def powerflowreact(model, i):
if i in gb:
return model.Qg[i]-Qd[i] == sum(model.vm[i]*model.vm[j]*abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in range(nb))
else:
return sum(model.vm[i]*model.vm[j]*abs(y[i, j]) * sin(model.va[i] - model.va[j] -
ang(y[i, j])) for j in range(nb)) == -Qd[i]
def powerflowact(model, i):
if i in gb:
return model.Pg[i]-Pd[i] == sum(model.vm[i]*model.vm[j]*abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in range(nb))
else:
return sum(model.vm[i]*model.vm[j]*abs(y[i, j]) * cos(model.va[i] - model.va[j] -
ang(y[i, j])) for j in range(nb)) == -Pd[i]
def qto(model, i):
if i in tp:
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / model.tr[i] * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
else:
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / tr0[i] * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
def pto(model, i):
if i in tp:
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / model.tr[i] * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
else:
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / tr0[i] * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
def test_model_mfcc(model, input_path, output_path, neighbor, nffts, normal_flag=0):
_, s = wavfile.read(input_path)
_, _, Zxx = stft(s, freq)
Zxx1 = log((abss(Zxx)).T+1e-7)
y_input = combine_with_mfcc(input_path, neighbor=neighbor, nfft=nffts, normal_flag=normal_flag)
y = model.predict(y_input)
y = (np.delete(y, np.s_[-39:], axis=1)).T # delete mfcc data
ypreComplex = exp(y) * exp(complex(0, 1) * ang(Zxx)) if normal_flag == 0 \
else exp(unnormalize(y, Zxx1)) * exp(complex(0, 1) * ang(Zxx))
# ypreComplex = unnormalize(exp(y) * exp(complex(0, 1) * ang(Zxx)), abss(Zxx)) # wrong code
_, xrec = istft(ypreComplex, freq)
dataWrite = xrec.astype(np.int16)
wavfile.write(output_path, freq, dataWrite)
def test_model(model, input_path, output_path, neighbor, nffts, normal_flag=0):
_, s = wavfile.read(input_path)
_, _, Zxx = stft(s, freq)
Zxx1 = log((abss(Zxx)).T+1e-7)
y_input = make_window_buffer(input_path, neighbor=neighbor, nfft=nffts, normal_flag=normal_flag)
y = model.predict(y_input).T
# print(y.shape, unnormalize(y, abss(Zxx)).shape, unnormalize(y, abss(Zxx)).dtype)
ypreComplex = exp(y) * exp(complex(0, 1) * ang(Zxx)) if normal_flag == 0 \
else exp(unnormalize(y, Zxx1)) * exp(complex(0, 1) * ang(Zxx))
# ypreComplex = unnormalize(exp(y) * exp(complex(0, 1) * ang(Zxx)), abss(Zxx)) # wrong code
_, xrec = istft(ypreComplex, freq)
dataWrite = xrec.astype(np.int16)
wavfile.write(output_path, freq, dataWrite)
def test_GRU(model, input_path, output_path):
_, s = wavfile.read(input_path)
_, _, Zxx = stft(s, freq)
Zxx1 = log((abss(Zxx)).T + 1e-7)
print(Zxx1.shape)
yt = pack_GRU(input_path)
y = model.predict(np.reshape(yt, [1, -1, 22]))
y = unpack_GRU(y)
print(y.shape)
ypreComplex = exp(y.T * Zxx1.T) * exp(complex(0, 1) * ang(Zxx))
_, xrec = istft(ypreComplex, freq)
dataWrite = xrec.astype(np.int16)
wavfile.write(output_path, freq, dataWrite)
def powerflowreact(model, i):
bfrom_i = tp[find(fr[tp] == i)] # branches from bus i with transformer
bto_i = tp[find(to[tp] == i)] # branches to bus i with transformer
allbut_i = find(bus[:, BUS_I] != i) # Set of other buses
sh = sd[find(sd == i)] # Detect shunt elements
if i in gb:
return model.Qg[i]-Qd[i] == \
sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * sin(model.va[i] - model.va[to[j]] - ang(yk[j]))
* (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * sin(model.va[i] - model.va[fr[j]] - ang(yk[j]))
* (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j] ** 2 - 1) * np.sin(- ang(yk[j]))
for j in bfrom_i) - imag(y[i, i]) - sum(model.Bs[j] for j in sh))
else:
return sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * sin(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * sin(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j] ** 2 - 1) * np.sin(- ang(yk[j]))
for j in bfrom_i) - imag(y[i, i]) - sum(model.Bs[j] for j in sh)) == - Qd[i]
def powerflowact(model, i):
bfrom_i = tp[find(fr[tp] == i)] # branches from bus i with transformer
bto_i = tp[find(to[tp] == i)] # branches to bus i with transformer
allbut_i = find(bus[:, BUS_I] != i) # Set of other buses
if i in gb:
return model.Pg[i]-Pd[i] == sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j]**2 - 1) *
np.cos(- ang(yk[j])) for j in bfrom_i) + real(y[i, i]))
else:
return sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j]**2 - 1) *
np.cos(- ang(yk[j])) for j in bfrom_i) + real(y[i, i])) == -Pd[i]
def solveropfnlp_2(ppc, solver="ipopt"):
if solver == "ipopt":
opt = SolverFactory("ipopt", executable="/home/iso/PycharmProjects/opfLC_python3/Python3/py_solvers/ipopt-linux64/ipopt")
if solver == "bonmin":
opt = SolverFactory("bonmin", executable="/home/iso/PycharmProjects/opfLC_python3/Python3/py_solvers/bonmin-linux64/bonmin")
if solver == "knitro":
opt = SolverFactory("knitro", executable="D:/ICT/Artelys/Knitro 10.2.1/knitroampl/knitroampl")
ppc = ext2int(ppc) # convert to continuous indexing starting from 0
# Gather information about the system
# =============================================================
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
nb = bus.shape[0] # number of buses
ng = gen.shape[0] # number of generators
nl = branch.shape[0] # number of lines
# generator buses
gb = tolist(np.array(gen[:, GEN_BUS]).astype(int))
sb = find((bus[:, BUS_TYPE] == REF)) # slack bus index
fr = branch[:, F_BUS].astype(int) # from bus indices
to = branch[:, T_BUS].astype(int) # to bus indices
tr = branch[:, TAP] # transformation ratios
tr[find(tr == 0)] = 1 # set to 1 transformation ratios that are 0
r = branch[:, BR_R] # branch resistances
x = branch[:, BR_X] # branch reactances
b = branch[:, BR_B] # branch susceptances
start_time = time.clock()
# Admittance matrix computation
# =============================================================
y = makeYbus(baseMVA, bus, branch)[0] # admittance matrix
yk = 1./(r+x*1j) # branch admittance
yft = yk + 0.5j*b # branch admittance + susceptance
gk = yk.real # branch resistance
yk = yk/tr # include /tr in yk
# Optimization
# =============================================================
branch[find(branch[:, RATE_A] == 0), RATE_A] = 9999 # set undefined Sflow limit to 9999
Smax = branch[:, RATE_A] / baseMVA # Max. Sflow
# Power demand parameters
Pd = bus[:, PD] / baseMVA
Qd = bus[:, QD] / baseMVA
# Max and min Pg and Qg
Pg_max = zeros(nb)
Pg_max[gb] = gen[:, PMAX] / baseMVA
Pg_min = zeros(nb)
Pg_min[gb] = gen[:, PMIN] / baseMVA
Qg_max = zeros(nb)
Qg_max[gb] = gen[:, QMAX] / baseMVA
Qg_min = zeros(nb)
Qg_min[gb] = gen[:, QMIN] / baseMVA
# Vmax and Vmin vectors
Vmax = bus[:, VMAX]
Vmin = bus[:, VMIN]
vm = bus[:, VM]
va = bus[:, VA]*pi/180
# create a new optimization model
model = ConcreteModel()
# Define sets
# ------------
model.bus = Set(ordered=True, initialize=range(nb)) # Set of all buses
model.gen = Set(ordered=True, initialize=gb) # Set of buses with generation
model.line = Set(ordered=True, initialize=range(nl)) # Set of all lines
# Define variables
# -----------------
# Voltage magnitudes vector (vm)
model.vm = Var(model.bus)
# Voltage angles vector (va)
model.va = Var(model.bus)
# Reactive power generation, synchronous machines(SM) (Qg)
model.Qg = Var(model.gen)
Qg0 = zeros(nb)
Qg0[gb] = gen[:, QG]/baseMVA
# Active power generation, synchronous machines(SM) (Pg)
model.Pg = Var(model.gen)
Pg0 = zeros(nb)
Pg0[gb] = gen[:, PG] / baseMVA
# Active and reactive power from at all branches
model.Pf = Var(model.line)
model.Qf = Var(model.line)
# Active and reactive power to at all branches
model.Pt = Var(model.line)
model.Qt = Var(model.line)
# Warm start the problem
# ------------------------
for i in range(nb):
model.vm[i] = vm[i]
model.va[i] = va[i]
if i in gb:
model.Pg[i] = Pg0[i]
model.Qg[i] = Qg0[i]
for i in range(nl):
model.Pf[i] = vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.cos(-ang(yft[i])) -\
vm[fr[i]] * vm[to[i]] * abs(yk[i]) * np.cos(va[fr[i]] - va[to[i]] - ang(yk[i]))
model.Qf[i] = vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.sin(-ang(yft[i])) -\
vm[fr[i]] * vm[to[i]] * abs(yk[i]) * np.sin(va[fr[i]] - va[to[i]] - ang(yk[i]))
model.Pt[i] = vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) -\
vm[to[i]] * vm[fr[i]] * abs(yk[i]) * np.cos(va[to[i]] - va[fr[i]] - ang(yk[i]))
model.Qt[i] = vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) -\
vm[to[i]] * vm[fr[i]] * abs(yk[i]) * np.sin(va[to[i]] - va[fr[i]] - ang(yk[i]))
# Define constraints
# ----------------------------
# Equalities:
# ------------
# Active power flow equalities
def powerflowact(model, i):
if i in gb:
return model.Pg[i]-Pd[i] == sum(model.vm[i]*model.vm[j]*abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in range(nb))
else:
return sum(model.vm[i]*model.vm[j]*abs(y[i, j]) * cos(model.va[i] - model.va[j] -
ang(y[i, j])) for j in range(nb)) == -Pd[i]
model.const1 = Constraint(model.bus, rule=powerflowact)
# Reactive power flow equalities
def powerflowreact(model, i):
if i in gb:
return model.Qg[i]-Qd[i] == sum(model.vm[i]*model.vm[j]*abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in range(nb))
else:
return sum(model.vm[i]*model.vm[j]*abs(y[i, j]) * sin(model.va[i] - model.va[j] -
ang(y[i, j])) for j in range(nb)) == -Qd[i]
model.const2 = Constraint(model.bus, rule=powerflowreact)
# Active power from
def pfrom(model, i):
return model.Pf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.cos(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) * \
cos(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
model.const3 = Constraint(model.line, rule=pfrom)
# Reactive power from
def qfrom(model, i):
return model.Qf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.sin(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) * \
sin(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
model.const4 = Constraint(model.line, rule=qfrom)
# Active power to
def pto(model, i):
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
model.const5 = Constraint(model.line, rule=pto)
# Reactive power to
def qto(model, i):
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
model.const6 = Constraint(model.line, rule=qto)
# Slack bus phase angle
model.const7 = Constraint(expr=model.va[sb[0]] == 0)
# Inequalities:
# ----------------
# Active power generator limits Pg_min <= Pg <= Pg_max
def genplimits(model, i):
return Pg_min[i] <= model.Pg[i] <= Pg_max[i]
model.const8 = Constraint(model.gen, rule=genplimits)
# Reactive power generator limits Qg_min <= Qg <= Qg_max
def genqlimits(model, i):
return Qg_min[i] <= model.Qg[i] <= Qg_max[i]
model.const9 = Constraint(model.gen, rule=genqlimits)
# Voltage constraints ( Vmin <= V <= Vmax )
def vlimits(model, i):
return Vmin[i] <= model.vm[i] <= Vmax[i]
model.const10 = Constraint(model.bus, rule=vlimits)
# Sfrom line limit
def sfrommax(model, i):
return model.Pf[i]**2 + model.Qf[i]**2 <= Smax[i]**2
model.const11 = Constraint(model.line, rule=sfrommax)
# Sto line limit
def stomax(model, i):
return model.Pt[i]**2 + model.Qt[i]**2 <= Smax[i]**2
model.const12 = Constraint(model.line, rule=stomax)
# Set objective function
# ------------------------
def obj_fun(model):
return sum(gk[i] * ((model.vm[fr[i]] / tr[i])**2 + model.vm[to[i]]**2 -
2/tr[i] * model.vm[fr[i]] * model.vm[to[i]] *
cos(model.va[fr[i]] - model.va[to[i]])) for i in range(nl))
model.obj = Objective(rule=obj_fun, sense=minimize)
mt = time.clock() - start_time # Modeling time
# Execute solve command with the selected solver
# ------------------------------------------------
start_time = time.clock()
results = opt.solve(model, tee=True)
et = time.clock() - start_time # Elapsed time
print(results)
# Update the case info with the optimized variables
# ==================================================
for i in range(nb):
bus[i, VM] = model.vm[i].value # Bus voltage magnitudes
bus[i, VA] = model.va[i].value*180/pi # Bus voltage angles
# Include Pf - Qf - Pt - Qt in the branch matrix
branchsol = zeros((nl, 17))
branchsol[:, :-4] = branch
for i in range(nl):
branchsol[i, PF] = model.Pf[i].value * baseMVA
branchsol[i, QF] = model.Qf[i].value * baseMVA
branchsol[i, PT] = model.Pt[i].value * baseMVA
branchsol[i, QT] = model.Qt[i].value * baseMVA
# Update gen matrix variables
for i in range(ng):
gen[i, PG] = model.Pg[gb[i]].value * baseMVA
gen[i, QG] = model.Qg[gb[i]].value * baseMVA
gen[i, VG] = bus[gb[i], VM]
# Convert to external (original) numbering and save case results
ppc = int2ext(ppc)
ppc['bus'][:, 1:] = bus[:, 1:]
branchsol[:, 0:2] = ppc['branch'][:, 0:2]
ppc['branch'] = branchsol
ppc['gen'][:, 1:] = gen[:, 1:]
ppc['obj'] = value(obj_fun(model))
ppc['ploss'] = value(obj_fun(model)) * baseMVA
ppc['et'] = et
ppc['mt'] = mt
ppc['success'] = 1
# ppc solved case is returned
return ppc
def qto(model, i):
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
def pto(model, i):
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
def qfrom(model, i):
return model.Qf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.sin(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) * \
sin(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
def pfrom(model, i):
return model.Pf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (tr[i] ** 2) * np.cos(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) * \
cos(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
def solveropfnlp_4(ppc, solver="ipopt"):
if solver == "ipopt":
opt = SolverFactory(
"ipopt",
executable=
"/home/iso/PycharmProjects/opfLC_python3/Python3/py_solvers/ipopt-linux64/ipopt"
)
if solver == "bonmin":
opt = SolverFactory(
"bonmin",
executable=
"/home/iso/PycharmProjects/opfLC_python3/Python3/py_solvers/bonmin-linux64/bonmin"
)
if solver == "knitro":
opt = SolverFactory(
"knitro",
executable="D:/ICT/Artelys/Knitro 10.2.1/knitroampl/knitroampl")
ppc = ext2int(ppc) # convert to continuous indexing starting from 0
# Gather information about the system
# =============================================================
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
nb = bus.shape[0] # number of buses
ng = gen.shape[0] # number of generators
nl = branch.shape[0] # number of lines
# generator buses
gb = tolist(np.array(gen[:, GEN_BUS]).astype(int))
sb = find((bus[:, BUS_TYPE] == REF)) # slack bus index
fr = branch[:, F_BUS].astype(int) # from bus indices
to = branch[:, T_BUS].astype(int) # to bus indices
tr0 = copy(branch[:, TAP]) # transformation ratios
tr0[find(tr0 == 0)] = 1 # set to 1 transformation ratios that are 0
tp = find(branch[:, TAP] != 0) # lines with tap changers
ntp = find(branch[:, TAP] == 0) # lines without tap changers
# Tap changer settings
dudtap = 0.01 # Voltage per unit variation with tap changes
tapmax = 10 # Highest tap changer setting
tapmin = -10 # Lowest tap changer setting
# Shunt element options
stepmax = 1 # maximum step of the shunt element
Bs0 = bus[:, BS] / baseMVA # shunt elements susceptance
sd = find(bus[:, BS] != 0) # buses with shunt devices
r = branch[:, BR_R] # branch resistances
x = branch[:, BR_X] # branch reactances
b = branch[:, BR_B] # branch susceptances
start_time = time.clock()
# Admittance matrix computation
# =============================================================
# Set tap ratios and shunt elements to neutral position
branch[:, TAP] = 1
bus[:, BS] = 0
y = makeYbus(baseMVA, bus, branch)[0] # admittance matrix
yk = 1. / (r + x * 1j) # branch admittance
yft = yk + 0.5j * b # branch admittance + susceptance
gk = yk.real # branch resistance
# Optimization
# =============================================================
branch[find(branch[:, RATE_A] == 0),
RATE_A] = 9999 # set undefined Sflow limit to 9999
Smax = branch[:, RATE_A] / baseMVA # Max. Sflow
# Power demand parameters
Pd = bus[:, PD] / baseMVA
Qd = bus[:, QD] / baseMVA
# Max and min Pg and Qg
Pg_max = zeros(nb)
Pg_max[gb] = gen[:, PMAX] / baseMVA
Pg_min = zeros(nb)
Pg_min[gb] = gen[:, PMIN] / baseMVA
Qg_max = zeros(nb)
Qg_max[gb] = gen[:, QMAX] / baseMVA
Qg_min = zeros(nb)
Qg_min[gb] = gen[:, QMIN] / baseMVA
# Vmax and Vmin vectors
Vmax = bus[:, VMAX]
Vmin = bus[:, VMIN]
vm = bus[:, VM]
va = bus[:, VA] * pi / 180
# create a new optimization model
model = ConcreteModel()
# Define sets
# ------------
model.bus = Set(ordered=True, initialize=range(nb)) # Set of all buses
model.gen = Set(ordered=True,
initialize=gb) # Set of buses with generation
model.line = Set(ordered=True, initialize=range(nl)) # Set of all lines
model.taps = Set(ordered=True,
initialize=tp) # Set of all lines with tap changers
model.shunt = Set(ordered=True,
initialize=sd) # Set of buses with shunt elements
# Define variables
# -----------------
# Voltage magnitudes vector (vm)
model.vm = Var(model.bus)
# Voltage angles vector (va)
model.va = Var(model.bus)
# Reactive power generation, synchronous machines(SM) (Qg)
model.Qg = Var(model.gen)
Qg0 = zeros(nb)
Qg0[gb] = gen[:, QG] / baseMVA
# Active power generation, synchronous machines(SM) (Pg)
model.Pg = Var(model.gen)
Pg0 = zeros(nb)
Pg0[gb] = gen[:, PG] / baseMVA
# Active and reactive power from at all branches
model.Pf = Var(model.line)
model.Qf = Var(model.line)
# Active and reactive power to at all branches
model.Pt = Var(model.line)
model.Qt = Var(model.line)
# Transformation ratios
model.tr = Var(model.taps)
# Tap changer positions + their bounds
model.tap = Var(model.taps, bounds=(tapmin, tapmax))
# Shunt susceptance
model.Bs = Var(model.shunt)
# Shunt positions + their bounds
model.s = Var(model.shunt, bounds=(0, stepmax))
# Warm start the problem
# ------------------------
for i in range(nb):
model.vm[i] = vm[i]
model.va[i] = va[i]
if i in gb:
model.Pg[i] = Pg0[i]
model.Qg[i] = Qg0[i]
for i in range(nl):
model.Pf[i] = vm[fr[i]] ** 2 * abs(yft[i]) / (tr0[i] ** 2) * np.cos(-ang(yft[i])) -\
vm[fr[i]] * vm[to[i]] * abs(yk[i]) / tr0[i] * np.cos(va[fr[i]] - va[to[i]] - ang(yk[i]))
model.Qf[i] = vm[fr[i]] ** 2 * abs(yft[i]) / (tr0[i] ** 2) * np.sin(-ang(yft[i])) -\
vm[fr[i]] * vm[to[i]] * abs(yk[i]) / tr0[i] * np.sin(va[fr[i]] - va[to[i]] - ang(yk[i]))
model.Pt[i] = vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) -\
vm[to[i]] * vm[fr[i]] * abs(yk[i]) / tr0[i] * np.cos(va[to[i]] - va[fr[i]] - ang(yk[i]))
model.Qt[i] = vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) -\
vm[to[i]] * vm[fr[i]] * abs(yk[i]) / tr0[i] * np.sin(va[to[i]] - va[fr[i]] - ang(yk[i]))
for i in tp:
model.tr[i] = tr0[i]
for i in sd:
model.Bs[i] = Bs0[i]
# Define constraints
# ----------------------------
# Equalities:
# ------------
# Active power flow equalities
def powerflowact(model, i):
bfrom_i = tp[find(fr[tp] == i)] # branches from bus i with transformer
bto_i = tp[find(to[tp] == i)] # branches to bus i with transformer
allbut_i = find(bus[:, BUS_I] != i) # Set of other buses
if i in gb:
return model.Pg[i]-Pd[i] == sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j]**2 - 1) *
np.cos(- ang(yk[j])) for j in bfrom_i) + real(y[i, i]))
else:
return sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
cos(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * cos(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j]**2 - 1) *
np.cos(- ang(yk[j])) for j in bfrom_i) + real(y[i, i])) == -Pd[i]
model.const1 = Constraint(model.bus, rule=powerflowact)
# Reactive power flow equalities
def powerflowreact(model, i):
bfrom_i = tp[find(fr[tp] == i)] # branches from bus i with transformer
bto_i = tp[find(to[tp] == i)] # branches to bus i with transformer
allbut_i = find(bus[:, BUS_I] != i) # Set of other buses
sh = sd[find(sd == i)] # Detect shunt elements
if i in gb:
return model.Qg[i]-Qd[i] == \
sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * sin(model.va[i] - model.va[to[j]] - ang(yk[j]))
* (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * sin(model.va[i] - model.va[fr[j]] - ang(yk[j]))
* (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j] ** 2 - 1) * np.sin(- ang(yk[j]))
for j in bfrom_i) - imag(y[i, i]) - sum(model.Bs[j] for j in sh))
else:
return sum(model.vm[i] * model.vm[j] * abs(y[i, j]) *
sin(model.va[i] - model.va[j] - ang(y[i, j])) for j in allbut_i) - \
sum(model.vm[i] * model.vm[to[j]] * abs(yk[j]) * sin(model.va[i] -
model.va[to[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bfrom_i) - \
sum(model.vm[i] * model.vm[fr[j]] * abs(yk[j]) * sin(model.va[i] -
model.va[fr[j]] - ang(yk[j])) * (1 / model.tr[j] - 1) for j in bto_i) + \
model.vm[i] ** 2 * (sum(abs(yk[j]) * (1 / model.tr[j] ** 2 - 1) * np.sin(- ang(yk[j]))
for j in bfrom_i) - imag(y[i, i]) - sum(model.Bs[j] for j in sh)) == - Qd[i]
model.const2 = Constraint(model.bus, rule=powerflowreact)
# Active power from
def pfrom(model, i):
if i in tp:
return model.Pf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (model.tr[i] ** 2) * np.cos(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) / model.tr[i] * \
cos(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
else:
return model.Pf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / tr0[i] ** 2 * np.cos(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) / tr0[i] * \
cos(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
model.const3 = Constraint(model.line, rule=pfrom)
# Reactive power from
def qfrom(model, i):
if i in tp:
return model.Qf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / (model.tr[i] ** 2) * np.sin(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) / model.tr[i] * \
sin(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
else:
return model.Qf[i] == model.vm[fr[i]] ** 2 * abs(yft[i]) / tr0[i] ** 2 * np.sin(-ang(yft[i])) - \
model.vm[fr[i]] * model.vm[to[i]] * abs(yk[i]) / tr0[i] * \
sin(model.va[fr[i]] - model.va[to[i]] - ang(yk[i]))
model.const4 = Constraint(model.line, rule=qfrom)
# Active power to
def pto(model, i):
if i in tp:
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / model.tr[i] * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
else:
return model.Pt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.cos(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / tr0[i] * \
cos(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
model.const5 = Constraint(model.line, rule=pto)
# Reactive power to
def qto(model, i):
if i in tp:
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / model.tr[i] * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
else:
return model.Qt[i] == model.vm[to[i]] ** 2 * abs(yft[i]) * np.sin(-ang(yft[i])) - \
model.vm[to[i]] * model.vm[fr[i]] * abs(yk[i]) / tr0[i] * \
sin(model.va[to[i]] - model.va[fr[i]] - ang(yk[i]))
model.const6 = Constraint(model.line, rule=qto)
# Slack bus phase angle
model.const7 = Constraint(expr=model.va[sb[0]] == 0)
# Transformation ratio equalities
def trfunc(model, i):
return model.tr[i] == 1 + dudtap * model.tap[i]
model.const8 = Constraint(model.taps, rule=trfunc)
# Shunt susceptance equality
def shuntfunc(model, i):
return model.Bs[i] == model.s[i] / stepmax * Bs0[i]
model.const9 = Constraint(model.shunt, rule=shuntfunc)
# Inequalities:
# ----------------
# Active power generator limits Pg_min <= Pg <= Pg_max
def genplimits(model, i):
return Pg_min[i] <= model.Pg[i] <= Pg_max[i]
model.const10 = Constraint(model.gen, rule=genplimits)
# Reactive power generator limits Qg_min <= Qg <= Qg_max
def genqlimits(model, i):
return Qg_min[i] <= model.Qg[i] <= Qg_max[i]
model.const11 = Constraint(model.gen, rule=genqlimits)
# Voltage constraints ( Vmin <= V <= Vmax )
def vlimits(model, i):
return Vmin[i] <= model.vm[i] <= Vmax[i]
model.const12 = Constraint(model.bus, rule=vlimits)
# Sfrom line limit
def sfrommax(model, i):
return model.Pf[i]**2 + model.Qf[i]**2 <= Smax[i]**2
model.const13 = Constraint(model.line, rule=sfrommax)
# Sto line limit
def stomax(model, i):
return model.Pt[i]**2 + model.Qt[i]**2 <= Smax[i]**2
model.const14 = Constraint(model.line, rule=stomax)
# Set objective function
# ------------------------
def obj_fun(model):
return sum(gk[i] * ((model.vm[fr[i]] / model.tr[i])**2 + model.vm[to[i]]**2 -
2 / model.tr[i] * model.vm[fr[i]] * model.vm[to[i]] *
cos(model.va[fr[i]] - model.va[to[i]])) for i in tp) + \
sum(gk[i] * ((model.vm[fr[i]] / tr0[i]) ** 2 + model.vm[to[i]] ** 2 -
2 / tr0[i] * model.vm[fr[i]] * model.vm[to[i]] *
cos(model.va[fr[i]] - model.va[to[i]])) for i in ntp)
model.obj = Objective(rule=obj_fun, sense=minimize)
mt = time.clock() - start_time # Modeling time
# Execute solve command with the selected solver
# ------------------------------------------------
start_time = time.clock()
results = opt.solve(model, tee=True)
et = time.clock() - start_time # Elapsed time
print(results)
# Update the case info with the optimized variables and approximate the continuous variables to discrete values
# ==============================================================================================================
for i in range(nb):
if i in sd:
bus[i, BS] = round(model.s[i].value) * Bs0[i] * baseMVA
bus[i, VM] = model.vm[i].value # Bus voltage magnitudes
bus[i, VA] = model.va[i].value * 180 / pi # Bus voltage angles
# Update transformation ratios
for i in range(nl):
if i in tp:
branch[i, TAP] = 1 + dudtap * round(model.tap[i].value)
# Update gen matrix variables
for i in range(ng):
gen[i, PG] = model.Pg[gb[i]].value * baseMVA
gen[i, QG] = model.Qg[gb[i]].value * baseMVA
gen[i, VG] = bus[gb[i], VM]
# Convert to external (original) numbering and save case results
ppc = int2ext(ppc)
ppc['bus'][:, 1:] = bus[:, 1:]
branch[:, 0:2] = ppc['branch'][:, 0:2]
ppc['branch'] = branch
ppc['gen'][:, 1:] = gen[:, 1:]
# Execute a second optimization with only the discrete approximated values (requires solveropfnlp_2)
sol = solveropfnlp_2(ppc)
sol['mt'] = sol['mt'] + mt
sol['et'] = sol['et'] + et
sol['tap'] = zeros((tp.shape[0], 1))
for i in range(tp.shape[0]):
sol['tap'][i] = round(model.tap[tp[i]].value)
sol['shunt'] = zeros((sd.shape[0], 1))
for i in range(sd.shape[0]):
sol['shunt'][i] = round(model.s[sd[i]].value)
# ppc solved case is returned
return sol
