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