def eval_jac_g(x, flag, user_data=None):
"""Calculates the Jacobi matrix.
If the flag is true, returns a tuple (row, col) to indicate the
sparse Jacobi matrix's structure.
If the flag is false, returns the values of the Jacobi matrix
with length nnzj.
"""
Js = user_data['Js']
if flag:
return (Js.row, Js.col)
else:
om = user_data['om']
Ybus = user_data['Ybus']
Yf = user_data['Yf']
Yt = user_data['Yt']
ppopt = user_data['ppopt']
il = user_data['il']
A = user_data['A']
_, _, dhn, dgn = opf_consfcn(x, om, Ybus, Yf, Yt, ppopt, il)
if A is not None and issparse(A):
J = vstack([dgn.T, dhn.T, A], 'coo')
else:
J = vstack([dgn.T, dhn.T], 'coo')
## FIXME: Extend PyIPOPT to handle changes in sparsity structure
nnzj = Js.nnz
Jd = zeros(nnzj)
Jc = J.tocsc()
for i in range(nnzj):
Jd[i] = Jc[Js.row[i], Js.col[i]]
return Jd
def eval_jac_g(x, flag, user_data=None):
"""Calculates the Jacobi matrix.
If the flag is true, returns a tuple (row, col) to indicate the
sparse Jacobi matrix's structure.
If the flag is false, returns the values of the Jacobi matrix
with length nnzj.
"""
Js = user_data['Js']
if flag:
return (Js.row, Js.col)
else:
om = user_data['om']
Ybus = user_data['Ybus']
Yf = user_data['Yf']
Yt = user_data['Yt']
ppopt = user_data['ppopt']
il = user_data['il']
A = user_data['A']
_, _, dhn, dgn = opf_consfcn(x, om, Ybus, Yf, Yt, ppopt, il)
if A is not None and issparse(A):
J = vstack([dgn.T, dhn.T, A], 'coo')
else:
J = vstack([dgn.T, dhn.T], 'coo')
## FIXME: Extend PyIPOPT to handle changes in sparsity structure
nnzj = Js.nnz
Jd = zeros(nnzj)
Jc = J.tocsc()
for i in range(nnzj):
Jd[i] = Jc[Js.row[i], Js.col[i]]
return Jd
def eval_g(x, user_data=None):
"""Calculates the constraint values and returns an array.
"""
om = user_data['om']
Ybus = user_data['Ybus']
Yf = user_data['Yf']
Yt = user_data['Yt']
ppopt = user_data['ppopt']
il = user_data['il']
A = user_data['A']
hn, gn, _, _ = opf_consfcn(x, om, Ybus, Yf, Yt, ppopt, il)
if A is not None and issparse(A):
c = r_[gn, hn, A * x]
else:
c = r_[gn, hn]
return c
def eval_g(x, user_data=None):
"""Calculates the constraint values and returns an array.
"""
om = user_data['om']
Ybus = user_data['Ybus']
Yf = user_data['Yf']
Yt = user_data['Yt']
ppopt = user_data['ppopt']
il = user_data['il']
A = user_data['A']
hn, gn, _, _ = opf_consfcn(x, om, Ybus, Yf, Yt, ppopt, il)
if A is not None and issparse(A):
c = r_[gn, hn, A * x]
else:
c = r_[gn, hn]
return c
def pipsopf_solver(om, ppopt, out_opt=None):
"""Solves AC optimal power flow using PIPS.
Inputs are an OPF model object, a PYPOWER options vector and
a dict containing keys (can be empty) for each of the desired
optional output fields.
outputs are a C{results} dict, C{success} flag and C{raw} output dict.
C{results} is a PYPOWER case dict (ppc) with the usual baseMVA, bus
branch, gen, gencost fields, along with the following additional
fields:
- C{order} see 'help ext2int' for details of this field
- C{x} final value of optimization variables (internal order)
- C{f} final objective function value
- C{mu} shadow prices on ...
- C{var}
- C{l} lower bounds on variables
- C{u} upper bounds on variables
- C{nln}
- C{l} lower bounds on nonlinear constraints
- C{u} upper bounds on nonlinear constraints
- C{lin}
- C{l} lower bounds on linear constraints
- C{u} upper bounds on linear constraints
C{success} is C{True} if solver converged successfully, C{False} otherwise
C{raw} is a raw output dict in form returned by MINOS
- xr final value of optimization variables
- pimul constraint multipliers
- info solver specific termination code
- output solver specific output information
@see: L{opf}, L{pips}
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Richard Lincoln
"""
##----- initialization -----
## optional output
if out_opt is None:
out_opt = {}
## options
verbose = ppopt['VERBOSE']
feastol = ppopt['PDIPM_FEASTOL']
gradtol = ppopt['PDIPM_GRADTOL']
comptol = ppopt['PDIPM_COMPTOL']
costtol = ppopt['PDIPM_COSTTOL']
max_it = ppopt['PDIPM_MAX_IT']
max_red = ppopt['SCPDIPM_RED_IT']
step_control = (ppopt['OPF_ALG'] == 565) ## OPF_ALG == 565, PIPS-sc
if feastol == 0:
feastol = ppopt['OPF_VIOLATION']
opt = { 'feastol': feastol,
'gradtol': gradtol,
'comptol': comptol,
'costtol': costtol,
'max_it': max_it,
'max_red': max_red,
'step_control': step_control,
'cost_mult': 1e-4,
'verbose': verbose }
## unpack data
ppc = om.get_ppc()
baseMVA, bus, gen, branch, gencost = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"], ppc["gencost"]
vv, _, nn, _ = om.get_idx()
## problem dimensions
nb = bus.shape[0] ## number of buses
nl = branch.shape[0] ## number of branches
ny = om.getN('var', 'y') ## number of piece-wise linear costs
## linear constraints
A, l, u = om.linear_constraints()
## bounds on optimization vars
_, xmin, xmax = om.getv()
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## try to select an interior initial point
ll, uu = xmin.copy(), xmax.copy()
ll[xmin == -Inf] = -1e10 ## replace Inf with numerical proxies
uu[xmax == Inf] = 1e10
x0 = (ll + uu) / 2
Varefs = bus[bus[:, BUS_TYPE] == REF, VA] * (pi / 180)
## angles set to first reference angle
x0[vv["i1"]["Va"]:vv["iN"]["Va"]] = Varefs[0]
if ny > 0:
ipwl = find(gencost[:, MODEL] == PW_LINEAR)
# PQ = r_[gen[:, PMAX], gen[:, QMAX]]
# c = totcost(gencost[ipwl, :], PQ[ipwl])
c = gencost.flatten('F')[sub2ind(gencost.shape, ipwl, NCOST+2*gencost[ipwl, NCOST])] ## largest y-value in CCV data
x0[vv["i1"]["y"]:vv["iN"]["y"]] = max(c) + 0.1 * abs(max(c))
# x0[vv["i1"]["y"]:vv["iN"]["y"]] = c + 0.1 * abs(c)
## find branches with flow limits
il = find((branch[:, RATE_A] != 0) & (branch[:, RATE_A] < 1e10))
nl2 = len(il) ## number of constrained lines
##----- run opf -----
f_fcn = lambda x, return_hessian=False: opf_costfcn(x, om, return_hessian)
gh_fcn = lambda x: opf_consfcn(x, om, Ybus, Yf[il, :], Yt[il,:], ppopt, il)
hess_fcn = lambda x, lmbda, cost_mult: opf_hessfcn(x, lmbda, om, Ybus, Yf[il, :], Yt[il, :], ppopt, il, cost_mult)
solution = pips(f_fcn, x0, A, l, u, xmin, xmax, gh_fcn, hess_fcn, opt)
x, f, info, lmbda, output = solution["x"], solution["f"], \
solution["eflag"], solution["lmbda"], solution["output"]
success = (info > 0)
## update solution data
Va = x[vv["i1"]["Va"]:vv["iN"]["Va"]]
Vm = x[vv["i1"]["Vm"]:vv["iN"]["Vm"]]
Pg = x[vv["i1"]["Pg"]:vv["iN"]["Pg"]]
Qg = x[vv["i1"]["Qg"]:vv["iN"]["Qg"]]
V = Vm * exp(1j * Va)
##----- calculate return values -----
## update voltages & generator outputs
bus[:, VA] = Va * 180 / pi
bus[:, VM] = Vm
gen[:, PG] = Pg * baseMVA
gen[:, QG] = Qg * baseMVA
gen[:, VG] = Vm[ gen[:, GEN_BUS].astype(int) ]
## compute branch flows
Sf = V[ branch[:, F_BUS].astype(int) ] * conj(Yf * V) ## cplx pwr at "from" bus, p["u"].
St = V[ branch[:, T_BUS].astype(int) ] * conj(Yt * V) ## cplx pwr at "to" bus, p["u"].
branch[:, PF] = Sf.real * baseMVA
branch[:, QF] = Sf.imag * baseMVA
branch[:, PT] = St.real * baseMVA
branch[:, QT] = St.imag * baseMVA
## line constraint is actually on square of limit
## so we must fix multipliers
muSf = zeros(nl)
muSt = zeros(nl)
if len(il) > 0:
muSf[il] = \
2 * lmbda["ineqnonlin"][:nl2] * branch[il, RATE_A] / baseMVA
muSt[il] = \
2 * lmbda["ineqnonlin"][nl2:nl2+nl2] * branch[il, RATE_A] / baseMVA
## update Lagrange multipliers
bus[:, MU_VMAX] = lmbda["upper"][vv["i1"]["Vm"]:vv["iN"]["Vm"]]
bus[:, MU_VMIN] = lmbda["lower"][vv["i1"]["Vm"]:vv["iN"]["Vm"]]
gen[:, MU_PMAX] = lmbda["upper"][vv["i1"]["Pg"]:vv["iN"]["Pg"]] / baseMVA
gen[:, MU_PMIN] = lmbda["lower"][vv["i1"]["Pg"]:vv["iN"]["Pg"]] / baseMVA
gen[:, MU_QMAX] = lmbda["upper"][vv["i1"]["Qg"]:vv["iN"]["Qg"]] / baseMVA
gen[:, MU_QMIN] = lmbda["lower"][vv["i1"]["Qg"]:vv["iN"]["Qg"]] / baseMVA
bus[:, LAM_P] = \
lmbda["eqnonlin"][nn["i1"]["Pmis"]:nn["iN"]["Pmis"]] / baseMVA
bus[:, LAM_Q] = \
lmbda["eqnonlin"][nn["i1"]["Qmis"]:nn["iN"]["Qmis"]] / baseMVA
branch[:, MU_SF] = muSf / baseMVA
branch[:, MU_ST] = muSt / baseMVA
## package up results
nlnN = om.getN('nln')
## extract multipliers for nonlinear constraints
kl = find(lmbda["eqnonlin"] < 0)
ku = find(lmbda["eqnonlin"] > 0)
nl_mu_l = zeros(nlnN)
nl_mu_u = r_[zeros(2*nb), muSf, muSt]
nl_mu_l[kl] = -lmbda["eqnonlin"][kl]
nl_mu_u[ku] = lmbda["eqnonlin"][ku]
mu = {
'var': {'l': lmbda["lower"], 'u': lmbda["upper"]},
'nln': {'l': nl_mu_l, 'u': nl_mu_u},
'lin': {'l': lmbda["mu_l"], 'u': lmbda["mu_u"]} }
results = ppc
results["bus"], results["branch"], results["gen"], \
results["om"], results["x"], results["mu"], results["f"] = \
bus, branch, gen, om, x, mu, f
pimul = r_[
results["mu"]["nln"]["l"] - results["mu"]["nln"]["u"],
results["mu"]["lin"]["l"] - results["mu"]["lin"]["u"],
-ones(ny > 0),
results["mu"]["var"]["l"] - results["mu"]["var"]["u"],
]
raw = {'xr': x, 'pimul': pimul, 'info': info, 'output': output}
return results, success, raw
def opf_hessfcn(x, lmbda, om, Ybus, Yf, Yt, ppopt, il=None, cost_mult=1.0):
"""Evaluates Hessian of Lagrangian for AC OPF.
Hessian evaluation function for AC optimal power flow, suitable
for use with L{pips}.
Examples::
Lxx = opf_hessfcn(x, lmbda, om, Ybus, Yf, Yt, ppopt)
Lxx = opf_hessfcn(x, lmbda, om, Ybus, Yf, Yt, ppopt, il)
Lxx = opf_hessfcn(x, lmbda, om, Ybus, Yf, Yt, ppopt, il, cost_mult)
@param x: optimization vector
@param lmbda: C{eqnonlin} - Lagrange multipliers on power balance
equations. C{ineqnonlin} - Kuhn-Tucker multipliers on constrained
branch flows.
@param om: OPF model object
@param Ybus: bus admittance matrix
@param Yf: admittance matrix for "from" end of constrained branches
@param Yt: admittance matrix for "to" end of constrained branches
@param ppopt: PYPOWER options vector
@param il: (optional) vector of branch indices corresponding to
branches with flow limits (all others are assumed to be unconstrained).
The default is C{range(nl)} (all branches). C{Yf} and C{Yt} contain
only the rows corresponding to C{il}.
@param cost_mult: (optional) Scale factor to be applied to the cost
(default = 1).
@return: Hessian of the Lagrangian.
@see: L{opf_costfcn}, L{opf_consfcn}
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
"""
##----- initialize -----
## unpack data
ppc = om.get_ppc()
baseMVA, bus, gen, branch, gencost = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"], ppc["gencost"]
cp = om.get_cost_params()
N, Cw, H, dd, rh, kk, mm = \
cp["N"], cp["Cw"], cp["H"], cp["dd"], cp["rh"], cp["kk"], cp["mm"]
vv, _, _, _ = om.get_idx()
## unpack needed parameters
nb = bus.shape[0] ## number of buses
nl = branch.shape[0] ## number of branches
ng = gen.shape[0] ## number of dispatchable injections
nxyz = len(x) ## total number of control vars of all types
## set default constrained lines
if il is None:
il = arange(nl) ## all lines have limits by default
nl2 = len(il) ## number of constrained lines
## grab Pg & Qg
Pg = x[vv["i1"]["Pg"]:vv["iN"]["Pg"]] ## active generation in p.u.
Qg = x[vv["i1"]["Qg"]:vv["iN"]["Qg"]] ## reactive generation in p.u.
## put Pg & Qg back in gen
gen[:, PG] = Pg * baseMVA ## active generation in MW
gen[:, QG] = Qg * baseMVA ## reactive generation in MVAr
## reconstruct V
Va = x[vv["i1"]["Va"]:vv["iN"]["Va"]]
Vm = x[vv["i1"]["Vm"]:vv["iN"]["Vm"]]
V = Vm * exp(1j * Va)
nxtra = nxyz - 2 * nb
pcost = gencost[arange(ng), :]
if gencost.shape[0] > ng:
qcost = gencost[arange(ng, 2 * ng), :]
else:
qcost = array([])
## ----- evaluate d2f -----
d2f_dPg2 = zeros(ng) #sparse((ng, 1)) ## w.r.t. p.u. Pg
d2f_dQg2 = zeros(ng) #sparse((ng, 1)) ## w.r.t. p.u. Qg
ipolp = find(pcost[:, MODEL] == POLYNOMIAL)
d2f_dPg2[ipolp] = \
baseMVA**2 * polycost(pcost[ipolp, :], Pg[ipolp] * baseMVA, 2)
if any(qcost): ## Qg is not free
ipolq = find(qcost[:, MODEL] == POLYNOMIAL)
d2f_dQg2[ipolq] = \
baseMVA**2 * polycost(qcost[ipolq, :], Qg[ipolq] * baseMVA, 2)
i = r_[arange(vv["i1"]["Pg"], vv["iN"]["Pg"]),
arange(vv["i1"]["Qg"], vv["iN"]["Qg"])]
# d2f = sparse((vstack([d2f_dPg2, d2f_dQg2]).toarray().flatten(),
# (i, i)), shape=(nxyz, nxyz))
d2f = sparse((r_[d2f_dPg2, d2f_dQg2], (i, i)), (nxyz, nxyz))
## generalized cost
if issparse(N) and N.nnz > 0:
nw = N.shape[0]
r = N * x - rh ## Nx - rhat
iLT = find(r < -kk) ## below dead zone
iEQ = find((r == 0) & (kk == 0)) ## dead zone doesn't exist
iGT = find(r > kk) ## above dead zone
iND = r_[iLT, iEQ, iGT] ## rows that are Not in the Dead region
iL = find(dd == 1) ## rows using linear function
iQ = find(dd == 2) ## rows using quadratic function
LL = sparse((ones(len(iL)), (iL, iL)), (nw, nw))
QQ = sparse((ones(len(iQ)), (iQ, iQ)), (nw, nw))
kbar = sparse((r_[ones(len(iLT)),
zeros(len(iEQ)), -ones(len(iGT))], (iND, iND)),
(nw, nw)) * kk
rr = r + kbar ## apply non-dead zone shift
M = sparse((mm[iND], (iND, iND)), (nw, nw)) ## dead zone or scale
diagrr = sparse((rr, (arange(nw), arange(nw))), (nw, nw))
## linear rows multiplied by rr(i), quadratic rows by rr(i)^2
w = M * (LL + QQ * diagrr) * rr
HwC = H * w + Cw
AA = N.T * M * (LL + 2 * QQ * diagrr)
d2f = d2f + AA * H * AA.T + 2 * N.T * M * QQ * \
sparse((HwC, (arange(nw), arange(nw))), (nw, nw)) * N
d2f = d2f * cost_mult
##----- evaluate Hessian of power balance constraints -----
nlam = len(lmbda["eqnonlin"]) / 2
lamP = lmbda["eqnonlin"][:nlam]
lamQ = lmbda["eqnonlin"][nlam:nlam + nlam]
Gpaa, Gpav, Gpva, Gpvv = d2Sbus_dV2(Ybus, V, lamP)
Gqaa, Gqav, Gqva, Gqvv = d2Sbus_dV2(Ybus, V, lamQ)
d2G = vstack([
hstack([
vstack([hstack([Gpaa, Gpav]),
hstack([Gpva, Gpvv])]).real +
vstack([hstack([Gqaa, Gqav]),
hstack([Gqva, Gqvv])]).imag,
sparse((2 * nb, nxtra))
]),
hstack([sparse(
(nxtra, 2 * nb)), sparse((nxtra, nxtra))])
], "csr")
##----- evaluate Hessian of flow constraints -----
nmu = len(lmbda["ineqnonlin"]) / 2
muF = lmbda["ineqnonlin"][:nmu]
muT = lmbda["ineqnonlin"][nmu:nmu + nmu]
if ppopt['OPF_FLOW_LIM'] == 2: ## current
dIf_dVa, dIf_dVm, dIt_dVa, dIt_dVm, If, It = dIbr_dV(branch, Yf, Yt, V)
Hfaa, Hfav, Hfva, Hfvv = d2AIbr_dV2(dIf_dVa, dIf_dVm, If, Yf, V, muF)
Htaa, Htav, Htva, Htvv = d2AIbr_dV2(dIt_dVa, dIt_dVm, It, Yt, V, muT)
else:
f = branch[il, F_BUS].astype(int) ## list of "from" buses
t = branch[il, T_BUS].astype(int) ## list of "to" buses
## connection matrix for line & from buses
Cf = sparse((ones(nl2), (arange(nl2), f)), (nl2, nb))
## connection matrix for line & to buses
Ct = sparse((ones(nl2), (arange(nl2), t)), (nl2, nb))
dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St = \
dSbr_dV(branch[il,:], Yf, Yt, V)
if ppopt['OPF_FLOW_LIM'] == 1: ## real power
Hfaa, Hfav, Hfva, Hfvv = d2ASbr_dV2(dSf_dVa.real, dSf_dVm.real,
Sf.real, Cf, Yf, V, muF)
Htaa, Htav, Htva, Htvv = d2ASbr_dV2(dSt_dVa.real, dSt_dVm.real,
St.real, Ct, Yt, V, muT)
else: ## apparent power
Hfaa, Hfav, Hfva, Hfvv = \
d2ASbr_dV2(dSf_dVa, dSf_dVm, Sf, Cf, Yf, V, muF)
Htaa, Htav, Htva, Htvv = \
d2ASbr_dV2(dSt_dVa, dSt_dVm, St, Ct, Yt, V, muT)
d2H = vstack([
hstack([
vstack([hstack([Hfaa, Hfav]),
hstack([Hfva, Hfvv])]) +
vstack([hstack([Htaa, Htav]),
hstack([Htva, Htvv])]),
sparse((2 * nb, nxtra))
]),
hstack([sparse(
(nxtra, 2 * nb)), sparse((nxtra, nxtra))])
], "csr")
##----- do numerical check using (central) finite differences -----
if 0:
nx = len(x)
step = 1e-5
num_d2f = sparse((nx, nx))
num_d2G = sparse((nx, nx))
num_d2H = sparse((nx, nx))
for i in range(nx):
xp = x
xm = x
xp[i] = x[i] + step / 2
xm[i] = x[i] - step / 2
# evaluate cost & gradients
_, dfp = opf_costfcn(xp, om)
_, dfm = opf_costfcn(xm, om)
# evaluate constraints & gradients
_, _, dHp, dGp = opf_consfcn(xp, om, Ybus, Yf, Yt, ppopt, il)
_, _, dHm, dGm = opf_consfcn(xm, om, Ybus, Yf, Yt, ppopt, il)
num_d2f[:, i] = cost_mult * (dfp - dfm) / step
num_d2G[:, i] = (dGp - dGm) * lmbda["eqnonlin"] / step
num_d2H[:, i] = (dHp - dHm) * lmbda["ineqnonlin"] / step
d2f_err = max(max(abs(d2f - num_d2f)))
d2G_err = max(max(abs(d2G - num_d2G)))
d2H_err = max(max(abs(d2H - num_d2H)))
if d2f_err > 1e-6:
print('Max difference in d2f: %g' % d2f_err)
if d2G_err > 1e-5:
print('Max difference in d2G: %g' % d2G_err)
if d2H_err > 1e-6:
print('Max difference in d2H: %g' % d2H_err)
return d2f + d2G + d2H
def pipsopf_solver(om, ppopt, out_opt=None):
"""Solves AC optimal power flow using PIPS.
Inputs are an OPF model object, a PYPOWER options vector and
a dict containing keys (can be empty) for each of the desired
optional output fields.
outputs are a C{results} dict, C{success} flag and C{raw} output dict.
C{results} is a PYPOWER case dict (ppc) with the usual baseMVA, bus
branch, gen, gencost fields, along with the following additional
fields:
- C{order} see 'help ext2int' for details of this field
- C{x} final value of optimization variables (internal order)
- C{f} final objective function value
- C{mu} shadow prices on ...
- C{var}
- C{l} lower bounds on variables
- C{u} upper bounds on variables
- C{nln}
- C{l} lower bounds on nonlinear constraints
- C{u} upper bounds on nonlinear constraints
- C{lin}
- C{l} lower bounds on linear constraints
- C{u} upper bounds on linear constraints
C{success} is C{True} if solver converged successfully, C{False} otherwise
C{raw} is a raw output dict in form returned by MINOS
- xr final value of optimization variables
- pimul constraint multipliers
- info solver specific termination code
- output solver specific output information
@see: L{opf}, L{pips}
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Richard Lincoln
"""
##----- initialization -----
## optional output
if out_opt is None:
out_opt = {}
## options
verbose = ppopt['VERBOSE']
feastol = ppopt['PDIPM_FEASTOL']
gradtol = ppopt['PDIPM_GRADTOL']
comptol = ppopt['PDIPM_COMPTOL']
costtol = ppopt['PDIPM_COSTTOL']
max_it = ppopt['PDIPM_MAX_IT']
max_red = ppopt['SCPDIPM_RED_IT']
step_control = (ppopt['OPF_ALG'] == 565) ## OPF_ALG == 565, PIPS-sc
if feastol == 0:
feastol = ppopt['OPF_VIOLATION']
opt = { 'feastol': feastol,
'gradtol': gradtol,
'comptol': comptol,
'costtol': costtol,
'max_it': max_it,
'max_red': max_red,
'step_control': step_control,
'cost_mult': 1e-4,
'verbose': verbose }
## unpack data
ppc = om.get_ppc()
baseMVA, bus, gen, branch, gencost = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"], ppc["gencost"]
vv, _, nn, _ = om.get_idx()
## problem dimensions
nb = bus.shape[0] ## number of buses
nl = branch.shape[0] ## number of branches
ny = om.getN('var', 'y') ## number of piece-wise linear costs
## linear constraints
A, l, u = om.linear_constraints()
## bounds on optimization vars
_, xmin, xmax = om.getv()
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## try to select an interior initial point
ll, uu = xmin.copy(), xmax.copy()
ll[xmin == -Inf] = -1e10 ## replace Inf with numerical proxies
uu[xmax == Inf] = 1e10
x0 = (ll + uu) / 2
Varefs = bus[bus[:, BUS_TYPE] == REF, VA] * (pi / 180)
## angles set to first reference angle
x0[vv["i1"]["Va"]:vv["iN"]["Va"]] = Varefs[0]
if ny > 0:
ipwl = find(gencost[:, MODEL] == PW_LINEAR)
# PQ = r_[gen[:, PMAX], gen[:, QMAX]]
# c = totcost(gencost[ipwl, :], PQ[ipwl])
c = gencost.flatten('F')[sub2ind(gencost.shape, ipwl, NCOST+2*gencost[ipwl, NCOST])] ## largest y-value in CCV data
x0[vv["i1"]["y"]:vv["iN"]["y"]] = max(c) + 0.1 * abs(max(c))
# x0[vv["i1"]["y"]:vv["iN"]["y"]] = c + 0.1 * abs(c)
## find branches with flow limits
il = find((branch[:, RATE_A] != 0) & (branch[:, RATE_A] < 1e10))
nl2 = len(il) ## number of constrained lines
##----- run opf -----
f_fcn = lambda x, return_hessian=False: opf_costfcn(x, om, return_hessian)
gh_fcn = lambda x: opf_consfcn(x, om, Ybus, Yf[il, :], Yt[il,:], ppopt, il)
hess_fcn = lambda x, lmbda, cost_mult: opf_hessfcn(x, lmbda, om, Ybus, Yf[il, :], Yt[il, :], ppopt, il, cost_mult)
solution = pips(f_fcn, x0, A, l, u, xmin, xmax, gh_fcn, hess_fcn, opt)
x, f, info, lmbda, output = solution["x"], solution["f"], \
solution["eflag"], solution["lmbda"], solution["output"]
success = (info > 0)
## update solution data
Va = x[vv["i1"]["Va"]:vv["iN"]["Va"]]
Vm = x[vv["i1"]["Vm"]:vv["iN"]["Vm"]]
Pg = x[vv["i1"]["Pg"]:vv["iN"]["Pg"]]
Qg = x[vv["i1"]["Qg"]:vv["iN"]["Qg"]]
V = Vm * exp(1j * Va)
##----- calculate return values -----
## update voltages & generator outputs
bus[:, VA] = Va * 180 / pi
bus[:, VM] = Vm
gen[:, PG] = Pg * baseMVA
gen[:, QG] = Qg * baseMVA
gen[:, VG] = Vm[ gen[:, GEN_BUS].astype(int) ]
## compute branch flows
Sf = V[ branch[:, F_BUS].astype(int) ] * conj(Yf * V) ## cplx pwr at "from" bus, p["u"].
St = V[ branch[:, T_BUS].astype(int) ] * conj(Yt * V) ## cplx pwr at "to" bus, p["u"].
branch[:, PF] = Sf.real * baseMVA
branch[:, QF] = Sf.imag * baseMVA
branch[:, PT] = St.real * baseMVA
branch[:, QT] = St.imag * baseMVA
## line constraint is actually on square of limit
## so we must fix multipliers
muSf = zeros(nl)
muSt = zeros(nl)
if len(il) > 0:
muSf[il] = \
2 * lmbda["ineqnonlin"][:nl2] * branch[il, RATE_A] / baseMVA
muSt[il] = \
2 * lmbda["ineqnonlin"][nl2:nl2+nl2] * branch[il, RATE_A] / baseMVA
## update Lagrange multipliers
bus[:, MU_VMAX] = lmbda["upper"][vv["i1"]["Vm"]:vv["iN"]["Vm"]]
bus[:, MU_VMIN] = lmbda["lower"][vv["i1"]["Vm"]:vv["iN"]["Vm"]]
gen[:, MU_PMAX] = lmbda["upper"][vv["i1"]["Pg"]:vv["iN"]["Pg"]] / baseMVA
gen[:, MU_PMIN] = lmbda["lower"][vv["i1"]["Pg"]:vv["iN"]["Pg"]] / baseMVA
gen[:, MU_QMAX] = lmbda["upper"][vv["i1"]["Qg"]:vv["iN"]["Qg"]] / baseMVA
gen[:, MU_QMIN] = lmbda["lower"][vv["i1"]["Qg"]:vv["iN"]["Qg"]] / baseMVA
bus[:, LAM_P] = \
lmbda["eqnonlin"][nn["i1"]["Pmis"]:nn["iN"]["Pmis"]] / baseMVA
bus[:, LAM_Q] = \
lmbda["eqnonlin"][nn["i1"]["Qmis"]:nn["iN"]["Qmis"]] / baseMVA
branch[:, MU_SF] = muSf / baseMVA
branch[:, MU_ST] = muSt / baseMVA
## package up results
nlnN = om.getN('nln')
## extract multipliers for nonlinear constraints
kl = find(lmbda["eqnonlin"] < 0)
ku = find(lmbda["eqnonlin"] > 0)
nl_mu_l = zeros(nlnN)
nl_mu_u = r_[zeros(2*nb), muSf, muSt]
nl_mu_l[kl] = -lmbda["eqnonlin"][kl]
nl_mu_u[ku] = lmbda["eqnonlin"][ku]
mu = {
'var': {'l': lmbda["lower"], 'u': lmbda["upper"]},
'nln': {'l': nl_mu_l, 'u': nl_mu_u},
'lin': {'l': lmbda["mu_l"], 'u': lmbda["mu_u"]} }
results = ppc
results["bus"], results["branch"], results["gen"], \
results["om"], results["x"], results["mu"], results["f"] = \
bus, branch, gen, om, x, mu, f
pimul = r_[
results["mu"]["nln"]["l"] - results["mu"]["nln"]["u"],
results["mu"]["lin"]["l"] - results["mu"]["lin"]["u"],
-ones(ny),
results["mu"]["var"]["l"] - results["mu"]["var"]["u"],
]
raw = {'xr': x, 'pimul': pimul, 'info': info, 'output': output}
return results, success, raw
def opf_hessfcn(x, lmbda, om, Ybus, Yf, Yt, ppopt, il=None, cost_mult=1.0):
"""Evaluates Hessian of Lagrangian for AC OPF.
Hessian evaluation function for AC optimal power flow, suitable
for use with L{pips}.
Examples::
Lxx = opf_hessfcn(x, lmbda, om, Ybus, Yf, Yt, ppopt)
Lxx = opf_hessfcn(x, lmbda, om, Ybus, Yf, Yt, ppopt, il)
Lxx = opf_hessfcn(x, lmbda, om, Ybus, Yf, Yt, ppopt, il, cost_mult)
@param x: optimization vector
@param lmbda: C{eqnonlin} - Lagrange multipliers on power balance
equations. C{ineqnonlin} - Kuhn-Tucker multipliers on constrained
branch flows.
@param om: OPF model object
@param Ybus: bus admittance matrix
@param Yf: admittance matrix for "from" end of constrained branches
@param Yt: admittance matrix for "to" end of constrained branches
@param ppopt: PYPOWER options vector
@param il: (optional) vector of branch indices corresponding to
branches with flow limits (all others are assumed to be unconstrained).
The default is C{range(nl)} (all branches). C{Yf} and C{Yt} contain
only the rows corresponding to C{il}.
@param cost_mult: (optional) Scale factor to be applied to the cost
(default = 1).
@return: Hessian of the Lagrangian.
@see: L{opf_costfcn}, L{opf_consfcn}
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
"""
##----- initialize -----
## unpack data
ppc = om.get_ppc()
baseMVA, bus, gen, branch, gencost = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"], ppc["gencost"]
cp = om.get_cost_params()
N, Cw, H, dd, rh, kk, mm = \
cp["N"], cp["Cw"], cp["H"], cp["dd"], cp["rh"], cp["kk"], cp["mm"]
vv, _, _, _ = om.get_idx()
## unpack needed parameters
nb = bus.shape[0] ## number of buses
nl = branch.shape[0] ## number of branches
ng = gen.shape[0] ## number of dispatchable injections
nxyz = len(x) ## total number of control vars of all types
## set default constrained lines
if il is None:
il = arange(nl) ## all lines have limits by default
nl2 = len(il) ## number of constrained lines
## grab Pg & Qg
Pg = x[vv["i1"]["Pg"]:vv["iN"]["Pg"]] ## active generation in p.u.
Qg = x[vv["i1"]["Qg"]:vv["iN"]["Qg"]] ## reactive generation in p.u.
## put Pg & Qg back in gen
gen[:, PG] = Pg * baseMVA ## active generation in MW
gen[:, QG] = Qg * baseMVA ## reactive generation in MVAr
## reconstruct V
Va = x[vv["i1"]["Va"]:vv["iN"]["Va"]]
Vm = x[vv["i1"]["Vm"]:vv["iN"]["Vm"]]
V = Vm * exp(1j * Va)
nxtra = nxyz - 2 * nb
pcost = gencost[arange(ng), :]
if gencost.shape[0] > ng:
qcost = gencost[arange(ng, 2 * ng), :]
else:
qcost = array([])
## ----- evaluate d2f -----
d2f_dPg2 = zeros(ng)#sparse((ng, 1)) ## w.r.t. p.u. Pg
d2f_dQg2 = zeros(ng)#sparse((ng, 1)) ## w.r.t. p.u. Qg
ipolp = find(pcost[:, MODEL] == POLYNOMIAL)
d2f_dPg2[ipolp] = \
baseMVA**2 * polycost(pcost[ipolp, :], Pg[ipolp] * baseMVA, 2)
if any(qcost): ## Qg is not free
ipolq = find(qcost[:, MODEL] == POLYNOMIAL)
d2f_dQg2[ipolq] = \
baseMVA**2 * polycost(qcost[ipolq, :], Qg[ipolq] * baseMVA, 2)
i = r_[arange(vv["i1"]["Pg"], vv["iN"]["Pg"]),
arange(vv["i1"]["Qg"], vv["iN"]["Qg"])]
# d2f = sparse((vstack([d2f_dPg2, d2f_dQg2]).toarray().flatten(),
# (i, i)), shape=(nxyz, nxyz))
d2f = sparse((r_[d2f_dPg2, d2f_dQg2], (i, i)), (nxyz, nxyz))
## generalized cost
if issparse(N) and N.nnz > 0:
nw = N.shape[0]
r = N * x - rh ## Nx - rhat
iLT = find(r < -kk) ## below dead zone
iEQ = find((r == 0) & (kk == 0)) ## dead zone doesn't exist
iGT = find(r > kk) ## above dead zone
iND = r_[iLT, iEQ, iGT] ## rows that are Not in the Dead region
iL = find(dd == 1) ## rows using linear function
iQ = find(dd == 2) ## rows using quadratic function
LL = sparse((ones(len(iL)), (iL, iL)), (nw, nw))
QQ = sparse((ones(len(iQ)), (iQ, iQ)), (nw, nw))
kbar = sparse((r_[ones(len(iLT)), zeros(len(iEQ)), -ones(len(iGT))],
(iND, iND)), (nw, nw)) * kk
rr = r + kbar ## apply non-dead zone shift
M = sparse((mm[iND], (iND, iND)), (nw, nw)) ## dead zone or scale
diagrr = sparse((rr, (arange(nw), arange(nw))), (nw, nw))
## linear rows multiplied by rr(i), quadratic rows by rr(i)^2
w = M * (LL + QQ * diagrr) * rr
HwC = H * w + Cw
AA = N.T * M * (LL + 2 * QQ * diagrr)
d2f = d2f + AA * H * AA.T + 2 * N.T * M * QQ * \
sparse((HwC, (arange(nw), arange(nw))), (nw, nw)) * N
d2f = d2f * cost_mult
##----- evaluate Hessian of power balance constraints -----
nlam = len(lmbda["eqnonlin"]) / 2
lamP = lmbda["eqnonlin"][:nlam]
lamQ = lmbda["eqnonlin"][nlam:nlam + nlam]
Gpaa, Gpav, Gpva, Gpvv = d2Sbus_dV2(Ybus, V, lamP)
Gqaa, Gqav, Gqva, Gqvv = d2Sbus_dV2(Ybus, V, lamQ)
d2G = vstack([
hstack([
vstack([hstack([Gpaa, Gpav]),
hstack([Gpva, Gpvv])]).real +
vstack([hstack([Gqaa, Gqav]),
hstack([Gqva, Gqvv])]).imag,
sparse((2 * nb, nxtra))]),
hstack([
sparse((nxtra, 2 * nb)),
sparse((nxtra, nxtra))
])
], "csr")
##----- evaluate Hessian of flow constraints -----
nmu = len(lmbda["ineqnonlin"]) / 2
muF = lmbda["ineqnonlin"][:nmu]
muT = lmbda["ineqnonlin"][nmu:nmu + nmu]
if ppopt['OPF_FLOW_LIM'] == 2: ## current
dIf_dVa, dIf_dVm, dIt_dVa, dIt_dVm, If, It = dIbr_dV(Yf, Yt, V)
Hfaa, Hfav, Hfva, Hfvv = d2AIbr_dV2(dIf_dVa, dIf_dVm, If, Yf, V, muF)
Htaa, Htav, Htva, Htvv = d2AIbr_dV2(dIt_dVa, dIt_dVm, It, Yt, V, muT)
else:
f = branch[il, F_BUS].astype(int) ## list of "from" buses
t = branch[il, T_BUS].astype(int) ## list of "to" buses
## connection matrix for line & from buses
Cf = sparse((ones(nl2), (arange(nl2), f)), (nl2, nb))
## connection matrix for line & to buses
Ct = sparse((ones(nl2), (arange(nl2), t)), (nl2, nb))
dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St = \
dSbr_dV(branch[il,:], Yf, Yt, V)
if ppopt['OPF_FLOW_LIM'] == 1: ## real power
Hfaa, Hfav, Hfva, Hfvv = d2ASbr_dV2(dSf_dVa.real, dSf_dVm.real,
Sf.real, Cf, Yf, V, muF)
Htaa, Htav, Htva, Htvv = d2ASbr_dV2(dSt_dVa.real, dSt_dVm.real,
St.real, Ct, Yt, V, muT)
else: ## apparent power
Hfaa, Hfav, Hfva, Hfvv = \
d2ASbr_dV2(dSf_dVa, dSf_dVm, Sf, Cf, Yf, V, muF)
Htaa, Htav, Htva, Htvv = \
d2ASbr_dV2(dSt_dVa, dSt_dVm, St, Ct, Yt, V, muT)
d2H = vstack([
hstack([
vstack([hstack([Hfaa, Hfav]),
hstack([Hfva, Hfvv])]) +
vstack([hstack([Htaa, Htav]),
hstack([Htva, Htvv])]),
sparse((2 * nb, nxtra))
]),
hstack([
sparse((nxtra, 2 * nb)),
sparse((nxtra, nxtra))
])
], "csr")
##----- do numerical check using (central) finite differences -----
if 0:
nx = len(x)
step = 1e-5
num_d2f = sparse((nx, nx))
num_d2G = sparse((nx, nx))
num_d2H = sparse((nx, nx))
for i in range(nx):
xp = x
xm = x
xp[i] = x[i] + step / 2
xm[i] = x[i] - step / 2
# evaluate cost & gradients
_, dfp = opf_costfcn(xp, om)
_, dfm = opf_costfcn(xm, om)
# evaluate constraints & gradients
_, _, dHp, dGp = opf_consfcn(xp, om, Ybus, Yf, Yt, ppopt, il)
_, _, dHm, dGm = opf_consfcn(xm, om, Ybus, Yf, Yt, ppopt, il)
num_d2f[:, i] = cost_mult * (dfp - dfm) / step
num_d2G[:, i] = (dGp - dGm) * lmbda["eqnonlin"] / step
num_d2H[:, i] = (dHp - dHm) * lmbda["ineqnonlin"] / step
d2f_err = max(max(abs(d2f - num_d2f)))
d2G_err = max(max(abs(d2G - num_d2G)))
d2H_err = max(max(abs(d2H - num_d2H)))
if d2f_err > 1e-6:
print('Max difference in d2f: %g' % d2f_err)
if d2G_err > 1e-5:
print('Max difference in d2G: %g' % d2G_err)
if d2H_err > 1e-6:
print('Max difference in d2H: %g' % d2H_err)
return d2f + d2G + d2H
def opf_execute(om, ppopt):
"""Executes the OPF specified by an OPF model object.
C{results} are returned with internal indexing, all equipment
in-service, etc.
@see: L{opf}, L{opf_setup}
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
##----- setup -----
## options
dc = ppopt['PF_DC'] ## 1 = DC OPF, 0 = AC OPF
alg = ppopt['OPF_ALG']
verbose = ppopt['VERBOSE']
## build user-defined costs
om.build_cost_params()
## get indexing
vv, ll, nn, _ = om.get_idx()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v['Version'], v['Date']))
##----- run DC OPF solver -----
if dc:
if verbose > 0:
stdout.write(' -- DC Optimal Power Flow\n')
results, success, raw = dcopf_solver(om, ppopt)
else:
##----- run AC OPF solver -----
if verbose > 0:
stdout.write(' -- AC Optimal Power Flow\n')
## if OPF_ALG not set, choose best available option
if alg == 0:
alg = 560 ## MIPS
## update deprecated algorithm codes to new, generalized formulation equivalents
if alg == 100 | alg == 200: ## CONSTR
alg = 300
elif alg == 120 | alg == 220: ## dense LP
alg = 320
elif alg == 140 | alg == 240: ## sparse (relaxed) LP
alg = 340
elif alg == 160 | alg == 260: ## sparse (full) LP
alg = 360
ppopt['OPF_ALG_POLY'] = alg
## run specific AC OPF solver
if alg == 560 or alg == 565: ## PIPS
results, success, raw = pipsopf_solver(om, ppopt)
elif alg == 580: ## IPOPT # pragma: no cover
try:
__import__('pyipopt')
results, success, raw = ipoptopf_solver(om, ppopt)
except ImportError:
raise ImportError('OPF_ALG %d requires IPOPT '
'(see https://projects.coin-or.org/Ipopt/)' %
alg)
else:
stderr.write(
'opf_execute: OPF_ALG %d is not a valid algorithm code\n' %
alg)
if ('output' not in raw) or ('alg' not in raw['output']):
raw['output']['alg'] = alg
if success:
if not dc:
## copy bus voltages back to gen matrix
results['gen'][:, VG] = results['bus'][
results['gen'][:, GEN_BUS].astype(int), VM]
## gen PQ capability curve multipliers
if (ll['N']['PQh'] > 0) | (ll['N']['PQl'] > 0): # pragma: no cover
mu_PQh = results['mu']['lin']['l'][
ll['i1']['PQh']:ll['iN']['PQh']] - results['mu']['lin'][
'u'][ll['i1']['PQh']:ll['iN']['PQh']]
mu_PQl = results['mu']['lin']['l'][
ll['i1']['PQl']:ll['iN']['PQl']] - results['mu']['lin'][
'u'][ll['i1']['PQl']:ll['iN']['PQl']]
Apqdata = om.userdata('Apqdata')
results['gen'] = update_mupq(results['baseMVA'],
results['gen'], mu_PQh, mu_PQl,
Apqdata)
## compute g, dg, f, df, d2f if requested by RETURN_RAW_DER = 1
if ppopt['RETURN_RAW_DER']: # pragma: no cover
## move from results to raw if using v4.0 of MINOPF or TSPOPF
if 'dg' in results:
raw = {}
raw['dg'] = results['dg']
raw['g'] = results['g']
## compute g, dg, unless already done by post-v4.0 MINOPF or TSPOPF
if 'dg' not in raw:
ppc = om.get_ppc()
Ybus, Yf, Yt = makeYbus(ppc['baseMVA'], ppc['bus'],
ppc['branch'])
g, geq, dg, dgeq = opf_consfcn(results['x'], om, Ybus, Yf,
Yt, ppopt)
raw['g'] = r_[geq, g]
raw['dg'] = r_[dgeq.T, dg.T] ## true Jacobian organization
## compute df, d2f
_, df, d2f = opf_costfcn(results['x'], om, True)
raw['df'] = df
raw['d2f'] = d2f
## delete g and dg fieldsfrom results if using v4.0 of MINOPF or TSPOPF
if 'dg' in results:
del results['dg']
del results['g']
## angle limit constraint multipliers
if ll['N']['ang'] > 0:
iang = om.userdata('iang')
results['branch'][iang, MU_ANGMIN] = results['mu']['lin']['l'][
ll['i1']['ang']:ll['iN']['ang']] * pi / 180
results['branch'][iang, MU_ANGMAX] = results['mu']['lin']['u'][
ll['i1']['ang']:ll['iN']['ang']] * pi / 180
else:
## assign empty g, dg, f, df, d2f if requested by RETURN_RAW_DER = 1
if not dc and ppopt['RETURN_RAW_DER']:
raw['dg'] = array([])
raw['g'] = array([])
raw['df'] = array([])
raw['d2f'] = array([])
## assign values and limit shadow prices for variables
if om.var['order']:
results['var'] = {'val': {}, 'mu': {'l': {}, 'u': {}}}
for name in om.var['order']:
if om.getN('var', name):
idx = arange(vv['i1'][name], vv['iN'][name])
results['var']['val'][name] = results['x'][idx]
results['var']['mu']['l'][name] = results['mu']['var']['l'][idx]
results['var']['mu']['u'][name] = results['mu']['var']['u'][idx]
## assign shadow prices for linear constraints
if om.lin['order']:
results['lin'] = {'mu': {'l': {}, 'u': {}}}
for name in om.lin['order']:
if om.getN('lin', name):
idx = arange(ll['i1'][name], ll['iN'][name])
results['lin']['mu']['l'][name] = results['mu']['lin']['l'][idx]
results['lin']['mu']['u'][name] = results['mu']['lin']['u'][idx]
## assign shadow prices for nonlinear constraints
if not dc:
if om.nln['order']:
results['nln'] = {'mu': {'l': {}, 'u': {}}}
for name in om.nln['order']:
if om.getN('nln', name):
idx = arange(nn['i1'][name], nn['iN'][name])
results['nln']['mu']['l'][name] = results['mu']['nln']['l'][
idx]
results['nln']['mu']['u'][name] = results['mu']['nln']['u'][
idx]
## assign values for components of user cost
if om.cost['order']:
results['cost'] = {}
for name in om.cost['order']:
if om.getN('cost', name):
results['cost'][name] = om.compute_cost(results['x'], name)
## if single-block PWL costs were converted to POLY, insert dummy y into x
## Note: The "y" portion of x will be nonsense, but everything should at
## least be in the expected locations.
pwl1 = om.userdata('pwl1')
if (len(pwl1) > 0) and (alg != 545) and (alg != 550):
## get indexing
vv, _, _, _ = om.get_idx()
if dc:
nx = vv['iN']['Pg']
else:
nx = vv['iN']['Qg']
y = zeros(len(pwl1))
raw['xr'] = r_[raw['xr'][:nx], y, raw['xr'][nx:]]
results['x'] = r_[results['x'][:nx], y, results['x'][nx:]]
return results, success, raw
def opf_execute(om, ppopt):
"""Executes the OPF specified by an OPF model object.
C{results} are returned with internal indexing, all equipment
in-service, etc.
@see: L{opf}, L{opf_setup}
@author: Ray Zimmerman (PSERC Cornell)
"""
##----- setup -----
## options
dc = ppopt['PF_DC'] ## 1 = DC OPF, 0 = AC OPF
alg = ppopt['OPF_ALG']
verbose = ppopt['VERBOSE']
## build user-defined costs
om.build_cost_params()
## get indexing
vv, ll, nn, _ = om.get_idx()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v['Version'], v['Date']))
##----- run DC OPF solver -----
if dc:
if verbose > 0:
stdout.write(' -- DC Optimal Power Flow\n')
results, success, raw = dcopf_solver(om, ppopt)
else:
##----- run AC OPF solver -----
if verbose > 0:
stdout.write(' -- AC Optimal Power Flow\n')
## if OPF_ALG not set, choose best available option
if alg == 0:
alg = 560 ## MIPS
## update deprecated algorithm codes to new, generalized formulation equivalents
if alg == 100 | alg == 200: ## CONSTR
alg = 300
elif alg == 120 | alg == 220: ## dense LP
alg = 320
elif alg == 140 | alg == 240: ## sparse (relaxed) LP
alg = 340
elif alg == 160 | alg == 260: ## sparse (full) LP
alg = 360
ppopt['OPF_ALG_POLY'] = alg
## run specific AC OPF solver
if alg == 560 or alg == 565: ## PIPS
results, success, raw = pipsopf_solver(om, ppopt)
elif alg == 580: ## IPOPT
try:
__import__('pyipopt')
results, success, raw = ipoptopf_solver(om, ppopt)
except ImportError:
raise ImportError('OPF_ALG %d requires IPOPT '
'(see https://projects.coin-or.org/Ipopt/)' %
alg)
else:
stderr.write('opf_execute: OPF_ALG %d is not a valid algorithm code\n' % alg)
if ('output' not in raw) or ('alg' not in raw['output']):
raw['output']['alg'] = alg
if success:
if not dc:
## copy bus voltages back to gen matrix
results['gen'][:, VG] = results['bus'][results['gen'][:, GEN_BUS].astype(int), VM]
## gen PQ capability curve multipliers
if (ll['N']['PQh'] > 0) | (ll['N']['PQl'] > 0):
mu_PQh = results['mu']['lin']['l'][ll['i1']['PQh']:ll['iN']['PQh']] - results['mu']['lin']['u'][ll['i1']['PQh']:ll['iN']['PQh']]
mu_PQl = results['mu']['lin']['l'][ll['i1']['PQl']:ll['iN']['PQl']] - results['mu']['lin']['u'][ll['i1']['PQl']:ll['iN']['PQl']]
Apqdata = om.userdata('Apqdata')
results['gen'] = update_mupq(results['baseMVA'], results['gen'], mu_PQh, mu_PQl, Apqdata)
## compute g, dg, f, df, d2f if requested by RETURN_RAW_DER = 1
if ppopt['RETURN_RAW_DER']:
## move from results to raw if using v4.0 of MINOPF or TSPOPF
if 'dg' in results:
raw = {}
raw['dg'] = results['dg']
raw['g'] = results['g']
## compute g, dg, unless already done by post-v4.0 MINOPF or TSPOPF
if 'dg' not in raw:
ppc = om.get_ppc()
Ybus, Yf, Yt = makeYbus(ppc['baseMVA'], ppc['bus'], ppc['branch'])
g, geq, dg, dgeq = opf_consfcn(results['x'], om, Ybus, Yf, Yt, ppopt)
raw['g'] = r_[geq, g]
raw['dg'] = r_[dgeq.T, dg.T] ## true Jacobian organization
## compute df, d2f
_, df, d2f = opf_costfcn(results['x'], om, True)
raw['df'] = df
raw['d2f'] = d2f
## delete g and dg fieldsfrom results if using v4.0 of MINOPF or TSPOPF
if 'dg' in results:
del results['dg']
del results['g']
## angle limit constraint multipliers
if ll['N']['ang'] > 0:
iang = om.userdata('iang')
results['branch'][iang, MU_ANGMIN] = results['mu']['lin']['l'][ll['i1']['ang']:ll['iN']['ang']] * pi / 180
results['branch'][iang, MU_ANGMAX] = results['mu']['lin']['u'][ll['i1']['ang']:ll['iN']['ang']] * pi / 180
else:
## assign empty g, dg, f, df, d2f if requested by RETURN_RAW_DER = 1
if not dc and ppopt['RETURN_RAW_DER']:
raw['dg'] = array([])
raw['g'] = array([])
raw['df'] = array([])
raw['d2f'] = array([])
## assign values and limit shadow prices for variables
if om.var['order']:
results['var'] = {'val': {}, 'mu': {'l': {}, 'u': {}}}
for name in om.var['order']:
if om.getN('var', name):
idx = arange(vv['i1'][name], vv['iN'][name])
results['var']['val'][name] = results['x'][idx]
results['var']['mu']['l'][name] = results['mu']['var']['l'][idx]
results['var']['mu']['u'][name] = results['mu']['var']['u'][idx]
## assign shadow prices for linear constraints
if om.lin['order']:
results['lin'] = {'mu': {'l': {}, 'u': {}}}
for name in om.lin['order']:
if om.getN('lin', name):
idx = arange(ll['i1'][name], ll['iN'][name])
results['lin']['mu']['l'][name] = results['mu']['lin']['l'][idx]
results['lin']['mu']['u'][name] = results['mu']['lin']['u'][idx]
## assign shadow prices for nonlinear constraints
if not dc:
if om.nln['order']:
results['nln'] = {'mu': {'l': {}, 'u': {}}}
for name in om.nln['order']:
if om.getN('nln', name):
idx = arange(nn['i1'][name], nn['iN'][name])
results['nln']['mu']['l'][name] = results['mu']['nln']['l'][idx]
results['nln']['mu']['u'][name] = results['mu']['nln']['u'][idx]
## assign values for components of user cost
if om.cost['order']:
results['cost'] = {}
for name in om.cost['order']:
if om.getN('cost', name):
results['cost'][name] = om.compute_cost(results['x'], name)
## if single-block PWL costs were converted to POLY, insert dummy y into x
## Note: The "y" portion of x will be nonsense, but everything should at
## least be in the expected locations.
pwl1 = om.userdata('pwl1')
if (len(pwl1) > 0) and (alg != 545) and (alg != 550):
## get indexing
vv, _, _, _ = om.get_idx()
if dc:
nx = vv['iN']['Pg']
else:
nx = vv['iN']['Qg']
y = zeros(len(pwl1))
raw['xr'] = r_[raw['xr'][:nx], y, raw['xr'][nx:]]
results['x'] = r_[results['x'][:nx], y, results['x'][nx:]]
return results, success, raw
