def _run_bfsw_ppc(ppc, ppopt=None):
"""
SPARSE version of distribution power flow solution according to [1]
:References:
[1] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions", IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, July 2003.
:param ppc: matpower-style case data
:return: results (pypower style), success (flag about PF convergence)
"""
ppci = ppc
ppopt = ppoption(ppopt)
baseMVA, bus, gen, branch = \
ppci["baseMVA"], ppci["bus"], ppci["gen"], ppci["branch"]
nbus = bus.shape[0]
# get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
# depth-first-search bus ordering and generating Direct Load Flow matrix DLF = BCBV * BIBC
DLF, ppc_bfsw, buses_ordered_bfsw = bibc_bcbv(ppci)
baseMVA_bfsw, bus_bfsw, gen_bfsw, branch_bfsw = \
ppc_bfsw["baseMVA"], ppc_bfsw["bus"], ppc_bfsw["gen"], ppc_bfsw["branch"]
time_start = time() # starting pf calculation timing
# initialize voltages to flat start and buses with gens to their setpoints
V0 = np.ones(nbus, dtype=complex)
V0[gen[:, GEN_BUS].astype(int)] = gen[:, VG]
Sbus_bfsw = makeSbus(baseMVA_bfsw, bus_bfsw, gen_bfsw)
# update data matrices with solution
Ybus_bfsw, Yf_bfsw, Yt_bfsw = makeYbus(baseMVA_bfsw, bus_bfsw, branch_bfsw)
## get bus index lists of each type of bus
ref_bfsw, pv_bfsw, pq_bfsw = bustypes(bus_bfsw, gen_bfsw)
# #----- run the power flow -----
V_final, success = bfsw(DLF, bus_bfsw, gen_bfsw, branch_bfsw, baseMVA_bfsw, Ybus_bfsw, Sbus_bfsw, V0,
ref_bfsw, pv_bfsw, pq_bfsw, ppopt=ppopt)
V_final = V_final[np.argsort(buses_ordered_bfsw)] # return bus voltages in original bus order
# #----- output results to ppc ------
ppci["et"] = time() - time_start # pf time end
# generate results for original bus ordering
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V_final, ref, pv, pq)
ppci["success"] = success
ppci["bus"], ppci["gen"], ppci["branch"] = bus, gen, branch
return ppci, success
def admmopf_consfcn(self, x=None):
if x is None:
x = self.pb['x0']
nx = len(x)
nb = self.region['nb']
ng = self.region['ng']
iv = self.idx['var']
bus = self.region['bus']
gen = self.region['gen']
Ybus = self.region['Ybus']
ridx = self.idx['rbus']['int']
# idx ranges
iVa = iv['iVa']
iVm = iv['iVm']
iPg = iv['iPg']
iQg = iv['iQg']
# grab Pg and Qg
gen[:, PG] = x[iPg]
gen[:, QG] = x[iQg]
# rebuid Sbus
Sbus = makeSbus(1, bus, gen)
# reconstruct V
Va, Vm = x[iVa], x[iVm]
V = Vm * exp(1j * Va)
# evaluate power flow equations
mis = V * conj(Ybus * V) - Sbus
g = r_[mis.real, mis.imag]
row = ridx + [i + nb for i in ridx]
g = g[row]
# ---- evaluate constraint gradients -------
# compute partials of injected bus powers
dSbus_dVm, dSbus_dVa = dSbus_dV(Ybus, V) # w.r.t. V
neg_Cg = sparse((-ones(ng), (gen[:, GEN_BUS], range(ng))), (nb, ng))
# construct Jacobian of equality constraints (power flow) and transpose it
dg = lil_matrix((2 * nb, nx))
blank = sparse((nb, ng))
dg = vstack([ \
#P mismatch w.r.t Va, Vm, Pg, Qg
hstack([dSbus_dVa.real, dSbus_dVm.real, neg_Cg, blank]),
# Q mismatch w.r.t Va, Vm, Pg, Qg
hstack([dSbus_dVa.imag, dSbus_dVm.imag, blank, neg_Cg])
], "csr")
dg = dg[row, :]
dg = dg.T
h = None
dh = None
return h, g, dh, dg
def _run_dc_pf(ppci):
t0 = time()
baseMVA, bus, gen, branch, ref, pv, pq, on, gbus, _ = _get_pf_variables_from_ppci(
ppci)
ppci["bus"][:, VM] = 1.0
## initial state
Va0 = bus[:, VA] * (pi / 180.)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(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] = real(gen[refgen, PG] + (B[ref, :] * Va - Pbus[ref]) * baseMVA)
success = True
# store results from DC powerflow for AC powerflow
ppci = _store_results_from_pf_in_ppci(ppci, bus, gen, branch)
ppci["et"] = time() - t0
ppci["success"] = success
return ppci
def _run_ac_pf_without_qlims_enforced(ppci, recycle, makeYbus, ppopt):
baseMVA, bus, gen, branch, ref, pv, pq, on, gbus, V0 = _get_pf_variables_from_ppci(
ppci)
ppci, Ybus, Yf, Yt = _get_Y_bus(ppci, recycle, makeYbus, baseMVA, bus,
branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the power flow
V, success = _call_power_flow_function(baseMVA, bus, branch, Ybus, Sbus,
V0, ref, pv, pq, ppopt)
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V, ref,
pv, pq)
return ppci, success, bus, gen, branch
def _run_ac_pf_without_qlims_enforced(ppci, options):
numba, makeYbus = _import_numba_extensions_if_flag_is_true(
options["numba"])
baseMVA, bus, gen, branch, ref, pv, pq, on, gbus, V0 = _get_pf_variables_from_ppci(
ppci)
ppci, Ybus, Yf, Yt = _get_Y_bus(ppci, options, makeYbus, baseMVA, bus,
branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the newton power flow
V, success, _ = newtonpf(Ybus, Sbus, V0, pv, pq, options, numba)
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V, ref,
pv, pq)
return ppci, success, bus, gen, branch
def _run_fbsw_dense(ppc, ppopt=None):
"""
DENSE version of distribution power flow solution according to [1]
:References:
[1] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions", IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, July 2003.
:param ppc: matpower-style case data
:return: results (pypower style), success (flag about PF convergence)
"""
# time_start = time()
# path_search = nx.shortest_path
# ppci = ext2int(ppc)
ppci = ppc
ppopt = ppoption(ppopt)
baseMVA, bus, gen, branch = \
ppci["baseMVA"], ppci["bus"], ppci["gen"], ppci["branch"]
nbus = bus.shape[0]
# get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
root_bus = ref[
0] # reference bus is assumed as root bus for a radial network
DLF, ppc_fbsw, buses_ordered_fbsw = bibc_bcbv_dense(ppci)
baseMVA_fbsw, bus_fbsw, gen_fbsw, branch_fbsw = \
ppc_fbsw["baseMVA"], ppc_fbsw["bus"], ppc_fbsw["gen"], ppc_fbsw["branch"]
time_start = time()
# initialize voltages to flat start and buses with gens to their setpoints
V0 = np.ones(nbus, dtype=complex)
V0[gen[:, GEN_BUS].astype(int)] = gen[:, VG]
Sbus_fbsw = makeSbus(baseMVA_fbsw, bus_fbsw, gen_fbsw)
# update data matrices with solution
Ybus_fbsw, Yf_fbsw, Yt_fbsw = makeYbus(baseMVA_fbsw, bus_fbsw, branch_fbsw)
## get bus index lists of each type of bus
ref_fbsw, pv_fbsw, pq_fbsw = bustypes(bus_fbsw, gen_fbsw)
##----- run the power flow -----
# ###
# LF initialization and calculation
V_final, success = fbsw_dense(DLF,
bus_fbsw,
gen_fbsw,
branch_fbsw,
baseMVA_fbsw,
Ybus_fbsw,
Sbus_fbsw,
V0,
ref_fbsw,
pv_fbsw,
pq_fbsw,
ppopt=ppopt)
V_final = V_final[np.argsort(
buses_ordered_fbsw)] # return bus voltages in original bus order
ppci["et"] = time() - time_start
Sbus = makeSbus(baseMVA, bus, gen)
# update data matrices with solution
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V_final,
ref, pv, pq)
ppci["success"] = success
##----- output results -----
ppci["bus"], ppci["gen"], ppci["branch"] = bus, gen, branch
results = ppci
return results, success
def fbsw(DLF,
bus,
gen,
branch,
baseMVA,
Ybus,
Sbus,
V0,
ref,
pv,
pq,
ppopt=None,
tol_inner=1e-2):
"""
distribution power flow solution according to [1]
:param DLF: direct-Load-Flow matrix which relates bus current injections to voltage drops from the root bus
:param bus: buses martix
:param gen: generators matrix
:param branch: branches matrix
:param baseMVA:
:param Ybus: bus admittance matrix
:param Sbus: vector of power injections
:param V0: initial voltage state vector
:param ref: reference bus index
:param pv: PV buses indices
:param pq: PQ buses indices
:return: power flow result
:References:
[1] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions", IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, July 2003.
"""
## options
if ppopt is None:
ppopt = ppoption()
tol = ppopt['PF_TOL']
max_it = ppopt['PF_MAX_IT_GS'] # maximum iterations from Gauss-Seidel
verbose = ppopt['VERBOSE']
nbus = bus.shape[0]
mask_root = ~(bus[:, BUS_TYPE] == 3) # mask for eliminating root bus
root_bus_i = ref
Vref = V0[ref]
bus_ind_mask_dict = dict(zip(bus[mask_root, BUS_I], range(nbus - 1)))
# detect PV buses
busPV_ind_mask = [bus_ind_mask_dict[pvbus] for pvbus in pv]
genPV_ind = np.where(np.in1d(gen[:, GEN_BUS], pv))[0].flatten()
# compute shunt admittance
# if Psh is the real power consumed by the shunt at V = 1.0 p.u. and Qsh is the reactive power injected by
# the shunt at V = 1.0 p.u. then Psh - j Qsh = V * conj(Ysh * V) = conj(Ysh) = Gs - j Bs,
# vector of shunt admittances
Ysh = (bus[:, GS] + 1j * bus[:, BS]) / baseMVA
# Line charging susceptance BR_B is also added as shunt admittance:
# summation of charging susceptances per each bus
Gch_f = -np.bincount(branch[:, F_BUS].real.astype(int),
weights=branch[:, BR_B].imag / 2,
minlength=nbus)
Bch_f = np.bincount(branch[:, F_BUS].real.astype(int),
weights=branch[:, BR_B].real / 2,
minlength=nbus)
Gch_t = -np.bincount(branch[:, T_BUS].real.astype(int),
weights=branch[:, BR_B].imag / 2,
minlength=nbus)
Bch_t = np.bincount(branch[:, T_BUS].real.astype(int),
weights=branch[:, BR_B].real / 2,
minlength=nbus)
Ysh += (Gch_f + Gch_t) + 1j * (
Bch_f + Bch_t) # adding line charging to shunt impedance vector
V_iter = V0[mask_root].copy() # initial voltage vector without root bus
V = V0.copy()
Iinj = np.conj(
Sbus[mask_root] /
V_iter) - Ysh[mask_root] * V_iter # Initial current injections
n_iter = 0
converged = 0
while not converged and n_iter < max_it:
n_iter_inner = 0
n_iter += 1
deltaV = DLF * Iinj
V_new = np.ones(nbus - 1) * Vref + deltaV
# ##
# inner loop for considering PV buses
inner_loop_converged = False
V_inner = V_new.copy()
success_inner = 1
while not inner_loop_converged and len(pv) > 0:
Vmis = (np.abs(gen[genPV_ind, VG]))**2 - (np.abs(
V_inner[busPV_ind_mask]))**2
dQ = (Vmis /
(2 * DLF[busPV_ind_mask, busPV_ind_mask].A1.imag)).flatten()
gen[genPV_ind, QG] += dQ
if (gen[genPV_ind, QG] < gen[genPV_ind, QMIN]).any():
Qviol_ind = np.argwhere(
(gen[genPV_ind, QG] < gen[genPV_ind, QMIN])).flatten()
gen[genPV_ind[Qviol_ind], QG] = gen[genPV_ind[Qviol_ind], QMIN]
elif (gen[genPV_ind, QG] > gen[genPV_ind, QMAX]).any():
Qviol_ind = np.argwhere(
(gen[genPV_ind, QG] < gen[genPV_ind, QMIN])).flatten()
gen[genPV_ind[Qviol_ind], QG] = gen[genPV_ind[Qviol_ind], QMAX]
Sbus = makeSbus(baseMVA, bus, gen)
Iinj = np.conj(
Sbus[mask_root] / V_inner) - Ysh[mask_root] * V_inner
deltaV = DLF * Iinj
V_inner = np.ones(nbus - 1) * V0[root_bus_i] + deltaV
if n_iter_inner > 20 or np.any(
np.abs(V_inner[busPV_ind_mask]) > 2):
success_inner = 0
break
# raise ConvergenceError("\n\t inner iterations (PV nodes) did not converge!!!")
n_iter_inner += 1
if np.all(np.abs(dQ) < tol_inner
): # inner loop termination criterion
inner_loop_converged = True
V_new = V_inner.copy()
if not success_inner:
if verbose:
sys.stdout.write(
"\nFwd-back sweep power flow did not converge in "
"{0} iterations.\n".format(n_iter))
break
# testing termination criterion -
V = np.insert(V_new, root_bus_i, Vref)
mis = V * np.conj(Ybus * V) - Sbus
F = np.r_[mis[pv].real, mis[pq].real, mis[pq].imag]
# check tolerance
normF = np.linalg.norm(F, np.Inf)
# deltaVmax = np.max(np.abs(V_new - V_iter))
if normF < tol:
converged = 1
if verbose:
sys.stdout.write("\nFwd-back sweep power flow converged in "
"{0} iterations.\n".format(n_iter))
V_iter = V_new.copy() # update iterating complex voltage vector
# updating injected currents
Iinj = np.conj(Sbus[mask_root] / V_iter) - Ysh[mask_root] * V_iter
return V, converged
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 runcpf(basecasedata=None,
targetcasedata=None,
ppopt=None,
fname='',
solvedcase=''):
# default arguments
if basecasedata is None:
basecasedata = join(dirname(__file__), 'case9')
if targetcasedata is None:
targetcasedata = join(dirname(__file__), 'case9target')
ppopt = ppoption(ppopt)
# options
verbose = ppopt["VERBOSE"]
step = ppopt["CPF_STEP"]
parameterization = ppopt["CPF_PARAMETERIZATION"]
adapt_step = ppopt["CPF_ADAPT_STEP"]
cb_args = ppopt["CPF_USER_CALLBACK_ARGS"]
# set up callbacks
callback_names = ["cpf_default_callback"]
if len(ppopt["CPF_USER_CALLBACK"]) > 0:
if isinstance(ppopt["CPF_USER_CALLBACK"], list):
callback_names = r_[callback_names, ppopt["CPF_USER_CALLBACK"]]
else:
callback_names.append(ppopt["CPF_USER_CALLBACK"])
callbacks = []
for callback_name in callback_names:
callbacks.append(getattr(cpf_callbacks, callback_name))
# read base case data
ppcbase = loadcase(basecasedata)
nb = ppcbase["bus"].shape[0]
# add zero columns to branch for flows if needed
if ppcbase["branch"].shape[1] < QT:
ppcbase["branch"] = c_[ppcbase["branch"],
zeros((ppcbase["branch"].shape[0],
QT - ppcbase["branch"].shape[1] + 1))]
# convert to internal indexing
ppcbase = ext2int(ppcbase)
baseMVAb, busb, genb, branchb = \
ppcbase["baseMVA"], ppcbase["bus"], ppcbase["gen"], ppcbase["branch"]
# get bus index lists of each type of bus
ref, pv, pq = bustypes(busb, genb)
# generator info
onb = find(genb[:, GEN_STATUS] > 0) # which generators are on?
gbusb = genb[onb, GEN_BUS].astype(int) # what buses are they at?
# read target case data
ppctarget = loadcase(targetcasedata)
# add zero columns to branch for flows if needed
if ppctarget["branch"].shape[1] < QT:
ppctarget["branch"] = c_[ppctarget["branch"],
zeros(
(ppctarget["branch"].shape[0],
QT - ppctarget["branch"].shape[1] + 1))]
# convert to internal indexing
ppctarget = ext2int(ppctarget)
baseMVAt, bust, gent, brancht = \
ppctarget["baseMVA"], ppctarget["bus"], ppctarget["gen"], ppctarget["branch"]
# get bus index lists of each type of bus
# ref, pv, pq = bustypes(bust, gent)
# generator info
ont = find(gent[:, GEN_STATUS] > 0) # which generators are on?
gbust = gent[ont, 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"]))
stdout.write(' -- AC Continuation Power Flow\n')
# initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = busb[:, VM] * exp(1j * pi / 180 * busb[:, VA])
vcb = ones(V0.shape) # create mask of voltage-controlled buses
vcb[pq] = 0 # exclude PQ buses
k = find(vcb[gbusb]) # in-service gens at v-c buses
V0[gbusb[k]] = genb[onb[k], VG] / abs(V0[gbusb[k]]) * V0[gbusb[k]]
# build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVAb, busb, branchb)
# compute base case complex bus power injections (generation - load)
Sbusb = makeSbus(baseMVAb, busb, genb)
# compute target case complex bus power injections (generation - load)
Sbust = makeSbus(baseMVAt, bust, gent)
# scheduled transfer
Sxfr = Sbust - Sbusb
# Run the base case power flow solution
if verbose > 2:
ppopt_pf = ppoption(ppopt, VERBOSE=max(0, verbose - 1))
else:
ppopt_pf = ppoption(ppopt, VERBOSE=max(0, verbose - 2))
lam = 0
V, success, iterations = newtonpf(Ybus, Sbusb, V0, ref, pv, pq, ppopt_pf)
if verbose > 2:
print('step %3d : lambda = %6.3f\n' % (0, 0))
elif verbose > 1:
print('step %3d : lambda = %6.3f, %2d Newton steps\n',
(0, 0, iterations))
lamprv = lam # lam at previous step
Vprv = V # V at previous step
continuation = 1
cont_steps = 0
# input args for callbacks
cb_data = {
"ppc_base": ppcbase,
"ppc_target": ppctarget,
"Sxfr": Sxfr,
"Ybus": Ybus,
"Yf": Yf,
"Yt": Yt,
"ref": ref,
"pv": pv,
"pq": pq,
"ppopt": ppopt
}
cb_state = {}
# invoke callbacks
for k in range(len(callbacks)):
cb_state, _ = callbacks[k](cont_steps, V, lam, V, lam, cb_data,
cb_state, cb_args)
if linalg.norm(Sxfr) == 0:
if verbose:
print(
'base case and target case have identical load and generation\n'
)
continuation = 0
V0 = V
lam0 = lam
# tangent predictor z = [dx;dlam]
z = zeros(2 * len(V) + 1)
z[-1] = 1.0
while continuation:
cont_steps = cont_steps + 1
# prediction for next step
V0, lam0, z = cpf_predictor(V, lam, Ybus, Sxfr, pv, pq, step, z, Vprv,
lamprv, parameterization)
# save previous voltage, lambda before updating
Vprv = V
lamprv = lam
# correction
V, success, i, lam = cpf_corrector(Ybus, Sbusb, V0, ref, pv, pq, lam0,
Sxfr, Vprv, lamprv, z, step,
parameterization, ppopt_pf)
if not success:
continuation = 0
if verbose:
print(
'step %3d : lambda = %6.3f, corrector did not converge in %d iterations\n'
% (cont_steps, lam, i))
break
if verbose > 2:
print('step %3d : lambda = %6.3f\n' % (cont_steps, lam))
elif verbose > 1:
print('step %3d : lambda = %6.3f, %2d corrector Newton steps\n' %
(cont_steps, lam, i))
# invoke callbacks
for k in range(len(callbacks)):
cb_state, _ = callbacks[k](cont_steps, V, lam, V0, lam0, cb_data,
cb_state, cb_args)
if isinstance(ppopt["CPF_STOP_AT"], str):
if ppopt["CPF_STOP_AT"].upper() == "FULL":
if abs(lam) < 1e-8: # traced the full continuation curve
if verbose:
print(
'\nTraced full continuation curve in %d continuation steps\n'
% cont_steps)
continuation = 0
elif lam < lamprv and lam - step < 0: # next step will overshoot
step = lam # modify step-size
parameterization = 1 # change to natural parameterization
adapt_step = False # disable step-adaptivity
else: # == 'NOSE'
if lam < lamprv: # reached the nose point
if verbose:
print(
'\nReached steady state loading limit in %d continuation steps\n'
% cont_steps)
continuation = 0
else:
if lam < lamprv:
if verbose:
print(
'\nReached steady state loading limit in %d continuation steps\n'
% cont_steps)
continuation = 0
elif abs(ppopt["CPF_STOP_AT"] -
lam) < 1e-8: # reached desired lambda
if verbose:
print(
'\nReached desired lambda %3.2f in %d continuation steps\n'
% (ppopt["CPF_STOP_AT"], cont_steps))
continuation = 0
# will reach desired lambda in next step
elif lam + step > ppopt["CPF_STOP_AT"]:
step = ppopt["CPF_STOP_AT"] - lam # modify step-size
parameterization = 1 # change to natural parameterization
adapt_step = False # disable step-adaptivity
if adapt_step and continuation:
pvpq = r_[pv, pq]
# Adapt stepsize
cpf_error = linalg.norm(
r_[angle(V[pq]), abs(V[pvpq]), lam] -
r_[angle(V0[pq]), abs(V0[pvpq]), lam0], inf)
if cpf_error < ppopt["CPF_ERROR_TOL"]:
# Increase stepsize
step = step * ppopt["CPF_ERROR_TOL"] / cpf_error
if step > ppopt["CPF_STEP_MAX"]:
step = ppopt["CPF_STEP_MAX"]
else:
# decrese stepsize
step = step * ppopt["CPF_ERROR_TOL"] / cpf_error
if step < ppopt["CPF_STEP_MIN"]:
step = ppopt["CPF_STEP_MIN"]
# invoke callbacks
if success:
cpf_results = {}
for k in range(len(callbacks)):
cb_state, cpf_results = callbacks[k](cont_steps,
V,
lam,
V0,
lam0,
cb_data,
cb_state,
cb_args,
results=cpf_results,
is_final=True)
else:
cpf_results["iterations"] = i
# update bus and gen matrices to reflect the loading and generation
# at the noise point
bust[:, PD] = busb[:, PD] + lam * (bust[:, PD] - busb[:, PD])
bust[:, QD] = busb[:, QD] + lam * (bust[:, QD] - busb[:, QD])
gent[:, PG] = genb[:, PG] + lam * (gent[:, PG] - genb[:, PG])
# update data matrices with solution
bust, gent, brancht = pfsoln(baseMVAt, bust, gent, brancht, Ybus, Yf, Yt,
V, ref, pv, pq)
ppctarget["et"] = time() - t0
ppctarget["success"] = success
# ----- output results -----
# convert back to original bus numbering & print results
ppctarget["bus"], ppctarget["gen"], ppctarget[
"branch"] = bust, gent, brancht
if success:
n = cpf_results["iterations"] + 1
cpf_results["V_p"] = i2e_data(ppctarget, cpf_results["V_p"],
full((nb, n), nan), "bus", 0)
cpf_results["V_c"] = i2e_data(ppctarget, cpf_results["V_c"],
full((nb, n), nan), "bus", 0)
results = int2ext(ppctarget)
results["cpf"] = cpf_results
# 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 admmopf_consfcn(self, x=None):
if x is None:
x = self.pb['x0']
nx = len(x)
nb = self.region['nb']
ng = self.region['ng']
iv = self.idx['var']
bus = self.region['bus']
gen = self.region['gen']
branch = self.region['branch']
Ybus = self.region['Ybus']
Yf = self.region['Yf']
Yt = self.region['Yt']
baseMVA = self.region['baseMVA']
ridx = self.idx['rbus']['int']
# idx ranges
iVa = iv['iVa']
iVm = iv['iVm']
iPg = iv['iPg']
iQg = iv['iQg']
# grab Pg and Qg
gen[:, PG] = x[iPg]
gen[:, QG] = x[iQg]
# rebuid Sbus
Sbus = makeSbus(1, bus, gen)
# reconstruct V
Va, Vm = x[iVa], x[iVm]
V = Vm * exp(1j * Va)
# evaluate power flow equations
mis = V * conj(Ybus * V) - Sbus
g = r_[mis.real, mis.imag]
row = ridx + [i + nb for i in ridx]
g = g[row]
il = find((branch[:, RATE_A] != 0) & (branch[:, RATE_A] < 1e10))
nl2 = len(il)
Ybus2, Yf2, Yt2 = makeYbus(baseMVA, bus, branch)
Yf = Yf2[il, :]
Yt = Yt2[il, :]
if nl2 > 0:
flow_max = (branch[il, RATE_A] / baseMVA)**2
flow_max[flow_max == 0] = Inf
## compute branch power flows
## complex power injected at "from" bus (p.u.)
Sf = V[branch[il, F_BUS].astype(int)] * conj(Yf * V)
## complex power injected at "to" bus (p.u.)
St = V[branch[il, T_BUS].astype(int)] * conj(Yt * V)
h = r_[Sf.real**2 - flow_max, ## branch P limits (from bus)
St.real**2 - flow_max] ## branch P limits (to bus)
else:
h = array([])
# ---- evaluate constraint gradients -------
# compute partials of injected bus powers
dSbus_dVm, dSbus_dVa = dSbus_dV(Ybus, V) # w.r.t. V
neg_Cg = sparse((-ones(ng), (gen[:, GEN_BUS], range(ng))), (nb, ng))
# construct Jacobian of equality constraints (power flow) and transpose it
dg = lil_matrix((2 * nb, nx))
blank = sparse((nb, ng))
dg = vstack([ \
#P mismatch w.r.t Va, Vm, Pg, Qg
hstack([dSbus_dVa.real, dSbus_dVm.real, neg_Cg, blank]),
# Q mismatch w.r.t Va, Vm, Pg, Qg
hstack([dSbus_dVa.imag, dSbus_dVm.imag, blank, neg_Cg])
], "csr")
dg = dg[row, :]
dg = dg.T
if nl2 > 0:
## compute partials of Flows w.r.t. V ## power
dFf_dVa, dFf_dVm, dFt_dVa, dFt_dVm, Ff, Ft = \
dSbr_dV(branch[il, :], Yf, Yt, V)
dFf_dVa = dFf_dVa.real
dFf_dVm = dFf_dVm.real
dFt_dVa = dFt_dVa.real
dFt_dVm = dFt_dVm.real
Ff = Ff.real
Ft = Ft.real
## squared magnitude of flow (of complex power or current, or real power)
df_dVa, df_dVm, dt_dVa, dt_dVm = \
dAbr_dV(dFf_dVa, dFf_dVm, dFt_dVa, dFt_dVm, Ff, Ft)
## construct Jacobian of inequality constraints (branch limits)
## and transpose it.
dh = lil_matrix((2 * nl2, nx))
dh[:, r_[iVa, iVm].T] = vstack(
[
hstack([df_dVa, df_dVm]), ## "from" flow limit
hstack([dt_dVa, dt_dVm]) ## "to" flow limit
],
"csr")
dh = dh.T
else:
dh = None
h = array([])
dh = None
return h, g, dh, dg
def _bfswpf(DLF, bus, gen, branch, baseMVA, Ybus, Sbus, Ibus, V0, ref, pv, pq,
buses_ordered_bfs_nets, enforce_q_lims, tolerance_kva,
max_iteration, **kwargs):
"""
distribution power flow solution according to [1]
:param DLF: direct-Load-Flow matrix which relates bus current injections to voltage drops from the root bus
:param bus: buses martix
:param gen: generators matrix
:param branch: branches matrix
:param baseMVA:
:param Ybus: bus admittance matrix
:param Sbus: vector of power injections
:param V0: initial voltage state vector
:param ref: reference bus index
:param pv: PV buses indices
:param pq: PQ buses indices
:param buses_ordered_bfs_nets: buses ordered according to breadth-first search
:return: power flow result
"""
# setting options
tolerance_mva = tolerance_kva * 1e-3
max_it = max_iteration # maximum iterations
verbose = kwargs["VERBOSE"] # verbose is set in run._runpppf() #
# tolerance for the inner loop for PV nodes
if 'tolerance_kva_pv' in kwargs:
tol_mva_inner = kwargs['tolerance_kva_pv'] * 1e-3
else:
tol_mva_inner = 1.e-2
if 'max_iter_pv' in kwargs:
max_iter_pv = kwargs['max_iter_pv']
else:
max_iter_pv = 20
nobus = bus.shape[0]
ngen = gen.shape[0]
mask_root = ~(bus[:, BUS_TYPE] == 3) # mask for eliminating root bus
norefs = len(ref)
Ysh = _makeYsh_bfsw(bus, branch, baseMVA)
# detect generators on PV buses which have status ON
gen_pv = np.in1d(gen[:, GEN_BUS], pv) & (gen[:, GEN_STATUS] > 0)
qg_lim = np.zeros(
ngen, dtype=bool) # initialize generators which violated Q limits
Iinj = np.conj(Sbus / V0) - Ysh * V0 + Ibus # Initial current injections
# initiate reference voltage vector
V_ref = np.ones(nobus, dtype=complex)
for neti, buses_ordered_bfs in enumerate(buses_ordered_bfs_nets):
V_ref[buses_ordered_bfs] *= V0[ref[neti]]
V = V0.copy()
n_iter = 0
converged = 0
if verbose:
print(' -- AC Power Flow (Backward/Forward sweep)\n')
while not converged and n_iter < max_it:
n_iter_inner = 0
n_iter += 1
deltaV = DLF * Iinj[mask_root]
V[mask_root] = V_ref[mask_root] + deltaV
# ##
# inner loop for considering PV buses
# TODO improve PV buses inner loop
inner_loop_converged = False
while not inner_loop_converged and len(pv) > 0:
pvi = pv - norefs # internal PV buses indices, assuming reference node is always 0
Vmis = (np.abs(gen[gen_pv, VG]))**2 - (np.abs(V[pv]))**2
# TODO improve getting values from sparse DLF matrix - DLF[pvi, pvi] is unefficient
dQ = (Vmis / (2 * DLF[pvi, pvi].A1.imag)).flatten()
gen[gen_pv, QG] += dQ
if enforce_q_lims: # check Q violation limits
## find gens with violated Q constraints
qg_max_lim = (gen[:, QG] > gen[:, QMAX]) & gen_pv
qg_min_lim = (gen[:, QG] < gen[:, QMIN]) & gen_pv
if qg_min_lim.any():
gen[qg_min_lim, QG] = gen[qg_min_lim, QMIN]
bus[gen[qg_min_lim, GEN_BUS].astype(int),
BUS_TYPE] = 1 # convert to PQ bus
if qg_max_lim.any():
gen[qg_max_lim, QG] = gen[qg_max_lim, QMAX]
bus[gen[qg_max_lim, GEN_BUS].astype(int),
BUS_TYPE] = 1 # convert to PQ bus
# TODO: correct: once all the PV buses are converted to PQ buses, conversion back to PV is not possible
qg_lim_new = qg_min_lim | qg_max_lim
if qg_lim_new.any():
pq2pv = (qg_lim != qg_lim_new) & qg_lim
# convert PQ to PV bus
if pq2pv.any():
bus[gen[qg_max_lim, GEN_BUS].astype(int),
BUS_TYPE] = 2 # convert to PV bus
qg_lim = qg_lim_new.copy()
ref, pv, pq = bustypes(bus, gen)
# avoid calling makeSbus, update only Sbus for pv nodes
Sbus = makeSbus(baseMVA, bus, gen)
Iinj = np.conj(Sbus / V) - Ysh * V + Ibus
deltaV = DLF * Iinj[mask_root]
V[mask_root] = V_ref[mask_root] + deltaV
if n_iter_inner > max_iter_pv:
raise LoadflowNotConverged(
" FBSW Power Flow did not converge - inner iterations for PV nodes "
"reached maximum value of {0}!".format(max_iter_pv))
n_iter_inner += 1
if np.all(np.abs(dQ) < tol_mva_inner
): # inner loop termination criterion
inner_loop_converged = True
# testing termination criterion -
F = _evaluate_Fx(Ybus, V, Sbus, pv, pq, Ibus=Ibus)
# check tolerance
converged = _check_for_convergence(F, tolerance_mva)
if converged and verbose:
print("\nFwd-back sweep power flow converged in "
"{0} iterations.\n".format(n_iter))
# updating injected currents
Iinj = np.conj(Sbus / V) - Ysh * V + Ibus
return V, converged
def _bfswpf(DLF, bus, gen, branch, baseMVA, Ybus, bus_ord_bfsw, Sbus, V0, ref,
pv, pq, enforce_q_lims, tolerance_kva, max_iteration, **kwargs):
"""
distribution power flow solution according to [1]
:param DLF: direct-Load-Flow matrix which relates bus current injections to voltage drops from the root bus
:param bus: buses martix
:param gen: generators matrix
:param branch: branches matrix
:param baseMVA:
:param Ybus: bus admittance matrix
:param Sbus: vector of power injections
:param V0: initial voltage state vector
:param ref: reference bus index
:param pv: PV buses indices
:param pq: PQ buses indices
:return: power flow result
:References:
[1] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions", IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, July 2003.
"""
# setting options
tolerance_mva = tolerance_kva * 1e-3
max_it = max_iteration # maximum iterations
verbose = kwargs["VERBOSE"] # verbose is set in run._runpppf() #
# tolerance for the inner loop for PV nodes
if 'tolerance_kva_pv' in kwargs:
tol_mva_inner = kwargs['tolerance_kva_pv'] * 1e-3
else:
tol_mva_inner = 1.e-2
if 'max_iter_pv' in kwargs:
max_iter_pv = kwargs['max_iter_pv']
else:
max_iter_pv = 20
nbus = bus.shape[0]
ngen = gen.shape[0]
mask_root = ~(bus[:, BUS_TYPE] == 3) # mask for eliminating root bus
root_bus_i = ref
Vref = V0[ref]
# bus_ind_mask_dict = dict(zip(bus[mask_root, BUS_I], range(nbus - 1)))
bus_ord_bfsw_inv = np.argsort(bus_ord_bfsw)
# compute shunt admittance
# if Psh is the real power consumed by the shunt at V = 1.0 p.u. and Qsh is the reactive power injected by
# the shunt at V = 1.0 p.u. then Psh - j Qsh = V * conj(Ysh * V) = conj(Ysh) = Gs - j Bs,
# vector of shunt admittances
Ysh = (bus[:, GS] + 1j * bus[:, BS]) / baseMVA
# Line charging susceptance BR_B is also added as shunt admittance:
# summation of charging susceptances per each bus
stat = branch[:, BR_STATUS] ## ones at in-service branches
Ys = stat / (branch[:, BR_R] + 1j * branch[:, BR_X])
ysh = (-branch[:, BR_B].imag + 1j * (branch[:, BR_B].real)) / 2
tap = branch[:, TAP] # * np.exp(1j * np.pi / 180 * branch[:, SHIFT])
ysh_f = Ys * (1 - tap) / (tap * np.conj(tap)) + ysh / (tap * np.conj(tap))
ysh_t = Ys * (tap - 1) / tap + ysh
Gch = (np.bincount(
branch[:, F_BUS].real.astype(int), weights=ysh_f.real, minlength=nbus)
+ np.bincount(branch[:, T_BUS].real.astype(int),
weights=ysh_t.real,
minlength=nbus))
Bch = (np.bincount(
branch[:, F_BUS].real.astype(int), weights=ysh_f.imag, minlength=nbus)
+ np.bincount(branch[:, T_BUS].real.astype(int),
weights=ysh_t.imag,
minlength=nbus))
Ysh += Gch + 1j * Bch
# Gch_f = - np.bincount(branch[:, F_BUS].real.astype(int), weights=branch[:, BR_B].imag / 2, minlength=nbus)
# Bch_f = np.bincount(branch[:, F_BUS].real.astype(int), weights=branch[:, BR_B].real / 2, minlength=nbus)
# Gch_t = - np.bincount(branch[:, T_BUS].real.astype(int), weights=branch[:, BR_B].imag / 2, minlength=nbus)
# Bch_t = np.bincount(branch[:, T_BUS].real.astype(int), weights=branch[:, BR_B].real / 2, minlength=nbus)
# Ysh += (Gch_f + Gch_t) + 1j * (Bch_f + Bch_t) # adding line charging to shunt impedance vector
# detect generators on PV buses which have status ON
gen_pv = np.in1d(gen[:, GEN_BUS], pv) & (gen[:, GEN_STATUS] > 0)
qg_lim = np.zeros(
ngen, dtype=bool) #initialize generators which violated Q limits
V_iter = V0[mask_root].copy() # initial voltage vector without root bus
V = V0.copy()
Iinj = np.conj(Sbus / V) - Ysh * V # Initial current injections
n_iter = 0
converged = 0
if verbose:
print(' -- AC Power Flow Backward/Forward sweep\n')
while not converged and n_iter < max_it:
n_iter_inner = 0
n_iter += 1
deltaV = DLF * Iinj[mask_root]
V_iter = np.ones(nbus - 1) * Vref + deltaV
# ##
# inner loop for considering PV buses
inner_loop_converged = False
while not inner_loop_converged and len(pv) > 0:
pvi = pv - 1 # internal PV buses indices, assuming reference node is always 0
Vmis = (np.abs(gen[gen_pv, VG]))**2 - (np.abs(V_iter[pvi]))**2
dQ = (Vmis / (2 * DLF[pvi, pvi].A1.imag)).flatten()
gen[gen_pv, QG] += dQ
if enforce_q_lims: #check Q violation limits
## find gens with violated Q constraints
qg_max_lim = (gen[:, QG] > gen[:, QMAX]) & gen_pv
qg_min_lim = (gen[:, QG] < gen[:, QMIN]) & gen_pv
if qg_min_lim.any():
gen[qg_min_lim, QG] = gen[qg_min_lim, QMIN]
bus[gen[qg_min_lim, GEN_BUS].astype(int),
BUS_TYPE] = 1 # convert to PQ bus
if qg_max_lim.any():
gen[qg_max_lim, QG] = gen[qg_max_lim, QMAX]
bus[gen[qg_max_lim, GEN_BUS].astype(int),
BUS_TYPE] = 1 # convert to PQ bus
# TODO: correct: once all the PV buses are converted to PQ buses, conversion back to PV is not possible
qg_lim_new = qg_min_lim | qg_max_lim
if qg_lim_new.any():
pq2pv = (qg_lim != qg_lim_new) & qg_lim
# convert PQ to PV bus
if pq2pv.any():
bus[gen[qg_max_lim, GEN_BUS].astype(int),
BUS_TYPE] = 2 # convert to PV bus
qg_lim = qg_lim_new.copy()
ref, pv, pq = bustypes(bus, gen)
Sbus = makeSbus(baseMVA, bus, gen)
V = np.insert(V_iter, root_bus_i, Vref)
Iinj = np.conj(Sbus / V) - Ysh * V
deltaV = DLF * Iinj[mask_root]
V_iter = np.ones(nbus - 1) * V0[root_bus_i] + deltaV
if n_iter_inner > max_iter_pv:
raise LoadflowNotConverged(
" FBSW Power Flow did not converge - inner iterations for PV nodes "
"reached maximum value of {0}!".format(max_iter_pv))
n_iter_inner += 1
if np.all(np.abs(dQ) < tol_mva_inner
): # inner loop termination criterion
inner_loop_converged = True
# testing termination criterion -
V = np.insert(V_iter, root_bus_i, Vref)
mis = V * np.conj(Ybus * V) - Sbus
F = np.r_[mis[pv].real, mis[pq].real, mis[pq].imag]
# check tolerance
normF = np.linalg.norm(F, np.Inf)
if normF < tolerance_mva:
converged = 1
if verbose:
print("\nFwd-back sweep power flow converged in "
"{0} iterations.\n".format(n_iter))
elif n_iter == max_it:
raise LoadflowNotConverged(
" FBSW Power Flow did not converge - "
"reached maximum iterations = {0}!".format(max_it))
# updating injected currents
Iinj = np.conj(Sbus / V) - Ysh * V
return V, converged
def opf_consfcn(x, om, Ybus, Yf, Yt, ppopt, il=None, *args):
"""Evaluates nonlinear constraints and their Jacobian for OPF.
Constraint evaluation function for AC optimal power flow, suitable
for use with L{pips}. Computes constraint vectors and their gradients.
@param x: optimization vector
@param om: OPF model object
@param Ybus: bus admittance matrix
@param Yf: admittance matrix for "from" end of constrained branches
@param Yt: admittance matrix for "to" end of constrained branches
@param ppopt: PYPOWER options vector
@param il: (optional) vector of branch indices corresponding to
branches with flow limits (all others are assumed to be
unconstrained). The default is C{range(nl)} (all branches).
C{Yf} and C{Yt} contain only the rows corresponding to C{il}.
@return: C{h} - vector of inequality constraint values (flow limits)
limit^2 - flow^2, where the flow can be apparent power real power or
current, depending on value of C{OPF_FLOW_LIM} in C{ppopt} (only for
constrained lines). C{g} - vector of equality constraint values (power
balances). C{dh} - (optional) inequality constraint gradients, column
j is gradient of h(j). C{dg} - (optional) equality constraint gradients.
@see: L{opf_costfcn}, L{opf_hessfcn}
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Ray Zimmerman (PSERC Cornell)
"""
##----- initialize -----
## unpack data
ppc = om.get_ppc()
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
vv, _, _, _ = om.get_idx()
## problem dimensions
nb = bus.shape[0] ## number of buses
nl = branch.shape[0] ## number of branches
ng = gen.shape[0] ## number of dispatchable injections
nxyz = len(x) ## total number of control vars of all types
## set default constrained lines
if il is None:
il = arange(nl) ## all lines have limits by default
nl2 = len(il) ## number of constrained lines
## grab Pg & Qg
Pg = x[vv["i1"]["Pg"]:vv["iN"]["Pg"]] ## active generation in p.u.
Qg = x[vv["i1"]["Qg"]:vv["iN"]["Qg"]] ## reactive generation in p.u.
## put Pg & Qg back in gen
gen[:, PG] = Pg * baseMVA ## active generation in MW
gen[:, QG] = Qg * baseMVA ## reactive generation in MVAr
## rebuild Sbus
Sbus = makeSbus(baseMVA, bus, gen) ## net injected power in p.u.
## ----- evaluate constraints -----
## reconstruct V
Va = x[vv["i1"]["Va"]:vv["iN"]["Va"]]
Vm = x[vv["i1"]["Vm"]:vv["iN"]["Vm"]]
V = Vm * exp(1j * Va)
## evaluate power flow equations
mis = V * conj(Ybus * V) - Sbus
##----- evaluate constraint function values -----
## first, the equality constraints (power flow)
g = r_[mis.real, ## active power mismatch for all buses
mis.imag] ## reactive power mismatch for all buses
## then, the inequality constraints (branch flow limits)
if nl2 > 0:
flow_max = (branch[il, RATE_A] / baseMVA)**2
flow_max[flow_max == 0] = Inf
if ppopt['OPF_FLOW_LIM'] == 2: ## current magnitude limit, |I|
If = Yf * V
It = Yt * V
h = r_[If * conj(If) - flow_max, ## branch I limits (from bus)
It * conj(It) - flow_max].real ## branch I limits (to bus)
else:
## compute branch power flows
## complex power injected at "from" bus (p.u.)
Sf = V[branch[il, F_BUS].astype(int)] * conj(Yf * V)
## complex power injected at "to" bus (p.u.)
St = V[branch[il, T_BUS].astype(int)] * conj(Yt * V)
if ppopt['OPF_FLOW_LIM'] == 1: ## active power limit, P (Pan Wei)
h = r_[Sf.real**2 - flow_max, ## branch P limits (from bus)
St.real**2 - flow_max] ## branch P limits (to bus)
else: ## apparent power limit, |S|
h = r_[Sf * conj(Sf) - flow_max, ## branch S limits (from bus)
St * conj(St) -
flow_max].real ## branch S limits (to bus)
else:
h = zeros((0, 1))
##----- evaluate partials of constraints -----
## index ranges
iVa = arange(vv["i1"]["Va"], vv["iN"]["Va"])
iVm = arange(vv["i1"]["Vm"], vv["iN"]["Vm"])
iPg = arange(vv["i1"]["Pg"], vv["iN"]["Pg"])
iQg = arange(vv["i1"]["Qg"], vv["iN"]["Qg"])
iVaVmPgQg = r_[iVa, iVm, iPg, iQg].T
## compute partials of injected bus powers
dSbus_dVm, dSbus_dVa = dSbus_dV(Ybus, V) ## w.r.t. V
## Pbus w.r.t. Pg, Qbus w.r.t. Qg
neg_Cg = sparse((-ones(ng), (gen[:, GEN_BUS], list(range(ng)))), (nb, ng))
## construct Jacobian of equality constraints (power flow) and transpose it
dg = lil_matrix((2 * nb, nxyz))
blank = sparse((nb, ng))
dg[:, iVaVmPgQg] = vstack(
[
## P mismatch w.r.t Va, Vm, Pg, Qg
hstack([dSbus_dVa.real, dSbus_dVm.real, neg_Cg, blank]),
## Q mismatch w.r.t Va, Vm, Pg, Qg
hstack([dSbus_dVa.imag, dSbus_dVm.imag, blank, neg_Cg])
],
"csr")
dg = dg.T
if nl2 > 0:
## compute partials of Flows w.r.t. V
if ppopt['OPF_FLOW_LIM'] == 2: ## current
dFf_dVa, dFf_dVm, dFt_dVa, dFt_dVm, Ff, Ft = \
dIbr_dV(branch[il, :], Yf, Yt, V)
else: ## power
dFf_dVa, dFf_dVm, dFt_dVa, dFt_dVm, Ff, Ft = \
dSbr_dV(branch[il, :], Yf, Yt, V)
if ppopt['OPF_FLOW_LIM'] == 1: ## real part of flow (active power)
dFf_dVa = dFf_dVa.real
dFf_dVm = dFf_dVm.real
dFt_dVa = dFt_dVa.real
dFt_dVm = dFt_dVm.real
Ff = Ff.real
Ft = Ft.real
## squared magnitude of flow (of complex power or current, or real power)
df_dVa, df_dVm, dt_dVa, dt_dVm = \
dAbr_dV(dFf_dVa, dFf_dVm, dFt_dVa, dFt_dVm, Ff, Ft)
## construct Jacobian of inequality constraints (branch limits)
## and transpose it.
dh = lil_matrix((2 * nl2, nxyz))
dh[:, r_[iVa, iVm].T] = vstack(
[
hstack([df_dVa, df_dVm]), ## "from" flow limit
hstack([dt_dVa, dt_dVm]) ## "to" flow limit
],
"csr")
dh = dh.T
else:
dh = None
return h, g, dh, dg
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
def opf_consfcn(x, om, Ybus, Yf, Yt, ppopt, il=None, *args):
"""Evaluates nonlinear constraints and their Jacobian for OPF.
Constraint evaluation function for AC optimal power flow, suitable
for use with L{pips}. Computes constraint vectors and their gradients.
@param x: optimization vector
@param om: OPF model object
@param Ybus: bus admittance matrix
@param Yf: admittance matrix for "from" end of constrained branches
@param Yt: admittance matrix for "to" end of constrained branches
@param ppopt: PYPOWER options vector
@param il: (optional) vector of branch indices corresponding to
branches with flow limits (all others are assumed to be
unconstrained). The default is C{range(nl)} (all branches).
C{Yf} and C{Yt} contain only the rows corresponding to C{il}.
@return: C{h} - vector of inequality constraint values (flow limits)
limit^2 - flow^2, where the flow can be apparent power real power or
current, depending on value of C{OPF_FLOW_LIM} in C{ppopt} (only for
constrained lines). C{g} - vector of equality constraint values (power
balances). C{dh} - (optional) inequality constraint gradients, column
j is gradient of h(j). C{dg} - (optional) equality constraint gradients.
@see: L{opf_costfcn}, L{opf_hessfcn}
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
##----- initialize -----
## unpack data
ppc = om.get_ppc()
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
vv, _, _, _ = om.get_idx()
## problem dimensions
nb = bus.shape[0] ## number of buses
nl = branch.shape[0] ## number of branches
ng = gen.shape[0] ## number of dispatchable injections
nxyz = len(x) ## total number of control vars of all types
## set default constrained lines
if il is None:
il = arange(nl) ## all lines have limits by default
nl2 = len(il) ## number of constrained lines
## grab Pg & Qg
Pg = x[vv["i1"]["Pg"]:vv["iN"]["Pg"]] ## active generation in p.u.
Qg = x[vv["i1"]["Qg"]:vv["iN"]["Qg"]] ## reactive generation in p.u.
## put Pg & Qg back in gen
gen[:, PG] = Pg * baseMVA ## active generation in MW
gen[:, QG] = Qg * baseMVA ## reactive generation in MVAr
## rebuild Sbus
Sbus = makeSbus(baseMVA, bus, gen) ## net injected power in p.u.
## ----- evaluate constraints -----
## reconstruct V
Va = x[vv["i1"]["Va"]:vv["iN"]["Va"]]
Vm = x[vv["i1"]["Vm"]:vv["iN"]["Vm"]]
V = Vm * exp(1j * Va)
## evaluate power flow equations
mis = V * conj(Ybus * V) - Sbus
##----- evaluate constraint function values -----
## first, the equality constraints (power flow)
g = r_[ mis.real, ## active power mismatch for all buses
mis.imag ] ## reactive power mismatch for all buses
## then, the inequality constraints (branch flow limits)
if nl2 > 0:
flow_max = (branch[il, RATE_A] / baseMVA)**2
flow_max[flow_max == 0] = Inf
if ppopt['OPF_FLOW_LIM'] == 2: ## current magnitude limit, |I|
If = Yf * V
It = Yt * V
h = r_[ If * conj(If) - flow_max, ## branch I limits (from bus)
It * conj(It) - flow_max ].real ## branch I limits (to bus)
else:
## compute branch power flows
## complex power injected at "from" bus (p.u.)
Sf = V[ branch[il, F_BUS].astype(int) ] * conj(Yf * V)
## complex power injected at "to" bus (p.u.)
St = V[ branch[il, T_BUS].astype(int) ] * conj(Yt * V)
if ppopt['OPF_FLOW_LIM'] == 1: ## active power limit, P (Pan Wei)
h = r_[ Sf.real**2 - flow_max, ## branch P limits (from bus)
St.real**2 - flow_max ] ## branch P limits (to bus)
else: ## apparent power limit, |S|
h = r_[ Sf * conj(Sf) - flow_max, ## branch S limits (from bus)
St * conj(St) - flow_max ].real ## branch S limits (to bus)
else:
h = zeros((0,1))
##----- evaluate partials of constraints -----
## index ranges
iVa = arange(vv["i1"]["Va"], vv["iN"]["Va"])
iVm = arange(vv["i1"]["Vm"], vv["iN"]["Vm"])
iPg = arange(vv["i1"]["Pg"], vv["iN"]["Pg"])
iQg = arange(vv["i1"]["Qg"], vv["iN"]["Qg"])
iVaVmPgQg = r_[iVa, iVm, iPg, iQg].T
## compute partials of injected bus powers
dSbus_dVm, dSbus_dVa = dSbus_dV(Ybus, V) ## w.r.t. V
## Pbus w.r.t. Pg, Qbus w.r.t. Qg
neg_Cg = sparse((-ones(ng), (gen[:, GEN_BUS], range(ng))), (nb, ng))
## construct Jacobian of equality constraints (power flow) and transpose it
dg = lil_matrix((2 * nb, nxyz))
blank = sparse((nb, ng))
dg[:, iVaVmPgQg] = vstack([
## P mismatch w.r.t Va, Vm, Pg, Qg
hstack([dSbus_dVa.real, dSbus_dVm.real, neg_Cg, blank]),
## Q mismatch w.r.t Va, Vm, Pg, Qg
hstack([dSbus_dVa.imag, dSbus_dVm.imag, blank, neg_Cg])
], "csr")
dg = dg.T
if nl2 > 0:
## compute partials of Flows w.r.t. V
if ppopt['OPF_FLOW_LIM'] == 2: ## current
dFf_dVa, dFf_dVm, dFt_dVa, dFt_dVm, Ff, Ft = \
dIbr_dV(branch[il, :], Yf, Yt, V)
else: ## power
dFf_dVa, dFf_dVm, dFt_dVa, dFt_dVm, Ff, Ft = \
dSbr_dV(branch[il, :], Yf, Yt, V)
if ppopt['OPF_FLOW_LIM'] == 1: ## real part of flow (active power)
dFf_dVa = dFf_dVa.real
dFf_dVm = dFf_dVm.real
dFt_dVa = dFt_dVa.real
dFt_dVm = dFt_dVm.real
Ff = Ff.real
Ft = Ft.real
## squared magnitude of flow (of complex power or current, or real power)
df_dVa, df_dVm, dt_dVa, dt_dVm = \
dAbr_dV(dFf_dVa, dFf_dVm, dFt_dVa, dFt_dVm, Ff, Ft)
## construct Jacobian of inequality constraints (branch limits)
## and transpose it.
dh = lil_matrix((2 * nl2, nxyz))
dh[:, r_[iVa, iVm].T] = vstack([
hstack([df_dVa, df_dVm]), ## "from" flow limit
hstack([dt_dVa, dt_dVm]) ## "to" flow limit
], "csr")
dh = dh.T
else:
dh = None
return h, g, dh, dg
def _run_bfswpf(ppc, options, **kwargs):
"""
SPARSE version of distribution power flow solution according to [1]
:References:
[1] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions", IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, July 2003.
:param ppc: matpower-style case data
:return: results (pypower style), success (flag about PF convergence)
"""
time_start = time() # starting pf calculation timing
tap_shift = ppc['branch'][:, SHIFT].real
enforce_q_lims, tolerance_kva, max_iteration, calculate_voltage_angles, numba = _get_options(
options)
numba, makeYbus = _import_numba_extensions_if_flag_is_true(numba)
ppci = ppc
baseMVA, bus, gen, branch = \
ppci["baseMVA"], ppci["bus"], ppci["gen"], ppci["branch"]
nbus = bus.shape[0]
# generate results for original bus ordering
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
# get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
# creating networkx graph from list of branches
G = nx.Graph()
G.add_edges_from((int(fb), int(tb), {
"shift": float(shift)
}) for fb, tb, shift in list(
zip(branch[:, F_BUS].real, branch[:, T_BUS].real, tap_shift)))
if not nx.is_connected(G):
Graphs = list(nx.connected_component_subgraphs(G))
else:
Graphs = [G]
V_final = np.zeros(nbus, dtype=complex)
V_tapshifts = np.zeros(nbus)
for subi, G in enumerate(Graphs):
ppci_sub = _cut_ppc(ppci, G.nodes())
nbus_sub = len(G)
# depth-first-search bus ordering and generating Direct Load Flow matrix DLF = BCBV * BIBC
DLF, ppc_bfsw, buses_ordered_bfsw = _bibc_bcbv(ppci_sub, G)
ppc_bfsw['branch'][:, SHIFT] = 0
baseMVA_bfsw, bus_bfsw, gen_bfsw, branch_bfsw, ref_bfsw, pv_bfsw, pq_bfsw,\
on, gbus, V0 = _get_pf_variables_from_ppci(ppc_bfsw)
Sbus_bfsw = makeSbus(baseMVA_bfsw, bus_bfsw, gen_bfsw)
Ybus_bfsw, Yf_bfsw, Yt_bfsw = makeYbus(baseMVA_bfsw, bus_bfsw,
branch_bfsw)
# #----- run the power flow -----
V_res, success = _bfswpf(DLF, bus_bfsw, gen_bfsw, branch_bfsw, baseMVA,
Ybus_bfsw, buses_ordered_bfsw, Sbus_bfsw, V0,
ref_bfsw, pv_bfsw, pq_bfsw, enforce_q_lims,
tolerance_kva, max_iteration, **kwargs)
V_final[
buses_ordered_bfsw] = V_res # return bus voltages in original bus order
# TODO: find the better way to consider transformer phase shift and remove this workaround
if calculate_voltage_angles:
predecessors = nx.bfs_predecessors(G, ref[subi])
branches = list(zip(branch[:, F_BUS].real, branch[:, T_BUS].real))
for bus_start in predecessors.iterkeys():
bus_pred = bus_start
bus_next = bus_start
while predecessors.get(bus_next) is not None:
bus_next = predecessors.get(bus_pred)
shift_angle = G.get_edge_data(bus_pred, bus_next)['shift']
if (bus_pred, bus_next) in branches:
V_tapshifts[bus_start] += shift_angle
else:
V_tapshifts[bus_start] -= shift_angle
bus_pred = bus_next
V_final *= np.exp(1j * np.pi / 180 * V_tapshifts)
# #----- output results to ppc ------
ppci["et"] = time() - time_start # pf time end
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V_final,
ref, pv, pq)
ppci["success"] = success
ppci["bus"], ppci["gen"], ppci["branch"] = bus, gen, branch
return ppci, success
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 runpf_fast(Ybus,
Yf,
Yt,
ref,
pv,
pq,
on,
ppc,
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
## options
## read data
#ppc = loadcase(casedata)
## convert to internal indexing
ppc["branch"][:, [0, 1]] -= 1
ppc["bus"][:, 0] -= 1
ppc["gen"][:, 0] -= 1
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
#print(gen[:, GEN_STATUS])
#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()
V0 = bus[:, VM] * exp(1j * 0.017453292519943295 * bus[:, VA])
V0[gbus] = gen[on, VG] / abs(V0[gbus]) * V0[gbus]
## 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
V, success, i = newtonpf_fast(Ybus, Sbus, V0, ref, pv, pq, ppopt)
## update data matrices with solution
#bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V, ref, pv, pq)
bus[:, VM] = abs(V)
bus[:, VA] = angle(V) * 180 / pi
#UNTIL HERE
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
ppc["branch"][:, [0, 1]] += 1
ppc["bus"][:, 0] += 1
ppc["gen"][:, 0] += 1
return ppc, success, i
def _run_bfswpf(ppci, options, **kwargs):
"""
SPARSE version of distribution power flow solution according to [1]
:References:
[1] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions",
IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, July 2003.
:param ppci: matpower-style case data
:param options: pf options
:return: results (pypower style), success (flag about PF convergence)
"""
time_start = time() # starting pf calculation timing
baseMVA, bus, gen, branch, ref, pv, pq, \
on, gbus, V0 = _get_pf_variables_from_ppci(ppci)
enforce_q_lims, tolerance_kva, max_iteration, calculate_voltage_angles, numba = _get_options(
options)
numba, makeYbus = _import_numba_extensions_if_flag_is_true(numba)
nobus = bus.shape[0]
nobranch = branch.shape[0]
# generate Sbus
Sbus = makeSbus(baseMVA, bus, gen)
# generate results for original bus ordering
# Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
ppci, Ybus, Yf, Yt = _get_Y_bus(ppci, options, makeYbus, baseMVA, bus,
branch)
# creating network graph from list of branches
bus_from = branch[:, F_BUS].real.astype(int)
bus_to = branch[:, T_BUS].real.astype(int)
G = csr_matrix((np.ones(nobranch), (bus_from, bus_to)),
shape=(nobus, nobus))
# create spanning trees using breadth-first-search
# TODO add efficiency warning if a network is heavy-meshed
G_trees = []
for refbus in ref:
G_trees.append(csgraph.breadth_first_tree(G, refbus, directed=False))
# depth-first-search bus ordering and generating Direct Load Flow matrix DLF = BCBV * BIBC
ppci, DLF, buses_ordered_bfs_nets = _get_bibc_bcbv(
ppci, options, bus, branch, G)
# if there are trafos with phase-shift calculate Ybus without phase-shift for bfswpf
any_trafo_shift = (branch[:, SHIFT] != 0).any()
if any_trafo_shift:
branch_noshift = branch.copy()
branch_noshift[:, SHIFT] = 0
Ybus_noshift, Yf_noshift, _ = makeYbus(baseMVA, bus, branch_noshift)
else:
Ybus_noshift = Ybus.copy()
# get current injections for constant-current loads
Ibus = _get_ibus(ppci)
# #----- run the power flow -----
V_final, success = _bfswpf(DLF, bus, gen, branch, baseMVA, Ybus_noshift,
Sbus, Ibus, V0, ref, pv, pq,
buses_ordered_bfs_nets, enforce_q_lims,
tolerance_kva, max_iteration, **kwargs)
# if phase-shifting trafos are present adjust final state vector angles accordingly
if calculate_voltage_angles and any_trafo_shift:
brch_shift_mask = branch[:, SHIFT] != 0
trafos_shift = dict(
list(
zip(
list(
zip(branch[brch_shift_mask, F_BUS].real.astype(int),
branch[brch_shift_mask, T_BUS].real.astype(int))),
branch[brch_shift_mask, SHIFT].real)))
for trafo_ind, shift_degree in iteritems(trafos_shift):
neti = 0
# if multiple reference nodes, find in which network trafo is located
if len(ref) > 0:
for refbusi in range(len(ref)):
if trafo_ind[0] in buses_ordered_bfs_nets[refbusi]:
neti = refbusi
break
G_tree = G_trees[neti]
buses_ordered_bfs = buses_ordered_bfs_nets[neti]
if (np.argwhere(buses_ordered_bfs == trafo_ind[0]) <
np.argwhere(buses_ordered_bfs == trafo_ind[1])):
lv_bus = trafo_ind[1]
shift_degree *= -1
else:
lv_bus = trafo_ind[0]
buses_shifted_from_root = csgraph.breadth_first_order(
G_tree, lv_bus, directed=True, return_predecessors=False)
V_final[buses_shifted_from_root] *= np.exp(1j * np.pi / 180 *
shift_degree)
# #----- output results to ppc ------
ppci["et"] = time() - time_start # pf time end
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V_final,
ref)
# bus, gen, branch = pfsoln_bfsw(baseMVA, bus, gen, branch, V_final, ref, pv, pq, BIBC, ysh_f,ysh_t,Iinj, Sbus)
ppci["success"] = success
ppci["bus"], ppci["gen"], ppci["branch"] = bus, gen, branch
return ppci, success
def _bfswpf(DLF, bus, gen, branch, baseMVA, Ybus, Sbus, V0, ref, pv, pq,
buses_ordered_bfs_nets, enforce_q_lims, tolerance_kva,
max_iteration, **kwargs):
"""
distribution power flow solution according to [1]
:param DLF: direct-Load-Flow matrix which relates bus current injections to voltage drops from the root bus
:param bus: buses martix
:param gen: generators matrix
:param branch: branches matrix
:param baseMVA:
:param Ybus: bus admittance matrix
:param Sbus: vector of power injections
:param V0: initial voltage state vector
:param ref: reference bus index
:param pv: PV buses indices
:param pq: PQ buses indices
:return: power flow result
"""
# setting options
tolerance_mva = tolerance_kva * 1e-3
max_it = max_iteration # maximum iterations
verbose = kwargs["VERBOSE"] # verbose is set in run._runpppf() #
# tolerance for the inner loop for PV nodes
if 'tolerance_kva_pv' in kwargs:
tol_mva_inner = kwargs['tolerance_kva_pv'] * 1e-3
else:
tol_mva_inner = 1.e-2
if 'max_iter_pv' in kwargs:
max_iter_pv = kwargs['max_iter_pv']
else:
max_iter_pv = 20
nobus = bus.shape[0]
ngen = gen.shape[0]
mask_root = ~(bus[:, BUS_TYPE] == 3) # mask for eliminating root bus
norefs = len(ref)
# compute shunt admittance
# if Psh is the real power consumed by the shunt at V = 1.0 p.u. and Qsh is the reactive power injected by
# the shunt at V = 1.0 p.u. then Psh - j Qsh = V * conj(Ysh * V) = conj(Ysh) = Gs - j Bs,
# vector of shunt admittances
Ysh = (bus[:, GS] + 1j * bus[:, BS]) / baseMVA
# Line charging susceptance BR_B is also added as shunt admittance:
# summation of charging susceptances per each bus
stat = branch[:, BR_STATUS] ## ones at in-service branches
Ys = stat / (branch[:, BR_R] + 1j * branch[:, BR_X])
ysh = (-branch[:, BR_B].imag + 1j * (branch[:, BR_B].real)) / 2
tap = branch[:, TAP] # * np.exp(1j * np.pi / 180 * branch[:, SHIFT])
ysh_f = Ys * (1 - tap) / (tap * np.conj(tap)) + ysh / (tap * np.conj(tap))
ysh_t = Ys * (tap - 1) / tap + ysh
Gch = (np.bincount(
branch[:, F_BUS].real.astype(int), weights=ysh_f.real, minlength=nobus)
+ np.bincount(branch[:, T_BUS].real.astype(int),
weights=ysh_t.real,
minlength=nobus))
Bch = (np.bincount(
branch[:, F_BUS].real.astype(int), weights=ysh_f.imag, minlength=nobus)
+ np.bincount(branch[:, T_BUS].real.astype(int),
weights=ysh_t.imag,
minlength=nobus))
Ysh += Gch + 1j * Bch
# detect generators on PV buses which have status ON
gen_pv = np.in1d(gen[:, GEN_BUS], pv) & (gen[:, GEN_STATUS] > 0)
qg_lim = np.zeros(
ngen, dtype=bool) #initialize generators which violated Q limits
Iinj = np.conj(Sbus / V0) - Ysh * V0 # Initial current injections
# initiate reference voltage vector
V_ref = np.ones(nobus, dtype=complex)
for neti, buses_ordered_bfs in enumerate(buses_ordered_bfs_nets):
V_ref[buses_ordered_bfs] *= V0[ref[neti]]
V = V0.copy()
n_iter = 0
converged = 0
if verbose:
print(' -- AC Power Flow (Backward/Forward sweep)\n')
while not converged and n_iter < max_it:
n_iter_inner = 0
n_iter += 1
deltaV = DLF * Iinj[mask_root]
V[mask_root] = V_ref[mask_root] + deltaV
# ##
# inner loop for considering PV buses
# TODO improve PV buses inner loop
inner_loop_converged = False
while not inner_loop_converged and len(pv) > 0:
pvi = pv - norefs # internal PV buses indices, assuming reference node is always 0
Vmis = (np.abs(gen[gen_pv, VG]))**2 - (np.abs(V[pv]))**2
# TODO improve getting values from sparse DLF matrix - DLF[pvi, pvi] is unefficient
dQ = (Vmis / (2 * DLF[pvi, pvi].A1.imag)).flatten()
gen[gen_pv, QG] += dQ
if enforce_q_lims: #check Q violation limits
## find gens with violated Q constraints
qg_max_lim = (gen[:, QG] > gen[:, QMAX]) & gen_pv
qg_min_lim = (gen[:, QG] < gen[:, QMIN]) & gen_pv
if qg_min_lim.any():
gen[qg_min_lim, QG] = gen[qg_min_lim, QMIN]
bus[gen[qg_min_lim, GEN_BUS].astype(int),
BUS_TYPE] = 1 # convert to PQ bus
if qg_max_lim.any():
gen[qg_max_lim, QG] = gen[qg_max_lim, QMAX]
bus[gen[qg_max_lim, GEN_BUS].astype(int),
BUS_TYPE] = 1 # convert to PQ bus
# TODO: correct: once all the PV buses are converted to PQ buses, conversion back to PV is not possible
qg_lim_new = qg_min_lim | qg_max_lim
if qg_lim_new.any():
pq2pv = (qg_lim != qg_lim_new) & qg_lim
# convert PQ to PV bus
if pq2pv.any():
bus[gen[qg_max_lim, GEN_BUS].astype(int),
BUS_TYPE] = 2 # convert to PV bus
qg_lim = qg_lim_new.copy()
ref, pv, pq = bustypes(bus, gen)
# avoid calling makeSbus, update only Sbus for pv nodes
Sbus = makeSbus(baseMVA, bus, gen)
Iinj = np.conj(Sbus / V) - Ysh * V
deltaV = DLF * Iinj[mask_root]
V[mask_root] = V_ref[mask_root] + deltaV
if n_iter_inner > max_iter_pv:
raise LoadflowNotConverged(
" FBSW Power Flow did not converge - inner iterations for PV nodes "
"reached maximum value of {0}!".format(max_iter_pv))
n_iter_inner += 1
if np.all(np.abs(dQ) < tol_mva_inner
): # inner loop termination criterion
inner_loop_converged = True
# testing termination criterion -
mis = V * np.conj(Ybus * V) - Sbus
F = np.r_[mis[pv].real, mis[pq].real, mis[pq].imag]
# check tolerance
normF = np.linalg.norm(F, np.Inf)
if normF < tolerance_mva:
converged = 1
if verbose:
print("\nFwd-back sweep power flow converged in "
"{0} iterations.\n".format(n_iter))
elif n_iter == max_it:
raise LoadflowNotConverged(
" FBSW Power Flow did not converge - "
"reached maximum iterations = {0}!".format(max_it))
# updating injected currents
Iinj = np.conj(Sbus / V) - Ysh * V
return V, converged