def opf_setup(ppc, ppopt):
"""Constructs an OPF model object from a PYPOWER case dict.
Assumes that ppc is a PYPOWER case dict with internal indexing,
all equipment in-service, etc.
@see: L{opf}, L{ext2int}, L{opf_execute}
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Richard Lincoln
Modified by University of Kassel (Friederike Meier): Bugfix in line 110
"""
## options
dc = ppopt['PF_DC'] ## 1 = DC OPF, 0 = AC OPF
alg = ppopt['OPF_ALG']
verbose = ppopt['VERBOSE']
## data dimensions
nb = ppc['bus'].shape[0] ## number of buses
nl = ppc['branch'].shape[0] ## number of branches
ng = ppc['gen'].shape[0] ## number of dispatchable injections
if 'A' in ppc:
nusr = ppc['A'].shape[0] ## number of linear user constraints
else:
nusr = 0
if 'N' in ppc:
nw = ppc['N'].shape[0] ## number of general cost vars, w
else:
nw = 0
if dc:
## ignore reactive costs for DC
ppc['gencost'], _ = pqcost(ppc['gencost'], ng)
## reduce A and/or N from AC dimensions to DC dimensions, if needed
if nusr or nw: # pragma: no cover
acc = r_[nb + arange(nb), 2 * nb + ng + arange(ng)] ## Vm and Qg columns
if nusr and (ppc['A'].shape[1] >= 2*nb + 2*ng):
## make sure there aren't any constraints on Vm or Qg
if ppc['A'][:, acc].nnz > 0:
stderr.write('opf_setup: attempting to solve DC OPF with user constraints on Vm or Qg\n')
# FIXME: delete sparse matrix columns
bcc = delete(arange(ppc['A'].shape[1]), acc)
ppc['A'] = ppc['A'].tolil()[:, bcc].tocsr() ## delete Vm and Qg columns
if nw and (ppc['N'].shape[1] >= 2*nb + 2*ng):
## make sure there aren't any costs on Vm or Qg
if ppc['N'][:, acc].nnz > 0:
ii, _ = nonzero(ppc['N'][:, acc])
_, ii = unique(ii, return_index=True) ## indices of w with potential non-zero cost terms from Vm or Qg
if any(ppc['Cw'][ii]) | ( ('H' in ppc) & (len(ppc['H']) > 0) &
any(any(ppc['H'][:, ii])) ):
stderr.write('opf_setup: attempting to solve DC OPF with user costs on Vm or Qg\n')
# FIXME: delete sparse matrix columns
bcc = delete(arange(ppc['N'].shape[1]), acc)
ppc['N'] = ppc['N'].tolil()[:, bcc].tocsr() ## delete Vm and Qg columns
## convert single-block piecewise-linear costs into linear polynomial cost
pwl1 = find((ppc['gencost'][:, MODEL] == PW_LINEAR) & (ppc['gencost'][:, NCOST] == 2))
# p1 = array([])
if len(pwl1) > 0:
x0 = ppc['gencost'][pwl1, COST]
y0 = ppc['gencost'][pwl1, COST + 1]
x1 = ppc['gencost'][pwl1, COST + 2]
y1 = ppc['gencost'][pwl1, COST + 3]
m = (y1 - y0) / (x1 - x0)
b = y0 - m * x0
ppc['gencost'][pwl1, MODEL] = POLYNOMIAL
ppc['gencost'][pwl1, NCOST] = 2
ppc['gencost'][pwl1, COST:COST + 2] = r_['1',m.reshape(len(m),1), b.reshape(len(b),1)] # changed from ppc['gencost'][pwl1, COST:COST + 2] = r_[m, b] because we need to make sure, that m and b have the same shape, resulted in a value error due to shape mismatch before
## create (read-only) copies of individual fields for convenience
baseMVA, bus, gen, branch, gencost, _, lbu, ubu, ppopt, \
_, fparm, H, Cw, z0, zl, zu, userfcn, _ = opf_args(ppc, ppopt)
## warn if there is more than one reference bus
refs = find(bus[:, BUS_TYPE] == REF)
if len(refs) > 1 and verbose > 0:
errstr = '\nopf_setup: Warning: Multiple reference buses.\n' + \
' For a system with islands, a reference bus in each island\n' + \
' may help convergence, but in a fully connected system such\n' + \
' a situation is probably not reasonable.\n\n'
stdout.write(errstr)
## set up initial variables and bounds
gbus = gen[:, GEN_BUS].astype(int)
Va = bus[:, VA] * (pi / 180.0)
Vm = bus[:, VM].copy()
Vm[gbus] = gen[:, VG] ## buses with gens, init Vm from gen data
Pg = gen[:, PG] / baseMVA
Qg = gen[:, QG] / baseMVA
Pmin = gen[:, PMIN] / baseMVA
Pmax = gen[:, PMAX] / baseMVA
Qmin = gen[:, QMIN] / baseMVA
Qmax = gen[:, QMAX] / baseMVA
if dc: ## DC model
## more problem dimensions
nv = 0 ## number of voltage magnitude vars
nq = 0 ## number of Qg vars
q1 = array([]) ## index of 1st Qg column in Ay
## power mismatch constraints
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
neg_Cg = sparse((-ones(ng), (gen[:, GEN_BUS], arange(ng))), (nb, ng)) ## Pbus w.r.t. Pg
Amis = hstack([B, neg_Cg], 'csr')
bmis = -(bus[:, PD] + bus[:, GS]) / baseMVA - Pbusinj
## branch flow constraints
il = find((branch[:, RATE_A] != 0) & (branch[:, RATE_A] < 1e10))
nl2 = len(il) ## number of constrained lines
lpf = -Inf * ones(nl2)
upf = branch[il, RATE_A] / baseMVA - Pfinj[il]
upt = branch[il, RATE_A] / baseMVA + Pfinj[il]
user_vars = ['Va', 'Pg']
ycon_vars = ['Pg', 'y']
else: ## AC model
## more problem dimensions
nv = nb ## number of voltage magnitude vars
nq = ng ## number of Qg vars
q1 = ng ## index of 1st Qg column in Ay
## dispatchable load, constant power factor constraints
Avl, lvl, uvl, _ = makeAvl(baseMVA, gen)
## generator PQ capability curve constraints
Apqh, ubpqh, Apql, ubpql, Apqdata = makeApq(baseMVA, gen)
user_vars = ['Va', 'Vm', 'Pg', 'Qg']
ycon_vars = ['Pg', 'Qg', 'y']
## voltage angle reference constraints
Vau = Inf * ones(nb)
Val = -Vau
Vau[refs] = Va[refs]
Val[refs] = Va[refs]
## branch voltage angle difference limits
Aang, lang, uang, iang = makeAang(baseMVA, branch, nb, ppopt)
## basin constraints for piece-wise linear gen cost variables
if alg == 545 or alg == 550: ## SC-PDIPM or TRALM, no CCV cost vars # pragma: no cover
ny = 0
Ay = None
by = array([])
else:
ipwl = find(gencost[:, MODEL] == PW_LINEAR) ## piece-wise linear costs
ny = ipwl.shape[0] ## number of piece-wise linear cost vars
Ay, by = makeAy(baseMVA, ng, gencost, 1, q1, 1+ng+nq)
if any((gencost[:, MODEL] != POLYNOMIAL) & (gencost[:, MODEL] != PW_LINEAR)):
stderr.write('opf_setup: some generator cost rows have invalid MODEL value\n')
## more problem dimensions
nx = nb+nv + ng+nq ## number of standard OPF control variables
if nusr: # pragma: no cover
nz = ppc['A'].shape[1] - nx ## number of user z variables
if nz < 0:
stderr.write('opf_setup: user supplied A matrix must have at least %d columns.\n' % nx)
else:
nz = 0 ## number of user z variables
if nw: ## still need to check number of columns of N
if ppc['N'].shape[1] != nx:
stderr.write('opf_setup: user supplied N matrix must have %d columns.\n' % nx)
## construct OPF model object
om = opf_model(ppc)
if len(pwl1) > 0:
om.userdata('pwl1', pwl1)
if dc:
om.userdata('Bf', Bf)
om.userdata('Pfinj', Pfinj)
om.userdata('iang', iang)
om.add_vars('Va', nb, Va, Val, Vau)
om.add_vars('Pg', ng, Pg, Pmin, Pmax)
om.add_constraints('Pmis', Amis, bmis, bmis, ['Va', 'Pg']) ## nb
om.add_constraints('Pf', Bf[il, :], lpf, upf, ['Va']) ## nl
om.add_constraints('Pt', -Bf[il, :], lpf, upt, ['Va']) ## nl
om.add_constraints('ang', Aang, lang, uang, ['Va']) ## nang
else:
om.userdata('Apqdata', Apqdata)
om.userdata('iang', iang)
om.add_vars('Va', nb, Va, Val, Vau)
om.add_vars('Vm', nb, Vm, bus[:, VMIN], bus[:, VMAX])
om.add_vars('Pg', ng, Pg, Pmin, Pmax)
om.add_vars('Qg', ng, Qg, Qmin, Qmax)
om.add_constraints('Pmis', nb, 'nonlinear')
om.add_constraints('Qmis', nb, 'nonlinear')
om.add_constraints('Sf', nl, 'nonlinear')
om.add_constraints('St', nl, 'nonlinear')
om.add_constraints('PQh', Apqh, array([]), ubpqh, ['Pg', 'Qg']) ## npqh
om.add_constraints('PQl', Apql, array([]), ubpql, ['Pg', 'Qg']) ## npql
om.add_constraints('vl', Avl, lvl, uvl, ['Pg', 'Qg']) ## nvl
om.add_constraints('ang', Aang, lang, uang, ['Va']) ## nang
## y vars, constraints for piece-wise linear gen costs
if ny > 0:
om.add_vars('y', ny)
om.add_constraints('ycon', Ay, array([]), by, ycon_vars) ## ncony
## add user vars, constraints and costs (as specified via A, ..., N, ...)
if nz > 0: # pragma: no cover
om.add_vars('z', nz, z0, zl, zu)
user_vars.append('z')
if nusr: # pragma: no cover
om.add_constraints('usr', ppc['A'], lbu, ubu, user_vars) ## nusr
if nw: # pragma: no cover
user_cost = {}
user_cost['N'] = ppc['N']
user_cost['Cw'] = Cw
if len(fparm) > 0:
user_cost['dd'] = fparm[:, 0]
user_cost['rh'] = fparm[:, 1]
user_cost['kk'] = fparm[:, 2]
user_cost['mm'] = fparm[:, 3]
# if len(H) > 0:
user_cost['H'] = H
om.add_costs('usr', user_cost, user_vars)
## execute userfcn callbacks for 'formulation' stage
run_userfcn(userfcn, 'formulation', om)
return om
def opf_setup(ppc, ppopt):
"""Constructs an OPF model object from a PYPOWER case dict.
Assumes that ppc is a PYPOWER case dict with internal indexing,
all equipment in-service, etc.
@see: L{opf}, L{ext2int}, L{opf_execute}
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Richard Lincoln
"""
## options
dc = ppopt['PF_DC'] ## 1 = DC OPF, 0 = AC OPF
alg = ppopt['OPF_ALG']
verbose = ppopt['VERBOSE']
## data dimensions
nb = ppc['bus'].shape[0] ## number of buses
nl = ppc['branch'].shape[0] ## number of branches
ng = ppc['gen'].shape[0] ## number of dispatchable injections
if 'A' in ppc:
nusr = ppc['A'].shape[0] ## number of linear user constraints
else:
nusr = 0
if 'N' in ppc:
nw = ppc['N'].shape[0] ## number of general cost vars, w
else:
nw = 0
if dc:
## ignore reactive costs for DC
ppc['gencost'], _ = pqcost(ppc['gencost'], ng)
## reduce A and/or N from AC dimensions to DC dimensions, if needed
if nusr or nw:
acc = r_[nb + arange(nb), 2 * nb + ng + arange(ng)] ## Vm and Qg columns
if nusr and (ppc['A'].shape[1] >= 2*nb + 2*ng):
## make sure there aren't any constraints on Vm or Qg
if ppc['A'][:, acc].nnz > 0:
stderr.write('opf_setup: attempting to solve DC OPF with user constraints on Vm or Qg\n')
# FIXME: delete sparse matrix columns
bcc = delete(arange(ppc['A'].shape[1]), acc)
ppc['A'] = ppc['A'].tolil()[:, bcc].tocsr() ## delete Vm and Qg columns
if nw and (ppc['N'].shape[1] >= 2*nb + 2*ng):
## make sure there aren't any costs on Vm or Qg
if ppc['N'][:, acc].nnz > 0:
ii, _ = nonzero(ppc['N'][:, acc])
_, ii = unique(ii, return_index=True) ## indices of w with potential non-zero cost terms from Vm or Qg
if any(ppc['Cw'][ii]) | ( ('H' in ppc) & (len(ppc['H']) > 0) &
any(any(ppc['H'][:, ii])) ):
stderr.write('opf_setup: attempting to solve DC OPF with user costs on Vm or Qg\n')
# FIXME: delete sparse matrix columns
bcc = delete(arange(ppc['N'].shape[1]), acc)
ppc['N'] = ppc['N'].tolil()[:, bcc].tocsr() ## delete Vm and Qg columns
## convert single-block piecewise-linear costs into linear polynomial cost
pwl1 = find((ppc['gencost'][:, MODEL] == PW_LINEAR) & (ppc['gencost'][:, NCOST] == 2))
# p1 = array([])
if len(pwl1) > 0:
x0 = ppc['gencost'][pwl1, COST]
y0 = ppc['gencost'][pwl1, COST + 1]
x1 = ppc['gencost'][pwl1, COST + 2]
y1 = ppc['gencost'][pwl1, COST + 3]
m = (y1 - y0) / (x1 - x0)
b = y0 - m * x0
ppc['gencost'][pwl1, MODEL] = POLYNOMIAL
ppc['gencost'][pwl1, NCOST] = 2
ppc['gencost'][pwl1, COST:COST + 2] = r_[m, b]
## create (read-only) copies of individual fields for convenience
baseMVA, bus, gen, branch, gencost, _, lbu, ubu, ppopt, \
_, fparm, H, Cw, z0, zl, zu, userfcn, _ = opf_args(ppc, ppopt)
## warn if there is more than one reference bus
refs = find(bus[:, BUS_TYPE] == REF)
if len(refs) > 1 and verbose > 0:
errstr = '\nopf_setup: Warning: Multiple reference buses.\n' + \
' For a system with islands, a reference bus in each island\n' + \
' may help convergence, but in a fully connected system such\n' + \
' a situation is probably not reasonable.\n\n'
stdout.write(errstr)
## set up initial variables and bounds
gbus = gen[:, GEN_BUS].astype(int)
Va = bus[:, VA] * (pi / 180.0)
Vm = bus[:, VM].copy()
Vm[gbus] = gen[:, VG] ## buses with gens, init Vm from gen data
Pg = gen[:, PG] / baseMVA
Qg = gen[:, QG] / baseMVA
Pmin = gen[:, PMIN] / baseMVA
Pmax = gen[:, PMAX] / baseMVA
Qmin = gen[:, QMIN] / baseMVA
Qmax = gen[:, QMAX] / baseMVA
if dc: ## DC model
## more problem dimensions
nv = 0 ## number of voltage magnitude vars
nq = 0 ## number of Qg vars
q1 = array([]) ## index of 1st Qg column in Ay
## power mismatch constraints
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
neg_Cg = sparse((-ones(ng), (gen[:, GEN_BUS], arange(ng))), (nb, ng)) ## Pbus w.r.t. Pg
Amis = hstack([B, neg_Cg], 'csr')
bmis = -(bus[:, PD] + bus[:, GS]) / baseMVA - Pbusinj
## branch flow constraints
il = find((branch[:, RATE_A] != 0) & (branch[:, RATE_A] < 1e10))
nl2 = len(il) ## number of constrained lines
lpf = -Inf * ones(nl2)
upf = branch[il, RATE_A] / baseMVA - Pfinj[il]
upt = branch[il, RATE_A] / baseMVA + Pfinj[il]
user_vars = ['Va', 'Pg']
ycon_vars = ['Pg', 'y']
else: ## AC model
## more problem dimensions
nv = nb ## number of voltage magnitude vars
nq = ng ## number of Qg vars
q1 = ng ## index of 1st Qg column in Ay
## dispatchable load, constant power factor constraints
Avl, lvl, uvl, _ = makeAvl(baseMVA, gen)
## generator PQ capability curve constraints
Apqh, ubpqh, Apql, ubpql, Apqdata = makeApq(baseMVA, gen)
user_vars = ['Va', 'Vm', 'Pg', 'Qg']
ycon_vars = ['Pg', 'Qg', 'y']
## voltage angle reference constraints
Vau = Inf * ones(nb)
Val = -Vau
Vau[refs] = Va[refs]
Val[refs] = Va[refs]
## branch voltage angle difference limits
Aang, lang, uang, iang = makeAang(baseMVA, branch, nb, ppopt)
## basin constraints for piece-wise linear gen cost variables
if alg == 545 or alg == 550: ## SC-PDIPM or TRALM, no CCV cost vars
ny = 0
Ay = None
by = array([])
else:
ipwl = find(gencost[:, MODEL] == PW_LINEAR) ## piece-wise linear costs
ny = ipwl.shape[0] ## number of piece-wise linear cost vars
Ay, by = makeAy(baseMVA, ng, gencost, 1, q1, 1+ng+nq)
if any((gencost[:, MODEL] != POLYNOMIAL) & (gencost[:, MODEL] != PW_LINEAR)):
stderr.write('opf_setup: some generator cost rows have invalid MODEL value\n')
## more problem dimensions
nx = nb+nv + ng+nq; ## number of standard OPF control variables
if nusr:
nz = ppc['A'].shape[1] - nx ## number of user z variables
if nz < 0:
stderr.write('opf_setup: user supplied A matrix must have at least %d columns.\n' % nx)
else:
nz = 0 ## number of user z variables
if nw: ## still need to check number of columns of N
if ppc['N'].shape[1] != nx:
stderr.write('opf_setup: user supplied N matrix must have %d columns.\n' % nx)
## construct OPF model object
om = opf_model(ppc)
if len(pwl1) > 0:
om.userdata('pwl1', pwl1)
if dc:
om.userdata('Bf', Bf)
om.userdata('Pfinj', Pfinj)
om.userdata('iang', iang)
om.add_vars('Va', nb, Va, Val, Vau)
om.add_vars('Pg', ng, Pg, Pmin, Pmax)
om.add_constraints('Pmis', Amis, bmis, bmis, ['Va', 'Pg']) ## nb
om.add_constraints('Pf', Bf[il, :], lpf, upf, ['Va']) ## nl
om.add_constraints('Pt', -Bf[il, :], lpf, upt, ['Va']) ## nl
om.add_constraints('ang', Aang, lang, uang, ['Va']) ## nang
else:
om.userdata('Apqdata', Apqdata)
om.userdata('iang', iang)
om.add_vars('Va', nb, Va, Val, Vau)
om.add_vars('Vm', nb, Vm, bus[:, VMIN], bus[:, VMAX])
om.add_vars('Pg', ng, Pg, Pmin, Pmax)
om.add_vars('Qg', ng, Qg, Qmin, Qmax)
om.add_constraints('Pmis', nb, 'nonlinear')
om.add_constraints('Qmis', nb, 'nonlinear')
om.add_constraints('Sf', nl, 'nonlinear')
om.add_constraints('St', nl, 'nonlinear')
om.add_constraints('PQh', Apqh, array([]), ubpqh, ['Pg', 'Qg']) ## npqh
om.add_constraints('PQl', Apql, array([]), ubpql, ['Pg', 'Qg']) ## npql
om.add_constraints('vl', Avl, lvl, uvl, ['Pg', 'Qg']) ## nvl
om.add_constraints('ang', Aang, lang, uang, ['Va']) ## nang
## y vars, constraints for piece-wise linear gen costs
if ny > 0:
om.add_vars('y', ny)
om.add_constraints('ycon', Ay, array([]), by, ycon_vars) ## ncony
## add user vars, constraints and costs (as specified via A, ..., N, ...)
if nz > 0:
om.add_vars('z', nz, z0, zl, zu)
user_vars.append('z')
if nusr:
om.add_constraints('usr', ppc['A'], lbu, ubu, user_vars) ## nusr
if nw:
user_cost = {}
user_cost['N'] = ppc['N']
user_cost['Cw'] = Cw
if len(fparm) > 0:
user_cost['dd'] = fparm[:, 0]
user_cost['rh'] = fparm[:, 1]
user_cost['kk'] = fparm[:, 2]
user_cost['mm'] = fparm[:, 3]
# if len(H) > 0:
user_cost['H'] = H
om.add_costs('usr', user_cost, user_vars)
## execute userfcn callbacks for 'formulation' stage
run_userfcn(userfcn, 'formulation', om)
return om