def is_bounded(self, config):
"""
Return True if the uncertainty set is bounded, else False.
"""
# === Determine bounds on all uncertain params
bounding_model = ConcreteModel()
bounding_model.util = Block() # So that boundedness checks work for Cardinality and FactorModel sets
bounding_model.uncertain_param_vars = IndexedVar(range(len(config.uncertain_params)), initialize=1)
for idx, param in enumerate(config.uncertain_params):
bounding_model.uncertain_param_vars[idx].value = param.value
bounding_model.add_component("uncertainty_set_constraint",
config.uncertainty_set.set_as_constraint(
uncertain_params=bounding_model.uncertain_param_vars,
model=bounding_model,
config=config
))
for idx, param in enumerate(list(bounding_model.uncertain_param_vars.values())):
bounding_model.add_component("lb_obj_" + str(idx), Objective(expr=param, sense=minimize))
bounding_model.add_component("ub_obj_" + str(idx), Objective(expr=param, sense=maximize))
for o in bounding_model.component_data_objects(Objective):
o.deactivate()
for i in range(len(bounding_model.uncertain_param_vars)):
for limit in ("lb", "ub"):
getattr(bounding_model, limit + "_obj_" + str(i)).activate()
res = config.global_solver.solve(bounding_model, tee=False)
getattr(bounding_model, limit + "_obj_" + str(i)).deactivate()
if not check_optimal_termination(res):
return False
return True
def __init__(self, pyomo_model):
"""Constructor"""
args = (Set(),)
kwargs = {}
self.pyomo_model = pyomo_model
Objective.__init__(self, *args, **kwargs)
#self._data = ObjectiveDataSequence( poek_model )
self._data = []
def add_bounds_for_uncertain_parameters(model, config):
'''
This function solves a set of optimization problems to determine bounds on the uncertain parameters
given the uncertainty set description. These bounds will be added as additional constraints to the uncertainty_set_constr
constraint. Should only be called once set_as_constraint() has been called on the separation_model object.
:param separation_model: the model on which to add the bounds
:param config: solver config
:return:
'''
# === Determine bounds on all uncertain params
uncertain_param_bounds = []
bounding_model = ConcreteModel()
bounding_model.util = Block()
bounding_model.util.uncertain_param_vars = IndexedVar(
model.util.uncertain_param_vars.index_set())
for tup in model.util.uncertain_param_vars.items():
bounding_model.util.uncertain_param_vars[tup[0]].value = tup[1].value
bounding_model.add_component(
"uncertainty_set_constraint",
config.uncertainty_set.set_as_constraint(
uncertain_params=bounding_model.util.uncertain_param_vars,
model=bounding_model,
config=config))
for idx, param in enumerate(
list(bounding_model.util.uncertain_param_vars.values())):
bounding_model.add_component("lb_obj_" + str(idx),
Objective(expr=param, sense=minimize))
bounding_model.add_component("ub_obj_" + str(idx),
Objective(expr=param, sense=maximize))
for o in bounding_model.component_data_objects(Objective):
o.deactivate()
for i in range(len(bounding_model.util.uncertain_param_vars)):
bounds = []
for limit in ("lb", "ub"):
getattr(bounding_model, limit + "_obj_" + str(i)).activate()
res = config.global_solver.solve(bounding_model, tee=False)
bounds.append(bounding_model.util.uncertain_param_vars[i].value)
getattr(bounding_model, limit + "_obj_" + str(i)).deactivate()
uncertain_param_bounds.append(bounds)
# === Add bounds as constraints to uncertainty_set_constraint ConstraintList
for idx, bound in enumerate(uncertain_param_bounds):
model.util.uncertain_param_vars[idx].setlb(bound[0])
model.util.uncertain_param_vars[idx].setub(bound[1])
return
def make_model_2():
m = ConcreteModel()
m.x = Var(initialize=0.1, bounds=(0, 1))
m.y = Var(initialize=0.1, bounds=(0, 1))
m.obj = Objective(expr=-m.x**2 - m.y**2)
m.c = Constraint(expr=m.y <= pe.exp(-m.x))
return m
def is_empty_intersection(self, uncertain_params, nlp_solver):
"""
Determine if intersection is empty
Args:
uncertain_params: list of uncertain parameters
nlp_solver: a Pyomo Solver object for solving NLPs
"""
# === Non-emptiness check for the set intersection
is_empty_intersection = True
if any(a_set.type == "discrete" for a_set in self.all_sets):
disc_sets = (a_set for a_set in self.all_sets if a_set.type == "discrete")
disc_set = min(disc_sets, key=lambda x: len(x.scenarios)) # minimum set of scenarios
# === Ensure there is at least one scenario from this discrete set which is a member of all other sets
for scenario in disc_set.scenarios:
if all(a_set.point_in_set(point=scenario) for a_set in self.all_sets):
is_empty_intersection = False
break
else:
# === Compile constraints and solve NLP
m = ConcreteModel()
m.obj = Objective(expr=0) # dummy objective required if using baron
m.param_vars = Var(uncertain_params.index_set())
for a_set in self.all_sets:
m.add_component(a_set.type + "_constraints", a_set.set_as_constraint(uncertain_params=m.param_vars))
try:
res = nlp_solver.solve(m)
except:
raise ValueError("Solver terminated with an error while checking set intersection non-emptiness.")
if check_optimal_termination(res):
is_empty_intersection = False
return is_empty_intersection
def build_Block_with_objects():
"""Build an empty Block"""
obj = Block(concrete=True)
obj.construct()
obj.x = Var()
obj.x._domain = None
obj.c = Constraint()
obj.o = Objective()
return obj
def build_BlockData_with_objects():
"""Build an empty _BlockData"""
obj = _BlockData(build_BlockData_with_objects.owner)
obj.x = Var()
obj.x._domain = None
obj.c = Constraint()
obj.o = Objective()
obj._component = None
return obj
def make_model_tri(n, small_val=1e-7, big_val=1e2):
m = ConcreteModel()
m.x = Var(range(n), initialize=0.5)
def c_rule(m, i):
return big_val*m.x[i-1] + small_val*m.x[i] + big_val*m.x[i+1] == 1
m.c = Constraint(range(1,n-1), rule=c_rule)
m.obj = Objective(expr=small_val*sum((m.x[i]-1)**2 for i in range(n)))
return m
def make_model():
m = ConcreteModel()
m.x = Var([1,2,3], initialize=0)
m.f = Var([1,2,3], initialize=0)
m.F = Var(initialize=0)
m.f[1].fix(1)
m.f[2].fix(2)
m.sum_con = Constraint(expr=
(1 == m.x[1] + m.x[2] + m.x[3]))
def bilin_rule(m, i):
return m.F*m.x[i] == m.f[i]
m.bilin_con = Constraint([1,2,3], rule=bilin_rule)
m.obj = Objective(expr=m.F**2)
return m
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 model_is_valid(model):
'''
Possibilities:
Deterministic model has a single objective
Deterministic model has no objective
Deterministic model has multiple objectives
:param model: the deterministic model
:return: True if it satisfies certain properties, else False.
'''
objectives = list(model.component_data_objects(Objective))
for o in objectives:
o.deactivate()
if len(objectives) == 1:
'''
Ensure objective is a minimization. If not, change the sense.
'''
obj = objectives[0]
if obj.sense is not minimize:
sympy_obj = sympyify_expression(-obj.expr)
# Use sympy to distribute the negation so the method for determining first/second stage costs is valid
min_obj = Objective(expr=sympy2pyomo_expression(
sympy_obj[1].simplify(), sympy_obj[0]))
model.del_component(obj)
model.add_component(
unique_component_name(model, obj.name + '_min'), min_obj)
return True
elif len(objectives) > 1:
'''
User should deactivate all Objectives in the model except the one represented by the output of
first_stage_objective + second_stage_objective
'''
return False
else:
'''
No Objective objects provided as part of the model, please provide an Objective to your model so that
PyROS can infer first- and second-stage objective.
'''
return False
def solveropfnlp_2(ppc, solver="ipopt"):
if solver == "ipopt":
opt = SolverFactory("ipopt", executable="/home/iso/PycharmProjects/opfLC_python3/Python3/py_solvers/ipopt-linux64/ipopt")
if solver == "bonmin":
opt = SolverFactory("bonmin", executable="/home/iso/PycharmProjects/opfLC_python3/Python3/py_solvers/bonmin-linux64/bonmin")
if solver == "knitro":
opt = SolverFactory("knitro", executable="D:/ICT/Artelys/Knitro 10.2.1/knitroampl/knitroampl")
ppc = ext2int(ppc) # convert to continuous indexing starting from 0
# Gather information about the system
# =============================================================
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
nb = bus.shape[0] # number of buses
ng = gen.shape[0] # number of generators
nl = branch.shape[0] # number of lines
# generator buses
gb = tolist(np.array(gen[:, GEN_BUS]).astype(int))
sb = find((bus[:, BUS_TYPE] == REF)) # slack bus index
fr = branch[:, F_BUS].astype(int) # from bus indices
to = branch[:, T_BUS].astype(int) # to bus indices
tr = branch[:, TAP] # transformation ratios
tr[find(tr == 0)] = 1 # set to 1 transformation ratios that are 0
r = branch[:, BR_R] # branch resistances
x = branch[:, BR_X] # branch reactances
b = branch[:, BR_B] # branch susceptances
start_time = time.clock()
# Admittance matrix computation
# =============================================================
y = makeYbus(baseMVA, bus, branch)[0] # admittance matrix
yk = 1./(r+x*1j) # branch admittance
yft = yk + 0.5j*b # branch admittance + susceptance
gk = yk.real # branch resistance
yk = yk/tr # include /tr in yk
# Optimization
# =============================================================
branch[find(branch[:, RATE_A] == 0), RATE_A] = 9999 # set undefined Sflow limit to 9999
Smax = branch[:, RATE_A] / baseMVA # Max. Sflow
# Power demand parameters
Pd = bus[:, PD] / baseMVA
Qd = bus[:, QD] / baseMVA
# Max and min Pg and Qg
Pg_max = zeros(nb)
Pg_max[gb] = gen[:, PMAX] / baseMVA
Pg_min = zeros(nb)
Pg_min[gb] = gen[:, PMIN] / baseMVA
Qg_max = zeros(nb)
Qg_max[gb] = gen[:, QMAX] / baseMVA
Qg_min = zeros(nb)
Qg_min[gb] = gen[:, QMIN] / baseMVA
# Vmax and Vmin vectors
Vmax = bus[:, VMAX]
Vmin = bus[:, VMIN]
vm = bus[:, VM]
va = bus[:, VA]*pi/180
# create a new optimization model
model = ConcreteModel()
# Define sets
# ------------
model.bus = Set(ordered=True, initialize=range(nb)) # Set of all buses
model.gen = Set(ordered=True, initialize=gb) # Set of buses with generation
model.line = Set(ordered=True, initialize=range(nl)) # Set of all lines
# Define variables
# -----------------
# Voltage magnitudes vector (vm)
model.vm = Var(model.bus)
# Voltage angles vector (va)
model.va = Var(model.bus)
# Reactive power generation, synchronous machines(SM) (Qg)
model.Qg = Var(model.gen)
Qg0 = zeros(nb)
Qg0[gb] = gen[:, QG]/baseMVA
# Active power generation, synchronous machines(SM) (Pg)
model.Pg = Var(model.gen)
Pg0 = zeros(nb)
Pg0[gb] = gen[:, PG] / baseMVA
# Active and reactive power from at all branches
model.Pf = Var(model.line)
model.Qf = Var(model.line)
# Active and reactive power to at all branches
model.Pt = Var(model.line)
model.Qt = Var(model.line)
# Warm start the problem
# ------------------------
for i in range(nb):
model.vm[i] = vm[i]
model.va[i] = va[i]
if i in gb:
model.Pg[i] = Pg0[i]
model.Qg[i] = Qg0[i]
for i in range(nl):
model.Pf[i] = vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.cos(-ang(yft[i])) -\
vm[fr[i]] * vm[to[i]] * abs(yk[i]) * np.cos(va[fr[i]] - va[to[i]] - ang(yk[i]))
model.Qf[i] = vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.sin(-ang(yft[i])) -\
vm[fr[i]] * vm[to[i]] * abs(yk[i]) * np.sin(va[fr[i]] - va[to[i]] - ang(yk[i]))
model.Pt[i] = vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) -\
vm[to[i]] * vm[fr[i]] * abs(yk[i]) * np.cos(va[to[i]] - va[fr[i]] - ang(yk[i]))
model.Qt[i] = vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) -\
vm[to[i]] * vm[fr[i]] * abs(yk[i]) * np.sin(va[to[i]] - va[fr[i]] - ang(yk[i]))
# Define constraints
# ----------------------------
# Equalities:
# ------------
# Active power flow equalities
def powerflowact(model, i):
if i in gb:
return model.Pg[i]-Pd[i] == sum(model.vm[i]*model.vm[j]*abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in range(nb))
else:
return sum(model.vm[i]*model.vm[j]*abs(y[i, j]) * cos(model.va[i] - model.va[j] -
ang(y[i, j])) for j in range(nb)) == -Pd[i]
model.const1 = Constraint(model.bus, rule=powerflowact)
# Reactive power flow equalities
def powerflowreact(model, i):
if i in gb:
return model.Qg[i]-Qd[i] == sum(model.vm[i]*model.vm[j]*abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in range(nb))
else:
return sum(model.vm[i]*model.vm[j]*abs(y[i, j]) * sin(model.va[i] - model.va[j] -
ang(y[i, j])) for j in range(nb)) == -Qd[i]
model.const2 = Constraint(model.bus, rule=powerflowreact)
# Active power from
def pfrom(model, i):
return model.Pf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.cos(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) * \
cos(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
model.const3 = Constraint(model.line, rule=pfrom)
# Reactive power from
def qfrom(model, i):
return model.Qf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.sin(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) * \
sin(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
model.const4 = Constraint(model.line, rule=qfrom)
# Active power to
def pto(model, i):
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
model.const5 = Constraint(model.line, rule=pto)
# Reactive power to
def qto(model, i):
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
model.const6 = Constraint(model.line, rule=qto)
# Slack bus phase angle
model.const7 = Constraint(expr=model.va[sb[0]] == 0)
# Inequalities:
# ----------------
# Active power generator limits Pg_min <= Pg <= Pg_max
def genplimits(model, i):
return Pg_min[i] <= model.Pg[i] <= Pg_max[i]
model.const8 = Constraint(model.gen, rule=genplimits)
# Reactive power generator limits Qg_min <= Qg <= Qg_max
def genqlimits(model, i):
return Qg_min[i] <= model.Qg[i] <= Qg_max[i]
model.const9 = Constraint(model.gen, rule=genqlimits)
# Voltage constraints ( Vmin <= V <= Vmax )
def vlimits(model, i):
return Vmin[i] <= model.vm[i] <= Vmax[i]
model.const10 = Constraint(model.bus, rule=vlimits)
# Sfrom line limit
def sfrommax(model, i):
return model.Pf[i]**2 + model.Qf[i]**2 <= Smax[i]**2
model.const11 = Constraint(model.line, rule=sfrommax)
# Sto line limit
def stomax(model, i):
return model.Pt[i]**2 + model.Qt[i]**2 <= Smax[i]**2
model.const12 = Constraint(model.line, rule=stomax)
# Set objective function
# ------------------------
def obj_fun(model):
return sum(gk[i] * ((model.vm[fr[i]] / tr[i])**2 + model.vm[to[i]]**2 -
2/tr[i] * model.vm[fr[i]] * model.vm[to[i]] *
cos(model.va[fr[i]] - model.va[to[i]])) for i in range(nl))
model.obj = Objective(rule=obj_fun, sense=minimize)
mt = time.clock() - start_time # Modeling time
# Execute solve command with the selected solver
# ------------------------------------------------
start_time = time.clock()
results = opt.solve(model, tee=True)
et = time.clock() - start_time # Elapsed time
print(results)
# Update the case info with the optimized variables
# ==================================================
for i in range(nb):
bus[i, VM] = model.vm[i].value # Bus voltage magnitudes
bus[i, VA] = model.va[i].value*180/pi # Bus voltage angles
# Include Pf - Qf - Pt - Qt in the branch matrix
branchsol = zeros((nl, 17))
branchsol[:, :-4] = branch
for i in range(nl):
branchsol[i, PF] = model.Pf[i].value * baseMVA
branchsol[i, QF] = model.Qf[i].value * baseMVA
branchsol[i, PT] = model.Pt[i].value * baseMVA
branchsol[i, QT] = model.Qt[i].value * baseMVA
# Update gen matrix variables
for i in range(ng):
gen[i, PG] = model.Pg[gb[i]].value * baseMVA
gen[i, QG] = model.Qg[gb[i]].value * baseMVA
gen[i, VG] = bus[gb[i], VM]
# Convert to external (original) numbering and save case results
ppc = int2ext(ppc)
ppc['bus'][:, 1:] = bus[:, 1:]
branchsol[:, 0:2] = ppc['branch'][:, 0:2]
ppc['branch'] = branchsol
ppc['gen'][:, 1:] = gen[:, 1:]
ppc['obj'] = value(obj_fun(model))
ppc['ploss'] = value(obj_fun(model)) * baseMVA
ppc['et'] = et
ppc['mt'] = mt
ppc['success'] = 1
# ppc solved case is returned
return ppc
def solveropfnlp_4(ppc, solver="ipopt"):
if solver == "ipopt":
opt = SolverFactory(
"ipopt",
executable=
"/home/iso/PycharmProjects/opfLC_python3/Python3/py_solvers/ipopt-linux64/ipopt"
)
if solver == "bonmin":
opt = SolverFactory(
"bonmin",
executable=
"/home/iso/PycharmProjects/opfLC_python3/Python3/py_solvers/bonmin-linux64/bonmin"
)
if solver == "knitro":
opt = SolverFactory(
"knitro",
executable="D:/ICT/Artelys/Knitro 10.2.1/knitroampl/knitroampl")
ppc = ext2int(ppc) # convert to continuous indexing starting from 0
# Gather information about the system
# =============================================================
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
nb = bus.shape[0] # number of buses
ng = gen.shape[0] # number of generators
nl = branch.shape[0] # number of lines
# generator buses
gb = tolist(np.array(gen[:, GEN_BUS]).astype(int))
sb = find((bus[:, BUS_TYPE] == REF)) # slack bus index
fr = branch[:, F_BUS].astype(int) # from bus indices
to = branch[:, T_BUS].astype(int) # to bus indices
tr0 = copy(branch[:, TAP]) # transformation ratios
tr0[find(tr0 == 0)] = 1 # set to 1 transformation ratios that are 0
tp = find(branch[:, TAP] != 0) # lines with tap changers
ntp = find(branch[:, TAP] == 0) # lines without tap changers
# Tap changer settings
dudtap = 0.01 # Voltage per unit variation with tap changes
tapmax = 10 # Highest tap changer setting
tapmin = -10 # Lowest tap changer setting
# Shunt element options
stepmax = 1 # maximum step of the shunt element
Bs0 = bus[:, BS] / baseMVA # shunt elements susceptance
sd = find(bus[:, BS] != 0) # buses with shunt devices
r = branch[:, BR_R] # branch resistances
x = branch[:, BR_X] # branch reactances
b = branch[:, BR_B] # branch susceptances
start_time = time.clock()
# Admittance matrix computation
# =============================================================
# Set tap ratios and shunt elements to neutral position
branch[:, TAP] = 1
bus[:, BS] = 0
y = makeYbus(baseMVA, bus, branch)[0] # admittance matrix
yk = 1. / (r + x * 1j) # branch admittance
yft = yk + 0.5j * b # branch admittance + susceptance
gk = yk.real # branch resistance
# Optimization
# =============================================================
branch[find(branch[:, RATE_A] == 0),
RATE_A] = 9999 # set undefined Sflow limit to 9999
Smax = branch[:, RATE_A] / baseMVA # Max. Sflow
# Power demand parameters
Pd = bus[:, PD] / baseMVA
Qd = bus[:, QD] / baseMVA
# Max and min Pg and Qg
Pg_max = zeros(nb)
Pg_max[gb] = gen[:, PMAX] / baseMVA
Pg_min = zeros(nb)
Pg_min[gb] = gen[:, PMIN] / baseMVA
Qg_max = zeros(nb)
Qg_max[gb] = gen[:, QMAX] / baseMVA
Qg_min = zeros(nb)
Qg_min[gb] = gen[:, QMIN] / baseMVA
# Vmax and Vmin vectors
Vmax = bus[:, VMAX]
Vmin = bus[:, VMIN]
vm = bus[:, VM]
va = bus[:, VA] * pi / 180
# create a new optimization model
model = ConcreteModel()
# Define sets
# ------------
model.bus = Set(ordered=True, initialize=range(nb)) # Set of all buses
model.gen = Set(ordered=True,
initialize=gb) # Set of buses with generation
model.line = Set(ordered=True, initialize=range(nl)) # Set of all lines
model.taps = Set(ordered=True,
initialize=tp) # Set of all lines with tap changers
model.shunt = Set(ordered=True,
initialize=sd) # Set of buses with shunt elements
# Define variables
# -----------------
# Voltage magnitudes vector (vm)
model.vm = Var(model.bus)
# Voltage angles vector (va)
model.va = Var(model.bus)
# Reactive power generation, synchronous machines(SM) (Qg)
model.Qg = Var(model.gen)
Qg0 = zeros(nb)
Qg0[gb] = gen[:, QG] / baseMVA
# Active power generation, synchronous machines(SM) (Pg)
model.Pg = Var(model.gen)
Pg0 = zeros(nb)
Pg0[gb] = gen[:, PG] / baseMVA
# Active and reactive power from at all branches
model.Pf = Var(model.line)
model.Qf = Var(model.line)
# Active and reactive power to at all branches
model.Pt = Var(model.line)
model.Qt = Var(model.line)
# Transformation ratios
model.tr = Var(model.taps)
# Tap changer positions + their bounds
model.tap = Var(model.taps, bounds=(tapmin, tapmax))
# Shunt susceptance
model.Bs = Var(model.shunt)
# Shunt positions + their bounds
model.s = Var(model.shunt, bounds=(0, stepmax))
# Warm start the problem
# ------------------------
for i in range(nb):
model.vm[i] = vm[i]
model.va[i] = va[i]
if i in gb:
model.Pg[i] = Pg0[i]
model.Qg[i] = Qg0[i]
for i in range(nl):
model.Pf[i] = vm[fr[i]] ** 2 * abs(yft[i]) / (tr0[i] ** 2) * np.cos(-ang(yft[i])) -\
vm[fr[i]] * vm[to[i]] * abs(yk[i]) / tr0[i] * np.cos(va[fr[i]] - va[to[i]] - ang(yk[i]))
model.Qf[i] = vm[fr[i]] ** 2 * abs(yft[i]) / (tr0[i] ** 2) * np.sin(-ang(yft[i])) -\
vm[fr[i]] * vm[to[i]] * abs(yk[i]) / tr0[i] * np.sin(va[fr[i]] - va[to[i]] - ang(yk[i]))
model.Pt[i] = vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) -\
vm[to[i]] * vm[fr[i]] * abs(yk[i]) / tr0[i] * np.cos(va[to[i]] - va[fr[i]] - ang(yk[i]))
model.Qt[i] = vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) -\
vm[to[i]] * vm[fr[i]] * abs(yk[i]) / tr0[i] * np.sin(va[to[i]] - va[fr[i]] - ang(yk[i]))
for i in tp:
model.tr[i] = tr0[i]
for i in sd:
model.Bs[i] = Bs0[i]
# Define constraints
# ----------------------------
# Equalities:
# ------------
# Active power flow equalities
def powerflowact(model, i):
bfrom_i = tp[find(fr[tp] == i)] # branches from bus i with transformer
bto_i = tp[find(to[tp] == i)] # branches to bus i with transformer
allbut_i = find(bus[:, BUS_I] != i) # Set of other buses
if i in gb:
return model.Pg[i]-Pd[i] == sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j]**2 - 1) *
np.cos(- ang(yk[j])) for j in bfrom_i) + real(y[i, i]))
else:
return sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j]**2 - 1) *
np.cos(- ang(yk[j])) for j in bfrom_i) + real(y[i, i])) == -Pd[i]
model.const1 = Constraint(model.bus, rule=powerflowact)
# Reactive power flow equalities
def powerflowreact(model, i):
bfrom_i = tp[find(fr[tp] == i)] # branches from bus i with transformer
bto_i = tp[find(to[tp] == i)] # branches to bus i with transformer
allbut_i = find(bus[:, BUS_I] != i) # Set of other buses
sh = sd[find(sd == i)] # Detect shunt elements
if i in gb:
return model.Qg[i]-Qd[i] == \
sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * sin(model.va[i] - model.va[to[j]] - ang(yk[j]))
* (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * sin(model.va[i] - model.va[fr[j]] - ang(yk[j]))
* (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j] ** 2 - 1) * np.sin(- ang(yk[j]))
for j in bfrom_i) - imag(y[i, i]) - sum(model.Bs[j] for j in sh))
else:
return sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * sin(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * sin(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j] ** 2 - 1) * np.sin(- ang(yk[j]))
for j in bfrom_i) - imag(y[i, i]) - sum(model.Bs[j] for j in sh)) == - Qd[i]
model.const2 = Constraint(model.bus, rule=powerflowreact)
# Active power from
def pfrom(model, i):
if i in tp:
return model.Pf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (model.tr[i] ** 2) * np.cos(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) / model.tr[i] * \
cos(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
else:
return model.Pf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / tr0[i] ** 2 * np.cos(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) / tr0[i] * \
cos(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
model.const3 = Constraint(model.line, rule=pfrom)
# Reactive power from
def qfrom(model, i):
if i in tp:
return model.Qf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (model.tr[i] ** 2) * np.sin(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) / model.tr[i] * \
sin(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
else:
return model.Qf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / tr0[i] ** 2 * np.sin(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) / tr0[i] * \
sin(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
model.const4 = Constraint(model.line, rule=qfrom)
# Active power to
def pto(model, i):
if i in tp:
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / model.tr[i] * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
else:
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / tr0[i] * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
model.const5 = Constraint(model.line, rule=pto)
# Reactive power to
def qto(model, i):
if i in tp:
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / model.tr[i] * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
else:
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / tr0[i] * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
model.const6 = Constraint(model.line, rule=qto)
# Slack bus phase angle
model.const7 = Constraint(expr=model.va[sb[0]] == 0)
# Transformation ratio equalities
def trfunc(model, i):
return model.tr[i] == 1 + dudtap * model.tap[i]
model.const8 = Constraint(model.taps, rule=trfunc)
# Shunt susceptance equality
def shuntfunc(model, i):
return model.Bs[i] == model.s[i] / stepmax * Bs0[i]
model.const9 = Constraint(model.shunt, rule=shuntfunc)
# Inequalities:
# ----------------
# Active power generator limits Pg_min <= Pg <= Pg_max
def genplimits(model, i):
return Pg_min[i] <= model.Pg[i] <= Pg_max[i]
model.const10 = Constraint(model.gen, rule=genplimits)
# Reactive power generator limits Qg_min <= Qg <= Qg_max
def genqlimits(model, i):
return Qg_min[i] <= model.Qg[i] <= Qg_max[i]
model.const11 = Constraint(model.gen, rule=genqlimits)
# Voltage constraints ( Vmin <= V <= Vmax )
def vlimits(model, i):
return Vmin[i] <= model.vm[i] <= Vmax[i]
model.const12 = Constraint(model.bus, rule=vlimits)
# Sfrom line limit
def sfrommax(model, i):
return model.Pf[i]**2 + model.Qf[i]**2 <= Smax[i]**2
model.const13 = Constraint(model.line, rule=sfrommax)
# Sto line limit
def stomax(model, i):
return model.Pt[i]**2 + model.Qt[i]**2 <= Smax[i]**2
model.const14 = Constraint(model.line, rule=stomax)
# Set objective function
# ------------------------
def obj_fun(model):
return sum(gk[i] * ((model.vm[fr[i]] / model.tr[i])**2 + model.vm[to[i]]**2 -
2 / model.tr[i] * model.vm[fr[i]] * model.vm[to[i]] *
cos(model.va[fr[i]] - model.va[to[i]])) for i in tp) + \
sum(gk[i] * ((model.vm[fr[i]] / tr0[i]) ** 2 + model.vm[to[i]] ** 2 -
2 / tr0[i] * model.vm[fr[i]] * model.vm[to[i]] *
cos(model.va[fr[i]] - model.va[to[i]])) for i in ntp)
model.obj = Objective(rule=obj_fun, sense=minimize)
mt = time.clock() - start_time # Modeling time
# Execute solve command with the selected solver
# ------------------------------------------------
start_time = time.clock()
results = opt.solve(model, tee=True)
et = time.clock() - start_time # Elapsed time
print(results)
# Update the case info with the optimized variables and approximate the continuous variables to discrete values
# ==============================================================================================================
for i in range(nb):
if i in sd:
bus[i, BS] = round(model.s[i].value) * Bs0[i] * baseMVA
bus[i, VM] = model.vm[i].value # Bus voltage magnitudes
bus[i, VA] = model.va[i].value * 180 / pi # Bus voltage angles
# Update transformation ratios
for i in range(nl):
if i in tp:
branch[i, TAP] = 1 + dudtap * round(model.tap[i].value)
# Update gen matrix variables
for i in range(ng):
gen[i, PG] = model.Pg[gb[i]].value * baseMVA
gen[i, QG] = model.Qg[gb[i]].value * baseMVA
gen[i, VG] = bus[gb[i], VM]
# Convert to external (original) numbering and save case results
ppc = int2ext(ppc)
ppc['bus'][:, 1:] = bus[:, 1:]
branch[:, 0:2] = ppc['branch'][:, 0:2]
ppc['branch'] = branch
ppc['gen'][:, 1:] = gen[:, 1:]
# Execute a second optimization with only the discrete approximated values (requires solveropfnlp_2)
sol = solveropfnlp_2(ppc)
sol['mt'] = sol['mt'] + mt
sol['et'] = sol['et'] + et
sol['tap'] = zeros((tp.shape[0], 1))
for i in range(tp.shape[0]):
sol['tap'][i] = round(model.tap[tp[i]].value)
sol['shunt'] = zeros((sd.shape[0], 1))
for i in range(sd.shape[0]):
sol['shunt'][i] = round(model.s[sd[i]].value)
# ppc solved case is returned
return sol
def __build_objectives(self):
"""
Initialize different objectives
:return:
"""
self.model.Slack = Var(within=NonNegativeReals)
def _decl_slack(model):
return model.Slack == 10**6 * sum(
comp.obj_slack() for comp in self.iter_components())
self.model.decl_slack = Constraint(rule=_decl_slack)
def obj_energy(model):
return model.Slack + sum(comp.obj_energy()
for comp in self.iter_components())
def obj_cost(model):
return model.Slack + sum(comp.obj_fuel_cost()
for comp in self.iter_components()) + sum(
comp.obj_elec_cost()
for comp in self.iter_components())
def obj_cost_ramp(model):
return model.Slack + sum(comp.obj_cost_ramp()
for comp in self.iter_components())
def obj_co2(model):
return model.Slack + sum(comp.obj_co2()
for comp in self.iter_components())
def obj_co2_fuel_cost(model):
return model.Slack + sum(
comp.obj_co2_cost() + comp.obj_fuel_cost()
for comp in self.iter_components())
self.model.OBJ_ENERGY = Objective(rule=obj_energy, sense=minimize)
self.model.OBJ_COST = Objective(rule=obj_cost, sense=minimize)
self.model.OBJ_COST_RAMP = Objective(rule=obj_cost_ramp,
sense=minimize)
self.model.OBJ_CO2 = Objective(rule=obj_co2, sense=minimize)
self.model.OBJ_COST_CO2_FUEL = Objective(rule=obj_co2_fuel_cost,
sense=minimize)
self.objectives = {
'energy': self.model.OBJ_ENERGY,
'cost': self.model.OBJ_COST,
'cost_ramp': self.model.OBJ_COST_RAMP,
'co2': self.model.OBJ_CO2,
'cost_fuel_co2': self.model.OBJ_COST_CO2_FUEL
}
for objective in self.objectives.values():
objective.deactivate()
if self.temperature_driven:
def obj_temp(model):
return model.Slack + sum(comp.obj_temp()
for comp in self.iter_components())
self.model.OBJ_TEMP = Objective(rule=obj_temp, sense=minimize)
self.objectives['temp'] = self.model.OBJ_TEMP
# 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 flow_rule(model, k):
if k == value(model.s) or k == value(model.t):
return Constraint.Skip
inFlow = sum(model.f[i,j] for (i,j) in model.A if j == k)
outFlow = sum(model.f[i,j] for (i,j) in model.A if i == k)
return inFlow == outFlow
model.flow = Constraint(model.N, rule=flow_rule)
def create_model(self):
"""
Create and return the mathematical model.
"""
if options.DEBUG:
logging.info("Creating model for day %d" % self.day_id)
# Obtain the orders book
book = self.orders
complexOrders = self.complexOrders
# Create the optimization model
model = ConcreteModel()
model.periods = Set(initialize=book.periods)
maxPeriod = max(book.periods)
model.bids = Set(initialize=range(len(book.bids)))
model.L = Set(initialize=book.locations)
model.sBids = Set(initialize=[
i for i in range(len(book.bids)) if book.bids[i].type == 'SB'
])
model.bBids = Set(initialize=[
i for i in range(len(book.bids)) if book.bids[i].type == 'BB'
])
model.cBids = RangeSet(len(complexOrders)) # Complex orders
model.C = RangeSet(len(self.connections))
model.directions = RangeSet(2) # 1 == up, 2 = down TODO: clean
# Variables
model.xs = Var(model.sBids, domain=Reals,
bounds=(0.0, 1.0)) # Single period bids acceptance
model.xb = Var(model.bBids, domain=Binary) # Block bids acceptance
model.xc = Var(model.cBids, domain=Binary) # Complex orders acceptance
model.pi = Var(model.L * model.periods,
domain=Reals,
bounds=self.priceCap) # Market prices
model.s = Var(model.bids, domain=NonNegativeReals) # Bids
model.sc = Var(model.cBids, domain=NonNegativeReals) # complex orders
model.complexVolume = Var(model.cBids, model.periods,
domain=Reals) # Bids
model.pi_lg_up = Var(model.cBids * model.periods,
domain=NonNegativeReals) # Market prices
model.pi_lg_down = Var(model.cBids * model.periods,
domain=NonNegativeReals) # Market prices
model.pi_lg = Var(model.cBids * model.periods,
domain=Reals) # Market prices
def flowBounds(m, c, d, t):
capacity = self.connections[c - 1].capacity_up[t] if d == 1 else \
self.connections[c - 1].capacity_down[t]
return (0, capacity)
model.f = Var(model.C * model.directions * model.periods,
domain=NonNegativeReals,
bounds=flowBounds)
model.u = Var(model.C * model.directions * model.periods,
domain=NonNegativeReals)
# Objective
def primalObj(m):
# Single period bids cost
expr = summation(
{i: book.bids[i].price * book.bids[i].volume
for i in m.sBids}, m.xs)
# Block bids cost
expr += summation(
{
i: book.bids[i].price * sum(book.bids[i].volumes.values())
for i in m.bBids
}, m.xb)
return -expr
if options.PRIMAL and not options.DUAL:
model.obj = Objective(rule=primalObj, sense=maximize)
def primalDualObj(m):
return primalObj(m) + sum(1e-5 * m.xc[i] for i in model.cBids)
if options.PRIMAL and options.DUAL:
model.obj = Objective(rule=primalDualObj, sense=maximize)
# Complex order constraint
if options.PRIMAL and options.DUAL:
model.deactivate_suborders = ConstraintList()
for o in model.cBids:
sub_ids = complexOrders[o - 1].ids
curves = complexOrders[o - 1].curves
for id in sub_ids:
bid = book.bids[id]
if bid.period <= complexOrders[o - 1].SSperiods and bid.price == \
curves[bid.period].bids[0].price:
pass # This bid, first step of the cruve in the scheduled stop periods, is not automatically deactivated when MIC constraint is not satisfied
else:
model.deactivate_suborders.add(
model.xs[id] <= model.xc[o])
# Ramping constraints for complex orders
def complex_volume_def_rule(m, o, p):
sub_ids = complexOrders[o - 1].ids
return m.complexVolume[o, p] == sum(m.xs[i] * book.bids[i].volume
for i in sub_ids
if book.bids[i].period == p)
if options.PRIMAL:
model.complex_volume_def = Constraint(model.cBids,
model.periods,
rule=complex_volume_def_rule)
def complex_lg_down_rule(m, o, p):
if p + 1 > maxPeriod or complexOrders[o - 1].ramp_down == None:
return Constraint.Skip
else:
return m.complexVolume[o, p] - m.complexVolume[o, p + 1] <= complexOrders[
o - 1].ramp_down * \
m.xc[o]
if options.PRIMAL and options.APPLY_LOAD_GRADIENT:
model.complex_lg_down = Constraint(model.cBids,
model.periods,
rule=complex_lg_down_rule)
def complex_lg_up_rule(m, o, p):
if p + 1 > maxPeriod or complexOrders[o - 1].ramp_up == None:
return Constraint.Skip
else:
return m.complexVolume[o, p + 1] - m.complexVolume[
o, p] <= complexOrders[o - 1].ramp_up
if options.PRIMAL and options.APPLY_LOAD_GRADIENT:
model.complex_lg_up = Constraint(
model.cBids, model.periods,
rule=complex_lg_up_rule) # Balance constraint
# Energy balance constraints
balanceExpr = {l: {t: 0.0 for t in model.periods} for l in model.L}
for i in model.sBids: # Simple bids
bid = book.bids[i]
balanceExpr[bid.location][bid.period] += bid.volume * model.xs[i]
for i in model.bBids: # Block bids
bid = book.bids[i]
for t, v in bid.volumes.items():
balanceExpr[bid.location][t] += v * model.xb[i]
def balanceCstr(m, l, t):
export = 0.0
for c in model.C:
if self.connections[c - 1].from_id == l:
export += m.f[c, 1, t] - m.f[c, 2, t]
elif self.connections[c - 1].to_id == l:
export += m.f[c, 2, t] - m.f[c, 1, t]
return balanceExpr[l][t] == export
if options.PRIMAL:
model.balance = Constraint(model.L * book.periods,
rule=balanceCstr)
# Surplus of single period bids
def sBidSurplus(m, i): # For the "usual" step orders
bid = book.bids[i]
if i in self.plain_single_orders:
return m.s[i] >= (m.pi[bid.location, bid.period] -
bid.price) * bid.volume
else:
return Constraint.Skip
if options.DUAL:
model.sBidSurplus = Constraint(model.sBids, rule=sBidSurplus)
# Surplus definition for complex suborders accounting for impact of load gradient condition
if options.DUAL:
model.complex_sBidSurplus = ConstraintList()
for o in model.cBids:
sub_ids = complexOrders[o - 1].ids
l = complexOrders[o - 1].location
for i in sub_ids:
bid = book.bids[i]
model.complex_sBidSurplus.add(
model.s[i] >=
(model.pi[l, bid.period] + model.pi_lg[o, bid.period] -
bid.price) * bid.volume)
def LG_price_def_rule(m, o, p):
l = complexOrders[o - 1].location
exp = 0
if options.APPLY_LOAD_GRADIENT:
D = complexOrders[o - 1].ramp_down
U = complexOrders[o - 1].ramp_up
if D is not None:
exp += (m.pi_lg_down[o, p - 1] if p > 1 else
0) - (m.pi_lg_down[o, p] if p < maxPeriod else 0)
if U is not None:
exp -= (m.pi_lg_up[o, p - 1] if p > 1 else
0) - (m.pi_lg_up[o, p] if p < maxPeriod else 0)
return m.pi_lg[o, p] == exp
if options.DUAL:
model.LG_price_def = Constraint(model.cBids,
model.periods,
rule=LG_price_def_rule)
# Surplus of block bids
def bBidSurplus(m, i):
bid = book.bids[i]
bidVolume = -sum(bid.volumes.values())
bigM = (self.priceCap[1] -
self.priceCap[0]) * bidVolume # FIXME tighten BIGM
return m.s[i] + sum([
m.pi[bid.location, t] * -v for t, v in bid.volumes.items()
]) >= bid.cost * bidVolume + bigM * (1 - m.xb[i])
if options.DUAL:
model.bBidSurplus = Constraint(model.bBids, rule=bBidSurplus)
# Surplus of complex orders
def cBidSurplus(m, o):
complexOrder = complexOrders[o - 1]
sub_ids = complexOrder.ids
if book.bids[sub_ids[0]].volume > 0: # supply
bigM = sum((self.priceCap[1] - book.bids[i].price) *
book.bids[i].volume for i in sub_ids)
else:
bigM = sum((book.bids[i].price - self.priceCap[0]) *
book.bids[i].volume for i in sub_ids)
return m.sc[o] + bigM * (1 - m.xc[o]) >= sum(m.s[i]
for i in sub_ids)
if options.DUAL:
model.cBidSurplus = Constraint(model.cBids, rule=cBidSurplus)
# Surplus of complex orders
def cBidSurplus_2(m, o):
complexOrder = complexOrders[o - 1]
expr = 0
for i in complexOrder.ids:
bid = book.bids[i]
if (bid.period <= complexOrder.SSperiods) and (
bid.price
== complexOrder.curves[bid.period].bids[0].price):
expr += m.s[i]
return m.sc[o] >= expr
if options.DUAL:
model.cBidSurplus_2 = Constraint(
model.cBids, rule=cBidSurplus_2) # MIC constraint
def cMIC(m, o):
complexOrder = complexOrders[o - 1]
if complexOrder.FT == 0 and complexOrder.VT == 0:
return Constraint.Skip
expr = 0
bigM = complexOrder.FT
for i in complexOrder.ids:
bid = book.bids[i]
if (bid.period <= complexOrder.SSperiods) and (
bid.price
== complexOrder.curves[bid.period].bids[0].price):
bigM += (bid.volume * (self.priceCap[1] - bid.price)
) # FIXME assumes order is supply
expr += bid.volume * m.xs[i] * (bid.price - complexOrder.VT)
return m.sc[o] + expr + bigM * (1 - m.xc[o]) >= complexOrder.FT
if options.DUAL and options.PRIMAL:
model.cMIC = Constraint(model.cBids, rule=cMIC)
# Dual connections capacity
def dualCapacity(m, c, t):
exportPrices = 0.0
for l in m.L:
if l == self.connections[c - 1].from_id:
exportPrices += m.pi[l, t]
elif l == self.connections[c - 1].to_id:
exportPrices -= m.pi[l, t]
return m.u[c, 1, t] - m.u[c, 2, t] + exportPrices == 0.0
if options.DUAL:
model.dualCapacity = Constraint(model.C * model.periods,
rule=dualCapacity)
# Dual optimality
def dualObj(m):
dualObj = summation(m.s) + summation(m.sc)
for o in m.cBids:
sub_ids = complexOrders[o - 1].ids
for id in sub_ids:
dualObj -= m.s[
id] # Remove contribution of complex suborders which were accounted for in prevous summation over single bids
if options.APPLY_LOAD_GRADIENT:
ramp_down = complexOrders[o - 1].ramp_down
ramp_up = complexOrders[o - 1].ramp_up
for p in m.periods:
if p == maxPeriod:
continue
if ramp_down is not None:
dualObj += ramp_down * m.pi_lg_down[
o, p] # Add contribution of load gradient
if ramp_up is not None:
dualObj += ramp_up * m.pi_lg_up[
o, p] # Add contribution of load gradient
for c in model.C:
for t in m.periods:
dualObj += self.connections[c - 1].capacity_up[t] * m.u[c,
1,
t]
dualObj += self.connections[c -
1].capacity_down[t] * m.u[c, 2,
t]
return dualObj
if not options.PRIMAL:
model.obj = Objective(rule=dualObj, sense=minimize)
def primalEqualsDual(m):
return primalObj(m) >= dualObj(m)
if options.DUAL and options.PRIMAL:
model.primalEqualsDual = Constraint(rule=primalEqualsDual)
self.model = model
def representative(duration_repr,
selection,
VWat=75000,
solArea=2 * (18300 + 15000),
VSTC=75000,
pipe_model='ExtensivePipe',
time_step=3600):
"""
Args:
duration_repr:
selection:
VWat:
solArea:
VSTC:
pipe_model:
time_step:
Returns:
"""
unit_sec = 3600 * 24 # Seconds per unit time of duration (seconds per day)
netGraph = CaseFuture.make_graph(repr=True)
import pandas as pd
import time
begin = time.clock()
topmodel = ConcreteModel()
# In[14]:
optimizers = {}
epoch = pd.Timestamp('20140101')
for start_day, duration in selection.items():
start_time = epoch + pd.Timedelta(days=start_day)
optmodel = Modesto(graph=netGraph, pipe_model=pipe_model)
for comp in optmodel.get_node_components(
filter_type='StorageCondensed').values():
comp.set_reps(num_reps=int(duration))
topmodel.add_component(name='repr_' + str(start_day),
val=optmodel.model)
#####################
# Assign parameters #
#####################
optmodel = CaseFuture.set_params(
optmodel,
pipe_model=pipe_model,
repr=True,
horizon=duration_repr * unit_sec,
time_step=time_step,
)
optmodel.change_param(node='SolarArray',
comp='solar',
param='area',
val=solArea)
optmodel.change_param(node='SolarArray',
comp='tank',
param='volume',
val=VSTC)
optmodel.change_param(node='WaterscheiGarden',
comp='tank',
param='volume',
val=VWat)
optmodel.change_param(node='Production',
comp='backup',
param='ramp',
val=0)
optmodel.change_param(node='Production',
comp='backup',
param='ramp_cost',
val=0)
optmodel.compile(start_time=start_time)
# optmodel.set_objective('energy')
optimizers[start_day] = optmodel
# In[ ]:
##############################
# Compile aggregated problem #
##############################
selected_days = selection.keys()
for i, next_day in enumerate(selected_days):
current = selected_days[i - 1]
next_heat = optimizers[next_day].get_node_components(
filter_type='StorageCondensed')
current_heat = optimizers[current].get_node_components(
filter_type='StorageCondensed')
for component_id in next_heat:
# Link begin and end of representative periods
def _link_stor(m):
"""
Args:
m:
Returns:
"""
return next_heat[component_id].get_heat_stor_init() == \
current_heat[component_id].get_heat_stor_final()
topmodel.add_component(name='_'.join(
[component_id, str(current), 'eq']),
val=Constraint(rule=_link_stor))
# print 'State equation added for storage {} in representative week starting on day {}'.format(
# component_id,
# current)
# In[ ]:
def _top_objective(m):
"""
Args:
m:
Returns:
"""
return 365 / (duration_repr * (365 // duration_repr)) * sum(
repetitions * optimizers[start_day].get_objective(objtype='energy',
get_value=False)
for start_day, repetitions in selection.items())
# Factor 365/364 to make up for missing day
# set get_value to False to return object instead of value of the objective function
topmodel.obj = Objective(rule=_top_objective, sense=minimize)
# In[ ]:
end = time.clock()
# print 'Writing time:', str(end - begin)
return topmodel, optimizers
def minimize_dr_vars(model_data, config):
"""
Decision rule polishing: For a given optimal design (x) determined in separation,
and the optimal value for control vars (z), choose min magnitude decision_rule_var
values.
"""
#config.progress_logger.info("Executing decision rule variable polishing solve.")
model = model_data.master_model
polishing_model = model.clone()
first_stage_variables = polishing_model.scenarios[
0, 0].util.first_stage_variables
decision_rule_vars = polishing_model.scenarios[0,
0].util.decision_rule_vars
polishing_model.obj.deactivate()
index_set = decision_rule_vars[0].index_set()
polishing_model.tau_vars = []
# ==========
for idx in range(len(decision_rule_vars)):
polishing_model.scenarios[0, 0].add_component(
"polishing_var_" + str(idx),
Var(index_set, initialize=1e6, domain=NonNegativeReals))
polishing_model.tau_vars.append(
getattr(polishing_model.scenarios[0, 0],
"polishing_var_" + str(idx)))
# ==========
this_iter = polishing_model.scenarios[
max(polishing_model.scenarios.keys())[0], 0]
nom_block = polishing_model.scenarios[0, 0]
if config.objective_focus == ObjectiveType.nominal:
obj_val = value(this_iter.second_stage_objective +
this_iter.first_stage_objective)
polishing_model.scenarios[0,0].polishing_constraint = \
Constraint(expr=obj_val >= nom_block.second_stage_objective + nom_block.first_stage_objective)
elif config.objective_focus == ObjectiveType.worst_case:
polishing_model.zeta.fix(
) # Searching equivalent optimal solutions given optimal zeta
# === Make absolute value constraints on polishing_vars
polishing_model.scenarios[
0, 0].util.absolute_var_constraints = cons = ConstraintList()
uncertain_params = nom_block.util.uncertain_params
if config.decision_rule_order == 1:
for i, tau in enumerate(polishing_model.tau_vars):
for j in range(len(this_iter.util.decision_rule_vars[i])):
if j == 0:
cons.add(
-tau[j] <= this_iter.util.decision_rule_vars[i][j])
cons.add(this_iter.util.decision_rule_vars[i][j] <= tau[j])
else:
cons.add(
-tau[j] <= this_iter.util.decision_rule_vars[i][j] *
uncertain_params[j - 1])
cons.add(this_iter.util.decision_rule_vars[i][j] *
uncertain_params[j - 1] <= tau[j])
elif config.decision_rule_order == 2:
l = list(range(len(uncertain_params)))
index_pairs = list(it.combinations(l, 2))
for i, tau in enumerate(polishing_model.tau_vars):
Z = this_iter.util.decision_rule_vars[i]
indices = list(k for k in range(len(Z)))
for r in indices:
if r == 0:
cons.add(-tau[r] <= Z[r])
cons.add(Z[r] <= tau[r])
elif r <= len(uncertain_params) and r > 0:
cons.add(-tau[r] <= Z[r] * uncertain_params[r - 1])
cons.add(Z[r] * uncertain_params[r - 1] <= tau[r])
elif r <= len(indices) - len(uncertain_params) - 1 and r > len(
uncertain_params):
cons.add(-tau[r] <= Z[r] * uncertain_params[index_pairs[
r - len(uncertain_params) - 1][0]] * uncertain_params[
index_pairs[r - len(uncertain_params) - 1][1]])
cons.add(Z[r] * uncertain_params[index_pairs[
r - len(uncertain_params) - 1][0]] *
uncertain_params[index_pairs[
r - len(uncertain_params) - 1][1]] <= tau[r])
elif r > len(indices) - len(uncertain_params) - 1:
cons.add(-tau[r] <= Z[r] *
uncertain_params[r - len(index_pairs) -
len(uncertain_params) - 1]**2)
cons.add(Z[r] * uncertain_params[r - len(index_pairs) -
len(uncertain_params) -
1]**2 <= tau[r])
else:
raise NotImplementedError(
"Decision rule variable polishing has not been generalized to decision_rule_order "
+ str(config.decision_rule_order) + ".")
polishing_model.scenarios[0,0].polishing_obj = \
Objective(expr=sum(sum(tau[j] for j in tau.index_set()) for tau in polishing_model.tau_vars))
# === Fix design
for d in first_stage_variables:
d.fix()
# === Unfix DR vars
num_dr_vars = len(model.scenarios[
0, 0].util.decision_rule_vars[0]) # there is at least one dr var
num_uncertain_params = len(config.uncertain_params)
if model.const_efficiency_applied:
for d in decision_rule_vars:
for i in range(1, num_dr_vars):
d[i].fix(0)
d[0].unfix()
elif model.linear_efficiency_applied:
for d in decision_rule_vars:
d.unfix()
for i in range(num_uncertain_params + 1, num_dr_vars):
d[i].fix(0)
else:
for d in decision_rule_vars:
d.unfix()
# === Unfix all control var values
for block in polishing_model.scenarios.values():
for c in block.util.second_stage_variables:
c.unfix()
if model.const_efficiency_applied:
for d in block.util.decision_rule_vars:
for i in range(1, num_dr_vars):
d[i].fix(0)
d[0].unfix()
elif model.linear_efficiency_applied:
for d in block.util.decision_rule_vars:
d.unfix()
for i in range(num_uncertain_params + 1, num_dr_vars):
d[i].fix(0)
else:
for d in block.util.decision_rule_vars:
d.unfix()
# === Solve the polishing model
polish_soln = MasterResult()
solver = config.global_solver
if not solver.available():
raise RuntimeError("NLP solver %s is not available." % config.solver)
try:
results = solver.solve(polishing_model, tee=config.tee)
polish_soln.termination_condition = results.solver.termination_condition
except ValueError as err:
polish_soln.pyros_termination_condition = pyrosTerminationCondition.subsolver_error
polish_soln.termination_condition = tc.error
raise
polish_soln.fsv_values = list(
v.value
for v in polishing_model.scenarios[0, 0].util.first_stage_variables)
polish_soln.ssv_values = list(
v.value
for v in polishing_model.scenarios[0, 0].util.second_stage_variables)
polish_soln.first_stage_objective = value(nom_block.first_stage_objective)
polish_soln.second_stage_objective = value(
nom_block.second_stage_objective)
# === Process solution by termination condition
acceptable = [tc.optimal, tc.locallyOptimal, tc.feasible]
if polish_soln.termination_condition not in acceptable:
return results
for i, d in enumerate(
model_data.master_model.scenarios[0, 0].util.decision_rule_vars):
for index in d:
d[index].set_value(polishing_model.scenarios[
0, 0].util.decision_rule_vars[i][index].value,
skip_validation=True)
return results
def _dualize(self, block, unfixed=[]):
"""
Generate the dual of a block
"""
#
# Collect linear terms from the block
#
A, b_coef, c_rhs, c_sense, d_sense, vnames, cnames, v_domain = collect_linear_terms(
block, unfixed)
##print(A)
##print(vnames)
##print(cnames)
##print(list(A.keys()))
##print("---")
##print(A.keys())
##print(c_sense)
##print(c_rhs)
#
# Construct the block
#
if isinstance(block, Model):
dual = ConcreteModel()
else:
dual = Block()
dual.construct()
_vars = {}
def getvar(name, ndx=None):
v = _vars.get((name, ndx), None)
if v is None:
v = Var()
if ndx is None:
v_name = name
elif type(ndx) is tuple:
v_name = "%s[%s]" % (name, ','.join(map(str, ndx)))
else:
v_name = "%s[%s]" % (name, str(ndx))
setattr(dual, v_name, v)
_vars[name, ndx] = v
return v
#
# Construct the objective
#
if d_sense == minimize:
dual.o = Objective(expr=sum(-b_coef[name, ndx] * getvar(name, ndx)
for name, ndx in b_coef),
sense=d_sense)
else:
dual.o = Objective(expr=sum(b_coef[name, ndx] * getvar(name, ndx)
for name, ndx in b_coef),
sense=d_sense)
#
# Construct the constraints
#
for cname in A:
for ndx, terms in iteritems(A[cname]):
expr = 0
for term in terms:
expr += term.coef * getvar(term.var, term.ndx)
if not (cname, ndx) in c_rhs:
c_rhs[cname, ndx] = 0.0
if c_sense[cname, ndx] == 'e':
e = expr - c_rhs[cname, ndx] == 0
elif c_sense[cname, ndx] == 'l':
e = expr - c_rhs[cname, ndx] <= 0
else:
e = expr - c_rhs[cname, ndx] >= 0
c = Constraint(expr=e)
if ndx is None:
c_name = cname
elif type(ndx) is tuple:
c_name = "%s[%s]" % (cname, ','.join(map(str, ndx)))
else:
c_name = "%s[%s]" % (cname, str(ndx))
setattr(dual, c_name, c)
#
for (name, ndx), domain in iteritems(v_domain):
v = getvar(name, ndx)
flag = type(ndx) is tuple and (ndx[-1] == 'lb'
or ndx[-1] == 'ub')
if domain == 1:
v.domain = NonNegativeReals
elif domain == -1:
v.domain = NonPositiveReals
else:
# TODO: verify that this case is possible
v.domain = Reals
return dual
for x in e.states:
con = getattr(e.d1, x + '_icc')
with open("states_sens.txt", "a") as f:
for key in con.keys():
if not con[key].active:
continue
f.write(x + "\t" + str(key) + "\n")
f.close()
# con.set_suffix_value(e.d1.dcdp, 1)
var = getattr(e.d1, x)
for key in var.keys():
if key[1] == e.ncp_t:
if var[key].stale:
continue
con[key[2:]].set_suffix_value(e.d1.dcdp, ii)
var[key].set_suffix_value(e.d1.var_order, ii)
ii += 1
f = open("suf0.txt", "w")
e.d1.var_order.pprint(ostream=f)
f.close()
e.d1.dum_of = Objective(expr=1, sense=minimize)
e.d1.write_nl(name="whatevs0.nl")
kaug = SolverFactory("k_aug",
executable="/home/dav0/k2/KKT_matrix/src/kmatrix/k_aug")
kaug.options["compute_dsdp"] = ""
f = open("suf1.txt", "w")
e.d1.var_order.pprint(ostream=f)
f.close()
e.d1.write_nl(name="whatevs.nl")
kaug.solve(e.d1, tee=True)
# Turn off file determinism
def find_target_ss(self, ref_state=None, **kwargs):
"""Attempt to find a second steady state
Args:
ref_state (dict): Contains the reference state with value key "state", (j,): value
kwargs (dict): Optional arguments
Returns
None"""
if ref_state:
self.ref_state = ref_state
else:
if not ref_state:
self.journalist("W", self._iteration_count,
"find_target_ss", "No reference state was given, using default")
if not self.ref_state:
self.journalist("W", self._iteration_count,
"find_target_ss", "No default reference state was given, exit")
sys.exit()
weights_ref = dict.fromkeys(self.ref_state.keys())
for i in self.ref_state.keys():
v = getattr(self.SteadyRef, i[0])
vkey = i[1]
vss0 = value(v[(0, 1) + vkey])
val = abs(self.ref_state[i] - vss0)
if val < 1e-09:
val = 1e+06
else:
val = 1/val
weights_ref[i] = val
weights = kwargs.pop("weights", weights_ref)
self.journalist("I", self._iteration_count, "find_target_ss", "Attempting to find steady state")
del self.SteadyRef2
self.SteadyRef2 = self.d_mod(1, 1, steady=True)
self.SteadyRef2.name = "SteadyRef2 (reference)"
for u in self.u:
cv = getattr(self.SteadyRef2, u) #: Get the param
c_val = [value(cv[i]) for i in cv.keys()] #: Current value
self.SteadyRef2.del_component(cv) #: Delete the param
self.SteadyRef2.add_component(u, Var(self.SteadyRef2.fe_t, initialize=lambda m, i: c_val[i-1]))
self.SteadyRef2.equalize_u(direction="r_to_u")
cc = getattr(self.SteadyRef2, u + "_c") #: Get the constraint
ce = getattr(self.SteadyRef2, u + "_e") #: Get the expression
cv = getattr(self.SteadyRef2, 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()
self.SteadyRef2.create_bounds()
self.SteadyRef2.equalize_u(direction="r_to_u")
for vs in self.SteadyRef.component_objects(Var, active=True): #: Load_guess
vt = getattr(self.SteadyRef2, vs.getname())
for ks in vs.keys():
vt[ks].set_value(value(vs[ks]))
ofexp = 0
for i in self.ref_state.keys():
v = getattr(self.SteadyRef2, i[0])
val = value((v[(0, 1) + vkey]))
vkey = i[1]
ofexp += weights[i] * (v[(0, 1) + vkey] - self.ref_state[i])**2
# ofexp += -weights[i] * (v[(1, 1) + vkey])**2 #- self.ref_state[i])**2
self.SteadyRef2.obfun_SteadyRef2 = Objective(expr=ofexp, sense=minimize)
tst = self.solve_dyn(self.SteadyRef2, iter_max=10000, stop_if_nopt=True, halt_on_ampl_error=False, **kwargs)
# self.SteadyRef2.write_nl(name="steady.nl")
# self.SteadyRef2.write_nl()
# self.SteadyRef2.snap_shot(filename="mom.py")
# sys.exit()
if tst != 0:
self.SteadyRef2.display(filename="failed_SteadyRef2.txt")
self.SteadyRef2.write(filename="failed_SteadyRef2.nl",
format=ProblemFormat.nl,
io_options={"symbolic_solver_labels": True})
# sys.exit(-1)
self.journalist("I", self._iteration_count, "find_target_ss", "Target: solve done")
for i in self.ref_state.keys():
v = getattr(self.SteadyRef2, i[0])
vkey = i[1]
val = value(v[(0, 1) + vkey])
print("target {:}".format(i[0]), "key {:}".format(i[1]), "weight {:f}".format(weights[i]),
"value {:f}".format(val))
for u in self.u:
v = getattr(self.SteadyRef2, u)
val = value(v[0])
print("target {:}".format(u), " value {:f}".format(val))
self.update_targets_nmpc()
def __init__(
self,
data,
target,
number_regions,
lam,
epsilon=0.01,
f_star=None,
selected_features=None,
):
super().__init__(
name="OplraRegression %d regions (f*=%s)" % (number_regions, f_star)
)
self.number_regions = number_regions
# Data and parameters
self.data = data
self.target = target
self.lam = lam
self.U1 = 1.5
self.U2 = sum(self.target)
self.epsilon = epsilon
# Indices of the model
self.s = Set(initialize=data.index.tolist())
self.r = Set(initialize=range(self.number_regions))
if self.number_regions == 1 or selected_features is None:
self.f = Set(initialize=self.data.columns, ordered=True)
else:
self.f = Set(initialize=selected_features, ordered=True)
# Variables common to both FEATURE_SELECTION and PIECEWISE models
self.W = Var(self.r, self.f, initialize=0)
self.absW = Var(self.r, self.f, domain=NonNegativeReals, initialize=0)
self.B = Var(self.r, initialize=0)
self.Pred = Var(self.r, self.s, initialize=0)
self.D = Var(self.s, domain=NonNegativeReals, initialize=0)
self.F = Var(self.r, self.s, domain=Binary, initialize=0)
self.z = Var(domain=NonNegativeReals, initialize=0)
self.mae = Var(domain=NonNegativeReals, initialize=0)
self.reg = Var(domain=NonNegativeReals, initialize=0)
if self.number_regions > 1 and f_star is None:
raise ValueError("f_star is invalid.")
self.fStar = f_star
# Specific variables and constraints according to model (number of regions)
if self.number_regions == 1:
self.sf = Var(self.f, domain=Binary, initialize=0)
# Old equations used for explicit feature selection
# self.number_features = Constraint(rule=OplraPyomoModel.number_features_rule)
# self.upper_bound_coeff = Constraint(self.r, self.f, rule=OplraPyomoModel.upper_bound_coeff_rule)
# self.lower_bound_coeff = Constraint(self.r, self.f, rule=OplraPyomoModel.lower_bound_coeff_rule)
else:
self.isSimpleLinear = False
self.fStar = self.fStar
self.X = Var(self.r, bounds=(0.0, 1.0), initialize=0)
self.rr = Set(within=self.r)
self.breakpoint_order = Constraint(
self.r, rule=OplraPyomoModel.breakpoint_order_rule
)
self.region_divider1 = Constraint(
self.r, self.s, rule=OplraPyomoModel.region_divider1_rule
)
self.region_divider2 = Constraint(
self.r, self.s, rule=OplraPyomoModel.region_divider2_rule
)
# Common constraints
self.sample_to_region = Constraint(
self.s, rule=OplraPyomoModel.sample_to_region_rule
)
self.prediction = Constraint(
self.r, self.s, rule=OplraPyomoModel.prediction_rule
)
self.abs_error1 = Constraint(
self.r, self.s, rule=OplraPyomoModel.abs_error1_rule
)
self.abs_error2 = Constraint(
self.r, self.s, rule=OplraPyomoModel.abs_error2_rule
)
self.abs_coeff1 = Constraint(
self.r, self.f, rule=OplraPyomoModel.abs_coeff1_rule
)
self.abs_coeff2 = Constraint(
self.r, self.f, rule=OplraPyomoModel.abs_coeff2_rule
)
self.mae_eqn = Constraint(rule=OplraPyomoModel.mae_rule)
self.reg_eqn = Constraint(rule=OplraPyomoModel.regularisation_rule)
self.z_eqn = Constraint(rule=OplraPyomoModel.z_rule)
self.obj = Objective(rule=OplraPyomoModel.objective_rule, sense=minimize)
def _dualize(self, block, unfixed=[]):
"""
Generate the dual of a block
"""
#
# Collect linear terms from the block
#
A, b_coef, c_rhs, c_sense, d_sense, vnames, cnames, v_domain = collect_linear_terms(
block, unfixed)
#
# Construct the block
#
if isinstance(block, Model):
dual = ConcreteModel()
else:
dual = Block()
for v, is_indexed in vnames:
if is_indexed:
setattr(dual, v + '_Index', Set(dimen=None))
setattr(dual, v, Var(getattr(dual, v + '_Index')))
else:
setattr(dual, v, Var())
for cname, is_indexed in cnames:
if is_indexed:
setattr(dual, cname + '_Index', Set(dimen=None))
setattr(dual, cname,
Constraint(getattr(dual, cname + '_Index')))
setattr(dual, cname + '_lower_',
Var(getattr(dual, cname + '_Index')))
setattr(dual, cname + '_upper_',
Var(getattr(dual, cname + '_Index')))
else:
setattr(dual, cname, Constraint())
setattr(dual, cname + '_lower_', Var())
setattr(dual, cname + '_upper_', Var())
dual.construct()
#
# Add variables
#
# TODO: revisit this hack. We shouldn't be calling
# _getitem_when_not_present()
#
for name, ndx in b_coef:
v = getattr(dual, name)
if not ndx in v:
v._getitem_when_not_present(ndx)
#
# Construct the objective
#
if d_sense == minimize:
dual.o = Objective(expr=sum(-b_coef[name, ndx] *
getattr(dual, name)[ndx]
for name, ndx in b_coef),
sense=d_sense)
else:
dual.o = Objective(expr=sum(b_coef[name, ndx] *
getattr(dual, name)[ndx]
for name, ndx in b_coef),
sense=d_sense)
#
# Construct the constraints
#
for cname in A:
c = getattr(dual, cname)
c_index = getattr(dual, cname +
"_Index") if c.is_indexed() else None
for ndx, terms in iteritems(A[cname]):
if not c_index is None and not ndx in c_index:
c_index.add(ndx)
expr = 0
for term in terms:
v = getattr(dual, term.var)
if not term.ndx in v:
v.add(term.ndx)
expr += term.coef * v[term.ndx]
if not (cname, ndx) in c_rhs:
c_rhs[cname, ndx] = 0.0
if c_sense[cname, ndx] == 'e':
c.add(ndx, expr - c_rhs[cname, ndx] == 0)
elif c_sense[cname, ndx] == 'l':
c.add(ndx, expr - c_rhs[cname, ndx] <= 0)
else:
c.add(ndx, expr - c_rhs[cname, ndx] >= 0)
for (name, ndx), domain in iteritems(v_domain):
v = getattr(dual, name)
flag = type(ndx) is tuple and (ndx[-1] == 'lb'
or ndx[-1] == 'ub')
if domain == 1:
if flag:
v[ndx].domain = NonNegativeReals
else:
v.domain = NonNegativeReals
elif domain == -1:
if flag:
v[ndx].domain = NonPositiveReals
else:
v.domain = NonPositiveReals
else:
if flag:
# TODO: verify that this case is possible
v[ndx].domain = Reals
else:
v.domain = Reals
return dual
def __init__(self, **kwargs):
# Values for the suffixes of input files
self.int_file_mhe_suf = int()
self.res_file_mhe_suf = str()
self.int_file_nmpc_suf = int()
self.res_file_nmpc_suf = str()
self.res_file_suf = str(int(time.time()))
self.d_mod = kwargs.pop('d_mod', None)
self.nfe_t = kwargs.pop('nfe_t', 5)
self.ncp_t = kwargs.pop('ncp_t', 3)
self._t = kwargs.pop('_t', 100)
self.states = kwargs.pop('states', [])
self.u = kwargs.pop('u', []) #: The inputs (controls)
self.hi_t = self._t / self.nfe_t
self.ss = self.d_mod(1, 1, steady=True)
self.ss2 = object
self.d1 = self.d_mod(1, self.ncp_t, _t=self.hi_t)
self.d2 = object()
self.ss.name = "ss"
self.d1.name = "d1"
self.ipopt = SolverFactory("ipopt")
self.asl_ipopt = SolverFactory("asl:ipopt")
self.k_aug = SolverFactory(
"k_aug", executable="/home/dav0/k2/KKT_matrix/src/kmatrix/k_aug")
self.k_aug_sens = SolverFactory(
"k_aug", executable="/home/dav0/k2/KKT_matrix/src/kmatrix/k_aug")
self.dot_driver = SolverFactory(
"dot_driver",
executable=
"/home/dav0/k2/KKT_matrix/src/kmatrix/dot_driver/dot_driver")
# self.k_aug.options["eig_rh"] = ""
self.asl_ipopt.options["halt_on_ampl_error"] = "yes"
# self.ipopt.options["print_user_options"] = "yes"
# self.k_aug.options["deb_kkt"] = ""
self.ss.ofun = Objective(expr=1, sense=minimize)
self.dyn = object()
self.l_state = []
self.l_vals = []
self._c_it = 0
self.ccl = []
self.iput = []
self.sp = []
self._kt_list = []
self._dt_list = []
self._ipt_list = []
self._k_timing = ["0", "0", "0"]
self._dot_timing = "0"
self.ip_time = 0
self._stall_iter = 0
self._window_keep = self.nfe_t + 2
self._u_plant = {} #: key: (ui, time)
for i in self.u:
u = getattr(self.d1, i)
for t in range(0, self._window_keep):
self._u_plant[(i, t)] = value(u[1])
self.curr_u = dict.fromkeys(self.u, 0.0)
self.state_vars = {}
self.curr_estate = {} #: Current estimated state (for the olnmpc)
self.curr_rstate = {} #: Current real state (for the olnmpc)
self.curr_state_offset = {} #: Current offset of measurement
self.curr_pstate = {} #: Current offset of measurement
self.curr_state_noise = {} #: Current noise of the state
self.curr_state_target = {} #: Current target state
self.curr_u_target = {} #: Current control state
self.xp_l = []
self.xp_key = {}
with open("ipopt.opt", "w") as f:
f.close()
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 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)
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
def ROSolver_iterative_solve(model_data, config):
'''
GRCS algorithm implementation
:model_data: ROSolveData object with deterministic model information
:config: ConfigBlock for the instance being solved
'''
# === The "violation" e.g. uncertain parameter values added to the master problem are nominal in iteration 0
# User can supply a nominal_uncertain_param_vals if they want to set nominal to a certain point,
# Otherwise, the default init value for the params is used as nominal_uncertain_param_vals
violation = list(p for p in config.nominal_uncertain_param_vals)
# === Do coefficient matching
constraints = [
c for c in model_data.working_model.component_data_objects(Constraint)
if c.equality and c not in ComponentSet(
model_data.working_model.util.decision_rule_eqns)
]
model_data.working_model.util.h_x_q_constraints = ComponentSet()
for c in constraints:
coeff_matching_success, robust_infeasible = coefficient_matching(
model=model_data.working_model,
constraint=c,
uncertain_params=model_data.working_model.util.uncertain_params,
config=config)
if not coeff_matching_success and not robust_infeasible:
raise ValueError(
"Equality constraint \"%s\" cannot be guaranteed to be robustly feasible, "
"given the current partitioning between first-stage, second-stage and state variables. "
"You might consider editing this constraint to reference some second-stage "
"and/or state variable(s)." % c.name)
elif not coeff_matching_success and robust_infeasible:
config.progress_logger.info(
"PyROS has determined that the model is robust infeasible. "
"One reason for this is that equality constraint \"%s\" cannot be satisfied "
"against all realizations of uncertainty, "
"given the current partitioning between first-stage, second-stage and state variables. "
"You might consider editing this constraint to reference some (additional) second-stage "
"and/or state variable(s)." % c.name)
return None, None
else:
pass
# h(x,q) == 0 becomes h'(x) == 0
for c in model_data.working_model.util.h_x_q_constraints:
c.deactivate()
# === Build the master problem and master problem data container object
master_data = master_problem_methods.initial_construct_master(model_data)
# === If using p_robustness, add ConstraintList for additional constraints
if config.p_robustness:
master_data.master_model.p_robust_constraints = ConstraintList()
# === Add scenario_0
master_data.master_model.scenarios[0, 0].transfer_attributes_from(
master_data.original.clone())
if len(master_data.master_model.scenarios[
0, 0].util.uncertain_params) != len(violation):
raise ValueError
# === Set the nominal uncertain parameters to the violation values
for i, v in enumerate(violation):
master_data.master_model.scenarios[
0, 0].util.uncertain_params[i].value = v
# === Add objective function (assuming minimization of costs) with nominal second-stage costs
if config.objective_focus is ObjectiveType.nominal:
master_data.master_model.obj = Objective(
expr=master_data.master_model.scenarios[0,
0].first_stage_objective +
master_data.master_model.scenarios[0, 0].second_stage_objective)
elif config.objective_focus is ObjectiveType.worst_case:
# === Worst-case cost objective
master_data.master_model.zeta = Var(initialize=value(
master_data.master_model.scenarios[0, 0].first_stage_objective +
master_data.master_model.scenarios[0, 0].second_stage_objective))
master_data.master_model.obj = Objective(
expr=master_data.master_model.zeta)
master_data.master_model.scenarios[0, 0].epigraph_constr = Constraint(
expr=master_data.master_model.scenarios[0,
0].first_stage_objective +
master_data.master_model.scenarios[0, 0].second_stage_objective <=
master_data.master_model.zeta)
master_data.master_model.scenarios[
0,
0].util.first_stage_variables.append(master_data.master_model.zeta)
# === Add deterministic constraints to ComponentSet on original so that these become part of separation model
master_data.original.util.deterministic_constraints = \
ComponentSet(c for c in master_data.original.component_data_objects(Constraint, descend_into=True))
# === Make separation problem model once before entering the solve loop
separation_model = separation_problem_methods.make_separation_problem(
model_data=master_data, config=config)
# === Create separation problem data container object and add information to catalog during solve
separation_data = SeparationProblemData()
separation_data.separation_model = separation_model
separation_data.points_separated = [
] # contains last point separated in the separation problem
separation_data.points_added_to_master = [
config.nominal_uncertain_param_vals
] # explicitly robust against in master
separation_data.constraint_violations = [
] # list of constraint violations for each iteration
separation_data.total_global_separation_solves = 0 # number of times global solve is used
separation_data.timing = master_data.timing # timing object
# === Keep track of subsolver termination statuses from each iteration
separation_data.separation_problem_subsolver_statuses = []
# === Nominal information
nominal_data = Block()
nominal_data.nom_fsv_vals = []
nominal_data.nom_ssv_vals = []
nominal_data.nom_first_stage_cost = 0
nominal_data.nom_second_stage_cost = 0
nominal_data.nom_obj = 0
# === Time information
timing_data = Block()
timing_data.total_master_solve_time = 0
timing_data.total_separation_local_time = 0
timing_data.total_separation_global_time = 0
timing_data.total_dr_polish_time = 0
dr_var_lists_original = []
dr_var_lists_polished = []
k = 0
while config.max_iter == -1 or k < config.max_iter:
master_data.iteration = k
# === Add p-robust constraint if iteration > 0
if k > 0 and config.p_robustness:
master_problem_methods.add_p_robust_constraint(
model_data=master_data, config=config)
# === Solve Master Problem
config.progress_logger.info("PyROS working on iteration %s..." % k)
master_soln = master_problem_methods.solve_master(
model_data=master_data, config=config)
#config.progress_logger.info("Done solving Master Problem!")
master_soln.master_problem_subsolver_statuses = []
# === Keep track of total time and subsolver termination conditions
timing_data.total_master_solve_time += get_time_from_solver(
master_soln.results)
timing_data.total_master_solve_time += get_time_from_solver(
master_soln.feasibility_problem_results)
master_soln.master_problem_subsolver_statuses.append(
master_soln.results.solver.termination_condition)
# === Check for robust infeasibility or error or time-out in master problem solve
if master_soln.master_subsolver_results[
1] is pyrosTerminationCondition.robust_infeasible:
term_cond = pyrosTerminationCondition.robust_infeasible
output_logger(config=config, robust_infeasible=True)
elif master_soln.pyros_termination_condition is pyrosTerminationCondition.subsolver_error:
term_cond = pyrosTerminationCondition.subsolver_error
else:
term_cond = None
if term_cond == pyrosTerminationCondition.subsolver_error or \
term_cond == pyrosTerminationCondition.robust_infeasible:
update_grcs_solve_data(pyros_soln=model_data,
k=k,
term_cond=term_cond,
nominal_data=nominal_data,
timing_data=timing_data,
separation_data=separation_data,
master_soln=master_soln)
return model_data, []
# === Check if time limit reached
elapsed = get_main_elapsed_time(model_data.timing)
if config.time_limit:
if elapsed >= config.time_limit:
output_logger(config=config, time_out=True, elapsed=elapsed)
update_grcs_solve_data(
pyros_soln=model_data,
k=k,
term_cond=pyrosTerminationCondition.time_out,
nominal_data=nominal_data,
timing_data=timing_data,
separation_data=separation_data,
master_soln=master_soln)
return model_data, []
# === Save nominal information
if k == 0:
for val in master_soln.fsv_vals:
nominal_data.nom_fsv_vals.append(val)
for val in master_soln.ssv_vals:
nominal_data.nom_ssv_vals.append(val)
nominal_data.nom_first_stage_cost = master_soln.first_stage_objective
nominal_data.nom_second_stage_cost = master_soln.second_stage_objective
nominal_data.nom_obj = value(master_data.master_model.obj)
if (
# === Decision rule polishing (do not polish on first iteration if no ssv or if decision_rule_order = 0)
(config.decision_rule_order != 0
and len(config.second_stage_variables) > 0 and k != 0)):
# === Save initial values of DR vars to file
for varslist in master_data.master_model.scenarios[
0, 0].util.decision_rule_vars:
vals = []
for dvar in varslist.values():
vals.append(dvar.value)
dr_var_lists_original.append(vals)
polishing_results = master_problem_methods.minimize_dr_vars(
model_data=master_data, config=config)
timing_data.total_dr_polish_time += get_time_from_solver(
polishing_results)
#=== Save after polish
for varslist in master_data.master_model.scenarios[
0, 0].util.decision_rule_vars:
vals = []
for dvar in varslist.values():
vals.append(dvar.value)
dr_var_lists_polished.append(vals)
# === Set up for the separation problem
separation_data.opt_fsv_vals = [
v.value for v in master_soln.master_model.scenarios[
0, 0].util.first_stage_variables
]
separation_data.opt_ssv_vals = master_soln.ssv_vals
# === Provide master model scenarios to separation problem for initialization options
separation_data.master_scenarios = master_data.master_model.scenarios
if config.objective_focus is ObjectiveType.worst_case:
separation_model.util.zeta = value(master_soln.master_model.obj)
# === Solve Separation Problem
separation_data.iteration = k
separation_data.master_nominal_scenario = master_data.master_model.scenarios[
0, 0]
separation_data.master_model = master_data.master_model
separation_solns, violating_realizations, constr_violations, is_global, \
local_sep_time, global_sep_time = \
separation_problem_methods.solve_separation_problem(model_data=separation_data, config=config)
for sep_soln_list in separation_solns:
for s in sep_soln_list:
separation_data.separation_problem_subsolver_statuses.append(
s.termination_condition)
if is_global:
separation_data.total_global_separation_solves += 1
timing_data.total_separation_local_time += local_sep_time
timing_data.total_separation_global_time += global_sep_time
separation_data.constraint_violations.append(constr_violations)
if not any(s.found_violation for solve_data_list in separation_solns
for s in solve_data_list):
separation_data.points_separated = []
else:
separation_data.points_separated = violating_realizations
# === Check if time limit reached
elapsed = get_main_elapsed_time(model_data.timing)
if config.time_limit:
if elapsed >= config.time_limit:
output_logger(config=config, time_out=True, elapsed=elapsed)
termination_condition = pyrosTerminationCondition.time_out
update_grcs_solve_data(pyros_soln=model_data,
k=k,
term_cond=termination_condition,
nominal_data=nominal_data,
timing_data=timing_data,
separation_data=separation_data,
master_soln=master_soln)
return model_data, separation_solns
# === Check if we exit due to solver returning unsatisfactory statuses (not in permitted_termination_conditions)
local_solve_term_conditions = {
TerminationCondition.optimal, TerminationCondition.locallyOptimal,
TerminationCondition.globallyOptimal
}
global_solve_term_conditions = {
TerminationCondition.optimal, TerminationCondition.globallyOptimal
}
if (is_global and any((s.termination_condition not in global_solve_term_conditions)
for sep_soln_list in separation_solns for s in sep_soln_list)) or \
(not is_global and any((s.termination_condition not in local_solve_term_conditions)
for sep_soln_list in separation_solns for s in sep_soln_list)):
termination_condition = pyrosTerminationCondition.subsolver_error
update_grcs_solve_data(pyros_soln=model_data,
k=k,
term_cond=termination_condition,
nominal_data=nominal_data,
timing_data=timing_data,
separation_data=separation_data,
master_soln=master_soln)
return model_data, separation_solns
# === Check if we terminate due to robust optimality or feasibility
if not any(s.found_violation for sep_soln_list in separation_solns
for s in sep_soln_list) and is_global:
if config.solve_master_globally and config.objective_focus is ObjectiveType.worst_case:
output_logger(config=config, robust_optimal=True)
termination_condition = pyrosTerminationCondition.robust_optimal
else:
output_logger(config=config, robust_feasible=True)
termination_condition = pyrosTerminationCondition.robust_feasible
update_grcs_solve_data(pyros_soln=model_data,
k=k,
term_cond=termination_condition,
nominal_data=nominal_data,
timing_data=timing_data,
separation_data=separation_data,
master_soln=master_soln)
return model_data, separation_solns
# === Add block to master at violation
master_problem_methods.add_scenario_to_master(master_data,
violating_realizations)
separation_data.points_added_to_master.append(violating_realizations)
k += 1
output_logger(config=config, max_iter=True)
update_grcs_solve_data(pyros_soln=model_data,
k=k,
term_cond=pyrosTerminationCondition.max_iter,
nominal_data=nominal_data,
timing_data=timing_data,
separation_data=separation_data,
master_soln=master_soln)
# === In this case we still return the final solution objects for the last iteration
return model_data, separation_solns
def GradientsTool(self):
self.journalizer("E", self._c_it, "GradientsTool", "Begin")
src = self.d1
src.dum_objfun = Objective(expr=1, sense=minimize)
self.d1.var_order = Suffix(direction=Suffix.EXPORT)
self.d1.con_order = Suffix(direction=Suffix.EXPORT)
src.pprint(filename="first.txt")
#: Fix/Deactivate irrelevant stuff
for var in src.component_objects(Var, active=True):
if not var.is_indexed():
var.fix()
for index in var.keys():
if type(index) != tuple:
var.fix()
continue
try:
if index[1] == self.ncp_t:
continue
var[index].fix()
except IndexError:
var.fix()
print("Variable not indexed by time",
var.name,
file=sys.stderr)
for con in src.component_objects(Constraint, active=True):
if not con.is_indexed():
con.deactivate()
for index in con.keys():
if type(index) != tuple:
con.deactivate()
continue
try:
if index[1] == self.ncp_t:
continue
con[index].deactivate()
except IndexError:
con.deactivate()
print("Constraint not indexed by time",
con.name,
file=sys.stderr)
for i in self.states: #: deactivate collocation related equations
# con = getattr(src, "cp_" + i) #: continuation
# con.deactivate()
con = getattr(src, "dvar_t_" + i) #: derivative vars
con.deactivate()
con = getattr(src, i + "_icc") #: initial-conditions
con.deactivate()
var = getattr(src, "d" + i + "_dt")
var.fix()
var = getattr(src, i)
var.fix() #: left with the av
con = getattr(src, "de_" + i) #: initial-conditions
con.deactivate()
# colcount = 0
# for i in src.component_data_objects(Var, active=True):
# if i.is_fixed():
# continue
# print(i)
# colcount += 1
# i.set_suffix_value(src.var_order, colcount)
# print(colcount)
self.d1.write_nl(name="dgy.nl")
sfxdict = dict()
self.parse_rc("dgy.row", sfxdict)
colcount = 1
for ob in sfxdict.keys():
con = getattr(src, sfxdict[ob][0])
con[sfxdict[ob][1]].set_suffix_value(src.con_order, colcount)
colcount += 1
sfxdict = dict()
self.parse_rc("dgy.col", sfxdict)
colcount = 1
for ob in sfxdict.keys():
var = getattr(src, sfxdict[ob][0])
var[sfxdict[ob][1]].set_suffix_value(src.var_order, colcount)
colcount += 1
print("Colcount\t", str(colcount), file=sys.stderr)
src.write_nl(name="dgy.nl")
#: Now dgx
for var in src.component_objects(Var):
var.fix() #: Fix everything
for i in self.states:
var = getattr(src, i)
for index in var.keys():
try:
if index[1] == self.ncp_t:
var[index].unfix()
except IndexError:
print("Something whent wrong :(\t",
var.name,
file=sys.stderr)
self.d1.write_nl(name="dgx.nl")
sfxdict = dict()
self.parse_rc("dgx.col", sfxdict)
colcount = 1
for ob in sfxdict.keys():
var = getattr(src, sfxdict[ob][0])
var[sfxdict[ob][1]].set_suffix_value(src.var_order, colcount)
colcount += 1
src.write_nl(name="dgx.nl")
#: Now dfy
for var in src.component_objects(Var):
var.unfix()
for var in src.component_objects(Var):
if not var.is_indexed():
var.fix()
for index in var.keys():
if type(index) != tuple:
var.fix()
continue
try:
if index[1] == self.ncp_t:
continue
var[index].fix()
except IndexError:
var.fix()
print("Variable not indexed by time",
var.name,
file=sys.stderr)
for con in src.component_objects(Constraint):
con.deactivate() #: deactivate everything
for i in self.states: #: deactivate collocation related terms
var = getattr(src, "d" + i + "_dt")
var.fix()
var = getattr(src, i)
var.fix() #: left with the av
con = getattr(src, "de_" + i)
for index in con.keys():
if index[1] == self.ncp_t:
con[index].activate()
self.d1.reconstruct()
# self.d1.write_nl(name="dfy.nl")
sfxdict = dict()
self.parse_rc("dgx.col", sfxdict)
colcount = 1
for ob in sfxdict.keys():
col = getattr(src, "de_" + sfxdict[ob][0])
col[sfxdict[ob][1]].set_suffix_value(src.con_order, colcount)
colcount += 1
src.write_nl(name="dfy.nl")
#: Now dfx
for var in src.component_objects(Var):
var.fix() #: Fix everything
for i in self.states:
var = getattr(src, i)
for index in var.keys():
try:
if index[1] == self.ncp_t:
var[index].unfix()
except IndexError:
print("Something whent wrong :(\t",
var.name,
file=sys.stderr)
# var.pprint()
self.d1.reconstruct()
src.pprint(filename="second.txt")
self.d1.write_nl(name="dfx.nl")
