def point_in_set(self, point):
"""
Calculates if supplied ``point`` is contained in the uncertainty set. Returns True or False.
Args:
point: The point being checked for membership in the set.
The coordinates of the point should be supplied in the same order as the elements of ``uncertain_params``
that is to be supplied to the PyROS solve statement.
This point must match the dimension of the uncertain parameters of the set.
"""
# === Ensure point is of correct dimensionality as the uncertain parameters
if len(point) != self.dim:
raise AttributeError("Point must have same dimensions as uncertain parameters.")
m = ConcreteModel()
the_params = []
for i in range(self.dim):
m.add_component("x_%s" % i, Var(initialize=point[i]))
the_params.append(getattr(m, "x_%s" % i))
# === Generate constraint for set
set_constraint = self.set_as_constraint(uncertain_params=the_params)
# === value() returns True if the constraint is satisfied, False else.
is_in_set = all(value(con.expr) for con in set_constraint.values())
return is_in_set
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_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 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 test_remove(self):
model = self.model
model = ConcreteModel()
index = range(5)
model.c = self._ctype(self._cdatatype(self._arg) for i in index)
for i in index:
cdata = model.c[0]
self.assertEqual(cdata in model.c, True)
model.c.remove(cdata)
self.assertEqual(cdata in model.c, False)
def compile(self, start_time='20140101', recompile=False):
"""
Compile the optimization problem
:param start_time: Start time of this modesto instance. Either a pandas Timestamp object or a string of format
'yyyymmdd'. Default '20140101'.
:param recompile: True if model should be recompiled. If False, only mutable parameters are reloaded.
:return:
"""
# Set time
if isinstance(start_time, str):
self.start_time = pd.Timestamp(start_time)
elif isinstance(start_time, pd.Timestamp):
self.start_time = start_time
else:
raise IOError(
"start_time specifier not recognized. Should be "
"either string of format 'yyyymmdd' or pd.Timestamp.")
# Check if not compiled already
if self.compiled:
if not recompile and not self.temperature_driven:
self.logger.info(
'Model was already compiled. Only changing mutable parameters.'
)
else:
self.model = ConcreteModel()
self.compiled = False
for comp in self.components:
self.components[comp].reinit()
self.logger.info('Recompiling model.')
# Check whether all necessary parameters are there
self.check_data()
self.update_time(self.start_time)
# Components
for name in self.get_edges():
edge_obj = self.get_component(name=name)
edge_obj.compile(self.model, start_time)
nodes = self.get_nodes()
for node in nodes:
node_obj = self.get_component(name=node)
node_obj.compile(self.model, start_time)
if not self.compiled or recompile:
self.__build_objectives()
self.compiled = True # Change compilation flag
return
def make_model():
m = ConcreteModel()
m.time = ContinuousSet(bounds=(0, 10))
m.space = ContinuousSet(bounds=(0, 5))
m.set1 = Set(initialize=['a', 'b', 'c'])
m.set2 = Set(initialize=['d', 'e', 'f'])
m.fs = Block()
m.fs.v0 = Var(m.space, initialize=1)
@m.fs.Block()
def b1(b):
b.v = Var(m.time, m.space, initialize=1)
b.dv = DerivativeVar(b.v, wrt=m.time, initialize=0)
b.con = Constraint(m.time,
m.space,
rule=lambda b, t, x: b.dv[t, x] == 7 - b.v[t, x])
# Inconsistent
@b.Block(m.time)
def b2(b, t):
b.v = Var(initialize=2)
@m.fs.Block(m.time, m.space)
def b2(b, t, x):
b.v = Var(m.set1, initialize=2)
@b.Block(m.set1)
def b3(b, c):
b.v = Var(m.set2, initialize=3)
@b.Constraint(m.set2)
def con(b, s):
return (5 * b.v[s] == m.fs.b2[m.time.first(),
m.space.first()].v[c])
# inconsistent
@m.fs.Constraint(m.time)
def con1(fs, t):
return fs.b1.v[t, m.space.last()] == 5
# Will be inconsistent
@m.fs.Constraint(m.space)
def con2(fs, x):
return fs.b1.v[m.time.first(), x] == fs.v0[x]
# will be consistent
disc = TransformationFactory('dae.collocation')
disc.apply_to(m, wrt=m.time, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
disc.apply_to(m, wrt=m.space, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
return m
def test_setitem(self):
model = self.model
model = ConcreteModel()
model.c = self._ctype()
index = ['a', 1, None, (1, ), (1, 2)]
for i in index:
self.assertTrue(i not in model.c)
for cnt, i in enumerate(index, 1):
model.c[i] = self._cdatatype(self._arg())
self.assertEqual(len(model.c), cnt)
self.assertTrue(i in model.c)
def two_kp_model(type):
model = ConcreteModel()
# Define input files
xlsx = pd.ExcelFile(
f"{Path(__file__).parent.absolute()}/input/{type}.xlsx",
engine="openpyxl")
a = pd.read_excel(xlsx, index_col=0, sheet_name="a").to_numpy()
b = pd.read_excel(xlsx, index_col=0, sheet_name="b").to_numpy()
c = pd.read_excel(xlsx, index_col=0, sheet_name="c").to_numpy()
# Define variables
model.ITEMS = Set(initialize=range(len(a[0])))
model.x = Var(model.ITEMS, within=Binary)
# --------------------------------------
# Define the objective functions
# --------------------------------------
def objective1(model):
return sum(c[0][i] * model.x[i] for i in model.ITEMS)
def objective2(model):
return sum(c[1][i] * model.x[i] for i in model.ITEMS)
# --------------------------------------
# Define the regular constraints
# --------------------------------------
def constraint1(model):
return sum(a[0][i] * model.x[i] for i in model.ITEMS) <= b[0][0]
def constraint2(model):
return sum(a[1][i] * model.x[i] for i in model.ITEMS) <= b[1][0]
# --------------------------------------
# Add components to the model
# --------------------------------------
# Add the constraints to the model
model.con1 = Constraint(rule=constraint1)
model.con2 = Constraint(rule=constraint2)
# Add the objective functions to the model using ObjectiveList(). Note
# that the first index is 1 instead of 0!
model.obj_list = ObjectiveList()
model.obj_list.add(expr=objective1(model), sense=maximize)
model.obj_list.add(expr=objective2(model), sense=maximize)
# By default deactivate all the objective functions
for o in range(len(model.obj_list)):
model.obj_list[o + 1].deactivate()
return model
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 test_active(self):
model = self.model
model = ConcreteModel()
model.c = self._ctype()
self.assertEqual(model.c.active, True)
model.c.deactivate()
self.assertEqual(model.c.active, False)
model.c.append(self._cdatatype(self._arg))
self.assertEqual(model.c.active, True)
model.c.deactivate()
self.assertEqual(model.c.active, False)
model.c.insert(0, self._cdatatype(self._arg))
self.assertEqual(model.c.active, True)
def initial_construct_master(model_data):
"""
Constructs the iteration 0 master problem
return: a MasterProblemData object containing the master_model object
"""
m = ConcreteModel()
m.scenarios = Block(NonNegativeIntegers, NonNegativeIntegers)
master_data = MasterProblemData()
master_data.original = model_data.working_model.clone()
master_data.master_model = m
master_data.timing = model_data.timing
return master_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 two_objective_model():
model = ConcreteModel()
# Define variables
model.x1 = Var(within=NonNegativeReals)
model.x2 = Var(within=NonNegativeReals)
# --------------------------------------
# Define the objective functions
# --------------------------------------
def objective1(model):
return model.x1
def objective2(model):
return 3 * model.x1 + 4 * model.x2
# --------------------------------------
# Define the regular constraints
# --------------------------------------
def constraint1(model):
return model.x1 <= 20
def constraint2(model):
return model.x2 <= 40
def constraint3(model):
return 5 * model.x1 + 4 * model.x2 <= 200
# --------------------------------------
# Add components to the model
# --------------------------------------
# Add the constraints to the model
model.con1 = Constraint(rule=constraint1)
model.con2 = Constraint(rule=constraint2)
model.con3 = Constraint(rule=constraint3)
# Add the objective functions to the model using ObjectiveList(). Note
# that the first index is 1 instead of 0!
model.obj_list = ObjectiveList()
model.obj_list.add(expr=objective1(model), sense=maximize)
model.obj_list.add(expr=objective2(model), sense=maximize)
# By default deactivate all the objective functions
for o in range(len(model.obj_list)):
model.obj_list[o + 1].deactivate()
return model
def convert(options=Bunch(), parser=None, model_format=None):
global _format
if not model_format is None:
_format = model_format
#
# Import plugins
#
import pyomo.environ
if options.model.save_file is None:
if _format == ProblemFormat.cpxlp:
options.model.save_file = 'unknown.lp'
else:
options.model.save_file = 'unknown.' + str(_format)
options.model.save_format = _format
data = Bunch(options=options)
model_data = None
try:
pyomo.scripting.util.setup_environment(data)
pyomo.scripting.util.apply_preprocessing(data, parser=parser)
if data.error:
return Bunch()
model_data = pyomo.scripting.util.create_model(data)
model_data.options = options
except:
# TBD: I should be able to call this function in the case of
# an exception to perform cleanup. However, as it stands
# calling finalize with its default keyword value for
# model(=None) results in an a different error related to
# task port values. Not sure how to interpret that.
pyomo.scripting.util.finalize(data,
model=ConcreteModel(),
instance=None,
results=None)
raise
else:
pyomo.scripting.util.finalize(data, model=model_data.model)
return model_data
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 __init__(self, pipe_model, graph, repr_days=None):
"""
This class allows setting up optimization problems for district energy systems
:param horizon: The horizon of the optimization problem, in seconds
:param time_step: The time step between two points
:param objective: String describing the objective of the optimization problem
:param pipe_model: String describing the type of model to be used for the pipes
:param graph: networkx object, describing the structure of the network
:param repr_days: None if regular optimization. Dict of days of year
mapped to representative days if used.
"""
self.model = ConcreteModel(name=Modesto)
self.results = None
self.pipe_model = pipe_model
if pipe_model == 'NodeMethod':
self.temperature_driven = True
else:
self.temperature_driven = False
self.allow_flow_reversal = True
self.start_time = None
if repr_days is not None:
self.repr_days = {i: int(round(j)) for i, j in repr_days.items()}
else:
self.repr_days = repr_days
self.graph = graph
self.edges = {}
self.nodes = {}
self.components = {}
self.params = self.create_params()
self.logger = logging.getLogger('modesto.main.Modesto')
self.build(graph)
self.compiled = False
self.objectives = {}
self.act_objective = None
def setUp(self):
self.model = ConcreteModel()
self.model.x = Var()
# set by derived class
self._arg = None
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
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
#!/usr/bin/env python # -*- coding: utf-8 -*- from pyomo.core.base import ConcreteModel from pyomo.environ import * import math # Creation of a Concrete Model model = ConcreteModel() ## Define sets ## # Sets # i Sources / 0, 1, 2, 3, 4 / # j Destinations / 0, 1, 2, 3, 4 / # d Days / 0, 1 / # t Times / 0, 1, 2, 3, 4 /; model.t = Set(initialize=range(0, 96), doc='Times') model.d = Set(initialize=range(0, 2), doc='Days') model.i = Set(initialize=range(0, 10), doc='Sources') model.j = SetOf(model.i) ## Define parameters ## # Parameters # T(i) Time spent at activity i # / 0 0 # 1 1 # 2 1 # 3 1 # 4 1 / # W(i) Weights for activity i
def unit_commitment_model():
model = ConcreteModel()
# Define input files
xlsx = pd.ExcelFile(
f"{Path(__file__).parent.absolute()}/input/unit_commitment.xlsx",
engine="openpyxl",
)
system_demand = Helper.read_excel(xlsx, "SystemDemand")
storage_systems = Helper.read_excel(xlsx, "StorageSystems")
generators = Helper.read_excel(xlsx, "Generators")
generator_step_size = Helper.read_excel(xlsx, "GeneratorStepSize")
generator_step_cost = Helper.read_excel(xlsx, "GeneratorStepCost")
pv_generation = Helper.read_excel(xlsx, "PVGeneration")
# Define sets
model.T = Set(ordered=True, initialize=system_demand.index)
model.I = Set(ordered=True, initialize=generators.index)
model.F = Set(ordered=True, initialize=generator_step_size.columns)
model.S = Set(ordered=True, initialize=storage_systems.index)
# Define parameters
model.Pmax = Param(model.I, within=NonNegativeReals, mutable=True)
model.Pmin = Param(model.I, within=NonNegativeReals, mutable=True)
model.RU = Param(model.I, within=NonNegativeReals, mutable=True)
model.RD = Param(model.I, within=NonNegativeReals, mutable=True)
model.SUC = Param(model.I, within=NonNegativeReals, mutable=True)
model.SDC = Param(model.I, within=NonNegativeReals, mutable=True)
model.Pini = Param(model.I, within=NonNegativeReals, mutable=True)
model.uini = Param(model.I, within=Binary, mutable=True)
model.C = Param(model.I, model.F, within=NonNegativeReals, mutable=True)
model.B = Param(model.I, model.F, within=NonNegativeReals, mutable=True)
model.SystemDemand = Param(model.T, within=NonNegativeReals, mutable=True)
model.Emissions = Param(model.I, within=NonNegativeReals, mutable=True)
model.PV = Param(model.T, within=NonNegativeReals, mutable=True)
model.ESS_Pmax = Param(model.S, within=NonNegativeReals, mutable=True)
model.ESS_SOEmax = Param(model.S, within=NonNegativeReals, mutable=True)
model.ESS_SOEini = Param(model.S, within=NonNegativeReals, mutable=True)
model.ESS_Eff = Param(model.S, within=NonNegativeReals, mutable=True)
# Give values to parameters of the generators
for i in model.I:
model.Pmin[i] = generators.loc[i, "Pmin"]
model.Pmax[i] = generators.loc[i, "Pmax"]
model.RU[i] = generators.loc[i, "RU"]
model.RD[i] = generators.loc[i, "RD"]
model.SUC[i] = generators.loc[i, "SUC"]
model.SDC[i] = generators.loc[i, "SDC"]
model.Pini[i] = generators.loc[i, "Pini"]
model.uini[i] = generators.loc[i, "uini"]
model.Emissions[i] = generators.loc[i, "Emissions"]
for f in model.F:
model.B[i, f] = generator_step_size.loc[i, f]
model.C[i, f] = generator_step_cost.loc[i, f]
# Add system demand and PV generation
for t in model.T:
model.SystemDemand[t] = system_demand.loc[t, "SystemDemand"]
model.PV[t] = pv_generation.loc[t, "PVGeneration"]
# Give values to ESS parameters
for s in model.S:
model.ESS_Pmax[s] = storage_systems.loc[s, "Power"]
model.ESS_SOEmax[s] = storage_systems.loc[s, "Energy"]
model.ESS_SOEini[s] = storage_systems.loc[s, "SOEini"]
model.ESS_Eff[s] = storage_systems.loc[s, "Eff"]
# Define decision variables
model.P = Var(model.I, model.T, within=NonNegativeReals)
model.Pres = Var(model.T, within=NonNegativeReals)
model.b = Var(model.I, model.F, model.T, within=NonNegativeReals)
model.u = Var(model.I, model.T, within=Binary)
model.CSU = Var(model.I, model.T, within=NonNegativeReals)
model.CSD = Var(model.I, model.T, within=NonNegativeReals)
model.SOE = Var(model.S, model.T, within=NonNegativeReals)
model.Pch = Var(model.S, model.T, within=NonNegativeReals)
model.Pdis = Var(model.S, model.T, within=NonNegativeReals)
model.u_ess = Var(model.S, model.T, within=Binary)
# --------------------------------------
# Define the objective functions
# --------------------------------------
def cost_objective(model):
return sum(
sum(
sum(model.C[i, f] * model.b[i, f, t]
for f in model.F) + model.CSU[i, t] + model.CSD[i, t]
for i in model.I) for t in model.T)
def emissions_objective(model):
return sum(
sum(model.P[i, t] * model.Emissions[i] for i in model.I)
for t in model.T)
def unmet_objective(model):
return sum(model.Pres[t] for t in model.T)
# --------------------------------------
# Define the regular constraints
# --------------------------------------
def power_decomposition_rule1(model, i, t):
return model.P[i, t] == sum(model.b[i, f, t] for f in model.F)
def power_decomposition_rule2(model, i, f, t):
return model.b[i, f, t] <= model.B[i, f]
def power_min_rule(model, i, t):
return model.P[i, t] >= model.Pmin[i] * model.u[i, t]
def power_max_rule(model, i, t):
return model.P[i, t] <= model.Pmax[i] * model.u[i, t]
def ramp_up_rule(model, i, t):
if model.T.ord(t) == 1:
return model.P[i, t] - model.Pini[i] <= 60 * model.RU[i]
if model.T.ord(t) > 1:
return model.P[i, t] - model.P[i,
model.T.prev(t)] <= 60 * model.RU[i]
def ramp_down_rule(model, i, t):
if model.T.ord(t) == 1:
return (model.Pini[i] - model.P[i, t]) <= 60 * model.RD[i]
if model.T.ord(t) > 1:
return (model.P[i, model.T.prev(t)] -
model.P[i, t]) <= 60 * model.RD[i]
def start_up_cost(model, i, t):
if model.T.ord(t) == 1:
return model.CSU[i, t] >= model.SUC[i] * (model.u[i, t] -
model.uini[i])
if model.T.ord(t) > 1:
return model.CSU[i, t] >= model.SUC[i] * (
model.u[i, t] - model.u[i, model.T.prev(t)])
def shut_down_cost(model, i, t):
if model.T.ord(t) == 1:
return model.CSD[i, t] >= model.SDC[i] * (model.uini[i] -
model.u[i, t])
if model.T.ord(t) > 1:
return model.CSD[i, t] >= model.SDC[i] * (
model.u[i, model.T.prev(t)] - model.u[i, t])
def ESS_SOEupdate(model, s, t):
if model.T.ord(t) == 1:
return (model.SOE[s, t] == model.ESS_SOEini[s] +
model.ESS_Eff[s] * model.Pch[s, t] -
model.Pdis[s, t] / model.ESS_Eff[s])
if model.T.ord(t) > 1:
return (model.SOE[s, t] == model.SOE[s, model.T.prev(t)] +
model.ESS_Eff[s] * model.Pch[s, t] -
model.Pdis[s, t] / model.ESS_Eff[s])
def ESS_SOElimit(model, s, t):
return model.SOE[s, t] <= model.ESS_SOEmax[s]
def ESS_Charging(model, s, t):
return model.Pch[s, t] <= model.ESS_Pmax[s] * model.u_ess[s, t]
def ESS_Discharging(model, s, t):
return model.Pdis[s, t] <= model.ESS_Pmax[s] * (1 - model.u_ess[s, t])
def Balance(model, t):
return model.PV[t] + sum(model.P[i, t] for i in model.I) + sum(
model.Pdis[s, t]
for s in model.S) == model.SystemDemand[t] - model.Pres[t] + sum(
model.Pch[s, t] for s in model.S)
def Pres_max(model, t):
return model.Pres[t] <= 0.1 * model.SystemDemand[t]
# --------------------------------------
# Add components to the model
# --------------------------------------
# Add the constraints to the model
model.power_decomposition_rule1 = Constraint(
model.I, model.T, rule=power_decomposition_rule1)
model.power_decomposition_rule2 = Constraint(
model.I, model.F, model.T, rule=power_decomposition_rule2)
model.power_min_rule = Constraint(model.I, model.T, rule=power_min_rule)
model.power_max_rule = Constraint(model.I, model.T, rule=power_max_rule)
model.start_up_cost = Constraint(model.I, model.T, rule=start_up_cost)
model.shut_down_cost = Constraint(model.I, model.T, rule=shut_down_cost)
model.ConSOEUpdate = Constraint(model.S, model.T, rule=ESS_SOEupdate)
model.ConCharging = Constraint(model.S, model.T, rule=ESS_Charging)
model.ConDischarging = Constraint(model.S, model.T, rule=ESS_Discharging)
model.ConSOElimit = Constraint(model.S, model.T, rule=ESS_SOElimit)
model.ConGenUp = Constraint(model.I, model.T, rule=ramp_up_rule)
model.ConGenDown = Constraint(model.I, model.T, rule=ramp_down_rule)
model.ConBalance = Constraint(model.T, rule=Balance)
model.Pres_max = Constraint(model.T, rule=Pres_max)
# Add the objective functions to the model using ObjectiveList(). Note
# that the first index is 1 instead of 0!
model.obj_list = ObjectiveList()
model.obj_list.add(expr=cost_objective(model), sense=minimize)
model.obj_list.add(expr=emissions_objective(model), sense=minimize)
model.obj_list.add(expr=unmet_objective(model), sense=minimize)
# By default deactivate all the objective functions
for o in range(len(model.obj_list)):
model.obj_list[o + 1].deactivate()
return model
def 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 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
return m.Vm[i, k] == m.x[i, k] * ((1/2288) * 0.2685**(1 + (1 - m.T[i, k]/512.4)**0.2453)) + \
(1 - m.x[i, k]) * ((1/1235) * 0.27136**(1 + (1 - m.T[i, k]/536.4)**0.24))
else:
return Constraint.Skip
# Initial conditions for the given noisy-filter
def acm(m, k):
return m.M[0, k] == m.M_ic[k]
def acx(m, k):
return m.x[0, k] == m.x_ic[k]
# ---------------------------------------------------------------------------------------------------------------------
mod = ConcreteModel()
mod.t = ContinuousSet(bounds=(0, 1))
mod.Ntray = Ntray = 42
mod.tray = Set(initialize=[i for i in range(1, mod.Ntray + 1)])
mod.feed = Param(mod.tray,
initialize=lambda m, k: 57.5294 if k == 21 else 0.0,
mutable=True)
mod.xf = Param(initialize=0.32, mutable=True) # feed mole fraction
mod.hf = Param(initialize=9081.3) # feed enthalpy
mod.hlm0 = Param(initialize=2.6786e-04)
mod.hlma = Param(initialize=-0.14779)
mod.hlmb = Param(initialize=97.4289)
def three_objective_model():
model = ConcreteModel()
# Define variables
model.LIGN = Var(within=NonNegativeReals)
model.LIGN1 = Var(within=NonNegativeReals)
model.LIGN2 = Var(within=NonNegativeReals)
model.OIL = Var(within=NonNegativeReals)
model.OIL2 = Var(within=NonNegativeReals)
model.OIL3 = Var(within=NonNegativeReals)
model.NG = Var(within=NonNegativeReals)
model.NG1 = Var(within=NonNegativeReals)
model.NG2 = Var(within=NonNegativeReals)
model.NG3 = Var(within=NonNegativeReals)
model.RES = Var(within=NonNegativeReals)
model.RES1 = Var(within=NonNegativeReals)
model.RES3 = Var(within=NonNegativeReals)
# --------------------------------------
# Define the objective functions
# --------------------------------------
def objective1(model):
return 30 * model.LIGN + 75 * model.OIL + 60 * model.NG + 90 * model.RES
def objective2(model):
return 1.44 * model.LIGN + 0.72 * model.OIL + 0.45 * model.NG
def objective3(model):
return model.OIL + model.NG
# --------------------------------------
# Define the regular constraints
# --------------------------------------
def constraint1(model):
return model.LIGN - model.LIGN1 - model.LIGN2 == 0
def constraint2(model):
return model.OIL - model.OIL2 - model.OIL3 == 0
def constraint3(model):
return model.NG - model.NG1 - model.NG2 - model.NG3 == 0
def constraint4(model):
return model.RES - model.RES1 - model.RES3 == 0
def constraint5(model):
return model.LIGN <= 31000
def constraint6(model):
return model.OIL <= 15000
def constraint7(model):
return model.NG <= 22000
def constraint8(model):
return model.RES <= 10000
def constraint9(model):
return model.LIGN1 + model.NG1 + model.RES1 >= 38400
def constraint10(model):
return model.LIGN2 + model.OIL2 + model.NG2 >= 19200
def constraint11(model):
return model.OIL3 + model.NG3 + model.RES3 >= 6400
# --------------------------------------
# Add components to the model
# --------------------------------------
# Add the constraints to the model
model.con1 = Constraint(rule=constraint1)
model.con2 = Constraint(rule=constraint2)
model.con3 = Constraint(rule=constraint3)
model.con4 = Constraint(rule=constraint4)
model.con5 = Constraint(rule=constraint5)
model.con6 = Constraint(rule=constraint6)
model.con7 = Constraint(rule=constraint7)
model.con8 = Constraint(rule=constraint8)
model.con9 = Constraint(rule=constraint9)
model.con10 = Constraint(rule=constraint10)
model.con11 = Constraint(rule=constraint11)
# Add the objective functions to the model using ObjectiveList(). Note
# that the first index is 1 instead of 0!
model.obj_list = ObjectiveList()
model.obj_list.add(expr=objective1(model), sense=minimize)
model.obj_list.add(expr=objective2(model), sense=minimize)
model.obj_list.add(expr=objective3(model), sense=minimize)
# By default deactivate all the objective functions
for o in range(len(model.obj_list)):
model.obj_list[o + 1].deactivate()
return model
def test_len1(self):
model = self.model
model = ConcreteModel()
model.c = self._ctype()
self.assertEqual(len(model.c), 0)
def test_get_index_set_except(self):
'''
Tests:
For components indexed by 0, 1, 2, 3, 4 sets:
get_index_set_except one, then two (if any) of those sets
check two items that should be in set_except
insert item(s) back into these sets via index_getter
'''
m = ConcreteModel()
m.time = ContinuousSet(bounds=(0, 10))
m.space = ContinuousSet(bounds=(0, 10))
m.set1 = Set(initialize=['a', 'b', 'c'])
m.set2 = Set(initialize=['d', 'e', 'f'])
m.v = Var()
m.v1 = Var(m.time)
m.v2 = Var(m.time, m.space)
m.v3 = Var(m.time, m.space, m.set1)
m.v4 = Var(m.time, m.space, m.set1, m.set2)
# Multi-dimensional set:
m.set3 = Set(initialize=[('a', 1), ('b', 2)])
m.v5 = Var(m.set3)
m.v6 = Var(m.time, m.space, m.set3)
m.v7 = Var(m.set3, m.space, m.time)
disc = TransformationFactory('dae.collocation')
disc.apply_to(m, wrt=m.time, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
disc.apply_to(m, wrt=m.space, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
# Want this to give a TypeError
# info = get_index_set_except(m.v, m.time)
# Indexed by one set
info = get_index_set_except(m.v1, m.time)
set_except = info['set_except']
index_getter = info['index_getter']
self.assertTrue(set_except == [None])
# Variable is not indexed by anything except time
# Test that index_getter returns only the new value given,
# regardless of whether it was part of the set excluded (time):
self.assertEqual(index_getter((), -1), -1)
# Indexed by two sets
info = get_index_set_except(m.v2, m.time)
set_except = info['set_except']
index_getter = info['index_getter']
self.assertTrue(m.space[1] in set_except
and m.space.last() in set_except)
# Here (2,) is the partial index, corresponding to space.
# Can be provided as a scalar or tuple. 4, the time index,
# should be inserted before (2,)
self.assertEqual(index_getter((2,), 4), (4, 2))
self.assertEqual(index_getter(2, 4), (4, 2))
# Case where every set is "omitted," now for multiple sets
info = get_index_set_except(m.v2, m.space, m.time)
set_except = info['set_except']
index_getter = info['index_getter']
self.assertTrue(set_except == [None])
# 5, 7 are the desired index values for space, time
# index_getter should put them in the right order for m.v2,
# even if they are not valid indices for m.v2
self.assertEqual(index_getter((), 5, 7), (7, 5))
# Indexed by three sets
info = get_index_set_except(m.v3, m.time)
# In this case set_except is a product of the two non-time sets
# indexing v3
set_except = info['set_except']
index_getter = info['index_getter']
self.assertTrue((m.space[1], 'b') in set_except
and (m.space.last(), 'a') in set_except)
# index_getter inserts a scalar index into an index of length 2
self.assertEqual(index_getter((2, 'b'), 7), (7, 2, 'b'))
info = get_index_set_except(m.v3, m.space, m.time)
# Two sets omitted. Now set_except is just set1
set_except = info['set_except']
index_getter = info['index_getter']
self.assertTrue('a' in set_except)
# index_getter inserts the two new indices in the right order
self.assertEqual(index_getter('b', 1.2, 1.1), (1.1, 1.2, 'b'))
# Indexed by four sets
info = get_index_set_except(m.v4, m.set1, m.space)
# set_except is a product, and there are two indices to insert
set_except = info['set_except']
index_getter = info['index_getter']
self.assertTrue((m.time[1], 'd') in set_except)
self.assertEqual(index_getter((4, 'f'), 'b', 8), (4, 8, 'b', 'f'))
# The intended usage of this function looks something like:
index_set = m.v4.index_set()
for partial_index in set_except:
complete_index = index_getter(partial_index, 'a', m.space[2])
self.assertTrue(complete_index in index_set)
# Do something for every index of v4 at 'a' and space[2]
# Indexed by a multi-dimensional set
info = get_index_set_except(m.v5, m.set3)
set_except = info['set_except']
index_getter = info['index_getter']
self.assertEqual(set_except, [None])
self.assertEqual(index_getter((), ('a', 1)), ('a', 1))
info = get_index_set_except(m.v6, m.set3, m.time)
set_except = info['set_except']
index_getter = info['index_getter']
self.assertTrue(m.space[1] in set_except)
self.assertEqual(index_getter(m.space[1], ('b', 2), m.time[1]),
(m.time[1], m.space[1], 'b', 2))
info = get_index_set_except(m.v7, m.time)
set_except = info['set_except']
index_getter = info['index_getter']
self.assertIn(('a', 1, m.space[1]), set_except)
self.assertEqual(index_getter(('a', 1, m.space[1]), m.time[1]),
('a', 1, m.space[1], m.time[1]))
m.v8 = Var(m.time, m.set3, m.time)
with self.assertRaises(ValueError):
info = get_index_set_except(m.v8, m.time)
with self.assertRaises(ValueError):
info = get_index_set_except(m.v8, m.space)
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 test_indexed_by(self):
m = ConcreteModel()
m.time = ContinuousSet(bounds=(0, 10))
m.space = ContinuousSet(bounds=(0, 10))
m.set = Set(initialize=['a', 'b', 'c'])
m.set2 = Set(initialize=[('a', 1), ('b', 2)])
m.v = Var()
m.v1 = Var(m.time)
m.v2 = Var(m.time, m.space)
m.v3 = Var(m.set, m.space, m.time)
m.v4 = Var(m.time, m.set2)
m.v5 = Var(m.set2, m.time, m.space)
@m.Block()
def b(b):
b.v = Var()
b.v1 = Var(m.time)
b.v2 = Var(m.time, m.space)
b.v3 = Var(m.set, m.space, m.time)
@m.Block(m.time)
def b1(b):
b.v = Var()
b.v1 = Var(m.space)
b.v2 = Var(m.space, m.set)
@m.Block(m.time, m.space)
def b2(b):
b.v = Var()
b.v1 = Var(m.set)
@b.Block()
def b(bl):
bl.v = Var()
bl.v1 = Var(m.set)
bl.v2 = Var(m.time)
@m.Block(m.set2, m.time)
def b3(b):
b.v = Var()
b.v1 = Var(m.space)
@b.Block(m.space)
def b(bb):
bb.v = Var(m.set)
disc = TransformationFactory('dae.collocation')
disc.apply_to(m, wrt=m.time, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
disc.apply_to(m, wrt=m.space, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
self.assertFalse(is_explicitly_indexed_by(m.v, m.time))
self.assertTrue(is_explicitly_indexed_by(m.b.v2, m.space))
self.assertTrue(is_explicitly_indexed_by(m.b.v3, m.time, m.space))
self.assertFalse(is_in_block_indexed_by(m.v1, m.time))
self.assertFalse(is_in_block_indexed_by(m.v2, m.set))
self.assertTrue(is_in_block_indexed_by(m.b1[m.time[1]].v2, m.time))
self.assertTrue(is_in_block_indexed_by(
m.b2[m.time[1], m.space[1]].b.v1, m.time))
self.assertTrue(is_in_block_indexed_by(
m.b2[m.time[1], m.space[1]].b.v2, m.time))
self.assertTrue(is_explicitly_indexed_by(
m.b2[m.time[1], m.space[1]].b.v2, m.time))
self.assertFalse(is_in_block_indexed_by(
m.b2[m.time[1], m.space[1]].b.v1, m.set))
self.assertFalse(is_in_block_indexed_by(
m.b2[m.time[1], m.space[1]].b.v1,
m.space, stop_at=m.b2[m.time[1], m.space[1]]))
# Explicit indexing with multi-dimensional set:
self.assertTrue(is_explicitly_indexed_by(m.v4, m.time, m.set2))
self.assertTrue(is_explicitly_indexed_by(m.v5, m.time, m.set2, m.space))
# Implicit indexing with multi-dimensional set:
self.assertTrue(is_in_block_indexed_by(
m.b3['a', 1, m.time[1]].v, m.set2))
self.assertTrue(is_in_block_indexed_by(
m.b3['a', 1, m.time[1]].v, m.time))
self.assertTrue(is_in_block_indexed_by(
m.b3['a', 1, m.time[1]].v1[m.space[1]], m.set2))
self.assertFalse(is_in_block_indexed_by(
m.b3['a', 1, m.time[1]].v1[m.space[1]], m.space))
self.assertTrue(is_in_block_indexed_by(
m.b3['b', 2, m.time[2]].b[m.space[2]].v['b'], m.set2))
self.assertTrue(is_in_block_indexed_by(
m.b3['b', 2, m.time[2]].b[m.space[2]].v['b'], m.time))
self.assertTrue(is_in_block_indexed_by(
m.b3['b', 2, m.time[2]].b[m.space[2]].v['b'], m.space))
self.assertFalse(is_in_block_indexed_by(
m.b3['b', 2, m.time[2]].b[m.space[2]].v['b'], m.set))
self.assertFalse(is_in_block_indexed_by(
m.b3['b', 2, m.time[2]].b[m.space[2]].v['b'], m.time,
stop_at=m.b3['b', 2, m.time[2]]))
self.assertFalse(is_in_block_indexed_by(
m.b3['b', 2, m.time[2]].b[m.space[2]].v['b'], m.time,
stop_at=m.b3))
