def test_to_ppc_and_mpc():
# pypower cases to validate
functions = [
'case4gs', 'case6ww', 'case14', 'case30', 'case24_ieee_rts', 'case39'
]
for fn in functions:
# get pypower results
pypower_module = __import__('pypower.' + fn)
pypower_submodule = getattr(pypower_module, fn)
pypower_function = getattr(pypower_submodule, fn)
ppc_net = pypower_function()
# get net from pandapower
res_pypower, status_pypower = runpf(ppc_net,
ppopt=ppoption(VERBOSE=0,
OUT_ALL=0))
pandapower_module = __import__('pandapower', fromlist=['networks'])
pandapower_function = getattr(pandapower_module.networks, fn)
net = pandapower_function()
reset_results(net)
# convert to ppc
ppc = cv.to_ppc(net)
# convert to mpc
mpc = cv.to_mpc(net)
# runpf from converted ppc
res_converted_pp, status_converted_pp = runpf(
ppc, ppopt=ppoption(VERBOSE=0, OUT_ALL=0))
if status_converted_pp and status_pypower:
# get lookup pp2ppc
bus_lookup = net["_pd2ppc_lookups"]["bus"]
# check for equality in bus voltages
pp_buses = bus_lookup[res_converted_pp['bus'][:,
BUS_I].astype(int)]
assert np.allclose(res_converted_pp['bus'][pp_buses, VM:VA + 1],
res_pypower['bus'][:, VM:VA + 1])
# ToDo: check equality of branch and gen values
# pp_gen = bus_lookup[res_converted_pp['bus'][:, BUS_I].astype(int)]
# assert np.allclose(res_pypower['gen'][res_pypower['order']['gen']['e2i'], PG:QG+1]
# , res_converted_pp['gen'][:, PG:QG+1])
else:
raise LoadflowNotConverged("Loadflow did not converge!")
def rundcpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a DC power flow.
@see: L{runpf}
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt, PF_DC=True)
return runpf(casedata, ppopt, fname, solvedcase)
def rundcpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a DC power flow.
@see: L{runpf}
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt, PF_DC=True)
return runpf(casedata, ppopt, fname, solvedcase)
def calc_power_flow(case):
# we run an opf, but real power output is fixed everywhere except a single quasi-slack bus,
# so it just adjusts the voltage setpoints to minimize losses (and hopefully get more
# reasonable solutions than a raw power flow)
#results = runopf(case)
#results = runpf(case, ppopt=ppoption(ENFORCE_Q_LIMS=1))[0]
results = runpf(case, ppopt=ppoption(OUT_ALL=0))[0]
slack_bus = case["slack_bus"]
slack_gens = case["slack_gens"]
# add in the extra slack generation introduced by the model, so the results
# show the operating state accurately (even if it differs from the proposed state)
results["net_injection"][slack_bus] += (
np.sum(results["gen"][slack_gens, PG] - case["gen"][slack_gens, PG])
)
return results
def solve_pf(case, hour=None,
results=None,
ppopt=None,
set_generator_status=False,
fname='./runpf.log'):
ppopt = ppoption(ppopt)
fname = os.path.abspath(fname)
baseMVA = case.baseMVA
bus = case.bus.copy(deep=True)
branch = case.branch.copy(deep=True)
gen = case.gen.copy(deep=True)
gencost = case.gen.copy(deep=True)
if hour is not None:
logger.debug("Setting bus load based on case.load")
bus['PD'] = case.load.loc[hour]
bus = bus.fillna(0)
if hour is not None and results is not None:
logger.debug("Setting GEN_STATUS and PG")
if set_generator_status is True:
gen['GEN_STATUS'] = results.unit_commitment.loc[hour].astype(int)
gen['PG'] = results.power_generated.loc[hour]
value = [i + 1 for i in range(0, len(bus.index))]
bus_name = bus.index
bus.index = value
bus.index = bus.index.astype(int)
branch['F_BUS'] = branch['F_BUS'].apply(lambda x: value[bus_name.get_loc(x)]).astype(int)
branch['T_BUS'] = branch['T_BUS'].apply(lambda x: value[bus_name.get_loc(x)]).astype(int)
gen['GEN_BUS'] = gen['GEN_BUS'].apply(lambda x: value[bus_name.get_loc(x)]).astype(int)
bus = np.array(bus.reset_index())
branch = np.array(branch)
gen = np.array(gen)
gencost = np.array(gencost)
casedata = {'baseMVA': baseMVA,
'gencost': gencost,
'gen': gen,
'branch': branch,
'bus': bus}
return runpf(casedata, ppopt=ppopt, fname=fname)
def callrunpf(ppc_sol, verbose=0):
if ppc_sol['branch'].shape[
1] > 13: # check if more columns than expected are included
branchcopy = ppc_sol['branch'] # create copy of the original branch
branch = ppc_sol[
'branch'][:, 0:13] # copy solution branch data except Pf Qf Pt Qt
ppc_sol['branch'] = branch # replace the original branch
opt = ppoption(VERBOSE=verbose,
OUT_ALL=verbose) # set verbose value (0 by default)
r = runpf(ppc_sol, opt) # execute power flow
if 'branchcopy' in locals(): # check if branch copy was needed
ppc_sol['branch'] = branchcopy # return the original branch
# returns the power flow solution struct
return r[0]
def calc_power_flow(self):
""" Calculate power flow """
dialog = gui_pf_settings.Settings_ui(self)
result = dialog.getSettings()
if result:
# Run power flow
self.refresh_data()
results, success = runpf(gui_globals.ppc, gui_globals.ppopt)
bus = results["bus"]
printpf(results, sys.stdout)
if success:
self.show_status_message("Power flow successfully converged...")
else:
self.show_status_message("Power flow did not converge...")
gui_globals.ppc["bus"][:, VM] = np.round(bus[:, VM], 4)
gui_globals.ppc["bus"][:, VA] = np.round(bus[:, VA], 4)
self.refresh_data()
def monte_carlo_simulation(self, power_networks, ns=100, beta=0.05):
# mpc = ext2int(power_networks)
mpc = power_networks
nb = shape(mpc['bus'])[0] ## number of buses
result = zeros((ns, nb))
base_load_P = mpc["bus"][:, PD]
base_load_Q = mpc["bus"][:, QD]
for i in range(ns):
load_variation = random.randn(nb)
mpc["bus"][:, PD] = base_load_P * (1 + load_variation * 0.5)
mpc["bus"][:, QD] = base_load_Q * (1 + load_variation * 0.5)
power_result_temp = runpf.runpf(mpc, ppopt=self.opt)
result[i, :] = power_result_temp[0]["bus"][:, VM]
pyplot.hist(result)
pyplot.show()
return result
def validate_from_ppc(
ppc_net,
pp_net,
max_diff_values={
"vm_pu": 1e-6,
"va_degree": 1e-5,
"p_branch_kw": 1e-3,
"q_branch_kvar": 1e-3,
"p_gen_kw": 1e-3,
"q_gen_kvar": 1e-3
}):
"""
This function validates the pypower case files to pandapower net structure conversion via a \
comparison of loadflow calculations.
INPUT:
**ppc_net** - The pypower case file.
**pp_net** - The pandapower network.
OPTIONAL:
**max_diff_values** - Dict of maximal allowed difference values. The keys must be
'vm_pu', 'va_degree', 'p_branch_kw', 'q_branch_kvar', 'p_gen_kw' and 'q_gen_kvar' and
the values floats.
OUTPUT:
**conversion_success** - conversion_success is returned as False if pypower or pandapower
cannot calculate a power flow or if the maximum difference values (max_diff_values )
cannot be hold.
EXAMPLE:
import pandapower.converter as pc
from pypower import case4gs
ppc_net = case4gs.case4gs()
pp_net = cv.from_ppc(ppc_net, f_hz=60)
cv.validate_from_ppc(ppc_net, pp_net)
"""
# --- run a pypower power flow without print output
ppopt = ppoption.ppoption(VERBOSE=0, OUT_ALL=0)
ppc_res = runpf.runpf(ppc_net, ppopt)[0]
# --- store pypower power flow results
ppc_res_branch = ppc_res['branch'][:, 13:17]
ppc_res_bus = ppc_res['bus'][:, 7:9]
ppc_res_gen = ppc_res['gen'][:, 1:3]
# --- try to run a pandapower power flow
try:
pp.runpp(pp_net,
init="dc",
calculate_voltage_angles=True,
trafo_model="pi")
except:
try:
pp.runpp(pp_net,
calculate_voltage_angles=True,
init="flat",
trafo_model="pi")
except:
try:
pp.runpp(pp_net, trafo_model="pi")
except:
if (ppc_res['success'] == 1) & (~pp_net.converged):
logger.error(
'The validation of ppc conversion fails because the pandapower net'
' power flow does not converge.')
elif (ppc_res['success'] != 1) & (pp_net.converged):
logger.error(
'The validation of ppc conversion fails because the power flow of '
'the pypower case does not converge.')
elif (ppc_res['success'] != 1) & (~pp_net.converged):
logger.error(
'The power flow of both, the pypower case and the pandapower net, '
'do not converge.')
return False
# --- prepare power flow result comparison by reordering pp results as they are in ppc results
if (ppc_res['success'] == 1) & (pp_net.converged):
# --- pandapower bus result table
pp_res_bus = array(pp_net.res_bus[['vm_pu', 'va_degree']])
# --- pandapower gen result table
pp_res_gen = zeros([1, 2])
# consideration of parallel generators via storing how much generators have been considered
# each node
already_used_gen = Series(zeros([pp_net.bus.shape[0]]),
index=pp_net.bus.index).astype(int)
GENS = DataFrame(ppc_res['gen'][:, [0]].astype(int))
change_q_compare = []
for i, j in GENS.iterrows():
current_bus_idx = pp.get_element_index(pp_net, 'bus', name=j[0])
current_bus_type = int(ppc_res['bus'][current_bus_idx, 1])
# ext_grid
if current_bus_type == 3:
if already_used_gen.at[current_bus_idx] == 0:
pp_res_gen = append(
pp_res_gen,
array(pp_net.res_ext_grid[
pp_net.ext_grid.bus == current_bus_idx][[
'p_kw', 'q_kvar'
]])[already_used_gen.at[current_bus_idx]].reshape(
(1, 2)), 0)
already_used_gen.at[current_bus_idx] += 1
else:
pp_res_gen = append(
pp_res_gen,
array(pp_net.res_sgen[
pp_net.sgen.bus == current_bus_idx][[
'p_kw', 'q_kvar'
]])[already_used_gen.at[current_bus_idx] -
1].reshape((1, 2)), 0)
already_used_gen.at[current_bus_idx] += 1
change_q_compare += [j[0]]
# gen
elif current_bus_type == 2:
pp_res_gen = append(
pp_res_gen,
array(pp_net.res_gen[pp_net.gen.bus == current_bus_idx][[
'p_kw', 'q_kvar'
]])[already_used_gen.at[current_bus_idx]].reshape((1, 2)),
0)
if already_used_gen.at[current_bus_idx] > 0:
change_q_compare += [j[0]]
already_used_gen.at[current_bus_idx] += 1
# sgen
elif current_bus_type == 1:
pp_res_gen = append(
pp_res_gen,
array(pp_net.res_sgen[pp_net.sgen.bus == current_bus_idx][[
'p_kw', 'q_kvar'
]])[already_used_gen.at[current_bus_idx]].reshape((1, 2)),
0)
already_used_gen.at[current_bus_idx] += 1
pp_res_gen = pp_res_gen[1:, :] # delete initial zero row
# --- pandapower branch result table
pp_res_branch = zeros([1, 4])
# consideration of parallel branches via storing how much branches have been considered
# each node-to-node-connection
init1 = concat([pp_net.line.from_bus, pp_net.line.to_bus],
axis=1).drop_duplicates()
init2 = concat([pp_net.trafo.hv_bus, pp_net.trafo.lv_bus],
axis=1).drop_duplicates()
init1['hv_bus'] = nan
init1['lv_bus'] = nan
init2['from_bus'] = nan
init2['to_bus'] = nan
already_used_branches = concat([init1, init2], axis=0)
already_used_branches['number'] = zeros(
[already_used_branches.shape[0], 1]).astype(int)
BRANCHES = DataFrame(ppc_res['branch'][:, [0, 1, 8, 9]])
for i in BRANCHES.index:
from_bus = pp.get_element_index(pp_net,
'bus',
name=int(ppc_res['branch'][i, 0]))
to_bus = pp.get_element_index(pp_net,
'bus',
name=int(ppc_res['branch'][i, 1]))
from_vn_kv = ppc_res['bus'][from_bus, 9]
to_vn_kv = ppc_res['bus'][to_bus, 9]
ratio = BRANCHES[2].at[i]
angle = BRANCHES[3].at[i]
# from line results
if (from_vn_kv == to_vn_kv) & ((ratio == 0) |
(ratio == 1)) & (angle == 0):
pp_res_branch = append(
pp_res_branch,
array(
pp_net.res_line[(pp_net.line.from_bus == from_bus)
& (pp_net.line.to_bus == to_bus)]
[['p_from_kw', 'q_from_kvar', 'p_to_kw',
'q_to_kvar']])[int(already_used_branches.number.loc[
(already_used_branches.from_bus == from_bus)
& (already_used_branches.to_bus == to_bus)].
values)].reshape(1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.from_bus == from_bus)
& (already_used_branches.to_bus == to_bus)] += 1
# from trafo results
else:
if from_vn_kv >= to_vn_kv:
pp_res_branch = append(
pp_res_branch,
array(pp_net.res_trafo[
(pp_net.trafo.hv_bus == from_bus)
& (pp_net.trafo.lv_bus == to_bus)][[
'p_hv_kw', 'q_hv_kvar', 'p_lv_kw', 'q_lv_kvar'
]])[int(already_used_branches.number.loc[
(already_used_branches.hv_bus == from_bus)
& (already_used_branches.lv_bus == to_bus)].
values)].reshape(1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.hv_bus == from_bus)
& (already_used_branches.lv_bus == to_bus)] += 1
else: # switch hv-lv-connection of pypower connection buses
pp_res_branch = append(
pp_res_branch,
array(pp_net.res_trafo[
(pp_net.trafo.hv_bus == to_bus)
& (pp_net.trafo.lv_bus == from_bus)][[
'p_lv_kw', 'q_lv_kvar', 'p_hv_kw', 'q_hv_kvar'
]])[int(already_used_branches.number.loc[
(already_used_branches.hv_bus == to_bus)
& (already_used_branches.lv_bus == from_bus)].
values)].reshape(1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.hv_bus == to_bus)
& (already_used_branches.lv_bus == from_bus)] += 1
pp_res_branch = pp_res_branch[1:, :] # delete initial zero row
# --- do the power flow result comparison
diff_res_bus = ppc_res_bus - pp_res_bus
diff_res_branch = ppc_res_branch - pp_res_branch * 1e-3
diff_res_gen = ppc_res_gen + pp_res_gen * 1e-3
# comparison of buses with several generator units only as q sum
GEN_uniq = GENS.drop_duplicates()
for i in GEN_uniq.loc[GEN_uniq[0].isin(change_q_compare)].index:
next_is = GEN_uniq.index[GEN_uniq.index > i]
if len(next_is) > 0:
next_i = next_is[0]
else:
next_i = GENS.index[-1] + 1
if (next_i - i) > 1:
diff_res_gen[i:next_i, 1] = sum(diff_res_gen[i:next_i, 1])
# logger info
logger.debug(
"Maximum voltage magnitude difference between pypower and pandapower: "
"%.2e pu" % max(abs(diff_res_bus[:, 0])))
logger.debug(
"Maximum voltage angle difference between pypower and pandapower: "
"%.2e degree" % max(abs(diff_res_bus[:, 1])))
logger.debug(
"Maximum branch flow active power difference between pypower and pandapower: "
"%.2e kW" % max(abs(diff_res_branch[:, [0, 2]] * 1e3)))
logger.debug(
"Maximum branch flow reactive power difference between pypower and "
"pandapower: %.2e kVAr" %
max(abs(diff_res_branch[:, [1, 3]] * 1e3)))
logger.debug(
"Maximum active power generation difference between pypower and pandapower: "
"%.2e kW" % max(abs(diff_res_gen[:, 0] * 1e3)))
logger.debug(
"Maximum reactive power generation difference between pypower and pandapower: "
"%.2e kVAr" % max(abs(diff_res_gen[:, 1] * 1e3)))
if (max(abs(diff_res_bus[:, 0])) < 1e-3) & (max(abs(diff_res_bus[:, 1])) < 1e-3) & \
(max(abs(diff_res_branch[:, [0, 2]])) < 1e-3) & \
(max(abs(diff_res_branch[:, [1, 3]])) < 1e-3) & \
(max(abs(diff_res_gen)) > 1e-1).any():
logger.debug(
"The active/reactive power generation difference possibly results "
"because of a pypower error. Please validate "
"the results via pypower loadflow."
) # this occurs e.g. at ppc case9
# give a return
if type(max_diff_values) == dict:
if Series([
'q_gen_kvar', 'p_branch_kw', 'q_branch_kvar', 'p_gen_kw',
'va_degree', 'vm_pu'
]).isin(Series(list(max_diff_values.keys()))).all():
if (max(abs(diff_res_bus[:, 0])) < max_diff_values['vm_pu']) & \
(max(abs(diff_res_bus[:, 1])) < max_diff_values['va_degree']) & \
(max(abs(diff_res_branch[:, [0, 2]])) < max_diff_values['p_branch_kw'] /
1e3) & \
(max(abs(diff_res_branch[:, [1, 3]])) < max_diff_values['q_branch_kvar'] /
1e3) & \
(max(abs(diff_res_gen[:, 0])) < max_diff_values['p_gen_kw'] / 1e3) & \
(max(abs(diff_res_gen[:, 1])) < max_diff_values['q_gen_kvar'] / 1e3):
return True
else:
return False
else:
logger.debug('Not all requried dict keys are provided.')
else:
logger.debug("'max_diff_values' must be a dict.")
def run_sim(ppc, elements, dynopt=None, events=None, recorder=None):
"""
Run a time-domain simulation
Inputs:
ppc PYPOWER load flow case
elements Dictionary of dynamic model objects (machines, controllers, etc) with Object ID as key
events Events object
recorder Recorder object (empty)
Outputs:
recorder Recorder object (with data)
"""
#########
# SETUP #
#########
# Get version information
ver = pydyn_ver()
print('PYPOWER-Dynamics ' + ver['Version'] + ', ' + ver['Date'])
# Program options
if dynopt:
h = dynopt['h']
t_sim = dynopt['t_sim']
max_err = dynopt['max_err']
max_iter = dynopt['max_iter']
verbose = dynopt['verbose']
else:
# Default program options
h = 0.01 # step length (s)
t_sim = 5 # simulation time (s)
max_err = 0.0001 # Maximum error in network iteration (voltage mismatches)
max_iter = 25 # Maximum number of network iterations
verbose = False
# Make lists of current injection sources (generators, external grids, etc) and controllers
sources = []
controllers = []
for element in elements.values():
if element.__module__ in [
'pydyn.sym_order6a', 'pydyn.sym_order6b', 'pydyn.sym_order4',
'pydyn.ext_grid', 'pydyn.vsc_average', 'pydyn.asym_1cage',
'pydyn.asym_2cage'
]:
sources.append(element)
if element.__module__ == 'pydyn.controller':
controllers.append(element)
# Set up interfaces
interfaces = init_interfaces(elements)
##################
# INITIALISATION #
##################
print('Initialising models...')
# Run power flow and update bus voltages and angles in PYPOWER case object
results, success = runpf(ppc)
ppc["bus"][:, VM] = results["bus"][:, VM]
ppc["bus"][:, VA] = results["bus"][:, VA]
# Build Ybus matrix
ppc_int = ext2int(ppc)
baseMVA, bus, branch = ppc_int["baseMVA"], ppc_int["bus"], ppc_int[
"branch"]
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
# Build modified Ybus matrix
Ybus = mod_Ybus(Ybus, elements, bus, ppc_int['gen'], baseMVA)
# Calculate initial voltage phasors
v0 = bus[:, VM] * (np.cos(np.radians(bus[:, VA])) +
1j * np.sin(np.radians(bus[:, VA])))
# Initialise sources from load flow
for source in sources:
if source.__module__ in ['pydyn.asym_1cage', 'pydyn.asym_2cage']:
# Asynchronous machine
source_bus = int(ppc_int['bus'][source.bus_no, 0])
v_source = v0[source_bus]
source.initialise(v_source, 0)
else:
# Generator or VSC
source_bus = int(ppc_int['gen'][source.gen_no, 0])
S_source = np.complex(results["gen"][source.gen_no, 1] / baseMVA,
results["gen"][source.gen_no, 2] / baseMVA)
v_source = v0[source_bus]
source.initialise(v_source, S_source)
# Interface controllers and machines (for initialisation)
for intf in interfaces:
int_type = intf[0]
var_name = intf[1]
if int_type == 'OUTPUT':
# If an output, interface in the reverse direction for initialisation
intf[2].signals[var_name] = intf[3].signals[var_name]
else:
# Inputs are interfaced in normal direction during initialisation
intf[3].signals[var_name] = intf[2].signals[var_name]
# Initialise controllers
for controller in controllers:
controller.initialise()
#############
# MAIN LOOP #
#############
if events == None:
print('Warning: no events!')
# Factorise Ybus matrix
Ybus_inv = splu(Ybus)
y1 = []
v_prev = v0
print('Simulating...')
for t in range(int(t_sim / h) + 1):
if np.mod(t, 1 / h) == 0:
print('t=' + str(t * h) + 's')
# Interface controllers and machines
for intf in interfaces:
var_name = intf[1]
intf[3].signals[var_name] = intf[2].signals[var_name]
# Solve differential equations
for j in range(4):
# Solve step of differential equations
for element in elements.values():
element.solve_step(h, j)
# Interface with network equations
v_prev = solve_network(sources, v_prev, Ybus_inv, ppc_int,
len(bus), max_err, max_iter)
if recorder != None:
# Record signals or states
recorder.record_variables(t * h, elements)
if events != None:
# Check event stack
ppc, refactorise = events.handle_events(np.round(t * h, 5),
elements, ppc, baseMVA)
if refactorise == True:
# Rebuild Ybus from new ppc_int
prev_ybus = Ybus.todense().copy()
ppc_int = ext2int(ppc)
baseMVA, bus, branch = ppc_int["baseMVA"], ppc_int[
"bus"], ppc_int["branch"]
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
# Rebuild modified Ybus
Ybus = mod_Ybus(Ybus, elements, bus, ppc_int['gen'], baseMVA)
ybus_delta = np.array(Ybus - prev_ybus)
# Refactorise Ybus
Ybus_inv = splu(Ybus)
# Solve network equations
v_prev = solve_network(sources, v_prev, Ybus_inv, ppc_int,
len(bus), max_err, max_iter)
return recorder
def rundyn(casefile_pf, casefile_dyn, casefile_ev, pdopt=None):
""" Runs dynamic simulation.
@param casefile_pf: m-file with power flow data
@param casefile_dyn: m-file with dynamic data
@param casefile_ev: m-file with event data
@param pdopt: options vector
@rtype: tuple
@return: (Angles = generator angles,
Speeds = generator speeds,
Eq_tr = q component of transient voltage behind reactance,
Ed_tr = d component of transient voltage behind reactance,
Efd = Excitation voltage,
PM = mechanical power,
Voltages = bus voltages,
Stepsize = step size integration method,
Errest = estimation of integration error,
Failed = failed steps,
Time = time points)
@see: U{http://www.esat.kuleuven.be/electa/teaching/matdyn/}
"""
## Begin timing
t0 = time()
if pdopt is None:
pdopt = Pdoption()
method = pdopt[0]
tol = pdopt[1]
minstepsize = pdopt[2]
maxstepsize = pdopt[3]
output = bool(pdopt[4])
plots = bool(pdopt[5])
## Load all data
# Load dynamic simulation data
if output:
print '> Loading dynamic simulation data...'
global freq
freq, stepsize, stoptime = Loaddyn(casefile_dyn)
# Load generator data
Pgen0 = Loadgen(casefile_dyn, output)
# Load exciter data
Pexc0 = Loadexc(casefile_dyn)
# Load governor data
Pgov0 = Loadgov(casefile_dyn)
# Load event data
if len(casefile_ev) > 0:
event, buschange, linechange = Loadevents(casefile_ev)
else:
event = array([])
genmodel = Pgen0[:, 0]
excmodel = Pgen0[:, 1]
govmodel = Pgen0[:, 2]
## Initialization: Power Flow
# Power flow options
ppopt = ppoption()
ppopt["VERBOSE"] = False
ppopt["OUT_ALL"] = False
# Run power flow
baseMVA, bus, gen, branch, success = runpf(casefile_pf, ppopt)
if not success:
print '> Error: Power flow did not converge. Exiting...'
return
else:
if output:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b> Power flow converged\n'
U0 = bus[:, VM] * (cos(bus[:, VA] * pi / 180) + 1j * sin(bus[:, VA] * pi / 180 ))
U00 = copy(U0)
# Get generator info
on = find(gen[:, GEN_STATUS] > 0) ## which generators are on?
gbus = gen[on, GEN_BUS] ## what buses are they at?
ngen = len(gbus)
nbus = len(U0)
## Construct augmented Ybus
if output:
print '> Constructing augmented admittance matrix...'
Pl = bus[:, PD] / baseMVA ## load power
Ql = bus[:, QD] / baseMVA
xd_tr = zeros(ngen)
xd_tr[genmodel == 2] = Pgen0[genmodel == 2, 7] # 4th order model: xd_tr column 7
xd_tr[genmodel == 1] = Pgen0[genmodel == 1, 6] # classical model: xd_tr column 6
invYbus = AugYbus(baseMVA, bus, branch, xd_tr, gbus, Pl, Ql, U0)
## Calculate Initial machine state
if output:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b> Calculating initial state...\n'
Efd0, Xgen0 = GeneratorInit(Pgen0, U0(gbus), gen, baseMVA, genmodel)
omega0 = Xgen0[:, 0]
Id0, Iq0, Pe0 = MachineCurrents(Xgen0, Pgen0, U0[gbus], genmodel)
Vgen0 = r_[Id0, Iq0, Pe0]
## Exciter initial conditions
Vexc0 = abs(U0[gbus])
Xexc0, Pexc0 = ExciterInit(Efd0, Pexc0, Vexc0, excmodel)
## Governor initial conditions
Pm0 = copy(Pe0)
Xgov0, Pgov0 = GovernorInit(Pm0, Pgov0, omega0, govmodel)
Vgov0 = copy(omega0)
## Check Steady-state
Fexc0 = Exciter(Xexc0, Pexc0, Vexc0, excmodel)
Fgov0 = Governor(Xgov0, Pgov0, Vgov0, govmodel)
Fgen0 = Generator(Xgen0, Xexc0, Xgov0, Pgen0, Vgen0, genmodel)
# Check Generator Steady-state
if sum(sum(abs(Fgen0))) > 1e-6:
print '> Error: Generator not in steady-state\n> Exiting...\n'
return
# Check Exciter Steady-state
if sum(sum(abs(Fexc0))) > 1e-6:
print '> Error: Exciter not in steady-state\n> Exiting...\n'
return
# Check Governor Steady-state
if sum(sum(abs(Fgov0))) > 1e-6:
print '> Error: Governor not in steady-state\n> Exiting...\n'
return
if output:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b> System in steady-state\n'
## Initialization of main stability loop
t = -0.02 # simulate 0.02s without applying events
errest = 0
failed = 0
eulerfailed = 0
if method == 3 | method == 4:
stepsize = minstepsize
if not output:
print ' '
ev = 1
eventhappened = False
i = 0
## Allocate memory for variables
if output:
print '> Allocate memory...\n'
chunk = 5000
Time = zeros(chunk); Time[0, :] = t
Errest = zeros(chunk); Errest[0, :] = errest
Stepsize = zeros(chunk); Stepsize[0, :] = stepsize
# System variables
Voltages = zeros(chunk, len(U0)); Voltages[0, :] = U0
# Generator
Angles = zeros(chunk, ngen); Angles[0, :] = Xgen0[:, 0] * 180 / pi
Speeds = zeros(chunk, ngen); Speeds[0, :] = Xgen0[:, 2] / (2 * pi * freq)
Eq_tr = zeros(chunk, ngen); Eq_tr[0, :] = Xgen0[:, 2]
Ed_tr = zeros(chunk, ngen); Ed_tr[0, :] = Xgen0[:, 3]
# Exciter and governor
Efd = zeros(chunk,ngen); Efd[0, :] = Efd0[:, 0]
PM = zeros(chunk, ngen); PM[0, :] = Pm0[:, 0]
## Main stability loop
while t < stoptime + stepsize:
## Output
i += 1
if i % 45 == 0 & output:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b> %6.2f## completed' % t / stoptime * 100
## Numerical Method
if method == 1:
Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, U0, t, newstepsize = \
ModifiedEuler(t, Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, invYbus, gbus, genmodel, excmodel, govmodel, stepsize)
elif method == 2:
Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, U0, t, newstepsize = \
RungeKutta(t, Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, invYbus, gbus, genmodel, excmodel, govmodel, stepsize)
elif method == 3:
Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, U0, errest, failed, t, newstepsize = \
RungeKuttaFehlberg(t, Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, U0, invYbus, gbus, genmodel, excmodel, govmodel, tol, maxstepsize, stepsize)
elif method == 4:
Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, U0, errest, failed, t, newstepsize = \
RungeKuttaHighamHall(t, Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, U0, invYbus, gbus, genmodel, excmodel, govmodel, tol, maxstepsize, stepsize)
elif method == 5:
Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, U0, t, eulerfailed, newstepsize = \
ModifiedEuler2(t, Xgen0, Pgen0, Vgen0, Xexc0, Pexc0, Vexc0, Xgov0, Pgov0, Vgov0, invYbus, gbus, genmodel, excmodel, govmodel, stepsize)
if eulerfailed:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b> Error: No solution found. Try lowering tolerance or increasing maximum number of iterations in ModifiedEuler2. Exiting... \n'
return
if failed:
t -= stepsize
# End exactly at stop time
if t + newstepsize > stoptime:
newstepsize = stoptime - t
elif stepsize < minstepsize:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b> Error: No solution found with minimum step size. Exiting... \n'
return
## Allocate new memory chunk if matrices are full
if i > Time.shape[0]:
Stepsize = c_[Stepsize, zeros(chunk)];
Errest = c_[Errest, zeros(chunk)]
Time = c_[Time, zeros(chunk)]
Voltages = c_[Voltages, zeros(chunk, len(U0))]
Efd = c_[Efd, zeros(chunk, ngen)]
PM = c_[PM, zeros(chunk, ngen)]
Angles = c_[Angles, zeros(chunk,ngen)]
Speeds = c_[Speeds, zeros(chunk,ngen)]
Eq_tr = c_[Eq_tr, zeros(chunk,ngen)]
Ed_tr = c_[Ed_tr, zeros(chunk,ngen)]
## Save values
Stepsize[i, :] = stepsize.T
Errest[i, :] = errest.T
Time[i, :] = t
Voltages[i, :] = U0.T
# exc
Efd[i, :] = Xexc0[:, 0] * (genmodel > 1) # Set Efd to zero when using classical generator model
# gov
PM[i, :] = Xgov0[:, 0]
# gen
Angles[i, :] = Xgen0[:, 0] * 180 / pi
Speeds[i, :] = Xgen0[:, 1] / (2 * pi * freq)
Eq_tr[i, :] = Xgen0[:, 2]
Ed_tr[i, :] = Xgen0[:, 3]
## Adapt step size if event will occur in next step
if len(event) > 0 & ev <= event.shape[0] & (method == 3 | method == 4):
if t + newstepsize >= event[ev, 0]:
if event[ev, 0] - t < newstepsize:
newstepsize = event[ev, 0] - t
## Check for events
if len(event) > 0 & ev <= event.shape[0]:
for k in range(ev, event.shape[0]): # cycle through all events ..
if abs(t - event[ev, 0]) > 10 * EPS | ev > event.shape[0]: #.. that happen on time t
break
else:
eventhappened = True
if event[ev, 1] == 1:
bus[buschange[ev, 1], buschange[ev, 2]] = buschange[ev, 3]
if event[ev, 1] == 2:
branch[linechange[ev, 1], linechange[ev, 2]] = linechange[ev, 3]
ev += 1
if eventhappened:
# Refactorise
invYbus = AugYbus(baseMVA, bus, branch, xd_tr, gbus, bus[:, PD] / baseMVA, bus[:, QD] / baseMVA, U00)
U0 = SolveNetwork(Xgen0, Pgen0, invYbus, gbus, genmodel)
Id0, Iq0, Pe0 = MachineCurrents(Xgen0, Pgen0, U0(gbus), genmodel)
Vgen0 = Id0,Iq0,Pe0
Vexc0 = abs(U0[gbus])
# decrease stepsize after event occured
if method == 3 | method == 4:
newstepsize = minstepsize
i += 1 # if event occurs, save values at t- and t+
## Save values
Stepsize[i, :] = stepsize.T
Errest[i, :] = errest.T
Time[i, :] = t
Voltages[i, :] = U0.T
# exc
Efd[i, :] = Xexc0[:, 0] * (genmodel > 1) # Set Efd to zero when using classical generator model
# gov
PM[i, :] = Xgov0[:, 0]
# gen
Angles[i, :] = Xgen0[:, 0] * 180 / pi
Speeds[i, :] = Xgen0[:, 1] / (2 * pi * freq)
Eq_tr[i, :] = Xgen0[:, 2]
Ed_tr[i, :] = Xgen0[:, 3]
eventhappened = False
## Advance time
stepsize = newstepsize
t += stepsize
# end of main stability loop
## Output
if output:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b> 100## completed'
else:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b'
simulationtime = t0 - time()
if output:
print '\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b> Simulation completed in %5.2f seconds\n' % simulationtime
# Save only the first i elements
Angles = Angles[:i, :]
Speeds = Speeds[:i, :]
Eq_tr = Eq_tr[:i, :]
Ed_tr = Ed_tr[:i, :]
Efd = Efd[:i, :]
PM = PM[:i, :]
Voltages = Voltages[:i, :]
Stepsize = Stepsize[:i, :]
Errest = Errest[:i, :]
Time = Time[:i, :]
## Plot
if plots:
from pylab import close, figure, xlabel, ylabel, hold, plot, axis
close('all')
figure
xlabel('Time [s]')
ylabel('Angle [deg]')
hold(True)
plot(Time,Angles)
axis([0, Time(-1), -1, 1])
axis('auto y')
figure
xlabel('Time [s]')
ylabel('Speed [pu]')
hold(True)
plot(Time,Speeds)
axis([0, Time(-1), -1, 1])
axis('auto y')
figure
xlabel('Time [s]')
ylabel('Voltage [pu]')
hold(True)
plot(Time, abs(Voltages))
axis([0, Time(-1), -1, 1])
axis('auto y')
figure
xlabel('Time [s]')
ylabel('Excitation voltage [pu]')
hold(True)
plot(Time, Efd)
axis([0, Time(-1), -1, 1])
axis('auto y')
figure
xlabel('Time [s]')
ylabel('Turbine Power [pu]')
hold(True)
plot(Time, PM)
axis([0, Time(-1), -1, 1])
axis('auto y')
figure
hold(True)
xlabel('Time [s]')
ylabel('Step size')
plot(Time, Stepsize, '-o')
axis([0, Time(-1), -1, 1])
axis('auto y')
return Angles, Speeds, Eq_tr, Ed_tr, Efd, PM, Voltages, Stepsize, Errest, Time
def t_dcline(quiet=False):
"""Tests for DC line extension in L{{toggle_dcline}.
@author: Ray Zimmerman (PSERC Cornell)
"""
num_tests = 50
t_begin(num_tests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_dcline')
if quiet:
verbose = False
else:
verbose = False
t0 = ''
ppopt = ppoption(OPF_VIOLATION=1e-6,
PDIPM_GRADTOL=1e-8,
PDIPM_COMPTOL=1e-8,
PDIPM_COSTTOL=1e-9)
ppopt = ppoption(ppopt, OPF_ALG=560, OPF_ALG_DC=200)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=verbose)
## set up indices
ib_data = r_[arange(BUS_AREA + 1), arange(BASE_KV, VMIN + 1)]
ib_voltage = arange(VM, VA + 1)
ib_lam = arange(LAM_P, LAM_Q + 1)
ib_mu = arange(MU_VMAX, MU_VMIN + 1)
ig_data = r_[[GEN_BUS, QMAX, QMIN], arange(MBASE, APF + 1)]
ig_disp = array([PG, QG, VG])
ig_mu = arange(MU_PMAX, MU_QMIN + 1)
ibr_data = arange(ANGMAX + 1)
ibr_flow = arange(PF, QT + 1)
ibr_mu = array([MU_SF, MU_ST])
ibr_angmu = array([MU_ANGMIN, MU_ANGMAX])
## load case
ppc0 = loadcase(casefile)
del ppc0['dclinecost']
ppc = ppc0
ppc = toggle_dcline(ppc, 'on')
ppc = toggle_dcline(ppc, 'off')
ndc = ppc['dcline'].shape[0]
## run AC OPF w/o DC lines
t = ''.join([t0, 'AC OPF (no DC lines) : '])
r0 = runopf(ppc0, ppopt)
success = r0['success']
t_ok(success, [t, 'success'])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['f'], r0['f'], 8, [t, 'f'])
t_is(r['bus'][:, ib_data], r0['bus'][:, ib_data], 10, [t, 'bus data'])
t_is(r['bus'][:, ib_voltage], r0['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(r['bus'][:, ib_lam], r0['bus'][:, ib_lam], 3, [t, 'bus lambda'])
t_is(r['bus'][:, ib_mu], r0['bus'][:, ib_mu], 2, [t, 'bus mu'])
t_is(r['gen'][:, ig_data], r0['gen'][:, ig_data], 10, [t, 'gen data'])
t_is(r['gen'][:, ig_disp], r0['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(r['gen'][:, ig_mu], r0['gen'][:, ig_mu], 3, [t, 'gen mu'])
t_is(r['branch'][:, ibr_data], r0['branch'][:, ibr_data], 10,
[t, 'branch data'])
t_is(r['branch'][:, ibr_flow], r0['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
t_is(r['branch'][:, ibr_mu], r0['branch'][:, ibr_mu], 2, [t, 'branch mu'])
t = ''.join([t0, 'AC PF (no DC lines) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(rp['gen'][:, ig_disp], r['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## run with DC lines
t = ''.join([t0, 'AC OPF (with DC lines) : '])
ppc = toggle_dcline(ppc, 'on')
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected = array([[10, 8.9, -10, 10, 1.0674, 1.0935],
[2.2776, 2.2776, 0, 0, 1.0818, 1.0665],
[0, 0, 0, 0, 1.0000, 1.0000],
[10, 9.5, 0.0563, -10, 1.0778, 1.0665]])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected, 4, [t, 'P Q V'])
expected = array([[0, 0.8490, 0.6165, 0, 0, 0.2938],
[0, 0, 0, 0.4290, 0.0739, 0], [0, 0, 0, 0, 0, 0],
[0, 7.2209, 0, 0, 0.0739, 0]])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected, 3, [t, 'mu'])
t = ''.join([t0, 'AC PF (with DC lines) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1 = toggle_dcline(ppc1, 'on')
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
#t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['gen'][:2, ig_disp], r['gen'][:2, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['gen'][2, PG], r['gen'][2, PG], 3, [t, 'gen dispatch'])
t_is(rp['gen'][2, QG] + rp['dcline'][0, c.QF],
r['gen'][2, QG] + r['dcline'][0, c.QF], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## add appropriate P and Q injections and check angles and generation when running PF
t = ''.join([t0, 'AC PF (with equivalent injections) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
for k in range(ndc):
if ppc1['dcline'][k, c.BR_STATUS]:
ff = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.F_BUS])
tt = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.T_BUS])
ppc1['bus'][ff, PD] = ppc1['bus'][ff, PD] + r['dcline'][k, c.PF]
ppc1['bus'][ff, QD] = ppc1['bus'][ff, QD] - r['dcline'][k, c.QF]
ppc1['bus'][tt, PD] = ppc1['bus'][tt, PD] - r['dcline'][k, c.PT]
ppc1['bus'][tt, QD] = ppc1['bus'][tt, QD] - r['dcline'][k, c.QT]
ppc1['bus'][ff, VM] = r['dcline'][k, c.VF]
ppc1['bus'][tt, VM] = r['dcline'][k, c.VT]
ppc1['bus'][ff, BUS_TYPE] = PV
ppc1['bus'][tt, BUS_TYPE] = PV
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(rp['gen'][:, ig_disp], r['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## test DC OPF
t = ''.join([t0, 'DC OPF (with DC lines) : '])
ppc = ppc0.copy()
ppc['gen'][0, PMIN] = 10
ppc['branch'][4, RATE_A] = 100
ppc = toggle_dcline(ppc, 'on')
r = rundcopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected = array([[10, 8.9, 0, 0, 1.01, 1], [2, 2, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 1], [10, 9.5, 0, 0, 1, 0.98]])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected, 4, [t, 'P Q V'])
expected = array([[0, 1.8602, 0, 0, 0, 0], [1.8507, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0.2681, 0, 0, 0, 0]])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected, 3, [t, 'mu'])
t = ''.join([t0, 'DC PF (with DC lines) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1 = toggle_dcline(ppc1, 'on')
ppc1['bus'][:, VA] = 0
rp = rundcpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(rp['gen'][:, ig_disp], r['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## add appropriate P injections and check angles and generation when running PF
t = ''.join([t0, 'DC PF (with equivalent injections) : '])
ppc1 = {
'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()
}
ppc1['bus'][:, VA] = 0
for k in range(ndc):
if ppc1['dcline'][k, c.BR_STATUS]:
ff = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.F_BUS])
tt = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.T_BUS])
ppc1['bus'][ff, PD] = ppc1['bus'][ff, PD] + r['dcline'][k, c.PF]
ppc1['bus'][tt, PD] = ppc1['bus'][tt, PD] - r['dcline'][k, c.PT]
ppc1['bus'][ff, BUS_TYPE] = PV
ppc1['bus'][tt, BUS_TYPE] = PV
rp = rundcpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is(rp['bus'][:, ib_voltage], r['bus'][:, ib_voltage], 3,
[t, 'bus voltage'])
t_is(rp['gen'][:, ig_disp], r['gen'][:, ig_disp], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:, ibr_flow], r['branch'][:, ibr_flow], 3,
[t, 'branch flow'])
## run with DC lines
t = ''.join([t0, 'AC OPF (with DC lines + poly cost) : '])
ppc = loadcase(casefile)
ppc = toggle_dcline(ppc, 'on')
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected1 = array([[10, 8.9, -10, 10, 1.0663, 1.0936],
[7.8429, 7.8429, 0, 0, 1.0809, 1.0667],
[0, 0, 0, 0, 1.0000, 1.0000],
[6.0549, 5.7522, -0.5897, -10, 1.0778, 1.0667]])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
expected2 = array([[0, 0.7605, 0.6226, 0, 0, 0.2980],
[0, 0, 0, 0.4275, 0.0792, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0.0792, 0]])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
ppc['dclinecost'][3, :8] = array([2, 0, 0, 4, 0, 0, 7.3, 0])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
t = ''.join([t0, 'AC OPF (with DC lines + pwl cost) : '])
ppc['dclinecost'][3, :8] = array([1, 0, 0, 2, 0, 0, 10, 73])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
t_end()
def t_loadcase(quiet=False):
"""Test that C{loadcase} works with an object as well as case file.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
t_begin(240, quiet)
## compare result of loading from M-file file to result of using data matrices
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_opf')
matfile = join(tdir, 't_mat9_opf')
pfcasefile = join(tdir, 't_case9_pf')
pfmatfile = join(tdir, 't_mat9_pf')
casefilev2 = join(tdir, 't_case9_opfv2')
matfilev2 = join(tdir, 't_mat9_opfv2')
pfcasefilev2 = join(tdir, 't_case9_pfv2')
pfmatfilev2 = join(tdir, 't_mat9_pfv2')
## read version 1 OPF data matrices
baseMVA, bus, gen, branch, areas, gencost = t_case9_opf()
## save as .mat file
savemat(matfile + '.mat', {'baseMVA': baseMVA, 'bus': bus, 'gen': gen,
'branch': branch, 'areas': areas, 'gencost': gencost}, oned_as='row')
## read version 2 OPF data matrices
ppc = t_case9_opfv2()
## save as .mat file
savemat(matfilev2 + '.mat', {'ppc': ppc}, oned_as='column')
## prepare expected matrices for v1 load
## (missing gen cap curve & branch ang diff lims)
tmp1 = (ppc['baseMVA'], ppc['bus'].copy(), ppc['gen'].copy(), ppc['branch'].copy(),
ppc['areas'].copy(), ppc['gencost'].copy())
tmp2 = (ppc['baseMVA'], ppc['bus'].copy(), ppc['gen'].copy(), ppc['branch'].copy(),
ppc['areas'].copy(), ppc['gencost'].copy())
## remove capability curves, angle difference limits
tmp1[2][1:3, [PC1, PC2, QC1MIN, QC1MAX, QC2MIN, QC2MAX]] = zeros((2,6))
tmp1[3][0, ANGMAX] = 360
tmp1[3][8, ANGMIN] = -360
baseMVA, bus, gen, branch, areas, gencost = tmp1
##----- load OPF data into individual matrices -----
t = 'loadcase(opf_PY_file_v1) without .py extension : '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = \
loadcase(casefile, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t_is(areas1, areas, 12, [t, 'areas'])
t_is(gencost1, gencost, 12, [t, 'gencost'])
t = 'loadcase(opf_PY_file_v1) with .py extension : '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = \
loadcase(casefile + '.py', False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t_is(areas1, areas, 12, [t, 'areas'])
t_is(gencost1, gencost, 12, [t, 'gencost'])
t = 'loadcase(opf_MAT_file_v1) without .mat extension : '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = \
loadcase(matfile, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t_is(areas1, areas, 12, [t, 'areas'])
t_is(gencost1, gencost, 12, [t, 'gencost'])
t = 'loadcase(opf_MAT_file_v1) with .mat extension : '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = \
loadcase(matfile + '.mat', False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t_is(areas1, areas, 12, [t, 'areas'])
t_is(gencost1, gencost, 12, [t, 'gencost'])
## prepare expected matrices for v2 load
baseMVA, bus, gen, branch, areas, gencost = tmp2
t = 'loadcase(opf_PY_file_v2) without .py extension : '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = \
loadcase(casefilev2, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t_is(areas1, areas, 12, [t, 'areas'])
t_is(gencost1, gencost, 12, [t, 'gencost'])
t = 'loadcase(opf_PY_file_v2) with .py extension : '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = \
loadcase(casefilev2 + '.py', False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t_is(areas1, areas, 12, [t, 'areas'])
t_is(gencost1, gencost, 12, [t, 'gencost'])
t = 'loadcase(opf_MAT_file_v2) without .mat extension : '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = \
loadcase(matfilev2, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t_is(areas1, areas, 12, [t, 'areas'])
t_is(gencost1, gencost, 12, [t, 'gencost'])
t = 'loadcase(opf_MAT_file_v2) with .mat extension : '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = \
loadcase(matfilev2 + '.mat', False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t_is(areas1, areas, 12, [t, 'areas'])
t_is(gencost1, gencost, 12, [t, 'gencost'])
## prepare expected matrices for v1 load
baseMVA, bus, gen, branch, areas, gencost = tmp1
t = 'loadcase(opf_struct_v1) (no version): '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = t_case9_opf()
c = {}
c['baseMVA'] = baseMVA1
c['bus'] = bus1.copy()
c['gen'] = gen1.copy()
c['branch'] = branch1.copy()
c['areas'] = areas1.copy()
c['gencost'] = gencost1.copy()
baseMVA2, bus2, gen2, branch2, areas2, gencost2 = loadcase(c, False)
t_is(baseMVA2, baseMVA, 12, [t, 'baseMVA'])
t_is(bus2, bus, 12, [t, 'bus'])
t_is(gen2, gen, 12, [t, 'gen'])
t_is(branch2, branch, 12, [t, 'branch'])
t_is(areas2, areas, 12, [t, 'areas'])
t_is(gencost2, gencost, 12, [t, 'gencost'])
t = 'loadcase(opf_struct_v1) (version=\'1\'): '
c['version'] = '1'
baseMVA2, bus2, gen2, branch2, areas2, gencost2 = loadcase(c, False)
t_is(baseMVA2, baseMVA, 12, [t, 'baseMVA'])
t_is(bus2, bus, 12, [t, 'bus'])
t_is(gen2, gen, 12, [t, 'gen'])
t_is(branch2, branch, 12, [t, 'branch'])
t_is(areas2, areas, 12, [t, 'areas'])
t_is(gencost2, gencost, 12, [t, 'gencost'])
## prepare expected matrices for v2 load
baseMVA, bus, gen, branch, areas, gencost = tmp2
t = 'loadcase(opf_struct_v2) (no version): '
c = {}
c['baseMVA'] = baseMVA
c['bus'] = bus.copy()
c['gen'] = gen.copy()
c['branch'] = branch.copy()
c['areas'] = areas.copy()
c['gencost'] = gencost.copy()
baseMVA2, bus2, gen2, branch2, areas2, gencost2 = loadcase(c, False)
t_is(baseMVA2, baseMVA, 12, [t, 'baseMVA'])
t_is(bus2, bus, 12, [t, 'bus'])
t_is(gen2, gen, 12, [t, 'gen'])
t_is(branch2, branch, 12, [t, 'branch'])
t_is(areas2, areas, 12, [t, 'areas'])
t_is(gencost2, gencost, 12, [t, 'gencost'])
t = 'loadcase(opf_struct_v2) (version=''2''): '
c = {}
c['baseMVA'] = baseMVA
c['bus'] = bus.copy()
c['gen'] = gen.copy()
c['branch'] = branch.copy()
c['areas'] = areas.copy()
c['gencost'] = gencost.copy()
c['version'] = '2'
baseMVA2, bus2, gen2, branch2, areas2, gencost2 = loadcase(c, False)
t_is(baseMVA2, baseMVA, 12, [t, 'baseMVA'])
t_is(bus2, bus, 12, [t, 'bus'])
t_is(gen2, gen, 12, [t, 'gen'])
t_is(branch2, branch, 12, [t, 'branch'])
t_is(areas2, areas, 12, [t, 'areas'])
t_is(gencost2, gencost, 12, [t, 'gencost'])
##----- load OPF data into struct -----
## prepare expected matrices for v1 load
baseMVA, bus, gen, branch, areas, gencost = tmp1
t = 'ppc = loadcase(opf_PY_file_v1) without .py extension : '
ppc1 = loadcase(casefile)
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t_is(ppc1['areas'], areas, 12, [t, 'areas'])
t_is(ppc1['gencost'], gencost, 12, [t, 'gencost'])
t = 'ppc = loadcase(opf_PY_file_v1) with .py extension : '
ppc1 = loadcase(casefile + '.py')
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t_is(ppc1['areas'], areas, 12, [t, 'areas'])
t_is(ppc1['gencost'], gencost, 12, [t, 'gencost'])
t = 'ppc = loadcase(opf_MAT_file_v1) without .mat extension : '
ppc1 = loadcase(matfile)
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t_is(ppc1['areas'], areas, 12, [t, 'areas'])
t_is(ppc1['gencost'], gencost, 12, [t, 'gencost'])
t = 'ppc = loadcase(opf_MAT_file_v1) with .mat extension : '
ppc1 = loadcase(matfile + '.mat')
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t_is(ppc1['areas'], areas, 12, [t, 'areas'])
t_is(ppc1['gencost'], gencost, 12, [t, 'gencost'])
## prepare expected matrices for v2 load
baseMVA, bus, gen, branch, areas, gencost = tmp2
t = 'ppc = loadcase(opf_PY_file_v2) without .m extension : '
ppc1 = loadcase(casefilev2)
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t_is(ppc1['areas'], areas, 12, [t, 'areas'])
t_is(ppc1['gencost'], gencost, 12, [t, 'gencost'])
t = 'ppc = loadcase(opf_PY_file_v2) with .py extension : '
ppc1 = loadcase(casefilev2 + '.py')
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t_is(ppc1['areas'], areas, 12, [t, 'areas'])
t_is(ppc1['gencost'], gencost, 12, [t, 'gencost'])
t = 'ppc = loadcase(opf_MAT_file_v2) without .mat extension : '
ppc1 = loadcase(matfilev2)
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t_is(ppc1['areas'], areas, 12, [t, 'areas'])
t_is(ppc1['gencost'], gencost, 12, [t, 'gencost'])
t = 'ppc = loadcase(opf_MAT_file_v2) with .mat extension : '
ppc1 = loadcase(matfilev2 + '.mat')
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t_is(ppc1['areas'], areas, 12, [t, 'areas'])
t_is(ppc1['gencost'], gencost, 12, [t, 'gencost'])
## prepare expected matrices for v1 load
baseMVA, bus, gen, branch, areas, gencost = tmp1
t = 'ppc = loadcase(opf_struct_v1) (no version): '
baseMVA1, bus1, gen1, branch1, areas1, gencost1 = t_case9_opf()
c = {}
c['baseMVA'] = baseMVA1
c['bus'] = bus1.copy()
c['gen'] = gen1.copy()
c['branch'] = branch1.copy()
c['areas'] = areas1.copy()
c['gencost'] = gencost1.copy()
ppc2 = loadcase(c)
t_is(ppc2['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc2['bus'], bus, 12, [t, 'bus'])
t_is(ppc2['gen'], gen, 12, [t, 'gen'])
t_is(ppc2['branch'], branch, 12, [t, 'branch'])
t_is(ppc2['areas'], areas, 12, [t, 'areas'])
t_is(ppc2['gencost'], gencost, 12, [t, 'gencost'])
t = 'ppc = loadcase(opf_struct_v1) (version=''1''): '
c['version'] = '1'
ppc2 = loadcase(c)
t_is(ppc2['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc2['bus'], bus, 12, [t, 'bus'])
t_is(ppc2['gen'], gen, 12, [t, 'gen'])
t_is(ppc2['branch'], branch, 12, [t, 'branch'])
t_is(ppc2['areas'], areas, 12, [t, 'areas'])
t_is(ppc2['gencost'], gencost, 12, [t, 'gencost'])
## prepare expected matrices for v2 load
baseMVA, bus, gen, branch, areas, gencost = tmp2
t = 'ppc = loadcase(opf_struct_v2) (no version): '
c = {}
c['baseMVA'] = baseMVA
c['bus'] = bus.copy()
c['gen'] = gen.copy()
c['branch'] = branch.copy()
c['areas'] = areas.copy()
c['gencost'] = gencost.copy()
ppc2 = loadcase(c)
t_is(ppc2['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc2['bus'], bus, 12, [t, 'bus'])
t_is(ppc2['gen'], gen, 12, [t, 'gen'])
t_is(ppc2['branch'], branch, 12, [t, 'branch'])
t_is(ppc2['areas'], areas, 12, [t, 'areas'])
t_is(ppc2['gencost'], gencost, 12, [t, 'gencost'])
t_ok(ppc2['version'] == '2', [t, 'version'])
t = 'ppc = loadcase(opf_struct_v2) (version=''2''): '
c = {}
c['baseMVA'] = baseMVA
c['bus'] = bus.copy()
c['gen'] = gen.copy()
c['branch'] = branch.copy()
c['areas'] = areas.copy()
c['gencost'] = gencost.copy()
c['version'] = '2'
ppc2 = loadcase(c)
t_is(ppc2['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc2['bus'], bus, 12, [t, 'bus'])
t_is(ppc2['gen'], gen, 12, [t, 'gen'])
t_is(ppc2['branch'], branch, 12, [t, 'branch'])
t_is(ppc2['areas'], areas, 12, [t, 'areas'])
t_is(ppc2['gencost'], gencost, 12, [t, 'gencost'])
## read version 1 PF data matrices
baseMVA, bus, gen, branch = t_case9_pf()
savemat(pfmatfile + '.mat',
{'baseMVA': baseMVA, 'bus': bus, 'gen': gen, 'branch': branch},
oned_as='column')
## read version 2 PF data matrices
ppc = t_case9_pfv2()
tmp = (ppc['baseMVA'], ppc['bus'].copy(),
ppc['gen'].copy(), ppc['branch'].copy())
baseMVA, bus, gen, branch = tmp
## save as .mat file
savemat(pfmatfilev2 + '.mat', {'ppc': ppc}, oned_as='column')
##----- load PF data into individual matrices -----
t = 'loadcase(pf_PY_file_v1) without .py extension : '
baseMVA1, bus1, gen1, branch1 = \
loadcase(pfcasefile, False, False, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t = 'loadcase(pf_PY_file_v1) with .py extension : '
baseMVA1, bus1, gen1, branch1 = \
loadcase(pfcasefile + '.py', False, False, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t = 'loadcase(pf_MAT_file_v1) without .mat extension : '
baseMVA1, bus1, gen1, branch1 = \
loadcase(pfmatfile, False, False, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t = 'loadcase(pf_MAT_file_v1) with .mat extension : '
baseMVA1, bus1, gen1, branch1 = \
loadcase(pfmatfile + '.mat', False, False, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t = 'loadcase(pf_PY_file_v2) without .py extension : '
baseMVA1, bus1, gen1, branch1 = \
loadcase(pfcasefilev2, False, False, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t = 'loadcase(pf_PY_file_v2) with .py extension : '
baseMVA1, bus1, gen1, branch1 = \
loadcase(pfcasefilev2 + '.py', False, False, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t = 'loadcase(pf_MAT_file_v2) without .mat extension : '
baseMVA1, bus1, gen1, branch1 = \
loadcase(pfmatfilev2, False, False, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t = 'loadcase(pf_MAT_file_v2) with .mat extension : '
baseMVA1, bus1, gen1, branch1 = \
loadcase(pfmatfilev2 + '.mat', False, False, False)
t_is(baseMVA1, baseMVA, 12, [t, 'baseMVA'])
t_is(bus1, bus, 12, [t, 'bus'])
t_is(gen1, gen, 12, [t, 'gen'])
t_is(branch1, branch, 12, [t, 'branch'])
t = 'loadcase(pf_struct_v1) (no version): '
baseMVA1, bus1, gen1, branch1 = t_case9_pf()
c = {}
c['baseMVA'] = baseMVA1
c['bus'] = bus1.copy()
c['gen'] = gen1.copy()
c['branch'] = branch1.copy()
baseMVA2, bus2, gen2, branch2 = loadcase(c, False, False, False)
t_is(baseMVA2, baseMVA, 12, [t, 'baseMVA'])
t_is(bus2, bus, 12, [t, 'bus'])
t_is(gen2, gen, 12, [t, 'gen'])
t_is(branch2, branch, 12, [t, 'branch'])
t = 'loadcase(pf_struct_v1) (version=''1''): '
c['version'] = '1'
baseMVA2, bus2, gen2, branch2 = loadcase(c, False, False, False)
t_is(baseMVA2, baseMVA, 12, [t, 'baseMVA'])
t_is(bus2, bus, 12, [t, 'bus'])
t_is(gen2, gen, 12, [t, 'gen'])
t_is(branch2, branch, 12, [t, 'branch'])
t = 'loadcase(pf_struct_v2) : '
c = {}
c['baseMVA'] = baseMVA
c['bus'] = bus.copy()
c['gen'] = gen.copy()
c['branch'] = branch.copy()
c['version'] = '2'
baseMVA2, bus2, gen2, branch2 = loadcase(c, False, False, False)
t_is(baseMVA2, baseMVA, 12, [t, 'baseMVA'])
t_is(bus2, bus, 12, [t, 'bus'])
t_is(gen2, gen, 12, [t, 'gen'])
t_is(branch2, branch, 12, [t, 'branch'])
##----- load PF data into struct -----
t = 'ppc = loadcase(pf_PY_file_v1) without .py extension : '
ppc1 = loadcase(pfcasefile)
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_PY_file_v1) with .py extension : '
ppc1 = loadcase(pfcasefile + '.py')
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_MAT_file_v1) without .mat extension : '
ppc1 = loadcase(pfmatfile)
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_MAT_file_v1) with .mat extension : '
ppc1 = loadcase(pfmatfile + '.mat')
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_PY_file_v2) without .py extension : '
ppc1 = loadcase(pfcasefilev2)
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_PY_file_v2) with .py extension : '
ppc1 = loadcase(pfcasefilev2 + '.py')
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_MAT_file_v2) without .mat extension : '
ppc1 = loadcase(pfmatfilev2)
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_MAT_file_v2) with .mat extension : '
ppc1 = loadcase(pfmatfilev2 + '.mat')
t_is(ppc1['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc1['bus'], bus, 12, [t, 'bus'])
t_is(ppc1['gen'], gen, 12, [t, 'gen'])
t_is(ppc1['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_struct_v1) (no version): '
baseMVA1, bus1, gen1, branch1 = t_case9_pf()
c = {}
c['baseMVA'] = baseMVA1
c['bus'] = bus1.copy()
c['gen'] = gen1.copy()
c['branch'] = branch1.copy()
ppc2 = loadcase(c)
t_is(ppc2['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc2['bus'], bus, 12, [t, 'bus'])
t_is(ppc2['gen'], gen, 12, [t, 'gen'])
t_is(ppc2['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_struct_v1) (version=''1''): '
c['version'] = '1'
ppc2 = loadcase(c)
t_is(ppc2['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc2['bus'], bus, 12, [t, 'bus'])
t_is(ppc2['gen'], gen, 12, [t, 'gen'])
t_is(ppc2['branch'], branch, 12, [t, 'branch'])
t = 'ppc = loadcase(pf_struct_v2) : '
c = {}
c['baseMVA'] = baseMVA
c['bus'] = bus.copy()
c['gen'] = gen.copy()
c['branch'] = branch.copy()
c['version'] = '2'
ppc2 = loadcase(c)
t_is(ppc2['baseMVA'], baseMVA, 12, [t, 'baseMVA'])
t_is(ppc2['bus'], bus, 12, [t, 'bus'])
t_is(ppc2['gen'], gen, 12, [t, 'gen'])
t_is(ppc2['branch'], branch, 12, [t, 'branch'])
## cleanup
os.remove(matfile + '.mat')
os.remove(pfmatfile + '.mat')
os.remove(matfilev2 + '.mat')
os.remove(pfmatfilev2 + '.mat')
t = 'runpf(my_PY_file)'
ppopt = ppoption(VERBOSE=0, OUT_ALL=0)
results3, success = runpf(pfcasefile, ppopt)
baseMVA3, bus3, gen3, branch3 = results3['baseMVA'], results3['bus'], \
results3['gen'], results3['branch']
t_ok( success, t )
t = 'runpf(my_object)'
results4, success = runpf(c, ppopt)
baseMVA4, bus4, gen4, branch4 = results4['baseMVA'], results4['bus'], \
results4['gen'], results4['branch']
t_ok( success, t )
t = 'runpf result comparison : '
t_is(baseMVA3, baseMVA4, 12, [t, 'baseMVA'])
t_is(bus3, bus4, 12, [t, 'bus'])
t_is(gen3, gen4, 12, [t, 'gen'])
t_is(branch3, branch4, 12, [t, 'branch'])
t = 'runpf(modified_struct)'
c['gen'][2, 1] = c['gen'][2, 1] + 1 ## increase gen 3 output by 1
results5, success = runpf(c, ppopt)
gen5 = results5['gen']
t_is(gen5[0, 1], gen4[0, 1] - 1, 1, t) ## slack bus output should decrease by 1
t_end()
def t_hessian(quiet=False):
"""Numerical tests of 2nd derivative code.
@author: Ray Zimmerman (PSERC Cornell)
"""
t_begin(44, quiet)
## run powerflow to get solved case
ppopt = ppoption(VERBOSE=0, OUT_ALL=0)
results, _ = runpf(case30(), ppopt)
baseMVA, bus, gen, branch = \
results['baseMVA'], results['bus'], results['gen'], results['branch']
## switch to internal bus numbering and build admittance matrices
_, bus, gen, branch = ext2int1(bus, gen, branch)
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
Vm = bus[:, VM]
Va = bus[:, VA] * (pi / 180)
V = Vm * exp(1j * Va)
f = branch[:, F_BUS] ## list of "from" buses
t = branch[:, T_BUS] ## list of "to" buses
nl = len(f)
nb = len(V)
Cf = sparse((ones(nl), (range(nl), f)), (nl, nb)) ## connection matrix for line & from buses
Ct = sparse((ones(nl), (range(nl), t)), (nl, nb)) ## connection matrix for line & to buses
pert = 1e-8
##----- check d2Sbus_dV2 code -----
t = ' - d2Sbus_dV2 (complex power injections)'
lam = 10 * random.rand(nb)
num_Haa = zeros((nb, nb), complex)
num_Hav = zeros((nb, nb), complex)
num_Hva = zeros((nb, nb), complex)
num_Hvv = zeros((nb, nb), complex)
dSbus_dVm, dSbus_dVa = dSbus_dV(Ybus, V)
Haa, Hav, Hva, Hvv = d2Sbus_dV2(Ybus, V, lam)
for i in range(nb):
Vap = V.copy()
Vap[i] = Vm[i] * exp(1j * (Va[i] + pert))
dSbus_dVm_ap, dSbus_dVa_ap = dSbus_dV(Ybus, Vap)
num_Haa[:, i] = (dSbus_dVa_ap - dSbus_dVa).T * lam / pert
num_Hva[:, i] = (dSbus_dVm_ap - dSbus_dVm).T * lam / pert
Vmp = V.copy()
Vmp[i] = (Vm[i] + pert) * exp(1j * Va[i])
dSbus_dVm_mp, dSbus_dVa_mp = dSbus_dV(Ybus, Vmp)
num_Hav[:, i] = (dSbus_dVa_mp - dSbus_dVa).T * lam / pert
num_Hvv[:, i] = (dSbus_dVm_mp - dSbus_dVm).T * lam / pert
t_is(Haa.todense(), num_Haa, 4, ['Haa', t])
t_is(Hav.todense(), num_Hav, 4, ['Hav', t])
t_is(Hva.todense(), num_Hva, 4, ['Hva', t])
t_is(Hvv.todense(), num_Hvv, 4, ['Hvv', t])
##----- check d2Sbr_dV2 code -----
t = ' - d2Sbr_dV2 (complex power flows)'
lam = 10 * random.rand(nl)
# lam = [1 zeros(nl-1, 1)]
num_Gfaa = zeros((nb, nb), complex)
num_Gfav = zeros((nb, nb), complex)
num_Gfva = zeros((nb, nb), complex)
num_Gfvv = zeros((nb, nb), complex)
num_Gtaa = zeros((nb, nb), complex)
num_Gtav = zeros((nb, nb), complex)
num_Gtva = zeros((nb, nb), complex)
num_Gtvv = zeros((nb, nb), complex)
dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, _, _ = dSbr_dV(branch, Yf, Yt, V)
Gfaa, Gfav, Gfva, Gfvv = d2Sbr_dV2(Cf, Yf, V, lam)
Gtaa, Gtav, Gtva, Gtvv = d2Sbr_dV2(Ct, Yt, V, lam)
for i in range(nb):
Vap = V.copy()
Vap[i] = Vm[i] * exp(1j * (Va[i] + pert))
dSf_dVa_ap, dSf_dVm_ap, dSt_dVa_ap, dSt_dVm_ap, Sf_ap, St_ap = \
dSbr_dV(branch, Yf, Yt, Vap)
num_Gfaa[:, i] = (dSf_dVa_ap - dSf_dVa).T * lam / pert
num_Gfva[:, i] = (dSf_dVm_ap - dSf_dVm).T * lam / pert
num_Gtaa[:, i] = (dSt_dVa_ap - dSt_dVa).T * lam / pert
num_Gtva[:, i] = (dSt_dVm_ap - dSt_dVm).T * lam / pert
Vmp = V.copy()
Vmp[i] = (Vm[i] + pert) * exp(1j * Va[i])
dSf_dVa_mp, dSf_dVm_mp, dSt_dVa_mp, dSt_dVm_mp, Sf_mp, St_mp = \
dSbr_dV(branch, Yf, Yt, Vmp)
num_Gfav[:, i] = (dSf_dVa_mp - dSf_dVa).T * lam / pert
num_Gfvv[:, i] = (dSf_dVm_mp - dSf_dVm).T * lam / pert
num_Gtav[:, i] = (dSt_dVa_mp - dSt_dVa).T * lam / pert
num_Gtvv[:, i] = (dSt_dVm_mp - dSt_dVm).T * lam / pert
t_is(Gfaa.todense(), num_Gfaa, 4, ['Gfaa', t])
t_is(Gfav.todense(), num_Gfav, 4, ['Gfav', t])
t_is(Gfva.todense(), num_Gfva, 4, ['Gfva', t])
t_is(Gfvv.todense(), num_Gfvv, 4, ['Gfvv', t])
t_is(Gtaa.todense(), num_Gtaa, 4, ['Gtaa', t])
t_is(Gtav.todense(), num_Gtav, 4, ['Gtav', t])
t_is(Gtva.todense(), num_Gtva, 4, ['Gtva', t])
t_is(Gtvv.todense(), num_Gtvv, 4, ['Gtvv', t])
##----- check d2Ibr_dV2 code -----
t = ' - d2Ibr_dV2 (complex currents)'
lam = 10 * random.rand(nl)
# lam = [1, zeros(nl-1)]
num_Gfaa = zeros((nb, nb), complex)
num_Gfav = zeros((nb, nb), complex)
num_Gfva = zeros((nb, nb), complex)
num_Gfvv = zeros((nb, nb), complex)
num_Gtaa = zeros((nb, nb), complex)
num_Gtav = zeros((nb, nb), complex)
num_Gtva = zeros((nb, nb), complex)
num_Gtvv = zeros((nb, nb), complex)
dIf_dVa, dIf_dVm, dIt_dVa, dIt_dVm, _, _ = dIbr_dV(branch, Yf, Yt, V)
Gfaa, Gfav, Gfva, Gfvv = d2Ibr_dV2(Yf, V, lam)
Gtaa, Gtav, Gtva, Gtvv = d2Ibr_dV2(Yt, V, lam)
for i in range(nb):
Vap = V.copy()
Vap[i] = Vm[i] * exp(1j * (Va[i] + pert))
dIf_dVa_ap, dIf_dVm_ap, dIt_dVa_ap, dIt_dVm_ap, If_ap, It_ap = \
dIbr_dV(branch, Yf, Yt, Vap)
num_Gfaa[:, i] = (dIf_dVa_ap - dIf_dVa).T * lam / pert
num_Gfva[:, i] = (dIf_dVm_ap - dIf_dVm).T * lam / pert
num_Gtaa[:, i] = (dIt_dVa_ap - dIt_dVa).T * lam / pert
num_Gtva[:, i] = (dIt_dVm_ap - dIt_dVm).T * lam / pert
Vmp = V.copy()
Vmp[i] = (Vm[i] + pert) * exp(1j * Va[i])
dIf_dVa_mp, dIf_dVm_mp, dIt_dVa_mp, dIt_dVm_mp, If_mp, It_mp = \
dIbr_dV(branch, Yf, Yt, Vmp)
num_Gfav[:, i] = (dIf_dVa_mp - dIf_dVa).T * lam / pert
num_Gfvv[:, i] = (dIf_dVm_mp - dIf_dVm).T * lam / pert
num_Gtav[:, i] = (dIt_dVa_mp - dIt_dVa).T * lam / pert
num_Gtvv[:, i] = (dIt_dVm_mp - dIt_dVm).T * lam / pert
t_is(Gfaa.todense(), num_Gfaa, 4, ['Gfaa', t])
t_is(Gfav.todense(), num_Gfav, 4, ['Gfav', t])
t_is(Gfva.todense(), num_Gfva, 4, ['Gfva', t])
t_is(Gfvv.todense(), num_Gfvv, 4, ['Gfvv', t])
t_is(Gtaa.todense(), num_Gtaa, 4, ['Gtaa', t])
t_is(Gtav.todense(), num_Gtav, 4, ['Gtav', t])
t_is(Gtva.todense(), num_Gtva, 4, ['Gtva', t])
t_is(Gtvv.todense(), num_Gtvv, 4, ['Gtvv', t])
##----- check d2ASbr_dV2 code -----
t = ' - d2ASbr_dV2 (squared apparent power flows)'
lam = 10 * random.rand(nl)
# lam = [1 zeros(nl-1, 1)]
num_Gfaa = zeros((nb, nb), complex)
num_Gfav = zeros((nb, nb), complex)
num_Gfva = zeros((nb, nb), complex)
num_Gfvv = zeros((nb, nb), complex)
num_Gtaa = zeros((nb, nb), complex)
num_Gtav = zeros((nb, nb), complex)
num_Gtva = zeros((nb, nb), complex)
num_Gtvv = zeros((nb, nb), complex)
dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St = dSbr_dV(branch, Yf, Yt, V)
dAf_dVa, dAf_dVm, dAt_dVa, dAt_dVm = \
dAbr_dV(dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St)
Gfaa, Gfav, Gfva, Gfvv = d2ASbr_dV2(dSf_dVa, dSf_dVm, Sf, Cf, Yf, V, lam)
Gtaa, Gtav, Gtva, Gtvv = d2ASbr_dV2(dSt_dVa, dSt_dVm, St, Ct, Yt, V, lam)
for i in range(nb):
Vap = V.copy()
Vap[i] = Vm[i] * exp(1j * (Va[i] + pert))
dSf_dVa_ap, dSf_dVm_ap, dSt_dVa_ap, dSt_dVm_ap, Sf_ap, St_ap = \
dSbr_dV(branch, Yf, Yt, Vap)
dAf_dVa_ap, dAf_dVm_ap, dAt_dVa_ap, dAt_dVm_ap = \
dAbr_dV(dSf_dVa_ap, dSf_dVm_ap, dSt_dVa_ap, dSt_dVm_ap, Sf_ap, St_ap)
num_Gfaa[:, i] = (dAf_dVa_ap - dAf_dVa).T * lam / pert
num_Gfva[:, i] = (dAf_dVm_ap - dAf_dVm).T * lam / pert
num_Gtaa[:, i] = (dAt_dVa_ap - dAt_dVa).T * lam / pert
num_Gtva[:, i] = (dAt_dVm_ap - dAt_dVm).T * lam / pert
Vmp = V.copy()
Vmp[i] = (Vm[i] + pert) * exp(1j * Va[i])
dSf_dVa_mp, dSf_dVm_mp, dSt_dVa_mp, dSt_dVm_mp, Sf_mp, St_mp = \
dSbr_dV(branch, Yf, Yt, Vmp)
dAf_dVa_mp, dAf_dVm_mp, dAt_dVa_mp, dAt_dVm_mp = \
dAbr_dV(dSf_dVa_mp, dSf_dVm_mp, dSt_dVa_mp, dSt_dVm_mp, Sf_mp, St_mp)
num_Gfav[:, i] = (dAf_dVa_mp - dAf_dVa).T * lam / pert
num_Gfvv[:, i] = (dAf_dVm_mp - dAf_dVm).T * lam / pert
num_Gtav[:, i] = (dAt_dVa_mp - dAt_dVa).T * lam / pert
num_Gtvv[:, i] = (dAt_dVm_mp - dAt_dVm).T * lam / pert
t_is(Gfaa.todense(), num_Gfaa, 2, ['Gfaa', t])
t_is(Gfav.todense(), num_Gfav, 2, ['Gfav', t])
t_is(Gfva.todense(), num_Gfva, 2, ['Gfva', t])
t_is(Gfvv.todense(), num_Gfvv, 2, ['Gfvv', t])
t_is(Gtaa.todense(), num_Gtaa, 2, ['Gtaa', t])
t_is(Gtav.todense(), num_Gtav, 2, ['Gtav', t])
t_is(Gtva.todense(), num_Gtva, 2, ['Gtva', t])
t_is(Gtvv.todense(), num_Gtvv, 2, ['Gtvv', t])
##----- check d2ASbr_dV2 code -----
t = ' - d2ASbr_dV2 (squared real power flows)'
lam = 10 * random.rand(nl)
# lam = [1 zeros(nl-1, 1)]
num_Gfaa = zeros((nb, nb), complex)
num_Gfav = zeros((nb, nb), complex)
num_Gfva = zeros((nb, nb), complex)
num_Gfvv = zeros((nb, nb), complex)
num_Gtaa = zeros((nb, nb), complex)
num_Gtav = zeros((nb, nb), complex)
num_Gtva = zeros((nb, nb), complex)
num_Gtvv = zeros((nb, nb), complex)
dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St = dSbr_dV(branch, Yf, Yt, V)
dAf_dVa, dAf_dVm, dAt_dVa, dAt_dVm = \
dAbr_dV(dSf_dVa.real, dSf_dVm.real, dSt_dVa.real, dSt_dVm.real, Sf.real, St.real)
Gfaa, Gfav, Gfva, Gfvv = d2ASbr_dV2(dSf_dVa.real, dSf_dVm.real, Sf.real, Cf, Yf, V, lam)
Gtaa, Gtav, Gtva, Gtvv = d2ASbr_dV2(dSt_dVa.real, dSt_dVm.real, St.real, Ct, Yt, V, lam)
for i in range(nb):
Vap = V.copy()
Vap[i] = Vm[i] * exp(1j * (Va[i] + pert))
dSf_dVa_ap, dSf_dVm_ap, dSt_dVa_ap, dSt_dVm_ap, Sf_ap, St_ap = \
dSbr_dV(branch, Yf, Yt, Vap)
dAf_dVa_ap, dAf_dVm_ap, dAt_dVa_ap, dAt_dVm_ap = \
dAbr_dV(dSf_dVa_ap.real, dSf_dVm_ap.real, dSt_dVa_ap.real, dSt_dVm_ap.real, Sf_ap.real, St_ap.real)
num_Gfaa[:, i] = (dAf_dVa_ap - dAf_dVa).T * lam / pert
num_Gfva[:, i] = (dAf_dVm_ap - dAf_dVm).T * lam / pert
num_Gtaa[:, i] = (dAt_dVa_ap - dAt_dVa).T * lam / pert
num_Gtva[:, i] = (dAt_dVm_ap - dAt_dVm).T * lam / pert
Vmp = V.copy()
Vmp[i] = (Vm[i] + pert) * exp(1j * Va[i])
dSf_dVa_mp, dSf_dVm_mp, dSt_dVa_mp, dSt_dVm_mp, Sf_mp, St_mp = \
dSbr_dV(branch, Yf, Yt, Vmp)
dAf_dVa_mp, dAf_dVm_mp, dAt_dVa_mp, dAt_dVm_mp = \
dAbr_dV(dSf_dVa_mp.real, dSf_dVm_mp.real, dSt_dVa_mp.real, dSt_dVm_mp.real, Sf_mp.real, St_mp.real)
num_Gfav[:, i] = (dAf_dVa_mp - dAf_dVa).T * lam / pert
num_Gfvv[:, i] = (dAf_dVm_mp - dAf_dVm).T * lam / pert
num_Gtav[:, i] = (dAt_dVa_mp - dAt_dVa).T * lam / pert
num_Gtvv[:, i] = (dAt_dVm_mp - dAt_dVm).T * lam / pert
t_is(Gfaa.todense(), num_Gfaa, 2, ['Gfaa', t])
t_is(Gfav.todense(), num_Gfav, 2, ['Gfav', t])
t_is(Gfva.todense(), num_Gfva, 2, ['Gfva', t])
t_is(Gfvv.todense(), num_Gfvv, 2, ['Gfvv', t])
t_is(Gtaa.todense(), num_Gtaa, 2, ['Gtaa', t])
t_is(Gtav.todense(), num_Gtav, 2, ['Gtav', t])
t_is(Gtva.todense(), num_Gtva, 2, ['Gtva', t])
t_is(Gtvv.todense(), num_Gtvv, 2, ['Gtvv', t])
##----- check d2AIbr_dV2 code -----
t = ' - d2AIbr_dV2 (squared current magnitudes)'
lam = 10 * random.rand(nl)
# lam = [1 zeros(nl-1, 1)]
num_Gfaa = zeros((nb, nb), complex)
num_Gfav = zeros((nb, nb), complex)
num_Gfva = zeros((nb, nb), complex)
num_Gfvv = zeros((nb, nb), complex)
num_Gtaa = zeros((nb, nb), complex)
num_Gtav = zeros((nb, nb), complex)
num_Gtva = zeros((nb, nb), complex)
num_Gtvv = zeros((nb, nb), complex)
dIf_dVa, dIf_dVm, dIt_dVa, dIt_dVm, If, It = dIbr_dV(branch, Yf, Yt, V)
dAf_dVa, dAf_dVm, dAt_dVa, dAt_dVm = \
dAbr_dV(dIf_dVa, dIf_dVm, dIt_dVa, dIt_dVm, If, It)
Gfaa, Gfav, Gfva, Gfvv = d2AIbr_dV2(dIf_dVa, dIf_dVm, If, Yf, V, lam)
Gtaa, Gtav, Gtva, Gtvv = d2AIbr_dV2(dIt_dVa, dIt_dVm, It, Yt, V, lam)
for i in range(nb):
Vap = V.copy()
Vap[i] = Vm[i] * exp(1j * (Va[i] + pert))
dIf_dVa_ap, dIf_dVm_ap, dIt_dVa_ap, dIt_dVm_ap, If_ap, It_ap = \
dIbr_dV(branch, Yf, Yt, Vap)
dAf_dVa_ap, dAf_dVm_ap, dAt_dVa_ap, dAt_dVm_ap = \
dAbr_dV(dIf_dVa_ap, dIf_dVm_ap, dIt_dVa_ap, dIt_dVm_ap, If_ap, It_ap)
num_Gfaa[:, i] = (dAf_dVa_ap - dAf_dVa).T * lam / pert
num_Gfva[:, i] = (dAf_dVm_ap - dAf_dVm).T * lam / pert
num_Gtaa[:, i] = (dAt_dVa_ap - dAt_dVa).T * lam / pert
num_Gtva[:, i] = (dAt_dVm_ap - dAt_dVm).T * lam / pert
Vmp = V.copy()
Vmp[i] = (Vm[i] + pert) * exp(1j * Va[i])
dIf_dVa_mp, dIf_dVm_mp, dIt_dVa_mp, dIt_dVm_mp, If_mp, It_mp = \
dIbr_dV(branch, Yf, Yt, Vmp)
dAf_dVa_mp, dAf_dVm_mp, dAt_dVa_mp, dAt_dVm_mp = \
dAbr_dV(dIf_dVa_mp, dIf_dVm_mp, dIt_dVa_mp, dIt_dVm_mp, If_mp, It_mp)
num_Gfav[:, i] = (dAf_dVa_mp - dAf_dVa).T * lam / pert
num_Gfvv[:, i] = (dAf_dVm_mp - dAf_dVm).T * lam / pert
num_Gtav[:, i] = (dAt_dVa_mp - dAt_dVa).T * lam / pert
num_Gtvv[:, i] = (dAt_dVm_mp - dAt_dVm).T * lam / pert
t_is(Gfaa.todense(), num_Gfaa, 3, ['Gfaa', t])
t_is(Gfav.todense(), num_Gfav, 3, ['Gfav', t])
t_is(Gfva.todense(), num_Gfva, 3, ['Gfva', t])
t_is(Gfvv.todense(), num_Gfvv, 2, ['Gfvv', t])
t_is(Gtaa.todense(), num_Gtaa, 3, ['Gtaa', t])
t_is(Gtav.todense(), num_Gtav, 3, ['Gtav', t])
t_is(Gtva.todense(), num_Gtva, 3, ['Gtva', t])
t_is(Gtvv.todense(), num_Gtvv, 2, ['Gtvv', t])
t_end()
def runacdcpf(caseac=None, casedc=None, pacdcopt=None, ppopt=None):
"""
Runs a sequential AC/DC power flow, optionally
returning the results, a convergence flag and the time.
Inputs (optional):
CASEAC : ac power flow data
either a PYPOWER case struct or a string containing the name
of the file with the data (default ac case is 'case5_stagg',
only used when both ac and dc power flow data are not defined)
(see also CASEFORMAT and LOADCASE and PYPOWER)
CASEDC : dc power flow data
either a PYACDCPF case struct or a string containing
the name of the file with the data (default dc case is
'case5_stagg_MTDCslack')
(see also LOADCASEDC)
PACDCOPT : PYACDCPF options vector to override default ac/dc power
flow options. Can be used to specify tolerances, inclusion of
limits, plot options and more (see also MACDCOPTION).
PPOPT : PYPOWER options vector to override default options
can be used to specify the solution algorithm, output options
termination tolerances, and more (see also MPOPTION).
Outputs:
RESULTSAC : results struct, with the following fields from the
input PYPOWER case: baseMVA, bus, branch, gen (but with solved
voltages, power flows, etc.)
RESULTSDC : results struct, with the following fields:
input PYACDCPF dc case: baseMVAac, baseMVAdc, pol, busdc, convdc,
branchdc (but with solved voltages, power flows, etc.)
CONVERGED : converge flag, can additionally be returned
TIMECALC : elapsed time, can additionally be returned
Examples of usage:
[resultsac, resultsdc, converged, te] = runacdcpf('case5_stagg', \
'case5_stagg_MTDCdroop');
@author:Jef Beerten (KU Leuven)
@author:Roni Irnawan (Aalborg University)
"""
## start of time calculation
t0 = time()
## add subdirectories to path
if caseac is None:
dirac = join(dirname(__file__), 'Cases', 'PowerflowAC')
caseac = join(dirac, 'case5_stagg')
# caseac = join(dirac, 'case3_inf')
# caseac = join(dirac, 'case24_ieee_rts1996_3zones')
# caseac = join(dirac, 'case24_ieee_rts1996_3zones_inf')
if casedc is None:
dirdc = join(dirname(__file__), 'Cases', 'PowerflowDC')
# casedc = join(dirdc, 'case5_stagg_HVDCptp')
# casedc = join(dirdc, 'case5_stagg_MTDCslack')
casedc = join(dirdc, 'case5_stagg_MTDCdroop')
# casedc = join(dirdc, 'case24_ieee_rts1996_MTDC')
## default arguments
ppopt = ppoption(ppopt)
ppopt["VERBOSE"] = 0
ppopt["OUT_ALL"] = 0
pacdcopt = pacdcoption(pacdcopt)
## options
tolacdc = pacdcopt["TOLACDC"]
itmaxacdc = pacdcopt["ITMAXACDC"]
toldc = pacdcopt["TOLDC"]
itmaxdc = pacdcopt["ITMAXDC"]
tolslackdroop = pacdcopt["TOLSLACKDROOP"]
itmaxslackdroop = pacdcopt["ITMAXSLACKDROOP"]
tolslackdroopint = pacdcopt["TOLSLACKDROOPINT"]
itmaxslackdroopint = pacdcopt["ITMAXSLACKDROOPINT"]
multslack = pacdcopt["MULTSLACK"]
limac = pacdcopt["LIMAC"]
limdc = pacdcopt["LIMDC"]
tollim = pacdcopt["TOLLIM"]
output = pacdcopt["OUTPUT"]
convplotopt = pacdcopt["CONVPLOTOPT"]
## ----- initialise -----
## read data
pdc = loadcasedc(casedc)
ppc = loadcase(caseac)
##----- Data preparation -----
## converter outage are considered as stations without ac grid connection
pdc, conv0busi, conv1, conv1i, conv0, conv0i = convout(pdc)
pdc['convdc'] = conv1 # only use converters without outage
## dc branch outages (remove branches from input data)
brchdc1, brchdc1i, brchdc0, brchdc0i = brchdcout(pdc)
pdc['branchdc'] = brchdc1 # only include branches in operation
## ac branch outages (remove branches from input data)
brch1, brch1i, brch0, brch0i = brchout(ppc)
ppc['branch'] = brch1 # only include branches in operation
## generator outages (remove non-operational generators from input data)
gon = where(ppc['gen'][:,GEN_STATUS] > 0)[0] # which gens are on?
goff = where(ppc['gen'][:,GEN_STATUS] == 0)[0] # which gens are off?
gen0 = ppc['gen'][goff,:] # non-operational gens data
ppc['gen'] = ppc['gen'][gon,:] #keep operational generators"
##----- External to internal numbering -----
## dc network external to internal bus numbering
i2edcpmt, i2edc, pdc = ext2intdc(pdc)
## ac network external to internal bus numbering
acdmbus, i2eac, pdc, ppc = ext2intac(pdc,ppc)
## sort matrices by new bus numbers
i2ebus = ppc['bus'][:,0].argsort()
i2egen = ppc['gen'][:,0].argsort()
i2ebrch = ppc['branch'][:,0].argsort()
i2ebusdc = pdc['busdc'][:,0].argsort()
i2econvdc = pdc['convdc'][:,0].argsort()
i2ebrchdc = pdc['branchdc'][:,0].argsort()
ppc['bus'] = ppc['bus'][i2ebus,:]
ppc['gen'] = ppc['gen'][i2egen,:]
ppc['branch'] = ppc['branch'][i2ebrch,:]
pdc['busdc'] = pdc['busdc'][i2ebusdc,:]
pdc['convdc'] = pdc['convdc'][i2econvdc,:]
pdc['branchdc'] = pdc['branchdc'][i2ebrchdc,:]
## Per unit external to internal data conversion
pdc = ext2intpu(ppc['baseMVA'],pdc)
##----- Additional data preparation & index initialisation -----
## zero rows addition to convdc matrix (dc buses without converter)
convdc1 = zeros((pdc['busdc'].shape[0]-pdc['convdc'].shape[0],
pdc['convdc'].shape[1]))
pdc['convdc'] = r_[pdc['convdc'],convdc1]
## indices initialisation
bdci = where(pdc['busdc'][:,BUSAC_I])[0].astype(int)
cdci = where(pdc['convdc'][:,CONVSTATUS] == 1)[0].astype(int)
slackdc = where(pdc['convdc'][:,CONVTYPE_DC] == DCSLACK)[0].astype(int)
droopdc = where(pdc['convdc'][:,CONVTYPE_DC] == DCDROOP)[0].astype(int)
ngriddc = pdc['busdc'][:,GRIDDC].max()
## convert to internal indexing
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
baseMVAac, baseMVAdc, pol, busdc, convdc, branchdc = \
pdc["baseMVAac"], pdc["baseMVAdc"], pdc["pol"], \
pdc["busdc"], pdc["convdc"], pdc["branchdc"]
##----- Violation check -----
## dc slack bus and distributed voltage bus violation check
gridviol = setdiff1d(arange(1,ngriddc+1),busdc[r_[slackdc, droopdc],GRIDDC])
if not gridviol.size == 0:
stdout.write('\nMultiple dc slack busses defined in grid %d \n' % (gridviol))
stderr.write('No droop controlled bus or slack bus defined for every dc grid !\n')
## remove multiple slack buses
if multslack == 0:
for ii in arange(1,ngriddc+1):
slackdcii = intersect1d(slackdc,where(busdc[:,GRIDDC] == ii)[0])
if slackdcii.size > 1:
convdc[slackdcii[0].astype(int),CONVTYPE_DC ] = DCSLACK
convdc[slackdcii[1:].astype(int),CONVTYPE_DC ] = DCNOSLACK
slackdcii = slackdcii[0]
## printout changes
stdout.write('\nMultiple dc slack busses defined in grid %d' %(ii))
stdout.write('\n Bus %d kept as the slack bus\n'%\
(i2edc[slackdcii.astype(int)+1]))
## redefine slack buses
slackdc = where(convdc[:,CONVTYPE_DC] == DCSLACK)[0].astype(int)
## define indices of slack, droop and power controlled buses
slackdroopdc = union1d(slackdc, droopdc)
noslackbdc = setdiff1d(where(busdc[:,BUSDC_I]), slackdc)
## remove converter and generator V control violations
vcontrvsc = where(convdc[:,CONVTYPE_AC] == PVC)[0]
vcontrgen = r_[where(bus[:,BUS_TYPE] == PV)[0], \
where(bus[:,BUS_TYPE] == REF)[0]]
## buses with V control conflicts
vconfl = intersect1d(vcontrvsc, vcontrgen).astype(int)
convdc[vconfl,CONVTYPE_AC] = PQC
convdc[vconfl,QCONV] = 0
convdc[:,QCONV] *= convdc[:,CONVTYPE_AC] == PQC
if not vconfl.size == 0:
stdout.write('Generator & VSC converter on the same bus')
stdout.write('\n Conflicting voltage control on bus %d' %(i2eac[vconfl+1]))
stdout.write('\n=> Corresponding VSC Converter set to PQ control without Q injections.\n')
##----- initialisation ac network -----
## dummy generator initialisation
Vcref = convdc[:,VCONV] # voltage setpoints
busVSC = bus.copy()
gendm = zeros((0,gen.shape[1]))
genPQ = zeros((0,1)).astype(int)
genPQi = zeros((0,1)).astype(int)
Qcmin_dum = -99999
Qcmax_dum = 99999
Pcmin_dum = 0
Pcmax_dum = 99999
## dummy generator addition
for ii in arange(convdc.shape[0]):
## change control from PQ to PV for buses with converter in PV control
if bus[ii,BUS_TYPE] == PQ and convdc[ii,CONVTYPE_AC] == PVC:
busVSC[ii,BUS_TYPE] = PV
## add dummy generator to V controlling converter bus without generator
if not any(gen[:,GEN_BUS] == bus[ii,BUS_I]):
gendm = r_[gendm, zeros((1,gen.shape[1]))]
gendm[-1,[GEN_BUS,PG,QG,QMAX,QMIN,VG,MBASE,GEN_STATUS,PMAX,PMIN]] = \
[ii+1,0,0,Qcmax_dum,Qcmin_dum,Vcref[ii],baseMVAac,1,Pcmax_dum,Pcmin_dum]
else:
genPQ = r_[genPQ,bus[ii,BUS_I]]
genPQii = where(gen[:,GEN_BUS] == bus[ii,BUS_I])[0]
genPQi = r_[genPQi,genPQii]
## define buses with dummy generator
# gdmbus = where(gendm[[where(bus[:,BUS_I]==x)[0][0] for x in \
# gendm[:,GEN_BUS]],GEN_BUS])[0]
if any(gendm[:,GEN_BUS]):
gdmbus = where(gendm[:,GEN_BUS] == bus[:,BUS_I])[0]
else:
gdmbus = []
## converter stations power injections into ac network
Pvsc = convdc[:,PCONV]/baseMVA
Qvsc = convdc[:,QCONV]/baseMVA
## dc voltage droop setpoints and parameters
PVdroop = zeros(busdc.shape[0])
Pdcset = zeros(busdc.shape[0])
Vdcset = zeros(busdc.shape[0])
dVdcset = zeros(busdc.shape[0])
PVdroop[cdci] = convdc[cdci,DROOP]*baseMVA
Pdcset[cdci] = convdc[cdci,PDCSET]/baseMVA
Vdcset[cdci] = convdc[cdci,VDCSET]
dVdcset[cdci] = convdc[cdci,DVDCSET]
## voltage droop converter power initialisation
Pvsc[droopdc] = Pdcset[droopdc] ## assumption: operating in reference set-point & no converter losses
## dc slack converter power injection initialisation
if slackdc.size != 0:
for ii in arange(1,ngriddc+1):
#slackdcii = where(convdc[busdc[:,GRIDDC]==ii,CONVTYPE_DC]==DCSLACK)[0]
slackdcii = intersect1d(where(busdc[:,GRIDDC]==ii)[0],\
where(convdc[:,CONVTYPE_DC]==DCSLACK)[0])
if slackdcii.size != 0:
#Pvscii = Pvsc*(convdc[busdc[:,GRIDDC]==1,CONVTYPE_DC]!=DCSLACK)
Pvscii = Pvsc*(equal(busdc[:,GRIDDC],ii)* \
not_equal(convdc[:,CONVTYPE_DC],DCSLACK))
Pvsc[slackdcii] = -Pvscii.sum()/slackdcii.shape[0]
## Inclusion of converters as loads
busVSC[cdci,PD] = bus[cdci,PD] - Pvsc[cdci]*baseMVA
busVSC[cdci,QD] = bus[cdci,QD] - Qvsc[cdci]*baseMVA
##----- initialisation of converter quantities -----
## per unit converter loss coefficients values
basekA = baseMVA/(sqrt(3)*convdc[:,BASEKVC])
lossa = convdc[:,LOSSA]/baseMVA
lossb = convdc[:,LOSSB]*basekA/baseMVA
losscr = convdc[:,LOSSCR]*basekA**2/baseMVA
lossci = convdc[:,LOSSCI]*basekA**2/baseMVA
## converter reactor parameters
Rc = convdc[:,RCONV]
Xc = convdc[:,XCONV]
Zc = Rc+j*Xc
## converter limits data
Icmax = convdc[:,ICMAX]
Vcmax = convdc[:,VCMAX]
Vcmin = convdc[:,VCMIN]
## filter reactance
Bf = convdc[:,BF]
## transformer parameters
Rtf = convdc[:,RTF]
Xtf = convdc[:,XTF]
Ztf = Rtf+j*Xtf
##----- initialisation of dc network quantities -----
## build dc bus matrix
Ybusdc, Yfdc, Ytdc = makeYbusdc( busdc, branchdc )
## detect ac islands errors (non-synchronised zones => to be solved independently)
zonecheck(bus, gen, branch, i2eac, output)
aczones = sort(unique(bus[:,ZONE])).astype(int)
##----- main iteration loop -----
## initialise
Vdc = busdc[:,VDC] #dc bus voltages
genVSC = r_[gen, gendm] #inclusion of dummy generators for ac solution
gendmidx = where(genVSC[:,GEN_BUS] == setdiff1d(genVSC[:,GEN_BUS], gen[:, GEN_BUS]))[0] # index of dummy generators in genVSC matrix
Ps = Pvsc #grid side converter power initialisation
Pdc = zeros(busdc.shape[0])
Ifdc = zeros(branchdc.shape[0])
Pfdc = zeros(branchdc.shape[0])
Ptdc = zeros(branchdc.shape[0])
## iteration options
it = 0
converged = 0
## main loop
while (not converged) and (it <= itmaxacdc):
## update iteration counter
it += 1
## reset grid side converter reactive power injection
Qs = Qvsc
Ss = Ps +j*Qs
##----- ac network power flow -----
## ac power flow with converters as loads (PQ mode) or load+generator (PV mode)
busVSCext = zeros((0,bus.shape[1]))
genVSCext = zeros((0,gen.shape[1]))
branchext = zeros((0,QT+1))
buszi = []
genVSCzi = []
brchzi = []
for i in arange(aczones.size):
## select buses, generators and branches in the specified ac zone
buszi = where(bus[:,ZONE] == aczones[i])[0]
genVSCzi = where(bus[[where(bus[:,BUS_I]==x)[0][0] for x in \
genVSC[:,GEN_BUS]],ZONE] == aczones[i])[0]
brchzi = where(bus[[where(bus[:,BUS_I]==x)[0][0] for x in \
branch[:,F_BUS]],ZONE] == aczones[i])[0]
busVSCz = busVSC[buszi,:]
genVSCz = genVSC[genVSCzi,:]
branchz = branch[brchzi,:]
## solve ac power flow for specified ac zone (if not infinite bus)
if busVSCz.shape[0]>1:
accaseVSCz = {}
accaseVSCz['baseMVA'] = baseMVA
accaseVSCz['bus'] = busVSCz
accaseVSCz['gen'] = genVSCz
accaseVSCz['branch'] = branchz
resultsz,successz = runpf(accaseVSCz, ppopt)
busVSCz = resultsz['bus']
genVSCz = resultsz['gen']
branchz = resultsz['branch']
## store solutions for specified ac zone in extended matrices
for k,idx in enumerate(buszi):
if busVSCext.shape[0] <= idx:
busVSCext = r_[busVSCext,zeros((idx-busVSCext.shape[0]+1, \
bus.shape[1]))]
busVSCext[idx,:] = busVSCz[k,:]
for k,idx in enumerate(genVSCzi):
if genVSCext.shape[0] <= idx:
genVSCext = r_[genVSCext,zeros((idx-genVSCext.shape[0]+1, \
gen.shape[1]))]
genVSCext[idx,:] = genVSCz[k,:]
for k,idx in enumerate(brchzi):
if branchext.shape[0] <= idx:
branchext = r_[branchext,zeros((idx-branchext.shape[0]+1, \
QT+1))]
branchext[idx,:] = branchz[k,:]
busVSC = busVSCext.copy()
genVSC = genVSCext.copy()
branch = branchext.copy()
## dummy generator update
gendm = genVSC[gen.shape[0]:,:]
## dummy generator on converter V controlled bus
Ss[gdmbus] = Ss[gdmbus] + j*gendm[:,QG]/baseMVA
## PQ generator on converter V controlled bus
Ss[genPQ] = Ss[genPQ] + \
j*(genVSC[genPQi,QG] - gen[genPQi,QG])/baseMVA
## update grid side converter power injections
Ps = real(Ss)
Qs = imag(Ss)
## generator reset
genVSC[gendmidx,QG] = 0
genVSC[genPQi,QG] = gen[genPQi,QG]
##----- Converter calculations -----
## converter reactor voltages and power
Vs = busVSC[bdci,VM]*exp(j*busVSC[bdci,VA]*pi/180)
Itf = conj(Ss/Vs) ## transformer current
Vf = Vs + Itf*Ztf ## filter side voltage
Ssf = Vf*conj(Itf) ## filter side transformer complex power
Qf = -Bf*abs(Vf)**2 ## filter reactive power
Scf = Ssf + j*Qf ## filter side converter complex power
Ic = conj(Scf/Vf) ## converter current
Vc = Vf + Ic*Zc ## converter side voltage
Sc = Vc*conj(Ic) ## converter side complex power
## converter active and reactive powers
Pc = real(Sc)
Qc = imag(Sc)
Pcf = real(Scf)
Qcf = imag(Scf)
Psf = real(Ssf)
Qsf = imag(Ssf)
## initialisation
Ps_old = Ps.copy()
if limac == 1:
##--- converter limit check ---
## initialisation
limviol = zeros((busdc.shape[0]))
SsL = zeros((busdc.shape[0]),dtype=complex)
plotarg = zeros((busdc.shape[0],17),dtype=complex)
for ii in arange(1,ngriddc+1):
## remove slack converters from limit check
cdcii = where(busdc[:,GRIDDC] == ii)[0]
ccdcslackii = where(intersect1d(cdcii,slackdc)==cdcii)[0]
if not ccdcslackii.size == 0:
cdcii = delete(cdcii,ccdcslackii) #remove slack converter
cdci0 = where(convdc[cdcii,CONV_BUS]==0)[0]
cdcii = delete(cdcii,cdci0) # remove zero elements (converter outages)
## converter limit check
for jj in arange(cdcii.size):
cvjj = cdcii[jj]
limviol[cvjj],SsL[cvjj], plotarg[cvjj,:] = convlim(Ss[cvjj], \
Vs[cvjj], Vc[cvjj], Ztf[cvjj], Bf[cvjj], Zc[cvjj], \
Icmax[cvjj], Vcmax[cvjj], Vcmin[cvjj], i2edc[cvjj+1], \
tollim, convplotopt)
## converter limit violations (1 = Q limit, 2 = P limit)
limviolii = limviol*(busdc[:,GRIDDC] == ii)
dSii = (SsL-Ss)*(busdc[:,GRIDDC] == ii)*(convdc[:,CONVTYPE_DC] != DCSLACK)
if (2 in limviolii) or (1 in limviolii):
if (2 in limviolii):
dSii = dSii*(limviolii==2)
dSiimaxi = where(abs(real(dSii)).max())[0]
stdout.write('\n Active power setpoint of converter %d changed from %.2f MW to %.2f MW.'%( \
i2edc[dSiimaxi+1], real(Ss[dSiimaxi])*baseMVA, real(SsL[dSiimaxi])*baseMVA))
stdout.write('\n Reactive power setpoint of converter %d changed from %.2f MVAr to %.2f MVAr.\n'%(\
i2edc[dSiimaxi+1], imag(Ss[dSiimaxi])*baseMVA, imag(SsL[dSiimaxi])*baseMVA))
else: ## if ismember(1, limviolii)
dSii = dSii*(limviolii==1)
dSiimaxi = argmax(abs(imag(dSii)))
stdout.write('\n Reactive power setpoint of converter %d changed from %.2f MVAr to %.2f MVAr. \n'%(\
i2edc[dSiimaxi+1], imag(Ss[dSiimaxi])*baseMVA, imag(SsL[dSiimaxi])*baseMVA))
## plot converter setpoint adaptation
if convplotopt != 0 :
convlimplot(plotarg[dSiimaxi,:], i2edc[dSiimaxi])
## update converter powers
Ss[dSiimaxi] = SsL[dSiimaxi]
Pvsc[dSiimaxi] = real(Ss[dSiimaxi])
Qvsc[dSiimaxi] = imag(Ss[dSiimaxi])
busVSC[dSiimaxi,PD] = bus[dSiimaxi,PD] - \
Pvsc[dSiimaxi]*baseMVA ## converter P injection from input files included as load
busVSC[dSiimaxi,QD] = bus[dSiimaxi,QD] - \
Qvsc[dSiimaxi]*baseMVA ## only Q from input files is included, not for V control
else:
dSiimaxi = []
## Remove voltage control on violated converter
if convdc[dSiimaxi, CONVTYPE_AC]==PVC:
convdc[dSiimaxi, CONVTYPE_AC] = PQC
stdout.write(' Voltage control at converter bus %d removed.\n'% i2edc[dSiimaxi+1])
busVSC[dSiimaxi, BUS_TYPE] = PQ
## Remove dummy generator (PV bus changed to PQ bus)
if dSiimaxi in gdmbus:
dSidx = where(gdmbus == dSiimaxi)[0]
dSgenidx = gendmidx[dSidx]
gendm = delete(gendm,dSidx)
genVSC = delete(genVSC,dSgenidx)
gdmbus = delete(gdmbus,dSidx)
gendmidx = where(genVSC[:,GEN_BUS] == np.setdiff1d(genVSC[:, GEN_BUS],gen[:, GEN_BUS])) ## index of dummy generators in genVSC matrix
## Remove VSC voltage control at genPQ bus
if dSiimaxi in genPQ:
dSidx = where(genPQ == dSiimaxi)[0]
genPQ = delete(genPQ,dSidx)
genPQi = delete(genPQi,dSidx)
## Remove droop control on violated converter
if convdc[dSiimaxi, CONVTYPE_DC]==DCDROOP:
convdc[dSiimaxi, CONVTYPE_DC] = DCNOSLACK
droopdc = setdiff1d(droopdc,dSiimaxi) ## remove converter from droop converters
slackdroopdc = setdiff1d(slackdroopdc,dSiimaxi) ## remove converter from slack/droop converters (additional loss iteration)
stdout.write(' Droop control at converter bus %d disabled.\n'%i2edc[dSiimaxi+1])
## recalculate converter quantities after limit check
Itf = conj(Ss/Vs) ## transformer current
Vf = Vs + Itf*Ztf ## filter side voltage
Ssf = Vf*conj(Itf) ## filter side transformer complex power
Qf = -Bf*abs(Vf)**2 ## filter reactive power
Scf = Ssf + j*Qf ## filter side converter complex power
Ic = conj(Scf/Vf) ## converter current
Vc = Vf + Ic*Zc ## converter side voltage
Sc = Vc*conj(Ic) ## converter side complex power
## converter active and reactive powers after limit check
Ps = real(Ss)
Qs = imag(Ss)
Pc = real(Sc)
Qc = imag(Sc)
Pcf = real(Scf)
Qcf = imag(Scf)
Psf = real(Ssf)
Qsf = imag(Ssf)
## converter losses and dc side power
Ploss = calclossac(Pc, Qc, Vc, lossa, lossb, losscr, lossci)
Pdc[cdci] = Pc[cdci] + Ploss[cdci]
##----- dc networks power flow -----
## calculate dc networks
Vdc, Pdc = dcnetworkpf(Ybusdc, Vdc, Pdc,slackdc, noslackbdc,\
droopdc, PVdroop, Pdcset, Vdcset, dVdcset, pol, toldc, itmaxdc)
## calculate dc line powers
Ifdc = Yfdc*Vdc ## current through dc lines
Vdcf = Vdc[[where(busdc[:,BUSDC_I]==x)[0][0] for x in \
branchdc[:,F_BUSDC]]]
Vdct = Vdc[[where(busdc[:,BUSDC_I]==x)[0][0] for x in \
branchdc[:,T_BUSDC]]]
Pfdc = pol*Vdcf*Ifdc ## power at the "from" bus
Ptdc = pol*Vdct*(-Ifdc) ## power at the "to" bus
##----- slack/droop bus voltage and converter loss -----
## Initialisation
Pc[slackdroopdc] = Pdc[slackdroopdc] - Ploss[slackdroopdc] ## Pc initialisation
itslack = 0
convergedslackdroop = 0
## dc slack bus loss calculation
while not convergedslackdroop and itslack<=itmaxslackdroop:
## update iteration counter and convergence variable
itslack += 1
Pcprev = Pc.copy()
## update slack bus powers Ps, Qc and voltage Vc
Ps[slackdroopdc], Qc[slackdroopdc], Vc[slackdroopdc] = calcslackdroop(
Pc[slackdroopdc], Qs[slackdroopdc], Vs[slackdroopdc], \
Vf[slackdroopdc], Vc[slackdroopdc], Ztf[slackdroopdc], \
Bf[slackdroopdc], Zc[slackdroopdc], \
tolslackdroopint, itmaxslackdroopint)
## update slack bus losses
Ploss[slackdroopdc] = calclossac(Pc[slackdroopdc], Qc[slackdroopdc], \
Vc[slackdroopdc], lossa[slackdroopdc], lossb[slackdroopdc], \
losscr[slackdroopdc], lossci[slackdroopdc])
## update slack bus converter side power Pc
Pc[slackdroopdc] = Pdc[slackdroopdc] - Ploss[slackdroopdc]
## slack bus tolerance check
if max(abs(Pcprev[slackdroopdc] - Pc[slackdroopdc])) < tolslackdroop:
convergedslackdroop = 1
if not convergedslackdroop:
stdout.write('\nSlackbus/Droop converter loss calculation of grid did NOT converge in %d iterations\n'% itslack)
## extended bus matrix update
busVSC[cdci,PD] = bus[cdci,PD] - Ps[cdci]*baseMVA
## convergence check
if abs(Ps_old - Ps).max() < tolacdc:
converged = 1
## end of iteration
timecalc = time() - t0
##----- Post processing -----
## convergence
if converged:
if output:
stdout.write('\nSequential solution method converged in %d iterations\n'%it)
else:
stdout.write('\nSequential solution method did NOT converge after %d iterations\n'%it)
## converter limit check
if limac == 1:
for ii in arange(cdci.size):
cvii = cdci[ii]
limviol, _, plotarg = convlim(Ss[cvii], Vs[cvii], Vc[cvii], Ztf[cvii], \
Bf[cvii], Zc[cvii], Icmax[cvii], Vcmax[cvii], Vcmin[cvii], i2edc[cvii+1], tollim, 1)
if limviol != 0: ## limits are hit
if (convdc[cvii,CONVTYPE_DC] == DCSLACK):
stdout.write('\n Slackbus converter %d is operating outside its limits.\n'%i2edc[cvii+1])
elif (convdc[cvii,CONVTYPE_DC] == DCNOSLACK):
stdout.write('\n Converter %d is operating outside its limits.\n'%i2edc[cvii+1])
if convplotopt == 2 :
convlimplot(plotarg, i2edc[cvii])
## update bus matrix
bus[:,VM] = busVSC[:,VM]
bus[:,VA] = busVSC[:,VA]
## dummy generators removal
gen = genVSC[arange(gen.shape[0]),:]
## update busdc matrix
busdc[:,PDC] = Pdc*baseMVA
busdc[:,VDC] = Vdc
## update convdc matrix
convdc[:,PCONV] = Ps*baseMVA
convdc[:,QCONV] = Qs*baseMVA
# new addition to convdc matrix
convdc = c_[convdc,abs(Vc)]
convdc = c_[convdc,angle(Vc)*180/pi]
convdc = c_[convdc,Pc*baseMVA]
convdc = c_[convdc,Qc*baseMVA]
convdc = c_[convdc,Ploss*baseMVA]
convdc = c_[convdc,abs(Vf)]
convdc = c_[convdc,angle(Vf)*180/pi]
convdc = c_[convdc,Psf*baseMVA]
convdc = c_[convdc,Qsf*baseMVA]
convdc = c_[convdc,Qcf*baseMVA]
## new addition to branchdc matrix
branchdc = c_[branchdc,Pfdc*baseMVA]
branchdc = c_[branchdc,Ptdc*baseMVA]
#----- internal to external bus renumbering -----
# remove dummy converters
convdc = convdc[cdci,:]
## convert to external indexing
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"] = \
baseMVA, bus, gen, branch
pdc["baseMVAac"], pdc["baseMVAdc"], pdc["pol"], \
pdc["busdc"], pdc["convdc"], pdc["branchdc"] = \
baseMVAac, baseMVAdc, pol, busdc, convdc, branchdc
## Per unit internal to external data conversion
pdc =int2extpu(ppc['baseMVA'],pdc);
## Undo the matrices sorting based on the bus numbers
ppc['bus'] = ppc['bus'][i2ebus.argsort(),:]
ppc['gen'] = ppc['gen'][i2egen.argsort(),:]
ppc['branch'] = ppc['branch'][i2ebrch.argsort(),:]
pdc['busdc'] = pdc['busdc'][i2ebusdc.argsort(),:]
pdc['convdc'] = pdc['convdc'][i2econvdc.argsort(),:]
pdc['branchdc'] = pdc['branchdc'][i2ebrchdc.argsort(),:]
## ac network internal to external bus numbering
pdc, ppc = int2extac(i2eac, acdmbus, pdc, ppc)
## dc network internal to external bus numbering
pdc = int2extdc(i2edcpmt, i2edc, pdc)
## generator outage inclusion
gen1 = ppc['gen'] ## operational generators
gen0[:,[PG, QG]] = 0 ## reset generator power injection
ppc['gen'] = zeros((gon.shape[0]+goff.shape[0], gen1.shape[1]));
ppc['gen'][gon,:] = gen1 ## include operational generators
ppc['gen'][goff,:] = gen0 ## include non-operational generators
## converter with outages inclusion
conv1 = pdc['convdc']
conv0 = c_[conv0, zeros((conv0.shape[0],conv1.shape[1] - conv0.shape[1]))]
pdc['convdc'][conv0i, :] = conv0
pdc['convdc'][conv1i, :] = conv1
if conv0busi.shape[0]>0:
pdc['busdc'][conv0busi[:,0], BUSAC_I] = conv0busi[:,1]
## dc branch outages inclusion
brchdc1 = pdc['branchdc']
brchdc0 = c_[brchdc0, zeros((brchdc0.shape[0], brchdc1.shape[1] - brchdc0.shape[1]))]
pdc['branchdc'][brchdc0i,:] = brchdc0
pdc['branchdc'][brchdc1i,:] = brchdc1
## ac branch outages inclusion
if ppc['branch'].shape[0] == 0: ## all infinite buses
# python start the index at 0
brch0 = c_[brch0, zeros((brch0.shape[0], QT + 1 - brch0.shape[1]))] # not necessary anymore after rewriting the code
ppc['branch'] = brch0;
else:
brch1 = ppc['branch']
brch0 = c_[brch0, zeros((brch0.shape[0], brch1.shape[1] - brch0.shape[1]))];
ppc['branch'][brch0i,:] = brch0
ppc['branch'][brch1i,:] = brch1
##----- output results -----
## print results
if output:
printpf(ppc['baseMVA'], ppc['bus'], ppc['gen'], ppc['branch'],None,converged,timecalc)
printdcpf(pdc['busdc'], pdc['convdc'], pdc['branchdc'])
##----- output results -----
# as dict
resultsac = {}
resultsac['baseMVA'] = baseMVA
resultsac['bus'] = bus
resultsac['gen'] = gen
resultsac['branch'] = branch
resultsdc = {}
resultsdc['baseMVAac'] = baseMVAac
resultsdc['baseMVAdc'] = baseMVAdc
resultsdc['pol'] = pol
resultsdc['busdc'] = busdc
resultsdc['convdc'] = convdc
resultsdc['branchdc'] = branchdc
# if nargout == 2 || nargout == 3 || nargout == 4
# baseMVA = resultsac;
# bus = resultsdc;
# gen = converged;
# branch = timecalc;
# end
input()
return resultsac, resultsdc, converged, timecalc
def t_dcline(quiet=False):
"""Tests for DC line extension in L{{toggle_dcline}.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
num_tests = 50
t_begin(num_tests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_dcline')
if quiet:
verbose = False
else:
verbose = False
t0 = ''
ppopt = ppoption(OPF_VIOLATION=1e-6, PDIPM_GRADTOL=1e-8,
PDIPM_COMPTOL=1e-8, PDIPM_COSTTOL=1e-9)
ppopt = ppoption(ppopt, OPF_ALG=560, OPF_ALG_DC=200)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=verbose)
## set up indices
ib_data = r_[arange(BUS_AREA + 1), arange(BASE_KV, VMIN + 1)]
ib_voltage = arange(VM, VA + 1)
ib_lam = arange(LAM_P, LAM_Q + 1)
ib_mu = arange(MU_VMAX, MU_VMIN + 1)
ig_data = r_[[GEN_BUS, QMAX, QMIN], arange(MBASE, APF + 1)]
ig_disp = array([PG, QG, VG])
ig_mu = arange(MU_PMAX, MU_QMIN + 1)
ibr_data = arange(ANGMAX + 1)
ibr_flow = arange(PF, QT + 1)
ibr_mu = array([MU_SF, MU_ST])
ibr_angmu = array([MU_ANGMIN, MU_ANGMAX])
## load case
ppc0 = loadcase(casefile)
del ppc0['dclinecost']
ppc = ppc0
ppc = toggle_dcline(ppc, 'on')
ppc = toggle_dcline(ppc, 'off')
ndc = ppc['dcline'].shape[0]
## run AC OPF w/o DC lines
t = ''.join([t0, 'AC OPF (no DC lines) : '])
r0 = runopf(ppc0, ppopt)
success = r0['success']
t_ok(success, [t, 'success'])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['f'], r0['f'], 8, [t, 'f'])
t_is( r['bus'][:,ib_data ], r0['bus'][:,ib_data ], 10, [t, 'bus data'])
t_is( r['bus'][:,ib_voltage], r0['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( r['bus'][:,ib_lam ], r0['bus'][:,ib_lam ], 3, [t, 'bus lambda'])
t_is( r['bus'][:,ib_mu ], r0['bus'][:,ib_mu ], 2, [t, 'bus mu'])
t_is( r['gen'][:,ig_data ], r0['gen'][:,ig_data ], 10, [t, 'gen data'])
t_is( r['gen'][:,ig_disp ], r0['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is( r['gen'][:,ig_mu ], r0['gen'][:,ig_mu ], 3, [t, 'gen mu'])
t_is(r['branch'][:,ibr_data ], r0['branch'][:,ibr_data ], 10, [t, 'branch data'])
t_is(r['branch'][:,ibr_flow ], r0['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
t_is(r['branch'][:,ibr_mu ], r0['branch'][:,ibr_mu ], 2, [t, 'branch mu'])
t = ''.join([t0, 'AC PF (no DC lines) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## run with DC lines
t = ''.join([t0, 'AC OPF (with DC lines) : '])
ppc = toggle_dcline(ppc, 'on')
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected = array([
[10, 8.9, -10, 10, 1.0674, 1.0935],
[2.2776, 2.2776, 0, 0, 1.0818, 1.0665],
[0, 0, 0, 0, 1.0000, 1.0000],
[10, 9.5, 0.0563, -10, 1.0778, 1.0665]
])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected, 4, [t, 'P Q V'])
expected = array([
[0, 0.8490, 0.6165, 0, 0, 0.2938],
[0, 0, 0, 0.4290, 0.0739, 0],
[0, 0, 0, 0, 0, 0],
[0, 7.2209, 0, 0, 0.0739, 0]
])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected, 3, [t, 'mu'])
t = ''.join([t0, 'AC PF (with DC lines) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1 = toggle_dcline(ppc1, 'on')
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
#t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is( rp['gen'][:2,ig_disp ], r['gen'][:2,ig_disp ], 3, [t, 'gen dispatch'])
t_is( rp['gen'][2,PG ], r['gen'][2,PG ], 3, [t, 'gen dispatch'])
t_is( rp['gen'][2,QG]+rp['dcline'][0,c.QF], r['gen'][2,QG]+r['dcline'][0,c.QF], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## add appropriate P and Q injections and check angles and generation when running PF
t = ''.join([t0, 'AC PF (with equivalent injections) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1['bus'][:, VM] = 1
ppc1['bus'][:, VA] = 0
for k in range(ndc):
if ppc1['dcline'][k, c.BR_STATUS]:
ff = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.F_BUS])
tt = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.T_BUS])
ppc1['bus'][ff, PD] = ppc1['bus'][ff, PD] + r['dcline'][k, c.PF]
ppc1['bus'][ff, QD] = ppc1['bus'][ff, QD] - r['dcline'][k, c.QF]
ppc1['bus'][tt, PD] = ppc1['bus'][tt, PD] - r['dcline'][k, c.PT]
ppc1['bus'][tt, QD] = ppc1['bus'][tt, QD] - r['dcline'][k, c.QT]
ppc1['bus'][ff, VM] = r['dcline'][k, c.VF]
ppc1['bus'][tt, VM] = r['dcline'][k, c.VT]
ppc1['bus'][ff, BUS_TYPE] = PV
ppc1['bus'][tt, BUS_TYPE] = PV
rp = runpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## test DC OPF
t = ''.join([t0, 'DC OPF (with DC lines) : '])
ppc = ppc0.copy()
ppc['gen'][0, PMIN] = 10
ppc['branch'][4, RATE_A] = 100
ppc = toggle_dcline(ppc, 'on')
r = rundcopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected = array([
[10, 8.9, 0, 0, 1.01, 1],
[2, 2, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 1],
[10, 9.5, 0, 0, 1, 0.98]
])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected, 4, [t, 'P Q V'])
expected = array([
[0, 1.8602, 0, 0, 0, 0],
[1.8507, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0.2681, 0, 0, 0, 0]
])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected, 3, [t, 'mu'])
t = ''.join([t0, 'DC PF (with DC lines) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1 = toggle_dcline(ppc1, 'on')
ppc1['bus'][:, VA] = 0
rp = rundcpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## add appropriate P injections and check angles and generation when running PF
t = ''.join([t0, 'DC PF (with equivalent injections) : '])
ppc1 = {'baseMVA': r['baseMVA'],
'bus': r['bus'][:, :VMIN + 1].copy(),
'gen': r['gen'][:, :APF + 1].copy(),
'branch': r['branch'][:, :ANGMAX + 1].copy(),
'gencost': r['gencost'].copy(),
'dcline': r['dcline'][:, :c.LOSS1 + 1].copy()}
ppc1['bus'][:, VA] = 0
for k in range(ndc):
if ppc1['dcline'][k, c.BR_STATUS]:
ff = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.F_BUS])
tt = find(ppc1['bus'][:, BUS_I] == ppc1['dcline'][k, c.T_BUS])
ppc1['bus'][ff, PD] = ppc1['bus'][ff, PD] + r['dcline'][k, c.PF]
ppc1['bus'][tt, PD] = ppc1['bus'][tt, PD] - r['dcline'][k, c.PT]
ppc1['bus'][ff, BUS_TYPE] = PV
ppc1['bus'][tt, BUS_TYPE] = PV
rp = rundcpf(ppc1, ppopt)
success = rp['success']
t_ok(success, [t, 'success'])
t_is( rp['bus'][:,ib_voltage], r['bus'][:,ib_voltage], 3, [t, 'bus voltage'])
t_is( rp['gen'][:,ig_disp ], r['gen'][:,ig_disp ], 3, [t, 'gen dispatch'])
t_is(rp['branch'][:,ibr_flow ], r['branch'][:,ibr_flow ], 3, [t, 'branch flow'])
## run with DC lines
t = ''.join([t0, 'AC OPF (with DC lines + poly cost) : '])
ppc = loadcase(casefile)
ppc = toggle_dcline(ppc, 'on')
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
expected1 = array([
[10, 8.9, -10, 10, 1.0663, 1.0936],
[7.8429, 7.8429, 0, 0, 1.0809, 1.0667],
[0, 0, 0, 0, 1.0000, 1.0000],
[6.0549, 5.7522, -0.5897, -10, 1.0778, 1.0667]
])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
expected2 = array([
[0, 0.7605, 0.6226, 0, 0, 0.2980],
[0, 0, 0, 0.4275, 0.0792, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0.0792, 0]
])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
ppc['dclinecost'][3, :8] = array([2, 0, 0, 4, 0, 0, 7.3, 0])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
t = ''.join([t0, 'AC OPF (with DC lines + pwl cost) : '])
ppc['dclinecost'][3, :8] = array([1, 0, 0, 2, 0, 0, 10, 73])
r = runopf(ppc, ppopt)
success = r['success']
t_ok(success, [t, 'success'])
t_is(r['dcline'][:, c.PF:c.VT + 1], expected1, 4, [t, 'P Q V'])
t_is(r['dcline'][:, c.MU_PMIN:c.MU_QMAXT + 1], expected2, 3, [t, 'mu'])
t_end()
def t_pf(quiet=False):
"""Tests for power flow solvers.
@author: Ray Zimmerman (PSERC Cornell)
"""
t_begin(33, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_pf')
verbose = not quiet
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
## get solved AC power flow case from MAT-file
## defines bus_soln, gen_soln, branch_soln
soln9_pf = loadmat(join(tdir, 'soln9_pf.mat'), struct_as_record=False)
bus_soln = soln9_pf['bus_soln']
gen_soln = soln9_pf['gen_soln']
branch_soln = soln9_pf['branch_soln']
## run Newton PF
t = 'Newton PF : '
ppopt = ppoption(ppopt, PF_ALG=1)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run fast-decoupled PF (XB version)
t = 'Fast Decoupled (XB) PF : '
ppopt = ppoption(ppopt, PF_ALG=2)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run fast-decoupled PF (BX version)
t = 'Fast Decoupled (BX) PF : '
ppopt = ppoption(ppopt, PF_ALG=3)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run Gauss-Seidel PF
t = 'Gauss-Seidel PF : '
ppopt = ppoption(ppopt, PF_ALG=4)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 5, [t, 'bus'])
t_is(gen, gen_soln, 5, [t, 'gen'])
t_is(branch, branch_soln, 5, [t, 'branch'])
## get solved AC power flow case from MAT-file
## defines bus_soln, gen_soln, branch_soln
soln9_dcpf = loadmat(join(tdir, 'soln9_dcpf.mat'), struct_as_record=False)
bus_soln = soln9_dcpf['bus_soln']
gen_soln = soln9_dcpf['gen_soln']
branch_soln = soln9_dcpf['branch_soln']
## run DC PF
t = 'DC PF : '
results, success = rundcpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## check Qg distribution, when Qmin = Qmax
t = 'check Qg : '
ppopt = ppoption(ppopt, PF_ALG=1, VERBOSE=0)
ppc = loadcase(casefile)
ppc['gen'][0, [QMIN, QMAX]] = [20, 20]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0, QG], 24.07, 2, [t, 'single gen, Qmin = Qmax'])
ppc['gen'] = r_[array([ppc['gen'][0, :]]), ppc['gen']]
ppc['gen'][0, [QMIN, QMAX]] = [10, 10]
ppc['gen'][1, [QMIN, QMAX]] = [0, 50]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [10, 14.07], 2, [t, '2 gens, Qmin = Qmax for one'])
ppc['gen'][0, [QMIN, QMAX]] = [10, 10]
ppc['gen'][1, [QMIN, QMAX]] = [-50, -50]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [12.03, 12.03], 2, [t, '2 gens, Qmin = Qmax for both'])
ppc['gen'][0, [QMIN, QMAX]] = [0, 50]
ppc['gen'][1, [QMIN, QMAX]] = [0, 100]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [8.02, 16.05], 2, [t, '2 gens, proportional'])
ppc['gen'][0, [QMIN, QMAX]] = [-50, 0]
ppc['gen'][1, [QMIN, QMAX]] = [50, 150]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [-50 + 8.02, 50 + 16.05], 2,
[t, '2 gens, proportional'])
## network with islands
t = 'network w/islands : DC PF : '
ppc0 = loadcase(casefile)
ppc0['gen'][0, PG] = 60
ppc0['gen'][0, [PMIN, PMAX, QMIN, QMAX, PG, QG]] = \
ppc0['gen'][0, [PMIN, PMAX, QMIN, QMAX, PG, QG]] / 2
ppc0['gen'] = r_[array([ppc0['gen'][0, :]]), ppc0['gen']]
ppc1 = ppc0.copy()
ppc = ppc0.copy()
nb = ppc['bus'].shape[0]
ppc1['bus'][:, BUS_I] = ppc1['bus'][:, BUS_I] + nb
ppc1['branch'][:, F_BUS] = ppc1['branch'][:, F_BUS] + nb
ppc1['branch'][:, T_BUS] = ppc1['branch'][:, T_BUS] + nb
ppc1['gen'][:, GEN_BUS] = ppc1['gen'][:, GEN_BUS] + nb
ppc['bus'] = r_[ppc['bus'], ppc1['bus']]
ppc['branch'] = r_[ppc['branch'], ppc1['branch']]
ppc['gen'] = r_[ppc['gen'], ppc1['gen']]
#ppopt = ppoption(ppopt, OUT_BUS=1, OUT_GEN=1, OUT_ALL=-1, VERBOSE=2)
ppopt = ppoption(ppopt, VERBOSE=verbose)
r = rundcpf(ppc, ppopt)
t_is(r['bus'][:9, VA], bus_soln[:, VA], 8, [t, 'voltage angles 1'])
t_is(r['bus'][10:18, VA], bus_soln[:, VA], 8, [t, 'voltage angles 2'])
Pg = r_[gen_soln[0, PG] - 30, 30, gen_soln[1:3, PG]]
t_is(r['gen'][:4, PG], Pg, 8, [t, 'active power generation 1'])
t_is(r['gen'][4:8, PG], Pg, 8, [t, 'active power generation 1'])
t = 'network w/islands : AC PF : '
## get solved AC power flow case from MAT-file
soln9_pf = loadmat(join(tdir, 'soln9_pf.mat'), struct_as_record=False)
bus_soln = soln9_pf['bus_soln']
gen_soln = soln9_pf['gen_soln']
branch_soln = soln9_pf['branch_soln']
r = runpf(ppc, ppopt)
t_is(r['bus'][:9, VA], bus_soln[:, VA], 8, [t, 'voltage angles 1'])
t_is(r['bus'][9:18, VA], bus_soln[:, VA], 8, [t, 'voltage angles 2'])
Pg = r_[gen_soln[0, PG] - 30, 30, gen_soln[1:3, PG]]
t_is(r['gen'][:4, PG], Pg, 8, [t, 'active power generation 1'])
t_is(r['gen'][4:8, PG], Pg, 8, [t, 'active power generation 1'])
t_end()
def main(train_case, storing_dir):
# Define the parameters of the training.
# More informations about the distributions used during training can be found
# in the notebook Sampling random power networks.
train_params = {
'num_samples': 1000,
'learning_rate': 3e-3,
}
# Define the parameters of the model.
model_params = {
'num_updates': 30, #10,
'latent_dim': 10,
'latent_layers': 2 #3
}
noise_params = {
'is_noise_active': 1.,
'r_min_coeff': -0.1,
'r_max_coeff': +0.1,
'x_min_coeff': -0.1,
'x_max_coeff': +0.1,
'b_min_coeff': -0.1,
'b_max_coeff': +0.1,
'ratio_min_coeff': -0.2,
'ratio_max_coeff': +0.2,
'angle_min_offset': -0.2,
'angle_max_offset': +0.2,
'Vg_min': 0.95,
'Vg_max': 1.05,
'Pd_min_coeff': -0.5,
'Pd_max_coeff': +0.5,
'Qd_min_coeff': -0.5,
'Qd_max_coeff': +0.5,
'Gs_min_coeff': -0.,
'Gs_max_coeff': +0.,
'Bs_min_coeff': -0.,
'Bs_max_coeff': +0.,
}
TEST_SIZE = 100
LEARN_ITER = 10
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
case_list = ['case9', 'case14', 'case30', 'case118']
error_NR_list = {}
error_DC_list = {}
error_GNS_list = {}
time_NR_list = {}
time_DC_list = {}
time_GNS_list = {}
timestr = time.strftime("%Y-%m-%d-%Hh%Mm%Ss")
expe_dir = 'expe_' + train_case + '_' + timestr
try:
os.stat(os.path.join(path, expe_dir))
except:
os.mkdir(os.path.join(path, expe_dir))
print("Building model that will be trained on " + train_case)
tf.reset_default_graph()
sess = tf.Session(config=config)
expe_path = os.path.join(path, expe_dir)
with tf.variable_scope('GNS', reuse=tf.AUTO_REUSE):
model = GNS(train_case, train_params, model_params, noise_params,
expe_path, sess)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print(" Learning on " + train_case + "...")
for learn_iter in tqdm.tqdm(range(LEARN_ITER)):
# Apply a gradient descent
sess.run(model.opt_op)
# Store the loss
model.store_summary(learn_iter)
print(" The model trained on " + train_case + " is ready to be tested!")
all_p_nr = {}
all_p_dc = {}
all_p_gns = {}
time_NR_list = {}
time_DC_list = {}
time_GNS_list = {}
for test_case in case_list:
print(" Testing on " + test_case + "...")
model.change_default_power_grid(case=test_case)
# Sample
all_p_nr[test_case] = None
all_p_dc[test_case] = None
all_p_gns[test_case] = None
time_NR_list[test_case] = []
time_DC_list[test_case] = []
time_GNS_list[test_case] = []
i = 0
while i < TEST_SIZE:
print(i)
# Sample a power grid
gens, buses, lines, p_gen, pft, ptf, qft, qtf = sess.run([
model.gens,
model.buses,
model.lines,
model.p_gen,
model.p_from_to[str(model_params['num_updates'])],
model.p_to_from[str(model_params['num_updates'])],
model.q_from_to[str(model_params['num_updates'])],
model.q_to_from[str(model_params['num_updates'])],
])
sample_id = 0
model.input_data['gen'][:, 1] = p_gen[str(
model_params['num_updates'])][sample_id] * 100
model.input_data['gen'][:, 5] = gens['Vg'][sample_id]
model.input_data['bus'][:, 2] = buses['Pd'][sample_id]
model.input_data['bus'][:, 3] = buses['Qd'][sample_id]
model.input_data['bus'][:, 4] = buses['Gs'][sample_id]
model.input_data['bus'][:, 5] = buses['Bs'][sample_id]
model.input_data['branch'][:, 2] = lines['r'][sample_id]
model.input_data['branch'][:, 3] = lines['x'][sample_id]
model.input_data['branch'][:, 4] = lines['b'][sample_id]
model.input_data['branch'][:, 8] = lines['ratio'][sample_id]
model.input_data[
'branch'][:, 9] = lines['angle'][sample_id] * 180 / np.pi
i += 1
try:
start = time.time()
a_nr = runpf(model.input_data)
time_NR_list[test_case].append(time.time() - start)
except:
print('did not converge')
i -= 1
continue
if a_nr[0]['success'] == 0:
print('did not converge')
i -= 1
continue
# Now try with DC approx
start = time.time()
a_dc = rundcpf(model.input_data)
time_DC_list[test_case].append(time.time() - start)
# Get flows from GNS
p_from_gns = pft[sample_id] * 100
p_to_gns = ptf[sample_id] * 100
q_from_gns = qft[sample_id] * 100
q_to_gns = qtf[sample_id] * 100
if all_p_gns[test_case] is None:
all_p_gns[test_case] = np.r_[p_from_gns, p_to_gns]
else:
all_p_gns[test_case] = np.c_[all_p_gns[test_case],
np.r_[p_from_gns, p_to_gns]]
# Get flows from Newton-Raphson
p_from_nr = a_nr[0]['branch'][:, 13]
q_from_nr = a_nr[0]['branch'][:, 14]
p_to_nr = a_nr[0]['branch'][:, 15]
q_to_nr = a_nr[0]['branch'][:, 16]
if all_p_nr[test_case] is None:
all_p_nr[test_case] = np.r_[p_from_nr, p_to_nr]
else:
all_p_nr[test_case] = np.c_[all_p_nr[test_case],
np.r_[p_from_nr, p_to_nr]]
# Get flows from DC approx
p_from_dc = a_dc[0]['branch'][:, 13]
q_from_dc = a_dc[0]['branch'][:, 14]
p_to_dc = a_dc[0]['branch'][:, 15]
q_to_dc = a_dc[0]['branch'][:, 16]
if all_p_dc[test_case] is None:
all_p_dc[test_case] = np.r_[p_from_dc, p_to_dc]
else:
all_p_dc[test_case] = np.c_[all_p_dc[test_case],
np.r_[p_from_dc, p_to_dc]]
# Now try with GNS
v_time, theta_time = sess.run([model.v, model.theta])
start = time.time()
v_time, theta_time = sess.run([model.v, model.theta])
time_GNS_list[test_case].append(
(time.time() - start) / train_params['num_samples'])
np.save(os.path.join(storing_dir, expe_dir + "_all_p_nr.npy"), all_p_nr)
np.save(os.path.join(storing_dir, expe_dir + "_all_p_gns.npy"), all_p_gns)
np.save(os.path.join(storing_dir, expe_dir + "_all_p_dc.npy"), all_p_dc)
np.save(os.path.join(storing_dir, expe_dir + "_time_nr.npy"), time_NR_list)
np.save(os.path.join(storing_dir, expe_dir + "_time_gns.npy"),
time_GNS_list)
np.save(os.path.join(storing_dir, expe_dir + "_time_dc.npy"), time_DC_list)
def run_pf(self, power_factor, power):
'''Run the power flow. This is repeated for each power bin contained in
the power factor data structure.
Args:
power_factor (list) [-]: List of tuples, val1 = array power output
in put, val2 = power factor.
power (float) [W]: Rated power of oec.
Attributes:
ppopt (dict) [-]: PyPower options.
ppc (dict) [-]: PyPower case definition. This contains all newtork
information.
result (dict) [-]: PyPower results.
all_success (list) [int]: Binary flag indicating if power solved,
1 = successful solution, 0 = unsuccessful solution.
all_active_powers (list) [MW]: Active power delivered onshore.
all_reactive_powers (list) [MVAr]: Reactive power delivered
onshore.
all_busbar_voltage (list) [pu]: Busbar voltage magnitudes.
all_busbar_angle (list) [deg]: Busbar voltage angles.
Returns:
Note:
The power flow is repeated for each defined power level. The length
of the lists above should equal this length.
'''
ppopt = ppoption.ppoption(VERBOSE=0, OUT_ALL=0)
ppc = {"version": '2'}
ppc["baseMVA"] = 100.0
ppc["bus"] = self.bus_data
ppc["branch"] = self.branch_data
all_active_powers = []
all_reactive_powers = []
all_success = []
all_busbar_voltage = []
all_busbar_angle = []
gen_active_powers = []
gen_reactive_powers = []
# for each generator output
for output_power in power_factor:
gen_power = output_power[0] * power/1000000 # power in MW
gen_reactive_power = gen_power * np.tan(np.arccos(output_power[1]))
for gen in range(1, self.n_devices+1):
self.gen_data[gen][1] = gen_power
self.gen_data[gen][2] = gen_reactive_power
ppc["gen"] = self.gen_data
result, success = runpf.runpf(ppc, ppopt)
all_active_powers.append(result['gen'][0][1])
all_reactive_powers.append(result['gen'][0][2])
all_success.append(success)
all_busbar_voltage.append(result['bus'][:,7])
all_busbar_angle.append(result['bus'][:,8])
gen_active_powers.append(result['gen'][:,1][1:])
gen_reactive_powers.append(result['gen'][:,2][1:])
self.flag = all_success
self.onshore_active_power = all_active_powers
self.onshore_reactive_power = all_reactive_powers
self.busbar_voltages = all_busbar_voltage
self.busbar_angles = all_busbar_angle
self.gen_active_powers = gen_active_powers
self.gen_reactive_powers = gen_reactive_powers
self.all_results = result
return result
def validate_from_ppc(
ppc_net,
net,
pf_type="runpp",
max_diff_values={
"bus_vm_pu": 1e-6,
"bus_va_degree": 1e-5,
"branch_p_mw": 1e-6,
"branch_q_mvar": 1e-6,
"gen_p_mw": 1e-6,
"gen_q_mvar": 1e-6
},
run=True):
"""
This function validates the pypower case files to pandapower net structure conversion via a \
comparison of loadflow calculation results. (Hence the opf cost conversion is not validated.)
INPUT:
**ppc_net** - The pypower case file, which must already contain the pypower powerflow
results or pypower must be importable.
**net** - The pandapower network.
OPTIONAL:
**pf_type** ("runpp", string) - Type of validated power flow. Possible are ("runpp",
"rundcpp", "runopp", "rundcopp")
**max_diff_values** - Dict of maximal allowed difference values. The keys must be
'vm_pu', 'va_degree', 'p_branch_mw', 'q_branch_mvar', 'p_gen_mw' and 'q_gen_mvar' and
the values floats.
**run** (True, bool or list of two bools) - changing the value to False avoids trying to run
(optimal) loadflows. Giving a list of two bools addresses first pypower and second
pandapower.
OUTPUT:
**conversion_success** - conversion_success is returned as False if pypower or pandapower
cannot calculate a powerflow or if the maximum difference values (max_diff_values )
cannot be hold.
EXAMPLE:
import pandapower.converter as pc
net = cv.from_ppc(ppc_net, f_hz=50)
conversion_success = cv.validate_from_ppc(ppc_net, net)
NOTE:
The user has to take care that the loadflow results already are included in the provided \
ppc_net or pypower is importable.
"""
# check in case of optimal powerflow comparison whether cost information exist
if "opp" in pf_type:
if not (len(net.polynomial_cost) | len(net.piecewise_linear_cost)):
if "gencost" in ppc_net:
if not len(ppc_net["gencost"]):
logger.debug(
'ppc and pandapower net do not include cost information.'
)
return True
else:
logger.error(
'The pandapower net does not include cost information.'
)
return False
else:
logger.debug(
'ppc and pandapower net do not include cost information.')
return True
# guarantee run parameter as list, for pypower and pandapower (optimal) powerflow run
run = [run, run] if isinstance(run, bool) else run
# --- check pypower powerflow success, if possible
if pypower_import and run[0]:
try:
if pf_type == "runpp":
ppc_net = runpf.runpf(ppc_net, ppopt)[0]
elif pf_type == "rundcpp":
ppc_net = rundcpf.rundcpf(ppc_net, ppopt)[0]
elif pf_type == "runopp":
ppc_net = runopf.runopf(ppc_net, ppopt)
elif pf_type == "rundcopp":
ppc_net = rundcopf.rundcopf(ppc_net, ppopt)
else:
raise ValueError("The pf_type %s is unknown" % pf_type)
except:
logger.debug("The pypower run did not work.")
ppc_success = True
if 'success' in ppc_net.keys():
if ppc_net['success'] != 1:
ppc_success = False
logger.error(
"The given ppc data indicates an unsuccessful pypower powerflow: "
+ "'ppc_net['success'] != 1'")
if (ppc_net['branch'].shape[1] < 17):
ppc_success = False
logger.error(
"The shape of given ppc data indicates missing pypower powerflow results."
)
# --- try to run a pandapower powerflow
if run[1]:
if pf_type == "runpp":
try:
pp.runpp(net,
init="dc",
calculate_voltage_angles=True,
trafo_model="pi")
except pp.LoadflowNotConverged:
try:
pp.runpp(net,
calculate_voltage_angles=True,
init="flat",
trafo_model="pi")
except pp.LoadflowNotConverged:
try:
pp.runpp(net,
trafo_model="pi",
calculate_voltage_angles=False)
if "bus_va_degree" in max_diff_values.keys():
max_diff_values[
"bus_va_degree"] = 1e2 if max_diff_values[
"bus_va_degree"] < 1e2 else max_diff_values[
"bus_va_degree"]
logger.info("voltage_angles could be calculated.")
except pp.LoadflowNotConverged:
logger.error(
'The pandapower powerflow does not converge.')
elif pf_type == "rundcpp":
try:
pp.rundcpp(net, trafo_model="pi")
except pp.LoadflowNotConverged:
logger.error('The pandapower dc powerflow does not converge.')
elif pf_type == "runopp":
try:
pp.runopp(net, init="flat", calculate_voltage_angles=True)
except pp.OPFNotConverged:
try:
pp.runopp(net, init="pf", calculate_voltage_angles=True)
except (pp.OPFNotConverged, pp.LoadflowNotConverged, KeyError):
try:
pp.runopp(net,
init="flat",
calculate_voltage_angles=False)
logger.info("voltage_angles could be calculated.")
if "bus_va_degree" in max_diff_values.keys():
max_diff_values[
"bus_va_degree"] = 1e2 if max_diff_values[
"bus_va_degree"] < 1e2 else max_diff_values[
"bus_va_degree"]
except pp.OPFNotConverged:
try:
pp.runopp(net,
init="pf",
calculate_voltage_angles=False)
if "bus_va_degree" in max_diff_values.keys():
max_diff_values[
"bus_va_degree"] = 1e2 if max_diff_values[
"bus_va_degree"] < 1e2 else max_diff_values[
"bus_va_degree"]
logger.info("voltage_angles could be calculated.")
except (pp.OPFNotConverged, pp.LoadflowNotConverged,
KeyError):
logger.error(
'The pandapower optimal powerflow does not converge.'
)
elif pf_type == "rundcopp":
try:
pp.rundcopp(net)
except pp.LoadflowNotConverged:
logger.error(
'The pandapower dc optimal powerflow does not converge.')
else:
raise ValueError("The pf_type %s is unknown" % pf_type)
# --- prepare powerflow result comparison by reordering pp results as they are in ppc results
if not ppc_success:
return False
if "opp" in pf_type:
if not net.OPF_converged:
return
elif not net.converged:
return False
# --- store pypower powerflow results
ppc_res = dict.fromkeys(ppc_elms)
ppc_res["branch"] = ppc_net['branch'][:, 13:17]
ppc_res["bus"] = ppc_net['bus'][:, 7:9]
ppc_res["gen"] = ppc_net['gen'][:, 1:3]
# --- pandapower bus result table
pp_res = dict.fromkeys(ppc_elms)
pp_res["bus"] = array(net.res_bus.sort_index()[['vm_pu', 'va_degree']])
# --- pandapower gen result table
pp_res["gen"] = zeros([1, 2])
# consideration of parallel generators via storing how much generators have been considered
# each node
# if in ppc is only one gen -> numpy initially uses one dim array -> change to two dim array
if len(ppc_net["gen"].shape) == 1:
ppc_net["gen"] = array(ppc_net["gen"], ndmin=2)
GENS = DataFrame(ppc_net['gen'][:, [0]].astype(int))
GEN_uniq = GENS.drop_duplicates()
already_used_gen = Series(zeros(GEN_uniq.shape[0]).astype(int),
index=[int(v) for v in GEN_uniq.values])
change_q_compare = []
for i, j in GENS.iterrows():
current_bus_type, current_bus_idx, same_bus_gen_idx, first_same_bus_in_service_gen_idx, \
last_same_bus_in_service_gen_idx = _gen_bus_info(ppc_net, i)
if current_bus_type == 3 and i == first_same_bus_in_service_gen_idx:
pp_res["gen"] = append(
pp_res["gen"],
array(net.res_ext_grid[net.ext_grid.bus == current_bus_idx][[
'p_mw', 'q_mvar'
]]).reshape((1, 2)), 0)
elif current_bus_type == 2 and i == first_same_bus_in_service_gen_idx:
pp_res["gen"] = append(
pp_res["gen"],
array(net.res_gen[net.gen.bus == current_bus_idx][[
'p_mw', 'q_mvar'
]]).reshape((1, 2)), 0)
else:
pp_res["gen"] = append(
pp_res["gen"],
array(net.res_sgen[net.sgen.bus == current_bus_idx][[
'p_mw', 'q_mvar'
]])[already_used_gen.at[int(j)]].reshape((1, 2)), 0)
already_used_gen.at[int(j)] += 1
change_q_compare += [int(j)]
pp_res["gen"] = pp_res["gen"][1:, :] # delete initial zero row
# --- pandapower branch result table
pp_res["branch"] = zeros([1, 4])
# consideration of parallel branches via storing how often branches were considered
# each node-to-node-connection
try:
init1 = concat([net.line.from_bus, net.line.to_bus], axis=1,
sort=True).drop_duplicates()
init2 = concat([net.trafo.hv_bus, net.trafo.lv_bus], axis=1,
sort=True).drop_duplicates()
except TypeError:
# legacy pandas < 0.21
init1 = concat([net.line.from_bus, net.line.to_bus],
axis=1).drop_duplicates()
init2 = concat([net.trafo.hv_bus, net.trafo.lv_bus],
axis=1).drop_duplicates()
init1['hv_bus'] = nan
init1['lv_bus'] = nan
init2['from_bus'] = nan
init2['to_bus'] = nan
try:
already_used_branches = concat([init1, init2], axis=0, sort=True)
except TypeError:
# pandas < 0.21 legacy
already_used_branches = concat([init1, init2], axis=0)
already_used_branches['number'] = zeros(
[already_used_branches.shape[0], 1]).astype(int)
BRANCHES = DataFrame(ppc_net['branch'][:, [0, 1, 8, 9]])
for i in BRANCHES.index:
from_bus = pp.get_element_index(net,
'bus',
name=int(ppc_net['branch'][i, 0]))
to_bus = pp.get_element_index(net,
'bus',
name=int(ppc_net['branch'][i, 1]))
from_vn_kv = ppc_net['bus'][from_bus, 9]
to_vn_kv = ppc_net['bus'][to_bus, 9]
ratio = BRANCHES[2].at[i]
angle = BRANCHES[3].at[i]
# from line results
if (from_vn_kv == to_vn_kv) & ((ratio == 0) |
(ratio == 1)) & (angle == 0):
pp_res["branch"] = append(
pp_res["branch"],
array(net.res_line[(net.line.from_bus == from_bus)
& (net.line.to_bus == to_bus)][[
'p_from_mw', 'q_from_mvar', 'p_to_mw',
'q_to_mvar'
]])
[int(already_used_branches.number.loc[
(already_used_branches.from_bus == from_bus) &
(already_used_branches.to_bus == to_bus)].values)].reshape(
1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.from_bus == from_bus)
& (already_used_branches.to_bus == to_bus)] += 1
# from trafo results
else:
if from_vn_kv >= to_vn_kv:
pp_res["branch"] = append(
pp_res["branch"],
array(net.res_trafo[(net.trafo.hv_bus == from_bus)
& (net.trafo.lv_bus == to_bus)]
[['p_hv_mw', 'q_hv_mvar', 'p_lv_mw', 'q_lv_mvar'
]])[int(already_used_branches.number.loc[
(already_used_branches.hv_bus == from_bus)
& (already_used_branches.lv_bus == to_bus)].
values)].reshape(1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.hv_bus == from_bus)
& (already_used_branches.lv_bus == to_bus)] += 1
else: # switch hv-lv-connection of pypower connection buses
pp_res["branch"] = append(
pp_res["branch"],
array(net.res_trafo[(net.trafo.hv_bus == to_bus)
& (net.trafo.lv_bus == from_bus)]
[['p_lv_mw', 'q_lv_mvar', 'p_hv_mw', 'q_hv_mvar'
]])[int(already_used_branches.number.loc[
(already_used_branches.hv_bus == to_bus)
& (already_used_branches.lv_bus == from_bus)].
values)].reshape(1, 4), 0)
already_used_branches.number.loc[
(already_used_branches.hv_bus == to_bus)
& (already_used_branches.lv_bus == from_bus)] += 1
pp_res["branch"] = pp_res["branch"][1:, :] # delete initial zero row
# --- do the powerflow result comparison
diff_res = dict.fromkeys(ppc_elms)
diff_res["bus"] = ppc_res["bus"] - pp_res["bus"]
diff_res["bus"][:, 1] -= diff_res["bus"][0, 1] # remove va_degree offset
diff_res["branch"] = ppc_res["branch"] - pp_res["branch"]
diff_res["gen"] = ppc_res["gen"] - pp_res["gen"]
# comparison of buses with several generator units only as q sum
for i in GEN_uniq.loc[GEN_uniq[0].isin(change_q_compare)].index:
next_is = GEN_uniq.index[GEN_uniq.index > i]
if len(next_is) > 0:
next_i = next_is[0]
else:
next_i = GENS.index[-1] + 1
if (next_i - i) > 1:
diff_res["gen"][i:next_i, 1] = sum(diff_res["gen"][i:next_i, 1])
# logger info
logger.debug(
"Maximum voltage magnitude difference between pypower and pandapower: "
"%.2e pu" % max_(abs(diff_res["bus"][:, 0])))
logger.debug(
"Maximum voltage angle difference between pypower and pandapower: "
"%.2e degree" % max_(abs(diff_res["bus"][:, 1])))
logger.debug(
"Maximum branch flow active power difference between pypower and pandapower: "
"%.2e MW" % max_(abs(diff_res["branch"][:, [0, 2]])))
logger.debug(
"Maximum branch flow reactive power difference between pypower and "
"pandapower: %.2e MVAr" % max_(abs(diff_res["branch"][:, [1, 3]])))
logger.debug(
"Maximum active power generation difference between pypower and pandapower: "
"%.2e MW" % max_(abs(diff_res["gen"][:, 0])))
logger.debug(
"Maximum reactive power generation difference between pypower and pandapower: "
"%.2e MVAr" % max_(abs(diff_res["gen"][:, 1])))
if _validate_diff_res(diff_res, {"bus_vm_pu": 1e-3, "bus_va_degree": 1e-3, "branch_p_mw": 1e-6,
"branch_q_mvar": 1e-6}) and \
(max_(abs(diff_res["gen"])) > 1e-1).any():
logger.debug(
"The active/reactive power generation difference possibly results "
"because of a pypower error. Please validate "
"the results via pypower loadflow."
) # this occurs e.g. at ppc case9
# give a return
if isinstance(max_diff_values, dict):
return _validate_diff_res(diff_res, max_diff_values)
else:
logger.debug("'max_diff_values' must be a dict.")
def run_sim(ppc, elements, dynopt=None, events=None, recorder=None):
"""
Run a time-domain simulation
Inputs:
ppc PYPOWER load flow case
elements Dictionary of dynamic model objects (machines, controllers, etc) with Object ID as key
events Events object
recorder Recorder object (empty)
Outputs:
recorder Recorder object (with data)
"""
#########
# SETUP #
#########
# Get version information
ver = pydyn_ver()
print('PYPOWER-Dynamics ' + ver['Version'] + ', ' + ver['Date'])
# Program options
if dynopt:
h = dynopt['h']
t_sim = dynopt['t_sim']
max_err = dynopt['max_err']
max_iter = dynopt['max_iter']
verbose = dynopt['verbose']
else:
# Default program options
h = 0.01 # step length (s)
t_sim = 5 # simulation time (s)
max_err = 0.0001 # Maximum error in network iteration (voltage mismatches)
max_iter = 25 # Maximum number of network iterations
verbose = False
if dynopt['sample_period']:
sample_rate = max(int(dynopt['sample_period'] / h) - 1, 0)
else:
sample_rate = 0
# Make lists of current injection sources (generators, external grids, etc) and controllers
sources = []
controllers = []
for element in elements.values():
if element.__module__ in [
'pydyn.sym_order6a', 'pydyn.sym_order6b', 'pydyn.sym_order4',
'pydyn.ext_grid', 'pydyn.vsc_average', 'pydyn.asym_1cage',
'pydyn.asym_2cage'
]:
sources.append(element)
if element.__module__ == 'pydyn.controller':
controllers.append(element)
# Set up interfaces
interfaces = init_interfaces(elements)
interfaces0 = init_interfaces0(elements)
# find events
events_controllers = []
# find blocks that create events in controllers
for element_id in elements.keys():
element = elements[element_id]
if element.__module__ == 'pydyn.controller':
for line in element.equations:
if line[1] == 'EVENT':
new_event = [element_id, line[0], line[2]] + line[3:]
events_controllers.append(new_event)
##################
# INITIALISATION #
##################
print('Initialising models...')
if not verbose:
ppopt = ppoption(VERBOSE=0, OUT_ALL=0)
else:
ppopt = ppoption()
#print('not verbose')
# Run power flow and update bus voltages and angles in PYPOWER case object
results, success = runpf(ppc, ppopt)
ppc["bus"][:, VM] = results["bus"][:, VM]
ppc["bus"][:, VA] = results["bus"][:, VA]
# Build Ybus matrix
ppc_int = ext2int(ppc)
baseMVA, bus, branch = ppc_int["baseMVA"], ppc_int["bus"], ppc_int[
"branch"]
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
# Build modified Ybus matrix
try:
Ybus = mod_Ybus(Ybus, elements, bus, ppc_int['gen'], baseMVA)
except:
bp()
Ybus = mod_Ybus(Ybus, elements, bus, ppc_int['gen'], baseMVA)
# Calculate initial voltage phasors
v0 = bus[:, VM] * (np.cos(np.radians(bus[:, VA])) +
1j * np.sin(np.radians(bus[:, VA])))
# Initialise sources from load flow
for source in sources:
if source.__module__ in ['pydyn.asym_1cage', 'pydyn.asym_2cage']:
# Asynchronous machine
source_bus = ppc_int['bus'][source.bus_no, 0].astype(np.int64)
v_source = v0[source_bus]
source.initialise(v_source, 0)
else:
# Generator or VSC
source_bus = ppc_int['gen'][source.gen_no, 0].astype(np.int64)
S_source = np.complex(results["gen"][source.gen_no, 1] / baseMVA,
results["gen"][source.gen_no, 2] / baseMVA)
v_source = v0[source_bus]
source.initialise(v_source, S_source)
# initialise bus
elements['bus'] = bus_int(ppc)
elements['sys_matrices'] = sys_matrices_int(ppc)
#elements['branch'] = ppc['branch']
# Do we need interfaces0?
# Interface controllers and machines (for initialisation)
#for intf in interfaces:
for k in range(len(interfaces)):
intf = interfaces[k]
intf0 = interfaces0[k]
int_type = intf[0]
var_name = intf0[1]
source_var = intf[1]
source_id = intf[2]
dest_var = intf[3]
dest_id = intf[4]
if int_type == 'OUTPUT':
# If an output, interface in the reverse direction for initialisation
#intf[2].signals[var_name] = intf[3].signals[var_name]
#if (intf0[2] != source_id) or (var_name != source_var) or (var_name != dest_var):
# bp()
elements[source_id].signals[source_var] = elements[
dest_id].signals[dest_var]
else:
# Inputs are interfaced in normal direction during initialisation
#intf[3].signals[var_name] = intf[2].signals[var_name]
#if (intf0[3] != dest_id) or (var_name != source_var) or (var_name != dest_var):
# bp()
elements[dest_id].signals[dest_var] = elements[source_id].signals[
source_var]
#try:
# element_source.signals[ var_name_source ] = element_dest.signals[ var_name_dest ]
#except:
# bp()
# Initialise controllers
for controller in controllers:
controller.initialise()
#############
# MAIN LOOP #
#############
sample_age = 0
if events == None:
print('Warning: no events!')
# Factorise Ybus matrix
Ybus_inv = splu(Ybus)
y1 = []
v_prev = v0
print('Simulating...')
for t in range(int(t_sim / h) + 1):
if np.mod(t, 1 / h) == 0 and verbose:
print('t=' + str(t * h) + 's')
# Interface controllers and machines
#for intf in interfaces:
for k in range(len(interfaces)):
intf = interfaces[k]
intf0 = interfaces0[k]
var_name = intf0[1]
source_var = intf[1]
source_id = intf[2]
dest_var = intf[3]
dest_id = intf[4]
#if var_name_dest not in element_dest.signals.keys():
#bp()
#element_dest.signals[ var_name_dest ] = element_source.signals[ var_name_source ]
#if (intf0[2] != source_id) or (var_name != source_var) or (var_name != dest_var) or (intf0[3] != dest_id):
# bp()
elements[dest_id].signals[dest_var] = elements[source_id].signals[
source_var]
#intf[3].signals[var_name] = intf[2].signals[var_name]
# Solve differential equations
for j in range(4):
# Solve step of differential equations
for element in elements.values():
try:
element.solve_step(h, j)
except:
bp()
element.solve_step(h, j)
# Interface with network equations
v_prev = solve_network(sources, v_prev, Ybus_inv, ppc_int,
len(bus), max_err, max_iter)
# check for events
for event_c in events_controllers:
new_event = None
ctrl = event_c[0]
ctrl_var = event_c[2]
var_result = event_c[1]
condition = elements[ctrl].signals[ctrl_var]
if condition >= 1:
#event_type = event_c[0]
#node = event_c[1]
new_event = [np.round(t * h, 5)] + event_c[3:]
#print(new_event)
try:
events.event_stack.append(new_event)
elements[ctrl].signals[var_result] = 1.0
#bp()
except:
bp()
else:
elements[ctrl].signals[var_result] = 0.0
if sample_age < sample_rate:
sample_age += 1
else:
sample_age = 0
if recorder != None:
# Record signals or states
recorder.record_variables(t * h, elements)
if events != None:
#if new_event != None:
# bp()
# Check event stack
ppc, refactorise = events.handle_events(np.round(t * h, 5),
elements, ppc, baseMVA)
if refactorise == True:
# Rebuild Ybus from new ppc_int
ppc_int = ext2int(ppc)
baseMVA, bus, branch = ppc_int["baseMVA"], ppc_int[
"bus"], ppc_int["branch"]
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
# Rebuild modified Ybus
Ybus = mod_Ybus(Ybus, elements, bus, ppc_int['gen'], baseMVA)
# Refactorise Ybus
Ybus_inv = splu(Ybus)
# Solve network equations
v_prev = solve_network(sources, v_prev, Ybus_inv, ppc_int,
len(bus), max_err, max_iter)
# update the voltage in 'bus' matrix
ppc['bus'][:, VM] = abs(v_prev)
ppc['bus'][:, VA] = 2 * np.arctan(v_prev.imag /
(abs(v_prev) + v_prev.real))
# update the system matrices
elements['bus'].update(ppc)
elements['sys_matrices'].update(ppc)
#bp()
return recorder
def t_pf(quiet=False):
"""Tests for power flow solvers.
@author: Ray Zimmerman (PSERC Cornell)
"""
t_begin(33, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_pf')
verbose = not quiet
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
## get solved AC power flow case from MAT-file
## defines bus_soln, gen_soln, branch_soln
soln9_pf = loadmat(join(tdir, 'soln9_pf.mat'), struct_as_record=False)
bus_soln = soln9_pf['bus_soln']
gen_soln = soln9_pf['gen_soln']
branch_soln = soln9_pf['branch_soln']
## run Newton PF
t = 'Newton PF : ';
ppopt = ppoption(ppopt, PF_ALG=1)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run fast-decoupled PF (XB version)
t = 'Fast Decoupled (XB) PF : ';
ppopt = ppoption(ppopt, PF_ALG=2)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run fast-decoupled PF (BX version)
t = 'Fast Decoupled (BX) PF : ';
ppopt = ppoption(ppopt, PF_ALG=3)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## run Gauss-Seidel PF
t = 'Gauss-Seidel PF : ';
ppopt = ppoption(ppopt, PF_ALG=4)
results, success = runpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 5, [t, 'bus'])
t_is(gen, gen_soln, 5, [t, 'gen'])
t_is(branch, branch_soln, 5, [t, 'branch'])
## get solved AC power flow case from MAT-file
## defines bus_soln, gen_soln, branch_soln
soln9_dcpf = loadmat(join(tdir, 'soln9_dcpf.mat'), struct_as_record=False)
bus_soln = soln9_dcpf['bus_soln']
gen_soln = soln9_dcpf['gen_soln']
branch_soln = soln9_dcpf['branch_soln']
## run DC PF
t = 'DC PF : '
results, success = rundcpf(casefile, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_ok(success, [t, 'success'])
t_is(bus, bus_soln, 6, [t, 'bus'])
t_is(gen, gen_soln, 6, [t, 'gen'])
t_is(branch, branch_soln, 6, [t, 'branch'])
## check Qg distribution, when Qmin = Qmax
t = 'check Qg : '
ppopt = ppoption(ppopt, PF_ALG=1, VERBOSE=0)
ppc = loadcase(casefile)
ppc['gen'][0, [QMIN, QMAX]] = [20, 20]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0, QG], 24.07, 2, [t, 'single gen, Qmin = Qmax'])
ppc['gen'] = r_[array([ ppc['gen'][0, :] ]), ppc['gen']]
ppc['gen'][0, [QMIN, QMAX]] = [10, 10]
ppc['gen'][1, [QMIN, QMAX]] = [ 0, 50]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [10, 14.07], 2, [t, '2 gens, Qmin = Qmax for one'])
ppc['gen'][0, [QMIN, QMAX]] = [10, 10]
ppc['gen'][1, [QMIN, QMAX]] = [-50, -50]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [12.03, 12.03], 2, [t, '2 gens, Qmin = Qmax for both'])
ppc['gen'][0, [QMIN, QMAX]] = [0, 50]
ppc['gen'][1, [QMIN, QMAX]] = [0, 100]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [8.02, 16.05], 2, [t, '2 gens, proportional'])
ppc['gen'][0, [QMIN, QMAX]] = [-50, 0]
ppc['gen'][1, [QMIN, QMAX]] = [50, 150]
results, success = runpf(ppc, ppopt)
bus, gen, branch = results['bus'], results['gen'], results['branch']
t_is(gen[0:2, QG], [-50 + 8.02, 50 + 16.05], 2, [t, '2 gens, proportional'])
## network with islands
t = 'network w/islands : DC PF : '
ppc0 = loadcase(casefile)
ppc0['gen'][0, PG] = 60
ppc0['gen'][0, [PMIN, PMAX, QMIN, QMAX, PG, QG]] = \
ppc0['gen'][0, [PMIN, PMAX, QMIN, QMAX, PG, QG]] / 2
ppc0['gen'] = r_[array([ ppc0['gen'][0, :] ]), ppc0['gen']]
ppc1 = ppc0.copy()
ppc = ppc0.copy()
nb = ppc['bus'].shape[0]
ppc1['bus'][:, BUS_I] = ppc1['bus'][:, BUS_I] + nb
ppc1['branch'][:, F_BUS] = ppc1['branch'][:, F_BUS] + nb
ppc1['branch'][:, T_BUS] = ppc1['branch'][:, T_BUS] + nb
ppc1['gen'][:, GEN_BUS] = ppc1['gen'][:, GEN_BUS] + nb
ppc['bus'] = r_[ppc['bus'], ppc1['bus']]
ppc['branch'] = r_[ppc['branch'], ppc1['branch']]
ppc['gen'] = r_[ppc['gen'], ppc1['gen']]
#ppopt = ppoption(ppopt, OUT_BUS=1, OUT_GEN=1, OUT_ALL=-1, VERBOSE=2)
ppopt = ppoption(ppopt, VERBOSE=verbose)
r = rundcpf(ppc, ppopt)
t_is(r['bus'][ :9, VA], bus_soln[:, VA], 8, [t, 'voltage angles 1'])
t_is(r['bus'][10:18, VA], bus_soln[:, VA], 8, [t, 'voltage angles 2'])
Pg = r_[gen_soln[0, PG] - 30, 30, gen_soln[1:3, PG]]
t_is(r['gen'][ :4, PG], Pg, 8, [t, 'active power generation 1'])
t_is(r['gen'][4:8, PG], Pg, 8, [t, 'active power generation 1'])
t = 'network w/islands : AC PF : '
## get solved AC power flow case from MAT-file
soln9_pf = loadmat(join(tdir, 'soln9_pf.mat'), struct_as_record=False)
bus_soln = soln9_pf['bus_soln']
gen_soln = soln9_pf['gen_soln']
branch_soln = soln9_pf['branch_soln']
r = runpf(ppc, ppopt)
t_is(r['bus'][ :9, VA], bus_soln[:, VA], 8, [t, 'voltage angles 1'])
t_is(r['bus'][9:18, VA], bus_soln[:, VA], 8, [t, 'voltage angles 2'])
Pg = r_[gen_soln[0, PG] - 30, 30, gen_soln[1:3, PG]]
t_is(r['gen'][ :4, PG], Pg, 8, [t, 'active power generation 1'])
t_is(r['gen'][4:8, PG], Pg, 8, [t, 'active power generation 1'])
t_end()
bus.index = value
bus.index = bus.index.astype(int)
branch['F_BUS'] = branch['F_BUS'].apply(
lambda x: value[bus_name.get_loc(x)]).astype(int)
branch['T_BUS'] = branch['T_BUS'].apply(
lambda x: value[bus_name.get_loc(x)]).astype(int)
gen['GEN_BUS'] = gen['GEN_BUS'].apply(
lambda x: value[bus_name.get_loc(x)]).astype(int)
bus = np.array(bus.reset_index())
branch = np.array(branch)
gen = np.array(gen)
gencost = np.array(gencost)
casedata = {
'baseMVA': baseMVA,
'gencost': gencost,
'gen': gen,
'branch': branch,
'bus': bus
}
return runpf(casedata, ppopt=ppopt, fname=fname)
def find_violated_lines(original_case_branch, results_case_branch):
s = (pd.Series(abs(results_case_branch[:, 13])) >
original_case_branch['RATE_A']) & (original_case_branch['RATE_A'] !=
0)
return s[s == True].index
def FlujosAC(self, Sbase, t_index):
for PVAC in self.PVAC:
# Actualizar matriz de generación con valores interpolados
self.genMatrix[PVAC.indice - 1,
1] = PVAC.P[t_index] / (Sbase * 1000000)
self.genMatrix[PVAC.indice - 1,
2] = PVAC.Q[t_index] / (Sbase * 1000000)
self.busMatrix = numpy.array([])
self.branchMatrix = numpy.array([])
for bus in self.Terminales:
bus.actualizar(t_index, Sbase)
self.busMatrix = numpy.concatenate((self.busMatrix, [
bus.ID, bus.Tipo, bus.P, bus.Q, 0, 0, 1, 1, 0, bus.Vnom, 1,
1.1, 0.9
]), 0)
i = 0
for branch in self.Branch:
self.branchMatrix = numpy.concatenate((self.branchMatrix, [
branch.Term1.ID, branch.Term2.ID, branch.R, branch.X, branch.Y,
branch.Snom, branch.Snom, branch.Snom, branch.Turns, 0, 1,
-360, 360
]), 0)
branch.branchindex = i
i = i + 1
# Crear caso para cargalo usando PYPOWER y correr flujos AC
RedACdict = dict()
RedACdict['baseMVA'] = Sbase
RedACdict['bus'] = self.busMatrix.reshape((len(self.Terminales), 13))
RedACdict['branch'] = self.branchMatrix.reshape((len(self.Branch), 13))
RedACdict['gen'] = self.genMatrix.reshape((len(self.PVAC) + 1, 21))
# Cargar caso de pypower con matrices actualizadas
caseRedAC = loadcase.loadcase(RedACdict)
# Correr flujo de potencia con PYPOWER
self.ACresults = runpf.runpf(caseRedAC)
fecha = datetime.strftime(self.fechas[t_index], '%Y-%m-%d %H:%M:%S')
# Guardar resultados de flujos para terminales
for bus in self.Terminales:
# Recuperar índice de bus en matriz de buses de pypower con resultados
busindex = numpy.where(
self.ACresults[0]['bus'][:, 0] == bus.ID)[0].item()
# Guardar resultados respectivos
bus.Results[fecha] = {
'V': self.ACresults[0]['bus'][busindex, 7].item(),
'delta': self.ACresults[0]['bus'][busindex, 8].item(),
'P': self.ACresults[0]['bus'][busindex, 2].item(),
'Q': self.ACresults[0]['bus'][busindex, 3].item()
}
# Guardar resultados de flujos para líneas y trafos
for branch in self.Branch:
# Recuperar potencias de entrada y salida de la rama
Pf = self.ACresults[0]['branch'][branch.branchindex, 13].item()
Qf = self.ACresults[0]['branch'][branch.branchindex, 14].item()
Pt = self.ACresults[0]['branch'][branch.branchindex, 15].item()
Qt = self.ACresults[0]['branch'][branch.branchindex, 16].item()
# Calcular pérdidas y cargabilidad de cada rama
Ploss = math.fabs(Pf - Pt)
Qloss = math.fabs(Qf - Qt)
Loading = math.sqrt(math.pow(Pf, 2) +
math.pow(Qf, 2)) * 100 / branch.Snom
# Guardar resultados respectivos
branch.Results[fecha] = {
'Pf': Pf,
'Qf': Qf,
'Ploss': Ploss,
'Qloss': Qloss,
'Loading': Loading
}
self.CDC.Results[fecha] = {
'P': self.ACresults[0]['gen'][0, 1].item(),
'Q': self.ACresults[0]['gen'][0, 2].item()
}
def t_jacobian(quiet=False):
"""Numerical tests of partial derivative code.
@author: Ray Zimmerman (PSERC Cornell)
"""
t_begin(28, quiet)
## run powerflow to get solved case
ppopt = ppoption(VERBOSE=0, OUT_ALL=0)
ppc = loadcase(case30())
results, _ = runpf(ppc, ppopt)
baseMVA, bus, gen, branch = \
results['baseMVA'], results['bus'], results['gen'], results['branch']
## switch to internal bus numbering and build admittance matrices
_, bus, gen, branch = ext2int1(bus, gen, branch)
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
Ybus_full = Ybus.todense()
Yf_full = Yf.todense()
Yt_full = Yt.todense()
Vm = bus[:, VM]
Va = bus[:, VA] * (pi / 180)
V = Vm * exp(1j * Va)
f = branch[:, F_BUS].astype(int) ## list of "from" buses
t = branch[:, T_BUS].astype(int) ## list of "to" buses
#nl = len(f)
nb = len(V)
pert = 1e-8
Vm = array([Vm]).T # column array
Va = array([Va]).T # column array
Vc = array([V]).T # column array
##----- check dSbus_dV code -----
## full matrices
dSbus_dVm_full, dSbus_dVa_full = dSbus_dV(Ybus_full, V)
## sparse matrices
dSbus_dVm, dSbus_dVa = dSbus_dV(Ybus, V)
dSbus_dVm_sp = dSbus_dVm.todense()
dSbus_dVa_sp = dSbus_dVa.todense()
## compute numerically to compare
Vmp = (Vm * ones((1, nb)) + pert*eye(nb)) * (exp(1j * Va) * ones((1, nb)))
Vap = (Vm * ones((1, nb))) * (exp(1j * (Va*ones((1, nb)) + pert*eye(nb))))
num_dSbus_dVm = (Vmp * conj(Ybus * Vmp) - Vc * ones((1, nb)) * conj(Ybus * Vc * ones((1, nb)))) / pert
num_dSbus_dVa = (Vap * conj(Ybus * Vap) - Vc * ones((1, nb)) * conj(Ybus * Vc * ones((1, nb)))) / pert
t_is(dSbus_dVm_sp, num_dSbus_dVm, 5, 'dSbus_dVm (sparse)')
t_is(dSbus_dVa_sp, num_dSbus_dVa, 5, 'dSbus_dVa (sparse)')
t_is(dSbus_dVm_full, num_dSbus_dVm, 5, 'dSbus_dVm (full)')
t_is(dSbus_dVa_full, num_dSbus_dVa, 5, 'dSbus_dVa (full)')
##----- check dSbr_dV code -----
## full matrices
dSf_dVa_full, dSf_dVm_full, dSt_dVa_full, dSt_dVm_full, _, _ = \
dSbr_dV(branch, Yf_full, Yt_full, V)
## sparse matrices
dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St = dSbr_dV(branch, Yf, Yt, V)
dSf_dVa_sp = dSf_dVa.todense()
dSf_dVm_sp = dSf_dVm.todense()
dSt_dVa_sp = dSt_dVa.todense()
dSt_dVm_sp = dSt_dVm.todense()
## compute numerically to compare
Vmpf = Vmp[f, :]
Vapf = Vap[f, :]
Vmpt = Vmp[t, :]
Vapt = Vap[t, :]
Sf2 = (Vc[f] * ones((1, nb))) * conj(Yf * Vc * ones((1, nb)))
St2 = (Vc[t] * ones((1, nb))) * conj(Yt * Vc * ones((1, nb)))
Smpf = Vmpf * conj(Yf * Vmp)
Sapf = Vapf * conj(Yf * Vap)
Smpt = Vmpt * conj(Yt * Vmp)
Sapt = Vapt * conj(Yt * Vap)
num_dSf_dVm = (Smpf - Sf2) / pert
num_dSf_dVa = (Sapf - Sf2) / pert
num_dSt_dVm = (Smpt - St2) / pert
num_dSt_dVa = (Sapt - St2) / pert
t_is(dSf_dVm_sp, num_dSf_dVm, 5, 'dSf_dVm (sparse)')
t_is(dSf_dVa_sp, num_dSf_dVa, 5, 'dSf_dVa (sparse)')
t_is(dSt_dVm_sp, num_dSt_dVm, 5, 'dSt_dVm (sparse)')
t_is(dSt_dVa_sp, num_dSt_dVa, 5, 'dSt_dVa (sparse)')
t_is(dSf_dVm_full, num_dSf_dVm, 5, 'dSf_dVm (full)')
t_is(dSf_dVa_full, num_dSf_dVa, 5, 'dSf_dVa (full)')
t_is(dSt_dVm_full, num_dSt_dVm, 5, 'dSt_dVm (full)')
t_is(dSt_dVa_full, num_dSt_dVa, 5, 'dSt_dVa (full)')
##----- check dAbr_dV code -----
## full matrices
dAf_dVa_full, dAf_dVm_full, dAt_dVa_full, dAt_dVm_full = \
dAbr_dV(dSf_dVa_full, dSf_dVm_full, dSt_dVa_full, dSt_dVm_full, Sf, St)
## sparse matrices
dAf_dVa, dAf_dVm, dAt_dVa, dAt_dVm = \
dAbr_dV(dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St)
dAf_dVa_sp = dAf_dVa.todense()
dAf_dVm_sp = dAf_dVm.todense()
dAt_dVa_sp = dAt_dVa.todense()
dAt_dVm_sp = dAt_dVm.todense()
## compute numerically to compare
num_dAf_dVm = (abs(Smpf)**2 - abs(Sf2)**2) / pert
num_dAf_dVa = (abs(Sapf)**2 - abs(Sf2)**2) / pert
num_dAt_dVm = (abs(Smpt)**2 - abs(St2)**2) / pert
num_dAt_dVa = (abs(Sapt)**2 - abs(St2)**2) / pert
t_is(dAf_dVm_sp, num_dAf_dVm, 4, 'dAf_dVm (sparse)')
t_is(dAf_dVa_sp, num_dAf_dVa, 4, 'dAf_dVa (sparse)')
t_is(dAt_dVm_sp, num_dAt_dVm, 4, 'dAt_dVm (sparse)')
t_is(dAt_dVa_sp, num_dAt_dVa, 4, 'dAt_dVa (sparse)')
t_is(dAf_dVm_full, num_dAf_dVm, 4, 'dAf_dVm (full)')
t_is(dAf_dVa_full, num_dAf_dVa, 4, 'dAf_dVa (full)')
t_is(dAt_dVm_full, num_dAt_dVm, 4, 'dAt_dVm (full)')
t_is(dAt_dVa_full, num_dAt_dVa, 4, 'dAt_dVa (full)')
##----- check dIbr_dV code -----
## full matrices
dIf_dVa_full, dIf_dVm_full, dIt_dVa_full, dIt_dVm_full, _, _ = \
dIbr_dV(branch, Yf_full, Yt_full, V)
## sparse matrices
dIf_dVa, dIf_dVm, dIt_dVa, dIt_dVm, _, _ = dIbr_dV(branch, Yf, Yt, V)
dIf_dVa_sp = dIf_dVa.todense()
dIf_dVm_sp = dIf_dVm.todense()
dIt_dVa_sp = dIt_dVa.todense()
dIt_dVm_sp = dIt_dVm.todense()
## compute numerically to compare
num_dIf_dVm = (Yf * Vmp - Yf * Vc * ones((1, nb))) / pert
num_dIf_dVa = (Yf * Vap - Yf * Vc * ones((1, nb))) / pert
num_dIt_dVm = (Yt * Vmp - Yt * Vc * ones((1, nb))) / pert
num_dIt_dVa = (Yt * Vap - Yt * Vc * ones((1, nb))) / pert
t_is(dIf_dVm_sp, num_dIf_dVm, 5, 'dIf_dVm (sparse)')
t_is(dIf_dVa_sp, num_dIf_dVa, 5, 'dIf_dVa (sparse)')
t_is(dIt_dVm_sp, num_dIt_dVm, 5, 'dIt_dVm (sparse)')
t_is(dIt_dVa_sp, num_dIt_dVa, 5, 'dIt_dVa (sparse)')
t_is(dIf_dVm_full, num_dIf_dVm, 5, 'dIf_dVm (full)')
t_is(dIf_dVa_full, num_dIf_dVa, 5, 'dIf_dVa (full)')
t_is(dIt_dVm_full, num_dIt_dVm, 5, 'dIt_dVm (full)')
t_is(dIt_dVa_full, num_dIt_dVa, 5, 'dIt_dVa (full)')
t_end()
def run_sim(ppc, elements, dynopt = None, events = None, recorder = None):
"""
Run a time-domain simulation
Inputs:
ppc PYPOWER load flow case
elements Dictionary of dynamic model objects (machines, controllers, etc) with Object ID as key
events Events object
recorder Recorder object (empty)
Outputs:
recorder Recorder object (with data)
"""
#########
# SETUP #
#########
# Get version information
ver = pydyn_ver()
print('PYPOWER-Dynamics ' + ver['Version'] + ', ' + ver['Date'])
# Program options
if dynopt:
h = dynopt['h']
t_sim = dynopt['t_sim']
max_err = dynopt['max_err']
max_iter = dynopt['max_iter']
verbose = dynopt['verbose']
else:
# Default program options
h = 0.01 # step length (s)
t_sim = 5 # simulation time (s)
max_err = 0.0001 # Maximum error in network iteration (voltage mismatches)
max_iter = 25 # Maximum number of network iterations
verbose = False
# Make lists of current injection sources (generators, external grids, etc) and controllers
sources = []
controllers = []
for element in elements.values():
if element.__module__ in ['pydyn.sym_order6a', 'pydyn.sym_order6b', 'pydyn.sym_order4', 'pydyn.ext_grid', 'pydyn.vsc_average', 'pydyn.asym_1cage', 'pydyn.asym_2cage']:
sources.append(element)
if element.__module__ == 'pydyn.controller':
controllers.append(element)
# Set up interfaces
interfaces = init_interfaces(elements)
##################
# INITIALISATION #
##################
print('Initialising models...')
# Run power flow and update bus voltages and angles in PYPOWER case object
results, success = runpf(ppc)
ppc["bus"][:, VM] = results["bus"][:, VM]
ppc["bus"][:, VA] = results["bus"][:, VA]
# Build Ybus matrix
ppc_int = ext2int(ppc)
baseMVA, bus, branch = ppc_int["baseMVA"], ppc_int["bus"], ppc_int["branch"]
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
# Build modified Ybus matrix
Ybus = mod_Ybus(Ybus, elements, bus, ppc_int['gen'], baseMVA)
# Calculate initial voltage phasors
v0 = bus[:, VM] * (np.cos(np.radians(bus[:, VA])) + 1j * np.sin(np.radians(bus[:, VA])))
# Initialise sources from load flow
for source in sources:
if source.__module__ in ['pydyn.asym_1cage', 'pydyn.asym_2cage']:
# Asynchronous machine
source_bus = ppc_int['bus'][source.bus_no,0]
v_source = v0[source_bus]
source.initialise(v_source,0)
else:
# Generator or VSC
source_bus = ppc_int['gen'][source.gen_no,0]
S_source = np.complex(results["gen"][source.gen_no, 1] / baseMVA, results["gen"][source.gen_no, 2] / baseMVA)
v_source = v0[source_bus]
source.initialise(v_source,S_source)
# Interface controllers and machines (for initialisation)
for intf in interfaces:
int_type = intf[0]
var_name = intf[1]
if int_type == 'OUTPUT':
# If an output, interface in the reverse direction for initialisation
intf[2].signals[var_name] = intf[3].signals[var_name]
else:
# Inputs are interfaced in normal direction during initialisation
intf[3].signals[var_name] = intf[2].signals[var_name]
# Initialise controllers
for controller in controllers:
controller.initialise()
#############
# MAIN LOOP #
#############
if events == None:
print('Warning: no events!')
# Factorise Ybus matrix
Ybus_inv = splu(Ybus)
y1 = []
v_prev = v0
print('Simulating...')
for t in range(int(t_sim / h) + 1):
if np.mod(t,1/h) == 0:
print('t=' + str(t*h) + 's')
# Interface controllers and machines
for intf in interfaces:
var_name = intf[1]
intf[3].signals[var_name] = intf[2].signals[var_name]
# Solve differential equations
for j in range(4):
# Solve step of differential equations
for element in elements.values():
element.solve_step(h,j)
# Interface with network equations
v_prev = solve_network(sources, v_prev, Ybus_inv, ppc_int, len(bus), max_err, max_iter)
if recorder != None:
# Record signals or states
recorder.record_variables(t*h, elements)
if events != None:
# Check event stack
ppc, refactorise = events.handle_events(np.round(t*h,5), elements, ppc, baseMVA)
if refactorise == True:
# Rebuild Ybus from new ppc_int
ppc_int = ext2int(ppc)
baseMVA, bus, branch = ppc_int["baseMVA"], ppc_int["bus"], ppc_int["branch"]
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
# Rebuild modified Ybus
Ybus = mod_Ybus(Ybus, elements, bus, ppc_int['gen'], baseMVA)
# Refactorise Ybus
Ybus_inv = splu(Ybus)
# Solve network equations
v_prev = solve_network(sources, v_prev, Ybus_inv, ppc_int, len(bus), max_err, max_iter)
return recorder