def _reset():
build_indexed_BlockWVars.model = Block(concrete=True)
build_indexed_BlockWVars.model.ndx = RangeSet(0, N - 1)
build_indexed_BlockWVars.indexed_Block_rule = _indexed_Block_rule
def _reset():
build_indexed_Constraint.model = Block(concrete=True)
build_indexed_Constraint.model.ndx = RangeSet(0, N - 1)
build_indexed_Constraint.rule = _con_rule
def _create_using(self, model, **kwds):
precision = kwds.pop('precision', 8)
user_discretize = kwds.pop('discretize', set())
verbose = kwds.pop('verbose', False)
M = model.clone()
# TODO: if discretize is not empty, we must translate those
# components over to the components on the cloned instance
_discretize = {}
if user_discretize:
for _var in user_discretize:
_v = M.find_component(_var.name)
if _v.component() is _v:
for _vv in _v.itervalues():
_discretize.setdefault(id(_vv), len(_discretize))
else:
_discretize.setdefault(id(_v), len(_discretize))
# Iterate over all Constraints and identify the bilinear and
# quadratic terms
bilinear_terms = []
quadratic_terms = []
for constraint in M.component_map(Constraint,
active=True).itervalues():
for cname, c in constraint._data.iteritems():
if c.body.polynomial_degree() != 2:
continue
self._collect_bilinear(c.body, bilinear_terms, quadratic_terms)
# We want to find the (minimum?) number of variables to
# discretize so that we cover all the bilinearities -- without
# discretizing both sides of any single bilinear expression.
# First step: figure out how many expressions each term appears
# in
_counts = {}
for q in quadratic_terms:
if not q[1].is_continuous():
continue
_id = id(q[1])
if _id not in _counts:
_counts[_id] = (q[1], set())
_counts[_id][1].add(_id)
for bi in bilinear_terms:
for i in (0, 1):
if not bi[i + 1].is_continuous():
continue
_id = id(bi[i + 1])
if _id not in _counts:
_counts[_id] = (bi[i + 1], set())
_counts[_id][1].add(id(bi[2 - i]))
_tmp_counts = dict(_counts)
# First, remove the variables that the user wants to have discretized
for _id in _discretize:
for _i in _tmp_counts[_id][1]:
if _i == _id:
continue
_tmp_counts[_i][1].remove(_id)
del _tmp_counts[_id]
# All quadratic terms must be discretized (?)
#for q in quadratic_terms:
# _id = id(q[1])
# if _id not in _tmp_counts:
# continue
# _discretize.setdefault(_id, len(_discretize))
# for _i in _tmp_counts[_id][1]:
# if _i == _id:
# continue
# _tmp_counts[_i][1].remove(_id)
# del _tmp_counts[_id]
# Now pick a (minimal) subset of the terms in bilinear expressions
while _tmp_counts:
_ct, _id = max((len(_tmp_counts[i][1]), i) for i in _tmp_counts)
if not _ct:
break
_discretize.setdefault(_id, len(_discretize))
for _i in list(_tmp_counts[_id][1]):
if _i == _id:
continue
_tmp_counts[_i][1].remove(_id)
del _tmp_counts[_id]
#
# Discretize things
#
# Define a block (namespace) for holding the disaggregated
# variables and new constraints
if False: # Set to true when the LP writer is fixed
M._radix_linearization = Block()
_block = M._radix_linearization
else:
_block = M
_block.DISCRETIZATION = RangeSet(precision)
_block.DISCRETIZED_VARIABLES = RangeSet(0, len(_discretize) - 1)
_block.z = Var(_block.DISCRETIZED_VARIABLES,
_block.DISCRETIZATION,
within=Binary)
_block.dv = Var(_block.DISCRETIZED_VARIABLES,
bounds=(0, 2**-precision))
# Actually discretize the terms we have marked for discretization
for _id, _idx in iteritems(_discretize):
if verbose:
logger.info("Discretizing variable %s as %s" %
(_counts[_id][0].name, _idx))
self._discretize_variable(_block, _counts[_id][0], _idx)
_known_bilinear = {}
# For each quadratic term, if it hasn't been discretized /
# generated, do so, and remember the resulting W term for later
# use...
#for _expr, _x1 in quadratic_terms:
# self._discretize_term( _expr, _x1, _x1,
# _block, _discretize, _known_bilinear )
# For each bilinear term, if it hasn't been discretized /
# generated, do so, and remember the resulting W term for later
# use...
for _expr, _x1, _x2 in bilinear_terms:
self._discretize_term(_expr, _x1, _x2, _block, _discretize,
_known_bilinear)
# Return the discretized instance!
return M
def _reset():
build_indexed_Var.model = Block(concrete=True)
build_indexed_Var.model.ndx = RangeSet(0, N - 1)
build_indexed_Var.bounds_rule = _bounds_rule
build_indexed_Var.initialize_rule = _initialize_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
