def t_makeLODF(quiet=False):
"""Tests for C{makeLODF}.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
ntests = 31
t_begin(ntests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_auction_case')
verbose = 0 #not quiet
## load case
ppc = loadcase(casefile)
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
r = rundcopf(ppc, ppopt)
baseMVA, bus, gen, branch = r['baseMVA'], r['bus'], r['gen'], r['branch']
_, bus, gen, branch = ext2int1(bus, gen, branch)
## compute injections and flows
F0 = branch[:, PF]
## create some PTDF matrices
H = makePTDF(baseMVA, bus, branch, 0)
## create some PTDF matrices
try:
LODF = makeLODF(branch, H)
except ZeroDivisionError:
pass
## take out non-essential lines one-by-one and see what happens
ppc['bus'] = bus
ppc['gen'] = gen
branch0 = branch
outages = r_[arange(12),
arange(13, 15),
arange(16, 18), [19],
arange(26, 33),
arange(34, 41)]
for k in outages:
ppc['branch'] = branch0.copy()
ppc['branch'][k, BR_STATUS] = 0
r, _ = rundcpf(ppc, ppopt)
baseMVA, bus, gen, branch = \
r['baseMVA'], r['bus'], r['gen'], r['branch']
F = branch[:, PF]
t_is(LODF[:, k], (F - F0) / F0[k], 6, 'LODF[:, %d]' % k)
t_end()
def t_makeLODF(quiet=False):
"""Tests for C{makeLODF}.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
ntests = 31
t_begin(ntests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_auction_case')
verbose = 0#not quiet
## load case
ppc = loadcase(casefile)
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
r = rundcopf(ppc, ppopt)
baseMVA, bus, gen, branch = r['baseMVA'], r['bus'], r['gen'], r['branch']
_, bus, gen, branch = ext2int1(bus, gen, branch)
## compute injections and flows
F0 = branch[:, PF]
## create some PTDF matrices
H = makePTDF(baseMVA, bus, branch, 0)
## create some PTDF matrices
try:
LODF = makeLODF(branch, H)
except ZeroDivisionError:
pass
## take out non-essential lines one-by-one and see what happens
ppc['bus'] = bus
ppc['gen'] = gen
branch0 = branch
outages = r_[arange(12), arange(13, 15), arange(16, 18),
[19], arange(26, 33), arange(34, 41)]
for k in outages:
ppc['branch'] = branch0.copy()
ppc['branch'][k, BR_STATUS] = 0
r, _ = rundcpf(ppc, ppopt)
baseMVA, bus, gen, branch = \
r['baseMVA'], r['bus'], r['gen'], r['branch']
F = branch[:, PF]
t_is(LODF[:, k], (F - F0) / F0[k], 6, 'LODF[:, %d]' % k)
t_end()
def solve_dcpf(case, hour=None,
results=None,
ppopt=None,
fname='./runpf.log'):
ppopt = ppoption(ppopt)
fname = os.path.abspath(fname)
baseMVA = case.baseMVA
bus = case.bus.copy(deep=True)
branch = case.branch.copy(deep=True)
gen = case.gen.copy(deep=True)
gencost = case.gen.copy(deep=True)
if hour is not None:
logger.debug("Setting bus load based on case.load")
bus['PD'] = case.load.loc[hour]
bus = bus.fillna(0)
if hour is not None and results is not None:
logger.debug("Setting GEN_STATUS and PG")
gen['GEN_STATUS'] = results.unit_commitment.loc[hour].astype(int)
gen['PG'] = results.power_generated.loc[hour]
value = [i + 1 for i in range(0, len(bus.index))]
bus_name = bus.index
bus.index = value
bus.index = bus.index.astype(int)
branch['F_BUS'] = branch['F_BUS'].apply(lambda x: value[bus_name.get_loc(x)]).astype(int)
branch['T_BUS'] = branch['T_BUS'].apply(lambda x: value[bus_name.get_loc(x)]).astype(int)
gen['GEN_BUS'] = gen['GEN_BUS'].apply(lambda x: value[bus_name.get_loc(x)]).astype(int)
bus = np.array(bus.reset_index())
branch = np.array(branch)
gen = np.array(gen)
gencost = np.array(gencost)
casedata = {'baseMVA': baseMVA,
'gencost': gencost,
'gen': gen,
'branch': branch,
'bus': bus}
return rundcpf(casedata, ppopt=ppopt, fname=fname)
def t_pf(quiet=False):
"""Tests for power flow solvers.
@author: Ray Zimmerman (PSERC Cornell)
"""
t_begin(33, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_pf')
verbose = not quiet
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
## get solved AC power flow case from MAT-file
## defines bus_soln, gen_soln, branch_soln
soln9_pf = loadmat(join(tdir, 'soln9_pf.mat'), struct_as_record=False)
bus_soln = soln9_pf['bus_soln']
gen_soln = soln9_pf['gen_soln']
branch_soln = soln9_pf['branch_soln']
## run Newton PF
t = 'Newton PF : ';
ppopt = ppoption(ppopt, PF_ALG=1)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run fast-decoupled PF (XB version)
t = 'Fast Decoupled (XB) PF : ';
ppopt = ppoption(ppopt, PF_ALG=2)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run fast-decoupled PF (BX version)
t = 'Fast Decoupled (BX) PF : ';
ppopt = ppoption(ppopt, PF_ALG=3)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run Gauss-Seidel PF
t = 'Gauss-Seidel PF : ';
ppopt = ppoption(ppopt, PF_ALG=4)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 5, [t, 'bus'])
t_is(gen, gen_soln, 5, [t, 'gen'])
t_is(branch, branch_soln, 5, [t, 'branch'])
## get solved AC power flow case from MAT-file
## defines bus_soln, gen_soln, branch_soln
soln9_dcpf = loadmat(join(tdir, 'soln9_dcpf.mat'), struct_as_record=False)
bus_soln = soln9_dcpf['bus_soln']
gen_soln = soln9_dcpf['gen_soln']
branch_soln = soln9_dcpf['branch_soln']
## run DC PF
t = 'DC PF : '
results, success = rundcpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## check Qg distribution, when Qmin = Qmax
t = 'check Qg : '
ppopt = ppoption(ppopt, PF_ALG=1, VERBOSE=0)
ppc = loadcase(casefile)
ppc['gen'][0, [QMIN, QMAX]] = [20, 20]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0, QG], 24.07, 2, [t, 'single gen, Qmin = Qmax'])
ppc['gen'] = r_[array([ ppc['gen'][0, :] ]), ppc['gen']]
ppc['gen'][0, [QMIN, QMAX]] = [10, 10]
ppc['gen'][1, [QMIN, QMAX]] = [ 0, 50]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [10, 14.07], 2, [t, '2 gens, Qmin = Qmax for one'])
ppc['gen'][0, [QMIN, QMAX]] = [10, 10]
ppc['gen'][1, [QMIN, QMAX]] = [-50, -50]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [12.03, 12.03], 2, [t, '2 gens, Qmin = Qmax for both'])
ppc['gen'][0, [QMIN, QMAX]] = [0, 50]
ppc['gen'][1, [QMIN, QMAX]] = [0, 100]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [8.02, 16.05], 2, [t, '2 gens, proportional'])
ppc['gen'][0, [QMIN, QMAX]] = [-50, 0]
ppc['gen'][1, [QMIN, QMAX]] = [50, 150]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [-50 + 8.02, 50 + 16.05], 2, [t, '2 gens, proportional'])
## network with islands
t = 'network w/islands : DC PF : '
ppc0 = loadcase(casefile)
ppc0['gen'][0, PG] = 60
ppc0['gen'][0, [PMIN, PMAX, QMIN, QMAX, PG, QG]] = \
ppc0['gen'][0, [PMIN, PMAX, QMIN, QMAX, PG, QG]] / 2
ppc0['gen'] = r_[array([ ppc0['gen'][0, :] ]), ppc0['gen']]
ppc1 = ppc0.copy()
ppc = ppc0.copy()
nb = ppc['bus'].shape[0]
ppc1['bus'][:, BUS_I] = ppc1['bus'][:, BUS_I] + nb
ppc1['branch'][:, F_BUS] = ppc1['branch'][:, F_BUS] + nb
ppc1['branch'][:, T_BUS] = ppc1['branch'][:, T_BUS] + nb
ppc1['gen'][:, GEN_BUS] = ppc1['gen'][:, GEN_BUS] + nb
ppc['bus'] = r_[ppc['bus'], ppc1['bus']]
ppc['branch'] = r_[ppc['branch'], ppc1['branch']]
ppc['gen'] = r_[ppc['gen'], ppc1['gen']]
#ppopt = ppoption(ppopt, OUT_BUS=1, OUT_GEN=1, OUT_ALL=-1, VERBOSE=2)
ppopt = ppoption(ppopt, VERBOSE=verbose)
r = rundcpf(ppc, ppopt)
t_is(r['bus'][ :9, VA], bus_soln[:, VA], 8, [t, 'voltage angles 1'])
t_is(r['bus'][10:18, VA], bus_soln[:, VA], 8, [t, 'voltage angles 2'])
Pg = r_[gen_soln[0, PG] - 30, 30, gen_soln[1:3, PG]]
t_is(r['gen'][ :4, PG], Pg, 8, [t, 'active power generation 1'])
t_is(r['gen'][4:8, PG], Pg, 8, [t, 'active power generation 1'])
t = 'network w/islands : AC PF : '
## get solved AC power flow case from MAT-file
soln9_pf = loadmat(join(tdir, 'soln9_pf.mat'), struct_as_record=False)
bus_soln = soln9_pf['bus_soln']
gen_soln = soln9_pf['gen_soln']
branch_soln = soln9_pf['branch_soln']
r = runpf(ppc, ppopt)
t_is(r['bus'][ :9, VA], bus_soln[:, VA], 8, [t, 'voltage angles 1'])
t_is(r['bus'][9:18, VA], bus_soln[:, VA], 8, [t, 'voltage angles 2'])
Pg = r_[gen_soln[0, PG] - 30, 30, gen_soln[1:3, PG]]
t_is(r['gen'][ :4, PG], Pg, 8, [t, 'active power generation 1'])
t_is(r['gen'][4:8, PG], Pg, 8, [t, 'active power generation 1'])
t_end()
def t_dcline(quiet=False):
"""Tests for DC line extension in L{{toggle_dcline}.
@author: Ray Zimmerman (PSERC Cornell)
"""
num_tests = 50
t_begin(num_tests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_dcline')
if quiet:
verbose = False
else:
verbose = False
t0 = ''
ppopt = ppoption(OPF_VIOLATION=1e-6,
PDIPM_GRADTOL=1e-8,
PDIPM_COMPTOL=1e-8,
PDIPM_COSTTOL=1e-9)
ppopt = ppoption(ppopt, OPF_ALG=560, OPF_ALG_DC=200)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=verbose)
## set up indices
ib_data = r_[arange(BUS_AREA + 1), arange(BASE_KV, VMIN + 1)]
ib_voltage = arange(VM, VA + 1)
ib_lam = arange(LAM_P, LAM_Q + 1)
ib_mu = arange(MU_VMAX, MU_VMIN + 1)
ig_data = r_[[GEN_BUS, QMAX, QMIN], arange(MBASE, APF + 1)]
ig_disp = array([PG, QG, VG])
ig_mu = arange(MU_PMAX, MU_QMIN + 1)
ibr_data = arange(ANGMAX + 1)
ibr_flow = arange(PF, QT + 1)
ibr_mu = array([MU_SF, MU_ST])
ibr_angmu = array([MU_ANGMIN, MU_ANGMAX])
## load case
ppc0 = loadcase(casefile)
del ppc0['dclinecost']
ppc = ppc0
ppc = toggle_dcline(ppc, 'on')
ppc = toggle_dcline(ppc, 'off')
ndc = ppc['dcline'].shape[0]
## run AC OPF w/o DC lines
t = ''.join([t0, 'AC OPF (no DC lines) : '])
r0 = runopf(ppc0, ppopt)
success = r0['success']
t_ok(success, [t, 'success'])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['f'], r0['f'], 8, [t, 'f'])
t_is(r['bus'][:, ib_data], r0['bus'][:, ib_data], 10, [t, 'bus data'])
t_is(r['bus'][:, ib_voltage], r0['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(r['bus'][:, ib_lam], r0['bus'][:, ib_lam], 3, [t, 'bus lambda'])
t_is(r['bus'][:, ib_mu], r0['bus'][:, ib_mu], 2, [t, 'bus mu'])
t_is(r['gen'][:, ig_data], r0['gen'][:, ig_data], 10, [t, 'gen data'])
t_is(r['gen'][:, ig_disp], r0['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(r['gen'][:, ig_mu], r0['gen'][:, ig_mu], 3, [t, 'gen mu'])
t_is(r['branch'][:, ibr_data], r0['branch'][:, ibr_data], 10,
[t, 'branch data'])
t_is(r['branch'][:, ibr_flow], r0['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
t_is(r['branch'][:, ibr_mu], r0['branch'][:, ibr_mu], 2, [t, 'branch mu'])
t = ''.join([t0, 'AC PF (no DC lines) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(rp['gen'][:, ig_disp], r['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## run with DC lines
t = ''.join([t0, 'AC OPF (with DC lines) : '])
ppc = toggle_dcline(ppc, 'on')
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected = array([[10, 8.9, -10, 10, 1.0674, 1.0935],
[2.2776, 2.2776, 0, 0, 1.0818, 1.0665],
[0, 0, 0, 0, 1.0000, 1.0000],
[10, 9.5, 0.0563, -10, 1.0778, 1.0665]])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected, 4, [t, 'P Q V'])
expected = array([[0, 0.8490, 0.6165, 0, 0, 0.2938],
[0, 0, 0, 0.4290, 0.0739, 0], [0, 0, 0, 0, 0, 0],
[0, 7.2209, 0, 0, 0.0739, 0]])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected, 3, [t, 'mu'])
t = ''.join([t0, 'AC PF (with DC lines) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1 = toggle_dcline(ppc1, 'on')
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
#t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['gen'][:2, ig_disp], r['gen'][:2, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['gen'][2, PG], r['gen'][2, PG], 3, [t, 'gen dispatch'])
t_is(rp['gen'][2, QG] + rp['dcline'][0, c.QF],
r['gen'][2, QG] + r['dcline'][0, c.QF], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## add appropriate P and Q injections and check angles and generation when running PF
t = ''.join([t0, 'AC PF (with equivalent injections) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
for k in range(ndc):
if ppc1['dcline'][k, c.BR_STATUS]:
ff = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.F_BUS])
tt = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.T_BUS])
ppc1['bus'][ff, PD] = ppc1['bus'][ff, PD] + r['dcline'][k, c.PF]
ppc1['bus'][ff, QD] = ppc1['bus'][ff, QD] - r['dcline'][k, c.QF]
ppc1['bus'][tt, PD] = ppc1['bus'][tt, PD] - r['dcline'][k, c.PT]
ppc1['bus'][tt, QD] = ppc1['bus'][tt, QD] - r['dcline'][k, c.QT]
ppc1['bus'][ff, VM] = r['dcline'][k, c.VF]
ppc1['bus'][tt, VM] = r['dcline'][k, c.VT]
ppc1['bus'][ff, BUS_TYPE] = PV
ppc1['bus'][tt, BUS_TYPE] = PV
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(rp['gen'][:, ig_disp], r['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## test DC OPF
t = ''.join([t0, 'DC OPF (with DC lines) : '])
ppc = ppc0.copy()
ppc['gen'][0, PMIN] = 10
ppc['branch'][4, RATE_A] = 100
ppc = toggle_dcline(ppc, 'on')
r = rundcopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected = array([[10, 8.9, 0, 0, 1.01, 1], [2, 2, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 1], [10, 9.5, 0, 0, 1, 0.98]])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected, 4, [t, 'P Q V'])
expected = array([[0, 1.8602, 0, 0, 0, 0], [1.8507, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0.2681, 0, 0, 0, 0]])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected, 3, [t, 'mu'])
t = ''.join([t0, 'DC PF (with DC lines) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1 = toggle_dcline(ppc1, 'on')
ppc1['bus'][:, VA] = 0
rp = rundcpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(rp['gen'][:, ig_disp], r['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## add appropriate P injections and check angles and generation when running PF
t = ''.join([t0, 'DC PF (with equivalent injections) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1['bus'][:, VA] = 0
for k in range(ndc):
if ppc1['dcline'][k, c.BR_STATUS]:
ff = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.F_BUS])
tt = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.T_BUS])
ppc1['bus'][ff, PD] = ppc1['bus'][ff, PD] + r['dcline'][k, c.PF]
ppc1['bus'][tt, PD] = ppc1['bus'][tt, PD] - r['dcline'][k, c.PT]
ppc1['bus'][ff, BUS_TYPE] = PV
ppc1['bus'][tt, BUS_TYPE] = PV
rp = rundcpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(rp['gen'][:, ig_disp], r['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## run with DC lines
t = ''.join([t0, 'AC OPF (with DC lines + poly cost) : '])
ppc = loadcase(casefile)
ppc = toggle_dcline(ppc, 'on')
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected1 = array([[10, 8.9, -10, 10, 1.0663, 1.0936],
[7.8429, 7.8429, 0, 0, 1.0809, 1.0667],
[0, 0, 0, 0, 1.0000, 1.0000],
[6.0549, 5.7522, -0.5897, -10, 1.0778, 1.0667]])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
expected2 = array([[0, 0.7605, 0.6226, 0, 0, 0.2980],
[0, 0, 0, 0.4275, 0.0792, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0.0792, 0]])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
ppc['dclinecost'][3, :8] = array([2, 0, 0, 4, 0, 0, 7.3, 0])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
t = ''.join([t0, 'AC OPF (with DC lines + pwl cost) : '])
ppc['dclinecost'][3, :8] = array([1, 0, 0, 2, 0, 0, 10, 73])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
t_end()
lambda x: value[bus_name.get_loc(x)]).astype(int)
bus = np.array(bus.reset_index())
branch = np.array(branch)
gen = np.array(gen)
gencost = np.array(gencost)
casedata = {
'baseMVA': baseMVA,
'gencost': gencost,
'gen': gen,
'branch': branch,
'bus': bus
}
return rundcpf(casedata, ppopt=ppopt, fname=fname)
def solve_dcpf(case,
hour=None,
results=None,
ppopt={'VERBOSE': False},
fname='./runpf.log'):
fname = os.path.abspath(fname)
baseMVA = case.baseMVA
bus = case.bus.copy(deep=True)
branch = case.branch.copy(deep=True)
gen = case.gen.copy(deep=True)
gencost = case.gen.copy(deep=True)
def t_dcline(quiet=False):
"""Tests for DC line extension in L{{toggle_dcline}.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
num_tests = 50
t_begin(num_tests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_dcline')
if quiet:
verbose = False
else:
verbose = False
t0 = ''
ppopt = ppoption(OPF_VIOLATION=1e-6, PDIPM_GRADTOL=1e-8,
PDIPM_COMPTOL=1e-8, PDIPM_COSTTOL=1e-9)
ppopt = ppoption(ppopt, OPF_ALG=560, OPF_ALG_DC=200)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=verbose)
## set up indices
ib_data = r_[arange(BUS_AREA + 1), arange(BASE_KV, VMIN + 1)]
ib_voltage = arange(VM, VA + 1)
ib_lam = arange(LAM_P, LAM_Q + 1)
ib_mu = arange(MU_VMAX, MU_VMIN + 1)
ig_data = r_[[GEN_BUS, QMAX, QMIN], arange(MBASE, APF + 1)]
ig_disp = array([PG, QG, VG])
ig_mu = arange(MU_PMAX, MU_QMIN + 1)
ibr_data = arange(ANGMAX + 1)
ibr_flow = arange(PF, QT + 1)
ibr_mu = array([MU_SF, MU_ST])
ibr_angmu = array([MU_ANGMIN, MU_ANGMAX])
## load case
ppc0 = loadcase(casefile)
del ppc0['dclinecost']
ppc = ppc0
ppc = toggle_dcline(ppc, 'on')
ppc = toggle_dcline(ppc, 'off')
ndc = ppc['dcline'].shape[0]
## run AC OPF w/o DC lines
t = ''.join([t0, 'AC OPF (no DC lines) : '])
r0 = runopf(ppc0, ppopt)
success = r0['success']
t_ok(success, [t, 'success'])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['f'], r0['f'], 8, [t, 'f'])
t_is( r['bus'][:,ib_data ], r0['bus'][:,ib_data ], 10, [t, 'bus data'])
t_is( r['bus'][:,ib_voltage], r0['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( r['bus'][:,ib_lam ], r0['bus'][:,ib_lam ], 3, [t, 'bus lambda'])
t_is( r['bus'][:,ib_mu ], r0['bus'][:,ib_mu ], 2, [t, 'bus mu'])
t_is( r['gen'][:,ig_data ], r0['gen'][:,ig_data ], 10, [t, 'gen data'])
t_is( r['gen'][:,ig_disp ], r0['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is( r['gen'][:,ig_mu ], r0['gen'][:,ig_mu ], 3, [t, 'gen mu'])
t_is(r['branch'][:,ibr_data ], r0['branch'][:,ibr_data ], 10, [t, 'branch data'])
t_is(r['branch'][:,ibr_flow ], r0['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
t_is(r['branch'][:,ibr_mu ], r0['branch'][:,ibr_mu ], 2, [t, 'branch mu'])
t = ''.join([t0, 'AC PF (no DC lines) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## run with DC lines
t = ''.join([t0, 'AC OPF (with DC lines) : '])
ppc = toggle_dcline(ppc, 'on')
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected = array([
[10, 8.9, -10, 10, 1.0674, 1.0935],
[2.2776, 2.2776, 0, 0, 1.0818, 1.0665],
[0, 0, 0, 0, 1.0000, 1.0000],
[10, 9.5, 0.0563, -10, 1.0778, 1.0665]
])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected, 4, [t, 'P Q V'])
expected = array([
[0, 0.8490, 0.6165, 0, 0, 0.2938],
[0, 0, 0, 0.4290, 0.0739, 0],
[0, 0, 0, 0, 0, 0],
[0, 7.2209, 0, 0, 0.0739, 0]
])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected, 3, [t, 'mu'])
t = ''.join([t0, 'AC PF (with DC lines) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1 = toggle_dcline(ppc1, 'on')
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
#t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is( rp['gen'][:2,ig_disp ], r['gen'][:2,ig_disp ], 3, [t, 'gen dispatch'])
t_is( rp['gen'][2,PG ], r['gen'][2,PG ], 3, [t, 'gen dispatch'])
t_is( rp['gen'][2,QG]+rp['dcline'][0,c.QF], r['gen'][2,QG]+r['dcline'][0,c.QF], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## add appropriate P and Q injections and check angles and generation when running PF
t = ''.join([t0, 'AC PF (with equivalent injections) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
for k in range(ndc):
if ppc1['dcline'][k, c.BR_STATUS]:
ff = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.F_BUS])
tt = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.T_BUS])
ppc1['bus'][ff, PD] = ppc1['bus'][ff, PD] + r['dcline'][k, c.PF]
ppc1['bus'][ff, QD] = ppc1['bus'][ff, QD] - r['dcline'][k, c.QF]
ppc1['bus'][tt, PD] = ppc1['bus'][tt, PD] - r['dcline'][k, c.PT]
ppc1['bus'][tt, QD] = ppc1['bus'][tt, QD] - r['dcline'][k, c.QT]
ppc1['bus'][ff, VM] = r['dcline'][k, c.VF]
ppc1['bus'][tt, VM] = r['dcline'][k, c.VT]
ppc1['bus'][ff, BUS_TYPE] = PV
ppc1['bus'][tt, BUS_TYPE] = PV
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## test DC OPF
t = ''.join([t0, 'DC OPF (with DC lines) : '])
ppc = ppc0.copy()
ppc['gen'][0, PMIN] = 10
ppc['branch'][4, RATE_A] = 100
ppc = toggle_dcline(ppc, 'on')
r = rundcopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected = array([
[10, 8.9, 0, 0, 1.01, 1],
[2, 2, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 1],
[10, 9.5, 0, 0, 1, 0.98]
])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected, 4, [t, 'P Q V'])
expected = array([
[0, 1.8602, 0, 0, 0, 0],
[1.8507, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0.2681, 0, 0, 0, 0]
])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected, 3, [t, 'mu'])
t = ''.join([t0, 'DC PF (with DC lines) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1 = toggle_dcline(ppc1, 'on')
ppc1['bus'][:, VA] = 0
rp = rundcpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## add appropriate P injections and check angles and generation when running PF
t = ''.join([t0, 'DC PF (with equivalent injections) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1['bus'][:, VA] = 0
for k in range(ndc):
if ppc1['dcline'][k, c.BR_STATUS]:
ff = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.F_BUS])
tt = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.T_BUS])
ppc1['bus'][ff, PD] = ppc1['bus'][ff, PD] + r['dcline'][k, c.PF]
ppc1['bus'][tt, PD] = ppc1['bus'][tt, PD] - r['dcline'][k, c.PT]
ppc1['bus'][ff, BUS_TYPE] = PV
ppc1['bus'][tt, BUS_TYPE] = PV
rp = rundcpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## run with DC lines
t = ''.join([t0, 'AC OPF (with DC lines + poly cost) : '])
ppc = loadcase(casefile)
ppc = toggle_dcline(ppc, 'on')
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected1 = array([
[10, 8.9, -10, 10, 1.0663, 1.0936],
[7.8429, 7.8429, 0, 0, 1.0809, 1.0667],
[0, 0, 0, 0, 1.0000, 1.0000],
[6.0549, 5.7522, -0.5897, -10, 1.0778, 1.0667]
])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
expected2 = array([
[0, 0.7605, 0.6226, 0, 0, 0.2980],
[0, 0, 0, 0.4275, 0.0792, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0.0792, 0]
])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
ppc['dclinecost'][3, :8] = array([2, 0, 0, 4, 0, 0, 7.3, 0])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
t = ''.join([t0, 'AC OPF (with DC lines + pwl cost) : '])
ppc['dclinecost'][3, :8] = array([1, 0, 0, 2, 0, 0, 10, 73])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
t_end()
def validate_from_ppc(
ppc_net,
net,
pf_type="runpp",
max_diff_values={
"bus_vm_pu": 1e-6,
"bus_va_degree": 1e-5,
"branch_p_mw": 1e-6,
"branch_q_mvar": 1e-6,
"gen_p_mw": 1e-6,
"gen_q_mvar": 1e-6
},
run=True):
"""
This function validates the pypower case files to pandapower net structure conversion via a \
comparison of loadflow calculation results. (Hence the opf cost conversion is not validated.)
INPUT:
**ppc_net** - The pypower case file, which must already contain the pypower powerflow
results or pypower must be importable.
**net** - The pandapower network.
OPTIONAL:
**pf_type** ("runpp", string) - Type of validated power flow. Possible are ("runpp",
"rundcpp", "runopp", "rundcopp")
**max_diff_values** - Dict of maximal allowed difference values. The keys must be
'vm_pu', 'va_degree', 'p_branch_mw', 'q_branch_mvar', 'p_gen_mw' and 'q_gen_mvar' and
the values floats.
**run** (True, bool or list of two bools) - changing the value to False avoids trying to run
(optimal) loadflows. Giving a list of two bools addresses first pypower and second
pandapower.
OUTPUT:
**conversion_success** - conversion_success is returned as False if pypower or pandapower
cannot calculate a powerflow or if the maximum difference values (max_diff_values )
cannot be hold.
EXAMPLE:
import pandapower.converter as pc
net = cv.from_ppc(ppc_net, f_hz=50)
conversion_success = cv.validate_from_ppc(ppc_net, net)
NOTE:
The user has to take care that the loadflow results already are included in the provided \
ppc_net or pypower is importable.
"""
# check in case of optimal powerflow comparison whether cost information exist
if "opp" in pf_type:
if not (len(net.polynomial_cost) | len(net.piecewise_linear_cost)):
if "gencost" in ppc_net:
if not len(ppc_net["gencost"]):
logger.debug(
'ppc and pandapower net do not include cost information.'
)
return True
else:
logger.error(
'The pandapower net does not include cost information.'
)
return False
else:
logger.debug(
'ppc and pandapower net do not include cost information.')
return True
# guarantee run parameter as list, for pypower and pandapower (optimal) powerflow run
run = [run, run] if isinstance(run, bool) else run
# --- check pypower powerflow success, if possible
if pypower_import and run[0]:
try:
if pf_type == "runpp":
ppc_net = runpf.runpf(ppc_net, ppopt)[0]
elif pf_type == "rundcpp":
ppc_net = rundcpf.rundcpf(ppc_net, ppopt)[0]
elif pf_type == "runopp":
ppc_net = runopf.runopf(ppc_net, ppopt)
elif pf_type == "rundcopp":
ppc_net = rundcopf.rundcopf(ppc_net, ppopt)
else:
raise ValueError("The pf_type %s is unknown" % pf_type)
except:
logger.debug("The pypower run did not work.")
ppc_success = True
if 'success' in ppc_net.keys():
if ppc_net['success'] != 1:
ppc_success = False
logger.error(
"The given ppc data indicates an unsuccessful pypower powerflow: "
+ "'ppc_net['success'] != 1'")
if (ppc_net['branch'].shape[1] < 17):
ppc_success = False
logger.error(
"The shape of given ppc data indicates missing pypower powerflow results."
)
# --- try to run a pandapower powerflow
if run[1]:
if pf_type == "runpp":
try:
pp.runpp(net,
init="dc",
calculate_voltage_angles=True,
trafo_model="pi")
except pp.LoadflowNotConverged:
try:
pp.runpp(net,
calculate_voltage_angles=True,
init="flat",
trafo_model="pi")
except pp.LoadflowNotConverged:
try:
pp.runpp(net,
trafo_model="pi",
calculate_voltage_angles=False)
if "bus_va_degree" in max_diff_values.keys():
max_diff_values[
"bus_va_degree"] = 1e2 if max_diff_values[
"bus_va_degree"] < 1e2 else max_diff_values[
"bus_va_degree"]
logger.info("voltage_angles could be calculated.")
except pp.LoadflowNotConverged:
logger.error(
'The pandapower powerflow does not converge.')
elif pf_type == "rundcpp":
try:
pp.rundcpp(net, trafo_model="pi")
except pp.LoadflowNotConverged:
logger.error('The pandapower dc powerflow does not converge.')
elif pf_type == "runopp":
try:
pp.runopp(net, init="flat", calculate_voltage_angles=True)
except pp.OPFNotConverged:
try:
pp.runopp(net, init="pf", calculate_voltage_angles=True)
except (pp.OPFNotConverged, pp.LoadflowNotConverged, KeyError):
try:
pp.runopp(net,
init="flat",
calculate_voltage_angles=False)
logger.info("voltage_angles could be calculated.")
if "bus_va_degree" in max_diff_values.keys():
max_diff_values[
"bus_va_degree"] = 1e2 if max_diff_values[
"bus_va_degree"] < 1e2 else max_diff_values[
"bus_va_degree"]
except pp.OPFNotConverged:
try:
pp.runopp(net,
init="pf",
calculate_voltage_angles=False)
if "bus_va_degree" in max_diff_values.keys():
max_diff_values[
"bus_va_degree"] = 1e2 if max_diff_values[
"bus_va_degree"] < 1e2 else max_diff_values[
"bus_va_degree"]
logger.info("voltage_angles could be calculated.")
except (pp.OPFNotConverged, pp.LoadflowNotConverged,
KeyError):
logger.error(
'The pandapower optimal powerflow does not converge.'
)
elif pf_type == "rundcopp":
try:
pp.rundcopp(net)
except pp.LoadflowNotConverged:
logger.error(
'The pandapower dc optimal powerflow does not converge.')
else:
raise ValueError("The pf_type %s is unknown" % pf_type)
# --- prepare powerflow result comparison by reordering pp results as they are in ppc results
if not ppc_success:
return False
if "opp" in pf_type:
if not net.OPF_converged:
return
elif not net.converged:
return False
# --- store pypower powerflow results
ppc_res = dict.fromkeys(ppc_elms)
ppc_res["branch"] = ppc_net['branch'][:, 13:17]
ppc_res["bus"] = ppc_net['bus'][:, 7:9]
ppc_res["gen"] = ppc_net['gen'][:, 1:3]
# --- pandapower bus result table
pp_res = dict.fromkeys(ppc_elms)
pp_res["bus"] = array(net.res_bus.sort_index()[['vm_pu', 'va_degree']])
# --- pandapower gen result table
pp_res["gen"] = zeros([1, 2])
# consideration of parallel generators via storing how much generators have been considered
# each node
# if in ppc is only one gen -> numpy initially uses one dim array -> change to two dim array
if len(ppc_net["gen"].shape) == 1:
ppc_net["gen"] = array(ppc_net["gen"], ndmin=2)
GENS = DataFrame(ppc_net['gen'][:, [0]].astype(int))
GEN_uniq = GENS.drop_duplicates()
already_used_gen = Series(zeros(GEN_uniq.shape[0]).astype(int),
index=[int(v) for v in GEN_uniq.values])
change_q_compare = []
for i, j in GENS.iterrows():
current_bus_type, current_bus_idx, same_bus_gen_idx, first_same_bus_in_service_gen_idx, \
last_same_bus_in_service_gen_idx = _gen_bus_info(ppc_net, i)
if current_bus_type == 3 and i == first_same_bus_in_service_gen_idx:
pp_res["gen"] = append(
pp_res["gen"],
array(net.res_ext_grid[net.ext_grid.bus == current_bus_idx][[
'p_mw', 'q_mvar'
]]).reshape((1, 2)), 0)
elif current_bus_type == 2 and i == first_same_bus_in_service_gen_idx:
pp_res["gen"] = append(
pp_res["gen"],
array(net.res_gen[net.gen.bus == current_bus_idx][[
'p_mw', 'q_mvar'
]]).reshape((1, 2)), 0)
else:
pp_res["gen"] = append(
pp_res["gen"],
array(net.res_sgen[net.sgen.bus == current_bus_idx][[
'p_mw', 'q_mvar'
]])[already_used_gen.at[int(j)]].reshape((1, 2)), 0)
already_used_gen.at[int(j)] += 1
change_q_compare += [int(j)]
pp_res["gen"] = pp_res["gen"][1:, :] # delete initial zero row
# --- pandapower branch result table
pp_res["branch"] = zeros([1, 4])
# consideration of parallel branches via storing how often branches were considered
# each node-to-node-connection
try:
init1 = concat([net.line.from_bus, net.line.to_bus], axis=1,
sort=True).drop_duplicates()
init2 = concat([net.trafo.hv_bus, net.trafo.lv_bus], axis=1,
sort=True).drop_duplicates()
except TypeError:
# legacy pandas < 0.21
init1 = concat([net.line.from_bus, net.line.to_bus],
axis=1).drop_duplicates()
init2 = concat([net.trafo.hv_bus, net.trafo.lv_bus],
axis=1).drop_duplicates()
init1['hv_bus'] = nan
init1['lv_bus'] = nan
init2['from_bus'] = nan
init2['to_bus'] = nan
try:
already_used_branches = concat([init1, init2], axis=0, sort=True)
except TypeError:
# pandas < 0.21 legacy
already_used_branches = concat([init1, init2], axis=0)
already_used_branches['number'] = zeros(
[already_used_branches.shape[0], 1]).astype(int)
BRANCHES = DataFrame(ppc_net['branch'][:, [0, 1, 8, 9]])
for i in BRANCHES.index:
from_bus = pp.get_element_index(net,
'bus',
name=int(ppc_net['branch'][i, 0]))
to_bus = pp.get_element_index(net,
'bus',
name=int(ppc_net['branch'][i, 1]))
from_vn_kv = ppc_net['bus'][from_bus, 9]
to_vn_kv = ppc_net['bus'][to_bus, 9]
ratio = BRANCHES[2].at[i]
angle = BRANCHES[3].at[i]
# from line results
if (from_vn_kv == to_vn_kv) & ((ratio == 0) |
(ratio == 1)) & (angle == 0):
pp_res["branch"] = append(
pp_res["branch"],
array(net.res_line[(net.line.from_bus == from_bus)
& (net.line.to_bus == to_bus)][[
'p_from_mw', 'q_from_mvar', 'p_to_mw',
'q_to_mvar'
]])
[int(already_used_branches.number.loc[
(already_used_branches.from_bus == from_bus) &
(already_used_branches.to_bus == to_bus)].values)].reshape(
1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.from_bus == from_bus)
& (already_used_branches.to_bus == to_bus)] += 1
# from trafo results
else:
if from_vn_kv >= to_vn_kv:
pp_res["branch"] = append(
pp_res["branch"],
array(net.res_trafo[(net.trafo.hv_bus == from_bus)
& (net.trafo.lv_bus == to_bus)]
[['p_hv_mw', 'q_hv_mvar', 'p_lv_mw', 'q_lv_mvar'
]])[int(already_used_branches.number.loc[
(already_used_branches.hv_bus == from_bus)
& (already_used_branches.lv_bus == to_bus)].
values)].reshape(1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.hv_bus == from_bus)
& (already_used_branches.lv_bus == to_bus)] += 1
else: # switch hv-lv-connection of pypower connection buses
pp_res["branch"] = append(
pp_res["branch"],
array(net.res_trafo[(net.trafo.hv_bus == to_bus)
& (net.trafo.lv_bus == from_bus)]
[['p_lv_mw', 'q_lv_mvar', 'p_hv_mw', 'q_hv_mvar'
]])[int(already_used_branches.number.loc[
(already_used_branches.hv_bus == to_bus)
& (already_used_branches.lv_bus == from_bus)].
values)].reshape(1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.hv_bus == to_bus)
& (already_used_branches.lv_bus == from_bus)] += 1
pp_res["branch"] = pp_res["branch"][1:, :] # delete initial zero row
# --- do the powerflow result comparison
diff_res = dict.fromkeys(ppc_elms)
diff_res["bus"] = ppc_res["bus"] - pp_res["bus"]
diff_res["bus"][:, 1] -= diff_res["bus"][0, 1] # remove va_degree offset
diff_res["branch"] = ppc_res["branch"] - pp_res["branch"]
diff_res["gen"] = ppc_res["gen"] - pp_res["gen"]
# comparison of buses with several generator units only as q sum
for i in GEN_uniq.loc[GEN_uniq[0].isin(change_q_compare)].index:
next_is = GEN_uniq.index[GEN_uniq.index > i]
if len(next_is) > 0:
next_i = next_is[0]
else:
next_i = GENS.index[-1] + 1
if (next_i - i) > 1:
diff_res["gen"][i:next_i, 1] = sum(diff_res["gen"][i:next_i, 1])
# logger info
logger.debug(
"Maximum voltage magnitude difference between pypower and pandapower: "
"%.2e pu" % max_(abs(diff_res["bus"][:, 0])))
logger.debug(
"Maximum voltage angle difference between pypower and pandapower: "
"%.2e degree" % max_(abs(diff_res["bus"][:, 1])))
logger.debug(
"Maximum branch flow active power difference between pypower and pandapower: "
"%.2e MW" % max_(abs(diff_res["branch"][:, [0, 2]])))
logger.debug(
"Maximum branch flow reactive power difference between pypower and "
"pandapower: %.2e MVAr" % max_(abs(diff_res["branch"][:, [1, 3]])))
logger.debug(
"Maximum active power generation difference between pypower and pandapower: "
"%.2e MW" % max_(abs(diff_res["gen"][:, 0])))
logger.debug(
"Maximum reactive power generation difference between pypower and pandapower: "
"%.2e MVAr" % max_(abs(diff_res["gen"][:, 1])))
if _validate_diff_res(diff_res, {"bus_vm_pu": 1e-3, "bus_va_degree": 1e-3, "branch_p_mw": 1e-6,
"branch_q_mvar": 1e-6}) and \
(max_(abs(diff_res["gen"])) > 1e-1).any():
logger.debug(
"The active/reactive power generation difference possibly results "
"because of a pypower error. Please validate "
"the results via pypower loadflow."
) # this occurs e.g. at ppc case9
# give a return
if isinstance(max_diff_values, dict):
return _validate_diff_res(diff_res, max_diff_values)
else:
logger.debug("'max_diff_values' must be a dict.")
def t_pf(quiet=False):
"""Tests for power flow solvers.
@author: Ray Zimmerman (PSERC Cornell)
"""
t_begin(33, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_pf')
verbose = not quiet
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
## get solved AC power flow case from MAT-file
## defines bus_soln, gen_soln, branch_soln
soln9_pf = loadmat(join(tdir, 'soln9_pf.mat'), struct_as_record=False)
bus_soln = soln9_pf['bus_soln']
gen_soln = soln9_pf['gen_soln']
branch_soln = soln9_pf['branch_soln']
## run Newton PF
t = 'Newton PF : '
ppopt = ppoption(ppopt, PF_ALG=1)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run fast-decoupled PF (XB version)
t = 'Fast Decoupled (XB) PF : '
ppopt = ppoption(ppopt, PF_ALG=2)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run fast-decoupled PF (BX version)
t = 'Fast Decoupled (BX) PF : '
ppopt = ppoption(ppopt, PF_ALG=3)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run Gauss-Seidel PF
t = 'Gauss-Seidel PF : '
ppopt = ppoption(ppopt, PF_ALG=4)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 5, [t, 'bus'])
t_is(gen, gen_soln, 5, [t, 'gen'])
t_is(branch, branch_soln, 5, [t, 'branch'])
## get solved AC power flow case from MAT-file
## defines bus_soln, gen_soln, branch_soln
soln9_dcpf = loadmat(join(tdir, 'soln9_dcpf.mat'), struct_as_record=False)
bus_soln = soln9_dcpf['bus_soln']
gen_soln = soln9_dcpf['gen_soln']
branch_soln = soln9_dcpf['branch_soln']
## run DC PF
t = 'DC PF : '
results, success = rundcpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## check Qg distribution, when Qmin = Qmax
t = 'check Qg : '
ppopt = ppoption(ppopt, PF_ALG=1, VERBOSE=0)
ppc = loadcase(casefile)
ppc['gen'][0, [QMIN, QMAX]] = [20, 20]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0, QG], 24.07, 2, [t, 'single gen, Qmin = Qmax'])
ppc['gen'] = r_[array([ppc['gen'][0, :]]), ppc['gen']]
ppc['gen'][0, [QMIN, QMAX]] = [10, 10]
ppc['gen'][1, [QMIN, QMAX]] = [0, 50]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [10, 14.07], 2, [t, '2 gens, Qmin = Qmax for one'])
ppc['gen'][0, [QMIN, QMAX]] = [10, 10]
ppc['gen'][1, [QMIN, QMAX]] = [-50, -50]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [12.03, 12.03], 2, [t, '2 gens, Qmin = Qmax for both'])
ppc['gen'][0, [QMIN, QMAX]] = [0, 50]
ppc['gen'][1, [QMIN, QMAX]] = [0, 100]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [8.02, 16.05], 2, [t, '2 gens, proportional'])
ppc['gen'][0, [QMIN, QMAX]] = [-50, 0]
ppc['gen'][1, [QMIN, QMAX]] = [50, 150]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [-50 + 8.02, 50 + 16.05], 2,
[t, '2 gens, proportional'])
## network with islands
t = 'network w/islands : DC PF : '
ppc0 = loadcase(casefile)
ppc0['gen'][0, PG] = 60
ppc0['gen'][0, [PMIN, PMAX, QMIN, QMAX, PG, QG]] = \
ppc0['gen'][0, [PMIN, PMAX, QMIN, QMAX, PG, QG]] / 2
ppc0['gen'] = r_[array([ppc0['gen'][0, :]]), ppc0['gen']]
ppc1 = ppc0.copy()
ppc = ppc0.copy()
nb = ppc['bus'].shape[0]
ppc1['bus'][:, BUS_I] = ppc1['bus'][:, BUS_I] + nb
ppc1['branch'][:, F_BUS] = ppc1['branch'][:, F_BUS] + nb
ppc1['branch'][:, T_BUS] = ppc1['branch'][:, T_BUS] + nb
ppc1['gen'][:, GEN_BUS] = ppc1['gen'][:, GEN_BUS] + nb
ppc['bus'] = r_[ppc['bus'], ppc1['bus']]
ppc['branch'] = r_[ppc['branch'], ppc1['branch']]
ppc['gen'] = r_[ppc['gen'], ppc1['gen']]
#ppopt = ppoption(ppopt, OUT_BUS=1, OUT_GEN=1, OUT_ALL=-1, VERBOSE=2)
ppopt = ppoption(ppopt, VERBOSE=verbose)
r = rundcpf(ppc, ppopt)
t_is(r['bus'][:9, VA], bus_soln[:, VA], 8, [t, 'voltage angles 1'])
t_is(r['bus'][10:18, VA], bus_soln[:, VA], 8, [t, 'voltage angles 2'])
Pg = r_[gen_soln[0, PG] - 30, 30, gen_soln[1:3, PG]]
t_is(r['gen'][:4, PG], Pg, 8, [t, 'active power generation 1'])
t_is(r['gen'][4:8, PG], Pg, 8, [t, 'active power generation 1'])
t = 'network w/islands : AC PF : '
## get solved AC power flow case from MAT-file
soln9_pf = loadmat(join(tdir, 'soln9_pf.mat'), struct_as_record=False)
bus_soln = soln9_pf['bus_soln']
gen_soln = soln9_pf['gen_soln']
branch_soln = soln9_pf['branch_soln']
r = runpf(ppc, ppopt)
t_is(r['bus'][:9, VA], bus_soln[:, VA], 8, [t, 'voltage angles 1'])
t_is(r['bus'][9:18, VA], bus_soln[:, VA], 8, [t, 'voltage angles 2'])
Pg = r_[gen_soln[0, PG] - 30, 30, gen_soln[1:3, PG]]
t_is(r['gen'][:4, PG], Pg, 8, [t, 'active power generation 1'])
t_is(r['gen'][4:8, PG], Pg, 8, [t, 'active power generation 1'])
t_end()
def main(train_case, storing_dir):
# Define the parameters of the training.
# More informations about the distributions used during training can be found
# in the notebook Sampling random power networks.
train_params = {
'num_samples': 1000,
'learning_rate': 3e-3,
}
# Define the parameters of the model.
model_params = {
'num_updates': 30, #10,
'latent_dim': 10,
'latent_layers': 2 #3
}
noise_params = {
'is_noise_active': 1.,
'r_min_coeff': -0.1,
'r_max_coeff': +0.1,
'x_min_coeff': -0.1,
'x_max_coeff': +0.1,
'b_min_coeff': -0.1,
'b_max_coeff': +0.1,
'ratio_min_coeff': -0.2,
'ratio_max_coeff': +0.2,
'angle_min_offset': -0.2,
'angle_max_offset': +0.2,
'Vg_min': 0.95,
'Vg_max': 1.05,
'Pd_min_coeff': -0.5,
'Pd_max_coeff': +0.5,
'Qd_min_coeff': -0.5,
'Qd_max_coeff': +0.5,
'Gs_min_coeff': -0.,
'Gs_max_coeff': +0.,
'Bs_min_coeff': -0.,
'Bs_max_coeff': +0.,
}
TEST_SIZE = 100
LEARN_ITER = 10
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
case_list = ['case9', 'case14', 'case30', 'case118']
error_NR_list = {}
error_DC_list = {}
error_GNS_list = {}
time_NR_list = {}
time_DC_list = {}
time_GNS_list = {}
timestr = time.strftime("%Y-%m-%d-%Hh%Mm%Ss")
expe_dir = 'expe_' + train_case + '_' + timestr
try:
os.stat(os.path.join(path, expe_dir))
except:
os.mkdir(os.path.join(path, expe_dir))
print("Building model that will be trained on " + train_case)
tf.reset_default_graph()
sess = tf.Session(config=config)
expe_path = os.path.join(path, expe_dir)
with tf.variable_scope('GNS', reuse=tf.AUTO_REUSE):
model = GNS(train_case, train_params, model_params, noise_params,
expe_path, sess)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print(" Learning on " + train_case + "...")
for learn_iter in tqdm.tqdm(range(LEARN_ITER)):
# Apply a gradient descent
sess.run(model.opt_op)
# Store the loss
model.store_summary(learn_iter)
print(" The model trained on " + train_case + " is ready to be tested!")
all_p_nr = {}
all_p_dc = {}
all_p_gns = {}
time_NR_list = {}
time_DC_list = {}
time_GNS_list = {}
for test_case in case_list:
print(" Testing on " + test_case + "...")
model.change_default_power_grid(case=test_case)
# Sample
all_p_nr[test_case] = None
all_p_dc[test_case] = None
all_p_gns[test_case] = None
time_NR_list[test_case] = []
time_DC_list[test_case] = []
time_GNS_list[test_case] = []
i = 0
while i < TEST_SIZE:
print(i)
# Sample a power grid
gens, buses, lines, p_gen, pft, ptf, qft, qtf = sess.run([
model.gens,
model.buses,
model.lines,
model.p_gen,
model.p_from_to[str(model_params['num_updates'])],
model.p_to_from[str(model_params['num_updates'])],
model.q_from_to[str(model_params['num_updates'])],
model.q_to_from[str(model_params['num_updates'])],
])
sample_id = 0
model.input_data['gen'][:, 1] = p_gen[str(
model_params['num_updates'])][sample_id] * 100
model.input_data['gen'][:, 5] = gens['Vg'][sample_id]
model.input_data['bus'][:, 2] = buses['Pd'][sample_id]
model.input_data['bus'][:, 3] = buses['Qd'][sample_id]
model.input_data['bus'][:, 4] = buses['Gs'][sample_id]
model.input_data['bus'][:, 5] = buses['Bs'][sample_id]
model.input_data['branch'][:, 2] = lines['r'][sample_id]
model.input_data['branch'][:, 3] = lines['x'][sample_id]
model.input_data['branch'][:, 4] = lines['b'][sample_id]
model.input_data['branch'][:, 8] = lines['ratio'][sample_id]
model.input_data[
'branch'][:, 9] = lines['angle'][sample_id] * 180 / np.pi
i += 1
try:
start = time.time()
a_nr = runpf(model.input_data)
time_NR_list[test_case].append(time.time() - start)
except:
print('did not converge')
i -= 1
continue
if a_nr[0]['success'] == 0:
print('did not converge')
i -= 1
continue
# Now try with DC approx
start = time.time()
a_dc = rundcpf(model.input_data)
time_DC_list[test_case].append(time.time() - start)
# Get flows from GNS
p_from_gns = pft[sample_id] * 100
p_to_gns = ptf[sample_id] * 100
q_from_gns = qft[sample_id] * 100
q_to_gns = qtf[sample_id] * 100
if all_p_gns[test_case] is None:
all_p_gns[test_case] = np.r_[p_from_gns, p_to_gns]
else:
all_p_gns[test_case] = np.c_[all_p_gns[test_case],
np.r_[p_from_gns, p_to_gns]]
# Get flows from Newton-Raphson
p_from_nr = a_nr[0]['branch'][:, 13]
q_from_nr = a_nr[0]['branch'][:, 14]
p_to_nr = a_nr[0]['branch'][:, 15]
q_to_nr = a_nr[0]['branch'][:, 16]
if all_p_nr[test_case] is None:
all_p_nr[test_case] = np.r_[p_from_nr, p_to_nr]
else:
all_p_nr[test_case] = np.c_[all_p_nr[test_case],
np.r_[p_from_nr, p_to_nr]]
# Get flows from DC approx
p_from_dc = a_dc[0]['branch'][:, 13]
q_from_dc = a_dc[0]['branch'][:, 14]
p_to_dc = a_dc[0]['branch'][:, 15]
q_to_dc = a_dc[0]['branch'][:, 16]
if all_p_dc[test_case] is None:
all_p_dc[test_case] = np.r_[p_from_dc, p_to_dc]
else:
all_p_dc[test_case] = np.c_[all_p_dc[test_case],
np.r_[p_from_dc, p_to_dc]]
# Now try with GNS
v_time, theta_time = sess.run([model.v, model.theta])
start = time.time()
v_time, theta_time = sess.run([model.v, model.theta])
time_GNS_list[test_case].append(
(time.time() - start) / train_params['num_samples'])
np.save(os.path.join(storing_dir, expe_dir + "_all_p_nr.npy"), all_p_nr)
np.save(os.path.join(storing_dir, expe_dir + "_all_p_gns.npy"), all_p_gns)
np.save(os.path.join(storing_dir, expe_dir + "_all_p_dc.npy"), all_p_dc)
np.save(os.path.join(storing_dir, expe_dir + "_time_nr.npy"), time_NR_list)
np.save(os.path.join(storing_dir, expe_dir + "_time_gns.npy"),
time_GNS_list)
np.save(os.path.join(storing_dir, expe_dir + "_time_dc.npy"), time_DC_list)
