVER_NAME == 'glpk':
results.options['tmlim'] = TIME_LIMIT
elif SOLVER_NAME == 'gurobi':
results.options['TimeLimit'] = TIME_LIMIT
com = results.solve(model, tee=True)
com.write()
#absgap = com.solution(0).gap
# get x results in matrix form
df_x = pd.DataFrame(columns=list(model.Segment), index=model.T)
for s in model.Segment:
for t in model.T:
for f in model.F:
if model.x[s, f, t].value > 0:
df_x.loc[t, [s]] = f
#df_x=df_x.reset_index()# return value is a dataframe of new time table
# 所有的决策变量都遍历一遍
# for v in instance.component_objects(Var, active=True):
# print("Variable", v)
# varobject = getattr(instance, str(v))
# for index in varobject:
# print(" ", index, varobject[index].value)
varobject = getattr(model, 'q')
q_data = {(i, pt): varobject[(i, pt)].value
for (i, pt), v in varobject.items() if varobject[(i, pt)] != 0}
df_q = pd.DataFrame.from_dict(q_data,
orient="index",
columns=["variable value"])
varobject2 = getattr(model, 'non_q')
nonq_data = {(m, pt): varobject2[(m, pt)].value
for (m, pt), v in varobject2.items()
if varobject2[(m, pt)] != 0}
# q = list(model.q.get_values().values())
# print('q = ', q)
profit = model.obj()
print('\nProfit = ', profit)
cost = value_s(model.obj_cost())
#revenue=value_s(model.obj_revenue())
print('cost is:' * 10, cost)
#print('revenue is:' * 10, revenue)
'''
print('\nDecision Variables')
#list_of_vars = [v.value for v in model.component_objects(ctype=Var, active=True, descend_into=True)]
#var_names = [v.name for v in model.component_objects(ctype=Var, active=True, descend_into=True) if v.value!=0]
# print("y=",y)
model.obj.display()
def pyomo_postprocess( options=None, instance=None, results=None ):
model.x.display()
pyomo_postprocess(None, model, results)
for v in model.component_objects(Var, active=True):
print("Variable component object", v)
varobject = getattr(model, str(v))
for index in varobject:
if varobject[index].value != 0:
print(" ", index, varobject[index].value)
'''
return df_x, q_data, profit, cost, nonq_data
"""
CSTR from Rodrigo's thesis
Returns:
cstr_rodrigo_dae: The model itmod. Without discretization.
"""
#: if steady == True fallback to steady-state computation
mod = ConcreteModel()
#: mod.nfe_t = nfe_t #:
#: mod.ncp_t = ncp_t
mod.discretized = False
ncstr = 1
mod.ncstr = Set(initialize=[i for i in range(0, ncstr)])
mod.t = ContinuousSet(bounds=(0, 1))
mod.Cainb = Param(default=1.0)
mod.Tinb = Param(default=275.0)
# mod.Tjinb = Param(default=250.0)
#: Our control var
mod.Tjinb = Var(mod.t, initialize=250)
mod.u1 = Param(mod.t, default=250,
mutable=True) #: We are making a sort-of port
def u1_rule(m, i):
return m.Tjinb[i] == m.u1[i]
# mod.u1_cdummy = Constraint(mod.t, rule=lambda m, i: m.Tjinb[i] == mod.u1[i])
mod.u1_cdummy = Constraint(mod.t, rule=u1_rule)
def __init__(self, **kwargs):
NmpcGen.__init__(self, **kwargs)
self.int_file_mhe_suf = int(time.time())-1
# Need a list of relevant measurements y
self.y = kwargs.pop('y', [])
self.y_vars = kwargs.pop('y_vars', {})
# Need a list or relevant noisy-states z
self.x_noisy = kwargs.pop('x_noisy', [])
self.x_vars = kwargs.pop('x_vars', {})
self.deact_ics = kwargs.pop('del_ics', True)
self.diag_Q_R = kwargs.pop('diag_QR', True) #: By default use diagonal matrices for Q and R matrices
self.u = kwargs.pop('u', [])
self.IgnoreProcessNoise = kwargs.pop('IgnoreProcessNoise', False)
print("-" * 120)
print("I[[create_lsmhe]] lsmhe (full) model created.")
print("-" * 120)
nstates = sum(len(self.x_vars[x]) for x in self.x_noisy)
self.journalizer("I", self._c_it, "MHE with \t", str(nstates) + "states")
self.journalizer("I", self._c_it, "MHE with \t", str(nstates*self.nfe_t*self.ncp_t) + "noise vars")
self.lsmhe = self.d_mod(self.nfe_t, self.ncp_t, _t=self._t)
self.lsmhe.name = "LSMHE (Least-Squares MHE)"
self.lsmhe.create_bounds()
#: create x_pi constraint
#: Create list of noisy-states vars
self.xkN_l = []
self.xkN_nexcl = []
self.xkN_key = {}
k = 0
for x in self.x_noisy:
n_s = getattr(self.lsmhe, x) #: Noisy-state
for jth in self.x_vars[x]: #: the jth variable
self.xkN_l.append(n_s[(1, 0) + jth])
self.xkN_nexcl.append(1) #: non-exclusion list for active bounds
self.xkN_key[(x, jth)] = k
k += 1
self.lsmhe.xkNk_mhe = Set(initialize=[i for i in range(0, len(self.xkN_l))]) #: Create set of noisy_states
self.lsmhe.x_0_mhe = Param(self.lsmhe.xkNk_mhe, initialize=0.0, mutable=True) #: Prior-state
self.lsmhe.wk_mhe = Param(self.lsmhe.fe_t, self.lsmhe.cp_ta, self.lsmhe.xkNk_mhe, initialize=0.0) \
if self.IgnoreProcessNoise else Expression(self.lsmhe.fe_t, self.lsmhe.cp_ta, self.lsmhe.xkNk_mhe) #: Model disturbance
self.lsmhe.PikN_mhe = Param(self.lsmhe.xkNk_mhe, self.lsmhe.xkNk_mhe,
initialize=lambda m, i, ii: 1. if i == ii else 0.0, mutable=True) #: Prior-Covariance
self.lsmhe.Q_mhe = Param(range(1, self.nfe_t), self.lsmhe.xkNk_mhe, initialize=1, mutable=True) if self.diag_Q_R\
else Param(range(1, self.nfe_t), self.lsmhe.xkNk_mhe, self.lsmhe.xkNk_mhe,
initialize=lambda m, t, i, ii: 1. if i == ii else 0.0, mutable=True) #: Disturbance-weight
j = 0
for i in self.x_noisy:
de_exp = getattr(self.lsmhe, "de_" + i)
for k in self.x_vars[i]:
for tfe in range(1, self.nfe_t+1):
for tcp in range(1, self.ncp_t + 1):
self.lsmhe.wk_mhe[tfe, tcp, j].set_value(de_exp[(tfe, tcp) + k]._body)
de_exp[(tfe, tcp) + k].deactivate()
j += 1
#: Create list of measurements vars
self.yk_l = {}
self.yk_key = {}
k = 0
self.yk_l[1] = []
for y in self.y:
m_v = getattr(self.lsmhe, y) #: Measured "state"
for jth in self.y_vars[y]: #: the jth variable
self.yk_l[1].append(m_v[(1, self.ncp_t) + jth])
self.yk_key[(y, jth)] = k #: The key needs to be created only once, that is why the loop was split
k += 1
for t in range(2, self.nfe_t + 1):
self.yk_l[t] = []
for y in self.y:
m_v = getattr(self.lsmhe, y) #: Measured "state"
for jth in self.y_vars[y]: #: the jth variable
self.yk_l[t].append(m_v[(t, self.ncp_t) + jth])
self.lsmhe.ykk_mhe = Set(initialize=[i for i in range(0, len(self.yk_l[1]))]) #: Create set of measured_vars
self.lsmhe.nuk_mhe = Var(self.lsmhe.fe_t, self.lsmhe.ykk_mhe, initialize=0.0) #: Measurement noise
self.lsmhe.yk0_mhe = Param(self.lsmhe.fe_t, self.lsmhe.ykk_mhe, initialize=1.0, mutable=True)
self.lsmhe.hyk_c_mhe = Constraint(self.lsmhe.fe_t, self.lsmhe.ykk_mhe,
rule=
lambda mod, t, i:mod.yk0_mhe[t, i] - self.yk_l[t][i] - mod.nuk_mhe[t, i] == 0.0)
self.lsmhe.hyk_c_mhe.deactivate()
self.lsmhe.R_mhe = Param(self.lsmhe.fe_t, self.lsmhe.ykk_mhe, initialize=1.0, mutable=True) if self.diag_Q_R else \
Param(self.lsmhe.fe_t, self.lsmhe.ykk_mhe, self.lsmhe.ykk_mhe,
initialize=lambda mod, t, i, ii: 1.0 if i == ii else 0.0, mutable=True)
f = open("file_cv.txt", "w")
f.close()
#: Constraints for the input noise
for u in self.u:
# cv = getattr(self.lsmhe, u) #: Get the param
# c_val = [value(cv[i]) for i in cv.keys()] #: Current value
# self.lsmhe.del_component(cv) #: Delete the param
# self.lsmhe.add_component(u + "_mhe", Var(self.lsmhe.fe_t, initialize=lambda m, i: c_val[i-1]))
self.lsmhe.add_component("w_" + u + "_mhe", Var(self.lsmhe.fe_t, initialize=0.0)) #: Noise for input
self.lsmhe.add_component("w_" + u + "c_mhe", Constraint(self.lsmhe.fe_t))
self.lsmhe.equalize_u(direction="r_to_u")
# cc = getattr(self.lsmhe, u + "_c") #: Get the constraint for input
con_w = getattr(self.lsmhe, "w_" + u + "c_mhe") #: Get the constraint-noisy
var_w = getattr(self.lsmhe, "w_" + u + "_mhe") #: Get the constraint-noisy
ce = getattr(self.lsmhe, u + "_e") #: Get the expression
cp = getattr(self.lsmhe, u) #: Get the param
con_w.rule = lambda m, i: cp[i] == ce[i] + var_w[i]
con_w.reconstruct()
con_w.deactivate()
# con_w.rule = lambda m, i: cp[i] == cv[i] + var_w[i]
# con_w.reconstruct()
# with open("file_cv.txt", "a") as f:
# cc.pprint(ostream=f)
# con_w.pprint(ostream=f)
# f.close()
self.lsmhe.U_mhe = Param(range(1, self.nfe_t + 1), self.u, initialize=1, mutable=True)
#: Deactivate icc constraints
if self.deact_ics:
pass
# for i in self.states:
# self.lsmhe.del_component(i + "_icc")
#: Maybe only for a subset of the states
else:
for i in self.states:
if i in self.x_noisy:
ic_con = getattr(self.lsmhe, i + "_icc")
for k in self.x_vars[i]:
ic_con[k].deactivate()
#: Put the noise in the continuation equations (finite-element)
j = 0
self.lsmhe.noisy_cont = ConstraintList()
for i in self.x_noisy:
# cp_con = getattr(self.lsmhe, "cp_" + i)
cp_exp = getattr(self.lsmhe, "noisy_" + i)
# self.lsmhe.del_component(cp_con)
for k in self.x_vars[i]: #: This should keep the same order
for t in range(1, self.nfe_t):
self.lsmhe.noisy_cont.add(cp_exp[t, k] == 0.0)
# self.lsmhe.noisy_cont.add(cp_exp[t, k] == 0.0)
j += 1
# cp_con.reconstruct()
j = 0
self.lsmhe.noisy_cont.deactivate()
#: Expressions for the objective function (least-squares)
self.lsmhe.Q_e_mhe = 0.0 if self.IgnoreProcessNoise else Expression(
expr=0.5 * sum(
sum(
sum(self.lsmhe.Q_mhe[1, k] * self.lsmhe.wk_mhe[i, j, k]**2 for k in self.lsmhe.xkNk_mhe) for j in range(1, self.ncp_t +1))
for i in range(1, self.nfe_t+1))) if self.diag_Q_R else Expression(
expr=sum(sum(self.lsmhe.wk_mhe[i, j] *
sum(self.lsmhe.Q_mhe[i, j, k] * self.lsmhe.wk_mhe[i, 1, k] for k in self.lsmhe.xkNk_mhe)
for j in self.lsmhe.xkNk_mhe) for i in range(1, self.nfe_t)))
self.lsmhe.R_e_mhe = Expression(
expr=0.5 * sum(
sum(
self.lsmhe.R_mhe[i, k] * self.lsmhe.nuk_mhe[i, k]**2 for k in self.lsmhe.ykk_mhe)
for i in self.lsmhe.fe_t)) if self.diag_Q_R else Expression(
expr=sum(sum(self.lsmhe.nuk_mhe[i, j] *
sum(self.lsmhe.R_mhe[i, j, k] * self.lsmhe.nuk_mhe[i, k] for k in self.lsmhe.ykk_mhe)
for j in self.lsmhe.ykk_mhe) for i in self.lsmhe.fe_t))
expr_u_obf = 0
for i in self.lsmhe.fe_t:
for u in self.u:
var_w = getattr(self.lsmhe, "w_" + u + "_mhe") #: Get the constraint-noisy
expr_u_obf += self.lsmhe.U_mhe[i, u] * var_w[i] ** 2
self.lsmhe.U_e_mhe = Expression(expr=0.5 * expr_u_obf) # how about this
# with open("file_cv.txt", "a") as f:
# self.lsmhe.U_e_mhe.pprint(ostream=f)
# f.close()
self.lsmhe.Arrival_e_mhe = Expression(
expr=0.5 * sum((self.xkN_l[j] - self.lsmhe.x_0_mhe[j]) *
sum(self.lsmhe.PikN_mhe[j, k] * (self.xkN_l[k] - self.lsmhe.x_0_mhe[k]) for k in self.lsmhe.xkNk_mhe)
for j in self.lsmhe.xkNk_mhe))
self.lsmhe.Arrival_dummy_e_mhe = Expression(
expr=100000.0 * sum((self.xkN_l[j] - self.lsmhe.x_0_mhe[j]) ** 2 for j in self.lsmhe.xkNk_mhe))
self.lsmhe.obfun_dum_mhe_deb = Objective(sense=minimize,
expr=self.lsmhe.Q_e_mhe)
self.lsmhe.obfun_dum_mhe = Objective(sense=minimize,
expr=self.lsmhe.R_e_mhe + self.lsmhe.Q_e_mhe + self.lsmhe.U_e_mhe) # no arrival
self.lsmhe.obfun_dum_mhe.deactivate()
self.lsmhe.obfun_mhe_first = Objective(sense=minimize,
expr=self.lsmhe.Arrival_dummy_e_mhe + self.lsmhe.Q_e_mhe)
self.lsmhe.obfun_mhe_first.deactivate()
self.lsmhe.obfun_mhe = Objective(sense=minimize,
expr=self.lsmhe.Arrival_dummy_e_mhe + self.lsmhe.R_e_mhe + self.lsmhe.Q_e_mhe + self.lsmhe.U_e_mhe)
self.lsmhe.obfun_mhe.deactivate()
# with open("file_cv.txt", "a") as f:
# self.lsmhe.obfun_mhe.pprint(ostream=f)
# f.close()
self._PI = {} #: Container of the KKT matrix
self.xreal_W = {}
self.curr_m_noise = {} #: Current measurement noise
self.curr_y_offset = {} #: Current offset of measurement
for y in self.y:
for j in self.y_vars[y]:
self.curr_m_noise[(y, j)] = 0.0
self.curr_y_offset[(y, j)] = 0.0
self.s_estimate = {}
self.s_real = {}
for x in self.x_noisy:
self.s_estimate[x] = []
self.s_real[x] = []
self.y_estimate = {}
self.y_real = {}
self.y_noise_jrnl = {}
self.yk0_jrnl = {}
for y in self.y:
self.y_estimate[y] = []
self.y_real[y] = []
self.y_noise_jrnl[y] = []
self.yk0_jrnl[y] = []
def compile(self, model, start_time):
"""
Build the structure of the optimization model
:param model: The entire optimization model
:param start_time: The optimization start time
:return:
"""
Component.compile(self, model, start_time)
self.dn = self.params['diameter'].v()
if self.dn is not 0:
self.mflo_max = self.mflo_max_list[self.dn]
Rs = self.Rs[self.dn]
self.f = self.f_mult * self.f_list[self.dn]
else:
self.mflo_max = 0
self.f = 0
Te = self.params["Te"]
self.temp_sup = self.params['temperature_supply'].v()
self.temp_ret = self.params['temperature_return'].v()
if self.compiled:
self.block.mass_flow_max = self.mflo_max
self.logger.debug('Redefining mass_flow_max')
self.construct_pumping_constraints()
if self.repr_days is None:
for t in self.TIME:
if self.dn is not 0:
self.block.heat_loss_nom[t] = (
self.temp_sup + self.temp_ret - 2 * Te.v(t)) / Rs
else:
self.block.heat_loss_nom[t] = 0
else:
for t in self.TIME:
for c in self.REPR_DAYS:
if self.dn is not 0:
self.block.heat_loss_nom[t,
c] = (self.temp_sup +
self.temp_ret -
2 * Te.v(t, c)) / Rs
else:
self.block.heat_loss_nom[t, c] = 0
else:
"""
Parameters and sets
"""
self.block.mass_flow_max = Param(initialize=self.mflo_max,
mutable=True)
# Maximal heat loss per unit length
def _heat_loss(b, t, c=None):
"""
Rule to calculate maximal heat loss per unit length
:param b: block identifier
:param t: time index
:param dn: DN index
:return: Heat loss in W/m
"""
if self.dn is not 0:
dq = (self.temp_sup + self.temp_ret - 2 * Te.v(t, c)) / Rs
else:
dq = 0
return dq
if self.repr_days is None:
self.block.heat_loss_nom = Param(self.TIME,
rule=_heat_loss,
mutable=True)
else:
self.block.heat_loss_nom = Param(self.TIME,
self.REPR_DAYS,
rule=_heat_loss,
mutable=True)
"""
Variables
"""
mflo_ub = (
-self.block.mass_flow_max,
self.block.mass_flow_max) if self.allow_flow_reversal else (
0, self.block.mass_flow_max)
# Real valued
if self.repr_days is None:
self.block.heat_flow_in = Var(self.TIME,
doc='Heat flow entering in-node')
self.block.heat_flow_out = Var(
self.TIME, doc='Heat flow exiting out-node')
self.block.mass_flow = Var(
self.TIME,
bounds=mflo_ub,
doc='Mass flow rate entering in-node and exiting out-node')
self.block.heat_loss_tot = Var(self.TIME,
within=NonNegativeReals,
doc='Total heat lost from pipe')
self.block.mass_flow_abs = Var(
self.TIME,
within=NonNegativeReals,
doc='Absolute value of mass flow rate')
else:
self.block.heat_flow_in = Var(self.TIME,
self.REPR_DAYS,
doc='Heat flow entering in-node')
self.block.heat_flow_out = Var(
self.TIME,
self.REPR_DAYS,
doc='Heat flow exiting out-node')
self.block.mass_flow = Var(
self.TIME,
self.REPR_DAYS,
bounds=mflo_ub,
doc='Mass flow rate entering in-node and exiting out-node')
self.block.heat_loss_tot = Var(self.TIME,
self.REPR_DAYS,
within=NonNegativeReals,
doc='Total heat lost from pipe')
self.block.mass_flow_abs = Var(
self.TIME,
self.REPR_DAYS,
within=NonNegativeReals,
doc='Absolute value of mass flow rate')
"""
Pipe model
"""
##############
# EQUALITIES #
##############
def _eq_heat_flow_bal(b, t, c=None):
if self.repr_days is None:
return b.heat_flow_in[t] == b.heat_loss_tot[t] + \
b.heat_flow_out[t]
else:
return b.heat_flow_in[t, c] == b.heat_loss_tot[t, c] + \
b.heat_flow_out[t, c]
if self.repr_days is None:
self.block.eq_heat_flow_bal = Constraint(
self.TIME, rule=_eq_heat_flow_bal)
else:
self.block.eq_heat_flow_bal = Constraint(
self.TIME, self.REPR_DAYS, rule=_eq_heat_flow_bal)
################
# INEQUALITIES #
################
def _mass_flow_pos(b, t, c=None):
if self.repr_days is None:
return b.mass_flow_abs[t] >= b.mass_flow[t]
else:
return b.mass_flow_abs[t, c] >= b.mass_flow[t, c]
def _mass_flow_neg(b, t, c=None):
if self.repr_days is None:
return b.mass_flow_abs[t] >= -b.mass_flow[t]
else:
return b.mass_flow_abs[t, c] >= -b.mass_flow[t, c]
if self.repr_days is None:
self.block.ineq_mass_flow_pos = Constraint(self.TIME,
rule=_mass_flow_pos)
self.block.ineq_mass_flow_neg = Constraint(self.TIME,
rule=_mass_flow_neg)
else:
self.block.ineq_mass_flow_pos = Constraint(self.TIME,
self.REPR_DAYS,
rule=_mass_flow_pos)
self.block.ineq_mass_flow_neg = Constraint(self.TIME,
self.REPR_DAYS,
rule=_mass_flow_neg)
def _eq_heat_loss(b, t, c=None):
if self.repr_days is None:
return b.heat_loss_tot[t] * (self.hl_setting * b.mass_flow_max) == b.heat_loss_nom[t] * \
b.mass_flow_abs[t] * self.length
else:
return b.heat_loss_tot[t, c] * (self.hl_setting * b.mass_flow_max) == b.heat_loss_nom[t, c] * \
b.mass_flow_abs[t, c] * self.length
if self.repr_days is None:
self.block.eq_heat_loss = Constraint(self.TIME,
rule=_eq_heat_loss)
else:
self.block.eq_heat_loss = Constraint(self.TIME,
self.REPR_DAYS,
rule=_eq_heat_loss)
self.construct_pumping_constraints()
self.logger.info('Optimization model Pipe {} compiled'.format(
self.name))
self.compiled = True
self.logger.debug('========================')
self.logger.debug(self.name)
self.logger.debug('DN:', str(self.dn))
def acm(m, k):
return m.M[0, k] == m.M_ic[k]
def acx(m, k):
return m.x[0, k] == m.x_ic[k]
# ---------------------------------------------------------------------------------------------------------------------
mod = ConcreteModel()
mod.t = ContinuousSet(bounds=(0, 1))
mod.Ntray = Ntray = 42
mod.tray = Set(initialize=[i for i in range(1, mod.Ntray + 1)])
mod.feed = Param(mod.tray,
initialize=lambda m, k: 57.5294 if k == 21 else 0.0,
mutable=True)
mod.xf = Param(initialize=0.32, mutable=True) # feed mole fraction
mod.hf = Param(initialize=9081.3) # feed enthalpy
mod.hlm0 = Param(initialize=2.6786e-04)
mod.hlma = Param(initialize=-0.14779)
mod.hlmb = Param(initialize=97.4289)
mod.hlmc = Param(initialize=-2.1045e04)
mod.hln0 = Param(initialize=4.0449e-04)
mod.hlna = Param(initialize=-0.1435)
mod.hlnb = Param(initialize=121.7981)
mod.hlnc = Param(initialize=-3.0718e04)
def coefficient_matching(model, constraint, uncertain_params, config):
'''
:param model: master problem model
:param constraint: the constraint from the master problem model
:param uncertain_params: the list of uncertain parameters
:param first_stage_variables: the list of effective first-stage variables (includes ssv if decision_rule_order = 0)
:return: True if the coefficient matching was successful, False if its proven robust_infeasible due to
constraints of the form 1 == 0
'''
# === Returned flags
successful_matching = True
robust_infeasible = False
# === Efficiency for q_LB = q_UB
actual_uncertain_params = []
for i in range(len(uncertain_params)):
if not is_certain_parameter(uncertain_param_index=i, config=config):
actual_uncertain_params.append(uncertain_params[i])
# === Add coefficient matching constraint list
if not hasattr(model, "coefficient_matching_constraints"):
model.coefficient_matching_constraints = ConstraintList()
if not hasattr(model, "swapped_constraints"):
model.swapped_constraints = ConstraintList()
variables_in_constraint = ComponentSet(identify_variables(constraint.expr))
params_in_constraint = ComponentSet(identify_mutable_parameters(constraint.expr))
first_stage_variables = model.util.first_stage_variables
second_stage_variables = model.util.second_stage_variables
# === Determine if we need to do DR expression/ssv substitution to
# make h(x,z,q) == 0 into h(x,d,q) == 0 (which is just h(x,q) == 0)
if all(v in ComponentSet(first_stage_variables) for v in variables_in_constraint) and \
any(q in ComponentSet(actual_uncertain_params) for q in params_in_constraint):
# h(x, q) == 0
pass
elif all(v in ComponentSet(first_stage_variables + second_stage_variables) for v in variables_in_constraint) and \
any(q in ComponentSet(actual_uncertain_params) for q in params_in_constraint):
constraint = substitute_ssv_in_dr_constraints(model=model, constraint=constraint)
variables_in_constraint = ComponentSet(identify_variables(constraint.expr))
params_in_constraint = ComponentSet(identify_mutable_parameters(constraint.expr))
else:
pass
if all(v in ComponentSet(first_stage_variables) for v in variables_in_constraint) and \
any(q in ComponentSet(actual_uncertain_params) for q in params_in_constraint):
# Swap param objects for variable objects in this constraint
model.param_set = []
for i in range(len(list(variables_in_constraint))):
# Initialize Params to non-zero value due to standard_repn bug
model.add_component("p_%s" % i, Param(initialize=1, mutable=True))
model.param_set.append(getattr(model, "p_%s" % i))
model.variable_set = []
for i in range(len(list(actual_uncertain_params))):
model.add_component("x_%s" % i, Var(initialize=1))
model.variable_set.append(getattr(model, "x_%s" % i))
original_var_to_param_map = list(zip(list(variables_in_constraint), model.param_set))
original_param_to_vap_map = list(zip(list(actual_uncertain_params), model.variable_set))
var_to_param_substitution_map_forward = {}
# Separation problem initialized to nominal uncertain parameter values
for var, param in original_var_to_param_map:
var_to_param_substitution_map_forward[id(var)] = param
param_to_var_substitution_map_forward = {}
# Separation problem initialized to nominal uncertain parameter values
for param, var in original_param_to_vap_map:
param_to_var_substitution_map_forward[id(param)] = var
var_to_param_substitution_map_reverse = {}
# Separation problem initialized to nominal uncertain parameter values
for var, param in original_var_to_param_map:
var_to_param_substitution_map_reverse[id(param)] = var
param_to_var_substitution_map_reverse = {}
# Separation problem initialized to nominal uncertain parameter values
for param, var in original_param_to_vap_map:
param_to_var_substitution_map_reverse[id(var)] = param
model.swapped_constraints.add(
replace_expressions(
expr=replace_expressions(expr=constraint.lower,
substitution_map=param_to_var_substitution_map_forward),
substitution_map=var_to_param_substitution_map_forward) ==
replace_expressions(
expr=replace_expressions(expr=constraint.body,
substitution_map=param_to_var_substitution_map_forward),
substitution_map=var_to_param_substitution_map_forward))
swapped = model.swapped_constraints[max(model.swapped_constraints.keys())]
val = generate_standard_repn(swapped.body, compute_values=False)
if val.constant is not None:
if type(val.constant) not in native_types:
temp_expr = replace_expressions(val.constant, substitution_map=var_to_param_substitution_map_reverse)
if temp_expr.is_potentially_variable():
model.coefficient_matching_constraints.add(expr=temp_expr == 0)
elif math.isclose(value(temp_expr), 0, rel_tol=COEFF_MATCH_REL_TOL, abs_tol=COEFF_MATCH_ABS_TOL):
pass
else:
successful_matching = False
robust_infeasible = True
elif math.isclose(value(val.constant), 0, rel_tol=COEFF_MATCH_REL_TOL, abs_tol=COEFF_MATCH_ABS_TOL):
pass
else:
successful_matching = False
robust_infeasible = True
if val.linear_coefs is not None:
for coeff in val.linear_coefs:
if type(coeff) not in native_types:
temp_expr = replace_expressions(coeff, substitution_map=var_to_param_substitution_map_reverse)
if temp_expr.is_potentially_variable():
model.coefficient_matching_constraints.add(expr=temp_expr == 0)
elif math.isclose(value(temp_expr), 0, rel_tol=COEFF_MATCH_REL_TOL, abs_tol=COEFF_MATCH_ABS_TOL):
pass
else:
successful_matching = False
robust_infeasible = True
elif math.isclose(value(coeff), 0, rel_tol=COEFF_MATCH_REL_TOL, abs_tol=COEFF_MATCH_ABS_TOL):
pass
else:
successful_matching = False
robust_infeasible = True
if val.quadratic_coefs:
for coeff in val.quadratic_coefs:
if type(coeff) not in native_types:
temp_expr = replace_expressions(coeff, substitution_map=var_to_param_substitution_map_reverse)
if temp_expr.is_potentially_variable():
model.coefficient_matching_constraints.add(expr=temp_expr == 0)
elif math.isclose(value(temp_expr), 0, rel_tol=COEFF_MATCH_REL_TOL, abs_tol=COEFF_MATCH_ABS_TOL):
pass
else:
successful_matching = False
robust_infeasible = True
elif math.isclose(value(coeff), 0, rel_tol=COEFF_MATCH_REL_TOL, abs_tol=COEFF_MATCH_ABS_TOL):
pass
else:
successful_matching = False
robust_infeasible = True
if val.nonlinear_expr is not None:
successful_matching = False
robust_infeasible = False
if successful_matching:
model.util.h_x_q_constraints.add(constraint)
for i in range(len(list(variables_in_constraint))):
model.del_component("p_%s" % i)
for i in range(len(list(params_in_constraint))):
model.del_component("x_%s" % i)
model.del_component("swapped_constraints")
model.del_component("swapped_constraints_index")
return successful_matching, robust_infeasible
def construct_pumping_constraints(self):
"""
Construct a set of constraints
:param n_segments: How many linear segments should be used to approximate the pumping power curve
:return:
"""
if self.dn is not 0:
di = self.di[self.dn]
else:
di = 1
if self.compiled:
for n in self.n_pump:
if self.dn is not 0:
self.block.pps[n] = 2 * self.f * self.length * (
self.mfs_ratio[n] *
self.mflo_max)**3 * 8 / (di**5 * 983**2 * pi**2)
else:
self.block.pps[n] = 0
else:
n_segments = self.n_pump_constr
self.n_pump = range(n_segments + 1)
self.mfs_ratio = np.linspace(0, 1, n_segments + 1)
self.block.pps = Param(self.n_pump, mutable=True)
for n in self.n_pump:
if self.dn is not 0:
self.block.pps[n] = 2 * self.f * self.length * (
self.mfs_ratio[n] *
self.mflo_max)**3 * 8 / (di**5 * 983**2 * pi**2)
else:
self.block.pps[n] = 0
if self.repr_days:
self.block.pumping_power = Var(self.TIME,
self.REPR_DAYS,
within=NonNegativeReals)
else:
self.block.pumping_power = Var(self.TIME,
within=NonNegativeReals)
for i in range(n_segments):
def _ineq_pumping(b, t, c=None):
if self.repr_days is None:
return b.pumping_power[t] * b.mass_flow_max >= (
b.mass_flow_abs[t] -
b.mass_flow_max * self.mfs_ratio[i]) / (
self.mfs_ratio[i + 1] - self.mfs_ratio[i]) * (
b.pps[i + 1] -
b.pps[i]) + b.mass_flow_max * b.pps[i]
else:
return b.pumping_power[t, c] * b.mass_flow_max >= (
b.mass_flow_abs[t, c] -
b.mass_flow_max * self.mfs_ratio[i]) / (
self.mfs_ratio[i + 1] - self.mfs_ratio[i]) * (
b.pps[i + 1] -
b.pps[i]) + b.mass_flow_max * b.pps[i]
if self.repr_days is None:
self.block.add_component(
'ineq_pumping_' + str(i),
Constraint(self.TIME, rule=_ineq_pumping))
else:
self.block.add_component(
'ineq_pumping_' + str(i),
Constraint(self.TIME,
self.REPR_DAYS,
rule=_ineq_pumping))
from pyomo.core.base import AbstractModel, Set, PositiveIntegers, Param, Var, Constraint, Objective, \
ConcreteModel, NonNegativeReals, value, maximize
model = AbstractModel()
# Nodes in the network
model.N = Set()
# Network arcs
model.A = Set(within=model.N*model.N)
# Source node
model.s = Param(within=model.N)
# Sink node
model.t = Param(within=model.N)
# Flow capacity limits
model.c = Param(model.A)
# The flow over each arc
model.f = Var(model.A, within=NonNegativeReals)
# Maximize the flow into the sink nodes
def total_rule(model):
return sum(model.f[i,j] for (i, j) in model.A if j == value(model.t))
model.total = Objective(rule=total_rule, sense=maximize)
# Enforce an upper limit on the flow across each arc
def limit_rule(model, i, j):
return model.f[i,j] <= model.c[i, j]
model.limit = Constraint(model.A, rule=limit_rule)
# Enforce flow through each node
def __init__(self, nfe_t, ncp_t, **kwargs):
#: type: (int, int, dict)
#: if steady == True fallback to steady-state computation
self.nfe_t = nfe_t #:
self.ncp_t = ncp_t
self.scheme = kwargs.pop('scheme', 'LAGRANGE-RADAU')
self.steady = kwargs.pop('steady', False)
self._t = kwargs.pop('_t', 1.0)
ncstr = kwargs.pop('n_cstr', 1)
ConcreteModel.__init__(self)
self.ncstr = Set(initialize=[i for i in range(0, ncstr)])
if self.steady:
self.t = Set(initialize=[1])
else:
self.t = ContinuousSet(bounds=(0, self._t))
self.Cainb = Param(default=1.0)
self.Tinb = Param(default=275.0)
self.Tjinb = Param(default=250.0)
self.V = Param(initialize=100)
self.UA = Param(initialize=20000 * 60)
self.rho = Param(initialize=1000)
self.Cp = Param(initialize=4.2)
self.Vw = Param(initialize=10)
self.rhow = Param(initialize=1000)
self.Cpw = Param(initialize=4.2)
self.k0 = Param(initialize=4.11e13)
self.E = Param(initialize=76534.704)
self.R = Param(initialize=8.314472)
self.Er = Param(initialize=lambda m: (value(self.E) / value(self.R)))
self.dH = Param(initialize=596619.)
self.F = Param(self.t, mutable=True, default=1.2000000000000000E+02)
self.Fw = Param(self.t, mutable=True, default=3.0000000000000000E+01)
# States
self.Ca = Var(self.t, self.ncstr, initialize=1.60659680385930765667001907104350E-02)
self.T = Var(self.t, self.ncstr, initialize=3.92336059452774350120307644829154E+02)
self.Tj = Var(self.t, self.ncstr, initialize=3.77995395658401662331016268581152E+02)
self.k = Var(self.t, self.ncstr, initialize=4.70706140E+02)
self.kdef = Constraint(self.t, self.ncstr)
#: These guys have to be zero at the steady-state (steady).
zero0 = dict.fromkeys(self.t * self.ncstr)
for key in zero0.keys():
zero0[key] = 0.0
if self.steady:
self.Cadot = zero0
self.Tdot = zero0
self.Tjdot = zero0
else:
self.Cadot = DerivativeVar(self.Ca, initialize=-3.58709135E+01)
self.Tdot = DerivativeVar(self.T, initialize=5.19191848E+03)
self.Tjdot = DerivativeVar(self.Tj, initialize=-9.70467399E+02)
#: These guys as well (steady).
self.Ca_ic = Param(self.ncstr, default=1.9193793974995963E-02)
self.T_ic = Param(self.ncstr, default=3.8400724261199036E+02)
self.Tj_ic = Param(self.ncstr, default=3.7127352272578315E+02)
# m.Ca_ic = Param(m.ncstr, default=1.9193793974995963E-02)
# m.T_ic = Param(m.ncstr, default=3.8400724261199036E+02)
# m.Tj_ic = Param(m.ncstr, default=3.7127352272578315E+02)
self.ODE_ca = Constraint(self.t, self.ncstr)
self.ODE_T = Constraint(self.t, self.ncstr)
self.ODE_Tj = Constraint(self.t, self.ncstr)
#: No need of these guys at steady.
if self.steady:
self.Ca_icc = None
self.T_icc = None
self.Tj_icc = None
else:
self.Ca_icc = Constraint(self.ncstr)
self.T_icc = Constraint(self.ncstr)
self.Tj_icc = Constraint(self.ncstr)
def _rule_k(m, i, n):
if i == 0:
return Constraint.Skip
else:
return m.k[i, n] == m.k0 * exp(-m.Er / m.T[i, n])
def _rule_ca(m, i, n):
if i == 0:
return Constraint.Skip
else:
rule = m.Cadot[i, n] == (m.F[i] / m.V) * (m.Cainb - m.Ca[i, n]) - 2 * m.k[i, n] * m.Ca[i, n] ** 2
return rule
def _rule_t(m, i, n):
if i == 0:
return Constraint.Skip
else:
return m.Tdot[i, n] == (m.F[i] / m.V) * (m.Tinb - m.T[i, n]) + \
2.0 * m.dH / (m.rho * m.Cp) * m.k[i, n] * m.Ca[i, n] ** 2 -\
m.UA / (m.V * m.rho * m.Cp) * (m.T[i, n] - m.Tj[i, n])
def _rule_tj(m, i, n):
if i == 0:
return Constraint.Skip
else:
return m.Tjdot[i, n] == \
(m.Fw[i] / m.Vw) * (m.Tjinb - m.Tj[i, n]) + m.UA / (m.Vw * m.rhow * m.Cpw) * (m.T[i, n] - m.Tj[i, n])
def _rule_ca0(m, n):
return m.Ca[0, n] == m.Ca_ic[n]
def _rule_t0(m, n):
return m.T[0, n] == m.T_ic[n]
def _rule_tj0(m, n):
return m.Tj[0, n] == m.Tj_ic[n]
# let Ca0 := 1.9193793974995963E-02 ;
# let T0 := 3.8400724261199036E+02 ;
# let Tj0 := 3.7127352272578315E+02 ;
self.kdef.rule = lambda m, i, n: _rule_k(m, i, n)
self.ODE_ca.rule = lambda m, i, n: _rule_ca(m, i, n)
self.ODE_T.rule = lambda m, i, n: _rule_t(m, i, n)
self.ODE_Tj.rule = lambda m, i, n: _rule_tj(m, i, n)
if self.steady:
pass
else:
self.Ca_icc.rule = lambda m, n: _rule_ca0(m, n)
self.T_icc.rule = lambda m, n: _rule_t0(m, n)
self.Tj_icc.rule = lambda m, n: _rule_tj0(m, n)
self.Ca_icc.reconstruct()
self.T_icc.reconstruct()
self.Tj_icc.reconstruct()
self.kdef.reconstruct()
self.ODE_ca.reconstruct()
self.ODE_T.reconstruct()
self.ODE_Tj.reconstruct()
# Declare at framework level
self.dual = Suffix(direction=Suffix.IMPORT_EXPORT)
self.ipopt_zL_out = Suffix(direction=Suffix.IMPORT)
self.ipopt_zU_out = Suffix(direction=Suffix.IMPORT)
self.ipopt_zL_in = Suffix(direction=Suffix.EXPORT)
self.ipopt_zU_in = Suffix(direction=Suffix.EXPORT)
self.discretizer = TransformationFactory('dae.collocation')
def dist_col_Rodrigo_ss(init0, bnd_set):
# collocation polynomial parameters
# polynomial roots (Radau)
m = ConcreteModel()
m.i_flag = init0
# set of finite elements and collocation points
m.fe = Set(initialize=[1])
m.cp = Set(initialize=[1])
m.bnd_set = bnd_set
if bnd_set:
print("Bounds_set")
Ntray = 42
m.Ntray = Ntray
m.tray = Set(initialize=[i for i in range(1, Ntray + 1)])
def __init_feed(m, t):
if t == 21:
return 57.5294
else:
return 0
m.feed = Param(m.tray, initialize=__init_feed)
m.xf = Param(initialize=0.32) # feed mole fraction
m.hf = Param(initialize=9081.3) # feed enthalpy
m.hlm0 = Param(initialize=2.6786e-04)
m.hlma = Param(initialize=-0.14779)
m.hlmb = Param(initialize=97.4289)
m.hlmc = Param(initialize=-2.1045e04)
m.hln0 = Param(initialize=4.0449e-04)
m.hlna = Param(initialize=-0.1435)
m.hlnb = Param(initialize=121.7981)
m.hlnc = Param(initialize=-3.0718e04)
m.r = Param(initialize=8.3147)
m.a = Param(initialize=6.09648)
m.b = Param(initialize=1.28862)
m.c1 = Param(initialize=1.016)
m.d = Param(initialize=15.6875)
m.l = Param(initialize=13.4721)
m.f = Param(initialize=2.615)
m.gm = Param(initialize=0.557)
m.Tkm = Param(initialize=512.6)
m.Pkm = Param(initialize=8.096e06)
m.gn = Param(initialize=0.612)
m.Tkn = Param(initialize=536.7)
m.Pkn = Param(initialize=5.166e06)
m.CapAm = Param(initialize=23.48)
m.CapBm = Param(initialize=3626.6)
m.CapCm = Param(initialize=-34.29)
m.CapAn = Param(initialize=22.437)
m.CapBn = Param(initialize=3166.64)
m.CapCn = Param(initialize=-80.15)
m.pstrip = Param(initialize=250)
m.prect = Param(initialize=190)
def _p_init(m, t):
ptray = 9.39e04
if t <= 20:
return _p_init(m, 21) + m.pstrip * (21 - t)
elif 20 < t < m.Ntray:
return ptray + m.prect * (m.Ntray - t)
elif t == m.Ntray:
return 9.39e04
m.p = Param(m.tray, initialize=_p_init)
m.T29_des = Param(initialize=343.15)
m.T15_des = Param(initialize=361.15)
m.Dset = Param(initialize=1.83728)
m.Qcset = Param(initialize=1.618890)
m.Qrset = Param(initialize=1.786050)
m.Recset = Param()
m.alpha_T29 = Param(initialize=1)
m.alpha_T15 = Param(initialize=1)
m.alpha_D = Param(initialize=1)
m.alpha_Qc = Param(initialize=1)
m.alpha_Qr = Param(initialize=1)
m.alpha_Rec = Param(initialize=1)
def _alpha_init(m, i):
if i <= 21:
return 0.62
else:
return 0.35
m.alpha = Param(m.tray, initialize=_alpha_init)
ME0 = {}
ME0[1] = 123790.826443232
ME0[2] = 3898.34923206106
ME0[3] = 3932.11766868415
ME0[4] = 3950.13107445914
ME0[5] = 3960.01212104318
ME0[6] = 3965.37146944881
ME0[7] = 3968.25340380767
ME0[8] = 3969.78910997468
ME0[9] = 3970.5965548502
ME0[10] = 3971.0110096803
ME0[11] = 3971.21368740283
ME0[12] = 3971.30232788932
ME0[13] = 3971.32958547037
ME0[14] = 3971.32380573089
ME0[15] = 3971.30024105555
ME0[16] = 3971.26709591428
ME0[17] = 3971.22878249852
ME0[18] = 3971.187673073
ME0[19] = 3971.14504284211
ME0[20] = 3971.10157713182
ME0[21] = 3971.05764415189
ME0[22] = 3611.00216267141
ME0[23] = 3766.84741932423
ME0[24] = 3896.87907072814
ME0[25] = 4004.98630195624
ME0[26] = 4092.49383654928
ME0[27] = 4161.86560059956
ME0[28] = 4215.98509169956
ME0[29] = 4257.69470716792
ME0[30] = 4289.54901779038
ME0[31] = 4313.71557755738
ME0[32] = 4331.9642075775
ME0[33] = 4345.70190802884
ME0[34] = 4356.02621744716
ME0[35] = 4363.78165047072
ME0[36] = 4369.61159802674
ME0[37] = 4374.00266939603
ME0[38] = 4377.32093116489
ME0[39] = 4379.84068162411
ME0[40] = 4381.76685527968
ME0[41] = 4383.25223100374
ME0[42] = 4736.04924276762
m.M_pred = Param(m.tray, initialize=ME0)
XE0 = {}
XE0[1] = 0.306547877605746
XE0[2] = 0.398184778678485
XE0[3] = 0.416675004386508
XE0[4] = 0.42676332128531
XE0[5] = 0.432244548463899
XE0[6] = 0.435193762178033
XE0[7] = 0.436764699693985
XE0[8] = 0.437589297877498
XE0[9] = 0.438010896454752
XE0[10] = 0.43821522113022
XE0[11] = 0.438302495819782
XE0[12] = 0.438326730875504
XE0[13] = 0.438317008813347
XE0[14] = 0.438288981487008
XE0[15] = 0.438251069561153
XE0[16] = 0.438207802087721
XE0[17] = 0.438161614415035
XE0[18] = 0.438113815737636
XE0[19] = 0.438065109638753
XE0[20] = 0.438015874079915
XE0[21] = 0.437966311972983
XE0[22] = 0.724835538043496
XE0[23] = 0.788208485334881
XE0[24] = 0.838605564838572
XE0[25] = 0.87793558673077
XE0[26] = 0.908189470853012
XE0[27] = 0.931224584141055
XE0[28] = 0.948635083197147
XE0[29] = 0.961724712952285
XE0[30] = 0.971527857048483
XE0[31] = 0.978848914860811
XE0[32] = 0.984304939392599
XE0[33] = 0.98836476845163
XE0[34] = 0.991382214572503
XE0[35] = 0.993622983870866
XE0[36] = 0.995285909293636
XE0[37] = 0.996519395295701
XE0[38] = 0.997433995899531
XE0[39] = 0.998111951760656
XE0[40] = 0.998614376770054
XE0[41] = 0.998986649363
XE0[42] = 0.999262443919619
m.x_pred = Param(m.tray, initialize=XE0)
# hold in each tray
def __m_init(m, i, j, t):
if m.i_flag:
if t < m.Ntray:
return 4000.
elif t == 1:
return 104340.
elif t == m.Ntray:
return 5000.
else:
return 0.
m.M = Var(m.fe, m.cp, m.tray, initialize=__m_init)
# m.M_0 = Var(m.fe, m.tray, initialize=1e07)
# temperatures
def __t_init(m, i, j, t):
if m.i_flag:
return ((370.781 - 335.753) / m.Ntray) * t + 370.781
else:
return 10.
m.T = Var(m.fe, m.cp, m.tray, initialize=__t_init)
# saturation pressures
m.pm = Var(m.fe, m.cp, m.tray, initialize=1e4)
m.pn = Var(m.fe, m.cp, m.tray, initialize=1e4)
# define l-v flowrate
def _v_init(m, i, j, t):
if m.i_flag:
return 44.
else:
return 0.
m.V = Var(m.fe, m.cp, m.tray, initialize=_v_init)
def _l_init(m, i, j, t):
if m.i_flag:
if 2 <= t <= 21:
return 83.
elif 22 <= t <= 42:
return 23
elif t == 1:
return 40
else:
return 0.
m.L = Var(m.fe, m.cp, m.tray, initialize=_l_init)
# mol frac l-v
def __x_init(m, i, j, t):
if m.i_flag:
return (0.999 / m.Ntray) * t
else:
return 1
m.x = Var(m.fe, m.cp, m.tray, initialize=__x_init)
#m.x_0 = Var(m.fe, m.tray)
# av
def __y_init(m, i, j, t):
if m.i_flag:
return ((0.99 - 0.005) / m.Ntray) * t + 0.005
else:
return 1
m.y = Var(m.fe, m.cp, m.tray, initialize=__y_init)
# enthalpy
m.hl = Var(m.fe, m.cp, m.tray, initialize=10000.)
def __hv_init(m, i, j, t):
if m.i_flag:
if t < m.Ntray:
return 5e4
else:
return 0.0
m.hv = Var(m.fe, m.cp, m.tray, initialize=__hv_init)
# reboiler & condenser heat
m.Qc = Var(m.fe, m.cp, initialize=1.6e06)
m.D = Var(m.fe, m.cp, initialize=18.33)
# vol holdups
m.Vm = Var(m.fe, m.cp, m.tray, initialize=6e-05)
def __mv_init(m, i, j, t):
if m.i_flag:
if 1 < t < m.Ntray:
return 0.23
else:
return 0.0
m.Mv = Var(m.fe, m.cp, m.tray, initialize=__mv_init)
m.Mv1 = Var(m.fe, m.cp, initialize=8.57)
m.Mvn = Var(m.fe, m.cp, initialize=0.203)
def _bound_set(m):
if m.bnd_set:
for key, value in m.M.iteritems():
value.setlb(1.0)
value.setub(1e7)
for key, value in m.Vm.iteritems():
value.setlb(-1.0)
value.setub(1e4)
for key, value in m.Mv.iteritems():
value.setlb(0.155 + 1e-06)
value.setub(1e4)
for key, value in m.Mv1.iteritems():
value.setlb(8.5 + 1e-06)
value.setub(1e4)
for key, value in m.Mvn.iteritems():
value.setlb(0.17 + 1e-06)
value.setub(1e4)
for key, value in m.y.iteritems():
value.setlb(0.0)
value.setub(1.0)
for key, value in m.x.iteritems():
value.setlb(0.0)
value.setub(1.0)
for key, value in m.L.iteritems():
value.setlb(0.0)
for key, value in m.V.iteritems():
value.setlb(0.0)
_bound_set(m)
m.Rec = Param(m.fe, initialize=7.72700925775773761472464684629813e-01)
# m.Rec = Param(m.fe, initialize=0.05)
m.Qr = Param(m.fe, initialize=1.78604740940007800236344337463379E+06)
# m.Qr = Param(m.fe, initialize=1.5e+02)
# mass balances
def _MODEtr(m, i, j, k):
if j > 0 and 1 < k < Ntray:
return 0.0 == (m.V[i, j, k - 1] - m.V[i, j, k] + m.L[i, j, k + 1] -
m.L[i, j, k] + m.feed[k])
else:
return Constraint.Skip
m.MODEtr = Constraint(m.fe, m.cp, m.tray, rule=_MODEtr)
# m.L[i, j, 1] = B
def _MODEr(m, i, j):
if j > 0:
return 0.0 == (m.L[i, j, 2] - m.L[i, j, 1] - m.V[i, j, 1])
else:
return Constraint.Skip
m.MODEr = Constraint(m.fe, m.cp, rule=_MODEr)
def _MODEc(m, i, j):
if j > 0:
return 0.0 == (m.V[i, j, Ntray - 1] - m.L[i, j, Ntray] - m.D[i, j])
else:
return Constraint.Skip
m.MODEc = Constraint(m.fe, m.cp, rule=_MODEc)
def _XODEtr(m, i, j, t):
if j > 0 and 1 < t < Ntray:
return 0.0 == (m.V[i, j, t - 1] * (m.y[i, j, t - 1] - m.x[i, j, t]) + \
m.L[i, j, t + 1] * (m.x[i, j, t + 1] - m.x[i, j, t]) - \
m.V[i, j, t] * (m.y[i, j, t] - m.x[i, j, t]) + \
m.feed[t] * (m.xf - m.x[i, j, t]))
else:
return Constraint.Skip
m.XODEtr = Constraint(m.fe, m.cp, m.tray, rule=_XODEtr)
def _xoder(m, i, j):
if j > 0:
return 0.0 == (m.L[i, j, 2] * (m.x[i, j, 2] - m.x[i, j, 1]) - \
m.V[i, j, 1] * (m.y[i, j, 1] - m.x[i, j, 1]))
else:
return Constraint.Skip
m.xoder = Constraint(m.fe, m.cp, rule=_xoder)
def _xodec(m, i, j):
if j > 0:
return 0.0 == \
(m.V[i, j, Ntray - 1] * (m.y[i, j, Ntray - 1] - m.x[i, j, Ntray]))
else:
return Constraint.Skip
m.xodec = Constraint(m.fe, m.cp, rule=_xodec)
def _hrc(m, i, j):
if j > 0:
return m.D[i, j] - m.Rec[i] * m.L[i, j, Ntray] == 0
else:
return Constraint.Skip
m.hrc = Constraint(m.fe, m.cp, rule=_hrc)
# Energy balance
def _gh(m, i, j, t):
if j > 0 and 1 < t < Ntray:
return 0.0 \
== (m.V[i, j, t-1] * (m.hv[i, j, t-1] - m.hl[i, j, t]) + m.L[i, j, t+1] * (m.hl[i, j, t+1] - m.hl[i, j, t]) - m.V[i, j, t] * (m.hv[i, j, t] - m.hl[i, j, t]) + m.feed[t] * (m.hf - m.hl[i, j, t]))
# return m.M[i, j, t] * (m.xdot[i, j, t] * ((m.hlm0 - m.hln0) * m.T[i, j, t]**3 + (m.hlma - m.hlna) * m.T[i, j, t]**2 + (m.hlmb - m.hlnb) * m.T[i, j, t] + m.hlmc - m.hlnc) + m.Tdot[i, j, t]*(3*m.hln0*m.T[i, j, t]**2 + 2*m.hlna * m.T[i, j, t] + m.hlnb + m.x[i, j, t]*(3*(m.hlm0 - m.hln0) * m.T[i, j, t]**2 + 2 * (m.hlma - m.hlna) * m.T[i, j, t] + m.hlmb - m.hlnb))) \
# M[i, q, c] * ( xdot[i, q, c] * (( hlm0 - hln0) * T[i, q, c] ^ 3 + ( hlma - hlna) * T[i, q, c] ^ 2 + ( hlmb - hlnb) * T[i, q, c] + hlmc - hlnc) + Tdot[i, q, c]*(3* hln0 * T[i, q, c] ^ 2 +2 * hlna * T[i, q, c] + hlnb + x[i, q, c]*(3*( hlm0 - hln0) * T[i, q, c] ^ 2 + 2 * ( hlma - hlna) * T[i, q, c] + hlmb - hlnb))) =
# V[i - 1, q, c]*(hv[i - 1, q, c] - hl[i, q, c]) + L[i + 1, q, c]*(hl[i + 1, q, c] - hl[i, q, c]) - V[i, q, c] * ( hv[i, q, c] - hl[i, q, c]) + feed[i] * ( hf - hl[i, q, c]);
else:
return Constraint.Skip
# M[i,q,c] *( xdot[i,q,c]* ((hlm0 - hln0) * T[i,q,c]^3 + ( hlma - hlna) * T[i,q,c]^2 + ( hlmb - hlnb) * T[i,q,c] + hlmc - hlnc) + Tdot[i,q,c] *(3* hln0* T[i,q,c]^2 + 2* hlna* T[i,q,c] + hlnb + x[i,q,c]* (3*( hlm0 - hln0) *T[i,q,c]^2 + 2* (hlma - hlna) *T[i,q,c]+ hlmb - hlnb)))
# V[i-1,q,c] *( hv[i-1,q,c] - hl[i,q,c] ) + L[i+1,q,c]* ( hl[i+1,q,c] - hl[i,q,c] ) - V[i,q,c] * ( hv[i,q,c] - hl[i,q,c]) + feed[i] * (hf - hl[i,q,c])
m.gh = Constraint(m.fe, m.cp, m.tray, rule=_gh)
def _ghb(m, i, j):
if j > 0:
return 0.0 == \
(m.L[i, j, 2] * (m.hl[i, j, 2] - m.hl[i, j, 1]) - m.V[i, j, 1] * (m.hv[i, j, 1] - m.hl[i, j, 1]) + m.Qr[i])
# M[1,q,c]* ( xdot[1,q,c] * (( hlm0 - hln0) *T[1,q,c]^3 + (hlma - hlna)* T[1,q,c]^2 + ( hlmb - hlnb) *T[1,q,c] + hlmc - hlnc) + Tdot[1,q,c]* (3* hln0 * T[1,q,c]^2 + 2 * hlna * T[1,q,c] + hlnb + x[1,q,c] * (3 * ( hlm0 - hln0) * T[1,q,c]^2 + 2*( hlma - hlna) *T[1,q,c] + hlmb - hlnb))) =
# L[2,q,c]* ( hl[2,q,c] - hl[1,q,c] ) - V[1,q,c] * ( hv[1,q,c] - hl[1,q,c] ) + Qr[q] ;
else:
return Constraint.Skip
m.ghb = Constraint(m.fe, m.cp, rule=_ghb)
def _ghc(m, i, j):
if j > 0:
return 0.0 == \
(m.V[i, j, Ntray - 1] * (m.hv[i, j, Ntray - 1] - m.hl[i, j, Ntray]) - m.Qc[i, j])
#M[Ntray, q, c] * (xdot[Ntray, q, c] * ((hlm0 - hln0) * T[Ntray, q, c] ^ 3 + (hlma - hlna) * T[Ntray, q, c] ^ 2 + (hlmb - hlnb) * T[Ntray, q, c] + hlmc - hlnc) + Tdot[Ntray, q, c] * (3 * hln0 * T[Ntray, q, c] ^ 2 + 2 * hlna * T[Ntray, q, c] + hlnb + x[Ntray, q, c] * (3 * ( hlm0 - hln0) * T[Ntray, q, c] ^ 2 + 2 * (hlma - hlna) * T[Ntray, q, c] + hlmb - hlnb))) =
# V[Ntray - 1, q, c] * (hv[Ntray - 1, q, c] - hl[Ntray, q, c]) - Qc[q, c];
else:
return Constraint.Skip
#M[Ntray, q, c] * ( xdot[Ntray, q, c] * (( hlm0 - hln0) * T[Ntray, q, c] ^ 3 + ( hlma - hlna) * T[Ntray, q, c] ^ 2 +(hlmb - hlnb) * T[Ntray, q, c] + hlmc - hlnc) + Tdot[Ntray, q, c] * (3 * hln0 * T[Ntray, q, c] ^ 2 + 2 * hlna * T[Ntray, q, c] + hlnb + x[Ntray, q, c] * (3 * ( hlm0 - hln0) * T[Ntray, q, c] ^ 2 + 2 * (hlma - hlna) * T[Ntray, q, c] + hlmb - hlnb))) =
# V[Ntray - 1, q, c] * (hv[Ntray - 1, q, c] - hl[Ntray, q, c]) - Qc[q, c];
m.ghc = Constraint(m.fe, m.cp, rule=_ghc)
def _hkl(m, i, j, t):
if j > 0:
return m.hl[i, j, t] == m.x[i, j, t] * (
m.hlm0 * m.T[i, j, t]**3 + m.hlma * m.T[i, j, t]**2 +
m.hlmb * m.T[i, j, t] + m.hlmc) + (1 - m.x[i, j, t]) * (
m.hln0 * m.T[i, j, t]**3 + m.hlna * m.T[i, j, t]**2 +
m.hlnb * m.T[i, j, t] + m.hlnc)
# hl[i, q, c] = x[i, q, c]*( hlm0 * T[i, q, c] ^ 3 + hlma * T[i, q, c] ^ 2 + hlmb * T[i, q, c] + hlmc ) + (1 - x[i, q, c] )*( hln0 * T[i, q, c] ^ 3 + hlna* T[i, q, c] ^ 2 + hlnb * T[i, q, c] + hlnc);
else:
return Constraint.Skip
# hl[i,q,c] = x[i,q,c]* ( hlm0* T[i,q,c]^3 + hlma * T[i,q,c]^2 + hlmb* T[i,q,c] + hlmc) + (1 - x[i,q,c])* ( hln0 *T[i,q,c] ^ 3 + hlna* T[i,q,c]^2 + hlnb *T[i,q,c] + hlnc) ;
m.hkl = Constraint(m.fe, m.cp, m.tray, rule=_hkl)
def _hkv(m, i, j, t):
if j > 0 and t < Ntray:
return m.hv[i, j, t] == m.y[i, j, t] * (
m.hlm0 * m.T[i, j, t]**3 + m.hlma * m.T[i, j, t]**2 +
m.hlmb * m.T[i, j, t] + m.hlmc +
m.r * m.Tkm * sqrt(1 - (m.p[t] / m.Pkm) *
(m.Tkm / m.T[i, j, t])**3) *
(m.a - m.b * m.T[i, j, t] / m.Tkm + m.c1 *
(m.T[i, j, t] / m.Tkm)**7 + m.gm *
(m.d - m.l * m.T[i, j, t] / m.Tkm + m.f *
(m.T[i, j, t] / m.Tkm)**7))) + (1 - m.y[i, j, t]) * (
m.hln0 * m.T[i, j, t]**3 + m.hlna * m.T[i, j, t]**2 +
m.hlnb * m.T[i, j, t] + m.hlnc +
m.r * m.Tkn * sqrt(1 - (m.p[t] / m.Pkn) *
(m.Tkn / m.T[i, j, t])**3) *
(m.a - m.b * m.T[i, j, t] / m.Tkn + m.c1 *
(m.T[i, j, t] / m.Tkn)**7 + m.gn *
(m.d - m.l * m.T[i, j, t] / m.Tkn + m.f *
(m.T[i, j, t] / m.Tkn)**7)))
else:
return Constraint.Skip
# hv[i,q,c] = y[i,q,c] * (hlm0 * T[i,q,c]^3 + hlma* T[i,q,c]^2 + hlmb * T[i,q,c] + hlmc + r* Tkm* sqrt(1 - ( p[i]/ Pkm)* ( Tkm/ T[i,q,c]) ^ 3 )*(a - b* T[i,q,c]/ Tkm + c1* ( T[i,q,c] / Tkm) ^7 + gm* ( d - l * T[i,q,c] /Tkm + f*( T[i,q,c] / Tkm)^7 ))) + (1 - y[i,q,c] )* ( hln0 * T[i,q,c] ^3 + hlna* T[i,q,c]^ 2 + hlnb* T[i,q,c] + hlnc + r * Tkn* sqrt(1 - ( p[i]/Pkn )*( Tkn/ T[i,q,c] )^3 )*( a - b * T[i,q,c] / Tkn + c1 * ( T[i,q,c] / Tkn)^7 + gn*( d - l* T[i,q,c]/ Tkn + f*( T[i,q,c] / Tkn) ^7 ))) ;
m.hkv = Constraint(m.fe, m.cp, m.tray, rule=_hkv)
def _lpm(m, i, j, t):
if j > 0:
return m.pm[i, j,
t] == exp(m.CapAm - m.CapBm / (m.T[i, j, t] + m.CapCm))
else:
return Constraint.Skip
m.lpm = Constraint(m.fe, m.cp, m.tray, rule=_lpm)
def _lpn(m, i, j, t):
if j > 0:
return m.pn[i, j,
t] == exp(m.CapAn - m.CapBn / (m.T[i, j, t] + m.CapCn))
else:
return Constraint.Skip
m.lpn = Constraint(m.fe, m.cp, m.tray, rule=_lpn)
def _dp(m, i, j, t):
if j > 0:
return m.p[t] == m.pm[i, j, t] * m.x[i, j, t] + (
1 - m.x[i, j, t]) * m.pn[i, j, t]
else:
return Constraint.Skip
m.dp = Constraint(m.fe, m.cp, m.tray, rule=_dp)
def _gy0(m, i, j):
if j > 0:
return m.y[i, j, 1] == m.x[i, j, 1] * m.pm[i, j, 1] / m.p[1]
else:
return Constraint.Skip
m.gy0 = Constraint(m.fe, m.cp, rule=_gy0)
def _gy(m, i, j, t):
if j > 0 and 1 < t < Ntray:
return m.y[i, j, t] == m.alpha[t] * m.x[i, j, t] * m.pm[
i, j, t] / m.p[t] + (1 - m.alpha[t]) * m.y[i, j, t - 1]
#y[i, q, c] = alpha[i] * x[i, q, c] * pm[i, q, c] / p[i] + (1 - alpha[i]) * y[i - 1, q, c];
else:
return Constraint.Skip
m.gy = Constraint(m.fe, m.cp, m.tray, rule=_gy)
def _dMV(m, i, j, t):
if j > 0 and 1 < t < Ntray:
return m.Mv[i, j, t] == m.Vm[i, j, t] * m.M[i, j, t]
else:
return Constraint.Skip
m.dMV = Constraint(m.fe, m.cp, m.tray, rule=_dMV)
def _dMv1(m, i, j):
if j > 0:
return m.Mv1[i, j] == m.Vm[i, j, 1] * m.M[i, j, 1]
else:
return Constraint.Skip
m.dMv1 = Constraint(m.fe, m.cp, rule=_dMv1)
def _dMvn(m, i, j):
if j > 0:
return m.Mvn[i, j] == m.Vm[i, j, Ntray] * m.M[i, j, Ntray]
else:
return Constraint.Skip
m.dMvn = Constraint(m.fe, m.cp, rule=_dMvn)
def _hyd(m, i, j, t):
if j > 0 and 1 < t < Ntray:
return m.L[i, j, t] * m.Vm[i, j, t] == 0.166 * (m.Mv[i, j, t] -
0.155)**1.5
# L[i,q,c]* Vm[i,q,c] = 0.166 * ( Mv[i,q,c] - 0.155)^1.5 ;
else:
return Constraint.Skip
m.hyd = Constraint(m.fe, m.cp, m.tray, rule=_hyd)
def _hyd1(m, i, j):
if j > 0:
return m.L[i, j, 1] * m.Vm[i, j,
1] == 0.166 * (m.Mv1[i, j] - 8.5)**1.5
else:
return Constraint.Skip
# L[i,q,c]*Vm[i,q,c] = 0.166*(Mv[i,q,c] - 0.155)^1.5 ;
m.hyd1 = Constraint(m.fe, m.cp, rule=_hyd1)
def _hydN(m, i, j):
if j > 0:
return m.L[i, j, Ntray] * m.Vm[i, j, Ntray] == 0.166 * (
m.Mvn[i, j] - 0.17)**1.5
else:
return Constraint.Skip
# L[i,q,c]*Vm[i,q,c] = 0.166*(Mv[i,q,c] - 0.155)^1.5 ;
m.hydN = Constraint(m.fe, m.cp, rule=_hydN)
def _dvm(m, i, j, t):
if j > 0:
return m.Vm[i, j, t] == m.x[i, j, t] * (
(1 / 2288) * 0.2685**
(1 +
(1 - m.T[i, j, t] / 512.4)**0.2453)) + (1 - m.x[i, j, t]) * (
(1 / 1235) * 0.27136**(1 +
(1 - m.T[i, j, t] / 536.4)**0.24))
else:
return Constraint.Skip
# Vm[i,q,c] = x[i,q,c] * ( 1/2288 * 0.2685^ (1 + (1 - T[i,q,c] /512.4)^0.2453)) + (1 - x[i,q,c]) * (1/1235 * 0.27136^ (1 + (1 - T[i,q,c] /536.4)^ 0.24)) ;
m.dvm = Constraint(m.fe, m.cp, m.tray, rule=_dvm)
return m
def compile(self, model, start_time):
"""
Build the structure of the optimization model
:param model: The entire optimization model
:param block:The pipe model object
:param start_time: The optimization start time
:return:
"""
Component.compile(self, model, start_time)
self.history_length = len(self.params['mass_flow_history'].v())
Tg = self.params['Tg'].v()
lines = self.params['lines'].v()
dn = self.params['diameter'].v()
time_step = self.params['time_step'].v()
n_steps = int(self.params['horizon'].v() / time_step)
self.block.all_time = Set(initialize=range(self.history_length +
n_steps),
ordered=True)
pipe_wall_rho = 7.85 * 10**3 # http://www.steel-grades.com/Steel-Grades/Structure-Steel/en-p235.html kg/m^3
pipe_wall_c = 461 # http://www.steel-grades.com/Steel-Grades/Structure-Steel/en-p235.html J/kg/K
pipe_wall_volume = np.pi * (self.Do[dn]**2 -
self.Di[dn]**2) / 4 * self.length
C = pipe_wall_volume * pipe_wall_c * pipe_wall_rho
surface = np.pi * self.Di[dn]**2 / 4 # cross sectional area of the pipe
Z = surface * self.rho * self.length # water mass in the pipe
# TODO Move capacity?
def _decl_mf(b, t):
return self.params['mass_flow'].v(t)
self.block.mass_flow = Param(self.TIME, rule=_decl_mf)
# Declare temperature variables ################################################################################
self.block.temperatures = Var(
lines,
self.block.all_time) # all temperatures (historical and future)
self.block.temperature_out_nhc = Var(
lines, self.block.all_time) # no heat capacity (only future)
self.block.temperature_out_nhl = Var(
lines, self.block.all_time) # no heat losses (only future)
self.block.temperature_out = Var(
lines, self.block.all_time) # no heat losses (only future)
self.block.temperature_in = Var(
lines, self.block.all_time) # incoming temperature (future)
# Declare list filled with all previous mass flows and future mass flows #######################################
def _decl_mf_history(b, t):
if t < n_steps:
return self.block.mass_flow[n_steps - t - 1]
else:
return self.params['mass_flow_history'].v(t - n_steps)
self.block.mf_history = Param(self.block.all_time,
rule=_decl_mf_history)
# Declare list filled with all previous temperatures for every optimization step ###############################
def _decl_temp_history(b, t, l):
if t < n_steps:
return b.temperatures[l,
t] == b.temperature_in[l,
n_steps - t - 1]
else:
return b.temperatures[l,
t] == self.params['temperature_history_'
+ l].v(t - n_steps)
self.block.def_temp_history = Constraint(self.block.all_time,
lines,
rule=_decl_temp_history)
# Initialize incoming temperature ##############################################################################
def _decl_init_temp_in(b, l):
return b.temperature_in[l, 0] == self.params[
'temperature_history_' + l].v(
0) # TODO better initialization??
self.block.decl_init_temp_in = Constraint(lines,
rule=_decl_init_temp_in)
# Define n #####################################################################################################
# Eq 3.4.7
def _decl_n(b, t):
sum_m = 0
for i in range(len(self.block.all_time) - (n_steps - 1 - t)):
sum_m += b.mf_history[n_steps - 1 - t + i] * time_step
if sum_m > Z:
return i
self.logger.warning('A proper value for n could not be calculated')
return i
self.block.n = Param(self.TIME, rule=_decl_n)
# Define R #####################################################################################################
# Eq 3.4.3
def _decl_r(b, t):
return sum(self.block.mf_history[i]
for i in range(n_steps - 1 - t, n_steps - 1 - t +
b.n[t] + 1)) * time_step
self.block.R = Param(self.TIME, rule=_decl_r)
# Define m #####################################################################################################
# Eq. 3.4.8
def _decl_m(b, t):
sum_m = 0
for i in range(len(self.block.all_time) - (n_steps - 1 - t)):
sum_m += b.mf_history[n_steps - 1 - t + i] * time_step
if sum_m > Z + self.block.mass_flow[t] * time_step:
return i
self.logger.warning('A proper value for m could not be calculated')
return i
self.block.m = Param(self.TIME, rule=_decl_m)
# Define Y #####################################################################################################
# Eq. 3.4.9
self.block.Y = Var(lines, self.TIME)
def _y(b, t, l):
return b.Y[l, t] == sum(
b.mf_history[i] * b.temperatures[l, i] * time_step
for i in range(n_steps - 1 - t + b.n[t] + 1, n_steps - 1 - t +
b.m[t]))
self.block.def_Y = Constraint(self.TIME, lines, rule=_y)
# Define S #####################################################################################################
# Eq 3.4.10 and 3.4.11
def _s(b, t):
if b.m[t] > b.n[t]:
return sum(b.mf_history[i] * time_step
for i in range(n_steps - 1 - t, n_steps - 1 - t +
b.m[t]))
else:
return b.R[t]
self.block.S = Param(self.TIME, rule=_s)
# Define outgoing temperature, without wall capacity and heat losses ###########################################
# Eq. 3.4.12
def _def_temp_out_nhc(b, t, l):
if b.mass_flow[t] == 0:
return Constraint.Skip
else:
return b.temperature_out_nhc[l, t] == (
(b.R[t] - Z) * b.temperatures[
l, n_steps - 1 - t + b.n[t]]
+ b.Y[l, t] + (
b.mass_flow[t] * time_step - b.S[t] + Z) *
b.temperatures[l, n_steps - 1 - t + b.m[t]]) \
/ b.mass_flow[t] / time_step
self.block.def_temp_out_nhc = Constraint(self.TIME,
lines,
rule=_def_temp_out_nhc)
# Pipe wall heat capacity ######################################################################################
# Eq. 3.4.20
self.block.K = 1 / self.Rs[self.params['diameter'].v()]
# Eq. 3.4.14
self.block.wall_temp = Var(lines, self.TIME)
def _decl_temp_out_nhl(b, t, l):
if t == 0:
return Constraint.Skip
elif b.mass_flow[t] == 0:
return Constraint.Skip
else:
return b.temperature_out_nhl[l, t] == (
b.temperature_out_nhc[l, t] * b.mass_flow[t]
* self.cp * time_step
+ C * b.wall_temp[l, t - 1]) / \
(C + b.mass_flow[t] * self.cp * time_step)
def _temp_wall(b, t, l):
if b.mass_flow[t] == 0:
if t == 0:
return Constraint.Skip
else:
return b.wall_temp[l, t] == Tg[t] + (
b.wall_temp[l, t - 1] - Tg[t]) * \
np.exp(-b.K * time_step /
(surface * self.rho * self.cp +
C / self.length))
else:
return b.wall_temp[l, t] == b.temperature_out_nhl[l, t]
# self.block.temp_wall = Constraint(self.TIME, lines, rule=_temp_wall)
# Eq. 3.4.15
def _init_temp_wall(b, l):
return b.wall_temp[l,
0] == self.params['wall_temperature_' + l].v()
self.block.init_temp_wall = Constraint(lines, rule=_init_temp_wall)
# def _decl_temp_out_nhl(b, t, l):
# if t == 0:
# return b.temperature_out_nhl[t, l] == (b.temperature_out_nhc[t, l] * (b.mass_flow[t]
# * self.cp * time_step - C/2)
# + C * b.wall_temp[t, l]) / \
# (C/2 + b.mass_flow[t] * self.cp * time_step)
# else:
# return b.temperature_out_nhl[t, l] == (b.temperature_out_nhc[t, l] * (b.mass_flow[t]
# * self.cp * time_step - C/2)
# + C * b.wall_temp[t-1, l]) / \
# (C/2 + b.mass_flow[t] * self.cp * time_step)
self.block.decl_temp_out_nhl = Constraint(self.TIME,
lines,
rule=_decl_temp_out_nhl)
# Eq. 3.4.18
# def _temp_wall(b, t, l):
# if t == 0:
# return Constraint.Skip
# elif b.mass_flow[t] == 0:
# return b.wall_temp[t, l] == Tg[t] + (b.wall_temp[t-1, l] - Tg[t]) * \
# np.exp(-b.K * time_step /
# (surface * self.rho * self.cp + C/self.length))
# else:
# return b.wall_temp[t, l] == b.wall_temp[t-1, l] + \
# ((b.temperature_out_nhc[t, l] - b.temperature_out_nhl[t, l]) *
# b.mass_flow[t] * self.cp * time_step) / C
self.block.temp_wall = Constraint(self.TIME, lines, rule=_temp_wall)
# Heat losses ##################################################################################################
# Eq. 3.4.24
def _tk(b, t):
if b.mass_flow[t] == 0:
return Constraint.Skip
else:
delta_time = time_step * ((b.R[t] - Z) * b.n[t]
+ sum(
b.mf_history[n_steps - 1 - t + i] * time_step * i
for i in range(b.n[t] + 1, b.m[t]))
+ (b.mass_flow[t] * time_step - b.S[
t] + Z) * b.m[t]) \
/ b.mass_flow[t] / time_step
return delta_time
self.block.tk = Param(self.TIME, rule=_tk)
# Eq. 3.4.27
def _temp_out(b, t, l):
if b.mass_flow[t] == 0:
if t == 0:
return b.temperature_out[l, t] == self.params[
'temperature_out_' + l].v()
else:
return b.temperature_out[l, t] == (
b.temperature_out[l, t - 1] - Tg[t]) * \
np.exp(-b.K * time_step /
(
surface * self.rho * self.cp + C / self.length)) \
+ Tg[t]
else:
return b.temperature_out[l, t] == Tg[t] + \
(b.temperature_out_nhl[l, t] - Tg[t]) * \
np.exp(-(b.K * b.tk[t]) /
(surface * self.rho * self.cp))
self.block.def_temp_out = Constraint(self.TIME, lines, rule=_temp_out)
def unit_commitment_model():
model = ConcreteModel()
# Define input files
xlsx = pd.ExcelFile(
f"{Path(__file__).parent.absolute()}/input/unit_commitment.xlsx",
engine="openpyxl",
)
system_demand = Helper.read_excel(xlsx, "SystemDemand")
storage_systems = Helper.read_excel(xlsx, "StorageSystems")
generators = Helper.read_excel(xlsx, "Generators")
generator_step_size = Helper.read_excel(xlsx, "GeneratorStepSize")
generator_step_cost = Helper.read_excel(xlsx, "GeneratorStepCost")
pv_generation = Helper.read_excel(xlsx, "PVGeneration")
# Define sets
model.T = Set(ordered=True, initialize=system_demand.index)
model.I = Set(ordered=True, initialize=generators.index)
model.F = Set(ordered=True, initialize=generator_step_size.columns)
model.S = Set(ordered=True, initialize=storage_systems.index)
# Define parameters
model.Pmax = Param(model.I, within=NonNegativeReals, mutable=True)
model.Pmin = Param(model.I, within=NonNegativeReals, mutable=True)
model.RU = Param(model.I, within=NonNegativeReals, mutable=True)
model.RD = Param(model.I, within=NonNegativeReals, mutable=True)
model.SUC = Param(model.I, within=NonNegativeReals, mutable=True)
model.SDC = Param(model.I, within=NonNegativeReals, mutable=True)
model.Pini = Param(model.I, within=NonNegativeReals, mutable=True)
model.uini = Param(model.I, within=Binary, mutable=True)
model.C = Param(model.I, model.F, within=NonNegativeReals, mutable=True)
model.B = Param(model.I, model.F, within=NonNegativeReals, mutable=True)
model.SystemDemand = Param(model.T, within=NonNegativeReals, mutable=True)
model.Emissions = Param(model.I, within=NonNegativeReals, mutable=True)
model.PV = Param(model.T, within=NonNegativeReals, mutable=True)
model.ESS_Pmax = Param(model.S, within=NonNegativeReals, mutable=True)
model.ESS_SOEmax = Param(model.S, within=NonNegativeReals, mutable=True)
model.ESS_SOEini = Param(model.S, within=NonNegativeReals, mutable=True)
model.ESS_Eff = Param(model.S, within=NonNegativeReals, mutable=True)
# Give values to parameters of the generators
for i in model.I:
model.Pmin[i] = generators.loc[i, "Pmin"]
model.Pmax[i] = generators.loc[i, "Pmax"]
model.RU[i] = generators.loc[i, "RU"]
model.RD[i] = generators.loc[i, "RD"]
model.SUC[i] = generators.loc[i, "SUC"]
model.SDC[i] = generators.loc[i, "SDC"]
model.Pini[i] = generators.loc[i, "Pini"]
model.uini[i] = generators.loc[i, "uini"]
model.Emissions[i] = generators.loc[i, "Emissions"]
for f in model.F:
model.B[i, f] = generator_step_size.loc[i, f]
model.C[i, f] = generator_step_cost.loc[i, f]
# Add system demand and PV generation
for t in model.T:
model.SystemDemand[t] = system_demand.loc[t, "SystemDemand"]
model.PV[t] = pv_generation.loc[t, "PVGeneration"]
# Give values to ESS parameters
for s in model.S:
model.ESS_Pmax[s] = storage_systems.loc[s, "Power"]
model.ESS_SOEmax[s] = storage_systems.loc[s, "Energy"]
model.ESS_SOEini[s] = storage_systems.loc[s, "SOEini"]
model.ESS_Eff[s] = storage_systems.loc[s, "Eff"]
# Define decision variables
model.P = Var(model.I, model.T, within=NonNegativeReals)
model.Pres = Var(model.T, within=NonNegativeReals)
model.b = Var(model.I, model.F, model.T, within=NonNegativeReals)
model.u = Var(model.I, model.T, within=Binary)
model.CSU = Var(model.I, model.T, within=NonNegativeReals)
model.CSD = Var(model.I, model.T, within=NonNegativeReals)
model.SOE = Var(model.S, model.T, within=NonNegativeReals)
model.Pch = Var(model.S, model.T, within=NonNegativeReals)
model.Pdis = Var(model.S, model.T, within=NonNegativeReals)
model.u_ess = Var(model.S, model.T, within=Binary)
# --------------------------------------
# Define the objective functions
# --------------------------------------
def cost_objective(model):
return sum(
sum(
sum(model.C[i, f] * model.b[i, f, t]
for f in model.F) + model.CSU[i, t] + model.CSD[i, t]
for i in model.I) for t in model.T)
def emissions_objective(model):
return sum(
sum(model.P[i, t] * model.Emissions[i] for i in model.I)
for t in model.T)
def unmet_objective(model):
return sum(model.Pres[t] for t in model.T)
# --------------------------------------
# Define the regular constraints
# --------------------------------------
def power_decomposition_rule1(model, i, t):
return model.P[i, t] == sum(model.b[i, f, t] for f in model.F)
def power_decomposition_rule2(model, i, f, t):
return model.b[i, f, t] <= model.B[i, f]
def power_min_rule(model, i, t):
return model.P[i, t] >= model.Pmin[i] * model.u[i, t]
def power_max_rule(model, i, t):
return model.P[i, t] <= model.Pmax[i] * model.u[i, t]
def ramp_up_rule(model, i, t):
if model.T.ord(t) == 1:
return model.P[i, t] - model.Pini[i] <= 60 * model.RU[i]
if model.T.ord(t) > 1:
return model.P[i, t] - model.P[i,
model.T.prev(t)] <= 60 * model.RU[i]
def ramp_down_rule(model, i, t):
if model.T.ord(t) == 1:
return (model.Pini[i] - model.P[i, t]) <= 60 * model.RD[i]
if model.T.ord(t) > 1:
return (model.P[i, model.T.prev(t)] -
model.P[i, t]) <= 60 * model.RD[i]
def start_up_cost(model, i, t):
if model.T.ord(t) == 1:
return model.CSU[i, t] >= model.SUC[i] * (model.u[i, t] -
model.uini[i])
if model.T.ord(t) > 1:
return model.CSU[i, t] >= model.SUC[i] * (
model.u[i, t] - model.u[i, model.T.prev(t)])
def shut_down_cost(model, i, t):
if model.T.ord(t) == 1:
return model.CSD[i, t] >= model.SDC[i] * (model.uini[i] -
model.u[i, t])
if model.T.ord(t) > 1:
return model.CSD[i, t] >= model.SDC[i] * (
model.u[i, model.T.prev(t)] - model.u[i, t])
def ESS_SOEupdate(model, s, t):
if model.T.ord(t) == 1:
return (model.SOE[s, t] == model.ESS_SOEini[s] +
model.ESS_Eff[s] * model.Pch[s, t] -
model.Pdis[s, t] / model.ESS_Eff[s])
if model.T.ord(t) > 1:
return (model.SOE[s, t] == model.SOE[s, model.T.prev(t)] +
model.ESS_Eff[s] * model.Pch[s, t] -
model.Pdis[s, t] / model.ESS_Eff[s])
def ESS_SOElimit(model, s, t):
return model.SOE[s, t] <= model.ESS_SOEmax[s]
def ESS_Charging(model, s, t):
return model.Pch[s, t] <= model.ESS_Pmax[s] * model.u_ess[s, t]
def ESS_Discharging(model, s, t):
return model.Pdis[s, t] <= model.ESS_Pmax[s] * (1 - model.u_ess[s, t])
def Balance(model, t):
return model.PV[t] + sum(model.P[i, t] for i in model.I) + sum(
model.Pdis[s, t]
for s in model.S) == model.SystemDemand[t] - model.Pres[t] + sum(
model.Pch[s, t] for s in model.S)
def Pres_max(model, t):
return model.Pres[t] <= 0.1 * model.SystemDemand[t]
# --------------------------------------
# Add components to the model
# --------------------------------------
# Add the constraints to the model
model.power_decomposition_rule1 = Constraint(
model.I, model.T, rule=power_decomposition_rule1)
model.power_decomposition_rule2 = Constraint(
model.I, model.F, model.T, rule=power_decomposition_rule2)
model.power_min_rule = Constraint(model.I, model.T, rule=power_min_rule)
model.power_max_rule = Constraint(model.I, model.T, rule=power_max_rule)
model.start_up_cost = Constraint(model.I, model.T, rule=start_up_cost)
model.shut_down_cost = Constraint(model.I, model.T, rule=shut_down_cost)
model.ConSOEUpdate = Constraint(model.S, model.T, rule=ESS_SOEupdate)
model.ConCharging = Constraint(model.S, model.T, rule=ESS_Charging)
model.ConDischarging = Constraint(model.S, model.T, rule=ESS_Discharging)
model.ConSOElimit = Constraint(model.S, model.T, rule=ESS_SOElimit)
model.ConGenUp = Constraint(model.I, model.T, rule=ramp_up_rule)
model.ConGenDown = Constraint(model.I, model.T, rule=ramp_down_rule)
model.ConBalance = Constraint(model.T, rule=Balance)
model.Pres_max = Constraint(model.T, rule=Pres_max)
# Add the objective functions to the model using ObjectiveList(). Note
# that the first index is 1 instead of 0!
model.obj_list = ObjectiveList()
model.obj_list.add(expr=cost_objective(model), sense=minimize)
model.obj_list.add(expr=emissions_objective(model), sense=minimize)
model.obj_list.add(expr=unmet_objective(model), sense=minimize)
# By default deactivate all the objective functions
for o in range(len(model.obj_list)):
model.obj_list[o + 1].deactivate()
return model
def __init__(self, nfe_t, ncp_t, **kwargs):
ConcreteModel.__init__(self)
steady = kwargs.pop('steady', False)
_t = kwargs.pop('_t', 1.0)
Ntray = kwargs.pop('Ntray', 42)
# --------------------------------------------------------------------------------------------------------------
# Orthogonal Collocation Parameters section
# Radau
self._alp_gauB_t = 1
self._bet_gauB_t = 0
if steady:
print("[I] " + str(self.__class__.__name__) + " NFE and NCP Overriden - Steady state mode")
self.nfe_t = 1
self.ncp_t = 1
else:
self.nfe_t = nfe_t
self.ncp_t = ncp_t
self.tau_t = collptsgen(self.ncp_t, self._alp_gauB_t, self._bet_gauB_t)
# start at zero
self.tau_i_t = {0: 0.}
# create a list
for ii in range(1, self.ncp_t + 1):
self.tau_i_t[ii] = self.tau_t[ii - 1]
# ======= SETS ======= #
# For finite element = 1 .. NFE
# This has to be > 0
self.fe_t = Set(initialize=[ii for ii in range(1, self.nfe_t + 1)])
# collocation points
# collocation points for differential variables
self.cp_t = Set(initialize=[ii for ii in range(0, self.ncp_t + 1)])
# collocation points for algebraic variables
self.cp_ta = Set(within=self.cp_t, initialize=[ii for ii in range(1, self.ncp_t + 1)])
# create collocation param
self.taucp_t = Param(self.cp_t, initialize=self.tau_i_t)
self.ldot_t = Param(self.cp_t, self.cp_t, initialize=
(lambda m, j, k: lgrdot(k, m.taucp_t[j], self.ncp_t, self._alp_gauB_t, self._bet_gauB_t))) #: watch out for this!
self.l1_t = Param(self.cp_t, initialize=
(lambda m, j: lgr(j, 1, self.ncp_t, self._alp_gauB_t, self._bet_gauB_t)))
# --------------------------------------------------------------------------------------------------------------
# Model parameters
self.Ntray = Ntray
self.tray = Set(initialize=[i for i in range(1, Ntray + 1)])
self.feed = Param(self.tray,
initialize=lambda m, t: 57.5294 if t == 21 else 0.0,
mutable=True)
self.xf = Param(initialize=0.32, mutable=True) # feed mole fraction
self.hf = Param(initialize=9081.3) # feed enthalpy
self.hlm0 = Param(initialize=2.6786e-04)
self.hlma = Param(initialize=-0.14779)
self.hlmb = Param(initialize=97.4289)
self.hlmc = Param(initialize=-2.1045e04)
self.hln0 = Param(initialize=4.0449e-04)
self.hlna = Param(initialize=-0.1435)
self.hlnb = Param(initialize=121.7981)
self.hlnc = Param(initialize=-3.0718e04)
self.r = Param(initialize=8.3147)
self.a = Param(initialize=6.09648)
self.b = Param(initialize=1.28862)
self.c1 = Param(initialize=1.016)
self.d = Param(initialize=15.6875)
self.l = Param(initialize=13.4721)
self.f = Param(initialize=2.615)
self.gm = Param(initialize=0.557)
self.Tkm = Param(initialize=512.6)
self.Pkm = Param(initialize=8.096e06)
self.gn = Param(initialize=0.612)
self.Tkn = Param(initialize=536.7)
self.Pkn = Param(initialize=5.166e06)
self.CapAm = Param(initialize=23.48)
self.CapBm = Param(initialize=3626.6)
self.CapCm = Param(initialize=-34.29)
self.CapAn = Param(initialize=22.437)
self.CapBn = Param(initialize=3166.64)
self.CapCn = Param(initialize=-80.15)
self.pstrip = Param(initialize=250)
self.prect = Param(initialize=190)
def _p_init(m, t):
ptray = 9.39e04
if t <= 20:
return _p_init(m, 21) + m.pstrip * (21 - t)
elif 20 < t < m.Ntray:
return ptray + m.prect * (m.Ntray - t)
elif t == m.Ntray:
return 9.39e04
self.p = Param(self.tray, initialize=_p_init)
self.T29_des = Param(initialize=343.15)
self.T15_des = Param(initialize=361.15)
self.Dset = Param(initialize=1.83728)
self.Qcset = Param(initialize=1.618890)
self.Qrset = Param(initialize=1.786050)
# self.Recset = Param()
self.alpha_T29 = Param(initialize=1)
self.alpha_T15 = Param(initialize=1)
self.alpha_D = Param(initialize=1)
self.alpha_Qc = Param(initialize=1)
self.alpha_Qr = Param(initialize=1)
self.alpha_Rec = Param(initialize=1)
def _alpha_init(m, i):
if i <= 21:
return 0.62
else:
return 0.35
self.alpha = Param(self.tray,
initialize=lambda m, t: 0.62 if t <= 21 else 0.35)
# --------------------------------------------------------------------------------------------------------------
#: First define differential state variables (state: x, ic-Param: x_ic, derivative-Var:dx_dt
#: States (differential) section
zero_tray = dict.fromkeys(self.tray)
zero3 = dict.fromkeys(self.fe_t * self.cp_t * self.tray)
for key in zero3.keys():
zero3[key] = 0.0
def __m_init(m, i, j, t):
if t < m.Ntray:
return 4000.
elif t == 1:
return 104340.
elif t == m.Ntray:
return 5000.
#: Liquid hold-up
self.M = Var(self.fe_t, self.cp_t, self.tray,
initialize=__m_init)
#: Mole-fraction
self.x = Var(self.fe_t, self.cp_t, self.tray, initialize=lambda m, i, j, t: 0.999 * t / m.Ntray)
#: Initial state-Param
self.M_ic = zero_tray if steady else Param(self.tray, initialize=0.0, mutable=True)
self.x_ic = zero_tray if steady else Param(self.tray, initialize=0.0, mutable=True)
#: Derivative-var
self.dM_dt = zero3 if steady else Var(self.fe_t, self.cp_t, self.tray, initialize=0.0)
self.dx_dt = zero3 if steady else Var(self.fe_t, self.cp_t, self.tray, initialize=0.0)
# --------------------------------------------------------------------------------------------------------------
# States (algebraic) section
# Tray temperature
self.T = Var(self.fe_t, self.cp_ta, self.tray,
initialize=lambda m, i, j, t: ((370.781 - 335.753) / m.Ntray) * t + 370.781)
self.Tdot = Var(self.fe_t, self.cp_ta, self.tray, initialize=1e-05) #: Not really a der_var
# saturation pressures
self.pm = Var(self.fe_t, self.cp_ta, self.tray, initialize=1e4)
self.pn = Var(self.fe_t, self.cp_ta, self.tray, initialize=1e4)
# Vapor mole flowrate
self.V = Var(self.fe_t, self.cp_ta, self.tray, initialize=44.0)
def _l_init(m, i, j, t):
if 2 <= t <= 21:
return 83.
elif 22 <= t <= 42:
return 23
elif t == 1:
return 40
# Liquid mole flowrate
self.L = Var(self.fe_t, self.cp_ta, self.tray, initialize=_l_init)
# Vapor mole frac & diff var
self.y = Var(self.fe_t, self.cp_ta, self.tray,
initialize=lambda m, i, j, t: ((0.99 - 0.005) / m.Ntray) * t + 0.005)
# Liquid enthalpy # enthalpy
self.hl = Var(self.fe_t, self.cp_ta, self.tray, initialize=10000.)
# Liquid enthalpy # enthalpy
self.hv = Var(self.fe_t, self.cp_ta, self.tray, initialize=5e+04)
# Re-boiler & condenser heat
self.Qc = Var(self.fe_t, self.cp_ta, initialize=1.6e06)
self.D = Var(self.fe_t, self.cp_ta, initialize=18.33)
# vol holdups
self.Vm = Var(self.fe_t, self.cp_ta, self.tray, initialize=6e-05)
self.Mv = Var(self.fe_t, self.cp_ta, self.tray,
initialize=lambda m, i, j, t: 0.23 if 1 < t < m.Ntray else 0.0)
self.Mv1 = Var(self.fe_t, self.cp_ta, initialize=8.57)
self.Mvn = Var(self.fe_t, self.cp_ta, initialize=0.203)
hi_t = dict.fromkeys(self.fe_t)
for key in hi_t.keys():
hi_t[key] = 1.0 if steady else _t/self.nfe_t
self.hi_t = hi_t if steady else Param(self.fe_t, initialize=hi_t)
# --------------------------------------------------------------------------------------------------------------
#: Controls
self.u1 = Param(self.fe_t, initialize=7.72700925775773761472464684629813E-01, mutable=True) #: Dummy
self.u2 = Param(self.fe_t, initialize=1.78604740940007800236344337463379E+06, mutable=True) #: Dummy
self.Rec = Var(self.fe_t, initialize=7.72700925775773761472464684629813E-01)
self.Qr = Var(self.fe_t, initialize=1.78604740940007800236344337463379E+06)
# --------------------------------------------------------------------------------------------------------------
#: Constraints for the differential states
#: Then the ode-Con:de_x, collocation-Con:dvar_t_x, noisy-Expr: noisy_x, cp-Constraint: cp_x, initial-Con: x_icc
#: Differential equations
self.de_M = Constraint(self.fe_t, self.cp_ta, self.tray, rule=m_ode)
self.de_x = Constraint(self.fe_t, self.cp_ta, self.tray, rule=x_ode)
#: Collocation equations
self.dvar_t_M = None if steady else Constraint(self.fe_t, self.cp_ta, self.tray, rule=M_COLL)
self.dvar_t_x = None if steady else Constraint(self.fe_t, self.cp_ta, self.tray, rule=x_coll)
#: Continuation equations (redundancy here)
if self.nfe_t > 1:
#: Noisy expressions
self.noisy_M = None if steady else Expression(self.fe_t, self.tray, rule=M_CONT)
self.noisy_x = None if steady else Expression(self.fe_t, self.tray, rule=x_cont)
#: Continuation equations
self.cp_M = None if steady else \
Constraint(self.fe_t, self.tray,
rule=lambda m, i, t: self.noisy_M[i, t] == 0.0 if i < self.nfe_t else Constraint.Skip)
self.cp_x = None if steady else \
Constraint(self.fe_t, self.tray,
rule=lambda m, i, t: self.noisy_x[i, t] == 0.0 if i < self.nfe_t else Constraint.Skip)
#: Initial condition-Constraints
self.M_icc = None if steady else Constraint(self.tray, rule=acm)
self.x_icc = None if steady else Constraint(self.tray, rule=acx)
# --------------------------------------------------------------------------------------------------------------
#: Constraint section (algebraic equations)
self.hrc = Constraint(self.fe_t, self.cp_ta, rule=hrc)
self.gh = Constraint(self.fe_t, self.cp_ta, self.tray, rule=gh)
self.ghb = Constraint(self.fe_t, self.cp_ta, rule=ghb)
self.ghc = Constraint(self.fe_t, self.cp_ta, rule=ghc)
self.hkl = Constraint(self.fe_t, self.cp_ta, self.tray, rule=hkl)
self.hkv = Constraint(self.fe_t, self.cp_ta, self.tray, rule=hkv)
self.lpself = Constraint(self.fe_t, self.cp_ta, self.tray, rule=lpm)
self.lpn = Constraint(self.fe_t, self.cp_ta, self.tray, rule=lpn)
self.dp = Constraint(self.fe_t, self.cp_ta, self.tray, rule=dp)
self.lTdot = Constraint(self.fe_t, self.cp_ta, self.tray, rule=lTdot)
self.gy0 = Constraint(self.fe_t, self.cp_ta, rule=gy0)
self.gy = Constraint(self.fe_t, self.cp_ta, self.tray, rule=gy)
self.dMV = Constraint(self.fe_t, self.cp_ta, self.tray, rule=dMV)
self.dMv1 = Constraint(self.fe_t, self.cp_ta, rule=dMv1)
self.dMvn = Constraint(self.fe_t, self.cp_ta, rule=dMvn)
self.hyd = Constraint(self.fe_t, self.cp_ta, self.tray, rule=hyd)
self.hyd1 = Constraint(self.fe_t, self.cp_ta, rule=hyd1)
self.hydN = Constraint(self.fe_t, self.cp_ta, rule=hydN)
self.dvself = Constraint(self.fe_t, self.cp_ta, self.tray, rule=dvm)
# --------------------------------------------------------------------------------------------------------------
#: Control constraint
self.u1_e = Expression(self.fe_t, rule=lambda m, i: self.Rec[i])
self.u2_e = Expression(self.fe_t, rule=lambda m, i: self.Qr[i])
self.u1_c = Constraint(self.fe_t, rule=lambda m, i: self.u1[i] == self.u1_e[i])
self.u2_c = Constraint(self.fe_t, rule=lambda m, i: self.u2[i] == self.u2_e[i])
# --------------------------------------------------------------------------------------------------------------
#: Suffixes
self.dual = Suffix(direction=Suffix.IMPORT_EXPORT)
self.ipopt_zL_out = Suffix(direction=Suffix.IMPORT)
self.ipopt_zU_out = Suffix(direction=Suffix.IMPORT)
self.ipopt_zL_in = Suffix(direction=Suffix.EXPORT)
self.ipopt_zU_in = Suffix(direction=Suffix.EXPORT)
def make_separation_problem(model_data, config):
"""
Swap out uncertain param Param objects for Vars
Add uncertainty set constraints and separation objectives
"""
separation_model = model_data.original.clone()
separation_model.del_component("coefficient_matching_constraints")
separation_model.del_component("coefficient_matching_constraints_index")
uncertain_params = separation_model.util.uncertain_params
separation_model.util.uncertain_param_vars = param_vars = Var(
range(len(uncertain_params)))
map_new_constraint_list_to_original_con = ComponentMap()
if config.objective_focus is ObjectiveType.worst_case:
separation_model.util.zeta = Param(initialize=0, mutable=True)
constr = Constraint(expr=separation_model.first_stage_objective +
separation_model.second_stage_objective -
separation_model.util.zeta <= 0)
separation_model.add_component("epigraph_constr", constr)
substitution_map = {}
#Separation problem initialized to nominal uncertain parameter values
for idx, var in enumerate(list(param_vars.values())):
param = uncertain_params[idx]
var.set_value(param.value, skip_validation=True)
substitution_map[id(param)] = var
separation_model.util.new_constraints = constraints = ConstraintList()
uncertain_param_set = ComponentSet(uncertain_params)
for c in separation_model.component_data_objects(Constraint):
if any(v in uncertain_param_set
for v in identify_mutable_parameters(c.expr)):
if c.equality:
constraints.add(
replace_expressions(expr=c.lower,
substitution_map=substitution_map) ==
replace_expressions(expr=c.body,
substitution_map=substitution_map))
elif c.lower is not None:
constraints.add(
replace_expressions(expr=c.lower,
substitution_map=substitution_map) <=
replace_expressions(expr=c.body,
substitution_map=substitution_map))
elif c.upper is not None:
constraints.add(
replace_expressions(expr=c.upper,
substitution_map=substitution_map) >=
replace_expressions(expr=c.body,
substitution_map=substitution_map))
else:
raise ValueError(
"Unable to parse constraint for building the separation problem."
)
c.deactivate()
map_new_constraint_list_to_original_con[constraints[
constraints.index_set().last()]] = c
separation_model.util.map_new_constraint_list_to_original_con = map_new_constraint_list_to_original_con
# === Add objectives first so that the uncertainty set
# Constraints do not get picked up into the set
# of performance constraints which become objectives
make_separation_objective_functions(separation_model, config)
add_uncertainty_set_constraints(separation_model, config)
# === Deactivate h(x,q) == 0 constraints
for c in separation_model.util.h_x_q_constraints:
c.deactivate()
return separation_model
def create_nmpc(self, **kwargs):
kwargs.pop("newnfe", self.nfe_tnmpc)
kwargs.pop("newncp", self.ncp_tnmpc)
self.journalist('W', self._iteration_count, "Initializing NMPC",
"With {:d} fe and {:d} cp".format(self.nfe_tnmpc, self.ncp_tnmpc))
_tnmpc = self.hi_t * self.nfe_tnmpc
self.olnmpc = self.d_mod(self.nfe_tnmpc, self.ncp_tnmpc, _t=_tnmpc)
self.olnmpc.name = "olnmpc (Open-Loop NMPC)"
self.olnmpc.create_bounds()
for u in self.u:
cv = getattr(self.olnmpc, u) #: Get the param
c_val = [value(cv[i]) for i in cv.keys()] #: Current value
self.olnmpc.del_component(cv) #: Delete the param
self.olnmpc.add_component(u, Var(self.olnmpc.fe_t, initialize=lambda m, i: c_val[i-1]))
self.olnmpc.equalize_u(direction="r_to_u")
cc = getattr(self.olnmpc, u + "_c") #: Get the constraint
ce = getattr(self.olnmpc, u + "_e") #: Get the expression
cv = getattr(self.olnmpc, u) #: Get the new variable
for k in cv.keys():
cv[k].setlb(self.u_bounds[u][0])
cv[k].setub(self.u_bounds[u][1])
cc.clear()
cc.rule = lambda m, i: cv[i] == ce[i]
cc.reconstruct()
#: Dictionary of the states for a particular time point i
self.xmpc_l = {}
#: Dictionary of the position for a state in the dictionary
self.xmpc_key = {}
#:
self.xmpc_l[0] = []
#: First build the name dictionary
k = 0
for x in self.states:
n_s = getattr(self.olnmpc, x) #: State
for j in self.state_vars[x]:
self.xmpc_l[0].append(n_s[(0, self.ncp_tnmpc) + j])
self.xmpc_key[(x, j)] = k
k += 1
#: Iterate over the rest
for t in range(1, self.nfe_tnmpc):
self.xmpc_l[t] = []
for x in self.states:
n_s = getattr(self.olnmpc, x) #: State
for j in self.state_vars[x]:
self.xmpc_l[t].append(n_s[(t, self.ncp_tnmpc) + j])
#: A set with the length of flattened states
self.olnmpc.xmpcS_nmpc = Set(initialize=[i for i in range(0, len(self.xmpc_l[0]))])
#: Create set of noisy_states
self.olnmpc.xmpc_ref_nmpc = Param(self.olnmpc.xmpcS_nmpc, initialize=0.0, mutable=True) #: Ref-state
self.olnmpc.Q_nmpc = Param(self.olnmpc.xmpcS_nmpc, initialize=1, mutable=True) #: Control-weight
# (diagonal Matrices)
self.olnmpc.Q_w_nmpc = Param(self.olnmpc.fe_t, initialize=1e-04, mutable=True)
self.olnmpc.R_w_nmpc = Param(self.olnmpc.fe_t, initialize=1e+02, mutable=True)
#: Build the xT*Q*x part
self.olnmpc.xQ_expr_nmpc = Expression(expr=sum(
sum(self.olnmpc.Q_w_nmpc[fe] *
self.olnmpc.Q_nmpc[k] * (self.xmpc_l[fe][k] - self.olnmpc.xmpc_ref_nmpc[k])**2
for k in self.olnmpc.xmpcS_nmpc)
for fe in range(0, self.nfe_tnmpc)))
#: Build the control list
self.umpc_l = {}
for t in range(0, self.nfe_tnmpc):
self.umpc_l[t] = []
for u in self.u:
uvar = getattr(self.olnmpc, u)
self.umpc_l[t].append(uvar[t])
#: Create set of u
self.olnmpc.umpcS_nmpc = Set(initialize=[i for i in range(0, len(self.umpc_l[0]))])
#: ref u
self.olnmpc.umpc_ref_nmpc = Param(self.olnmpc.umpcS_nmpc, initialize=0.0, mutable=True)
self.olnmpc.R_nmpc = Param(self.olnmpc.umpcS_nmpc, initialize=1, mutable=True) #: Control-weight
#: Build the uT * R * u expression
self.olnmpc.xR_expr_nmpc = Expression(expr=sum(
sum(self.olnmpc.R_w_nmpc[fe] *
self.olnmpc.R_nmpc[k] * (self.umpc_l[fe][k] - self.olnmpc.umpc_ref_nmpc[k]) ** 2 for k in
self.olnmpc.umpcS_nmpc)
for fe in range(0, self.nfe_tnmpc)))
self.olnmpc.objfun_nmpc = Objective(expr=self.olnmpc.xQ_expr_nmpc + self.olnmpc.xR_expr_nmpc)