def solve_full(self, bi, lmbd, show_progress=True):
solvers.options['show_progress'] = show_progress
# load problem data
self.bi = bi
self.lmbd = lmbd
self.c[1 + self.Nm:1 + self.Nm + self.Nb] = lmbd
self.h[1:1 + self.Nm] = -bi
t1 = time()
self.sol = solvers.conelp(self.c, self.G, self.h, self.dims, self.A,
self.b)
t2 = time()
t3 = time()
if self.sol['status'] in ('optimal'):
S = self.sol['s'][1 + self.Nm:]
self.sA[:] = S[self.I11] + 1j * S[self.I21]
lapack.heevr(self.sA,
self.sW,
jobz='V',
range='I',
uplo='L',
vl=0.0,
vu=0.0,
il=self.Nb,
iu=self.Nb,
Z=self.sZ)
self.beta_hat[:] = math.sqrt(self.sW[0]) * self.sZ[:, 0]
else:
raise RuntimeError('numerical problems')
t4 = time()
self.solver_time = t2 - t1
self.eig_time = t4 - t3
def solve_full(self, bi, lmbd, show_progress=True):
solvers.options['show_progress'] = show_progress
# load problem data
self.bi = bi
self.lmbd = lmbd
self.c[1 + self.Nm:1 + self.Nm + self.Nb] = lmbd
self.h[1:1 + self.Nm] = -bi
t1 = time()
self.sol = solvers.conelp(
self.c, self.G, self.h, self.dims, self.A, self.b)
t2 = time()
t3 = time()
if self.sol['status'] in ('optimal'):
S = self.sol['s'][1 + self.Nm:]
self.sA[:] = S[self.I11] + 1j*S[self.I21]
lapack.heevr(
self.sA, self.sW, jobz='V', range='I', uplo='L',
vl=0.0, vu=0.0, il=self.Nb, iu=self.Nb, Z=self.sZ)
self.beta_hat[:] = math.sqrt(self.sW[0])*self.sZ[:, 0]
else:
raise RuntimeError('numerical problems')
t4 = time()
self.solver_time = t2 - t1
self.eig_time = t4 - t3
def solve_linmap(self, bi, lmbd, linmap, show_progress=True):
solvers.options['show_progress'] = show_progress
# save all problem data
self.bi, self.lmbd = bi, lmbd
# count alive in map
Nm2 = int(sum(linmap))
self.Nm2 = Nm2
self.linmap = linmap
n_x1 = self.Nb * (self.Nb + 1) // 2
n_x2 = self.Nb * (self.Nb - 1) // 2
n_x = 1 + Nm2 + n_x1 + n_x2
c = matrix(0.0, (1 + Nm2 + n_x1 + n_x2, 1))
c[0] = 1.0
c[1 + Nm2:1 + Nm2 + self.Nb] = lmbd
G = matrix(0.0, (1 + Nm2 + (2 * self.Nb)**2, n_x))
G[:1 + Nm2, :1 + Nm2] = -eye(1 + Nm2)
G[1 + Nm2:, 1 + Nm2:] = self.G[1 + self.Nm:, 1 + self.Nm:]
h = matrix(0.0, (1 + Nm2 + (2 * self.Nb)**2, 1))
A = matrix(0.0, (Nm2, n_x))
lbi = matrix(0.0, (Nm2, 1))
A[:, 1:1 + Nm2] = eye(Nm2)
pos = 0
for i in range(self.Nm):
if linmap[i] != 0.0:
A[pos, 1 + Nm2:] = self.A[i, 1 + self.Nm:]
lbi[pos] = bi[i]
h[1 + pos] = -bi[i]
pos += 1
assert (pos == Nm2)
b = matrix(0.0, (Nm2, 1))
dims = {'l': 0, 'q': [1 + Nm2], 's': [2 * self.Nb]}
t1 = time()
self.sol = solvers.conelp(c, G, h, dims, A, b)
t2 = time()
self.lc = c
self.lG = G
self.lh = h
self.ldims = dims
self.lA = A
self.lb = b
self.lNm = Nm2
self.lbi = lbi
t3 = time()
if self.sol['status'] in ('optimal', 'unknown'):
S = self.sol['s'][1 + Nm2:]
self.sA[:] = S[self.I11] + 1j * S[self.I21]
lapack.heevr(self.sA,
self.sW,
jobz='V',
range='I',
uplo='L',
vl=0.0,
vu=0.0,
il=self.Nb,
iu=self.Nb,
Z=self.sZ)
self.beta_hat[:] = math.sqrt(self.sW[0]) * self.sZ[:, 0]
else:
raise RuntimeError('numerical problems')
t4 = time()
self.solver_time = t2 - t1
self.eig_time = t4 - t3
def solve_linmap(self, bi, lmbd, linmap, show_progress=True):
solvers.options['show_progress'] = show_progress
# save all problem data
self.bi, self.lmbd = bi, lmbd
# count alive in map
Nm2 = int(sum(linmap))
self.Nm2 = Nm2
self.linmap = linmap
n_x1 = self.Nb*(self.Nb + 1)//2
n_x2 = self.Nb*(self.Nb - 1)//2
n_x = 1 + Nm2 + n_x1 + n_x2
c = matrix(0.0, (1 + Nm2 + n_x1 + n_x2, 1))
c[0] = 1.0
c[1 + Nm2:1 + Nm2 + self.Nb] = lmbd
G = matrix(0.0, (1 + Nm2 + (2*self.Nb)**2, n_x))
G[:1 + Nm2, :1 + Nm2] = -eye(1 + Nm2)
G[1 + Nm2:, 1 + Nm2:] = self.G[1 + self.Nm:, 1 + self.Nm:]
h = matrix(0.0, (1 + Nm2 + (2*self.Nb)**2, 1))
A = matrix(0.0, (Nm2, n_x))
lbi = matrix(0.0, (Nm2, 1))
A[:, 1:1 + Nm2] = eye(Nm2)
pos = 0
for i in range(self.Nm):
if linmap[i] != 0.0:
A[pos, 1 + Nm2:] = self.A[i, 1 + self.Nm:]
lbi[pos] = bi[i]
h[1 + pos] = -bi[i]
pos += 1
assert(pos == Nm2)
b = matrix(0.0, (Nm2, 1))
dims = {'l': 0, 'q': [1 + Nm2], 's': [2*self.Nb]}
t1 = time()
self.sol = solvers.conelp(c, G, h, dims, A, b)
t2 = time()
self.lc = c
self.lG = G
self.lh = h
self.ldims = dims
self.lA = A
self.lb = b
self.lNm = Nm2
self.lbi = lbi
t3 = time()
if self.sol['status'] in ('optimal', 'unknown'):
S = self.sol['s'][1 + Nm2:]
self.sA[:] = S[self.I11] + 1j*S[self.I21]
lapack.heevr(
self.sA, self.sW, jobz='V', range='I', uplo='L',
vl=0.0, vu=0.0, il=self.Nb, iu=self.Nb, Z=self.sZ)
self.beta_hat[:] = math.sqrt(self.sW[0])*self.sZ[:, 0]
else:
raise RuntimeError('numerical problems')
t4 = time()
self.solver_time = t2 - t1
self.eig_time = t4 - t3
def solve(self, solver = "mosek"):
if self.to_real == False: raise ValueError("Solvers do not support complex-valued data.")
sol = {}
c,G,h,dims = self.problem_data
if solver == "mosek":
if self.__verbose:
msk.options[msk.mosek.iparam.log] = 1
else:
msk.options[msk.mosek.iparam.log] = 0
solsta,mu,zz = msk.conelp(c,G,matrix(h),dims)
sol['status'] = str(solsta).split('.')[-1]
elif solver == "cvxopt":
if self.__verbose:
options = {'show_progress':True}
else:
options = {'show_progress':False}
csol = solvers.conelp(c,G,matrix(h),dims,options=options)
zz = csol['z']
mu = csol['x']
sol['status'] = csol['status']
else:
raise ValueError("Unknown solver %s" % (solver))
if zz is None: return sol
sol['mu'] = mu
offset = self.offset
sol['t'] = zz[:offset['wpl']]
sol['wpl'] = zz[offset['wpl']:offset['wpu']]
sol['wpu'] = zz[offset['wpu']:offset['wql']]
sol['wql'] = zz[offset['wql']:offset['wqu']]
sol['wqu'] = zz[offset['wqu']:offset['ul']]
sol['ul'] = zz[offset['ul']:offset['uu']]
sol['uu'] = zz[offset['uu']:offset['z']]
sol['z'] = zz[offset['z']:offset['w']]
sol['w'] = zz[offset['w']:offset['X']]
if self.conversion:
dims = self.problem_data[3]
offset = dims['l'] + sum(dims['q'])
self.Xc = []
sol['eigratio'] = []
for k,sk in enumerate(dims['s']):
zk = matrix(zz[offset:offset+sk**2],(sk,sk))
offset += sk**2
zkr = 0.5*(zk[:sk//2,:sk//2] + zk[sk//2:,sk//2:])
zki = 0.5*(zk[sk//2:,:sk//2] - zk[:sk//2,sk//2:])
zki[::sk+1] = 0.0
zk = zkr + complex(0,1.0)*zki
self.Xc.append(zk)
ev = matrix(0.0,(zk.size[0],1),tc='d')
lapack.heev(+zk,ev)
ev = sorted(list(ev),reverse=True)
sol['eigratio'].append(ev[0]/ev[1])
# Build partial Hermitian matrix
z = matrix([zk[:] for zk in self.Xc])
blki,I,J,bn = self.blocks_to_sparse[0]
X = spmatrix(z[blki],I,J)
idx = [i for i,ij in enumerate(zip(X.I,X.J)) if ij[0] > ij[1]]
sol['X'] = chompack.tril(X) + spmatrix(X.V[idx].H, X.J[idx], X.I[idx], X.size)
else:
X = matrix(zz[offset['X']:],(2*self.nbus,2*self.nbus))
Xr = X[:self.nbus,:self.nbus]
Xi = X[self.nbus:,:self.nbus]
Xi[::self.nbus+1] = 0.0
X = Xr + complex(0.0,1.0)*Xi
sol['X'] = +X
V = matrix(0.0,(self.nbus,5),tc='z')
w = matrix(0.0,(self.nbus,1))
lapack.heevr(X, w, Z = V, jobz='V', range='I', il = self.nbus-4, iu = self.nbus)
sol['eigratio'] = [w[4]/w[3]]
if w[4]/w[3] < self.eigtol and self.__verbose:
print("Eigenvalue ratio smaller than %e."%(self.eigtol))
sol['eigs'] = w[:5]
V = V[:,-1]*sqrt(w[4])
sol['V'] = V
# Branch injections
sol['Sf'] = self.baseMVA*(sol['z'][1::6] + complex(0.0,1.0)*sol['z'][2::6])
sol['St'] = self.baseMVA*(sol['z'][4::6] + complex(0.0,1.0)*sol['z'][5::6])
# Generation
sol['Sg'] = (matrix([gen['Pmin'] for gen in self.generators]) +\
matrix([0.0 if gen['pslack'] is None else sol['wpl'][gen['pslack']] for gen in self.generators])) +\
complex(0.0,1.0)*(matrix([gen['Qmin'] for gen in self.generators]) +\
matrix([0.0 if gen['qslack'] is None else sol['wql'][gen['qslack']] for gen in self.generators]))
Pg = sol['Sg'].real()
Qg = sol['Sg'].imag()
for k,gen in enumerate(self.generators):
gen['Pg'] = Pg[k]
gen['Qg'] = Qg[k]
sol['Sg'] *= self.baseMVA
sol['cost'] = 0.0
for ii,gen in enumerate(self.generators):
for nk in range(gen['Pcost']['ncoef']):
sol['cost'] += gen['Pcost']['coef'][-1-nk]*(Pg[ii]*self.baseMVA)**nk
sol['cost_objective'] = -(self.problem_data[0].T*mu)[0]*self.cost_scale + self.const_cost
sol['Vm'] = sqrt(matrix(sol['X'][::self.nbus+1]).real())
return sol
