def estimate(self,
v_start=None,
delta_start=None,
calculate_voltage_angles=True):
"""
The function estimate is the main function of the module. It takes up to three input
arguments: v_start, delta_start and calculate_voltage_angles. The first two are the initial
state variables for the estimation process. Usually they can be initialized in a
"flat-start" condition: All voltages being 1.0 pu and all voltage angles being 0 degrees.
In this case, the parameters can be left at their default values (None). If the estimation
is applied continuously, using the results from the last estimation as the starting
condition for the current estimation can decrease the amount of iterations needed to
estimate the current state. The third parameter defines whether all voltage angles are
calculated absolutely, including phase shifts from transformers. If only the relative
differences between buses are required, this parameter can be set to False. Returned is a
boolean value, which is true after a successful estimation and false otherwise.
The resulting complex voltage will be written into the pandapower network. The result
fields are found res_bus_est of the pandapower network.
INPUT:
**net** - The net within this line should be created
**v_start** (np.array, shape=(1,), optional) - Vector with initial values for all
voltage magnitudes in p.u. (sorted by bus index)
**delta_start** (np.array, shape=(1,), optional) - Vector with initial values for all
voltage angles in degrees (sorted by bus index)
OPTIONAL:
**calculate_voltage_angles** - (bool) - Take into account absolute voltage angles and
phase shifts in transformers Default is True.
OUTPUT:
**successful** (boolean) - True if the estimation process was successful
Optional estimation variables:
The bus power injections can be accessed with *se.s_node_powers* and the estimated
values corresponding to the (noisy) measurement values with *se.hx*. (*hx* denotes h(x))
EXAMPLE:
success = estimate(np.array([1.0, 1.0, 1.0]), np.array([0.0, 0.0, 0.0]))
"""
if self.net is None:
raise UserWarning("Component was not initialized with a network.")
# add initial values for V and delta
# node voltages
# V<delta
if v_start is None:
v_start = np.ones(self.net.bus.shape[0])
if delta_start is None:
delta_start = np.zeros(self.net.bus.shape[0])
# initialize the ppc bus with the initial values given
vm_backup, va_backup = self.net.res_bus.vm_pu.copy(
), self.net.res_bus.va_degree.copy()
self.net.res_bus.vm_pu = v_start
self.net.res_bus.vm_pu[self.net.bus.index[self.net.bus.in_service ==
False]] = np.nan
self.net.res_bus.va_degree = delta_start
# select elements in service and convert pandapower ppc to ppc
self.net._options = {}
_add_ppc_options(self.net,
check_connectivity=False,
init="results",
trafo_model="t",
copy_constraints_to_ppc=False,
mode="pf",
enforce_q_lims=False,
calculate_voltage_angles=calculate_voltage_angles,
r_switch=0.0,
recycle=dict(_is_elements=False,
ppc=False,
Ybus=False))
self.net["_is_elements"] = _select_is_elements(self.net)
ppc, _ = _pd2ppc(self.net)
mapping_table = self.net["_pd2ppc_lookups"]["bus"]
br_cols = ppc["branch"].shape[1]
bs_cols = ppc["bus"].shape[1]
self.net.res_bus.vm_pu = vm_backup
self.net.res_bus.va_degree = va_backup
# add 6 columns to ppc[bus] for Vm, Vm std dev, P, P std dev, Q, Q std dev
bus_append = np.full((ppc["bus"].shape[0], 6),
np.nan,
dtype=ppc["bus"].dtype)
v_measurements = self.net.measurement[
(self.net.measurement.type == "v")
& (self.net.measurement.element_type == "bus")]
if len(v_measurements):
bus_positions = mapping_table[v_measurements.bus.values.astype(
int)]
bus_append[bus_positions, 0] = v_measurements.value.values
bus_append[bus_positions, 1] = v_measurements.std_dev.values
p_measurements = self.net.measurement[
(self.net.measurement.type == "p")
& (self.net.measurement.element_type == "bus")]
if len(p_measurements):
bus_positions = mapping_table[p_measurements.bus.values.astype(
int)]
bus_append[bus_positions,
2] = p_measurements.value.values * 1e3 / self.s_ref
bus_append[bus_positions,
3] = p_measurements.std_dev.values * 1e3 / self.s_ref
q_measurements = self.net.measurement[
(self.net.measurement.type == "q")
& (self.net.measurement.element_type == "bus")]
if len(q_measurements):
bus_positions = mapping_table[q_measurements.bus.values.astype(
int)]
bus_append[bus_positions,
4] = q_measurements.value.values * 1e3 / self.s_ref
bus_append[bus_positions,
5] = q_measurements.std_dev.values * 1e3 / self.s_ref
# add virtual measurements for artificial buses, which were created because
# of an open line switch. p/q are 0. and std dev is 1. (small value)
new_in_line_buses = np.setdiff1d(np.arange(ppc["bus"].shape[0]),
mapping_table[mapping_table >= 0])
bus_append[new_in_line_buses, 2] = 0.
bus_append[new_in_line_buses, 3] = 1.
bus_append[new_in_line_buses, 4] = 0.
bus_append[new_in_line_buses, 5] = 1.
# add 12 columns to mpc[branch] for Im_from, Im_from std dev, Im_to, Im_to std dev,
# P_from, P_from std dev, P_to, P_to std dev, Q_from,Q_from std dev, Q_to, Q_to std dev
branch_append = np.full((ppc["branch"].shape[0], 12),
np.nan,
dtype=ppc["branch"].dtype)
i_measurements = self.net.measurement[
(self.net.measurement.type == "i")
& (self.net.measurement.element_type == "line")]
if len(i_measurements):
meas_from = i_measurements[(i_measurements.bus.values.astype(
int) == self.net.line.from_bus[i_measurements.element]).values]
meas_to = i_measurements[(i_measurements.bus.values.astype(
int) == self.net.line.to_bus[i_measurements.element]).values]
ix_from = meas_from.element.values.astype(int)
ix_to = meas_to.element.values.astype(int)
i_a_to_pu_from = (self.net.bus.vn_kv[meas_from.bus] * 1e3 /
self.s_ref).values
i_a_to_pu_to = (self.net.bus.vn_kv[meas_to.bus] * 1e3 /
self.s_ref).values
branch_append[ix_from, 0] = meas_from.value.values * i_a_to_pu_from
branch_append[ix_from,
1] = meas_from.std_dev.values * i_a_to_pu_from
branch_append[ix_to, 2] = meas_to.value.values * i_a_to_pu_to
branch_append[ix_to, 3] = meas_to.std_dev.values * i_a_to_pu_to
p_measurements = self.net.measurement[
(self.net.measurement.type == "p")
& (self.net.measurement.element_type == "line")]
if len(p_measurements):
meas_from = p_measurements[(p_measurements.bus.values.astype(
int) == self.net.line.from_bus[p_measurements.element]).values]
meas_to = p_measurements[(p_measurements.bus.values.astype(
int) == self.net.line.to_bus[p_measurements.element]).values]
ix_from = meas_from.element.values.astype(int)
ix_to = meas_to.element.values.astype(int)
branch_append[ix_from,
4] = meas_from.value.values * 1e3 / self.s_ref
branch_append[ix_from,
5] = meas_from.std_dev.values * 1e3 / self.s_ref
branch_append[ix_to, 6] = meas_to.value.values * 1e3 / self.s_ref
branch_append[ix_to, 7] = meas_to.std_dev.values * 1e3 / self.s_ref
q_measurements = self.net.measurement[
(self.net.measurement.type == "q")
& (self.net.measurement.element_type == "line")]
if len(q_measurements):
meas_from = q_measurements[(q_measurements.bus.values.astype(
int) == self.net.line.from_bus[q_measurements.element]).values]
meas_to = q_measurements[(q_measurements.bus.values.astype(
int) == self.net.line.to_bus[q_measurements.element]).values]
ix_from = meas_from.element.values.astype(int)
ix_to = meas_to.element.values.astype(int)
branch_append[ix_from,
8] = meas_from.value.values * 1e3 / self.s_ref
branch_append[ix_from,
9] = meas_from.std_dev.values * 1e3 / self.s_ref
branch_append[ix_to, 10] = meas_to.value.values * 1e3 / self.s_ref
branch_append[ix_to,
11] = meas_to.std_dev.values * 1e3 / self.s_ref
i_tr_measurements = self.net.measurement[
(self.net.measurement.type == "i")
& (self.net.measurement.element_type == "transformer")]
if len(i_tr_measurements):
meas_from = i_tr_measurements[(
i_tr_measurements.bus.values.astype(int) ==
self.net.trafo.hv_bus[i_tr_measurements.element]).values]
meas_to = i_tr_measurements[(
i_tr_measurements.bus.values.astype(int) ==
self.net.trafo.lv_bus[i_tr_measurements.element]).values]
ix_from = meas_from.element.values.astype(int)
ix_to = meas_to.element.values.astype(int)
i_a_to_pu_from = (self.net.bus.vn_kv[meas_from.bus] * 1e3 /
self.s_ref).values
i_a_to_pu_to = (self.net.bus.vn_kv[meas_to.bus] * 1e3 /
self.s_ref).values
branch_append[ix_from, 0] = meas_from.value.values * i_a_to_pu_from
branch_append[ix_from,
1] = meas_from.std_dev.values * i_a_to_pu_from
branch_append[ix_to, 2] = meas_to.value.values * i_a_to_pu_to
branch_append[ix_to, 3] = meas_to.std_dev.values * i_a_to_pu_to
p_tr_measurements = self.net.measurement[
(self.net.measurement.type == "p")
& (self.net.measurement.element_type == "transformer")]
if len(p_tr_measurements):
meas_from = p_tr_measurements[(
p_tr_measurements.bus.values.astype(int) ==
self.net.trafo.hv_bus[p_tr_measurements.element]).values]
meas_to = p_tr_measurements[(
p_tr_measurements.bus.values.astype(int) ==
self.net.trafo.lv_bus[p_tr_measurements.element]).values]
ix_from = len(self.net.line) + meas_from.element.values.astype(int)
ix_to = len(self.net.line) + meas_to.element.values.astype(int)
branch_append[ix_from,
4] = meas_from.value.values * 1e3 / self.s_ref
branch_append[ix_from,
5] = meas_from.std_dev.values * 1e3 / self.s_ref
branch_append[ix_to, 6] = meas_to.value.values * 1e3 / self.s_ref
branch_append[ix_to, 7] = meas_to.std_dev.values * 1e3 / self.s_ref
q_tr_measurements = self.net.measurement[
(self.net.measurement.type == "q")
& (self.net.measurement.element_type == "transformer")]
if len(q_tr_measurements):
meas_from = q_tr_measurements[(
q_tr_measurements.bus.values.astype(int) ==
self.net.trafo.hv_bus[q_tr_measurements.element]).values]
meas_to = q_tr_measurements[(
q_tr_measurements.bus.values.astype(int) ==
self.net.trafo.lv_bus[q_tr_measurements.element]).values]
ix_from = len(self.net.line) + meas_from.element.values.astype(int)
ix_to = len(self.net.line) + meas_to.element.values.astype(int)
branch_append[ix_from,
8] = meas_from.value.values * 1e3 / self.s_ref
branch_append[ix_from,
9] = meas_from.std_dev.values * 1e3 / self.s_ref
branch_append[ix_to, 10] = meas_to.value.values * 1e3 / self.s_ref
branch_append[ix_to,
11] = meas_to.std_dev.values * 1e3 / self.s_ref
ppc["bus"] = np.hstack((ppc["bus"], bus_append))
ppc["branch"] = np.hstack((ppc["branch"], branch_append))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ppc_i = ext2int(ppc)
p_bus_not_nan = ~np.isnan(ppc_i["bus"][:, bs_cols + 2])
p_line_f_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 4])
p_line_t_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 6])
q_bus_not_nan = ~np.isnan(ppc_i["bus"][:, bs_cols + 4])
q_line_f_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 8])
q_line_t_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 10])
v_bus_not_nan = ~np.isnan(ppc_i["bus"][:, bs_cols + 0])
i_line_f_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 0])
i_line_t_not_nan = ~np.isnan(ppc_i["branch"][:, br_cols + 2])
# piece together our measurement vector z
z = np.concatenate(
(ppc_i["bus"][p_bus_not_nan,
bs_cols + 2], ppc_i["branch"][p_line_f_not_nan,
br_cols + 4],
ppc_i["branch"][p_line_t_not_nan,
br_cols + 6], ppc_i["bus"][q_bus_not_nan,
bs_cols + 4],
ppc_i["branch"][q_line_f_not_nan,
br_cols + 8], ppc_i["branch"][q_line_t_not_nan,
br_cols + 10],
ppc_i["bus"][v_bus_not_nan,
bs_cols + 0], ppc_i["branch"][i_line_f_not_nan,
br_cols + 0],
ppc_i["branch"][i_line_t_not_nan,
br_cols + 2])).real.astype(np.float64)
# number of nodes
n_active = len(np.where(ppc_i["bus"][:, 1] != 4)[0])
slack_buses = np.where(ppc_i["bus"][:, 1] == 3)[0]
# Check if observability criterion is fulfilled and the state estimation is possible
if len(z) < 2 * n_active - 1:
self.logger.error("System is not observable (cancelling)")
self.logger.error(
"Measurements available: %d. Measurements required: %d" %
(len(z), 2 * n_active - 1))
return False
# Set the starting values for all active buses
v_m = ppc_i["bus"][:, 7]
delta = ppc_i["bus"][:, 8] * np.pi / 180 # convert to rad
delta_masked = np.ma.array(delta, mask=False)
delta_masked.mask[slack_buses] = True
non_slack_buses = np.arange(len(delta))[~delta_masked.mask]
# Matrix calculation object
sem = wls_matrix_ops(ppc_i, slack_buses, non_slack_buses, self.s_ref,
bs_cols, br_cols)
# state vector
E = np.concatenate((delta_masked.compressed(), v_m))
# Covariance matrix R
r_cov = np.concatenate(
(ppc_i["bus"][p_bus_not_nan,
bs_cols + 3], ppc_i["branch"][p_line_f_not_nan,
br_cols + 5],
ppc_i["branch"][p_line_t_not_nan,
br_cols + 7], ppc_i["bus"][q_bus_not_nan,
bs_cols + 5],
ppc_i["branch"][q_line_f_not_nan,
br_cols + 9], ppc_i["branch"][q_line_t_not_nan,
br_cols + 11],
ppc_i["bus"][v_bus_not_nan,
bs_cols + 1], ppc_i["branch"][i_line_f_not_nan,
br_cols + 1],
ppc_i["branch"][i_line_t_not_nan,
br_cols + 3])).real.astype(np.float64)
r_inv = csr_matrix(np.linalg.inv(np.diagflat(r_cov)**2))
current_error = 100
current_iterations = 0
while current_error > self.tolerance and current_iterations < self.max_iterations:
self.logger.debug(" Starting iteration %d" %
(1 + current_iterations))
try:
# create h(x) for the current iteration
h_x = sem.create_hx(v_m, delta)
# Residual r
r = csr_matrix(z - h_x).T
# Jacobian matrix H
H = csr_matrix(sem.create_jacobian(v_m, delta))
# if not np.linalg.cond(H) < 1 / sys.float_info.epsilon:
# self.logger.error("Error in matrix H")
# Gain matrix G_m
# G_m = H^t * R^-1 * H
G_m = H.T * (r_inv * H)
# State Vector difference d_E
# d_E = G_m^-1 * (H' * R^-1 * r)
d_E = spsolve(G_m, H.T * (r_inv * r))
E += d_E
# Update V/delta
delta[non_slack_buses] = E[:len(non_slack_buses)]
v_m = np.squeeze(E[len(non_slack_buses):])
current_iterations += 1
current_error = np.max(np.abs(d_E))
self.logger.debug("Current error: %.4f" % current_error)
except np.linalg.linalg.LinAlgError:
self.logger.error(
"A problem appeared while using the linear algebra methods."
"Check and change the measurement set.")
return False
# Print output for results
if current_error <= self.tolerance:
successful = True
self.logger.info(
"WLS State Estimation successful (%d iterations)" %
current_iterations)
else:
successful = False
self.logger.info(
"WLS State Estimation not successful (%d/%d iterations" %
(current_iterations, self.max_iterations))
# write voltage into ppc
ppc_i["bus"][:, 7] = v_m
ppc_i["bus"][:, 8] = delta * 180 / np.pi # convert to degree
# calculate bus powers
v_cpx = v_m * np.exp(1j * delta)
bus_powers_conj = np.zeros(len(v_cpx), dtype=np.complex128)
for i in range(len(v_cpx)):
bus_powers_conj[i] = np.dot(sem.Y_bus[i, :], v_cpx) * np.conjugate(
v_cpx[i])
ppc_i["bus"][:, 2] = bus_powers_conj.real # saved in per unit
ppc_i["bus"][:, 3] = -bus_powers_conj.imag # saved in per unit
# convert to pandapower indices
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ppc = int2ext(ppc_i)
_set_buses_out_of_service(ppc)
# Store results, overwrite old results
self.net.res_bus_est = pd.DataFrame(
columns=["vm_pu", "va_degree", "p_kw", "q_kvar"],
index=self.net.bus.index)
self.net.res_line_est = pd.DataFrame(columns=[
"p_from_kw", "q_from_kvar", "p_to_kw", "q_to_kvar", "pl_kw",
"ql_kvar", "i_from_ka", "i_to_ka", "i_ka", "loading_percent"
],
index=self.net.line.index)
bus_idx = mapping_table[self.net["bus"].index.values]
self.net["res_bus_est"]["vm_pu"] = ppc["bus"][bus_idx][:, 7]
self.net["res_bus_est"]["va_degree"] = ppc["bus"][bus_idx][:, 8]
self.net.res_bus_est.p_kw = -get_values(
ppc["bus"][:, 2], self.net.bus.index,
mapping_table) * self.s_ref / 1e3
self.net.res_bus_est.q_kvar = -get_values(
ppc["bus"][:, 3], self.net.bus.index,
mapping_table) * self.s_ref / 1e3
self.net.res_line_est = calculate_line_results(self.net,
use_res_bus_est=True)
# Store some variables required for Chi^2 and r_N_max test:
self.R_inv = r_inv.toarray()
self.Gm = G_m.toarray()
self.r = r.toarray()
self.H = H.toarray()
self.Ht = self.H.T
self.hx = h_x
self.V = v_m
self.delta = delta
return successful
def runpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a power flow.
Runs a power flow [full AC Newton's method by default] and optionally
returns the solved values in the data matrices, a flag which is C{True} if
the algorithm was successful in finding a solution, and the elapsed
time in seconds. All input arguments are optional. If C{casename} is
provided it specifies the name of the input data file or dict
containing the power flow data. The default value is 'case9'.
If the ppopt is provided it overrides the default PYPOWER options
vector and can be used to specify the solution algorithm and output
options among other things. If the 3rd argument is given the pretty
printed output will be appended to the file whose name is given in
C{fname}. If C{solvedcase} is specified the solved case will be written
to a case file in PYPOWER format with the specified name. If C{solvedcase}
ends with '.mat' it saves the case as a MAT-file otherwise it saves it
as a Python-file.
If the C{ENFORCE_Q_LIMS} options is set to C{True} [default is false] then
if any generator reactive power limit is violated after running the AC
power flow, the corresponding bus is converted to a PQ bus, with Qg at
the limit, and the case is re-run. The voltage magnitude at the bus
will deviate from the specified value in order to satisfy the reactive
power limit. If the reference bus is converted to PQ, the first
remaining PV bus will be used as the slack bus for the next iteration.
This may result in the real power output at this generator being
slightly off from the specified values.
Enforcing of generator Q limits inspired by contributions from Mu Lin,
Lincoln University, New Zealand (1/14/05).
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
## options
verbose = ppopt["VERBOSE"]
qlim = ppopt["ENFORCE_Q_LIMS"] ## enforce Q limits on gens?
dc = ppopt["PF_DC"] ## use DC formulation?
## read data
ppc = loadcase(casedata)
## add zero columns to branch for flows if needed
if ppc["branch"].shape[1] < QT:
ppc["branch"] = c_[ppc["branch"],
zeros((ppc["branch"].shape[0],
QT - ppc["branch"].shape[1] + 1))]
## convert to internal indexing
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
## get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
## generator info
on = find(gen[:, GEN_STATUS] > 0) ## which generators are on?
gbus = gen[on, GEN_BUS].astype(int) ## what buses are they at?
##----- run the power flow -----
t0 = time()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v["Version"], v["Date"]))
if dc: # DC formulation
if verbose:
stdout.write(' -- DC Power Flow\n')
## initial state
Va0 = bus[:, VA] * (pi / 180)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
## adjusted for phase shifters and real shunts
Pbus = makeSbus(baseMVA, bus,
gen).real - Pbusinj - bus[:, GS] / baseMVA
## "run" the power flow
Va = dcpf(B, Pbus, Va0, ref, pv, pq)
## update data matrices with solution
branch[:, [QF, QT]] = zeros((branch.shape[0], 2))
branch[:, PF] = (Bf * Va + Pfinj) * baseMVA
branch[:, PT] = -branch[:, PF]
bus[:, VM] = ones(bus.shape[0])
bus[:, VA] = Va * (180 / pi)
## update Pg for slack generator (1st gen at ref bus)
## (note: other gens at ref bus are accounted for in Pbus)
## Pg = Pinj + Pload + Gs
## newPg = oldPg + newPinj - oldPinj
refgen = zeros(len(ref), dtype=int)
for k in range(len(ref)):
temp = find(gbus == ref[k])
refgen[k] = on[temp[0]]
gen[refgen,
PG] = gen[refgen, PG] + (B[ref, :] * Va - Pbus[ref]) * baseMVA
success = 1
else: ## AC formulation
alg = ppopt['PF_ALG']
if verbose > 0:
if alg == 1:
solver = 'Newton'
elif alg == 2:
solver = 'fast-decoupled, XB'
elif alg == 3:
solver = 'fast-decoupled, BX'
elif alg == 4:
solver = 'Gauss-Seidel'
else:
solver = 'unknown'
print(' -- AC Power Flow (%s)\n' % solver)
## initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = bus[:, VM] * exp(1j * pi / 180 * bus[:, VA])
V0[gbus] = gen[on, VG] / abs(V0[gbus]) * V0[gbus]
if qlim:
ref0 = ref ## save index and angle of
Varef0 = bus[ref0, VA] ## original reference bus(es)
limited = [] ## list of indices of gens @ Q lims
fixedQg = zeros(gen.shape[0]) ## Qg of gens at Q limits
repeat = True
while repeat:
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the power flow
alg = ppopt["PF_ALG"]
if alg == 1:
V, success, _ = newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
elif alg == 2 or alg == 3:
Bp, Bpp = makeB(baseMVA, bus, branch, alg)
V, success, _ = fdpf(Ybus, Sbus, V0, Bp, Bpp, ref, pv, pq,
ppopt)
elif alg == 4:
V, success, _ = gausspf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
else:
stderr.write('Only Newton'
's method, fast-decoupled, and '
'Gauss-Seidel power flow algorithms currently '
'implemented.\n')
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt,
V, ref, pv, pq)
if qlim: ## enforce generator Q limits
## find gens with violated Q constraints
gen_status = gen[:, GEN_STATUS] > 0
qg_max_lim = gen[:, QG] > gen[:, QMAX]
qg_min_lim = gen[:, QG] < gen[:, QMIN]
mx = find(gen_status & qg_max_lim)
mn = find(gen_status & qg_min_lim)
if len(mx) > 0 or len(
mn) > 0: ## we have some Q limit violations
# No PV generators
if len(pv) == 0:
if verbose:
if len(mx) > 0:
print(
'Gen %d [only one left] exceeds upper Q limit : INFEASIBLE PROBLEM\n'
% mx + 1)
else:
print(
'Gen %d [only one left] exceeds lower Q limit : INFEASIBLE PROBLEM\n'
% mn + 1)
success = 0
break
## one at a time?
if qlim == 2: ## fix largest violation, ignore the rest
k = argmax(r_[gen[mx, QG] - gen[mx, QMAX],
gen[mn, QMIN] - gen[mn, QG]])
if k > len(mx):
mn = mn[k - len(mx)]
mx = []
else:
mx = mx[k]
mn = []
if verbose and len(mx) > 0:
for i in range(len(mx)):
print('Gen ' + str(mx[i] + 1) +
' at upper Q limit, converting to PQ bus\n')
if verbose and len(mn) > 0:
for i in range(len(mn)):
print('Gen ' + str(mn[i] + 1) +
' at lower Q limit, converting to PQ bus\n')
## save corresponding limit values
fixedQg[mx] = gen[mx, QMAX]
fixedQg[mn] = gen[mn, QMIN]
mx = r_[mx, mn].astype(int)
## convert to PQ bus
gen[mx, QG] = fixedQg[mx] ## set Qg to binding
for i in range(
len(mx)
): ## [one at a time, since they may be at same bus]
gen[mx[i],
GEN_STATUS] = 0 ## temporarily turn off gen,
bi = gen[mx[i], GEN_BUS] ## adjust load accordingly,
bus[bi, [PD, QD]] = (bus[bi, [PD, QD]] -
gen[mx[i], [PG, QG]])
if len(ref) > 1 and any(bus[gen[mx, GEN_BUS],
BUS_TYPE] == REF):
raise ValueError('Sorry, PYPOWER cannot enforce Q '
'limits for slack buses in systems '
'with multiple slacks.')
bus[gen[mx, GEN_BUS].astype(int),
BUS_TYPE] = PQ ## & set bus type to PQ
## update bus index lists of each type of bus
ref_temp = ref
ref, pv, pq = bustypes(bus, gen)
if verbose and ref != ref_temp:
print('Bus %d is new slack bus\n' % ref)
limited = r_[limited, mx].astype(int)
else:
repeat = 0 ## no more generator Q limits violated
else:
repeat = 0 ## don't enforce generator Q limits, once is enough
if qlim and len(limited) > 0:
## restore injections from limited gens [those at Q limits]
gen[limited, QG] = fixedQg[limited] ## restore Qg value,
for i in range(
len(limited
)): ## [one at a time, since they may be at same bus]
bi = gen[limited[i], GEN_BUS] ## re-adjust load,
bus[bi,
[PD, QD]] = bus[bi, [PD, QD]] + gen[limited[i], [PG, QG]]
gen[limited[i], GEN_STATUS] = 1 ## and turn gen back on
if ref != ref0:
## adjust voltage angles to make original ref bus correct
bus[:, VA] = bus[:, VA] - bus[ref0, VA] + Varef0
ppc["et"] = time() - t0
ppc["success"] = success
##----- output results -----
## convert back to original bus numbering & print results
ppc["bus"], ppc["gen"], ppc["branch"] = bus, gen, branch
results = int2ext(ppc)
## zero out result fields of out-of-service gens & branches
if len(results["order"]["gen"]["status"]["off"]) > 0:
results["gen"][ix_(results["order"]["gen"]["status"]["off"],
[PG, QG])] = 0
if len(results["order"]["branch"]["status"]["off"]) > 0:
results["branch"][ix_(results["order"]["branch"]["status"]["off"],
[PF, QF, PT, QT])] = 0
if fname:
fd = None
try:
fd = open(fname, "a")
except Exception as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(results, fd, ppopt)
fd.close()
else:
printpf(results, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, results)
return results, success
def opf(*args):
"""Solves an optimal power flow.
Returns a C{results} dict.
The data for the problem can be specified in one of three ways:
1. a string (ppc) containing the file name of a PYPOWER case
which defines the data matrices baseMVA, bus, gen, branch, and
gencost (areas is not used at all, it is only included for
backward compatibility of the API).
2. a dict (ppc) containing the data matrices as fields.
3. the individual data matrices themselves.
The optional user parameters for user constraints (C{A, l, u}), user costs
(C{N, fparm, H, Cw}), user variable initializer (C{z0}), and user variable
limits (C{zl, zu}) can also be specified as fields in a case dict,
either passed in directly or defined in a case file referenced by name.
When specified, C{A, l, u} represent additional linear constraints on the
optimization variables, C{l <= A*[x z] <= u}. If the user specifies an C{A}
matrix that has more columns than the number of "C{x}" (OPF) variables,
then there are extra linearly constrained "C{z}" variables. For an
explanation of the formulation used and instructions for forming the
C{A} matrix, see the MATPOWER manual.
A generalized cost on all variables can be applied if input arguments
C{N}, C{fparm}, C{H} and C{Cw} are specified. First, a linear transformation
of the optimization variables is defined by means of C{r = N * [x z]}.
Then, to each element of C{r} a function is applied as encoded in the
C{fparm} matrix (see MATPOWER manual). If the resulting vector is named
C{w}, then C{H} and C{Cw} define a quadratic cost on w:
C{(1/2)*w'*H*w + Cw * w}. C{H} and C{N} should be sparse matrices and C{H}
should also be symmetric.
The optional C{ppopt} vector specifies PYPOWER options. If the OPF
algorithm is not explicitly set in the options PYPOWER will use the default
solver, based on a primal-dual interior point method. For the AC OPF this
is C{OPF_ALG = 560}. For the DC OPF, the default is C{OPF_ALG_DC = 200}.
See L{ppoption} for more details on the available OPF solvers and other OPF
options and their default values.
The solved case is returned in a single results dict (described
below). Also returned are the final objective function value (C{f}) and a
flag which is C{True} if the algorithm was successful in finding a solution
(success). Additional optional return values are an algorithm specific
return status (C{info}), elapsed time in seconds (C{et}), the constraint
vector (C{g}), the Jacobian matrix (C{jac}), and the vector of variables
(C{xr}) as well as the constraint multipliers (C{pimul}).
The single results dict is a PYPOWER case struct (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{et} elapsed time in seconds for solving OPF
- C{success} 1 if solver converged successfully, 0 otherwise
- C{om} OPF model object, see 'help opf_model'
- 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{g} (optional) constraint values
- C{dg} (optional) constraint 1st derivatives
- C{df} (optional) obj fun 1st derivatives (not yet implemented)
- C{d2f} (optional) obj fun 2nd derivatives (not yet implemented)
- C{raw} raw solver output in form returned by MINOS, and more
- C{xr} final value of optimization variables
- C{pimul} constraint multipliers
- C{info} solver specific termination code
- C{output} solver specific output information
- C{alg} algorithm code of solver used
- C{var}
- C{val} optimization variable values, by named block
- C{Va} voltage angles
- C{Vm} voltage magnitudes (AC only)
- C{Pg} real power injections
- C{Qg} reactive power injections (AC only)
- C{y} constrained cost variable (only if have pwl costs)
- (other) any user defined variable blocks
- C{mu} variable bound shadow prices, by named block
- C{l} lower bound shadow prices
- C{Va}, C{Vm}, C{Pg}, C{Qg}, C{y}, (other)
- C{u} upper bound shadow prices
- C{Va}, C{Vm}, C{Pg}, C{Qg}, C{y}, (other)
- C{nln} (AC only)
- C{mu} shadow prices on nonlinear constraints, by named block
- C{l} lower bounds
- C{Pmis} real power mismatch equations
- C{Qmis} reactive power mismatch equations
- C{Sf} flow limits at "from" end of branches
- C{St} flow limits at "to" end of branches
- C{u} upper bounds
- C{Pmis}, C{Qmis}, C{Sf}, C{St}
- C{lin}
- C{mu} shadow prices on linear constraints, by named block
- C{l} lower bounds
- C{Pmis} real power mistmatch equations (DC only)
- C{Pf} flow limits at "from" end of branches (DC only)
- C{Pt} flow limits at "to" end of branches (DC only)
- C{PQh} upper portion of gen PQ-capability curve(AC only)
- C{PQl} lower portion of gen PQ-capability curve(AC only)
- C{vl} constant power factor constraint for loads
- C{ycon} basin constraints for CCV for pwl costs
- (other) any user defined constraint blocks
- C{u} upper bounds
- C{Pmis}, C{Pf}, C{Pf}, C{PQh}, C{PQl}, C{vl}, C{ycon},
- (other)
- C{cost} user defined cost values, by named block
@see: L{runopf}, L{dcopf}, L{uopf}, L{caseformat}
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Richard Lincoln
"""
##----- initialization -----
t0 = time() ## start timer
## process input arguments
ppc, ppopt = opf_args2(*args)
## add zero columns to bus, gen, branch for multipliers, etc if needed
nb = shape(ppc['bus'])[0] ## number of buses
nl = shape(ppc['branch'])[0] ## number of branches
ng = shape(ppc['gen'])[0] ## number of dispatchable injections
if shape(ppc['bus'])[1] < MU_VMIN + 1:
ppc['bus'] = c_[ppc['bus'],
zeros((nb, MU_VMIN + 1 - shape(ppc['bus'])[1]))]
if shape(ppc['gen'])[1] < MU_QMIN + 1:
ppc['gen'] = c_[ppc['gen'],
zeros((ng, MU_QMIN + 1 - shape(ppc['gen'])[1]))]
if shape(ppc['branch'])[1] < MU_ANGMAX + 1:
ppc['branch'] = c_[ppc['branch'],
zeros(
(nl, MU_ANGMAX + 1 - shape(ppc['branch'])[1]))]
##----- convert to internal numbering, remove out-of-service stuff -----
ppc = ext2int(ppc)
##----- construct OPF model object -----
om = opf_setup(ppc, ppopt)
##----- execute the OPF -----
results, success, raw = opf_execute(om, ppopt)
##----- revert to original ordering, including out-of-service stuff -----
results = int2ext(results)
## zero out result fields of out-of-service gens & branches
if len(results['order']['gen']['status']['off']) > 0:
results['gen'][ix_(results['order']['gen']['status']['off'],
[PG, QG, MU_PMAX, MU_PMIN])] = 0
if len(results['order']['branch']['status']['off']) > 0:
results['branch'][ix_(
results['order']['branch']['status']['off'],
[PF, QF, PT, QT, MU_SF, MU_ST, MU_ANGMIN, MU_ANGMAX])] = 0
##----- finish preparing output -----
et = time() - t0 ## compute elapsed time
results['et'] = et
results['success'] = success
results['raw'] = raw
return results
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 runpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a power flow.
Runs a power flow [full AC Newton's method by default] and optionally
returns the solved values in the data matrices, a flag which is C{True} if
the algorithm was successful in finding a solution, and the elapsed
time in seconds. All input arguments are optional. If C{casename} is
provided it specifies the name of the input data file or dict
containing the power flow data. The default value is 'case9'.
If the ppopt is provided it overrides the default PYPOWER options
vector and can be used to specify the solution algorithm and output
options among other things. If the 3rd argument is given the pretty
printed output will be appended to the file whose name is given in
C{fname}. If C{solvedcase} is specified the solved case will be written
to a case file in PYPOWER format with the specified name. If C{solvedcase}
ends with '.mat' it saves the case as a MAT-file otherwise it saves it
as a Python-file.
If the C{ENFORCE_Q_LIMS} options is set to C{True} [default is false] then
if any generator reactive power limit is violated after running the AC
power flow, the corresponding bus is converted to a PQ bus, with Qg at
the limit, and the case is re-run. The voltage magnitude at the bus
will deviate from the specified value in order to satisfy the reactive
power limit. If the reference bus is converted to PQ, the first
remaining PV bus will be used as the slack bus for the next iteration.
This may result in the real power output at this generator being
slightly off from the specified values.
Enforcing of generator Q limits inspired by contributions from Mu Lin,
Lincoln University, New Zealand (1/14/05).
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
## options
verbose = ppopt["VERBOSE"]
qlim = ppopt["ENFORCE_Q_LIMS"] ## enforce Q limits on gens?
dc = ppopt["PF_DC"] ## use DC formulation?
## read data
ppc = loadcase(casedata)
## add zero columns to branch for flows if needed
if ppc["branch"].shape[1] < QT:
ppc["branch"] = c_[ppc["branch"],
zeros((ppc["branch"].shape[0],
QT - ppc["branch"].shape[1] + 1))]
## convert to internal indexing
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
## get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
## generator info
on = find(gen[:, GEN_STATUS] > 0) ## which generators are on?
gbus = gen[on, GEN_BUS].astype(int) ## what buses are they at?
##----- run the power flow -----
t0 = time()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v["Version"], v["Date"]))
if dc: # DC formulation
if verbose:
stdout.write(' -- DC Power Flow\n')
## initial state
Va0 = bus[:, VA] * (pi / 180)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
## adjusted for phase shifters and real shunts
Pbus = makeSbus(baseMVA, bus, gen).real - Pbusinj - bus[:, GS] / baseMVA
## "run" the power flow
Va = dcpf(B, Pbus, Va0, ref, pv, pq)
## update data matrices with solution
branch[:, [QF, QT]] = zeros((branch.shape[0], 2))
branch[:, PF] = (Bf * Va + Pfinj) * baseMVA
branch[:, PT] = -branch[:, PF]
bus[:, VM] = ones(bus.shape[0])
bus[:, VA] = Va * (180 / pi)
## update Pg for slack generator (1st gen at ref bus)
## (note: other gens at ref bus are accounted for in Pbus)
## Pg = Pinj + Pload + Gs
## newPg = oldPg + newPinj - oldPinj
refgen = zeros(len(ref), dtype=int)
for k in range(len(ref)):
temp = find(gbus == ref[k])
refgen[k] = on[temp[0]]
gen[refgen, PG] = gen[refgen, PG] + (B[ref, :] * Va - Pbus[ref]) * baseMVA
success = 1
else: ## AC formulation
alg = ppopt['PF_ALG']
if verbose > 0:
if alg == 1:
solver = 'Newton'
elif alg == 2:
solver = 'fast-decoupled, XB'
elif alg == 3:
solver = 'fast-decoupled, BX'
elif alg == 4:
solver = 'Gauss-Seidel'
else:
solver = 'unknown'
print(' -- AC Power Flow (%s)\n' % solver)
## initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = bus[:, VM] * exp(1j * pi/180 * bus[:, VA])
V0[gbus] = gen[on, VG] / abs(V0[gbus]) * V0[gbus]
if qlim:
ref0 = ref ## save index and angle of
Varef0 = bus[ref0, VA] ## original reference bus(es)
limited = [] ## list of indices of gens @ Q lims
fixedQg = zeros(gen.shape[0]) ## Qg of gens at Q limits
repeat = True
while repeat:
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the power flow
alg = ppopt["PF_ALG"]
if alg == 1:
V, success, _ = newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
elif alg == 2 or alg == 3:
Bp, Bpp = makeB(baseMVA, bus, branch, alg)
V, success, _ = fdpf(Ybus, Sbus, V0, Bp, Bpp, ref, pv, pq, ppopt)
elif alg == 4:
V, success, _ = gausspf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
else:
stderr.write('Only Newton''s method, fast-decoupled, and '
'Gauss-Seidel power flow algorithms currently '
'implemented.\n')
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V, ref, pv, pq)
if qlim: ## enforce generator Q limits
## find gens with violated Q constraints
gen_status = gen[:, GEN_STATUS] > 0
qg_max_lim = gen[:, QG] > gen[:, QMAX]
qg_min_lim = gen[:, QG] < gen[:, QMIN]
mx = find( gen_status & qg_max_lim )
mn = find( gen_status & qg_min_lim )
if len(mx) > 0 or len(mn) > 0: ## we have some Q limit violations
# No PV generators
if len(pv) == 0:
if verbose:
if len(mx) > 0:
print('Gen %d [only one left] exceeds upper Q limit : INFEASIBLE PROBLEM\n' % mx + 1)
else:
print('Gen %d [only one left] exceeds lower Q limit : INFEASIBLE PROBLEM\n' % mn + 1)
success = 0
break
## one at a time?
if qlim == 2: ## fix largest violation, ignore the rest
k = argmax(r_[gen[mx, QG] - gen[mx, QMAX],
gen[mn, QMIN] - gen[mn, QG]])
if k > len(mx):
mn = mn[k - len(mx)]
mx = []
else:
mx = mx[k]
mn = []
if verbose and len(mx) > 0:
for i in range(len(mx)):
print('Gen ' + str(mx[i] + 1) + ' at upper Q limit, converting to PQ bus\n')
if verbose and len(mn) > 0:
for i in range(len(mn)):
print('Gen ' + str(mn[i] + 1) + ' at lower Q limit, converting to PQ bus\n')
## save corresponding limit values
fixedQg[mx] = gen[mx, QMAX]
fixedQg[mn] = gen[mn, QMIN]
mx = r_[mx, mn].astype(int)
## convert to PQ bus
gen[mx, QG] = fixedQg[mx] ## set Qg to binding
for i in range(len(mx)): ## [one at a time, since they may be at same bus]
gen[mx[i], GEN_STATUS] = 0 ## temporarily turn off gen,
bi = gen[mx[i], GEN_BUS] ## adjust load accordingly,
bus[bi, [PD, QD]] = (bus[bi, [PD, QD]] - gen[mx[i], [PG, QG]])
if len(ref) > 1 and any(bus[gen[mx, GEN_BUS], BUS_TYPE] == REF):
raise ValueError('Sorry, PYPOWER cannot enforce Q '
'limits for slack buses in systems '
'with multiple slacks.')
bus[gen[mx, GEN_BUS].astype(int), BUS_TYPE] = PQ ## & set bus type to PQ
## update bus index lists of each type of bus
ref_temp = ref
ref, pv, pq = bustypes(bus, gen)
if verbose and ref != ref_temp:
print('Bus %d is new slack bus\n' % ref)
limited = r_[limited, mx].astype(int)
else:
repeat = 0 ## no more generator Q limits violated
else:
repeat = 0 ## don't enforce generator Q limits, once is enough
if qlim and len(limited) > 0:
## restore injections from limited gens [those at Q limits]
gen[limited, QG] = fixedQg[limited] ## restore Qg value,
for i in range(len(limited)): ## [one at a time, since they may be at same bus]
bi = gen[limited[i], GEN_BUS] ## re-adjust load,
bus[bi, [PD, QD]] = bus[bi, [PD, QD]] + gen[limited[i], [PG, QG]]
gen[limited[i], GEN_STATUS] = 1 ## and turn gen back on
if ref != ref0:
## adjust voltage angles to make original ref bus correct
bus[:, VA] = bus[:, VA] - bus[ref0, VA] + Varef0
ppc["et"] = time() - t0
ppc["success"] = success
##----- output results -----
## convert back to original bus numbering & print results
ppc["bus"], ppc["gen"], ppc["branch"] = bus, gen, branch
results = int2ext(ppc)
## zero out result fields of out-of-service gens & branches
if len(results["order"]["gen"]["status"]["off"]) > 0:
results["gen"][ix_(results["order"]["gen"]["status"]["off"], [PG, QG])] = 0
if len(results["order"]["branch"]["status"]["off"]) > 0:
results["branch"][ix_(results["order"]["branch"]["status"]["off"], [PF, QF, PT, QT])] = 0
if fname:
fd = None
try:
fd = open(fname, "a")
except Exception as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(results, fd, ppopt)
fd.close()
else:
printpf(results, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, results)
return results, success
def t_ext2int2ext(quiet=False):
"""Tests C{ext2int} and C{int2ext}.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
t_begin(85, quiet)
##----- ppc = e2i_data/i2e_data(ppc) -----
t = 'ppc = e2i_data(ppc) : '
ppce = loadcase(t_case_ext())
ppci = loadcase(t_case_int())
ppc = e2i_data(ppce)
t_is(ppc['bus'], ppci['bus'], 12, [t, 'bus'])
t_is(ppc['branch'], ppci['branch'], 12, [t, 'branch'])
t_is(ppc['gen'], ppci['gen'], 12, [t, 'gen'])
t_is(ppc['gencost'], ppci['gencost'], 12, [t, 'gencost'])
t_is(ppc['areas'], ppci['areas'], 12, [t, 'areas'])
t_is(ppc['A'], ppci['A'], 12, [t, 'A'])
t_is(ppc['N'], ppci['N'], 12, [t, 'N'])
t = 'ppc = e2i_data(ppc) - repeat : '
ppc = e2i_data(ppc)
t_is(ppc['bus'], ppci['bus'], 12, [t, 'bus'])
t_is(ppc['branch'], ppci['branch'], 12, [t, 'branch'])
t_is(ppc['gen'], ppci['gen'], 12, [t, 'gen'])
t_is(ppc['gencost'], ppci['gencost'], 12, [t, 'gencost'])
t_is(ppc['areas'], ppci['areas'], 12, [t, 'areas'])
t_is(ppc['A'], ppci['A'], 12, [t, 'A'])
t_is(ppc['N'], ppci['N'], 12, [t, 'N'])
t = 'ppc = i2e_data(ppc) : '
ppc = i2e_data(ppc)
t_is(ppc['bus'], ppce['bus'], 12, [t, 'bus'])
t_is(ppc['branch'], ppce['branch'], 12, [t, 'branch'])
t_is(ppc['gen'], ppce['gen'], 12, [t, 'gen'])
t_is(ppc['gencost'], ppce['gencost'], 12, [t, 'gencost'])
t_is(ppc['areas'], ppce['areas'], 12, [t, 'areas'])
t_is(ppc['A'], ppce['A'], 12, [t, 'A'])
t_is(ppc['N'], ppce['N'], 12, [t, 'N'])
##----- val = e2i_data/i2e_data(ppc, val, ...) -----
t = 'val = e2i_data(ppc, val, \'bus\')'
ppc = e2i_data(ppce)
got = e2i_data(ppc, ppce['xbus'], 'bus')
ex = ppce['xbus']
ex = delete(ex, 5, 0)
t_is(got, ex, 12, t)
t = 'val = i2e_data(ppc, val, oldval, \'bus\')'
tmp = ones(ppce['xbus'].shape)
tmp[5, :] = ppce['xbus'][5, :]
got = i2e_data(ppc, ex, tmp, 'bus')
t_is(got, ppce['xbus'], 12, t)
t = 'val = e2i_data(ppc, val, \'bus\', 1)'
got = e2i_data(ppc, ppce['xbus'], 'bus', 1)
ex = ppce['xbus']
ex = delete(ex, 5, 1)
t_is(got, ex, 12, t)
t = 'val = i2e_data(ppc, val, oldval, \'bus\', 1)'
tmp = ones(ppce['xbus'].shape)
tmp[:, 5] = ppce['xbus'][:, 5]
got = i2e_data(ppc, ex, tmp, 'bus', 1)
t_is(got, ppce['xbus'], 12, t)
t = 'val = e2i_data(ppc, val, \'gen\')'
got = e2i_data(ppc, ppce['xgen'], 'gen')
ex = ppce['xgen'][[3, 1, 0], :]
t_is(got, ex, 12, t)
t = 'val = i2e_data(ppc, val, oldval, \'gen\')'
tmp = ones(ppce['xgen'].shape)
tmp[2, :] = ppce['xgen'][2, :]
got = i2e_data(ppc, ex, tmp, 'gen')
t_is(got, ppce['xgen'], 12, t)
t = 'val = e2i_data(ppc, val, \'gen\', 1)'
got = e2i_data(ppc, ppce['xgen'], 'gen', 1)
ex = ppce['xgen'][:, [3, 1, 0]]
t_is(got, ex, 12, t)
t = 'val = i2e_data(ppc, val, oldval, \'gen\', 1)'
tmp = ones(ppce['xgen'].shape)
tmp[:, 2] = ppce['xgen'][:, 2]
got = i2e_data(ppc, ex, tmp, 'gen', 1)
t_is(got, ppce['xgen'], 12, t)
t = 'val = e2i_data(ppc, val, \'branch\')'
got = e2i_data(ppc, ppce['xbranch'], 'branch')
ex = ppce['xbranch']
ex = delete(ex, 6, 0)
t_is(got, ex, 12, t)
t = 'val = i2e_data(ppc, val, oldval, \'branch\')'
tmp = ones(ppce['xbranch'].shape)
tmp[6, :] = ppce['xbranch'][6, :]
got = i2e_data(ppc, ex, tmp, 'branch')
t_is(got, ppce['xbranch'], 12, t)
t = 'val = e2i_data(ppc, val, \'branch\', 1)'
got = e2i_data(ppc, ppce['xbranch'], 'branch', 1)
ex = ppce['xbranch']
ex = delete(ex, 6, 1)
t_is(got, ex, 12, t)
t = 'val = i2e_data(ppc, val, oldval, \'branch\', 1)'
tmp = ones(ppce['xbranch'].shape)
tmp[:, 6] = ppce['xbranch'][:, 6]
got = i2e_data(ppc, ex, tmp, 'branch', 1)
t_is(got, ppce['xbranch'], 12, t)
t = 'val = e2i_data(ppc, val, {\'branch\', \'gen\', \'bus\'})'
got = e2i_data(ppc, ppce['xrows'], ['branch', 'gen', 'bus'])
ex = r_[ppce['xbranch'][list(range(6)) + list(range(7, 10)), :4],
ppce['xgen'][[3, 1, 0], :],
ppce['xbus'][list(range(5)) + list(range(6, 10)), :4],
-1 * ones((2, 4))]
t_is(got, ex, 12, t)
t = 'val = i2e_data(ppc, val, oldval, {\'branch\', \'gen\', \'bus\'})'
tmp1 = ones(ppce['xbranch'][:, :4].shape)
tmp1[6, :4] = ppce['xbranch'][6, :4]
tmp2 = ones(ppce['xgen'].shape)
tmp2[2, :] = ppce['xgen'][2, :]
tmp3 = ones(ppce['xbus'][:, :4].shape)
tmp3[5, :4] = ppce['xbus'][5, :4]
tmp = r_[tmp1, tmp2, tmp3]
got = i2e_data(ppc, ex, tmp, ['branch', 'gen', 'bus'])
t_is(got, ppce['xrows'], 12, t)
t = 'val = e2i_data(ppc, val, {\'branch\', \'gen\', \'bus\'}, 1)'
got = e2i_data(ppc, ppce['xcols'], ['branch', 'gen', 'bus'], 1)
ex = r_[ppce['xbranch'][list(range(6)) + list(range(7, 10)), :4],
ppce['xgen'][[3, 1, 0], :],
ppce['xbus'][list(range(5)) + list(range(6, 10)), :4],
-1 * ones((2, 4))].T
t_is(got, ex, 12, t)
t = 'val = i2e_data(ppc, val, oldval, {\'branch\', \'gen\', \'bus\'}, 1)'
tmp1 = ones(ppce['xbranch'][:, :4].shape)
tmp1[6, :4] = ppce['xbranch'][6, :4]
tmp2 = ones(ppce['xgen'].shape)
tmp2[2, :] = ppce['xgen'][2, :]
tmp3 = ones(ppce['xbus'][:, :4].shape)
tmp3[5, :4] = ppce['xbus'][5, :4]
tmp = r_[tmp1, tmp2, tmp3].T
got = i2e_data(ppc, ex, tmp, ['branch', 'gen', 'bus'], 1)
t_is(got, ppce['xcols'], 12, t)
##----- ppc = e2i_field/i2e_field(ppc, field, ...) -----
t = 'ppc = e2i_field(ppc, field, \'bus\')'
ppc = e2i_field(ppce)
ex = ppce['xbus']
ex = delete(ex, 5, 0)
got = e2i_field(ppc, 'xbus', 'bus')
t_is(got['xbus'], ex, 12, t)
t = 'ppc = i2e_field(ppc, field, \'bus\')'
got = i2e_field(got, 'xbus', ordering='bus')
t_is(got['xbus'], ppce['xbus'], 12, t)
t = 'ppc = e2i_field(ppc, field, \'bus\', 1)'
ex = ppce['xbus']
ex = delete(ex, 5, 1)
got = e2i_field(ppc, 'xbus', 'bus', 1)
t_is(got['xbus'], ex, 12, t)
t = 'ppc = i2e_field(ppc, field, \'bus\', 1)'
got = i2e_field(got, 'xbus', ordering='bus', dim=1)
t_is(got['xbus'], ppce['xbus'], 12, t)
t = 'ppc = e2i_field(ppc, field, \'gen\')'
ex = ppce['xgen'][[3, 1, 0], :]
got = e2i_field(ppc, 'xgen', 'gen')
t_is(got['xgen'], ex, 12, t)
t = 'ppc = i2e_field(ppc, field, \'gen\')'
got = i2e_field(got, 'xgen', ordering='gen')
t_is(got['xgen'], ppce['xgen'], 12, t)
t = 'ppc = e2i_field(ppc, field, \'gen\', 1)'
ex = ppce['xgen'][:, [3, 1, 0]]
got = e2i_field(ppc, 'xgen', 'gen', 1)
t_is(got['xgen'], ex, 12, t)
t = 'ppc = i2e_field(ppc, field, \'gen\', 1)'
got = i2e_field(got, 'xgen', ordering='gen', dim=1)
t_is(got['xgen'], ppce['xgen'], 12, t)
t = 'ppc = e2i_field(ppc, field, \'branch\')'
ex = ppce['xbranch']
ex = delete(ex, 6, 0)
got = e2i_field(ppc, 'xbranch', 'branch')
t_is(got['xbranch'], ex, 12, t)
t = 'ppc = i2e_field(ppc, field, \'branch\')'
got = i2e_field(got, 'xbranch', ordering='branch')
t_is(got['xbranch'], ppce['xbranch'], 12, t)
t = 'ppc = e2i_field(ppc, field, \'branch\', 1)'
ex = ppce['xbranch']
ex = delete(ex, 6, 1)
got = e2i_field(ppc, 'xbranch', 'branch', 1)
t_is(got['xbranch'], ex, 12, t)
t = 'ppc = i2e_field(ppc, field, \'branch\', 1)'
got = i2e_field(got, 'xbranch', ordering='branch', dim=1)
t_is(got['xbranch'], ppce['xbranch'], 12, t)
t = 'ppc = e2i_field(ppc, field, {\'branch\', \'gen\', \'bus\'})'
ex = r_[ppce['xbranch'][list(range(6)) + list(range(7, 10)), :4],
ppce['xgen'][[3, 1, 0], :],
ppce['xbus'][list(range(5)) + list(range(6, 10)), :4],
-1 * ones((2, 4))]
got = e2i_field(ppc, 'xrows', ['branch', 'gen', 'bus'])
t_is(got['xrows'], ex, 12, t)
t = 'ppc = i2e_field(ppc, field, {\'branch\', \'gen\', \'bus\'})'
got = i2e_field(got, 'xrows', ordering=['branch', 'gen', 'bus'])
t_is(got['xrows'], ppce['xrows'], 12, t)
t = 'ppc = e2i_field(ppc, field, {\'branch\', \'gen\', \'bus\'}, 1)'
ex = r_[ppce['xbranch'][list(range(6)) + list(range(7, 10)), :4],
ppce['xgen'][[3, 1, 0], :],
ppce['xbus'][list(range(5)) + list(range(6, 10)), :4],
-1 * ones((2, 4))].T
got = e2i_field(ppc, 'xcols', ['branch', 'gen', 'bus'], 1)
t_is(got['xcols'], ex, 12, t)
t = 'ppc = i2e_field(ppc, field, {\'branch\', \'gen\', \'bus\'})'
got = i2e_field(got, 'xcols', ordering=['branch', 'gen', 'bus'], dim=1)
t_is(got['xcols'], ppce['xcols'], 12, t)
t = 'ppc = e2i_field(ppc, {\'field1\', \'field2\'}, ordering)'
ex = ppce['x']['more'][[3, 1, 0], :]
got = e2i_field(ppc, ['x', 'more'], 'gen')
t_is(got['x']['more'], ex, 12, t)
t = 'ppc = i2e_field(ppc, {\'field1\', \'field2\'}, ordering)'
got = i2e_field(got, ['x', 'more'], ordering='gen')
t_is(got['x']['more'], ppce['x']['more'], 12, t)
t = 'ppc = e2i_field(ppc, {\'field1\', \'field2\'}, ordering, 1)'
ex = ppce['x']['more'][:, [3, 1, 0]]
got = e2i_field(ppc, ['x', 'more'], 'gen', 1)
t_is(got['x']['more'], ex, 12, t)
t = 'ppc = i2e_field(ppc, {\'field1\', \'field2\'}, ordering, 1)'
got = i2e_field(got, ['x', 'more'], ordering='gen', dim=1)
t_is(got['x']['more'], ppce['x']['more'], 12, t)
##----- more ppc = ext2int/int2ext(ppc) -----
t = 'ppc = ext2int(ppc) - bus/gen/branch only : '
ppce = loadcase(t_case_ext())
ppci = loadcase(t_case_int())
del ppce['gencost']
del ppce['areas']
del ppce['A']
del ppce['N']
del ppci['gencost']
del ppci['areas']
del ppci['A']
del ppci['N']
ppc = ext2int(ppce)
t_is(ppc['bus'], ppci['bus'], 12, [t, 'bus'])
t_is(ppc['branch'], ppci['branch'], 12, [t, 'branch'])
t_is(ppc['gen'], ppci['gen'], 12, [t, 'gen'])
t = 'ppc = ext2int(ppc) - no areas/A : '
ppce = loadcase(t_case_ext())
ppci = loadcase(t_case_int())
del ppce['areas']
del ppce['A']
del ppci['areas']
del ppci['A']
ppc = ext2int(ppce)
t_is(ppc['bus'], ppci['bus'], 12, [t, 'bus'])
t_is(ppc['branch'], ppci['branch'], 12, [t, 'branch'])
t_is(ppc['gen'], ppci['gen'], 12, [t, 'gen'])
t_is(ppc['gencost'], ppci['gencost'], 12, [t, 'gencost'])
t_is(ppc['N'], ppci['N'], 12, [t, 'N'])
t = 'ppc = ext2int(ppc) - Qg cost, no N : '
ppce = loadcase(t_case_ext())
ppci = loadcase(t_case_int())
del ppce['N']
del ppci['N']
ppce['gencost'] = c_[ppce['gencost'], ppce['gencost']]
ppci['gencost'] = c_[ppci['gencost'], ppci['gencost']]
ppc = ext2int(ppce)
t_is(ppc['bus'], ppci['bus'], 12, [t, 'bus'])
t_is(ppc['branch'], ppci['branch'], 12, [t, 'branch'])
t_is(ppc['gen'], ppci['gen'], 12, [t, 'gen'])
t_is(ppc['gencost'], ppci['gencost'], 12, [t, 'gencost'])
t_is(ppc['areas'], ppci['areas'], 12, [t, 'areas'])
t_is(ppc['A'], ppci['A'], 12, [t, 'A'])
t = 'ppc = ext2int(ppc) - A, N are DC sized : '
ppce = loadcase(t_case_ext())
ppci = loadcase(t_case_int())
eVmQgcols = list(range(10, 20)) + list(range(24, 28))
iVmQgcols = list(range(9, 18)) + list(range(21, 24))
ppce['A'] = delete(ppce['A'], eVmQgcols, 1)
ppce['N'] = delete(ppce['N'], eVmQgcols, 1)
ppci['A'] = delete(ppci['A'], iVmQgcols, 1)
ppci['N'] = delete(ppci['N'], iVmQgcols, 1)
ppc = ext2int(ppce)
t_is(ppc['bus'], ppci['bus'], 12, [t, 'bus'])
t_is(ppc['branch'], ppci['branch'], 12, [t, 'branch'])
t_is(ppc['gen'], ppci['gen'], 12, [t, 'gen'])
t_is(ppc['gencost'], ppci['gencost'], 12, [t, 'gencost'])
t_is(ppc['areas'], ppci['areas'], 12, [t, 'areas'])
t_is(ppc['A'], ppci['A'], 12, [t, 'A'])
t_is(ppc['N'], ppci['N'], 12, [t, 'N'])
t = 'ppc = int2ext(ppc) - A, N are DC sized : '
ppc = int2ext(ppc)
t_is(ppc['bus'], ppce['bus'], 12, [t, 'bus'])
t_is(ppc['branch'], ppce['branch'], 12, [t, 'branch'])
t_is(ppc['gen'], ppce['gen'], 12, [t, 'gen'])
t_is(ppc['gencost'], ppce['gencost'], 12, [t, 'gencost'])
t_is(ppc['areas'], ppce['areas'], 12, [t, 'areas'])
t_is(ppc['A'], ppce['A'], 12, [t, 'A'])
t_is(ppc['N'], ppce['N'], 12, [t, 'N'])
t_end()
def opf(*args):
"""Solves an optimal power flow.
Returns a C{results} dict.
The data for the problem can be specified in one of three ways:
1. a string (ppc) containing the file name of a PYPOWER case
which defines the data matrices baseMVA, bus, gen, branch, and
gencost (areas is not used at all, it is only included for
backward compatibility of the API).
2. a dict (ppc) containing the data matrices as fields.
3. the individual data matrices themselves.
The optional user parameters for user constraints (C{A, l, u}), user costs
(C{N, fparm, H, Cw}), user variable initializer (C{z0}), and user variable
limits (C{zl, zu}) can also be specified as fields in a case dict,
either passed in directly or defined in a case file referenced by name.
When specified, C{A, l, u} represent additional linear constraints on the
optimization variables, C{l <= A*[x z] <= u}. If the user specifies an C{A}
matrix that has more columns than the number of "C{x}" (OPF) variables,
then there are extra linearly constrained "C{z}" variables. For an
explanation of the formulation used and instructions for forming the
C{A} matrix, see the MATPOWER manual.
A generalized cost on all variables can be applied if input arguments
C{N}, C{fparm}, C{H} and C{Cw} are specified. First, a linear transformation
of the optimization variables is defined by means of C{r = N * [x z]}.
Then, to each element of C{r} a function is applied as encoded in the
C{fparm} matrix (see MATPOWER manual). If the resulting vector is named
C{w}, then C{H} and C{Cw} define a quadratic cost on w:
C{(1/2)*w'*H*w + Cw * w}. C{H} and C{N} should be sparse matrices and C{H}
should also be symmetric.
The optional C{ppopt} vector specifies PYPOWER options. If the OPF
algorithm is not explicitly set in the options PYPOWER will use the default
solver, based on a primal-dual interior point method. For the AC OPF this
is C{OPF_ALG = 560}. For the DC OPF, the default is C{OPF_ALG_DC = 200}.
See L{ppoption} for more details on the available OPF solvers and other OPF
options and their default values.
The solved case is returned in a single results dict (described
below). Also returned are the final objective function value (C{f}) and a
flag which is C{True} if the algorithm was successful in finding a solution
(success). Additional optional return values are an algorithm specific
return status (C{info}), elapsed time in seconds (C{et}), the constraint
vector (C{g}), the Jacobian matrix (C{jac}), and the vector of variables
(C{xr}) as well as the constraint multipliers (C{pimul}).
The single results dict is a PYPOWER case struct (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{et} elapsed time in seconds for solving OPF
- C{success} 1 if solver converged successfully, 0 otherwise
- C{om} OPF model object, see 'help opf_model'
- 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{g} (optional) constraint values
- C{dg} (optional) constraint 1st derivatives
- C{df} (optional) obj fun 1st derivatives (not yet implemented)
- C{d2f} (optional) obj fun 2nd derivatives (not yet implemented)
- C{raw} raw solver output in form returned by MINOS, and more
- C{xr} final value of optimization variables
- C{pimul} constraint multipliers
- C{info} solver specific termination code
- C{output} solver specific output information
- C{alg} algorithm code of solver used
- C{var}
- C{val} optimization variable values, by named block
- C{Va} voltage angles
- C{Vm} voltage magnitudes (AC only)
- C{Pg} real power injections
- C{Qg} reactive power injections (AC only)
- C{y} constrained cost variable (only if have pwl costs)
- (other) any user defined variable blocks
- C{mu} variable bound shadow prices, by named block
- C{l} lower bound shadow prices
- C{Va}, C{Vm}, C{Pg}, C{Qg}, C{y}, (other)
- C{u} upper bound shadow prices
- C{Va}, C{Vm}, C{Pg}, C{Qg}, C{y}, (other)
- C{nln} (AC only)
- C{mu} shadow prices on nonlinear constraints, by named block
- C{l} lower bounds
- C{Pmis} real power mismatch equations
- C{Qmis} reactive power mismatch equations
- C{Sf} flow limits at "from" end of branches
- C{St} flow limits at "to" end of branches
- C{u} upper bounds
- C{Pmis}, C{Qmis}, C{Sf}, C{St}
- C{lin}
- C{mu} shadow prices on linear constraints, by named block
- C{l} lower bounds
- C{Pmis} real power mistmatch equations (DC only)
- C{Pf} flow limits at "from" end of branches (DC only)
- C{Pt} flow limits at "to" end of branches (DC only)
- C{PQh} upper portion of gen PQ-capability curve(AC only)
- C{PQl} lower portion of gen PQ-capability curve(AC only)
- C{vl} constant power factor constraint for loads
- C{ycon} basin constraints for CCV for pwl costs
- (other) any user defined constraint blocks
- C{u} upper bounds
- C{Pmis}, C{Pf}, C{Pf}, C{PQh}, C{PQl}, C{vl}, C{ycon},
- (other)
- C{cost} user defined cost values, by named block
@see: L{runopf}, L{dcopf}, L{uopf}, L{caseformat}
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Richard Lincoln
"""
##----- initialization -----
t0 = time() ## start timer
## process input arguments
ppc, ppopt = opf_args2(*args)
## add zero columns to bus, gen, branch for multipliers, etc if needed
nb = shape(ppc['bus'])[0] ## number of buses
nl = shape(ppc['branch'])[0] ## number of branches
ng = shape(ppc['gen'])[0] ## number of dispatchable injections
if shape(ppc['bus'])[1] < MU_VMIN + 1:
ppc['bus'] = c_[ppc['bus'], zeros((nb, MU_VMIN + 1 - shape(ppc['bus'])[1]))]
if shape(ppc['gen'])[1] < MU_QMIN + 1:
ppc['gen'] = c_[ppc['gen'], zeros((ng, MU_QMIN + 1 - shape(ppc['gen'])[1]))]
if shape(ppc['branch'])[1] < MU_ANGMAX + 1:
ppc['branch'] = c_[ppc['branch'], zeros((nl, MU_ANGMAX + 1 - shape(ppc['branch'])[1]))]
##----- convert to internal numbering, remove out-of-service stuff -----
ppc = ext2int(ppc)
##----- construct OPF model object -----
om = opf_setup(ppc, ppopt)
##----- execute the OPF -----
results, success, raw = opf_execute(om, ppopt)
##----- revert to original ordering, including out-of-service stuff -----
results = int2ext(results)
## zero out result fields of out-of-service gens & branches
if len(results['order']['gen']['status']['off']) > 0:
results['gen'][ ix_(results['order']['gen']['status']['off'], [PG, QG, MU_PMAX, MU_PMIN]) ] = 0
if len(results['order']['branch']['status']['off']) > 0:
results['branch'][ ix_(results['order']['branch']['status']['off'], [PF, QF, PT, QT, MU_SF, MU_ST, MU_ANGMIN, MU_ANGMAX]) ] = 0
##----- finish preparing output -----
et = time() - t0 ## compute elapsed time
results['et'] = et
results['success'] = success
results['raw'] = raw
return results
def i2e_data(ppc, val, oldval, ordering, dim=0):
"""Converts data from internal to external bus numbering.
For a case dict using internal indexing, this function can be
used to convert other data structures as well by passing in 3 or 4
extra parameters in addition to the case dict. If the value passed
in the 2nd argument C{val} is a column vector, it will be converted
according to the ordering specified by the 4th argument (C{ordering},
described below). If C{val} is an n-dimensional matrix, then the
optional 5th argument (C{dim}, default = 0) can be used to specify
which dimension to reorder. The 3rd argument (C{oldval}) is used to
initialize the return value before converting C{val} to external
indexing. In particular, any data corresponding to off-line gens
or branches or isolated buses or any connected gens or branches
will be taken from C{oldval}, with C[val} supplying the rest of the
returned data.
The C{ordering} argument is used to indicate whether the data
corresponds to bus-, gen- or branch-ordered data. It can be one
of the following three strings: 'bus', 'gen' or 'branch'. For
data structures with multiple blocks of data, ordered by bus,
gen or branch, they can be converted with a single call by
specifying C[ordering} as a list of strings.
Any extra elements, rows, columns, etc. beyond those indicated
in C{ordering}, are not disturbed.
Examples:
A_ext = i2e_data(ppc, A_int, A_orig, ['bus','bus','gen','gen'], 1)
Converts an A matrix for user-supplied OPF constraints from
internal to external ordering, where the columns of the A
matrix correspond to bus voltage angles, then voltage
magnitudes, then generator real power injections and finally
generator reactive power injections.
gencost_ext = i2e_data(ppc, gencost_int, gencost_orig, ['gen','gen'], 0)
Converts a C{gencost} matrix that has both real and reactive power
costs (in rows 1--ng and ng+1--2*ng, respectively).
@see: L{e2i_data}, L{i2e_field}, L{int2ext}.
"""
from pypower.int2ext import int2ext
if 'order' not in ppc:
sys.stderr.write('i2e_data: ppc does not have the \'order\' field '
'required for conversion back to external numbering.\n')
return
o = ppc["order"]
if o['state'] != 'i':
sys.stderr.write('i2e_data: ppc does not appear to be in internal '
'order\n')
return
if isinstance(ordering, str): ## single set
if ordering == 'gen':
v = get_reorder(val, o[ordering]["i2e"], dim)
else:
v = val
val = set_reorder(oldval, v, o[ordering]["status"]["on"], dim)
else: ## multiple sets
be = 0 ## base, external indexing
bi = 0 ## base, internal indexing
new_v = []
for ordr in ordering:
ne = o["ext"][ordr].shape[0]
ni = ppc[ordr].shape[0]
v = get_reorder(val, bi + arange(ni), dim)
oldv = get_reorder(oldval, be + arange(ne), dim)
new_v.append( int2ext(ppc, v, oldv, ordr, dim) )
be = be + ne
bi = bi + ni
ni = val.shape[dim]
if ni > bi: ## the rest
v = get_reorder(val, arange(bi, ni), dim)
new_v.append(v)
val = concatenate(new_v, dim)
return val
def runcpf(basecasedata=None,
targetcasedata=None,
ppopt=None,
fname='',
solvedcase=''):
# default arguments
if basecasedata is None:
basecasedata = join(dirname(__file__), 'case9')
if targetcasedata is None:
targetcasedata = join(dirname(__file__), 'case9target')
ppopt = ppoption(ppopt)
# options
verbose = ppopt["VERBOSE"]
step = ppopt["CPF_STEP"]
parameterization = ppopt["CPF_PARAMETERIZATION"]
adapt_step = ppopt["CPF_ADAPT_STEP"]
cb_args = ppopt["CPF_USER_CALLBACK_ARGS"]
# set up callbacks
callback_names = ["cpf_default_callback"]
if len(ppopt["CPF_USER_CALLBACK"]) > 0:
if isinstance(ppopt["CPF_USER_CALLBACK"], list):
callback_names = r_[callback_names, ppopt["CPF_USER_CALLBACK"]]
else:
callback_names.append(ppopt["CPF_USER_CALLBACK"])
callbacks = []
for callback_name in callback_names:
callbacks.append(getattr(cpf_callbacks, callback_name))
# read base case data
ppcbase = loadcase(basecasedata)
nb = ppcbase["bus"].shape[0]
# add zero columns to branch for flows if needed
if ppcbase["branch"].shape[1] < QT:
ppcbase["branch"] = c_[ppcbase["branch"],
zeros((ppcbase["branch"].shape[0],
QT - ppcbase["branch"].shape[1] + 1))]
# convert to internal indexing
ppcbase = ext2int(ppcbase)
baseMVAb, busb, genb, branchb = \
ppcbase["baseMVA"], ppcbase["bus"], ppcbase["gen"], ppcbase["branch"]
# get bus index lists of each type of bus
ref, pv, pq = bustypes(busb, genb)
# generator info
onb = find(genb[:, GEN_STATUS] > 0) # which generators are on?
gbusb = genb[onb, GEN_BUS].astype(int) # what buses are they at?
# read target case data
ppctarget = loadcase(targetcasedata)
# add zero columns to branch for flows if needed
if ppctarget["branch"].shape[1] < QT:
ppctarget["branch"] = c_[ppctarget["branch"],
zeros(
(ppctarget["branch"].shape[0],
QT - ppctarget["branch"].shape[1] + 1))]
# convert to internal indexing
ppctarget = ext2int(ppctarget)
baseMVAt, bust, gent, brancht = \
ppctarget["baseMVA"], ppctarget["bus"], ppctarget["gen"], ppctarget["branch"]
# get bus index lists of each type of bus
# ref, pv, pq = bustypes(bust, gent)
# generator info
ont = find(gent[:, GEN_STATUS] > 0) # which generators are on?
gbust = gent[ont, GEN_BUS].astype(int) # what buses are they at?
# ----- run the power flow -----
t0 = time()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v["Version"], v["Date"]))
stdout.write(' -- AC Continuation Power Flow\n')
# initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = busb[:, VM] * exp(1j * pi / 180 * busb[:, VA])
vcb = ones(V0.shape) # create mask of voltage-controlled buses
vcb[pq] = 0 # exclude PQ buses
k = find(vcb[gbusb]) # in-service gens at v-c buses
V0[gbusb[k]] = genb[onb[k], VG] / abs(V0[gbusb[k]]) * V0[gbusb[k]]
# build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVAb, busb, branchb)
# compute base case complex bus power injections (generation - load)
Sbusb = makeSbus(baseMVAb, busb, genb)
# compute target case complex bus power injections (generation - load)
Sbust = makeSbus(baseMVAt, bust, gent)
# scheduled transfer
Sxfr = Sbust - Sbusb
# Run the base case power flow solution
if verbose > 2:
ppopt_pf = ppoption(ppopt, VERBOSE=max(0, verbose - 1))
else:
ppopt_pf = ppoption(ppopt, VERBOSE=max(0, verbose - 2))
lam = 0
V, success, iterations = newtonpf(Ybus, Sbusb, V0, ref, pv, pq, ppopt_pf)
if verbose > 2:
print('step %3d : lambda = %6.3f\n' % (0, 0))
elif verbose > 1:
print('step %3d : lambda = %6.3f, %2d Newton steps\n',
(0, 0, iterations))
lamprv = lam # lam at previous step
Vprv = V # V at previous step
continuation = 1
cont_steps = 0
# input args for callbacks
cb_data = {
"ppc_base": ppcbase,
"ppc_target": ppctarget,
"Sxfr": Sxfr,
"Ybus": Ybus,
"Yf": Yf,
"Yt": Yt,
"ref": ref,
"pv": pv,
"pq": pq,
"ppopt": ppopt
}
cb_state = {}
# invoke callbacks
for k in range(len(callbacks)):
cb_state, _ = callbacks[k](cont_steps, V, lam, V, lam, cb_data,
cb_state, cb_args)
if linalg.norm(Sxfr) == 0:
if verbose:
print(
'base case and target case have identical load and generation\n'
)
continuation = 0
V0 = V
lam0 = lam
# tangent predictor z = [dx;dlam]
z = zeros(2 * len(V) + 1)
z[-1] = 1.0
while continuation:
cont_steps = cont_steps + 1
# prediction for next step
V0, lam0, z = cpf_predictor(V, lam, Ybus, Sxfr, pv, pq, step, z, Vprv,
lamprv, parameterization)
# save previous voltage, lambda before updating
Vprv = V
lamprv = lam
# correction
V, success, i, lam = cpf_corrector(Ybus, Sbusb, V0, ref, pv, pq, lam0,
Sxfr, Vprv, lamprv, z, step,
parameterization, ppopt_pf)
if not success:
continuation = 0
if verbose:
print(
'step %3d : lambda = %6.3f, corrector did not converge in %d iterations\n'
% (cont_steps, lam, i))
break
if verbose > 2:
print('step %3d : lambda = %6.3f\n' % (cont_steps, lam))
elif verbose > 1:
print('step %3d : lambda = %6.3f, %2d corrector Newton steps\n' %
(cont_steps, lam, i))
# invoke callbacks
for k in range(len(callbacks)):
cb_state, _ = callbacks[k](cont_steps, V, lam, V0, lam0, cb_data,
cb_state, cb_args)
if isinstance(ppopt["CPF_STOP_AT"], str):
if ppopt["CPF_STOP_AT"].upper() == "FULL":
if abs(lam) < 1e-8: # traced the full continuation curve
if verbose:
print(
'\nTraced full continuation curve in %d continuation steps\n'
% cont_steps)
continuation = 0
elif lam < lamprv and lam - step < 0: # next step will overshoot
step = lam # modify step-size
parameterization = 1 # change to natural parameterization
adapt_step = False # disable step-adaptivity
else: # == 'NOSE'
if lam < lamprv: # reached the nose point
if verbose:
print(
'\nReached steady state loading limit in %d continuation steps\n'
% cont_steps)
continuation = 0
else:
if lam < lamprv:
if verbose:
print(
'\nReached steady state loading limit in %d continuation steps\n'
% cont_steps)
continuation = 0
elif abs(ppopt["CPF_STOP_AT"] -
lam) < 1e-8: # reached desired lambda
if verbose:
print(
'\nReached desired lambda %3.2f in %d continuation steps\n'
% (ppopt["CPF_STOP_AT"], cont_steps))
continuation = 0
# will reach desired lambda in next step
elif lam + step > ppopt["CPF_STOP_AT"]:
step = ppopt["CPF_STOP_AT"] - lam # modify step-size
parameterization = 1 # change to natural parameterization
adapt_step = False # disable step-adaptivity
if adapt_step and continuation:
pvpq = r_[pv, pq]
# Adapt stepsize
cpf_error = linalg.norm(
r_[angle(V[pq]), abs(V[pvpq]), lam] -
r_[angle(V0[pq]), abs(V0[pvpq]), lam0], inf)
if cpf_error < ppopt["CPF_ERROR_TOL"]:
# Increase stepsize
step = step * ppopt["CPF_ERROR_TOL"] / cpf_error
if step > ppopt["CPF_STEP_MAX"]:
step = ppopt["CPF_STEP_MAX"]
else:
# decrese stepsize
step = step * ppopt["CPF_ERROR_TOL"] / cpf_error
if step < ppopt["CPF_STEP_MIN"]:
step = ppopt["CPF_STEP_MIN"]
# invoke callbacks
if success:
cpf_results = {}
for k in range(len(callbacks)):
cb_state, cpf_results = callbacks[k](cont_steps,
V,
lam,
V0,
lam0,
cb_data,
cb_state,
cb_args,
results=cpf_results,
is_final=True)
else:
cpf_results["iterations"] = i
# update bus and gen matrices to reflect the loading and generation
# at the noise point
bust[:, PD] = busb[:, PD] + lam * (bust[:, PD] - busb[:, PD])
bust[:, QD] = busb[:, QD] + lam * (bust[:, QD] - busb[:, QD])
gent[:, PG] = genb[:, PG] + lam * (gent[:, PG] - genb[:, PG])
# update data matrices with solution
bust, gent, brancht = pfsoln(baseMVAt, bust, gent, brancht, Ybus, Yf, Yt,
V, ref, pv, pq)
ppctarget["et"] = time() - t0
ppctarget["success"] = success
# ----- output results -----
# convert back to original bus numbering & print results
ppctarget["bus"], ppctarget["gen"], ppctarget[
"branch"] = bust, gent, brancht
if success:
n = cpf_results["iterations"] + 1
cpf_results["V_p"] = i2e_data(ppctarget, cpf_results["V_p"],
full((nb, n), nan), "bus", 0)
cpf_results["V_c"] = i2e_data(ppctarget, cpf_results["V_c"],
full((nb, n), nan), "bus", 0)
results = int2ext(ppctarget)
results["cpf"] = cpf_results
# zero out result fields of out-of-service gens & branches
if len(results["order"]["gen"]["status"]["off"]) > 0:
results["gen"][ix_(results["order"]["gen"]["status"]["off"],
[PG, QG])] = 0
if len(results["order"]["branch"]["status"]["off"]) > 0:
results["branch"][ix_(results["order"]["branch"]["status"]["off"],
[PF, QF, PT, QT])] = 0
if fname:
fd = None
try:
fd = open(fname, "a")
except Exception as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(results, fd, ppopt)
fd.close()
else:
printpf(results, stdout, ppopt)
# save solved case
if solvedcase:
savecase(solvedcase, results)
return results, success
def i2e_data(ppc, val, oldval, ordering, dim=0):
"""Converts data from internal to external bus numbering.
For a case dict using internal indexing, this function can be
used to convert other data structures as well by passing in 3 or 4
extra parameters in addition to the case dict. If the value passed
in the 2nd argument C{val} is a column vector, it will be converted
according to the ordering specified by the 4th argument (C{ordering},
described below). If C{val} is an n-dimensional matrix, then the
optional 5th argument (C{dim}, default = 0) can be used to specify
which dimension to reorder. The 3rd argument (C{oldval}) is used to
initialize the return value before converting C{val} to external
indexing. In particular, any data corresponding to off-line gens
or branches or isolated buses or any connected gens or branches
will be taken from C{oldval}, with C[val} supplying the rest of the
returned data.
The C{ordering} argument is used to indicate whether the data
corresponds to bus-, gen- or branch-ordered data. It can be one
of the following three strings: 'bus', 'gen' or 'branch'. For
data structures with multiple blocks of data, ordered by bus,
gen or branch, they can be converted with a single call by
specifying C[ordering} as a list of strings.
Any extra elements, rows, columns, etc. beyond those indicated
in C{ordering}, are not disturbed.
Examples:
A_ext = i2e_data(ppc, A_int, A_orig, ['bus','bus','gen','gen'], 1)
Converts an A matrix for user-supplied OPF constraints from
internal to external ordering, where the columns of the A
matrix correspond to bus voltage angles, then voltage
magnitudes, then generator real power injections and finally
generator reactive power injections.
gencost_ext = i2e_data(ppc, gencost_int, gencost_orig, ['gen','gen'], 0)
Converts a C{gencost} matrix that has both real and reactive power
costs (in rows 1--ng and ng+1--2*ng, respectively).
@see: L{e2i_data}, L{i2e_field}, L{int2ext}.
"""
if 'order' not in ppc:
sys.stderr.write('i2e_data: ppc does not have the \'order\' field '
'required for conversion back to external numbering.\n')
return
o = ppc["order"]
if o['state'] != 'i':
sys.stderr.write('i2e_data: ppc does not appear to be in internal '
'order\n')
return
if isinstance(ordering, str): ## single set
if ordering == 'gen':
v = get_reorder(val, o[ordering]["i2e"], dim)
else:
v = val
val = set_reorder(oldval, v, o[ordering]["status"]["on"], dim)
else: ## multiple sets
be = 0 ## base, external indexing
bi = 0 ## base, internal indexing
new_v = []
for ordr in ordering:
ne = o["ext"][ordr].shape[0]
ni = ppc[ordr].shape[0]
v = get_reorder(val, bi + arange(ni), dim)
oldv = get_reorder(oldval, be + arange(ne), dim)
new_v.append( int2ext(ppc, v, oldv, ordr, dim) )
be = be + ne
bi = bi + ni
ni = val.shape[dim]
if ni > bi: ## the rest
v = get_reorder(val, arange(bi, ni), dim)
new_v.append(v)
val = concatenate(new_v, dim)
return val
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