def _run_dc_pf(ppci):
t0 = time()
baseMVA, bus, gen, branch, ref, pv, pq, on, gbus, _, refgen = _get_pf_variables_from_ppci(
ppci)
## initial state
Va0 = bus[:, VA] * (pi / 180.)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(bus, branch)
## updates Bbus matrix
ppci['internal']['Bbus'] = B
## 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[:, VA] = Va * (180. / pi)
## update Pg for slack generators
## (note: other gens at ref bus are accounted for in Pbus)
## Pg = Pinj + Pload + Gs
## newPg = oldPg + newPinj - oldPinj
## ext_grid (refgen) buses
refgenbus = gen[refgen, GEN_BUS].astype(int)
## number of ext_grids (refgen) at those buses
ext_grids_bus = bincount(refgenbus)
gen[refgen,
PG] = real(gen[refgen, PG] + (B[refgenbus, :] * Va - Pbus[refgenbus]) *
baseMVA / ext_grids_bus[refgenbus])
# store results from DC powerflow for AC powerflow
et = time() - t0
success = True
iterations = 1
ppci = _store_results_from_pf_in_ppci(ppci, bus, gen, branch, success,
iterations, et)
return ppci
from pandapower.pypower.idx_bus import VA, GS
from pandapower.pypower.makeBdc import makeBdc
from pandapower.pypower.makeSbus import makeSbus
pp_net._pd2ppc_lookups = {
"bus": np.array([], dtype=int),
"ext_grid": np.array([], dtype=int),
"gen": np.array([], dtype=int),
"branch": np.array([], dtype=int)
}
# convert pandapower net to ppc
ppc, ppci = _pd2ppc(pp_net)
baseMVA, bus, gen, branch, ref, pv, pq, on, gbus, _, refgen = _get_pf_variables_from_ppci(
ppci)
Va0 = bus[:, VA] * (np.pi / 180.)
B, Bf, Pbusinj, Pfinj = makeBdc(bus, branch)
from pandapower.pypower.idx_brch import F_BUS, T_BUS, BR_X, TAP, SHIFT, BR_STATUS
Pbus = makeSbus(baseMVA, bus, gen) - Pbusinj - bus[:, GS] / baseMVA
Pbus_pp_ro = Pbus[pp_vect_converter]
error_p = np.abs(np.real(Sdc_me) - np.real(Pbus_pp_ro))
test_ok = True
#### pandapower DC algo (yet another one)
Va = copy.deepcopy(Va0)
pvpq = np.r_[pv, pq]
pvpq_matrix = B[pvpq.T, :].tocsc()[:, pvpq]
ref_matrix = np.transpose(Pbus[pvpq] - B[pvpq.T, :].tocsc()[:, ref] * Va0[ref])
Va[pvpq] = np.real(scipy.sparse.linalg.spsolve(pvpq_matrix, ref_matrix))
####
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(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
## execute userfcn callbacks for 'formulation' stage
run_userfcn(userfcn, 'formulation', om)
return om
def _aux_test(self, pn_net):
with tempfile.TemporaryDirectory() as path:
case_name = os.path.join(path, "this_case.json")
pp.to_json(pn_net, case_name)
real_init_file = pp.from_json(case_name)
backend = LightSimBackend()
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
backend.load_grid(case_name)
nb_sub = backend.n_sub
pp_net = backend.init_pp_backend._grid
# first i deactivate all slack bus in pp that are connected but not handled in ls
pp_net.ext_grid["in_service"].loc[:] = False
pp_net.ext_grid["in_service"].iloc[0] = True
conv = backend.runpf()
conv_pp = backend.init_pp_backend.runpf()
assert conv_pp, "Error: pandapower do not converge, impossible to perform the necessary checks"
assert conv, "Error: lightsim do not converge"
por_pp, qor_pp, vor_pp, aor_pp = copy.deepcopy(
backend.init_pp_backend.lines_or_info())
pex_pp, qex_pp, vex_pp, aex_pp = copy.deepcopy(
backend.init_pp_backend.lines_ex_info())
# I- Check for divergence and equality of flows"
por_ls, qor_ls, vor_ls, aor_ls = backend.lines_or_info()
max_mis = np.max(np.abs(por_ls - por_pp))
assert max_mis <= self.tol, f"Error: por do not match, maximum absolute error is {max_mis:.5f} MW"
max_mis = np.max(np.abs(qor_ls - qor_pp))
assert max_mis <= self.tol, f"Error: qor do not match, maximum absolute error is {max_mis:.5f} MVAr"
max_mis = np.max(np.abs(vor_ls - vor_pp))
assert max_mis <= self.tol, f"Error: vor do not match, maximum absolute error is {max_mis:.5f} kV"
max_mis = np.max(np.abs(aor_ls - aor_pp))
assert max_mis <= self.tol, f"Error: aor do not match, maximum absolute error is {max_mis:.5f} A"
# "II - Check for possible solver issues"
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
pp.runpp(backend.init_pp_backend._grid, v_debug=True)
v_tmp = backend.init_pp_backend._grid.res_bus[
"vm_pu"].values[:nb_sub] + 0j
v_tmp *= np.exp(
1j * np.pi / 180. *
backend.init_pp_backend._grid.res_bus["va_degree"].values[:nb_sub])
v_tmp = np.concatenate((v_tmp, v_tmp))
backend._grid.ac_pf(v_tmp, 1000, 1e-5)
Y_pp = backend.init_pp_backend._grid._ppc["internal"]["Ybus"]
Sbus = backend.init_pp_backend._grid._ppc["internal"]["Sbus"]
pv_ = backend.init_pp_backend._grid._ppc["internal"]["pv"]
pq_ = backend.init_pp_backend._grid._ppc["internal"]["pq"]
max_iter = 10
tol_this = 1e-8
All_Vms = backend.init_pp_backend._grid._ppc["internal"]["Vm_it"]
AllVas = backend.init_pp_backend._grid._ppc["internal"]["Va_it"]
for index_V in range(All_Vms.shape[1] - 1, -1, -1):
nb_iter = All_Vms.shape[1] - 1
# i check from easiest to hardest, so from the last iteartion of pandapower to the first iteration of pandapower
# take the same V as pandapower
V_init = All_Vms[:, index_V] * (np.cos(AllVas[:, index_V]) +
1j * np.sin(AllVas[:, index_V]))
# V_init *= np.exp(1j * AllVas[:, 0])
V_init_ref = copy.deepcopy(V_init)
solver = ClassSolver()
solver.solve(scipy.sparse.csc_matrix(Y_pp), V_init, Sbus, pv_, pq_,
max_iter, tol_this)
time_for_nr = solver.get_timers()[3]
if TIMER_INFO:
print(
f"\t Info: Time to perform {nb_iter - index_V} NR iterations for a grid with {nb_sub} "
f"buses: {1000. * time_for_nr:.2f}ms")
error_va = np.abs(
solver.get_Va() -
np.angle(backend.init_pp_backend._grid._ppc["internal"]["V"]))
assert np.max(error_va) <= self.tol, f"Error: VA do not match for iteration {index_V}, maximum absolute " \
f"error is {np.max(error_va):.5f} rad"
error_vm = np.abs(
np.abs(solver.get_Vm() - np.abs(
backend.init_pp_backend._grid._ppc["internal"]["V"])))
assert np.max(error_vm) <= self.tol, f"\t Error: VM do not match for iteration {index_V}, maximum absolute " \
f"error is {np.max(error_vm):.5f} pu"
solver.reset()
if TIMER_INFO:
print("")
# 'III - Check the data conversion'
pp_vect_converter = backend.init_pp_backend._grid._pd2ppc_lookups[
"bus"][:nb_sub]
pp_net = backend.init_pp_backend._grid
# 1) Checking Sbus conversion
Sbus_pp = backend.init_pp_backend._grid._ppc["internal"]["Sbus"]
Sbus_pp_right_order = Sbus_pp[pp_vect_converter]
Sbus_me = backend._grid.get_Sbus()
error_p = np.abs(np.real(Sbus_me) - np.real(Sbus_pp_right_order))
assert np.max(error_p) <= self.tol, f"\t Error: P do not match for Sbus, maximum absolute error is " \
f"{np.max(error_p):.5f} MW, \t Error: significative difference for bus " \
f"index (lightsim): {np.where(error_p > self.tol)[0]}"
error_q = np.abs(np.imag(Sbus_me) - np.imag(Sbus_pp_right_order))
assert np.max(error_q) <= self.tol, f"\t Error: Q do not match for Sbus, maximum absolute error is " \
f"{np.max(error_q):.5f} MVAr, \t Error: significative difference for bus " \
f"index (lightsim): {np.where(error_q > self.tol)[0]}"
# 2) Checking Ybus conversion"
Y_me = backend._grid.get_Ybus()
Y_pp = backend.init_pp_backend._grid._ppc["internal"]["Ybus"]
Y_pp_right_order = Y_pp[pp_vect_converter.reshape(nb_sub, 1),
pp_vect_converter.reshape(1, nb_sub)]
error_p = np.abs(np.real(Y_me) - np.real(Y_pp_right_order))
assert np.max(error_p) <= self.tol, f"Error: P do not match for Ybus, maximum absolute error " \
f"is {np.max(error_p):.5f}"
error_q = np.abs(np.imag(Y_me) - np.imag(Y_pp_right_order))
assert np.max(error_q) <= self.tol, f"\t Error: Q do not match for Ybus, maximum absolute error is " \
f"{np.max(error_q):.5f}"
# "IV - Check for the initialization (dc powerflow)"
# 1) check that the results are same for dc lightsim and dc pandapower
Vinit = np.ones(backend.nb_bus_total,
dtype=np.complex_) * pp_net["_options"]["init_vm_pu"]
backend._grid.deactivate_result_computation()
Vdc = backend._grid.dc_pf(Vinit, max_iter, tol_this)
backend._grid.reactivate_result_computation()
Ydc_me = backend._grid.get_Ybus()
Sdc_me = backend._grid.get_Sbus()
assert np.max(np.abs(V_init_ref[pp_vect_converter] - Vdc[:nb_sub])) <= 100.*self.tol,\
f"\t Error for the DC approximation: resulting voltages are different " \
f"{np.max(np.abs(V_init_ref[pp_vect_converter] - Vdc[:nb_sub])):.5f}pu"
if np.max(np.abs(V_init_ref[pp_vect_converter] -
Vdc[:nb_sub])) >= self.tol:
warnings.warn(
"\t Warning: maximum difference after DC approximation is "
"{np.max(np.abs(V_init_ref[pp_vect_converter] - Vdc[:nb_sub])):.5f} which is higher than "
"the tolerance (this is just a warning because we noticed this could happen even if the "
"results match perfectly. Probably some conversion issue with complex number and "
"radian / degree.")
# "2) check that the Sbus vector is same for PP and lightisim in DC"
from pandapower.pd2ppc import _pd2ppc
from pandapower.pf.run_newton_raphson_pf import _get_pf_variables_from_ppci
from pandapower.pypower.idx_brch import F_BUS, T_BUS, BR_X, TAP, SHIFT, BR_STATUS
from pandapower.pypower.idx_bus import VA, GS
from pandapower.pypower.makeBdc import makeBdc
from pandapower.pypower.makeSbus import makeSbus
pp_net._pd2ppc_lookups = {
"bus": np.array([], dtype=int),
"ext_grid": np.array([], dtype=int),
"gen": np.array([], dtype=int),
"branch": np.array([], dtype=int)
}
# convert pandapower net to ppc
ppc, ppci = _pd2ppc(pp_net)
baseMVA, bus, gen, branch, ref, pv, pq, on, gbus, _, refgen = _get_pf_variables_from_ppci(
ppci)
Va0 = bus[:, VA] * (np.pi / 180.)
B, Bf, Pbusinj, Pfinj = makeBdc(bus, branch)
Pbus = makeSbus(baseMVA, bus, gen) - Pbusinj - bus[:, GS] / baseMVA
Pbus_pp_ro = Pbus[pp_vect_converter]
error_p = np.abs(np.real(Sdc_me) - np.real(Pbus_pp_ro))
test_ok = True
#### pandapower DC algo (yet another one)
Va = copy.deepcopy(Va0)
pvpq = np.r_[pv, pq]
pvpq_matrix = B[pvpq.T, :].tocsc()[:, pvpq]
ref_matrix = np.transpose(Pbus[pvpq] -
B[pvpq.T, :].tocsc()[:, ref] * Va0[ref])
Va[pvpq] = np.real(scipy.sparse.linalg.spsolve(pvpq_matrix,
ref_matrix))
####
assert np.max(error_p) <= self.tol, f"\t Error: P do not match for Sbus (dc), maximum absolute error is " \
f"{np.max(error_p):.5f} MW, \nError: significative difference for bus " \
f"index (lightsim): {np.where(error_p > self.tol)[0]}"
error_q = np.abs(np.imag(Sdc_me) - np.imag(Pbus_pp_ro))
assert np.max(error_q) <= self.tol, f"\t Error: Q do not match for Sbus (dc), maximum absolute error is " \
f"{np.max(error_q):.5f} MVAr, \n\t Error: significative difference for " \
f"bus index (lightsim): {np.where(error_q > self.tol)[0]}"
# "3) check that the Ybus matrix is same for PP and lightisim in DC"
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
pp.rundcpp(pp_net)
Ydc_pp = backend.init_pp_backend._grid._ppc["internal"]["Bbus"]
Ydc_pp_right_order = Ydc_pp[pp_vect_converter.reshape(nb_sub, 1),
pp_vect_converter.reshape(1, nb_sub)]
error_p = np.abs(np.real(Ydc_me) - np.real(Ydc_pp_right_order))
assert np.max(error_p) <= self.tol, f"Error: P do not match for Ybus (dc mode), maximum absolute error " \
f"is {np.max(error_p):.5f}"
error_q = np.abs(np.imag(Ydc_me) - np.imag(Ydc_pp_right_order))
assert np.max(error_q) <= self.tol, f"\t Error: Q do not match for Ybus (dc mdoe), maximum absolute error " \
f"is {np.max(error_q):.5f}"
# "3) check that lightsim ac pf init with pp dc pf give same results (than pp)"
Vinit = np.ones(backend.nb_bus_total,
dtype=np.complex_) * pp_net["_options"]["init_vm_pu"]
Vinit[:nb_sub] = V_init_ref[pp_vect_converter]
conv = backend._grid.ac_pf(Vinit, max_iter, tol_this)
assert conv.shape[
0] > 0, "\t Error: the lightsim diverge when initialized with pandapower Vinit_dc"
lpor, lqor, lvor, laor = backend._grid.get_lineor_res()
tpor, tqor, tvor, taor = backend._grid.get_trafohv_res()
tpex, tqex, tvex, taex = backend._grid.get_trafolv_res()
nb_trafo = tpor.shape[0]
nb_powerline = lpor.shape[0]
p_or_me2 = np.concatenate((lpor, tpor))
q_or_me2 = np.concatenate((lqor, tqor))
v_or_me2 = np.concatenate((lvor, tvor))
a_or_me2 = 1000. * np.concatenate((laor, taor))
test_ok = True
# pdb.set_trace()
max_mis = np.max(np.abs(p_or_me2 - por_pp))
assert np.max(
error_q
) <= self.tol, f"\t Error: por do not match, maximum absolute error is {max_mis:.5f} MW"
max_mis = np.max(np.abs(q_or_me2 - qor_pp))
assert np.max(
error_q
) <= self.tol, f"\t Error: qor do not match, maximum absolute error is {max_mis:.5f} MVAr"
max_mis = np.max(np.abs(v_or_me2 - vor_pp))
assert np.max(
error_q
) <= self.tol, f"\t Error: vor do not match, maximum absolute error is {max_mis:.5f} kV"
max_mis = np.max(np.abs(a_or_me2 - aor_pp))
assert np.max(
error_q
) <= self.tol, f"\t Error: aor do not match, maximum absolute error is {max_mis:.5f} A"
# "V - Check trafo proper conversion to r,x, b"
from lightsim2grid_cpp import GridModel, PandaPowerConverter, SolverType
from pandapower.build_branch import _calc_branch_values_from_trafo_df, get_trafo_values
from pandapower.build_branch import _calc_nominal_ratio_from_dataframe, _calc_r_x_y_from_dataframe
from pandapower.build_branch import _calc_tap_from_dataframe, BASE_KV, _calc_r_x_from_dataframe
# my trafo parameters
converter = PandaPowerConverter()
converter.set_sn_mva(pp_net.sn_mva)
converter.set_f_hz(pp_net.f_hz)
tap_neutral = 1.0 * pp_net.trafo["tap_neutral"].values
tap_neutral[~np.isfinite(tap_neutral)] = 0.
if np.any(tap_neutral != 0.):
raise RuntimeError(
"lightsim converter supposes that tap_neutral is 0 for the transformers"
)
tap_step_pct = 1.0 * pp_net.trafo["tap_step_percent"].values
tap_step_pct[~np.isfinite(tap_step_pct)] = 0.
tap_pos = 1.0 * pp_net.trafo["tap_pos"].values
tap_pos[~np.isfinite(tap_pos)] = 0.
shift_ = 1.0 * pp_net.trafo["shift_degree"].values
shift_[~np.isfinite(shift_)] = 0.
is_tap_hv_side = pp_net.trafo["tap_side"].values == "hv"
is_tap_hv_side[~np.isfinite(is_tap_hv_side)] = True
if np.any(pp_net.trafo["tap_phase_shifter"].values):
raise RuntimeError(
"ideal phase shifter are not modeled. Please remove all trafo with "
"pp_net.trafo[\"tap_phase_shifter\"] set to True.")
tap_angles_ = 1.0 * pp_net.trafo["tap_step_degree"].values
tap_angles_[~np.isfinite(tap_angles_)] = 0.
tap_angles_ = np.deg2rad(tap_angles_)
trafo_r, trafo_x, trafo_b = \
converter.get_trafo_param(tap_step_pct,
tap_pos,
tap_angles_, # in radian !
is_tap_hv_side,
pp_net.bus.loc[pp_net.trafo["hv_bus"]]["vn_kv"],
pp_net.bus.loc[pp_net.trafo["lv_bus"]]["vn_kv"],
pp_net.trafo["vk_percent"].values,
pp_net.trafo["vkr_percent"].values,
pp_net.trafo["sn_mva"].values,
pp_net.trafo["pfe_kw"].values,
pp_net.trafo["i0_percent"].values,
)
# pandapower trafo parameters
ppc = copy.deepcopy(pp_net._ppc)
bus_lookup = pp_net["_pd2ppc_lookups"]["bus"]
trafo_df = pp_net["trafo"]
lv_bus = get_trafo_values(trafo_df, "lv_bus")
vn_lv = ppc["bus"][bus_lookup[lv_bus], BASE_KV]
vn_trafo_hv, vn_trafo_lv, shift_pp = _calc_tap_from_dataframe(
pp_net, trafo_df)
ratio = _calc_nominal_ratio_from_dataframe(ppc, trafo_df, vn_trafo_hv,
vn_trafo_lv, bus_lookup)
r_t, x_t, b_t = _calc_r_x_y_from_dataframe(pp_net, trafo_df,
vn_trafo_lv, vn_lv,
pp_net.sn_mva)
# check where there are mismatch if any
val_r_pp = r_t
val_r_me = trafo_r
all_equals_r = np.abs(val_r_pp - val_r_me) <= self.tol
if not np.all(all_equals_r):
test_ok = False
print(
f"\t Error: some trafo resistance are not equal, max error: {np.max(np.abs(val_r_pp - val_r_me)):.5f}"
)
val_x_pp = x_t
val_x_me = trafo_x
all_equals_x = np.abs(val_x_pp - val_x_me) <= self.tol
assert np.all(all_equals_x), f"\t Error: some trafo x are not equal, max error: " \
f"{np.max(np.abs(val_x_pp - val_x_me)):.5f}"
val_ib_pp = np.imag(b_t)
val_ib_me = np.imag(trafo_b)
all_equals_imag_b = np.abs(val_ib_pp - val_ib_me) <= self.tol
assert np.all(all_equals_imag_b), f"\t Error: some trafo (imag) b are not equal, max error: " \
f"{np.max(np.abs(val_ib_pp - val_ib_me)):.5f}"
val_reb_pp = np.real(b_t)
val_reb_me = np.real(trafo_b)
all_equals_real_b = np.abs(val_reb_pp - val_reb_me) <= self.tol
assert np.all(all_equals_real_b), f"\t Error: some trafo (real) b are not equal, max error: " \
f"{np.max(np.abs(val_reb_pp - val_reb_me)):.5f}"