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pflow.py
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pflow.py
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"""
Module for power flow calculation.
"""
import logging
from collections import OrderedDict
from andes.utils.misc import elapsed
from andes.routines.base import BaseRoutine
from andes.variables.report import Report
from andes.shared import np, matrix, sparse, newton_krylov, IP_ADD
logger = logging.getLogger(__name__)
class PFlow(BaseRoutine):
"""
Power flow calculation routine.
"""
def __init__(self, system=None, config=None):
super().__init__(system, config)
self.config.add(OrderedDict((('tol', 1e-6),
('max_iter', 25),
('method', 'NR'),
('check_conn', 1),
('n_factorize', 4),
('report', 1),
('degree', 0),
('init_tds', 0),
)))
self.config.add_extra("_help",
tol="convergence tolerance",
max_iter="max. number of iterations",
method="calculation method",
check_conn='check connectivity before power flow',
n_factorize="first N iterations to factorize Jacobian in dishonest method",
report="write output report",
degree='use degree in report',
init_tds="initialize TDS after PFlow",
)
self.config.add_extra("_alt",
tol="float",
method=("NR", "dishonest"),
check_conn=(0, 1),
max_iter=">=10",
n_factorize=">0",
report=(0, 1),
degree=(0, 1),
init_tds=(0, 1),
)
self.converged = False
self.inc = None
self.A = None
self.niter = None
self.mis = [1]
self.models = OrderedDict()
self.x_sol = None
self.y_sol = None
def init(self):
system = self.system
self.models = system.find_models('pflow')
self.converged = False
self.inc = None
self.A = None
self.niter = None
self.mis = [1]
self.x_sol = None
self.y_sol = None
self.system.set_var_arrays(self.models, inplace=True, alloc=False)
self.system.init(self.models, routine='pflow')
logger.info('Power flow initialized.')
# force precompile if numba is on - improves timing accuracy
if system.config.numba:
system.f_update(self.models)
system.g_update(self.models)
system.j_update(models=self.models)
return system.dae.xy
def nr_step(self):
"""
Single step using Newton-Raphson method.
Returns
-------
float
maximum absolute mismatch
"""
system = self.system
# evaluate discrete, differential, algebraic, and Jacobians
system.dae.clear_fg()
system.l_update_var(self.models, niter=self.niter, err=self.mis[-1])
system.s_update_var(self.models)
system.f_update(self.models)
system.g_update(self.models)
system.l_update_eq(self.models)
system.fg_to_dae()
if self.config.method == 'NR':
system.j_update(models=self.models)
elif self.config.method == 'dishonest':
if self.niter < self.config.n_factorize:
system.j_update(self.models)
# prepare and solve linear equations
self.inc = -matrix([matrix(system.dae.f),
matrix(system.dae.g)])
self.A = sparse([[system.dae.fx, system.dae.gx],
[system.dae.fy, system.dae.gy]])
if not self.config.linsolve:
self.inc = self.solver.solve(self.A, self.inc)
else:
self.inc = self.solver.linsolve(self.A, self.inc)
system.dae.x += np.ravel(np.array(self.inc[:system.dae.n]))
system.dae.y += np.ravel(np.array(self.inc[system.dae.n:]))
# find out variables associated with maximum mismatches
fmax = 0
if system.dae.n > 0:
fmax_idx = np.argmax(np.abs(system.dae.f))
fmax = system.dae.f[fmax_idx]
logger.debug("Max. diff mismatch %.10g on %s", fmax, system.dae.x_name[fmax_idx])
gmax_idx = np.argmax(np.abs(system.dae.g))
gmax = system.dae.g[gmax_idx]
logger.debug("Max. algeb mismatch %.10g on %s", gmax, system.dae.y_name[gmax_idx])
mis = max(abs(fmax), abs(gmax))
if self.niter == 0:
self.mis[0] = mis
else:
self.mis.append(mis)
system.vars_to_models()
return mis
def summary(self):
"""
Output a summary for the PFlow routine.
"""
ipadd_status = 'Standard (ipadd not available)'
# extract package name, `kvxopt` or `kvxopt`
sp_module = sparse.__module__
if '.' in sp_module:
sp_module = sp_module.split('.')[0]
if IP_ADD:
if self.system.config.ipadd:
ipadd_status = f'Fast in-place ({sp_module})'
else:
ipadd_status = 'Standard (ipadd disabled in config)'
out = list()
out.append('')
out.append('-> Power flow calculation')
out.append(f'{"Sparse solver":>16s}: {self.solver.sparselib.upper()}')
out.append(f'{"Solution method":>16s}: {self.config.method} method')
out.append(f'{"Sparse addition":>16s}: {ipadd_status}')
out_str = '\n'.join(out)
logger.info(out_str)
def run(self, **kwargs):
"""
Full Newton-Raphson method.
Returns
-------
bool
convergence status
"""
system = self.system
if self.config.check_conn == 1:
self.system.connectivity()
self.summary()
self.init()
if system.dae.m == 0:
logger.error("Loaded case contains no power flow element.")
system.exit_code = 1
return False
t0, _ = elapsed()
self.niter = 0
while True:
mis = self.nr_step()
logger.info('%d: |F(x)| = %.10g', self.niter, mis)
if mis < self.config.tol:
self.converged = True
break
if self.niter > self.config.max_iter:
break
if np.isnan(mis).any():
logger.error('NaN found in solution. Convergence not likely')
self.niter = self.config.max_iter + 1
break
if mis > 1e4 * self.mis[0]:
logger.error('Mismatch increased too fast. Convergence not likely.')
break
self.niter += 1
_, s1 = elapsed(t0)
if not self.converged:
if abs(self.mis[-1] - self.mis[-2]) < self.config.tol:
max_idx = np.argmax(np.abs(system.dae.xy))
name = system.dae.xy_name[max_idx]
logger.error('Mismatch is not correctable possibly due to large load-generation imbalance.')
logger.error('Largest mismatch on equation associated with <%s>', name)
else:
logger.error('Power flow failed after %d iterations for "%s".', self.niter + 1, system.files.case)
else:
logger.info('Converged in %d iterations in %s.', self.niter + 1, s1)
# make a copy of power flow solutions
self.x_sol = system.dae.x.copy()
self.y_sol = system.dae.y.copy()
if self.config.init_tds:
system.TDS.init()
if self.config.report:
system.PFlow.report()
system.exit_code = 0 if self.converged else 1
return self.converged
def report(self):
"""
Write power flow report to text file.
"""
if self.system.files.no_output is False:
r = Report(self.system)
r.write()
def _fg_wrapper(self, xy):
"""
Wrapper for algebraic equations to be used with Newton-Krylov general solver
Parameters
----------
xy
Returns
-------
"""
system = self.system
system.dae.x[:] = xy[:system.dae.n]
system.dae.y[:] = xy[system.dae.n:]
system.vars_to_models()
system.dae.clear_fg()
system.l_update_var(self.models, niter=self.niter, err=self.mis[-1])
system.f_update(self.models)
system.g_update(self.models)
system.l_update_eq(self.models)
system.fg_to_dae()
return system.dae.fg
def newton_krylov(self, verbose=False):
"""
Full Newton-Krylov method from SciPy.
Warnings
--------
The result might be wrong if discrete are in use!
Parameters
----------
verbose
True if verbose.
Returns
-------
np.array
Solutions `dae.xy`.
"""
system = self.system
system.init(system.exist.pflow)
v0 = system.dae.xy
try:
ret = newton_krylov(self._fg_wrapper, v0, verbose=verbose)
except ValueError as e:
logger.error('Mismatch is not correctable. Equations may be unsolvable.')
raise e
return ret