def makePTDF(baseMVA, bus, branch, slack=None):
"""Builds the DC PTDF matrix for a given choice of slack.
Returns the DC PTDF matrix for a given choice of slack. The matrix is
C{nbr x nb}, where C{nbr} is the number of branches and C{nb} is the
number of buses. The C{slack} can be a scalar (single slack bus) or an
C{nb x 1} column vector of weights specifying the proportion of the
slack taken up at each bus. If the C{slack} is not specified the
reference bus is used by default.
For convenience, C{slack} can also be an C{nb x nb} matrix, where each
column specifies how the slack should be handled for injections
at that bus.
@see: L{makeLODF}
@author: Ray Zimmerman (PSERC Cornell)
"""
## use reference bus for slack by default
if slack is None:
slack = find(bus[:, BUS_TYPE] == REF)
slack = slack[0]
## set the slack bus to be used to compute initial PTDF
if isscalar(slack):
slack_bus = slack
else:
slack_bus = 0 ## use bus 1 for temp slack bus
nb = bus.shape[0]
nbr = branch.shape[0]
noref = arange(1, nb) ## use bus 1 for voltage angle reference
noslack = find(arange(nb) != slack_bus)
## check that bus numbers are equal to indices to bus (one set of bus numbers)
if any(bus[:, BUS_I] != arange(nb)):
stderr.write('makePTDF: buses must be numbered consecutively')
## compute PTDF for single slack_bus
Bbus, Bf, _, _ = makeBdc(baseMVA, bus, branch)
Bbus, Bf = Bbus.todense(), Bf.todense()
H = zeros((nbr, nb))
H[:, noslack] = solve( Bbus[ix_(noslack, noref)].T, Bf[:, noref].T ).T
# = Bf[:, noref] * inv(Bbus[ix_(noslack, noref)])
## distribute slack, if requested
if not isscalar(slack):
if len(slack.shape) == 1: ## slack is a vector of weights
slack = slack / sum(slack) ## normalize weights
## conceptually, we want to do ...
## H = H * (eye(nb, nb) - slack * ones((1, nb)))
## ... we just do it more efficiently
v = dot(H, slack)
for k in range(nb):
H[:, k] = H[:, k] - v
else:
H = dot(H, slack)
return H
def userfcn_iflims_formulation(om, *args):
"""This is the 'formulation' stage userfcn callback that defines the
user costs and constraints for interface flow limits. It expects to
find an 'if' field in the ppc stored in om, as described above. The
optional args are not currently used.
"""
## initialize some things
ppc = om.get_ppc()
baseMVA, bus, branch = ppc['baseMVA'], ppc['bus'], ppc['branch']
ifmap = ppc['if']['map']
iflims = ppc['if']['lims']
## form B matrices for DC model
_, Bf, _, Pfinj = makeBdc(baseMVA, bus, branch)
n = Bf.shape[1] ## dim of theta
## form constraints
ifidx = unique(iflims[:, 0]) ## interface number list
nifs = len(ifidx) ## number of interfaces
Aif = lil_matrix((nifs, n))
lif = zeros(nifs)
uif = zeros(nifs)
for k in range(nifs):
## extract branch indices
br = ifmap[ifmap[:, 0] == ifidx[k], 1]
if len(br) == 0:
stderr.write(
'userfcn_iflims_formulation: interface %d has no in-service branches\n'
% k)
d = sign(br)
br = abs(br)
Ak = sparse((1, n)) ## Ak = sum( d(i) * Bf(i, :) )
bk = 0 ## bk = sum( d(i) * Pfinj(i) )
for i in range(len(br)):
Ak = Ak + d[i] * Bf[br[i], :]
bk = bk + d[i] * Pfinj[br[i]]
Aif[k, :] = Ak
lif[k] = iflims[k, 1] / baseMVA - bk
uif[k] = iflims[k, 2] / baseMVA - bk
## add interface constraint
om.add_constraints('iflims', Aif, lif, uif, ['Va']) ## nifs
return om
def userfcn_iflims_formulation(om, *args):
"""This is the 'formulation' stage userfcn callback that defines the
user costs and constraints for interface flow limits. It expects to
find an 'if' field in the ppc stored in om, as described above. The
optional args are not currently used.
"""
## initialize some things
ppc = om.get_ppc()
baseMVA, bus, branch = ppc['baseMVA'], ppc['bus'], ppc['branch']
ifmap = ppc['if']['map']
iflims = ppc['if']['lims']
## form B matrices for DC model
_, Bf, _, Pfinj = makeBdc(baseMVA, bus, branch)
n = Bf.shape[1] ## dim of theta
## form constraints
ifidx = unique(iflims[:, 0]) ## interface number list
nifs = len(ifidx) ## number of interfaces
Aif = lil_matrix((nifs, n))
lif = zeros(nifs)
uif = zeros(nifs)
for k in range(nifs):
## extract branch indices
br = ifmap[ifmap[:, 0] == ifidx[k], 1]
if len(br) == 0:
stderr.write('userfcn_iflims_formulation: interface %d has no in-service branches\n' % k)
d = sign(br)
br = abs(br)
Ak = sparse((1, n)) ## Ak = sum( d(i) * Bf(i, :) )
bk = 0 ## bk = sum( d(i) * Pfinj(i) )
for i in range(len(br)):
Ak = Ak + d[i] * Bf[br[i], :]
bk = bk + d[i] * Pfinj[br[i]]
Aif[k, :] = Ak
lif[k] = iflims[k, 1] / baseMVA - bk
uif[k] = iflims[k, 2] / baseMVA - bk
## add interface constraint
om.add_constraints('iflims', Aif, lif, uif, ['Va']) ## nifs
return om
def runpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a power flow.
Runs a power flow [full AC Newton's method by default] and optionally
returns the solved values in the data matrices, a flag which is C{True} if
the algorithm was successful in finding a solution, and the elapsed
time in seconds. All input arguments are optional. If C{casename} is
provided it specifies the name of the input data file or dict
containing the power flow data. The default value is 'case9'.
If the ppopt is provided it overrides the default PYPOWER options
vector and can be used to specify the solution algorithm and output
options among other things. If the 3rd argument is given the pretty
printed output will be appended to the file whose name is given in
C{fname}. If C{solvedcase} is specified the solved case will be written
to a case file in PYPOWER format with the specified name. If C{solvedcase}
ends with '.mat' it saves the case as a MAT-file otherwise it saves it
as a Python-file.
If the C{ENFORCE_Q_LIMS} options is set to C{True} [default is false] then
if any generator reactive power limit is violated after running the AC
power flow, the corresponding bus is converted to a PQ bus, with Qg at
the limit, and the case is re-run. The voltage magnitude at the bus
will deviate from the specified value in order to satisfy the reactive
power limit. If the reference bus is converted to PQ, the first
remaining PV bus will be used as the slack bus for the next iteration.
This may result in the real power output at this generator being
slightly off from the specified values.
Enforcing of generator Q limits inspired by contributions from Mu Lin,
Lincoln University, New Zealand (1/14/05).
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
## options
verbose = ppopt["VERBOSE"]
qlim = ppopt["ENFORCE_Q_LIMS"] ## enforce Q limits on gens?
dc = ppopt["PF_DC"] ## use DC formulation?
## read data
ppc = loadcase(casedata)
## add zero columns to branch for flows if needed
if ppc["branch"].shape[1] < QT:
ppc["branch"] = c_[ppc["branch"],
zeros((ppc["branch"].shape[0],
QT - ppc["branch"].shape[1] + 1))]
## convert to internal indexing
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
## get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
## generator info
on = find(gen[:, GEN_STATUS] > 0) ## which generators are on?
gbus = gen[on, GEN_BUS].astype(int) ## what buses are they at?
##----- run the power flow -----
t0 = time()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v["Version"], v["Date"]))
if dc: # DC formulation
if verbose:
stdout.write(' -- DC Power Flow\n')
## initial state
Va0 = bus[:, VA] * (pi / 180)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
## adjusted for phase shifters and real shunts
Pbus = makeSbus(baseMVA, bus,
gen).real - Pbusinj - bus[:, GS] / baseMVA
## "run" the power flow
Va = dcpf(B, Pbus, Va0, ref, pv, pq)
## update data matrices with solution
branch[:, [QF, QT]] = zeros((branch.shape[0], 2))
branch[:, PF] = (Bf * Va + Pfinj) * baseMVA
branch[:, PT] = -branch[:, PF]
bus[:, VM] = ones(bus.shape[0])
bus[:, VA] = Va * (180 / pi)
## update Pg for slack generator (1st gen at ref bus)
## (note: other gens at ref bus are accounted for in Pbus)
## Pg = Pinj + Pload + Gs
## newPg = oldPg + newPinj - oldPinj
refgen = zeros(len(ref), dtype=int)
for k in range(len(ref)):
temp = find(gbus == ref[k])
refgen[k] = on[temp[0]]
gen[refgen,
PG] = gen[refgen, PG] + (B[ref, :] * Va - Pbus[ref]) * baseMVA
success = 1
else: ## AC formulation
alg = ppopt['PF_ALG']
if verbose > 0:
if alg == 1:
solver = 'Newton'
elif alg == 2:
solver = 'fast-decoupled, XB'
elif alg == 3:
solver = 'fast-decoupled, BX'
elif alg == 4:
solver = 'Gauss-Seidel'
else:
solver = 'unknown'
print(' -- AC Power Flow (%s)\n' % solver)
## initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = bus[:, VM] * exp(1j * pi / 180 * bus[:, VA])
V0[gbus] = gen[on, VG] / abs(V0[gbus]) * V0[gbus]
if qlim:
ref0 = ref ## save index and angle of
Varef0 = bus[ref0, VA] ## original reference bus(es)
limited = [] ## list of indices of gens @ Q lims
fixedQg = zeros(gen.shape[0]) ## Qg of gens at Q limits
repeat = True
while repeat:
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the power flow
alg = ppopt["PF_ALG"]
if alg == 1:
V, success, _ = newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
elif alg == 2 or alg == 3:
Bp, Bpp = makeB(baseMVA, bus, branch, alg)
V, success, _ = fdpf(Ybus, Sbus, V0, Bp, Bpp, ref, pv, pq,
ppopt)
elif alg == 4:
V, success, _ = gausspf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
else:
stderr.write('Only Newton'
's method, fast-decoupled, and '
'Gauss-Seidel power flow algorithms currently '
'implemented.\n')
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt,
V, ref, pv, pq)
if qlim: ## enforce generator Q limits
## find gens with violated Q constraints
gen_status = gen[:, GEN_STATUS] > 0
qg_max_lim = gen[:, QG] > gen[:, QMAX]
qg_min_lim = gen[:, QG] < gen[:, QMIN]
mx = find(gen_status & qg_max_lim)
mn = find(gen_status & qg_min_lim)
if len(mx) > 0 or len(
mn) > 0: ## we have some Q limit violations
# No PV generators
if len(pv) == 0:
if verbose:
if len(mx) > 0:
print(
'Gen %d [only one left] exceeds upper Q limit : INFEASIBLE PROBLEM\n'
% mx + 1)
else:
print(
'Gen %d [only one left] exceeds lower Q limit : INFEASIBLE PROBLEM\n'
% mn + 1)
success = 0
break
## one at a time?
if qlim == 2: ## fix largest violation, ignore the rest
k = argmax(r_[gen[mx, QG] - gen[mx, QMAX],
gen[mn, QMIN] - gen[mn, QG]])
if k > len(mx):
mn = mn[k - len(mx)]
mx = []
else:
mx = mx[k]
mn = []
if verbose and len(mx) > 0:
for i in range(len(mx)):
print('Gen ' + str(mx[i] + 1) +
' at upper Q limit, converting to PQ bus\n')
if verbose and len(mn) > 0:
for i in range(len(mn)):
print('Gen ' + str(mn[i] + 1) +
' at lower Q limit, converting to PQ bus\n')
## save corresponding limit values
fixedQg[mx] = gen[mx, QMAX]
fixedQg[mn] = gen[mn, QMIN]
mx = r_[mx, mn].astype(int)
## convert to PQ bus
gen[mx, QG] = fixedQg[mx] ## set Qg to binding
for i in range(
len(mx)
): ## [one at a time, since they may be at same bus]
gen[mx[i],
GEN_STATUS] = 0 ## temporarily turn off gen,
bi = gen[mx[i], GEN_BUS] ## adjust load accordingly,
bus[bi, [PD, QD]] = (bus[bi, [PD, QD]] -
gen[mx[i], [PG, QG]])
if len(ref) > 1 and any(bus[gen[mx, GEN_BUS],
BUS_TYPE] == REF):
raise ValueError('Sorry, PYPOWER cannot enforce Q '
'limits for slack buses in systems '
'with multiple slacks.')
bus[gen[mx, GEN_BUS].astype(int),
BUS_TYPE] = PQ ## & set bus type to PQ
## update bus index lists of each type of bus
ref_temp = ref
ref, pv, pq = bustypes(bus, gen)
if verbose and ref != ref_temp:
print('Bus %d is new slack bus\n' % ref)
limited = r_[limited, mx].astype(int)
else:
repeat = 0 ## no more generator Q limits violated
else:
repeat = 0 ## don't enforce generator Q limits, once is enough
if qlim and len(limited) > 0:
## restore injections from limited gens [those at Q limits]
gen[limited, QG] = fixedQg[limited] ## restore Qg value,
for i in range(
len(limited
)): ## [one at a time, since they may be at same bus]
bi = gen[limited[i], GEN_BUS] ## re-adjust load,
bus[bi,
[PD, QD]] = bus[bi, [PD, QD]] + gen[limited[i], [PG, QG]]
gen[limited[i], GEN_STATUS] = 1 ## and turn gen back on
if ref != ref0:
## adjust voltage angles to make original ref bus correct
bus[:, VA] = bus[:, VA] - bus[ref0, VA] + Varef0
ppc["et"] = time() - t0
ppc["success"] = success
##----- output results -----
## convert back to original bus numbering & print results
ppc["bus"], ppc["gen"], ppc["branch"] = bus, gen, branch
results = int2ext(ppc)
## zero out result fields of out-of-service gens & branches
if len(results["order"]["gen"]["status"]["off"]) > 0:
results["gen"][ix_(results["order"]["gen"]["status"]["off"],
[PG, QG])] = 0
if len(results["order"]["branch"]["status"]["off"]) > 0:
results["branch"][ix_(results["order"]["branch"]["status"]["off"],
[PF, QF, PT, QT])] = 0
if fname:
fd = None
try:
fd = open(fname, "a")
except Exception as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(results, fd, ppopt)
fd.close()
else:
printpf(results, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, results)
return results, success
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 runpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a power flow.
Runs a power flow [full AC Newton's method by default] and optionally
returns the solved values in the data matrices, a flag which is C{True} if
the algorithm was successful in finding a solution, and the elapsed
time in seconds. All input arguments are optional. If C{casename} is
provided it specifies the name of the input data file or dict
containing the power flow data. The default value is 'case9'.
If the ppopt is provided it overrides the default PYPOWER options
vector and can be used to specify the solution algorithm and output
options among other things. If the 3rd argument is given the pretty
printed output will be appended to the file whose name is given in
C{fname}. If C{solvedcase} is specified the solved case will be written
to a case file in PYPOWER format with the specified name. If C{solvedcase}
ends with '.mat' it saves the case as a MAT-file otherwise it saves it
as a Python-file.
If the C{ENFORCE_Q_LIMS} options is set to C{True} [default is false] then
if any generator reactive power limit is violated after running the AC
power flow, the corresponding bus is converted to a PQ bus, with Qg at
the limit, and the case is re-run. The voltage magnitude at the bus
will deviate from the specified value in order to satisfy the reactive
power limit. If the reference bus is converted to PQ, the first
remaining PV bus will be used as the slack bus for the next iteration.
This may result in the real power output at this generator being
slightly off from the specified values.
Enforcing of generator Q limits inspired by contributions from Mu Lin,
Lincoln University, New Zealand (1/14/05).
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
## options
verbose = ppopt["VERBOSE"]
qlim = ppopt["ENFORCE_Q_LIMS"] ## enforce Q limits on gens?
dc = ppopt["PF_DC"] ## use DC formulation?
## read data
ppc = loadcase(casedata)
## add zero columns to branch for flows if needed
if ppc["branch"].shape[1] < QT:
ppc["branch"] = c_[ppc["branch"],
zeros((ppc["branch"].shape[0],
QT - ppc["branch"].shape[1] + 1))]
## convert to internal indexing
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
## get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
## generator info
on = find(gen[:, GEN_STATUS] > 0) ## which generators are on?
gbus = gen[on, GEN_BUS].astype(int) ## what buses are they at?
##----- run the power flow -----
t0 = time()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v["Version"], v["Date"]))
if dc: # DC formulation
if verbose:
stdout.write(' -- DC Power Flow\n')
## initial state
Va0 = bus[:, VA] * (pi / 180)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
## adjusted for phase shifters and real shunts
Pbus = makeSbus(baseMVA, bus, gen).real - Pbusinj - bus[:, GS] / baseMVA
## "run" the power flow
Va = dcpf(B, Pbus, Va0, ref, pv, pq)
## update data matrices with solution
branch[:, [QF, QT]] = zeros((branch.shape[0], 2))
branch[:, PF] = (Bf * Va + Pfinj) * baseMVA
branch[:, PT] = -branch[:, PF]
bus[:, VM] = ones(bus.shape[0])
bus[:, VA] = Va * (180 / pi)
## update Pg for slack generator (1st gen at ref bus)
## (note: other gens at ref bus are accounted for in Pbus)
## Pg = Pinj + Pload + Gs
## newPg = oldPg + newPinj - oldPinj
refgen = zeros(len(ref), dtype=int)
for k in range(len(ref)):
temp = find(gbus == ref[k])
refgen[k] = on[temp[0]]
gen[refgen, PG] = gen[refgen, PG] + (B[ref, :] * Va - Pbus[ref]) * baseMVA
success = 1
else: ## AC formulation
alg = ppopt['PF_ALG']
if verbose > 0:
if alg == 1:
solver = 'Newton'
elif alg == 2:
solver = 'fast-decoupled, XB'
elif alg == 3:
solver = 'fast-decoupled, BX'
elif alg == 4:
solver = 'Gauss-Seidel'
else:
solver = 'unknown'
print(' -- AC Power Flow (%s)\n' % solver)
## initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = bus[:, VM] * exp(1j * pi/180 * bus[:, VA])
V0[gbus] = gen[on, VG] / abs(V0[gbus]) * V0[gbus]
if qlim:
ref0 = ref ## save index and angle of
Varef0 = bus[ref0, VA] ## original reference bus(es)
limited = [] ## list of indices of gens @ Q lims
fixedQg = zeros(gen.shape[0]) ## Qg of gens at Q limits
repeat = True
while repeat:
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the power flow
alg = ppopt["PF_ALG"]
if alg == 1:
V, success, _ = newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
elif alg == 2 or alg == 3:
Bp, Bpp = makeB(baseMVA, bus, branch, alg)
V, success, _ = fdpf(Ybus, Sbus, V0, Bp, Bpp, ref, pv, pq, ppopt)
elif alg == 4:
V, success, _ = gausspf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
else:
stderr.write('Only Newton''s method, fast-decoupled, and '
'Gauss-Seidel power flow algorithms currently '
'implemented.\n')
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V, ref, pv, pq)
if qlim: ## enforce generator Q limits
## find gens with violated Q constraints
gen_status = gen[:, GEN_STATUS] > 0
qg_max_lim = gen[:, QG] > gen[:, QMAX]
qg_min_lim = gen[:, QG] < gen[:, QMIN]
mx = find( gen_status & qg_max_lim )
mn = find( gen_status & qg_min_lim )
if len(mx) > 0 or len(mn) > 0: ## we have some Q limit violations
# No PV generators
if len(pv) == 0:
if verbose:
if len(mx) > 0:
print('Gen %d [only one left] exceeds upper Q limit : INFEASIBLE PROBLEM\n' % mx + 1)
else:
print('Gen %d [only one left] exceeds lower Q limit : INFEASIBLE PROBLEM\n' % mn + 1)
success = 0
break
## one at a time?
if qlim == 2: ## fix largest violation, ignore the rest
k = argmax(r_[gen[mx, QG] - gen[mx, QMAX],
gen[mn, QMIN] - gen[mn, QG]])
if k > len(mx):
mn = mn[k - len(mx)]
mx = []
else:
mx = mx[k]
mn = []
if verbose and len(mx) > 0:
for i in range(len(mx)):
print('Gen ' + str(mx[i] + 1) + ' at upper Q limit, converting to PQ bus\n')
if verbose and len(mn) > 0:
for i in range(len(mn)):
print('Gen ' + str(mn[i] + 1) + ' at lower Q limit, converting to PQ bus\n')
## save corresponding limit values
fixedQg[mx] = gen[mx, QMAX]
fixedQg[mn] = gen[mn, QMIN]
mx = r_[mx, mn].astype(int)
## convert to PQ bus
gen[mx, QG] = fixedQg[mx] ## set Qg to binding
for i in range(len(mx)): ## [one at a time, since they may be at same bus]
gen[mx[i], GEN_STATUS] = 0 ## temporarily turn off gen,
bi = gen[mx[i], GEN_BUS] ## adjust load accordingly,
bus[bi, [PD, QD]] = (bus[bi, [PD, QD]] - gen[mx[i], [PG, QG]])
if len(ref) > 1 and any(bus[gen[mx, GEN_BUS], BUS_TYPE] == REF):
raise ValueError('Sorry, PYPOWER cannot enforce Q '
'limits for slack buses in systems '
'with multiple slacks.')
bus[gen[mx, GEN_BUS].astype(int), BUS_TYPE] = PQ ## & set bus type to PQ
## update bus index lists of each type of bus
ref_temp = ref
ref, pv, pq = bustypes(bus, gen)
if verbose and ref != ref_temp:
print('Bus %d is new slack bus\n' % ref)
limited = r_[limited, mx].astype(int)
else:
repeat = 0 ## no more generator Q limits violated
else:
repeat = 0 ## don't enforce generator Q limits, once is enough
if qlim and len(limited) > 0:
## restore injections from limited gens [those at Q limits]
gen[limited, QG] = fixedQg[limited] ## restore Qg value,
for i in range(len(limited)): ## [one at a time, since they may be at same bus]
bi = gen[limited[i], GEN_BUS] ## re-adjust load,
bus[bi, [PD, QD]] = bus[bi, [PD, QD]] + gen[limited[i], [PG, QG]]
gen[limited[i], GEN_STATUS] = 1 ## and turn gen back on
if ref != ref0:
## adjust voltage angles to make original ref bus correct
bus[:, VA] = bus[:, VA] - bus[ref0, VA] + Varef0
ppc["et"] = time() - t0
ppc["success"] = success
##----- output results -----
## convert back to original bus numbering & print results
ppc["bus"], ppc["gen"], ppc["branch"] = bus, gen, branch
results = int2ext(ppc)
## zero out result fields of out-of-service gens & branches
if len(results["order"]["gen"]["status"]["off"]) > 0:
results["gen"][ix_(results["order"]["gen"]["status"]["off"], [PG, QG])] = 0
if len(results["order"]["branch"]["status"]["off"]) > 0:
results["branch"][ix_(results["order"]["branch"]["status"]["off"], [PF, QF, PT, QT])] = 0
if fname:
fd = None
try:
fd = open(fname, "a")
except Exception as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(results, fd, ppopt)
fd.close()
else:
printpf(results, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, results)
return results, success
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
def _runpf(casedata=None, init='flat', ac=True, Numba=True, ppopt=None):
"""Runs a power flow.
Similar to runpf() from pypower. See Pypower documentation for more information.
Changes by University of Kassel (Florian Schaefer):
Numba can be used for pf calculations.
Changes in structure (AC as well as DC PF can be calculated)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
## options
verbose = ppopt["VERBOSE"]
## read data
ppci = loadcase(casedata)
# get data for calc
baseMVA, bus, gen, branch = \
ppci["baseMVA"], ppci["bus"], ppci["gen"], ppci["branch"]
## get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
## generator info
on = find(gen[:, GEN_STATUS] > 0) ## which generators are on?
gbus = gen[on, GEN_BUS].astype(int) ## what buses are they at?
##----- run the power flow -----
t0 = time()
if not ac or (ac and init == 'dc'): # DC formulation
if verbose:
print(' -- DC Power Flow\n')
## initial state
Va0 = bus[:, VA] * (pi / 180)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
## adjusted for phase shifters and real shunts
Pbus = makeSbus(baseMVA, bus, gen) - Pbusinj - bus[:, GS] / baseMVA
## "run" the power flow
Va = dcpf(B, Pbus, Va0, ref, pv, pq)
## update data matrices with solution
branch[:, [QF, QT]] = zeros((branch.shape[0], 2))
branch[:, PF] = (Bf * Va + Pfinj) * baseMVA
branch[:, PT] = -branch[:, PF]
bus[:, VM] = ones(bus.shape[0])
bus[:, VA] = Va * (180 / pi)
## update Pg for slack generator (1st gen at ref bus)
## (note: other gens at ref bus are accounted for in Pbus)
## Pg = Pinj + Pload + Gs
## newPg = oldPg + newPinj - oldPinj
refgen = zeros(len(ref), dtype=int)
for k in range(len(ref)):
temp = find(gbus == ref[k])
refgen[k] = on[temp[0]]
gen[refgen,
PG] = gen[refgen, PG] + (B[ref, :] * Va - Pbus[ref]) * baseMVA
success = 1
if ac and init == 'dc':
# get results from DC powerflow for AC powerflow
ppci["bus"], ppci["gen"], ppci["branch"] = bus, gen, branch
if ac: ## AC formulation
# options
qlim = ppopt["ENFORCE_Q_LIMS"] ## enforce Q limits on gens?
## check if numba is available and the corresponding flag
try:
from numba import _version as nb_version
# get Numba Version (in order to use it it must be > 0.25)
nbVersion = float(nb_version.version_version[:4])
if nbVersion < 0.25:
print(
'Warning: Numba version too old -> Upgrade to a version > 0.25. Numba is disabled\n'
)
Numba = False
except ImportError:
# raise UserWarning('Numba cannot be imported. Call runpp() with Numba=False!')
print(
'Warning: Numba cannot be imported. Numba is disabled. Call runpp() with Numba=False!\n'
)
Numba = False
if Numba:
from pandapower.pypower_extensions.makeYbus import makeYbus
else:
from pypower.makeYbus import makeYbus
alg = ppopt['PF_ALG']
if verbose > 0:
if alg == 1:
solver = 'Newton'
elif alg == 2:
solver = 'fast-decoupled, XB'
elif alg == 3:
solver = 'fast-decoupled, BX'
elif alg == 4:
solver = 'Gauss-Seidel'
else:
solver = 'unknown'
print(' -- AC Power Flow (%s)\n' % solver)
## initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = bus[:, VM] * exp(1j * pi / 180 * bus[:, VA])
V0[gbus] = gen[on, VG] / abs(V0[gbus]) * V0[gbus]
if qlim:
ref0 = ref ## save index and angle of
Varef0 = bus[ref0, VA] ## original reference bus(es)
limited = [] ## list of indices of gens @ Q lims
fixedQg = zeros(gen.shape[0]) ## Qg of gens at Q limits
repeat = True
while repeat:
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the power flow
alg = ppopt["PF_ALG"]
if alg == 1:
V, success, _ = newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt,
Numba)
elif alg == 2 or alg == 3:
Bp, Bpp = makeB(baseMVA, bus, branch, alg)
V, success, _ = fdpf(Ybus, Sbus, V0, Bp, Bpp, ref, pv, pq,
ppopt)
elif alg == 4:
V, success, _ = gausspf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
else:
raise ValueError(
'Only Newton'
's method, fast-decoupled, and '
'Gauss-Seidel power flow algorithms currently '
'implemented.\n')
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt,
V, ref, pv, pq)
if qlim: ## enforce generator Q limits
## find gens with violated Q constraints
gen_status = gen[:, GEN_STATUS] > 0
qg_max_lim = gen[:, QG] > gen[:, QMAX]
qg_min_lim = gen[:, QG] < gen[:, QMIN]
mx = find(gen_status & qg_max_lim)
mn = find(gen_status & qg_min_lim)
if len(mx) > 0 or len(
mn) > 0: ## we have some Q limit violations
# No PV generators
if len(pv) == 0:
if verbose:
if len(mx) > 0:
print(
'Gen %d [only one left] exceeds upper Q limit : INFEASIBLE PROBLEM\n'
% mx + 1)
else:
print(
'Gen %d [only one left] exceeds lower Q limit : INFEASIBLE PROBLEM\n'
% mn + 1)
success = 0
break
## one at a time?
if qlim == 2: ## fix largest violation, ignore the rest
k = argmax(r_[gen[mx, QG] - gen[mx, QMAX],
gen[mn, QMIN] - gen[mn, QG]])
if k > len(mx):
mn = mn[k - len(mx)]
mx = []
else:
mx = mx[k]
mn = []
if verbose and len(mx) > 0:
for i in range(len(mx)):
print('Gen ' + str(mx[i] + 1) +
' at upper Q limit, converting to PQ bus\n')
if verbose and len(mn) > 0:
for i in range(len(mn)):
print('Gen ' + str(mn[i] + 1) +
' at lower Q limit, converting to PQ bus\n')
## save corresponding limit values
fixedQg[mx] = gen[mx, QMAX]
fixedQg[mn] = gen[mn, QMIN]
mx = r_[mx, mn].astype(int)
## convert to PQ bus
gen[mx, QG] = fixedQg[mx] ## set Qg to binding
for i in range(
len(mx)
): ## [one at a time, since they may be at same bus]
gen[mx[i],
GEN_STATUS] = 0 ## temporarily turn off gen,
bi = gen[mx[i], GEN_BUS] ## adjust load accordingly,
bus[bi, [PD, QD]] = (bus[bi, [PD, QD]] -
gen[mx[i], [PG, QG]])
if len(ref) > 1 and any(bus[gen[mx, GEN_BUS].astype(int),
BUS_TYPE] == REF):
raise ValueError('Sorry, PYPOWER cannot enforce Q '
'limits for slack buses in systems '
'with multiple slacks.')
bus[gen[mx, GEN_BUS].astype(int),
BUS_TYPE] = PQ ## & set bus type to PQ
## update bus index lists of each type of bus
ref_temp = ref
ref, pv, pq = bustypes(bus, gen)
if verbose and ref != ref_temp:
print('Bus %d is new slack bus\n' % ref)
limited = r_[limited, mx].astype(int)
else:
repeat = 0 ## no more generator Q limits violated
else:
repeat = 0 ## don't enforce generator Q limits, once is enough
if qlim and len(limited) > 0:
## restore injections from limited gens [those at Q limits]
gen[limited, QG] = fixedQg[limited] ## restore Qg value,
for i in range(
len(limited
)): ## [one at a time, since they may be at same bus]
bi = gen[limited[i], GEN_BUS] ## re-adjust load,
bus[bi,
[PD, QD]] = bus[bi, [PD, QD]] + gen[limited[i], [PG, QG]]
gen[limited[i], GEN_STATUS] = 1 ## and turn gen back on
# if ref != ref0:
# ## adjust voltage angles to make original ref bus correct
# bus[:, VA] = bus[:, VA] - bus[ref0, VA] + Varef0
ppci["et"] = time() - t0
ppci["success"] = success
##----- output results -----
ppci["bus"], ppci["gen"], ppci["branch"] = bus, gen, branch
results = ppci
return results, success
