def runopf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs an optimal power flow.
@see: L{rundcopf}, L{runuopf}
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
##----- run the optimal power flow -----
r = opf(casedata, ppopt)
##----- output results -----
if fname:
fd = None
try:
fd = open(fname, "a")
except IOError as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(r, fd, ppopt)
fd.close()
else:
printpf(r, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, r)
return r
def _run_bfsw_ppc(ppc, ppopt=None):
"""
SPARSE version of distribution power flow solution according to [1]
:References:
[1] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions", IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, July 2003.
:param ppc: matpower-style case data
:return: results (pypower style), success (flag about PF convergence)
"""
ppci = ppc
ppopt = ppoption(ppopt)
baseMVA, bus, gen, branch = \
ppci["baseMVA"], ppci["bus"], ppci["gen"], ppci["branch"]
nbus = bus.shape[0]
# get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
# depth-first-search bus ordering and generating Direct Load Flow matrix DLF = BCBV * BIBC
DLF, ppc_bfsw, buses_ordered_bfsw = bibc_bcbv(ppci)
baseMVA_bfsw, bus_bfsw, gen_bfsw, branch_bfsw = \
ppc_bfsw["baseMVA"], ppc_bfsw["bus"], ppc_bfsw["gen"], ppc_bfsw["branch"]
time_start = time() # starting pf calculation timing
# initialize voltages to flat start and buses with gens to their setpoints
V0 = np.ones(nbus, dtype=complex)
V0[gen[:, GEN_BUS].astype(int)] = gen[:, VG]
Sbus_bfsw = makeSbus(baseMVA_bfsw, bus_bfsw, gen_bfsw)
# update data matrices with solution
Ybus_bfsw, Yf_bfsw, Yt_bfsw = makeYbus(baseMVA_bfsw, bus_bfsw, branch_bfsw)
## get bus index lists of each type of bus
ref_bfsw, pv_bfsw, pq_bfsw = bustypes(bus_bfsw, gen_bfsw)
# #----- run the power flow -----
V_final, success = bfsw(DLF, bus_bfsw, gen_bfsw, branch_bfsw, baseMVA_bfsw, Ybus_bfsw, Sbus_bfsw, V0,
ref_bfsw, pv_bfsw, pq_bfsw, ppopt=ppopt)
V_final = V_final[np.argsort(buses_ordered_bfsw)] # return bus voltages in original bus order
# #----- output results to ppc ------
ppci["et"] = time() - time_start # pf time end
# generate results for original bus ordering
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V_final, ref, pv, pq)
ppci["success"] = success
ppci["bus"], ppci["gen"], ppci["branch"] = bus, gen, branch
return ppci, success
def runopf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs an optimal power flow.
@see: L{rundcopf}, L{runuopf}
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
##----- run the optimal power flow -----
r = opf(casedata, ppopt)
##----- output results -----
if fname:
fd = None
try:
fd = open(fname, "a")
except IOError as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(r, fd, ppopt)
fd.close()
else:
printpf(r, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, r)
return r
def __init__(self):
self.name = "Stochastic Power Flow"
from pypower.ppoption import ppoption
opt = ppoption()
opt["VERBOSE"] = 0
opt["OUT_ALL"] = 0
self.opt = opt
def _optimal_powerflow(net, verbose, suppress_warnings, **kwargs):
ac = net["_options"]["ac"]
ppopt = ppoption(VERBOSE=verbose, OPF_FLOW_LIM=2, PF_DC=not ac, **kwargs)
net["OPF_converged"] = False
net["converged"] = False
_add_auxiliary_elements(net)
reset_results(net)
ppc, ppci = _pd2ppc(net)
if not ac:
ppci["bus"][:, VM] = 1.0
net["_ppc_opf"] = ppc
if len(net.dcline) > 0:
ppci = add_userfcn(ppci, 'formulation', _add_dcline_constraints, args=net)
if suppress_warnings:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
result = opf(ppci, ppopt)
else:
result = opf(ppci, ppopt)
net["_ppc_opf"] = result
if not result["success"]:
raise OPFNotConverged("Optimal Power Flow did not converge!")
# ppci doesn't contain out of service elements, but ppc does -> copy results accordingly
mode = net["_options"]["mode"]
result = _copy_results_ppci_to_ppc(result, ppc, mode=mode)
net["_ppc_opf"] = result
net["OPF_converged"] = True
_extract_results_opf(net, result)
_clean_up(net)
def init():
global ppc
global ppopt
ppc = {"version": '2'}
ppc["baseMVA"] = 100.0
ppc["bus"] = np.array([
[1, 3, 0, 0, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9],
[2, 2, 0, 0, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9],
[3, 2, 0, 0, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9],
[4, 1, 0, 0, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9],
[5, 1, 90, 30, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9],
[6, 1, 0, 0, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9],
[7, 1, 100, 35, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9],
[8, 1, 0, 0, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9],
[9, 1, 125, 50, 0, 0, 1, 1, 0, 345, 1, 1.1, 0.9]
])
ppc["branch"] = np.array([
[1, 4, 0, 0.0576, 0, 250, 250, 250, 0, 0, 1, -360, 360],
[4, 5, 0.017, 0.092, 0.158, 250, 250, 250, 0, 0, 1, -360, 360],
[5, 6, 0.039, 0.17, 0.358, 150, 150, 150, 0, 0, 1, -360, 360],
[3, 6, 0, 0.0586, 0, 300, 300, 300, 0, 0, 1, -360, 360],
[6, 7, 0.0119, 0.1008, 0.209, 150, 150, 150, 0, 0, 1, -360, 360],
[7, 8, 0.0085, 0.072, 0.149, 250, 250, 250, 0, 0, 1, -360, 360],
[8, 2, 0, 0.0625, 0, 250, 250, 250, 0, 0, 1, -360, 360],
[8, 9, 0.032, 0.161, 0.306, 250, 250, 250, 0, 0, 1, -360, 360],
[9, 4, 0.01, 0.085, 0.176, 250, 250, 250, 0, 0, 1, -360, 360]
])
ppc["gen"] = np.array([
[1, 0, 0, 300, -300, 1.0, 100, 1, 250, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[2, 163, 0, 300, -300, 1.0, 100, 1, 300, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[3, 85, 0, 300, -300, 1.0, 100, 1, 270, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
])
# OPF functions not yet implemented in GUI
"""
ppc["areas"] = np.array([
[1, 5]
])
ppc["gencost"] = np.array([
[2, 1500, 0, 3, 0.11, 5, 150],
[2, 2000, 0, 3, 0.085, 1.2, 600],
[2, 3000, 0, 3, 0.1225, 1, 335]
])
"""
ppopt = ppoption()
global filename
filename = ""
# Power flow settings
global pf_settings
pf_settings = {"Qlim": False, "max_iter": 25, "err_tol": 0.00001}
def runuopf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs an optimal power flow with unit-decommitment heuristic.
@see: L{rundcopf}, L{runuopf}
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
##----- run the unit de-commitment / optimal power flow -----
r = uopf(casedata, ppopt)
##----- output results -----
if fname:
fd = None
try:
fd = open(fname, "wb")
except Exception as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(r, fd, ppopt)
fd.close()
printpf(r, ppopt=ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, r)
return r
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 _runpppf(net, init, ac, calculate_voltage_angles, tolerance_kva, trafo_model,
trafo_loading, enforce_q_lims, suppress_warnings, Numba, **kwargs):
"""
Gets called by runpp or rundcpp with different arguments.
"""
net["converged"] = False
if (ac and not init == "results") or not ac:
reset_results(net)
# select elements in service (time consuming, so we do it once)
is_elems = _select_is_elements(net)
# convert pandapower net to ppc
ppc, ppci, bus_lookup = _pd2ppc(net, is_elems, calculate_voltage_angles, enforce_q_lims,
trafo_model, init_results=(init == "results"))
net["_ppc"] = ppc
if not "VERBOSE" in kwargs:
kwargs["VERBOSE"] = 0
# run the powerflow with or without warnings. If init='dc', AC PF will be
# initialized with DC voltages
if suppress_warnings:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
result = _runpf(ppci, init, ac, Numba, ppopt=ppopt.ppoption(ENFORCE_Q_LIMS=enforce_q_lims,
PF_TOL=tolerance_kva * 1e-3, **kwargs))[0]
else:
result = _runpf(ppci, init, ac, Numba, ppopt=ppopt.ppoption(ENFORCE_Q_LIMS=enforce_q_lims,
PF_TOL=tolerance_kva * 1e-3, **kwargs))[0]
# ppci doesn't contain out of service elements, but ppc does -> copy results accordingly
result = _copy_results_ppci_to_ppc(result, ppc)
# raise if PF was not successful. If DC -> success is always 1
if result["success"] != 1:
raise LoadflowNotConverged("Loadflow did not converge!")
else:
net["_ppc"] = result
net["converged"] = True
_extract_results(net, result, is_elems, bus_lookup, trafo_loading, ac)
_clean_up(net)
def _runpppf_dd(net, init, ac, calculate_voltage_angles, tolerance_kva,
trafo_model, trafo_loading, enforce_q_lims, numba, recycle,
**kwargs):
"""
Gets called by runpp or rundcpp with different arguments.
"""
net["converged"] = False
if (ac and not init == "results") or not ac:
reset_results(net)
# select elements in service (time consuming, so we do it once)
is_elems = _select_is_elements(net, recycle)
if recycle["ppc"] and "_ppc" in net and net[
"_ppc"] is not None and "_bus_lookup" in net:
# update the ppc from last cycle
ppc, ppci, bus_lookup = _update_ppc(net, is_elems, recycle,
calculate_voltage_angles,
enforce_q_lims, trafo_model)
else:
# convert pandapower net to ppc
ppc, ppci, bus_lookup = _pd2ppc(net,
is_elems,
calculate_voltage_angles,
enforce_q_lims,
trafo_model,
init_results=(init == "results"))
# store variables
net["_ppc"] = ppc
net["_bus_lookup"] = bus_lookup
net["_is_elems"] = is_elems
if not "VERBOSE" in kwargs:
kwargs["VERBOSE"] = 0
# run the powerflow
result = _run_fbsw(ppci,
ppopt=ppoption(ENFORCE_Q_LIMS=enforce_q_lims,
PF_TOL=tolerance_kva * 1e-3,
**kwargs))[0]
# ppci doesn't contain out of service elements, but ppc does -> copy results accordingly
result = _copy_results_ppci_to_ppc(result, ppc, bus_lookup)
# raise if PF was not successful. If DC -> success is always 1
if result["success"] != 1:
raise LoadflowNotConverged("Loadflow did not converge!")
else:
net["_ppc"] = result
net["converged"] = True
_extract_results(net, result, is_elems, bus_lookup, trafo_loading, ac)
_clean_up(net)
def t_makeLODF(quiet=False):
"""Tests for C{makeLODF}.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
ntests = 31
t_begin(ntests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_auction_case')
verbose = 0 #not quiet
## load case
ppc = loadcase(casefile)
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
r = rundcopf(ppc, ppopt)
baseMVA, bus, gen, branch = r['baseMVA'], r['bus'], r['gen'], r['branch']
_, bus, gen, branch = ext2int1(bus, gen, branch)
## compute injections and flows
F0 = branch[:, PF]
## create some PTDF matrices
H = makePTDF(baseMVA, bus, branch, 0)
## create some PTDF matrices
try:
LODF = makeLODF(branch, H)
except ZeroDivisionError:
pass
## take out non-essential lines one-by-one and see what happens
ppc['bus'] = bus
ppc['gen'] = gen
branch0 = branch
outages = r_[arange(12),
arange(13, 15),
arange(16, 18), [19],
arange(26, 33),
arange(34, 41)]
for k in outages:
ppc['branch'] = branch0.copy()
ppc['branch'][k, BR_STATUS] = 0
r, _ = rundcpf(ppc, ppopt)
baseMVA, bus, gen, branch = \
r['baseMVA'], r['bus'], r['gen'], r['branch']
F = branch[:, PF]
t_is(LODF[:, k], (F - F0) / F0[k], 6, 'LODF[:, %d]' % k)
t_end()
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 rundcopf(casedata=None, ppopt=None, fname="", solvedcase=""):
"""Runs a DC optimal power flow.
@see: L{runopf}, L{runduopf}
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), "case9")
ppopt = ppoption(ppopt, PF_DC=True)
return runopf(casedata, ppopt, fname, solvedcase)
def runduopf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a DC optimal power flow with unit-decommitment heuristic.
@see: L{rundcopf}, L{runuopf}
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt, PF_DC=True)
return runuopf(casedata, ppopt, fname, solvedcase)
def t_makeLODF(quiet=False):
"""Tests for C{makeLODF}.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
ntests = 31
t_begin(ntests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_auction_case')
verbose = 0#not quiet
## load case
ppc = loadcase(casefile)
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
r = rundcopf(ppc, ppopt)
baseMVA, bus, gen, branch = r['baseMVA'], r['bus'], r['gen'], r['branch']
_, bus, gen, branch = ext2int1(bus, gen, branch)
## compute injections and flows
F0 = branch[:, PF]
## create some PTDF matrices
H = makePTDF(baseMVA, bus, branch, 0)
## create some PTDF matrices
try:
LODF = makeLODF(branch, H)
except ZeroDivisionError:
pass
## take out non-essential lines one-by-one and see what happens
ppc['bus'] = bus
ppc['gen'] = gen
branch0 = branch
outages = r_[arange(12), arange(13, 15), arange(16, 18),
[19], arange(26, 33), arange(34, 41)]
for k in outages:
ppc['branch'] = branch0.copy()
ppc['branch'][k, BR_STATUS] = 0
r, _ = rundcpf(ppc, ppopt)
baseMVA, bus, gen, branch = \
r['baseMVA'], r['bus'], r['gen'], r['branch']
F = branch[:, PF]
t_is(LODF[:, k], (F - F0) / F0[k], 6, 'LODF[:, %d]' % k)
t_end()
def dcopf(*args, **kw_args):
"""Solves a DC optimal power flow.
This is a simple wrapper function around L{opf} that sets the C{PF_DC}
option to C{True} before calling L{opf}.
See L{opf} for the details of input and output arguments.
@see: L{rundcopf}
@author: Ray Zimmerman (PSERC Cornell)
"""
ppc, ppopt = opf_args2(*args, **kw_args)
ppopt = ppoption(ppopt, PF_DC=1)
return opf(ppc, ppopt)
def dcopf(*args, **kw_args):
"""Solves a DC optimal power flow.
This is a simple wrapper function around L{opf} that sets the C{PF_DC}
option to C{True} before calling L{opf}.
See L{opf} for the details of input and output arguments.
@see: L{rundcopf}
@author: Ray Zimmerman (PSERC Cornell)
"""
ppc, ppopt = opf_args2(*args, **kw_args);
ppopt = ppoption(ppopt, PF_DC=1)
return opf(ppc, ppopt)
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 rundcpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a DC power flow.
@see: L{runpf}
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
Changes by University of Kassel: Different runpf is imported
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt, PF_DC=True)
return runpf(casedata, ppopt, fname, solvedcase)
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 draw_network(fignr=754): from matplotlib.pyplot import figure, show import networkx as nx casedata = get_topology() ppc = casedata ppopt = ppoption(PF_ALG=2) ppc = ext2int(ppc) figure(fignr) g = nx.Graph() i = ppc['bus'][:, BUS_I].astype(int) g.add_nodes_from(i, bgcolor='green') #nx.draw_networkx_nodes(g,pos=nx.spring_layout(g)) fr = ppc['branch'][:, F_BUS].astype(int) to = ppc['branch'][:, T_BUS].astype(int) g.add_edges_from(zip(fr, to), color='magenta') nx.draw(g, with_labels=True, node_size=1000,node_color='skyblue',width=0.5) show()
def _get_options(options, **kwargs):
init_va_degree = options["init_va_degree"]
ac = options["ac"]
recycle = options["recycle"]
numba = options["numba"]
enforce_q_lims = options["enforce_q_lims"]
tolerance_kva = options["tolerance_kva"]
algorithm = options["algorithm"]
max_iteration = options["max_iteration"]
# algorithms implemented within pypower
algorithm_pypower_dict = {'nr': 1, 'fdbx': 2, 'fdxb': 3, 'gs': 4}
ppopt = ppoption(ENFORCE_Q_LIMS=enforce_q_lims, PF_TOL=tolerance_kva * 1e-3,
PF_ALG=algorithm_pypower_dict[algorithm], **kwargs)
ppopt['PF_MAX_IT'] = max_iteration
ppopt['PF_MAX_IT_GS'] = max_iteration
ppopt['PF_MAX_IT_FD'] = max_iteration
ppopt['VERBOSE'] = 0
return init_va_degree, ac, numba, recycle, ppopt
def draw_network(fignr=754):
from matplotlib.pyplot import figure, show
import networkx as nx
casedata = get_topology()
ppc = casedata
ppopt = ppoption(PF_ALG=2)
ppc = ext2int(ppc)
figure(fignr)
g = nx.Graph()
i = ppc['bus'][:, BUS_I].astype(int)
g.add_nodes_from(i, bgcolor='green')
#nx.draw_networkx_nodes(g,pos=nx.spring_layout(g))
fr = ppc['branch'][:, F_BUS].astype(int)
to = ppc['branch'][:, T_BUS].astype(int)
g.add_edges_from(zip(fr, to), color='magenta')
nx.draw(g,
with_labels=True,
node_size=1000,
node_color='skyblue',
width=0.5)
show()
def runopf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs an optimal power flow.
@see: L{rundcopf}, L{runuopf}
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
##----- run the optimal power flow -----
r = opf(casedata, ppopt)
##----- output results -----
if fname:
fd = None
try:
fd = open(fname, "wb")
except IOError, detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
def uopf(*args):
"""Solves combined unit decommitment / optimal power flow.
Solves a combined unit decommitment and optimal power flow for a single
time period. Uses an algorithm similar to dynamic programming. It proceeds
through a sequence of stages, where stage C{N} has C{N} generators shut
down, starting with C{N=0}. In each stage, it forms a list of candidates
(gens at their C{Pmin} limits) and computes the cost with each one of them
shut down. It selects the least cost case as the starting point for the
next stage, continuing until there are no more candidates to be shut down
or no more improvement can be gained by shutting something down.
If C{verbose} in ppopt (see L{ppoption} is C{true}, it prints progress
info, if it is > 1 it prints the output of each individual opf.
@see: L{opf}, L{runuopf}
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
##----- initialization -----
t0 = time() ## start timer
## process input arguments
ppc, ppopt = opf_args2(*args)
## options
verbose = ppopt["VERBOSE"]
if verbose: ## turn down verbosity one level for calls to opf
ppopt = ppoption(ppopt, VERBOSE=verbose - 1)
##----- do combined unit commitment/optimal power flow -----
## check for sum(Pmin) > total load, decommit as necessary
on = find( (ppc["gen"][:, GEN_STATUS] > 0) & ~isload(ppc["gen"]) ) ## gens in service
onld = find( (ppc["gen"][:, GEN_STATUS] > 0) & isload(ppc["gen"]) ) ## disp loads in serv
load_capacity = sum(ppc["bus"][:, PD]) - sum(ppc["gen"][onld, PMIN]) ## total load capacity
Pmin = ppc["gen"][on, PMIN]
while sum(Pmin) > load_capacity:
## shut down most expensive unit
avgPmincost = totcost(ppc["gencost"][on, :], Pmin) / Pmin
_, i = fairmax(avgPmincost) ## pick one with max avg cost at Pmin
i = on[i] ## convert to generator index
if verbose:
print('Shutting down generator %d so all Pmin limits can be satisfied.\n' % i)
## set generation to zero
ppc["gen"][i, [PG, QG, GEN_STATUS]] = 0
## update minimum gen capacity
on = find( (ppc["gen"][:, GEN_STATUS] > 0) & ~isload(ppc["gen"]) ) ## gens in service
Pmin = ppc["gen"][on, PMIN]
## run initial opf
results = opf(ppc, ppopt)
## best case so far
results1 = deepcopy(results)
## best case for this stage (ie. with n gens shut down, n=0,1,2 ...)
results0 = deepcopy(results1)
ppc["bus"] = results0["bus"].copy() ## use these V as starting point for OPF
while True:
## get candidates for shutdown
candidates = find((results0["gen"][:, MU_PMIN] > 0) & (results0["gen"][:, PMIN] > 0))
if len(candidates) == 0:
break
## do not check for further decommitment unless we
## see something better during this stage
done = True
for k in candidates:
## start with best for this stage
ppc["gen"] = results0["gen"].copy()
## shut down gen k
ppc["gen"][k, [PG, QG, GEN_STATUS]] = 0
## run opf
results = opf(ppc, ppopt)
## something better?
if results['success'] and (results["f"] < results1["f"]):
results1 = deepcopy(results)
k1 = k
done = False ## make sure we check for further decommitment
if done:
## decommits at this stage did not help, so let's quit
break
else:
## shutting something else down helps, so let's keep going
if verbose:
print('Shutting down generator %d.\n' % k1)
results0 = deepcopy(results1)
ppc["bus"] = results0["bus"].copy() ## use these V as starting point for OPF
## compute elapsed time
et = time() - t0
## finish preparing output
results0['et'] = et
return results0
def printpf(baseMVA, bus=None, gen=None, branch=None, f=None, success=None,
et=None, fd=None, ppopt=None):
"""Prints power flow results.
Prints power flow and optimal power flow results to C{fd} (a file
descriptor which defaults to C{stdout}), with the details of what
gets printed controlled by the optional C{ppopt} argument, which is a
PYPOWER options vector (see L{ppoption} for details).
The data can either be supplied in a single C{results} dict, or
in the individual arguments: C{baseMVA}, C{bus}, C{gen}, C{branch}, C{f},
C{success} and C{et}, where C{f} is the OPF objective function value,
C{success} is C{True} if the solution converged and C{False} otherwise,
and C{et} is the elapsed time for the computation in seconds. If C{f} is
given, it is assumed that the output is from an OPF run, otherwise it is
assumed to be a simple power flow run.
Examples::
ppopt = ppoptions(OUT_GEN=1, OUT_BUS=0, OUT_BRANCH=0)
fd = open(fname, 'w+b')
results = runopf(ppc)
printpf(results)
printpf(results, fd)
printpf(results, fd, ppopt)
printpf(baseMVA, bus, gen, branch, f, success, et)
printpf(baseMVA, bus, gen, branch, f, success, et, fd)
printpf(baseMVA, bus, gen, branch, f, success, et, fd, ppopt)
fd.close()
@author: Ray Zimmerman (PSERC Cornell)
"""
##----- initialization -----
## default arguments
if isinstance(baseMVA, dict):
have_results_struct = 1
results = baseMVA
if gen is None:
ppopt = ppoption() ## use default options
else:
ppopt = gen
if (ppopt['OUT_ALL'] == 0):
return ## nothin' to see here, bail out now
if bus is None:
fd = stdout ## print to stdout by default
else:
fd = bus
baseMVA, bus, gen, branch, success, et = \
results["baseMVA"], results["bus"], results["gen"], \
results["branch"], results["success"], results["et"]
if 'f' in results:
f = results["f"]
else:
f = None
else:
have_results_struct = 0
if ppopt is None:
ppopt = ppoption() ## use default options
if fd is None:
fd = stdout ## print to stdout by default
if ppopt['OUT_ALL'] == 0:
return ## nothin' to see here, bail out now
isOPF = f is not None ## FALSE -> only simple PF data, TRUE -> OPF data
## options
isDC = ppopt['PF_DC'] ## use DC formulation?
OUT_ALL = ppopt['OUT_ALL']
OUT_ANY = OUT_ALL == 1 ## set to true if any pretty output is to be generated
OUT_SYS_SUM = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_SYS_SUM'])
OUT_AREA_SUM = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_AREA_SUM'])
OUT_BUS = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_BUS'])
OUT_BRANCH = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_BRANCH'])
OUT_GEN = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_GEN'])
OUT_ANY = OUT_ANY | ((OUT_ALL == -1) and
(OUT_SYS_SUM or OUT_AREA_SUM or OUT_BUS or
OUT_BRANCH or OUT_GEN))
if OUT_ALL == -1:
OUT_ALL_LIM = ppopt['OUT_ALL_LIM']
elif OUT_ALL == 1:
OUT_ALL_LIM = 2
else:
OUT_ALL_LIM = 0
OUT_ANY = OUT_ANY or (OUT_ALL_LIM >= 1)
if OUT_ALL_LIM == -1:
OUT_V_LIM = ppopt['OUT_V_LIM']
OUT_LINE_LIM = ppopt['OUT_LINE_LIM']
OUT_PG_LIM = ppopt['OUT_PG_LIM']
OUT_QG_LIM = ppopt['OUT_QG_LIM']
else:
OUT_V_LIM = OUT_ALL_LIM
OUT_LINE_LIM = OUT_ALL_LIM
OUT_PG_LIM = OUT_ALL_LIM
OUT_QG_LIM = OUT_ALL_LIM
OUT_ANY = OUT_ANY or ((OUT_ALL_LIM == -1) and (OUT_V_LIM or OUT_LINE_LIM or OUT_PG_LIM or OUT_QG_LIM))
ptol = 1e-4 ## tolerance for displaying shadow prices
## create map of external bus numbers to bus indices
i2e = bus[:, BUS_I].astype(int)
e2i = zeros(max(i2e) + 1, int)
e2i[i2e] = arange(bus.shape[0])
## sizes of things
nb = bus.shape[0] ## number of buses
nl = branch.shape[0] ## number of branches
ng = gen.shape[0] ## number of generators
## zero out some data to make printout consistent for DC case
if isDC:
bus[:, r_[QD, BS]] = zeros((nb, 2))
gen[:, r_[QG, QMAX, QMIN]] = zeros((ng, 3))
branch[:, r_[BR_R, BR_B]] = zeros((nl, 2))
## parameters
ties = find(bus[e2i[branch[:, F_BUS].astype(int)], BUS_AREA] !=
bus[e2i[branch[:, T_BUS].astype(int)], BUS_AREA])
## area inter-ties
tap = ones(nl) ## default tap ratio = 1 for lines
xfmr = find(branch[:, TAP]) ## indices of transformers
tap[xfmr] = branch[xfmr, TAP] ## include transformer tap ratios
tap = tap * exp(1j * pi / 180 * branch[:, SHIFT]) ## add phase shifters
nzld = find((bus[:, PD] != 0.0) | (bus[:, QD] != 0.0))
sorted_areas = sort(bus[:, BUS_AREA])
## area numbers
s_areas = sorted_areas[r_[1, find(diff(sorted_areas)) + 1]]
nzsh = find((bus[:, GS] != 0.0) | (bus[:, BS] != 0.0))
allg = find( ~isload(gen) )
ong = find( (gen[:, GEN_STATUS] > 0) & ~isload(gen) )
onld = find( (gen[:, GEN_STATUS] > 0) & isload(gen) )
V = bus[:, VM] * exp(-1j * pi / 180 * bus[:, VA])
out = find(branch[:, BR_STATUS] == 0) ## out-of-service branches
nout = len(out)
if isDC:
loss = zeros(nl)
else:
loss = baseMVA * abs(V[e2i[ branch[:, F_BUS].astype(int) ]] / tap -
V[e2i[ branch[:, T_BUS].astype(int) ]])**2 / \
(branch[:, BR_R] - 1j * branch[:, BR_X])
fchg = abs(V[e2i[ branch[:, F_BUS].astype(int) ]] / tap)**2 * branch[:, BR_B] * baseMVA / 2
tchg = abs(V[e2i[ branch[:, T_BUS].astype(int) ]] )**2 * branch[:, BR_B] * baseMVA / 2
loss[out] = zeros(nout)
fchg[out] = zeros(nout)
tchg[out] = zeros(nout)
##----- print the stuff -----
if OUT_ANY:
## convergence & elapsed time
if success:
fd.write('\nConverged in %.2f seconds' % et)
else:
fd.write('\nDid not converge (%.2f seconds)\n' % et)
## objective function value
if isOPF:
fd.write('\nObjective Function Value = %.2f $/hr' % f)
if OUT_SYS_SUM:
fd.write('\n================================================================================')
fd.write('\n| System Summary |')
fd.write('\n================================================================================')
fd.write('\n\nHow many? How much? P (MW) Q (MVAr)')
fd.write('\n--------------------- ------------------- ------------- -----------------')
fd.write('\nBuses %6d Total Gen Capacity %7.1f %7.1f to %.1f' % (nb, sum(gen[allg, PMAX]), sum(gen[allg, QMIN]), sum(gen[allg, QMAX])))
fd.write('\nGenerators %5d On-line Capacity %7.1f %7.1f to %.1f' % (len(allg), sum(gen[ong, PMAX]), sum(gen[ong, QMIN]), sum(gen[ong, QMAX])))
fd.write('\nCommitted Gens %5d Generation (actual) %7.1f %7.1f' % (len(ong), sum(gen[ong, PG]), sum(gen[ong, QG])))
fd.write('\nLoads %5d Load %7.1f %7.1f' % (len(nzld)+len(onld), sum(bus[nzld, PD])-sum(gen[onld, PG]), sum(bus[nzld, QD])-sum(gen[onld, QG])))
fd.write('\n Fixed %5d Fixed %7.1f %7.1f' % (len(nzld), sum(bus[nzld, PD]), sum(bus[nzld, QD])))
fd.write('\n Dispatchable %5d Dispatchable %7.1f of %-7.1f%7.1f' % (len(onld), -sum(gen[onld, PG]), -sum(gen[onld, PMIN]), -sum(gen[onld, QG])))
fd.write('\nShunts %5d Shunt (inj) %7.1f %7.1f' % (len(nzsh),
-sum(bus[nzsh, VM]**2 * bus[nzsh, GS]), sum(bus[nzsh, VM]**2 * bus[nzsh, BS]) ))
fd.write('\nBranches %5d Losses (I^2 * Z) %8.2f %8.2f' % (nl, sum(loss.real), sum(loss.imag) ))
fd.write('\nTransformers %5d Branch Charging (inj) - %7.1f' % (len(xfmr), sum(fchg) + sum(tchg) ))
fd.write('\nInter-ties %5d Total Inter-tie Flow %7.1f %7.1f' % (len(ties), sum(abs(branch[ties, PF]-branch[ties, PT])) / 2, sum(abs(branch[ties, QF]-branch[ties, QT])) / 2))
fd.write('\nAreas %5d' % len(s_areas))
fd.write('\n')
fd.write('\n Minimum Maximum')
fd.write('\n ------------------------- --------------------------------')
minv = min(bus[:, VM])
mini = argmin(bus[:, VM])
maxv = max(bus[:, VM])
maxi = argmax(bus[:, VM])
fd.write('\nVoltage Magnitude %7.3f p.u. @ bus %-4d %7.3f p.u. @ bus %-4d' % (minv, bus[mini, BUS_I], maxv, bus[maxi, BUS_I]))
minv = min(bus[:, VA])
mini = argmin(bus[:, VA])
maxv = max(bus[:, VA])
maxi = argmax(bus[:, VA])
fd.write('\nVoltage Angle %8.2f deg @ bus %-4d %8.2f deg @ bus %-4d' % (minv, bus[mini, BUS_I], maxv, bus[maxi, BUS_I]))
if not isDC:
maxv = max(loss.real)
maxi = argmax(loss.real)
fd.write('\nP Losses (I^2*R) - %8.2f MW @ line %d-%d' % (maxv, branch[maxi, F_BUS], branch[maxi, T_BUS]))
maxv = max(loss.imag)
maxi = argmax(loss.imag)
fd.write('\nQ Losses (I^2*X) - %8.2f MVAr @ line %d-%d' % (maxv, branch[maxi, F_BUS], branch[maxi, T_BUS]))
if isOPF:
minv = min(bus[:, LAM_P])
mini = argmin(bus[:, LAM_P])
maxv = max(bus[:, LAM_P])
maxi = argmax(bus[:, LAM_P])
fd.write('\nLambda P %8.2f $/MWh @ bus %-4d %8.2f $/MWh @ bus %-4d' % (minv, bus[mini, BUS_I], maxv, bus[maxi, BUS_I]))
minv = min(bus[:, LAM_Q])
mini = argmin(bus[:, LAM_Q])
maxv = max(bus[:, LAM_Q])
maxi = argmax(bus[:, LAM_Q])
fd.write('\nLambda Q %8.2f $/MWh @ bus %-4d %8.2f $/MWh @ bus %-4d' % (minv, bus[mini, BUS_I], maxv, bus[maxi, BUS_I]))
fd.write('\n')
if OUT_AREA_SUM:
fd.write('\n================================================================================')
fd.write('\n| Area Summary |')
fd.write('\n================================================================================')
fd.write('\nArea # of # of Gens # of Loads # of # of # of # of')
fd.write('\n Num Buses Total Online Total Fixed Disp Shunt Brchs Xfmrs Ties')
fd.write('\n---- ----- ----- ------ ----- ----- ----- ----- ----- ----- -----')
for i in range(len(s_areas)):
a = s_areas[i]
ib = find(bus[:, BUS_AREA] == a)
ig = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a) & ~isload(gen))
igon = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a) & (gen[:, GEN_STATUS] > 0) & ~isload(gen))
ildon = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a) & (gen[:, GEN_STATUS] > 0) & isload(gen))
inzld = find((bus[:, BUS_AREA] == a) & logical_or(bus[:, PD], bus[:, QD]))
inzsh = find((bus[:, BUS_AREA] == a) & logical_or(bus[:, GS], bus[:, BS]))
ibrch = find((bus[e2i[branch[:, F_BUS].astype(int)], BUS_AREA] == a) & (bus[e2i[branch[:, T_BUS].astype(int)], BUS_AREA] == a))
in_tie = find((bus[e2i[branch[:, F_BUS].astype(int)], BUS_AREA] == a) & (bus[e2i[branch[:, T_BUS].astype(int)], BUS_AREA] != a))
out_tie = find((bus[e2i[branch[:, F_BUS].astype(int)], BUS_AREA] != a) & (bus[e2i[branch[:, T_BUS].astype(int)], BUS_AREA] == a))
if not any(xfmr + 1):
nxfmr = 0
else:
nxfmr = len(find((bus[e2i[branch[xfmr, F_BUS].astype(int)], BUS_AREA] == a) & (bus[e2i[branch[xfmr, T_BUS].astype(int)], BUS_AREA] == a)))
fd.write('\n%3d %6d %5d %5d %5d %5d %5d %5d %5d %5d %5d' %
(a, len(ib), len(ig), len(igon), \
len(inzld)+len(ildon), len(inzld), len(ildon), \
len(inzsh), len(ibrch), nxfmr, len(in_tie)+len(out_tie)))
fd.write('\n---- ----- ----- ------ ----- ----- ----- ----- ----- ----- -----')
fd.write('\nTot: %6d %5d %5d %5d %5d %5d %5d %5d %5d %5d' %
(nb, len(allg), len(ong), len(nzld)+len(onld),
len(nzld), len(onld), len(nzsh), nl, len(xfmr), len(ties)))
fd.write('\n')
fd.write('\nArea Total Gen Capacity On-line Gen Capacity Generation')
fd.write('\n Num MW MVAr MW MVAr MW MVAr')
fd.write('\n---- ------ ------------------ ------ ------------------ ------ ------')
for i in range(len(s_areas)):
a = s_areas[i]
ig = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a) & ~isload(gen))
igon = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a) & (gen[:, GEN_STATUS] > 0) & ~isload(gen))
fd.write('\n%3d %7.1f %7.1f to %-7.1f %7.1f %7.1f to %-7.1f %7.1f %7.1f' %
(a, sum(gen[ig, PMAX]), sum(gen[ig, QMIN]), sum(gen[ig, QMAX]),
sum(gen[igon, PMAX]), sum(gen[igon, QMIN]), sum(gen[igon, QMAX]),
sum(gen[igon, PG]), sum(gen[igon, QG]) ))
fd.write('\n---- ------ ------------------ ------ ------------------ ------ ------')
fd.write('\nTot: %7.1f %7.1f to %-7.1f %7.1f %7.1f to %-7.1f %7.1f %7.1f' %
(sum(gen[allg, PMAX]), sum(gen[allg, QMIN]), sum(gen[allg, QMAX]),
sum(gen[ong, PMAX]), sum(gen[ong, QMIN]), sum(gen[ong, QMAX]),
sum(gen[ong, PG]), sum(gen[ong, QG]) ))
fd.write('\n')
fd.write('\nArea Disp Load Cap Disp Load Fixed Load Total Load')
fd.write('\n Num MW MVAr MW MVAr MW MVAr MW MVAr')
fd.write('\n---- ------ ------ ------ ------ ------ ------ ------ ------')
Qlim = (gen[:, QMIN] == 0) * gen[:, QMAX] + (gen[:, QMAX] == 0) * gen[:, QMIN]
for i in range(len(s_areas)):
a = s_areas[i]
ildon = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a) & (gen[:, GEN_STATUS] > 0) & isload(gen))
inzld = find((bus[:, BUS_AREA] == a) & logical_or(bus[:, PD], bus[:, QD]))
fd.write('\n%3d %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f' %
(a, -sum(gen[ildon, PMIN]),
-sum(Qlim[ildon]),
-sum(gen[ildon, PG]), -sum(gen[ildon, QG]),
sum(bus[inzld, PD]), sum(bus[inzld, QD]),
-sum(gen[ildon, PG]) + sum(bus[inzld, PD]),
-sum(gen[ildon, QG]) + sum(bus[inzld, QD]) ))
fd.write('\n---- ------ ------ ------ ------ ------ ------ ------ ------')
fd.write('\nTot: %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f' %
(-sum(gen[onld, PMIN]),
-sum(Qlim[onld]),
-sum(gen[onld, PG]), -sum(gen[onld, QG]),
sum(bus[nzld, PD]), sum(bus[nzld, QD]),
-sum(gen[onld, PG]) + sum(bus[nzld, PD]),
-sum(gen[onld, QG]) + sum(bus[nzld, QD])) )
fd.write('\n')
fd.write('\nArea Shunt Inj Branch Series Losses Net Export')
fd.write('\n Num MW MVAr Charging MW MVAr MW MVAr')
fd.write('\n---- ------ ------ -------- ------ ------ ------ ------')
for i in range(len(s_areas)):
a = s_areas[i]
inzsh = find((bus[:, BUS_AREA] == a) & logical_or(bus[:, GS], bus[:, BS]))
ibrch = find((bus[e2i[branch[:, F_BUS].astype(int)], BUS_AREA] == a) & (bus[e2i[branch[:, T_BUS].astype(int)], BUS_AREA] == a) & branch[:, BR_STATUS].astype(bool))
in_tie = find((bus[e2i[branch[:, F_BUS].astype(int)], BUS_AREA] != a) & (bus[e2i[branch[:, T_BUS].astype(int)], BUS_AREA] == a) & branch[:, BR_STATUS].astype(bool))
out_tie = find((bus[e2i[branch[:, F_BUS].astype(int)], BUS_AREA] == a) & (bus[e2i[branch[:, T_BUS].astype(int)], BUS_AREA] != a) & branch[:, BR_STATUS].astype(bool))
fd.write('\n%3d %7.1f %7.1f %7.1f %7.2f %7.2f %7.1f %7.1f' %
(a, -sum(bus[inzsh, VM]**2 * bus[inzsh, GS]),
sum(bus[inzsh, VM]**2 * bus[inzsh, BS]),
sum(fchg[ibrch]) + sum(tchg[ibrch]) + sum(fchg[out_tie]) + sum(tchg[in_tie]),
sum(real(loss[ibrch])) + sum(real(loss[r_[in_tie, out_tie]])) / 2,
sum(imag(loss[ibrch])) + sum(imag(loss[r_[in_tie, out_tie]])) / 2,
sum(branch[in_tie, PT])+sum(branch[out_tie, PF]) - sum(real(loss[r_[in_tie, out_tie]])) / 2,
sum(branch[in_tie, QT])+sum(branch[out_tie, QF]) - sum(imag(loss[r_[in_tie, out_tie]])) / 2 ))
fd.write('\n---- ------ ------ -------- ------ ------ ------ ------')
fd.write('\nTot: %7.1f %7.1f %7.1f %7.2f %7.2f - -' %
(-sum(bus[nzsh, VM]**2 * bus[nzsh, GS]),
sum(bus[nzsh, VM]**2 * bus[nzsh, BS]),
sum(fchg) + sum(tchg), sum(real(loss)), sum(imag(loss)) ))
fd.write('\n')
## generator data
if OUT_GEN:
if isOPF:
genlamP = bus[e2i[gen[:, GEN_BUS].astype(int)], LAM_P]
genlamQ = bus[e2i[gen[:, GEN_BUS].astype(int)], LAM_Q]
fd.write('\n================================================================================')
fd.write('\n| Generator Data |')
fd.write('\n================================================================================')
fd.write('\n Gen Bus Status Pg Qg ')
if isOPF: fd.write(' Lambda ($/MVA-hr)')
fd.write('\n # # (MW) (MVAr) ')
if isOPF: fd.write(' P Q ')
fd.write('\n---- ----- ------ -------- --------')
if isOPF: fd.write(' -------- --------')
for k in range(len(ong)):
i = ong[k]
fd.write('\n%3d %6d %2d ' % (i, gen[i, GEN_BUS], gen[i, GEN_STATUS]))
if (gen[i, GEN_STATUS] > 0) & logical_or(gen[i, PG], gen[i, QG]):
fd.write('%10.2f%10.2f' % (gen[i, PG], gen[i, QG]))
else:
fd.write(' - - ')
if isOPF: fd.write('%10.2f%10.2f' % (genlamP[i], genlamQ[i]))
fd.write('\n -------- --------')
fd.write('\n Total: %9.2f%10.2f' % (sum(gen[ong, PG]), sum(gen[ong, QG])))
fd.write('\n')
if any(onld + 1):
fd.write('\n================================================================================')
fd.write('\n| Dispatchable Load Data |')
fd.write('\n================================================================================')
fd.write('\n Gen Bus Status Pd Qd ')
if isOPF: fd.write(' Lambda ($/MVA-hr)')
fd.write('\n # # (MW) (MVAr) ')
if isOPF: fd.write(' P Q ')
fd.write('\n---- ----- ------ -------- --------')
if isOPF: fd.write(' -------- --------')
for k in range(len(onld)):
i = onld[k]
fd.write('\n%3d %6d %2d ' % (i, gen[i, GEN_BUS], gen[i, GEN_STATUS]))
if (gen[i, GEN_STATUS] > 0) & logical_or(gen[i, PG], gen[i, QG]):
fd.write('%10.2f%10.2f' % (-gen[i, PG], -gen[i, QG]))
else:
fd.write(' - - ')
if isOPF: fd.write('%10.2f%10.2f' % (genlamP[i], genlamQ[i]))
fd.write('\n -------- --------')
fd.write('\n Total: %9.2f%10.2f' % (-sum(gen[onld, PG]), -sum(gen[onld, QG])))
fd.write('\n')
## bus data
if OUT_BUS:
fd.write('\n================================================================================')
fd.write('\n| Bus Data |')
fd.write('\n================================================================================')
fd.write('\n Bus Voltage Generation Load ')
if isOPF: fd.write(' Lambda($/MVA-hr)')
fd.write('\n # Mag(pu) Ang(deg) P (MW) Q (MVAr) P (MW) Q (MVAr)')
if isOPF: fd.write(' P Q ')
fd.write('\n----- ------- -------- -------- -------- -------- --------')
if isOPF: fd.write(' ------- -------')
for i in range(nb):
fd.write('\n%5d%7.3f%9.3f' % tuple(bus[i, [BUS_I, VM, VA]]))
if bus[i, BUS_TYPE] == REF:
fd.write('*')
else:
fd.write(' ')
g = find((gen[:, GEN_STATUS] > 0) & (gen[:, GEN_BUS] == bus[i, BUS_I]) &
~isload(gen))
ld = find((gen[:, GEN_STATUS] > 0) & (gen[:, GEN_BUS] == bus[i, BUS_I]) &
isload(gen))
if any(g + 1):
fd.write('%9.2f%10.2f' % (sum(gen[g, PG]), sum(gen[g, QG])))
else:
fd.write(' - - ')
if logical_or(bus[i, PD], bus[i, QD]) | any(ld + 1):
if any(ld + 1):
fd.write('%10.2f*%9.2f*' % (bus[i, PD] - sum(gen[ld, PG]),
bus[i, QD] - sum(gen[ld, QG])))
else:
fd.write('%10.2f%10.2f ' % tuple(bus[i, [PD, QD]]))
else:
fd.write(' - - ')
if isOPF:
fd.write('%9.3f' % bus[i, LAM_P])
if abs(bus[i, LAM_Q]) > ptol:
fd.write('%8.3f' % bus[i, LAM_Q])
else:
fd.write(' -')
fd.write('\n -------- -------- -------- --------')
fd.write('\n Total: %9.2f %9.2f %9.2f %9.2f' %
(sum(gen[ong, PG]), sum(gen[ong, QG]),
sum(bus[nzld, PD]) - sum(gen[onld, PG]),
sum(bus[nzld, QD]) - sum(gen[onld, QG])))
fd.write('\n')
## branch data
if OUT_BRANCH:
fd.write('\n================================================================================')
fd.write('\n| Branch Data |')
fd.write('\n================================================================================')
fd.write('\nBrnch From To From Bus Injection To Bus Injection Loss (I^2 * Z) ')
fd.write('\n # Bus Bus P (MW) Q (MVAr) P (MW) Q (MVAr) P (MW) Q (MVAr)')
fd.write('\n----- ----- ----- -------- -------- -------- -------- -------- --------')
for i in range(nl):
fd.write('\n%4d%7d%7d%10.2f%10.2f%10.2f%10.2f%10.3f%10.2f' %
(i, branch[i, F_BUS], branch[i, T_BUS],
branch[i, PF], branch[i, QF], branch[i, PT], branch[i, QT],
loss[i].real, loss[i].imag))
fd.write('\n -------- --------')
fd.write('\n Total:%10.3f%10.2f' %
(sum(real(loss)), sum(imag(loss))))
fd.write('\n')
##----- constraint data -----
if isOPF:
ctol = ppopt['OPF_VIOLATION'] ## constraint violation tolerance
## voltage constraints
if (not isDC) & (OUT_V_LIM == 2 | (OUT_V_LIM == 1 &
(any(bus[:, VM] < bus[:, VMIN] + ctol) |
any(bus[:, VM] > bus[:, VMAX] - ctol) |
any(bus[:, MU_VMIN] > ptol) |
any(bus[:, MU_VMAX] > ptol)))):
fd.write('\n================================================================================')
fd.write('\n| Voltage Constraints |')
fd.write('\n================================================================================')
fd.write('\nBus # Vmin mu Vmin |V| Vmax Vmax mu')
fd.write('\n----- -------- ----- ----- ----- --------')
for i in range(nb):
if (OUT_V_LIM == 2) | (OUT_V_LIM == 1 &
((bus[i, VM] < bus[i, VMIN] + ctol) |
(bus[i, VM] > bus[i, VMAX] - ctol) |
(bus[i, MU_VMIN] > ptol) |
(bus[i, MU_VMAX] > ptol))):
fd.write('\n%5d' % bus[i, BUS_I])
if ((bus[i, VM] < bus[i, VMIN] + ctol) |
(bus[i, MU_VMIN] > ptol)):
fd.write('%10.3f' % bus[i, MU_VMIN])
else:
fd.write(' - ')
fd.write('%8.3f%7.3f%7.3f' % tuple(bus[i, [VMIN, VM, VMAX]]))
if (bus[i, VM] > bus[i, VMAX] - ctol) | (bus[i, MU_VMAX] > ptol):
fd.write('%10.3f' % bus[i, MU_VMAX])
else:
fd.write(' - ')
fd.write('\n')
## generator P constraints
if (OUT_PG_LIM == 2) | \
((OUT_PG_LIM == 1) & (any(gen[ong, PG] < gen[ong, PMIN] + ctol) |
any(gen[ong, PG] > gen[ong, PMAX] - ctol) |
any(gen[ong, MU_PMIN] > ptol) |
any(gen[ong, MU_PMAX] > ptol))) | \
((not isDC) & ((OUT_QG_LIM == 2) |
((OUT_QG_LIM == 1) & (any(gen[ong, QG] < gen[ong, QMIN] + ctol) |
any(gen[ong, QG] > gen[ong, QMAX] - ctol) |
any(gen[ong, MU_QMIN] > ptol) |
any(gen[ong, MU_QMAX] > ptol))))):
fd.write('\n================================================================================')
fd.write('\n| Generation Constraints |')
fd.write('\n================================================================================')
if (OUT_PG_LIM == 2) | ((OUT_PG_LIM == 1) &
(any(gen[ong, PG] < gen[ong, PMIN] + ctol) |
any(gen[ong, PG] > gen[ong, PMAX] - ctol) |
any(gen[ong, MU_PMIN] > ptol) |
any(gen[ong, MU_PMAX] > ptol))):
fd.write('\n Gen Bus Active Power Limits')
fd.write('\n # # Pmin mu Pmin Pg Pmax Pmax mu')
fd.write('\n---- ----- ------- -------- -------- -------- -------')
for k in range(len(ong)):
i = ong[k]
if (OUT_PG_LIM == 2) | ((OUT_PG_LIM == 1) &
((gen[i, PG] < gen[i, PMIN] + ctol) |
(gen[i, PG] > gen[i, PMAX] - ctol) |
(gen[i, MU_PMIN] > ptol) | (gen[i, MU_PMAX] > ptol))):
fd.write('\n%4d%6d ' % (i, gen[i, GEN_BUS]))
if (gen[i, PG] < gen[i, PMIN] + ctol) | (gen[i, MU_PMIN] > ptol):
fd.write('%8.3f' % gen[i, MU_PMIN])
else:
fd.write(' - ')
if gen[i, PG]:
fd.write('%10.2f%10.2f%10.2f' % tuple(gen[i, [PMIN, PG, PMAX]]))
else:
fd.write('%10.2f - %10.2f' % tuple(gen[i, [PMIN, PMAX]]))
if (gen[i, PG] > gen[i, PMAX] - ctol) | (gen[i, MU_PMAX] > ptol):
fd.write('%9.3f' % gen[i, MU_PMAX])
else:
fd.write(' - ')
fd.write('\n')
## generator Q constraints
if (not isDC) & ((OUT_QG_LIM == 2) | ((OUT_QG_LIM == 1) &
(any(gen[ong, QG] < gen[ong, QMIN] + ctol) |
any(gen[ong, QG] > gen[ong, QMAX] - ctol) |
any(gen[ong, MU_QMIN] > ptol) |
any(gen[ong, MU_QMAX] > ptol)))):
fd.write('\nGen Bus Reactive Power Limits')
fd.write('\n # # Qmin mu Qmin Qg Qmax Qmax mu')
fd.write('\n--- --- ------- -------- -------- -------- -------')
for k in range(len(ong)):
i = ong[k]
if (OUT_QG_LIM == 2) | ((OUT_QG_LIM == 1) &
((gen[i, QG] < gen[i, QMIN] + ctol) |
(gen[i, QG] > gen[i, QMAX] - ctol) |
(gen[i, MU_QMIN] > ptol) |
(gen[i, MU_QMAX] > ptol))):
fd.write('\n%3d%5d' % (i, gen[i, GEN_BUS]))
if (gen[i, QG] < gen[i, QMIN] + ctol) | (gen[i, MU_QMIN] > ptol):
fd.write('%8.3f' % gen[i, MU_QMIN])
else:
fd.write(' - ')
if gen[i, QG]:
fd.write('%10.2f%10.2f%10.2f' % tuple(gen[i, [QMIN, QG, QMAX]]))
else:
fd.write('%10.2f - %10.2f' % tuple(gen[i, [QMIN, QMAX]]))
if (gen[i, QG] > gen[i, QMAX] - ctol) | (gen[i, MU_QMAX] > ptol):
fd.write('%9.3f' % gen[i, MU_QMAX])
else:
fd.write(' - ')
fd.write('\n')
## dispatchable load P constraints
if (OUT_PG_LIM == 2) | (OUT_QG_LIM == 2) | \
((OUT_PG_LIM == 1) & (any(gen[onld, PG] < gen[onld, PMIN] + ctol) |
any(gen[onld, PG] > gen[onld, PMAX] - ctol) |
any(gen[onld, MU_PMIN] > ptol) |
any(gen[onld, MU_PMAX] > ptol))) | \
((OUT_QG_LIM == 1) & (any(gen[onld, QG] < gen[onld, QMIN] + ctol) |
any(gen[onld, QG] > gen[onld, QMAX] - ctol) |
any(gen[onld, MU_QMIN] > ptol) |
any(gen[onld, MU_QMAX] > ptol))):
fd.write('\n================================================================================')
fd.write('\n| Dispatchable Load Constraints |')
fd.write('\n================================================================================')
if (OUT_PG_LIM == 2) | ((OUT_PG_LIM == 1) &
(any(gen[onld, PG] < gen[onld, PMIN] + ctol) |
any(gen[onld, PG] > gen[onld, PMAX] - ctol) |
any(gen[onld, MU_PMIN] > ptol) |
any(gen[onld, MU_PMAX] > ptol))):
fd.write('\nGen Bus Active Power Limits')
fd.write('\n # # Pmin mu Pmin Pg Pmax Pmax mu')
fd.write('\n--- --- ------- -------- -------- -------- -------')
for k in range(len(onld)):
i = onld[k]
if (OUT_PG_LIM == 2) | ((OUT_PG_LIM == 1) &
((gen[i, PG] < gen[i, PMIN] + ctol) |
(gen[i, PG] > gen[i, PMAX] - ctol) |
(gen[i, MU_PMIN] > ptol) |
(gen[i, MU_PMAX] > ptol))):
fd.write('\n%3d%5d' % (i, gen[i, GEN_BUS]))
if (gen[i, PG] < gen[i, PMIN] + ctol) | (gen[i, MU_PMIN] > ptol):
fd.write('%8.3f' % gen[i, MU_PMIN])
else:
fd.write(' - ')
if gen[i, PG]:
fd.write('%10.2f%10.2f%10.2f' % gen[i, [PMIN, PG, PMAX]])
else:
fd.write('%10.2f - %10.2f' % gen[i, [PMIN, PMAX]])
if (gen[i, PG] > gen[i, PMAX] - ctol) | (gen[i, MU_PMAX] > ptol):
fd.write('%9.3f' % gen[i, MU_PMAX])
else:
fd.write(' - ')
fd.write('\n')
## dispatchable load Q constraints
if (not isDC) & ((OUT_QG_LIM == 2) | ((OUT_QG_LIM == 1) &
(any(gen[onld, QG] < gen[onld, QMIN] + ctol) |
any(gen[onld, QG] > gen[onld, QMAX] - ctol) |
any(gen[onld, MU_QMIN] > ptol) |
any(gen[onld, MU_QMAX] > ptol)))):
fd.write('\nGen Bus Reactive Power Limits')
fd.write('\n # # Qmin mu Qmin Qg Qmax Qmax mu')
fd.write('\n--- --- ------- -------- -------- -------- -------')
for k in range(len(onld)):
i = onld[k]
if (OUT_QG_LIM == 2) | ((OUT_QG_LIM == 1) &
((gen[i, QG] < gen[i, QMIN] + ctol) |
(gen[i, QG] > gen[i, QMAX] - ctol) |
(gen[i, MU_QMIN] > ptol) |
(gen[i, MU_QMAX] > ptol))):
fd.write('\n%3d%5d' % (i, gen(i, GEN_BUS)))
if (gen[i, QG] < gen[i, QMIN] + ctol) | (gen[i, MU_QMIN] > ptol):
fd.write('%8.3f' % gen[i, MU_QMIN])
else:
fd.write(' - ')
if gen[i, QG]:
fd.write('%10.2f%10.2f%10.2f' % gen[i, [QMIN, QG, QMAX]])
else:
fd.write('%10.2f - %10.2f' % gen[i, [QMIN, QMAX]])
if (gen[i, QG] > gen[i, QMAX] - ctol) | (gen[i, MU_QMAX] > ptol):
fd.write('%9.3f' % gen[i, MU_QMAX])
else:
fd.write(' - ')
fd.write('\n')
## line flow constraints
if (ppopt['OPF_FLOW_LIM'] == 1) | isDC: ## P limit
Ff = branch[:, PF]
Ft = branch[:, PT]
strg = '\n # Bus Pf mu Pf |Pmax| Pt Pt mu Bus'
elif ppopt['OPF_FLOW_LIM'] == 2: ## |I| limit
Ff = abs( (branch[:, PF] + 1j * branch[:, QF]) / V[e2i[branch[:, F_BUS].astype(int)]] )
Ft = abs( (branch[:, PT] + 1j * branch[:, QT]) / V[e2i[branch[:, T_BUS].astype(int)]] )
strg = '\n # Bus |If| mu |If| |Imax| |It| |It| mu Bus'
else: ## |S| limit
Ff = abs(branch[:, PF] + 1j * branch[:, QF])
Ft = abs(branch[:, PT] + 1j * branch[:, QT])
strg = '\n # Bus |Sf| mu |Sf| |Smax| |St| |St| mu Bus'
if (OUT_LINE_LIM == 2) | ((OUT_LINE_LIM == 1) &
(any((branch[:, RATE_A] != 0) & (abs(Ff) > branch[:, RATE_A] - ctol)) |
any((branch[:, RATE_A] != 0) & (abs(Ft) > branch[:, RATE_A] - ctol)) |
any(branch[:, MU_SF] > ptol) |
any(branch[:, MU_ST] > ptol))):
fd.write('\n================================================================================')
fd.write('\n| Branch Flow Constraints |')
fd.write('\n================================================================================')
fd.write('\nBrnch From "From" End Limit "To" End To')
fd.write(strg)
fd.write('\n----- ----- ------- -------- -------- -------- ------- -----')
for i in range(nl):
if (OUT_LINE_LIM == 2) | ((OUT_LINE_LIM == 1) &
(((branch[i, RATE_A] != 0) & (abs(Ff[i]) > branch[i, RATE_A] - ctol)) |
((branch[i, RATE_A] != 0) & (abs(Ft[i]) > branch[i, RATE_A] - ctol)) |
(branch[i, MU_SF] > ptol) | (branch[i, MU_ST] > ptol))):
fd.write('\n%4d%7d' % (i, branch[i, F_BUS]))
if (Ff[i] > branch[i, RATE_A] - ctol) | (branch[i, MU_SF] > ptol):
fd.write('%10.3f' % branch[i, MU_SF])
else:
fd.write(' - ')
fd.write('%9.2f%10.2f%10.2f' %
(Ff[i], branch[i, RATE_A], Ft[i]))
if (Ft[i] > branch[i, RATE_A] - ctol) | (branch[i, MU_ST] > ptol):
fd.write('%10.3f' % branch[i, MU_ST])
else:
fd.write(' - ')
fd.write('%6d' % branch[i, T_BUS])
fd.write('\n')
## execute userfcn callbacks for 'printpf' stage
if have_results_struct and 'userfcn' in results:
if not isOPF: ## turn off option for all constraints if it isn't an OPF
ppopt = ppoption(ppopt, 'OUT_ALL_LIM', 0)
run_userfcn(results["userfcn"], 'printpf', results, fd, ppopt)
ppopt = ppoption(ppopt)
##----- run the optimal power flow -----
r = opf(casedata, ppopt)
##----- output results -----
if fname:
fd = None
try:
fd = open(fname, "a")
except IOError as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(r, fd, ppopt)
fd.close()
else:
printpf(r, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, r)
return r
if __name__ == '__main__':
ppopt = ppoption(OPF_ALG=580)
runopf(None, ppopt)
def run(self,
Testsys=case24_ieee_rts(),
BETAlimit=0.0017,
ITER_max=10000,
SIMUNIT=1000):
Nb = Testsys["bus"].shape[0] # Load test system，Nb为节点数，Ng为发电机组数，Nl为馈线数
Ng = Testsys["gen"].shape[0]
Nl = Testsys["branch"].shape[0]
# Set initial value
iter = 0
betavalue = float('inf') # The stopping criteria停止迭代的标准
row_index = 0
# Build matrices that have fix dimension to avoid changing size in each loop
eqstatus_total = np.zeros(
(ITER_max, Ng + Nl + 3)) # 建一个100000*（33+38+3）的矩阵
beta_table = np.zeros((1, ITER_max // SIMUNIT)) # "//"除法得到的才是整数
edns_table = np.zeros((1, ITER_max // SIMUNIT)) # 存放评价指标，大小为1*1000
lole_table = np.zeros((1, ITER_max // SIMUNIT))
plc_table = np.zeros((1, ITER_max // SIMUNIT))
genbus = np.nonzero((Testsys["bus"][:, PD]))[
0] # 第三列（python中坐标是2）是节点的有功功率，表示节点有有功负荷,此处返回该列非零元素的索引，共有17个元素非零
sizegenbus = genbus.shape[0] # 有负荷的节点数量赋值给sizegenbus
Testsys["load"] = sum(Testsys["bus"][:, PD]) # 系统需要的总有功功率
Testsys["gencost"] = np.tile([2, 0, 0, 3, 0, 0, 0],
(Ng, 1)) # np.tile建立重复矩阵块（设置机组费用）
# treat all load as negtive generator and set their parameters, then add these vitual generators to real gens
# 将所有载荷视为负发电机，并设置其参数，然后将这些发电机加到实际的发电机中
loadcost = np.tile([2, 0, 0, 3, 0, 1, 0],
(sizegenbus, 1)) # np.tile建立重复矩阵块（负荷的“机组费用”）
Testsys["gencost"] = np.append(Testsys["gencost"], loadcost, axis=0)
Index = copy.deepcopy(Testsys["gen"][0:sizegenbus, :]) # 将前17台机组的数据取出
Index[:, 0:10] = np.hstack(
(Testsys["bus"][genbus, 0].reshape(-1, 1),
-Testsys["bus"][genbus, 2].reshape(-1, 1),
-Testsys["bus"][genbus, 3].reshape(-1, 1),
np.zeros(
(sizegenbus, 1)), -Testsys["bus"][genbus, 3].reshape(-1, 1),
np.zeros((sizegenbus, 1)), Testsys["baseMVA"] * np.ones(
(sizegenbus, 1)), np.ones((sizegenbus, 1)),
np.zeros(
(sizegenbus, 1)), -Testsys["bus"][genbus, 2].reshape(-1, 1)))
# 负荷参数代替取出的机组数据，将负荷套入机组模型,上面矩阵取数注意与matlab相比坐标要减一
Testsys["gen"] = np.append(Testsys["gen"], Index, axis=0)
del Index
Testsys["bus"][genbus, 2:4] = 0 # 将原来节点中的第3、4列（有功、无功）负荷设为零
totalprob = failprob() # 引用前面定义的函数
ppopt = ppoption(
PF_DC=1, VERBOSE=0, OUT_ALL=0, OPF_ALG_DC=200, OPF_FLOW_LIM=1
) # 可以通过ppoption()采用默认变量来看里面需要什么样的输入，这个按照matlab来输入没问题吧？
result = runopf(casedata=Testsys, ppopt=ppopt)
while (betavalue > BETAlimit) & (iter < ITER_max):
eqstatus_indi = mc_sampling(
totalprob, SIMUNIT, Ng,
Nl) # eqstatus为元件的状态矩阵，为1表示元件故障，为0表示原件正常
eqstatus_indi = np.hstack(
(eqstatus_indi, np.ones((eqstatus_indi.shape[0], 1)),
np.zeros((eqstatus_indi.shape[0], 2))))
# 在eqstatus_indi矩阵中加入三列，第一列代表状态重复次数，第二列记载切负荷量大小（没有切负荷则为零），第三列记载是否为容量不足
eqstatus_indi, ia1 = np.unique(eqstatus_indi,
axis=0,
return_inverse=True) # 找出抽样中的相同结果
for i in range(eqstatus_indi.shape[0]):
eqstatus_indi[i,
Ng + Nl] = sum(ia1 == i) # 将重复记录次数在第Ng + Nl + 1
if iter:
x = 0
y = eqstatus_indi.shape[0]
for i in range(y):
indi_x = eqstatus_indi[x, 0:Ng + Nl]
for j in range(row_index):
if (indi_x == eqstatus_total[j, 0:Ng + Nl]).all():
eqstatus_total[j, Ng + Nl] = eqstatus_total[
j, Ng + Nl] + eqstatus_indi[
x,
Ng + Nl] # 遇见相同的，就在eqstatus_total的计数中累加次数
eqstatus_indi = np.delete(eqstatus_indi, x, axis=0)
x = x - 1
break
x = x + 1
parfortemp = np.zeros((eqstatus_indi.shape[0], 2))
para = [0] * eqstatus_indi.shape[0]
n_sample = [0] * eqstatus_indi.shape[0]
for i in range(eqstatus_indi.shape[0]):
para[i] = [0] * 5
para[i][0] = eqstatus_indi[i, 0:Ng + Nl]
para[i][1] = Testsys
para[i][2] = ppopt
para[i][3] = Ng
para[i][4] = Nl
with Pool(self.n_processors) as p:
load_shedding = list(p.map(mc_simulation, para))
parfortemp[:, 0] = np.asarray(load_shedding)
parfortemp[:, 1] = (parfortemp[:, 0]) != 0
eqstatus_indi[:, Ng + Nl + 1:Ng + Nl + 3] = parfortemp
eqstatus_total[row_index:row_index +
eqstatus_indi.shape[0], :] = eqstatus_indi
row_index = row_index + eqstatus_indi.shape[0]
else:
parfortemp = np.zeros((eqstatus_indi.shape[0], 2))
para = [0] * eqstatus_indi.shape[0]
c = [0] * eqstatus_indi.shape[0]
for i in range(eqstatus_indi.shape[0]):
para[i] = [0] * 5
para[i][0] = eqstatus_indi[i, 0:Ng + Nl]
para[i][1] = Testsys
para[i][2] = ppopt
para[i][3] = Ng
para[i][4] = Nl
with Pool(self.n_processors) as p:
load_shedding = list(p.map(mc_simulation, para)) # 计算负荷短缺值
parfortemp[:, 0] = np.asarray(load_shedding)
parfortemp[:, 1] = (parfortemp[:, 0]) != 0 # 记录是否负荷短缺
eqstatus_indi[:, Ng + Nl + 1:Ng + Nl + 3] = parfortemp
eqstatus_total[row_index:row_index +
eqstatus_indi.shape[0], :] = eqstatus_indi
row_index = row_index + eqstatus_indi.shape[0]
## Update index
edns = sum(
eqstatus_total[0:row_index, Ng + Nl] *
eqstatus_total[0:row_index, Ng + Nl + 1]) / (iter + SIMUNIT)
lole = sum(eqstatus_total[0:row_index, Ng + Nl] *
eqstatus_total[0:row_index, Ng + Nl + 2]) / (
iter + SIMUNIT) * 8760
plc = sum(
eqstatus_total[0:row_index, Ng + Nl] *
eqstatus_total[0:row_index, Ng + Nl + 2]) / (iter + SIMUNIT)
betavalue = (sum(eqstatus_total[0:row_index, Ng + Nl] *
(eqstatus_total[0:row_index, Ng + Nl + 1] - edns)
**2))**0.5 / (iter + SIMUNIT) / edns
beta_table[0, ((iter + SIMUNIT) // SIMUNIT) - 1] = betavalue
edns_table[0, ((iter + SIMUNIT) // SIMUNIT) - 1] = edns
lole_table[0, ((iter + SIMUNIT) // SIMUNIT) - 1] = lole
plc_table[0, ((iter + SIMUNIT) // SIMUNIT) - 1] = plc
iter = iter + SIMUNIT
return edns
def t_opf_userfcns(quiet=False):
"""Tests for userfcn callbacks (reserves/iflims) w/OPF.
Includes high-level tests of reserves and iflims implementations.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
t_begin(38, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case30_userfcns')
verbose = 0#not quiet
ppopt = ppoption(OPF_VIOLATION=1e-6, PDIPM_GRADTOL=1e-8,
PDIPM_COMPTOL=1e-8, PDIPM_COSTTOL=1e-9)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=verbose,
OPF_ALG=560, OPF_ALG_DC=200)
#ppopt = ppoption(ppopt, OUT_ALL=-1, VERBOSE=2, OUT_GEN=1)
## run the OPF with fixed reserves
t = 'fixed reserves : '
ppc = loadcase(casefile)
ppc = toggle_reserves(ppc, 'on')
r = runopf(ppc, ppopt)
t_ok(r['success'], [t, 'success'])
t_is(r['reserves']['R'], [25, 15, 0, 0, 19.3906, 0.6094], 4, [t, 'reserves.R'])
t_is(r['reserves']['prc'], [2, 2, 2, 2, 5.5, 5.5], 4, [t, 'reserves.prc'])
t_is(r['reserves']['mu']['Pmax'], [0, 0, 0, 0, 0.5, 0], 4, [t, 'reserves.mu.Pmax'])
t_is(r['reserves']['mu']['l'], [0, 0, 1, 2, 0, 0], 4, [t, 'reserves.mu.l'])
t_is(r['reserves']['mu']['u'], [0.1, 0, 0, 0, 0, 0], 4, [t, 'reserves.mu.u'])
t_ok('P' not in r['if'], [t, 'no iflims'])
t_is(r['reserves']['totalcost'], 177.8047, 4, [t, 'totalcost'])
t = 'toggle_reserves(ppc, \'off\') : ';
ppc = toggle_reserves(ppc, 'off')
r = runopf(ppc, ppopt)
t_ok(r['success'], [t, 'success'])
t_ok('R' not in r['reserves'], [t, 'no reserves'])
t_ok('P' not in r['if'], [t, 'no iflims'])
t = 'interface flow lims (DC) : '
ppc = loadcase(casefile)
ppc = toggle_iflims(ppc, 'on')
r = rundcopf(ppc, ppopt)
t_ok(r['success'], [t, 'success'])
t_is(r['if']['P'], [-15, 20], 4, [t, 'if.P'])
t_is(r['if']['mu']['l'], [4.8427, 0], 4, [t, 'if.mu.l'])
t_is(r['if']['mu']['u'], [0, 13.2573], 4, [t, 'if.mu.u'])
t_is(r['branch'][13, PF], 8.244, 3, [t, 'flow in branch 14'])
t_ok('R' not in r['reserves'], [t, 'no reserves'])
t = 'reserves + interface flow lims (DC) : '
ppc = loadcase(casefile)
ppc = toggle_reserves(ppc, 'on')
ppc = toggle_iflims(ppc, 'on')
r = rundcopf(ppc, ppopt)
t_ok(r['success'], [t, 'success'])
t_is(r['if']['P'], [-15, 20], 4, [t, 'if.P'])
t_is(r['if']['mu']['l'], [4.8427, 0], 4, [t, 'if.mu.l'])
t_is(r['if']['mu']['u'], [0, 38.2573], 4, [t, 'if.mu.u'])
t_is(r['reserves']['R'], [25, 15, 0, 0, 16.9, 3.1], 4, [t, 'reserves.R'])
t_is(r['reserves']['prc'], [2, 2, 2, 2, 5.5, 5.5], 4, [t, 'reserves.prc'])
t_is(r['reserves']['mu']['Pmax'], [0, 0, 0, 0, 0.5, 0], 4, [t, 'reserves.mu.Pmax'])
t_is(r['reserves']['mu']['l'], [0, 0, 1, 2, 0, 0], 4, [t, 'reserves.mu.l'])
t_is(r['reserves']['mu']['u'], [0.1, 0, 0, 0, 0, 0], 4, [t, 'reserves.mu.u'])
t_is(r['reserves']['totalcost'], 179.05, 4, [t, 'totalcost'])
t = 'interface flow lims (AC) : '
ppc = toggle_reserves(ppc, 'off')
r = runopf(ppc, ppopt)
t_ok(r['success'], [t, 'success'])
t_is(r['if']['P'], [-9.101, 21.432], 3, [t, 'if.P'])
t_is(r['if']['mu']['l'], [0, 0], 4, [t, 'if.mu.l'])
t_is(r['if']['mu']['u'], [0, 10.198], 3, [t, 'if.mu.u'])
t_ok('R' not in r['reserves'], [t, 'no reserves'])
t = 'interface flow lims (line out) : '
ppc = loadcase(casefile)
ppc = toggle_iflims(ppc, 'on')
ppc['branch'][11, BR_STATUS] = 0 ## take out line 6-10
r = rundcopf(ppc, ppopt)
t_ok(r['success'], [t, 'success'])
t_is(r['if']['P'], [-15, 20], 4, [t, 'if.P'])
t_is(r['if']['mu']['l'], [4.8427, 0], 4, [t, 'if.mu.l'])
t_is(r['if']['mu']['u'], [0, 13.2573], 4, [t, 'if.mu.u'])
t_is(r['branch'][13, PF], 10.814, 3, [t, 'flow in branch 14'])
t_ok('R' not in r['reserves'], [t, 'no reserves'])
# r['reserves']['R']
# r['reserves']['prc']
# r['reserves']['mu.Pmax']
# r['reserves']['mu']['l']
# r['reserves']['mu']['u']
# r['reserves']['totalcost']
#
# r['if']['P']
# r['if']['mu']['l']
# r['if']['mu']['u']
t_end()
ppopt = ppoption(ppopt)
##----- run the optimal power flow -----
r = opf(casedata, ppopt)
##----- output results -----
if fname:
fd = None
try:
fd = open(fname, "a")
except IOError as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(r, fd, ppopt)
fd.close()
else:
printpf(r, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, r)
return r
if __name__ == '__main__':
ppopt = ppoption(OPF_ALG=580)
runopf(None, ppopt)
def newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt=None):
"""Solves the power flow using a full Newton's method.
Solves for bus voltages given the full system admittance matrix (for
all buses), the complex bus power injection vector (for all buses),
the initial vector of complex bus voltages, and column vectors with
the lists of bus indices for the swing bus, PV buses, and PQ buses,
respectively. The bus voltage vector contains the set point for
generator (including ref bus) buses, and the reference angle of the
swing bus, as well as an initial guess for remaining magnitudes and
angles. C{ppopt} is a PYPOWER options vector which can be used to
set the termination tolerance, maximum number of iterations, and
output options (see L{ppoption} for details). Uses default options if
this parameter is not given. Returns the final complex voltages, a
flag which indicates whether it converged or not, and the number of
iterations performed.
@see: L{runpf}
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
## default arguments
if ppopt is None:
ppopt = ppoption()
## options
tol = ppopt['PF_TOL']
max_it = ppopt['PF_MAX_IT']
verbose = ppopt['VERBOSE']
## initialize
converged = 0
i = 0
V = V0
Va = angle(V)
Vm = abs(V)
## set up indexing for updating V
pvpq = r_[pv, pq]
npv = len(pv)
npq = len(pq)
j1 = 0; j2 = npv ## j1:j2 - V angle of pv buses
j3 = j2; j4 = j2 + npq ## j3:j4 - V angle of pq buses
j5 = j4; j6 = j4 + npq ## j5:j6 - V mag of pq buses
## evaluate F(x0)
mis = V * conj(Ybus * V) - Sbus
F = r_[ mis[pv].real,
mis[pq].real,
mis[pq].imag ]
## check tolerance
normF = linalg.norm(F, Inf)
if verbose > 1:
sys.stdout.write('\n it max P & Q mismatch (p.u.)')
sys.stdout.write('\n---- ---------------------------')
sys.stdout.write('\n%3d %10.3e' % (i, normF))
if normF < tol:
converged = 1
if verbose > 1:
sys.stdout.write('\nConverged!\n')
## do Newton iterations
while (not converged and i < max_it):
## update iteration counter
i = i + 1
## evaluate Jacobian
dS_dVm, dS_dVa = dSbus_dV(Ybus, V)
J11 = dS_dVa[array([pvpq]).T, pvpq].real
J12 = dS_dVm[array([pvpq]).T, pq].real
J21 = dS_dVa[array([pq]).T, pvpq].imag
J22 = dS_dVm[array([pq]).T, pq].imag
J = vstack([
hstack([J11, J12]),
hstack([J21, J22])
], format="csr")
## compute update step
dx = -1 * spsolve(J, F)
## update voltage
if npv:
Va[pv] = Va[pv] + dx[j1:j2]
if npq:
Va[pq] = Va[pq] + dx[j3:j4]
Vm[pq] = Vm[pq] + dx[j5:j6]
V = Vm * exp(1j * Va)
Vm = abs(V) ## update Vm and Va again in case
Va = angle(V) ## we wrapped around with a negative Vm
## evalute F(x)
mis = V * conj(Ybus * V) - Sbus
F = r_[ mis[pv].real,
mis[pq].real,
mis[pq].imag ]
## check for convergence
normF = linalg.norm(F, Inf)
if verbose > 1:
sys.stdout.write('\n%3d %10.3e' % (i, normF))
if normF < tol:
converged = 1
if verbose:
sys.stdout.write("\nNewton's method power flow converged in "
"%d iterations.\n" % i)
if verbose:
if not converged:
sys.stdout.write("\nNewton's method power did not converge in %d "
"iterations.\n" % i)
return V, converged, i
def t_makePTDF(quiet=False):
"""Tests for C{makePTDF}.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
ntests = 24
t_begin(ntests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_opf')
verbose = 0 #not quiet
## load case
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0)
r = rundcopf(casefile, ppopt)
baseMVA, bus, gen, branch = r['baseMVA'], r['bus'], r['gen'], r['branch']
_, bus, gen, branch = ext2int1(bus, gen, branch)
nb = bus.shape[0]
nbr = branch.shape[0]
ng = gen.shape[0]
## compute injections and flows
Cg = sparse((ones(ng), (gen[:, GEN_BUS], arange(ng))), (nb, ng))
Pg = Cg * gen[:, PG]
Pd = bus[:, PD]
P = Pg - Pd
ig = find(P > 0)
il = find(P <= 0)
F = branch[:, PF]
## create corresponding slack distribution matrices
e1 = zeros((nb, 1))
e1[0] = 1
e4 = zeros((nb, 1))
e4[3] = 1
D1 = eye(nb, nb) - dot(e1, ones((1, nb)))
D4 = eye(nb, nb) - dot(e4, ones((1, nb)))
Deq = eye(nb, nb) - ones((nb, 1)) / nb * ones((1, nb))
Dg = eye(nb) - matrix(Pd / sum(Pd)).T * ones(nb)
Dd = eye(nb) - matrix(Pg / sum(Pg)).T * ones(nb)
## create some PTDF matrices
H1 = makePTDF(baseMVA, bus, branch, 0)
H4 = makePTDF(baseMVA, bus, branch, 3)
Heq = makePTDF(baseMVA, bus, branch, ones(nb))
Hg = makePTDF(baseMVA, bus, branch, Pd)
Hd = makePTDF(baseMVA, bus, branch, Pg)
## matrices get properly transformed by slack dist matrices
t_is(H1, dot(H1, D1), 8, 'H1 == H1 * D1')
t_is(H4, dot(H1, D4), 8, 'H4 == H1 * D4')
t_is(Heq, dot(H1, Deq), 8, 'Heq == H1 * Deq')
t_is(Hg, dot(H1, Dg), 8, 'Hg == H1 * Dg')
t_is(Hd, dot(H1, Dd), 8, 'Hd == H1 * Dd')
t_is(H1, dot(Heq, D1), 8, 'H1 == Heq * D1')
t_is(H4, dot(Heq, D4), 8, 'H4 == Heq * D4')
t_is(Heq, dot(Heq, Deq), 8, 'Heq == Heq * Deq')
t_is(Hg, dot(Heq, Dg), 8, 'Hg == Heq * Dg')
t_is(Hd, dot(Heq, Dd), 8, 'Hd == Heq * Dd')
t_is(H1, dot(Hg, D1), 8, 'H1 == Hg * D1')
t_is(H4, dot(Hg, D4), 8, 'H4 == Hg * D4')
t_is(Heq, dot(Hg, Deq), 8, 'Heq == Hg * Deq')
t_is(Hg, dot(Hg, Dg), 8, 'Hg == Hg * Dg')
t_is(Hd, dot(Hg, Dd), 8, 'Hd == Hg * Dd')
## PTDFs can reconstruct flows
t_is(F, dot(H1, P), 3, 'Flow == H1 * P')
t_is(F, dot(H4, P), 3, 'Flow == H4 * P')
t_is(F, dot(Heq, P), 3, 'Flow == Heq * P')
t_is(F, dot(Hg, P), 3, 'Flow == Hg * P')
t_is(F, dot(Hd, P), 3, 'Flow == Hd * P')
## other
t_is(F, dot(Hg, Pg), 3, 'Flow == Hg * Pg')
t_is(F, dot(Hd, (-Pd)), 3, 'Flow == Hd * (-Pd)')
t_is(zeros(nbr), dot(Hg, (-Pd)), 3, 'zeros == Hg * (-Pd)')
t_is(zeros(nbr), dot(Hd, Pg), 3, 'zeros == Hd * Pg')
t_end()
def newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt=None):
"""Solves the power flow using a full Newton's method.
Solves for bus voltages given the full system admittance matrix (for
all buses), the complex bus power injection vector (for all buses),
the initial vector of complex bus voltages, and column vectors with
the lists of bus indices for the swing bus, PV buses, and PQ buses,
respectively. The bus voltage vector contains the set point for
generator (including ref bus) buses, and the reference angle of the
swing bus, as well as an initial guess for remaining magnitudes and
angles. C{ppopt} is a PYPOWER options vector which can be used to
set the termination tolerance, maximum number of iterations, and
output options (see L{ppoption} for details). Uses default options if
this parameter is not given. Returns the final complex voltages, a
flag which indicates whether it converged or not, and the number of
iterations performed.
@see: L{runpf}
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if ppopt is None:
ppopt = ppoption()
## options
tol = ppopt['PF_TOL']
max_it = ppopt['PF_MAX_IT']
verbose = ppopt['VERBOSE']
## initialize
converged = 0
i = 0
V = V0
Va = angle(V)
Vm = abs(V)
## set up indexing for updating V
pvpq = r_[pv, pq]
npv = len(pv)
npq = len(pq)
j1 = 0
j2 = npv ## j1:j2 - V angle of pv buses
j3 = j2
j4 = j2 + npq ## j3:j4 - V angle of pq buses
j5 = j4
j6 = j4 + npq ## j5:j6 - V mag of pq buses
## evaluate F(x0)
mis = V * conj(Ybus * V) - Sbus
F = r_[mis[pv].real, mis[pq].real, mis[pq].imag]
## check tolerance
normF = linalg.norm(F, Inf)
if verbose > 1:
sys.stdout.write('\n it max P & Q mismatch (p.u.)')
sys.stdout.write('\n---- ---------------------------')
sys.stdout.write('\n%3d %10.3e' % (i, normF))
if normF < tol:
converged = 1
if verbose > 1:
sys.stdout.write('\nConverged!\n')
## do Newton iterations
while (not converged and i < max_it):
## update iteration counter
i = i + 1
## evaluate Jacobian
dS_dVm, dS_dVa = dSbus_dV(Ybus, V)
J11 = dS_dVa[array([pvpq]).T, pvpq].real
J12 = dS_dVm[array([pvpq]).T, pq].real
J21 = dS_dVa[array([pq]).T, pvpq].imag
J22 = dS_dVm[array([pq]).T, pq].imag
J = vstack([hstack([J11, J12]), hstack([J21, J22])], format="csr")
## compute update step
dx = -1 * spsolve(J, F)
## update voltage
if npv:
Va[pv] = Va[pv] + dx[j1:j2]
if npq:
Va[pq] = Va[pq] + dx[j3:j4]
Vm[pq] = Vm[pq] + dx[j5:j6]
V = Vm * exp(1j * Va)
Vm = abs(V) ## update Vm and Va again in case
Va = angle(V) ## we wrapped around with a negative Vm
## evalute F(x)
mis = V * conj(Ybus * V) - Sbus
F = r_[mis[pv].real, mis[pq].real, mis[pq].imag]
## check for convergence
normF = linalg.norm(F, Inf)
if verbose > 1:
sys.stdout.write('\n%3d %10.3e' % (i, normF))
if normF < tol:
converged = 1
if verbose:
sys.stdout.write("\nNewton's method power flow converged in "
"%d iterations.\n" % i)
if verbose:
if not converged:
sys.stdout.write("\nNewton's method power did not converge in %d "
"iterations.\n" % i)
return V, converged, i
def t_opf_ipopt(quiet=False):
"""Tests for IPOPT-based AC optimal power flow.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
num_tests = 101
t_begin(num_tests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_opf')
verbose = 0#not quiet
t0 = 'IPOPT : '
ppopt = ppoption(OPF_VIOLATION=1e-6, PDIPM_GRADTOL=1e-8,
PDIPM_COMPTOL=1e-8, PDIPM_COSTTOL=1e-9)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=verbose, OPF_ALG=580)
## 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])
## get solved AC power flow case from MAT-file
soln9_opf = loadmat(join(tdir, 'soln9_opf.mat'), struct_as_record=True)
## defines bus_soln, gen_soln, branch_soln, f_soln
bus_soln = soln9_opf['bus_soln']
gen_soln = soln9_opf['gen_soln']
branch_soln = soln9_opf['branch_soln']
f_soln = soln9_opf['f_soln'][0]
## run OPF
t = t0
r = runopf(casefile, ppopt)
bus, gen, branch, f, success = \
r['bus'], r['gen'], r['branch'], r['f'], r['success']
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 2, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
## run with automatic conversion of single-block pwl to linear costs
t = ''.join([t0, '(single-block PWL) : '])
ppc = loadcase(casefile)
ppc['gencost'][2, NCOST] = 2
r = runopf(ppc, ppopt)
bus, gen, branch, f, success = \
r['bus'], r['gen'], r['branch'], r['f'], r['success']
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 2, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
xr = r_[r['var']['val']['Va'], r['var']['val']['Vm'], r['var']['val']['Pg'],
r['var']['val']['Qg'], 0, r['var']['val']['y']]
t_is(r['x'], xr, 8, [t, 'check on raw x returned from OPF'])
## get solved AC power flow case from MAT-file
soln9_opf_Plim = loadmat(join(tdir, 'soln9_opf_Plim.mat'), struct_as_record=True)
## defines bus_soln, gen_soln, branch_soln, f_soln
bus_soln = soln9_opf_Plim['bus_soln']
gen_soln = soln9_opf_Plim['gen_soln']
branch_soln = soln9_opf_Plim['branch_soln']
f_soln = soln9_opf_Plim['f_soln'][0]
## run OPF with active power line limits
t = ''.join([t0, '(P line lim) : '])
ppopt1 = ppoption(ppopt, OPF_FLOW_LIM=1)
r = runopf(casefile, ppopt1)
bus, gen, branch, f, success = \
r['bus'], r['gen'], r['branch'], r['f'], r['success']
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 2, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
##----- test OPF with quadratic gen costs moved to generalized costs -----
ppc = loadcase(casefile)
ppc['gencost'] = array([
[2, 1500, 0, 3, 0.11, 5, 0],
[2, 2000, 0, 3, 0.085, 1.2, 0],
[2, 3000, 0, 3, 0.1225, 1, 0]
])
r = runopf(ppc, ppopt)
bus_soln, gen_soln, branch_soln, f_soln, success = \
r['bus'], r['gen'], r['branch'], r['f'], r['success']
branch_soln = branch_soln[:, :MU_ST + 1]
A = None
l = array([])
u = array([])
nb = ppc['bus'].shape[0] # number of buses
ng = ppc['gen'].shape[0] # number of gens
thbas = 0; thend = thbas + nb
vbas = thend; vend = vbas + nb
pgbas = vend; pgend = pgbas + ng
# qgbas = pgend; qgend = qgbas + ng
nxyz = 2 * nb + 2 * ng
N = sparse((ppc['baseMVA'] * ones(ng), (arange(ng), arange(pgbas, pgend))), (ng, nxyz))
fparm = ones((ng, 1)) * array([[1, 0, 0, 1]])
ix = argsort(ppc['gen'][:, 0])
H = 2 * spdiags(ppc['gencost'][ix, 4], 0, ng, ng, 'csr')
Cw = ppc['gencost'][ix, 5]
ppc['gencost'][:, 4:7] = 0
## run OPF with quadratic gen costs moved to generalized costs
t = ''.join([t0, 'w/quadratic generalized gen cost : '])
r = opf(ppc, A, l, u, ppopt, N, fparm, H, Cw)
f, bus, gen, branch, success = \
r['f'], r['bus'], r['gen'], r['branch'], r['success']
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 2, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
t_is(r['cost']['usr'], f, 12, [t, 'user cost'])
##----- run OPF with extra linear user constraints & costs -----
## single new z variable constrained to be greater than or equal to
## deviation from 1 pu voltage at bus 1, linear cost on this z
## get solved AC power flow case from MAT-file
soln9_opf_extras1 = loadmat(join(tdir, 'soln9_opf_extras1.mat'), struct_as_record=True)
## defines bus_soln, gen_soln, branch_soln, f_soln
bus_soln = soln9_opf_extras1['bus_soln']
gen_soln = soln9_opf_extras1['gen_soln']
branch_soln = soln9_opf_extras1['branch_soln']
f_soln = soln9_opf_extras1['f_soln'][0]
row = [0, 0, 1, 1]
col = [9, 24, 9, 24]
A = sparse(([-1, 1, 1, 1], (row, col)), (2, 25))
u = array([Inf, Inf])
l = array([-1, 1])
N = sparse(([1], ([0], [24])), (1, 25)) ## new z variable only
fparm = array([[1, 0, 0, 1]]) ## w = r = z
H = sparse((1, 1)) ## no quadratic term
Cw = array([100.0])
t = ''.join([t0, 'w/extra constraints & costs 1 : '])
r = opf(casefile, A, l, u, ppopt, N, fparm, H, Cw)
f, bus, gen, branch, success = \
r['f'], r['bus'], r['gen'], r['branch'], r['success']
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 2, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
t_is(r['var']['val']['z'], 0.025419, 6, [t, 'user variable'])
t_is(r['cost']['usr'], 2.5419, 4, [t, 'user cost'])
##----- test OPF with capability curves -----
ppc = loadcase(join(tdir, 't_case9_opfv2'))
## remove angle diff limits
ppc['branch'][0, ANGMAX] = 360
ppc['branch'][8, ANGMIN] = -360
## get solved AC power flow case from MAT-file
soln9_opf_PQcap = loadmat(join(tdir, 'soln9_opf_PQcap.mat'), struct_as_record=True)
## defines bus_soln, gen_soln, branch_soln, f_soln
bus_soln = soln9_opf_PQcap['bus_soln']
gen_soln = soln9_opf_PQcap['gen_soln']
branch_soln = soln9_opf_PQcap['branch_soln']
f_soln = soln9_opf_PQcap['f_soln'][0]
## run OPF with capability curves
t = ''.join([t0, 'w/capability curves : '])
r = runopf(ppc, ppopt)
bus, gen, branch, f, success = \
r['bus'], r['gen'], r['branch'], r['f'], r['success']
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 2, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
##----- test OPF with angle difference limits -----
ppc = loadcase(join(tdir, 't_case9_opfv2'))
## remove capability curves
ppc['gen'][ix_(arange(1, 3),
[PC1, PC2, QC1MIN, QC1MAX, QC2MIN, QC2MAX])] = zeros((2, 6))
## get solved AC power flow case from MAT-file
soln9_opf_ang = loadmat(join(tdir, 'soln9_opf_ang.mat'), struct_as_record=True)
## defines bus_soln, gen_soln, branch_soln, f_soln
bus_soln = soln9_opf_ang['bus_soln']
gen_soln = soln9_opf_ang['gen_soln']
branch_soln = soln9_opf_ang['branch_soln']
f_soln = soln9_opf_ang['f_soln'][0]
## run OPF with angle difference limits
t = ''.join([t0, 'w/angle difference limits : '])
r = runopf(ppc, ppopt)
bus, gen, branch, f, success = \
r['bus'], r['gen'], r['branch'], r['f'], r['success']
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 1, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
t_is(branch[:, ibr_angmu ], branch_soln[:, ibr_angmu ], 2, [t, 'branch angle mu'])
##----- test OPF with ignored angle difference limits -----
## get solved AC power flow case from MAT-file
soln9_opf = loadmat(join(tdir, 'soln9_opf.mat'), struct_as_record=True)
## defines bus_soln, gen_soln, branch_soln, f_soln
bus_soln = soln9_opf['bus_soln']
gen_soln = soln9_opf['gen_soln']
branch_soln = soln9_opf['branch_soln']
f_soln = soln9_opf['f_soln'][0]
## run OPF with ignored angle difference limits
t = ''.join([t0, 'w/ignored angle difference limits : '])
ppopt1 = ppoption(ppopt, OPF_IGNORE_ANG_LIM=1)
r = runopf(ppc, ppopt1)
bus, gen, branch, f, success = \
r['bus'], r['gen'], r['branch'], r['f'], r['success']
## ang limits are not in this solution data, so let's remove them
branch[0, ANGMAX] = 360
branch[8, ANGMIN] = -360
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 2, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
t_end()
def runpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a power flow.
Runs a power flow [full AC Newton's method by default] and optionally
returns the solved values in the data matrices, a flag which is C{True} if
the algorithm was successful in finding a solution, and the elapsed
time in seconds. All input arguments are optional. If C{casename} is
provided it specifies the name of the input data file or dict
containing the power flow data. The default value is 'case9'.
If the ppopt is provided it overrides the default PYPOWER options
vector and can be used to specify the solution algorithm and output
options among other things. If the 3rd argument is given the pretty
printed output will be appended to the file whose name is given in
C{fname}. If C{solvedcase} is specified the solved case will be written
to a case file in PYPOWER format with the specified name. If C{solvedcase}
ends with '.mat' it saves the case as a MAT-file otherwise it saves it
as a Python-file.
If the C{ENFORCE_Q_LIMS} options is set to C{True} [default is false] then
if any generator reactive power limit is violated after running the AC
power flow, the corresponding bus is converted to a PQ bus, with Qg at
the limit, and the case is re-run. The voltage magnitude at the bus
will deviate from the specified value in order to satisfy the reactive
power limit. If the reference bus is converted to PQ, the first
remaining PV bus will be used as the slack bus for the next iteration.
This may result in the real power output at this generator being
slightly off from the specified values.
Enforcing of generator Q limits inspired by contributions from Mu Lin,
Lincoln University, New Zealand (1/14/05).
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
## options
verbose = ppopt["VERBOSE"]
qlim = ppopt["ENFORCE_Q_LIMS"] ## enforce Q limits on gens?
dc = ppopt["PF_DC"] ## use DC formulation?
## read data
ppc = loadcase(casedata)
## add zero columns to branch for flows if needed
if ppc["branch"].shape[1] < QT:
ppc["branch"] = c_[ppc["branch"],
zeros((ppc["branch"].shape[0],
QT - ppc["branch"].shape[1] + 1))]
## convert to internal indexing
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
## get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
## generator info
on = find(gen[:, GEN_STATUS] > 0) ## which generators are on?
gbus = gen[on, GEN_BUS].astype(int) ## what buses are they at?
##----- run the power flow -----
t0 = time()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v["Version"], v["Date"]))
if dc: # DC formulation
if verbose:
stdout.write(' -- DC Power Flow\n')
## initial state
Va0 = bus[:, VA] * (pi / 180)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
## adjusted for phase shifters and real shunts
Pbus = makeSbus(baseMVA, bus, gen).real - Pbusinj - bus[:, GS] / baseMVA
## "run" the power flow
Va = dcpf(B, Pbus, Va0, ref, pv, pq)
## update data matrices with solution
branch[:, [QF, QT]] = zeros((branch.shape[0], 2))
branch[:, PF] = (Bf * Va + Pfinj) * baseMVA
branch[:, PT] = -branch[:, PF]
bus[:, VM] = ones(bus.shape[0])
bus[:, VA] = Va * (180 / pi)
## update Pg for slack generator (1st gen at ref bus)
## (note: other gens at ref bus are accounted for in Pbus)
## Pg = Pinj + Pload + Gs
## newPg = oldPg + newPinj - oldPinj
refgen = zeros(len(ref), dtype=int)
for k in range(len(ref)):
temp = find(gbus == ref[k])
refgen[k] = on[temp[0]]
gen[refgen, PG] = gen[refgen, PG] + (B[ref, :] * Va - Pbus[ref]) * baseMVA
success = 1
else: ## AC formulation
alg = ppopt['PF_ALG']
if verbose > 0:
if alg == 1:
solver = 'Newton'
elif alg == 2:
solver = 'fast-decoupled, XB'
elif alg == 3:
solver = 'fast-decoupled, BX'
elif alg == 4:
solver = 'Gauss-Seidel'
else:
solver = 'unknown'
print(' -- AC Power Flow (%s)\n' % solver)
## initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = bus[:, VM] * exp(1j * pi/180 * bus[:, VA])
V0[gbus] = gen[on, VG] / abs(V0[gbus]) * V0[gbus]
if qlim:
ref0 = ref ## save index and angle of
Varef0 = bus[ref0, VA] ## original reference bus(es)
limited = [] ## list of indices of gens @ Q lims
fixedQg = zeros(gen.shape[0]) ## Qg of gens at Q limits
repeat = True
while repeat:
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the power flow
alg = ppopt["PF_ALG"]
if alg == 1:
V, success, _ = newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
elif alg == 2 or alg == 3:
Bp, Bpp = makeB(baseMVA, bus, branch, alg)
V, success, _ = fdpf(Ybus, Sbus, V0, Bp, Bpp, ref, pv, pq, ppopt)
elif alg == 4:
V, success, _ = gausspf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
else:
stderr.write('Only Newton''s method, fast-decoupled, and '
'Gauss-Seidel power flow algorithms currently '
'implemented.\n')
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V, ref, pv, pq)
if qlim: ## enforce generator Q limits
## find gens with violated Q constraints
gen_status = gen[:, GEN_STATUS] > 0
qg_max_lim = gen[:, QG] > gen[:, QMAX]
qg_min_lim = gen[:, QG] < gen[:, QMIN]
mx = find( gen_status & qg_max_lim )
mn = find( gen_status & qg_min_lim )
if len(mx) > 0 or len(mn) > 0: ## we have some Q limit violations
# No PV generators
if len(pv) == 0:
if verbose:
if len(mx) > 0:
print('Gen %d [only one left] exceeds upper Q limit : INFEASIBLE PROBLEM\n' % mx + 1)
else:
print('Gen %d [only one left] exceeds lower Q limit : INFEASIBLE PROBLEM\n' % mn + 1)
success = 0
break
## one at a time?
if qlim == 2: ## fix largest violation, ignore the rest
k = argmax(r_[gen[mx, QG] - gen[mx, QMAX],
gen[mn, QMIN] - gen[mn, QG]])
if k > len(mx):
mn = mn[k - len(mx)]
mx = []
else:
mx = mx[k]
mn = []
if verbose and len(mx) > 0:
for i in range(len(mx)):
print('Gen ' + str(mx[i] + 1) + ' at upper Q limit, converting to PQ bus\n')
if verbose and len(mn) > 0:
for i in range(len(mn)):
print('Gen ' + str(mn[i] + 1) + ' at lower Q limit, converting to PQ bus\n')
## save corresponding limit values
fixedQg[mx] = gen[mx, QMAX]
fixedQg[mn] = gen[mn, QMIN]
mx = r_[mx, mn].astype(int)
## convert to PQ bus
gen[mx, QG] = fixedQg[mx] ## set Qg to binding
for i in range(len(mx)): ## [one at a time, since they may be at same bus]
gen[mx[i], GEN_STATUS] = 0 ## temporarily turn off gen,
bi = gen[mx[i], GEN_BUS] ## adjust load accordingly,
bus[bi, [PD, QD]] = (bus[bi, [PD, QD]] - gen[mx[i], [PG, QG]])
if len(ref) > 1 and any(bus[gen[mx, GEN_BUS], BUS_TYPE] == REF):
raise ValueError('Sorry, PYPOWER cannot enforce Q '
'limits for slack buses in systems '
'with multiple slacks.')
bus[gen[mx, GEN_BUS].astype(int), BUS_TYPE] = PQ ## & set bus type to PQ
## update bus index lists of each type of bus
ref_temp = ref
ref, pv, pq = bustypes(bus, gen)
if verbose and ref != ref_temp:
print('Bus %d is new slack bus\n' % ref)
limited = r_[limited, mx].astype(int)
else:
repeat = 0 ## no more generator Q limits violated
else:
repeat = 0 ## don't enforce generator Q limits, once is enough
if qlim and len(limited) > 0:
## restore injections from limited gens [those at Q limits]
gen[limited, QG] = fixedQg[limited] ## restore Qg value,
for i in range(len(limited)): ## [one at a time, since they may be at same bus]
bi = gen[limited[i], GEN_BUS] ## re-adjust load,
bus[bi, [PD, QD]] = bus[bi, [PD, QD]] + gen[limited[i], [PG, QG]]
gen[limited[i], GEN_STATUS] = 1 ## and turn gen back on
if ref != ref0:
## adjust voltage angles to make original ref bus correct
bus[:, VA] = bus[:, VA] - bus[ref0, VA] + Varef0
ppc["et"] = time() - t0
ppc["success"] = success
##----- output results -----
## convert back to original bus numbering & print results
ppc["bus"], ppc["gen"], ppc["branch"] = bus, gen, branch
results = int2ext(ppc)
## zero out result fields of out-of-service gens & branches
if len(results["order"]["gen"]["status"]["off"]) > 0:
results["gen"][ix_(results["order"]["gen"]["status"]["off"], [PG, QG])] = 0
if len(results["order"]["branch"]["status"]["off"]) > 0:
results["branch"][ix_(results["order"]["branch"]["status"]["off"], [PF, QF, PT, QT])] = 0
if fname:
fd = None
try:
fd = open(fname, "a")
except Exception as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(results, fd, ppopt)
fd.close()
else:
printpf(results, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, results)
return results, success
def t_opf_dc_pips(quiet=False):
"""Tests for DC optimal power flow using PIPS solver.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
num_tests = 23
t_begin(num_tests, quiet)
tdir = dirname(__file__)
casefile = join(tdir, 't_case9_opf')
verbose = 0#not quiet
t0 = 'DC OPF (PIPS): '
ppopt = ppoption(VERBOSE=verbose, OUT_ALL=0, OPF_ALG_DC=200)
## run DC OPF
## 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])
## get solved DC power flow case from MAT-file
soln9_dcopf = loadmat(join(tdir, 'soln9_dcopf.mat'), struct_as_record=True)
## defines bus_soln, gen_soln, branch_soln, f_soln
bus_soln = soln9_dcopf['bus_soln']
gen_soln = soln9_dcopf['gen_soln']
branch_soln = soln9_dcopf['branch_soln']
f_soln = soln9_dcopf['f_soln'][0]
## run OPF
t = t0
r = rundcopf(casefile, ppopt)
bus, gen, branch, f, success = \
r['bus'], r['gen'], r['branch'], r['f'], r['success']
t_ok(success, [t, 'success'])
t_is(f, f_soln, 3, [t, 'f'])
t_is( bus[:, ib_data ], bus_soln[:, ib_data ], 10, [t, 'bus data'])
t_is( bus[:, ib_voltage], bus_soln[:, ib_voltage], 3, [t, 'bus voltage'])
t_is( bus[:, ib_lam ], bus_soln[:, ib_lam ], 3, [t, 'bus lambda'])
t_is( bus[:, ib_mu ], bus_soln[:, ib_mu ], 2, [t, 'bus mu'])
t_is( gen[:, ig_data ], gen_soln[:, ig_data ], 10, [t, 'gen data'])
t_is( gen[:, ig_disp ], gen_soln[:, ig_disp ], 3, [t, 'gen dispatch'])
t_is( gen[:, ig_mu ], gen_soln[:, ig_mu ], 3, [t, 'gen mu'])
t_is(branch[:, ibr_data ], branch_soln[:, ibr_data ], 10, [t, 'branch data'])
t_is(branch[:, ibr_flow ], branch_soln[:, ibr_flow ], 3, [t, 'branch flow'])
t_is(branch[:, ibr_mu ], branch_soln[:, ibr_mu ], 2, [t, 'branch mu'])
##----- run OPF with extra linear user constraints & costs -----
## two new z variables
## 0 <= z1, P2 - P1 <= z1
## 0 <= z2, P2 - P3 <= z2
## with A and N sized for DC opf
ppc = loadcase(casefile)
row = [0, 0, 0, 1, 1, 1]
col = [9, 10, 12, 10, 11, 13]
ppc['A'] = sparse(([-1, 1, -1, 1, -1, -1], (row, col)), (2, 14))
ppc['u'] = array([0, 0])
ppc['l'] = array([-Inf, -Inf])
ppc['zl'] = array([0, 0])
ppc['N'] = sparse(([1, 1], ([0, 1], [12, 13])), (2, 14)) ## new z variables only
ppc['fparm'] = ones((2, 1)) * array([[1, 0, 0, 1]]) ## w = r = z
ppc['H'] = sparse((2, 2)) ## no quadratic term
ppc['Cw'] = array([1000, 1])
t = ''.join([t0, 'w/extra constraints & costs 1 : '])
r = rundcopf(ppc, ppopt)
t_ok(r['success'], [t, 'success'])
t_is(r['gen'][0, PG], 116.15974, 4, [t, 'Pg1 = 116.15974'])
t_is(r['gen'][1, PG], 116.15974, 4, [t, 'Pg2 = 116.15974'])
t_is(r['var']['val']['z'], [0, 0.3348], 4, [t, 'user vars'])
t_is(r['cost']['usr'], 0.3348, 3, [t, 'user costs'])
## with A and N sized for AC opf
ppc = loadcase(casefile)
row = [0, 0, 0, 1, 1, 1]
col = [18, 19, 24, 19, 20, 25]
ppc['A'] = sparse(([-1, 1, -1, 1, -1, -1], (row, col)), (2, 26))
ppc['u'] = array([0, 0])
ppc['l'] = array([-Inf, -Inf])
ppc['zl'] = array([0, 0])
ppc['N'] = sparse(([1, 1], ([0, 1], [24, 25])), (2, 26)) ## new z variables only
ppc['fparm'] = ones((2, 1)) * array([[1, 0, 0, 1]]) ## w = r = z
ppc['H'] = sparse((2, 2)) ## no quadratic term
ppc['Cw'] = array([1000, 1])
t = ''.join([t0, 'w/extra constraints & costs 2 : '])
r = rundcopf(ppc, ppopt)
t_ok(r['success'], [t, 'success'])
t_is(r['gen'][0, PG], 116.15974, 4, [t, 'Pg1 = 116.15974'])
t_is(r['gen'][1, PG], 116.15974, 4, [t, 'Pg2 = 116.15974'])
t_is(r['var']['val']['z'], [0, 0.3348], 4, [t, 'user vars'])
t_is(r['cost']['usr'], 0.3348, 3, [t, 'user costs'])
t = ''.join([t0, 'infeasible : '])
## with A and N sized for DC opf
ppc = loadcase(casefile)
ppc['A'] = sparse(([1, 1], ([0, 0], [9, 10])), (1, 14)) ## Pg1 + Pg2
ppc['u'] = array([Inf])
ppc['l'] = array([600])
r = rundcopf(ppc, ppopt)
t_ok(not r['success'], [t, 'no success'])
t_end()
def t_runopf_w_res(quiet=False):
"""Tests C{runopf_w_res} and the associated callbacks.
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
t_begin(46, quiet)
verbose = 0#not quiet
tdir = dirname(__file__)
casefile = join(tdir, 't_case30_userfcns')
ppopt = ppoption(OPF_VIOLATION=1e-6, PDIPM_GRADTOL=1e-8,
PDIPM_COMPTOL=1e-8, PDIPM_COSTTOL=1e-9)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=verbose, OPF_ALG=560)
t = 'runopf_w_res(''t_case30_userfcns'') : '
r = runopf_w_res(casefile, ppopt)
t_is(r['reserves']['R'], [25, 15, 0, 0, 19.3906, 0.6094], 4, [t, 'R'])
t_is(r['reserves']['prc'], [2, 2, 2, 2, 5.5, 5.5], 6, [t, 'prc'])
t_is(r['reserves']['mu']['l'], [0, 0, 1, 2, 0, 0], 7, [t, 'mu.l'])
t_is(r['reserves']['mu']['u'], [0.1, 0, 0, 0, 0, 0], 7, [t, 'mu.u'])
t_is(r['reserves']['mu']['Pmax'], [0, 0, 0, 0, 0.5, 0], 7, [t, 'mu.Pmax'])
ppc = loadcase(casefile)
t_is(r['reserves']['cost'], ppc['reserves']['cost'], 12, [t, 'cost'])
t_is(r['reserves']['qty'], ppc['reserves']['qty'], 12, [t, 'qty'])
t_is(r['reserves']['totalcost'], 177.8047, 4, [t, 'totalcost'])
t = 'gen 5 no reserves : ';
ppc = loadcase(casefile)
ppc['reserves']['zones'][:, 4] = 0
ppc['reserves']['cost'] = delete(ppc['reserves']['cost'], 4)
ppc['reserves']['qty'] = delete(ppc['reserves']['qty'], 4)
r = runopf_w_res(ppc, ppopt)
t_is(r['reserves']['R'], [25, 15, 0, 0, 0, 20], 4, [t, 'R'])
t_is(r['reserves']['prc'], [2, 2, 2, 2, 0, 5.5], 6, [t, 'prc'])
t_is(r['reserves']['mu']['l'], [0, 0, 1, 2, 0, 0], 7, [t, 'mu.l'])
t_is(r['reserves']['mu']['u'], [0.1, 0, 0, 0, 0, 0], 6, [t, 'mu.u'])
t_is(r['reserves']['mu']['Pmax'], [0, 0, 0, 0, 0, 0], 7, [t, 'mu.Pmax'])
t_is(r['reserves']['cost'], ppc['reserves']['cost'], 12, [t, 'cost'])
t_is(r['reserves']['qty'], ppc['reserves']['qty'], 12, [t, 'qty'])
t_is(r['reserves']['totalcost'], 187.5, 4, [t, 'totalcost'])
t = 'extra offline gen : ';
ppc = loadcase(casefile)
idx = list(range(3)) + [4] + list(range(3, 6))
ppc['gen'] = ppc['gen'][idx, :]
ppc['gencost'] = ppc['gencost'][idx, :]
ppc['reserves']['zones'] = ppc['reserves']['zones'][:, idx]
ppc['reserves']['cost'] = ppc['reserves']['cost'][idx]
ppc['reserves']['qty'] = ppc['reserves']['qty'][idx]
ppc['gen'][3, GEN_STATUS] = 0
r = runopf_w_res(ppc, ppopt)
t_is(r['reserves']['R'], [25, 15, 0, 0, 0, 19.3906, 0.6094], 4, [t, 'R'])
t_is(r['reserves']['prc'], [2, 2, 2, 5.5, 2, 5.5, 5.5], 6, [t, 'prc'])
t_is(r['reserves']['mu']['l'], [0, 0, 1, 0, 2, 0, 0], 7, [t, 'mu.l'])
t_is(r['reserves']['mu']['u'], [0.1, 0, 0, 0, 0, 0, 0], 7, [t, 'mu.u'])
t_is(r['reserves']['mu']['Pmax'], [0, 0, 0, 0, 0, 0.5, 0], 7, [t, 'mu.Pmax'])
t_is(r['reserves']['cost'], ppc['reserves']['cost'], 12, [t, 'cost'])
t_is(r['reserves']['qty'], ppc['reserves']['qty'], 12, [t, 'qty'])
t_is(r['reserves']['totalcost'], 177.8047, 4, [t, 'totalcost'])
t = 'both extra & gen 6 no res : ';
ppc = loadcase(casefile)
idx = list(range(3)) + [4] + list(range(3, 6))
ppc['gen'] = ppc['gen'][idx, :]
ppc['gencost'] = ppc['gencost'][idx, :]
ppc['reserves']['zones'] = ppc['reserves']['zones'][:, idx]
ppc['reserves']['cost'] = ppc['reserves']['cost'][idx]
ppc['reserves']['qty'] = ppc['reserves']['qty'][idx]
ppc['gen'][3, GEN_STATUS] = 0
ppc['reserves']['zones'][:, 5] = 0
ppc['reserves']['cost'] = delete(ppc['reserves']['cost'], 5)
ppc['reserves']['qty'] = delete(ppc['reserves']['qty'], 5)
r = runopf_w_res(ppc, ppopt)
t_is(r['reserves']['R'], [25, 15, 0, 0, 0, 0, 20], 4, [t, 'R'])
t_is(r['reserves']['prc'], [2, 2, 2, 5.5, 2, 0, 5.5], 6, [t, 'prc'])
t_is(r['reserves']['mu']['l'], [0, 0, 1, 0, 2, 0, 0], 7, [t, 'mu.l'])
t_is(r['reserves']['mu']['u'], [0.1, 0, 0, 0, 0, 0, 0], 6, [t, 'mu.u'])
t_is(r['reserves']['mu']['Pmax'], [0, 0, 0, 0, 0, 0, 0], 7, [t, 'mu.Pmax'])
t_is(r['reserves']['cost'], ppc['reserves']['cost'], 12, [t, 'cost'])
t_is(r['reserves']['qty'], ppc['reserves']['qty'], 12, [t, 'qty'])
t_is(r['reserves']['totalcost'], 187.5, 4, [t, 'totalcost'])
t = 'no qty (Rmax) : '
ppc = loadcase(casefile)
del ppc['reserves']['qty']
r = runopf_w_res(ppc, ppopt)
t_is(r['reserves']['R'], [39.3826, 0.6174, 0, 0, 19.3818, 0.6182], 4, [t, 'R'])
t_is(r['reserves']['prc'], [2, 2, 2, 2, 5.5, 5.5], 5, [t, 'prc'])
t_is(r['reserves']['mu']['l'], [0, 0, 1, 2, 0, 0], 5, [t, 'mu.l'])
t_is(r['reserves']['mu']['u'], [0, 0, 0, 0, 0, 0], 7, [t, 'mu.u'])
t_is(r['reserves']['mu']['Pmax'], [0.1, 0, 0, 0, 0.5, 0], 5, [t, 'mu.Pmax'])
t_is(r['reserves']['cost'], ppc['reserves']['cost'], 12, [t, 'cost'])
t_is(r['reserves']['totalcost'], 176.3708, 4, [t, 'totalcost'])
t = 'RAMP_10, no qty (Rmax) : ';
ppc = loadcase(casefile)
del ppc['reserves']['qty']
ppc['gen'][0, RAMP_10] = 25
r = runopf_w_res(ppc, ppopt)
t_is(r['reserves']['R'], [25, 15, 0, 0, 19.3906, 0.6094], 4, [t, 'R'])
t_is(r['reserves']['prc'], [2, 2, 2, 2, 5.5, 5.5], 6, [t, 'prc'])
t_is(r['reserves']['mu']['l'], [0, 0, 1, 2, 0, 0], 7, [t, 'mu.l'])
t_is(r['reserves']['mu']['u'], [0.1, 0, 0, 0, 0, 0], 7, [t, 'mu.u'])
t_is(r['reserves']['mu']['Pmax'], [0, 0, 0, 0, 0.5, 0], 7, [t, 'mu.Pmax'])
t_is(r['reserves']['cost'], ppc['reserves']['cost'], 12, [t, 'cost'])
t_is(r['reserves']['totalcost'], 177.8047, 4, [t, 'totalcost'])
t_end()
def runpf(casedata=None, ppopt=None, fname='', solvedcase=''):
"""Runs a power flow.
Runs a power flow [full AC Newton's method by default] and optionally
returns the solved values in the data matrices, a flag which is C{True} if
the algorithm was successful in finding a solution, and the elapsed
time in seconds. All input arguments are optional. If C{casename} is
provided it specifies the name of the input data file or dict
containing the power flow data. The default value is 'case9'.
If the ppopt is provided it overrides the default PYPOWER options
vector and can be used to specify the solution algorithm and output
options among other things. If the 3rd argument is given the pretty
printed output will be appended to the file whose name is given in
C{fname}. If C{solvedcase} is specified the solved case will be written
to a case file in PYPOWER format with the specified name. If C{solvedcase}
ends with '.mat' it saves the case as a MAT-file otherwise it saves it
as a Python-file.
If the C{ENFORCE_Q_LIMS} options is set to C{True} [default is false] then
if any generator reactive power limit is violated after running the AC
power flow, the corresponding bus is converted to a PQ bus, with Qg at
the limit, and the case is re-run. The voltage magnitude at the bus
will deviate from the specified value in order to satisfy the reactive
power limit. If the reference bus is converted to PQ, the first
remaining PV bus will be used as the slack bus for the next iteration.
This may result in the real power output at this generator being
slightly off from the specified values.
Enforcing of generator Q limits inspired by contributions from Mu Lin,
Lincoln University, New Zealand (1/14/05).
@author: Ray Zimmerman (PSERC Cornell)
"""
## default arguments
if casedata is None:
casedata = join(dirname(__file__), 'case9')
ppopt = ppoption(ppopt)
## options
verbose = ppopt["VERBOSE"]
qlim = ppopt["ENFORCE_Q_LIMS"] ## enforce Q limits on gens?
dc = ppopt["PF_DC"] ## use DC formulation?
## read data
ppc = loadcase(casedata)
## add zero columns to branch for flows if needed
if ppc["branch"].shape[1] < QT:
ppc["branch"] = c_[ppc["branch"],
zeros((ppc["branch"].shape[0],
QT - ppc["branch"].shape[1] + 1))]
## convert to internal indexing
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = \
ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc["branch"]
## get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
## generator info
on = find(gen[:, GEN_STATUS] > 0) ## which generators are on?
gbus = gen[on, GEN_BUS].astype(int) ## what buses are they at?
##----- run the power flow -----
t0 = time()
if verbose > 0:
v = ppver('all')
stdout.write('PYPOWER Version %s, %s' % (v["Version"], v["Date"]))
if dc: # DC formulation
if verbose:
stdout.write(' -- DC Power Flow\n')
## initial state
Va0 = bus[:, VA] * (pi / 180)
## build B matrices and phase shift injections
B, Bf, Pbusinj, Pfinj = makeBdc(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
## adjusted for phase shifters and real shunts
Pbus = makeSbus(baseMVA, bus,
gen).real - Pbusinj - bus[:, GS] / baseMVA
## "run" the power flow
Va = dcpf(B, Pbus, Va0, ref, pv, pq)
## update data matrices with solution
branch[:, [QF, QT]] = zeros((branch.shape[0], 2))
branch[:, PF] = (Bf * Va + Pfinj) * baseMVA
branch[:, PT] = -branch[:, PF]
bus[:, VM] = ones(bus.shape[0])
bus[:, VA] = Va * (180 / pi)
## update Pg for slack generator (1st gen at ref bus)
## (note: other gens at ref bus are accounted for in Pbus)
## Pg = Pinj + Pload + Gs
## newPg = oldPg + newPinj - oldPinj
refgen = zeros(len(ref), dtype=int)
for k in range(len(ref)):
temp = find(gbus == ref[k])
refgen[k] = on[temp[0]]
gen[refgen,
PG] = gen[refgen, PG] + (B[ref, :] * Va - Pbus[ref]) * baseMVA
success = 1
else: ## AC formulation
alg = ppopt['PF_ALG']
if verbose > 0:
if alg == 1:
solver = 'Newton'
elif alg == 2:
solver = 'fast-decoupled, XB'
elif alg == 3:
solver = 'fast-decoupled, BX'
elif alg == 4:
solver = 'Gauss-Seidel'
else:
solver = 'unknown'
print(' -- AC Power Flow (%s)\n' % solver)
## initial state
# V0 = ones(bus.shape[0]) ## flat start
V0 = bus[:, VM] * exp(1j * pi / 180 * bus[:, VA])
V0[gbus] = gen[on, VG] / abs(V0[gbus]) * V0[gbus]
if qlim:
ref0 = ref ## save index and angle of
Varef0 = bus[ref0, VA] ## original reference bus(es)
limited = [] ## list of indices of gens @ Q lims
fixedQg = zeros(gen.shape[0]) ## Qg of gens at Q limits
repeat = True
while repeat:
## build admittance matrices
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
## compute complex bus power injections [generation - load]
Sbus = makeSbus(baseMVA, bus, gen)
## run the power flow
alg = ppopt["PF_ALG"]
if alg == 1:
V, success, _ = newtonpf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
elif alg == 2 or alg == 3:
Bp, Bpp = makeB(baseMVA, bus, branch, alg)
V, success, _ = fdpf(Ybus, Sbus, V0, Bp, Bpp, ref, pv, pq,
ppopt)
elif alg == 4:
V, success, _ = gausspf(Ybus, Sbus, V0, ref, pv, pq, ppopt)
else:
stderr.write('Only Newton'
's method, fast-decoupled, and '
'Gauss-Seidel power flow algorithms currently '
'implemented.\n')
## update data matrices with solution
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt,
V, ref, pv, pq)
if qlim: ## enforce generator Q limits
## find gens with violated Q constraints
gen_status = gen[:, GEN_STATUS] > 0
qg_max_lim = gen[:, QG] > gen[:, QMAX]
qg_min_lim = gen[:, QG] < gen[:, QMIN]
mx = find(gen_status & qg_max_lim)
mn = find(gen_status & qg_min_lim)
if len(mx) > 0 or len(
mn) > 0: ## we have some Q limit violations
# No PV generators
if len(pv) == 0:
if verbose:
if len(mx) > 0:
print(
'Gen %d [only one left] exceeds upper Q limit : INFEASIBLE PROBLEM\n'
% mx + 1)
else:
print(
'Gen %d [only one left] exceeds lower Q limit : INFEASIBLE PROBLEM\n'
% mn + 1)
success = 0
break
## one at a time?
if qlim == 2: ## fix largest violation, ignore the rest
k = argmax(r_[gen[mx, QG] - gen[mx, QMAX],
gen[mn, QMIN] - gen[mn, QG]])
if k > len(mx):
mn = mn[k - len(mx)]
mx = []
else:
mx = mx[k]
mn = []
if verbose and len(mx) > 0:
for i in range(len(mx)):
print('Gen ' + str(mx[i] + 1) +
' at upper Q limit, converting to PQ bus\n')
if verbose and len(mn) > 0:
for i in range(len(mn)):
print('Gen ' + str(mn[i] + 1) +
' at lower Q limit, converting to PQ bus\n')
## save corresponding limit values
fixedQg[mx] = gen[mx, QMAX]
fixedQg[mn] = gen[mn, QMIN]
mx = r_[mx, mn].astype(int)
## convert to PQ bus
gen[mx, QG] = fixedQg[mx] ## set Qg to binding
for i in range(
len(mx)
): ## [one at a time, since they may be at same bus]
gen[mx[i],
GEN_STATUS] = 0 ## temporarily turn off gen,
bi = gen[mx[i], GEN_BUS] ## adjust load accordingly,
bus[bi, [PD, QD]] = (bus[bi, [PD, QD]] -
gen[mx[i], [PG, QG]])
if len(ref) > 1 and any(bus[gen[mx, GEN_BUS],
BUS_TYPE] == REF):
raise ValueError('Sorry, PYPOWER cannot enforce Q '
'limits for slack buses in systems '
'with multiple slacks.')
bus[gen[mx, GEN_BUS].astype(int),
BUS_TYPE] = PQ ## & set bus type to PQ
## update bus index lists of each type of bus
ref_temp = ref
ref, pv, pq = bustypes(bus, gen)
if verbose and ref != ref_temp:
print('Bus %d is new slack bus\n' % ref)
limited = r_[limited, mx].astype(int)
else:
repeat = 0 ## no more generator Q limits violated
else:
repeat = 0 ## don't enforce generator Q limits, once is enough
if qlim and len(limited) > 0:
## restore injections from limited gens [those at Q limits]
gen[limited, QG] = fixedQg[limited] ## restore Qg value,
for i in range(
len(limited
)): ## [one at a time, since they may be at same bus]
bi = gen[limited[i], GEN_BUS] ## re-adjust load,
bus[bi,
[PD, QD]] = bus[bi, [PD, QD]] + gen[limited[i], [PG, QG]]
gen[limited[i], GEN_STATUS] = 1 ## and turn gen back on
if ref != ref0:
## adjust voltage angles to make original ref bus correct
bus[:, VA] = bus[:, VA] - bus[ref0, VA] + Varef0
ppc["et"] = time() - t0
ppc["success"] = success
##----- output results -----
## convert back to original bus numbering & print results
ppc["bus"], ppc["gen"], ppc["branch"] = bus, gen, branch
results = int2ext(ppc)
## zero out result fields of out-of-service gens & branches
if len(results["order"]["gen"]["status"]["off"]) > 0:
results["gen"][ix_(results["order"]["gen"]["status"]["off"],
[PG, QG])] = 0
if len(results["order"]["branch"]["status"]["off"]) > 0:
results["branch"][ix_(results["order"]["branch"]["status"]["off"],
[PF, QF, PT, QT])] = 0
if fname:
fd = None
try:
fd = open(fname, "a")
except Exception as detail:
stderr.write("Error opening %s: %s.\n" % (fname, detail))
finally:
if fd is not None:
printpf(results, fd, ppopt)
fd.close()
else:
printpf(results, stdout, ppopt)
## save solved case
if solvedcase:
savecase(solvedcase, results)
return results, success
def uopf(*args):
"""Solves combined unit decommitment / optimal power flow.
Solves a combined unit decommitment and optimal power flow for a single
time period. Uses an algorithm similar to dynamic programming. It proceeds
through a sequence of stages, where stage C{N} has C{N} generators shut
down, starting with C{N=0}. In each stage, it forms a list of candidates
(gens at their C{Pmin} limits) and computes the cost with each one of them
shut down. It selects the least cost case as the starting point for the
next stage, continuing until there are no more candidates to be shut down
or no more improvement can be gained by shutting something down.
If C{verbose} in ppopt (see L{ppoption} is C{true}, it prints progress
info, if it is > 1 it prints the output of each individual opf.
@see: L{opf}, L{runuopf}
@author: Ray Zimmerman (PSERC Cornell)
@author: Richard Lincoln
"""
##----- initialization -----
t0 = time() ## start timer
## process input arguments
ppc, ppopt = opf_args2(*args)
## options
verbose = ppopt["VERBOSE"]
if verbose: ## turn down verbosity one level for calls to opf
ppopt = ppoption(ppopt, VERBOSE=verbose - 1)
##----- do combined unit commitment/optimal power flow -----
## check for sum(Pmin) > total load, decommit as necessary
on = find((ppc["gen"][:, GEN_STATUS] > 0)
& ~isload(ppc["gen"])) ## gens in service
onld = find((ppc["gen"][:, GEN_STATUS] > 0)
& isload(ppc["gen"])) ## disp loads in serv
load_capacity = sum(ppc["bus"][:, PD]) - sum(
ppc["gen"][onld, PMIN]) ## total load capacity
Pmin = ppc["gen"][on, PMIN]
while sum(Pmin) > load_capacity:
## shut down most expensive unit
avgPmincost = totcost(ppc["gencost"][on, :], Pmin) / Pmin
_, i = fairmax(avgPmincost) ## pick one with max avg cost at Pmin
i = on[i] ## convert to generator index
if verbose:
print(
'Shutting down generator %d so all Pmin limits can be satisfied.\n'
% i)
## set generation to zero
ppc["gen"][i, [PG, QG, GEN_STATUS]] = 0
## update minimum gen capacity
on = find((ppc["gen"][:, GEN_STATUS] > 0)
& ~isload(ppc["gen"])) ## gens in service
Pmin = ppc["gen"][on, PMIN]
## run initial opf
results = opf(ppc, ppopt)
## best case so far
results1 = deepcopy(results)
## best case for this stage (ie. with n gens shut down, n=0,1,2 ...)
results0 = deepcopy(results1)
ppc["bus"] = results0["bus"].copy(
) ## use these V as starting point for OPF
while True:
## get candidates for shutdown
candidates = find((results0["gen"][:, MU_PMIN] > 0)
& (results0["gen"][:, PMIN] > 0))
if len(candidates) == 0:
break
## do not check for further decommitment unless we
## see something better during this stage
done = True
for k in candidates:
## start with best for this stage
ppc["gen"] = results0["gen"].copy()
## shut down gen k
ppc["gen"][k, [PG, QG, GEN_STATUS]] = 0
## run opf
results = opf(ppc, ppopt)
## something better?
if results['success'] and (results["f"] < results1["f"]):
results1 = deepcopy(results)
k1 = k
done = False ## make sure we check for further decommitment
if done:
## decommits at this stage did not help, so let's quit
break
else:
## shutting something else down helps, so let's keep going
if verbose:
print('Shutting down generator %d.\n' % k1)
results0 = deepcopy(results1)
ppc["bus"] = results0["bus"].copy(
) ## use these V as starting point for OPF
## compute elapsed time
et = time() - t0
## finish preparing output
results0['et'] = et
return results0
def t_runmarket(quiet=False):
"""Tests for code in C{runmkt}, C{smartmkt} and C{auction}.
@author: Ray Zimmerman (PSERC Cornell)
"""
n_tests = 20
t_begin(n_tests, quiet)
try:
from pypower.extras.smartmarket import runmarket
except ImportError:
t_skip(n_tests, 'smartmarket code not available')
t_end
return
ppc = loadcase('t_auction_case')
ppopt = ppoption(OPF_ALG=560, OUT_ALL_LIM=1, OUT_BRANCH=0, OUT_SYS_SUM=0)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=1)
#ppopt = ppoption(ppopt, OUT_GEN=1, OUT_BRANCH=0, OUT_SYS_SUM=0)
offers = {'P': {}, 'Q': {}}
bids = {'P': {}, 'Q': {}}
offers['P']['qty'] = array([[12, 24, 24], [12, 24, 24], [12, 24, 24],
[12, 24, 24], [12, 24, 24], [12, 24, 24]])
offers['P']['prc'] = array([[20, 50, 60], [20, 40, 70], [20, 42, 80],
[20, 44, 90], [20, 46, 75], [20, 48, 60]])
bids['P']['qty'] = array([[10, 10, 10], [10, 10, 10], [10, 10, 10]])
bids['P']['prc'] = array([
[100, 70, 60],
# [100, 64.3, 20],
# [100, 30.64545, 0],
[100, 50, 20],
[100, 60, 50]
])
offers['Q']['qty'] = [60, 60, 60, 60, 60, 60, 0, 0, 0]
offers['Q']['prc'] = [0, 0, 0, 0, 0, 3, 0, 0, 0]
bids.Q['qty'] = [15, 15, 15, 15, 15, 15, 15, 12, 7.5]
# bids.Q['prc'] = [ 0, 0, 0, 0, 0, 0, 0, 83.9056, 0 ]
bids.Q['prc'] = [0, 0, 0, 0, 0, 0, 0, 20, 0]
t = 'marginal Q offer, marginal PQ bid, auction_type = 5'
mkt = {'auction_type': 5, 't': [], 'u0': [], 'lim': []}
r, co, cb, _, _, _, _ = runmarket(ppc, offers, bids, mkt, ppopt)
co5 = co.copy()
cb5 = cb.copy()
# [ co['P']['qty'] co['P']['prc'] ]
# [ cb['P']['qty'] cb['P']['prc'] ]
# [ co['Q']['qty'] co['Q']['prc'] ]
# [ cb['Q']['qty'] cb['Q']['prc'] ]
i2e = r['bus'][:, BUS_I]
e2i = sparse((max(i2e), 1))
e2i[i2e] = list(range(r['bus'].size))
G = find(isload(r['gen']) == 0) ## real generators
L = find(isload(r['gen'])) ## dispatchable loads
Gbus = e2i[r['gen'][G, GEN_BUS]]
Lbus = e2i[r['gen'][L, GEN_BUS]]
t_is(co['P']['qty'],
ones((6, 1)) * [12, 24, 0], 2, [t, ' : gen P quantities'])
t_is(co['P']['prc'][0, :], 50.1578, 3, [t, ' : gen 1 P prices'])
t_is(cb['P']['qty'], [[10, 10, 10], [10, 0.196, 0], [10, 10, 0]], 2,
[t, ' : load P quantities'])
t_is(cb['P']['prc'][1, :], 56.9853, 4, [t, ' : load 2 P price'])
t_is(co['P']['prc'][:, 0], r['bus'][Gbus, LAM_P], 8,
[t, ' : gen P prices'])
t_is(cb['P']['prc'][:, 0], r['bus'][Lbus, LAM_P], 8,
[t, ' : load P prices'])
t_is(co['Q']['qty'],
[4.2722, 11.3723, 14.1472, 22.8939, 36.7886, 12.3375, 0, 0, 0], 2,
[t, ' : Q offer quantities'])
t_is(co['Q']['prc'], [0, 0, 0, 0, 0, 3, 0.4861, 2.5367, 1.3763], 4,
[t, ' : Q offer prices'])
t_is(cb['Q']['qty'], [0, 0, 0, 0, 0, 0, 15, 4.0785, 5], 2,
[t, ' : Q bid quantities'])
t_is(cb['Q']['prc'], [0, 0, 0, 0, 0, 3, 0.4861, 2.5367, 1.3763], 4,
[t, ' : Q bid prices'])
t_is(co['Q']['prc'], r['bus'][[Gbus, Lbus], LAM_Q], 8,
[t, ' : Q offer prices'])
t_is(cb['Q']['prc'], co['Q']['prc'], 8, [t, ' : Q bid prices'])
t = 'marginal Q offer, marginal PQ bid, auction_type = 0'
mkt['auction_type'] = 0
r, co, cb, _, _, _, _ = runmarket(ppc, offers, bids, mkt, ppopt)
t_is(co['P']['qty'], co5['P']['qty'], 8, [t, ' : gen P quantities'])
t_is(cb['P']['qty'], cb5['P']['qty'], 8, [t, ' : load P quantities'])
t_is(co['P']['prc'], offers['P']['prc'], 8, [t, ' : gen P prices'])
t_is(cb['P']['prc'], bids['P']['prc'], 8, [t, ' : load P prices'])
t_is(co['Q']['qty'], co5['Q']['qty'], 8, [t, ' : gen Q quantities'])
t_is(cb['Q']['qty'], cb5['Q']['qty'], 8, [t, ' : load Q quantities'])
t_is(co['Q']['prc'], offers['Q']['prc'], 8, [t, ' : gen Q prices'])
t_is(cb['Q']['prc'], bids['Q']['prc'], 8, [t, ' : load Q prices'])
t_end
def t_qps_pypower(quiet=False):
"""Tests of C{qps_pypower} QP solvers.
@author: Ray Zimmerman (PSERC Cornell)
"""
algs = [200, 250, 400, 500, 600, 700]
names = ['PIPS', 'sc-PIPS', 'IPOPT', 'CPLEX', 'MOSEK', 'Gurobi']
check = [None, None, 'ipopt', 'cplex', 'mosek', 'gurobipy']
n = 36
t_begin(n * len(algs), quiet)
for k in range(len(algs)):
if check[k] is not None and not have_fcn(check[k]):
t_skip(n, '%s not installed' % names[k])
else:
opt = {'verbose': 0, 'alg': algs[k]}
if names[k] == 'PIPS' or names[k] == 'sc-PIPS':
opt['pips_opt'] = {}
opt['pips_opt']['comptol'] = 1e-8
if names[k] == 'CPLEX':
# alg = 0 ## default uses barrier method with NaN bug in lower lim multipliers
alg = 2 ## use dual simplex
ppopt = ppoption(CPLEX_LPMETHOD = alg, CPLEX_QPMETHOD = min([4, alg]))
opt['cplex_opt'] = cplex_options([], ppopt)
if names[k] == 'MOSEK':
# alg = 5 ## use dual simplex
ppopt = ppoption()
# ppopt = ppoption(ppopt, MOSEK_LP_ALG = alg)
ppopt = ppoption(ppopt, MOSEK_GAP_TOL=1e-9)
opt['mosek_opt'] = mosek_options([], ppopt)
t = '%s - 3-d LP : ' % names[k]
## example from 'doc linprog'
c = array([-5, -4, -6], float)
A = sparse([[1, -1, 1],
[3, 2, 4],
[3, 2, 0]], dtype=float)
l = None
u = array([20, 42, 30], float)
xmin = array([0, 0, 0], float)
x0 = None
x, f, s, _, lam = qps_pypower(None, c, A, l, u, xmin, None, None, opt)
t_is(s, 1, 12, [t, 'success'])
t_is(x, [0, 15, 3], 6, [t, 'x'])
t_is(f, -78, 6, [t, 'f'])
t_is(lam['mu_l'], [0, 0, 0], 13, [t, 'lam.mu_l'])
t_is(lam['mu_u'], [0, 1.5, 0.5], 9, [t, 'lam.mu_u'])
t_is(lam['lower'], [1, 0, 0], 9, [t, 'lam.lower'])
t_is(lam['upper'], zeros(shape(x)), 13, [t, 'lam.upper'])
t = '%s - unconstrained 3-d quadratic : ' % names[k]
## from http://www.akiti.ca/QuadProgEx0Constr.html
H = sparse([
[ 5, -2, -1],
[-2, 4, 3],
[-1, 3, 5]
], dtype=float)
c = array([2, -35, -47], float)
x0 = array([0, 0, 0], float)
x, f, s, _, lam = qps_pypower(H, c, opt=opt)
t_is(s, 1, 12, [t, 'success'])
t_is(x, [3, 5, 7], 8, [t, 'x'])
t_is(f, -249, 13, [t, 'f'])
t_ok(len(lam['mu_l']) == 0, [t, 'lam.mu_l'])
t_ok(len(lam['mu_u']) == 0, [t, 'lam.mu_u'])
t_is(lam['lower'], zeros(shape(x)), 13, [t, 'lam.lower'])
t_is(lam['upper'], zeros(shape(x)), 13, [t, 'lam.upper'])
t = '%s - constrained 2-d QP : ' % names[k]
## example from 'doc quadprog'
H = sparse([[ 1, -1],
[-1, 2]], dtype=float)
c = array([-2, -6], float)
A = sparse([[ 1, 1],
[-1, 2],
[ 2, 1]], dtype=float)
l = None
u = array([2, 2, 3], float)
xmin = array([0, 0])
x0 = None
x, f, s, _, lam = qps_pypower(H, c, A, l, u, xmin, None, x0, opt)
t_is(s, 1, 12, [t, 'success'])
t_is(x, array([2., 4.]) / 3, 7, [t, 'x'])
t_is(f, -74. / 9, 6, [t, 'f'])
t_is(lam['mu_l'], [0., 0., 0.], 13, [t, 'lam.mu_l'])
t_is(lam['mu_u'], array([28., 4., 0.]) / 9, 7, [t, 'lam.mu_u'])
t_is(lam['lower'], zeros(shape(x)), 8, [t, 'lam.lower'])
t_is(lam['upper'], zeros(shape(x)), 13, [t, 'lam.upper'])
t = '%s - constrained 4-d QP : ' % names[k]
## from http://www.jmu.edu/docs/sasdoc/sashtml/iml/chap8/sect12.htm
H = sparse([[1003.1, 4.3, 6.3, 5.9],
[4.3, 2.2, 2.1, 3.9],
[6.3, 2.1, 3.5, 4.8],
[5.9, 3.9, 4.8, 10.0]])
c = zeros(4)
A = sparse([[ 1, 1, 1, 1],
[0.17, 0.11, 0.10, 0.18]])
l = array([1, 0.10])
u = array([1, Inf])
xmin = zeros(4)
x0 = array([1, 0, 0, 1], float)
x, f, s, _, lam = qps_pypower(H, c, A, l, u, xmin, None, x0, opt)
t_is(s, 1, 12, [t, 'success'])
t_is(x, array([0, 2.8, 0.2, 0]) / 3, 5, [t, 'x'])
t_is(f, 3.29 / 3, 6, [t, 'f'])
t_is(lam['mu_l'], array([6.58, 0]) / 3, 6, [t, 'lam.mu_l'])
t_is(lam['mu_u'], [0, 0], 13, [t, 'lam.mu_u'])
t_is(lam['lower'], [2.24, 0, 0, 1.7667], 4, [t, 'lam.lower'])
t_is(lam['upper'], zeros(shape(x)), 13, [t, 'lam.upper'])
t = '%s - (dict) constrained 4-d QP : ' % names[k]
p = {'H': H, 'A': A, 'l': l, 'u': u, 'xmin': xmin, 'x0': x0, 'opt': opt}
x, f, s, _, lam = qps_pypower(p)
t_is(s, 1, 12, [t, 'success'])
t_is(x, array([0, 2.8, 0.2, 0]) / 3, 5, [t, 'x'])
t_is(f, 3.29 / 3, 6, [t, 'f'])
t_is(lam['mu_l'], array([6.58, 0]) / 3, 6, [t, 'lam.mu_l'])
t_is(lam['mu_u'], [0, 0], 13, [t, 'lam.mu_u'])
t_is(lam['lower'], [2.24, 0, 0, 1.7667], 4, [t, 'lam.lower'])
t_is(lam['upper'], zeros(shape(x)), 13, [t, 'lam.upper'])
t = '%s - infeasible LP : ' % names[k]
p = {'A': sparse([1, 1]), 'c': array([1, 1]), 'u': array([-1]),
'xmin': array([0, 0]), 'opt': opt}
x, f, s, _, lam = qps_pypower(p)
t_ok(s <= 0, [t, 'no success'])
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 _run_fbsw_dense(ppc, ppopt=None):
"""
DENSE version of distribution power flow solution according to [1]
:References:
[1] Jen-Hao Teng, "A Direct Approach for Distribution System Load Flow Solutions", IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882-887, July 2003.
:param ppc: matpower-style case data
:return: results (pypower style), success (flag about PF convergence)
"""
# time_start = time()
# path_search = nx.shortest_path
# ppci = ext2int(ppc)
ppci = ppc
ppopt = ppoption(ppopt)
baseMVA, bus, gen, branch = \
ppci["baseMVA"], ppci["bus"], ppci["gen"], ppci["branch"]
nbus = bus.shape[0]
# get bus index lists of each type of bus
ref, pv, pq = bustypes(bus, gen)
root_bus = ref[
0] # reference bus is assumed as root bus for a radial network
DLF, ppc_fbsw, buses_ordered_fbsw = bibc_bcbv_dense(ppci)
baseMVA_fbsw, bus_fbsw, gen_fbsw, branch_fbsw = \
ppc_fbsw["baseMVA"], ppc_fbsw["bus"], ppc_fbsw["gen"], ppc_fbsw["branch"]
time_start = time()
# initialize voltages to flat start and buses with gens to their setpoints
V0 = np.ones(nbus, dtype=complex)
V0[gen[:, GEN_BUS].astype(int)] = gen[:, VG]
Sbus_fbsw = makeSbus(baseMVA_fbsw, bus_fbsw, gen_fbsw)
# update data matrices with solution
Ybus_fbsw, Yf_fbsw, Yt_fbsw = makeYbus(baseMVA_fbsw, bus_fbsw, branch_fbsw)
## get bus index lists of each type of bus
ref_fbsw, pv_fbsw, pq_fbsw = bustypes(bus_fbsw, gen_fbsw)
##----- run the power flow -----
# ###
# LF initialization and calculation
V_final, success = fbsw_dense(DLF,
bus_fbsw,
gen_fbsw,
branch_fbsw,
baseMVA_fbsw,
Ybus_fbsw,
Sbus_fbsw,
V0,
ref_fbsw,
pv_fbsw,
pq_fbsw,
ppopt=ppopt)
V_final = V_final[np.argsort(
buses_ordered_fbsw)] # return bus voltages in original bus order
ppci["et"] = time() - time_start
Sbus = makeSbus(baseMVA, bus, gen)
# update data matrices with solution
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
bus, gen, branch = pfsoln(baseMVA, bus, gen, branch, Ybus, Yf, Yt, V_final,
ref, pv, pq)
ppci["success"] = success
##----- output results -----
ppci["bus"], ppci["gen"], ppci["branch"] = bus, gen, branch
results = ppci
return results, success
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 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 opf_args(*args):
"""Parses and initializes OPF input arguments.
Returns the full set of initialized OPF input arguments, filling in
default values for missing arguments. See Examples below for the
possible calling syntax options.
Input arguments options::
opf_args(ppc)
opf_args(ppc, ppopt)
opf_args(ppc, userfcn, ppopt)
opf_args(ppc, A, l, u)
opf_args(ppc, A, l, u, ppopt)
opf_args(ppc, A, l, u, ppopt, N, fparm, H, Cw)
opf_args(ppc, A, l, u, ppopt, N, fparm, H, Cw, z0, zl, zu)
opf_args(baseMVA, bus, gen, branch, areas, gencost)
opf_args(baseMVA, bus, gen, branch, areas, gencost, ppopt)
opf_args(baseMVA, bus, gen, branch, areas, gencost, userfcn, ppopt)
opf_args(baseMVA, bus, gen, branch, areas, gencost, A, l, u)
opf_args(baseMVA, bus, gen, branch, areas, gencost, A, l, u, ppopt)
opf_args(baseMVA, bus, gen, branch, areas, gencost, A, l, u, ...
ppopt, N, fparm, H, Cw)
opf_args(baseMVA, bus, gen, branch, areas, gencost, A, l, u, ...
ppopt, N, fparm, H, Cw, z0, zl, zu)
The data for the problem can be specified in one of three ways:
1. a string (ppc) containing the file name of a PYPOWER case
which defines the data matrices baseMVA, bus, gen, branch, and
gencost (areas is not used at all, it is only included for
backward compatibility of the API).
2. a dict (ppc) containing the data matrices as fields.
3. the individual data matrices themselves.
The optional user parameters for user constraints (C{A, l, u}), user costs
(C{N, fparm, H, Cw}), user variable initializer (z0), and user variable
limits (C{zl, zu}) can also be specified as fields in a case dict,
either passed in directly or defined in a case file referenced by name.
When specified, C{A, l, u} represent additional linear constraints on the
optimization variables, C{l <= A*[x z] <= u}. If the user specifies an C{A}
matrix that has more columns than the number of "C{x}" (OPF) variables,
then there are extra linearly constrained "C{z}" variables. For an
explanation of the formulation used and instructions for forming the
C{A} matrix, see the MATPOWER manual.
A generalized cost on all variables can be applied if input arguments
C{N}, C{fparm}, C{H} and C{Cw} are specified. First, a linear
transformation of the optimization variables is defined by means of
C{r = N * [x z]}. Then, to each element of r a function is applied as
encoded in the C{fparm} matrix (see Matpower manual). If the resulting
vector is named C{w}, then C{H} and C{Cw} define a quadratic cost on
C{w}: C{(1/2)*w'*H*w + Cw * w}.
C{H} and C{N} should be sparse matrices and C{H} should also be symmetric.
The optional C{ppopt} vector specifies PYPOWER options. See L{ppoption}
for details and default values.
@author: Ray Zimmerman (PSERC Cornell)
@author: Carlos E. Murillo-Sanchez (PSERC Cornell & Universidad
Autonoma de Manizales)
@author: Richard Lincoln
"""
# nargin = len([arg for arg in [baseMVA, bus, gen, branch, areas, gencost,
# Au, lbu, ubu, ppopt, N, fparm, H, Cw,
# z0, zl, zu] if arg is not None])
nargin = len(args)
userfcn = array([])
## passing filename or dict
if isinstance(args[0], basestring) or isinstance(args[0], dict):
# ----opf( baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt, N, fparm, H, Cw, z0, zl, zu)
# 12 opf(casefile, Au, lbu, ubu, ppopt, N, fparm, H, Cw, z0, zl, zu)
# 9 opf(casefile, Au, lbu, ubu, ppopt, N, fparm, H, Cw)
# 5 opf(casefile, Au, lbu, ubu, ppopt)
# 4 opf(casefile, Au, lbu, ubu)
# 3 opf(casefile, userfcn, ppopt)
# 2 opf(casefile, ppopt)
# 1 opf(casefile)
if nargin in [1, 2, 3, 4, 5, 9, 12]:
casefile = args[0]
if nargin == 12:
baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt, N, fparm = args
zu = fparm
zl = N
z0 = ppopt
Cw = ubu
H = lbu
fparm = Au
N = gencost
ppopt = areas
ubu = branch
lbu = gen
Au = bus
elif nargin == 9:
baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu = args
zu = array([])
zl = array([])
z0 = array([])
Cw = ubu
H = lbu
fparm = Au
N = gencost
ppopt = areas
ubu = branch
lbu = gen
Au = bus
elif nargin == 5:
baseMVA, bus, gen, branch, areas = args
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ppopt = areas
ubu = branch
lbu = gen
Au = bus
elif nargin == 4:
baseMVA, bus, gen, branch = args
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ppopt = ppoption()
ubu = branch
lbu = gen
Au = bus
elif nargin == 3:
baseMVA, bus, gen = args
userfcn = bus
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ppopt = gen
ubu = array([])
lbu = array([])
Au = None
elif nargin == 2:
baseMVA, bus = args
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ppopt = bus
ubu = array([])
lbu = array([])
Au = None
elif nargin == 1:
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ppopt = ppoption()
ubu = array([])
lbu = array([])
Au = None
else:
stderr.write('opf_args: Incorrect input arg order, number or type\n')
ppc = loadcase(casefile)
baseMVA, bus, gen, branch, gencost = \
ppc['baseMVA'], ppc['bus'], ppc['gen'], ppc['branch'], ppc['gencost']
if 'areas' in ppc:
areas = ppc['areas']
else:
areas = array([])
if Au is None and 'A' in ppc:
Au, lbu, ubu = ppc["A"], ppc["l"], ppc["u"]
if N is None and 'N' in ppc: ## these two must go together
N, Cw = ppc["N"], ppc["Cw"]
if H is None and 'H' in ppc: ## will default to zeros
H = ppc["H"]
if (fparm is None or len(fparm) == 0) and 'fparm' in ppc: ## will default to [1 0 0 1]
fparm = ppc["fparm"]
if (z0 is None or len(z0) == 0) and 'z0' in ppc:
z0 = ppc["z0"]
if (zl is None or len(zl) == 0) and 'zl' in ppc:
zl = ppc["zl"]
if (zu is None or len(zu) == 0) and 'zu' in ppc:
zu = ppc["zu"]
if (userfcn is None or len(userfcn) == 0) and 'userfcn' in ppc:
userfcn = ppc['userfcn']
else: ## passing individual data matrices
# ----opf(baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt, N, fparm, H, Cw, z0, zl, zu)
# 17 opf(baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt, N, fparm, H, Cw, z0, zl, zu)
# 14 opf(baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt, N, fparm, H, Cw)
# 10 opf(baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt)
# 9 opf(baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu)
# 8 opf(baseMVA, bus, gen, branch, areas, gencost, userfcn, ppopt)
# 7 opf(baseMVA, bus, gen, branch, areas, gencost, ppopt)
# 6 opf(baseMVA, bus, gen, branch, areas, gencost)
if nargin in [6, 7, 8, 9, 10, 14, 17]:
if nargin == 17:
baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt, N, fparm, H, Cw, z0, zl, zu = args
elif nargin == 14:
baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt, N, fparm, H, Cw = args
zu = array([])
zl = array([])
z0 = array([])
elif nargin == 10:
baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu, ppopt = args
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
elif nargin == 9:
baseMVA, bus, gen, branch, areas, gencost, Au, lbu, ubu = args
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ppopt = ppoption()
elif nargin == 8:
baseMVA, bus, gen, branch, areas, gencost, userfcn, ppopt = args
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ubu = array([])
lbu = array([])
Au = None
elif nargin == 7:
baseMVA, bus, gen, branch, areas, gencost, ppopt = args
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ubu = array([])
lbu = array([])
Au = None
elif nargin == 6:
baseMVA, bus, gen, branch, areas, gencost = args
zu = array([])
zl = array([])
z0 = array([])
Cw = array([])
H = None
fparm = array([])
N = None
ppopt = ppoption()
ubu = array([])
lbu = array([])
Au = None
else:
stderr.write('opf_args: Incorrect input arg order, number or type\n')
if N is not None:
nw = N.shape[0]
else:
nw = 0
if nw:
if Cw.shape[0] != nw:
stderr.write('opf_args.m: dimension mismatch between N and Cw in '
'generalized cost parameters\n')
if len(fparm) > 0 and fparm.shape[0] != nw:
stderr.write('opf_args.m: dimension mismatch between N and fparm '
'in generalized cost parameters\n')
if (H is not None) and (H.shape[0] != nw | H.shape[0] != nw):
stderr.write('opf_args.m: dimension mismatch between N and H in '
'generalized cost parameters\n')
if Au is not None:
if Au.shape[0] > 0 and N.shape[1] != Au.shape[1]:
stderr.write('opf_args.m: A and N must have the same number '
'of columns\n')
## make sure N and H are sparse
if not issparse(N):
stderr.write('opf_args.m: N must be sparse in generalized cost '
'parameters\n')
if not issparse(H):
stderr.write('opf_args.m: H must be sparse in generalized cost parameters\n')
if Au is not None and not issparse(Au):
stderr.write('opf_args.m: Au must be sparse\n')
if ppopt == None or len(ppopt) == 0:
ppopt = ppoption()
return baseMVA, bus, gen, branch, gencost, Au, lbu, ubu, \
ppopt, N, fparm, H, Cw, z0, zl, zu, userfcn, areas
def printpf(baseMVA,
bus=None,
gen=None,
branch=None,
f=None,
success=None,
et=None,
fd=None,
ppopt=None):
"""Prints power flow results.
Prints power flow and optimal power flow results to C{fd} (a file
descriptor which defaults to C{stdout}), with the details of what
gets printed controlled by the optional C{ppopt} argument, which is a
PYPOWER options vector (see L{ppoption} for details).
The data can either be supplied in a single C{results} dict, or
in the individual arguments: C{baseMVA}, C{bus}, C{gen}, C{branch}, C{f},
C{success} and C{et}, where C{f} is the OPF objective function value,
C{success} is C{True} if the solution converged and C{False} otherwise,
and C{et} is the elapsed time for the computation in seconds. If C{f} is
given, it is assumed that the output is from an OPF run, otherwise it is
assumed to be a simple power flow run.
Examples::
ppopt = ppoptions(OUT_GEN=1, OUT_BUS=0, OUT_BRANCH=0)
fd = open(fname, 'w+b')
results = runopf(ppc)
printpf(results)
printpf(results, fd)
printpf(results, fd, ppopt)
printpf(baseMVA, bus, gen, branch, f, success, et)
printpf(baseMVA, bus, gen, branch, f, success, et, fd)
printpf(baseMVA, bus, gen, branch, f, success, et, fd, ppopt)
fd.close()
@author: Ray Zimmerman (PSERC Cornell)
"""
##----- initialization -----
## default arguments
if isinstance(baseMVA, dict):
have_results_struct = 1
results = baseMVA
if gen is None:
ppopt = ppoption() ## use default options
else:
ppopt = gen
if (ppopt['OUT_ALL'] == 0):
return ## nothin' to see here, bail out now
if bus is None:
fd = stdout ## print to stdout by default
else:
fd = bus
baseMVA, bus, gen, branch, success, et = \
results["baseMVA"], results["bus"], results["gen"], \
results["branch"], results["success"], results["et"]
if 'f' in results:
f = results["f"]
else:
f = None
else:
have_results_struct = 0
if ppopt is None:
ppopt = ppoption() ## use default options
if fd is None:
fd = stdout ## print to stdout by default
if ppopt['OUT_ALL'] == 0:
return ## nothin' to see here, bail out now
isOPF = f is not None ## FALSE -> only simple PF data, TRUE -> OPF data
## options
isDC = ppopt['PF_DC'] ## use DC formulation?
OUT_ALL = ppopt['OUT_ALL']
OUT_ANY = OUT_ALL == 1 ## set to true if any pretty output is to be generated
OUT_SYS_SUM = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_SYS_SUM'])
OUT_AREA_SUM = (OUT_ALL == 1) or ((OUT_ALL == -1)
and ppopt['OUT_AREA_SUM'])
OUT_BUS = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_BUS'])
OUT_BRANCH = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_BRANCH'])
OUT_GEN = (OUT_ALL == 1) or ((OUT_ALL == -1) and ppopt['OUT_GEN'])
OUT_ANY = OUT_ANY | (
(OUT_ALL == -1) and
(OUT_SYS_SUM or OUT_AREA_SUM or OUT_BUS or OUT_BRANCH or OUT_GEN))
if OUT_ALL == -1:
OUT_ALL_LIM = ppopt['OUT_ALL_LIM']
elif OUT_ALL == 1:
OUT_ALL_LIM = 2
else:
OUT_ALL_LIM = 0
OUT_ANY = OUT_ANY or (OUT_ALL_LIM >= 1)
if OUT_ALL_LIM == -1:
OUT_V_LIM = ppopt['OUT_V_LIM']
OUT_LINE_LIM = ppopt['OUT_LINE_LIM']
OUT_PG_LIM = ppopt['OUT_PG_LIM']
OUT_QG_LIM = ppopt['OUT_QG_LIM']
else:
OUT_V_LIM = OUT_ALL_LIM
OUT_LINE_LIM = OUT_ALL_LIM
OUT_PG_LIM = OUT_ALL_LIM
OUT_QG_LIM = OUT_ALL_LIM
OUT_ANY = OUT_ANY or (
(OUT_ALL_LIM == -1) and
(OUT_V_LIM or OUT_LINE_LIM or OUT_PG_LIM or OUT_QG_LIM))
ptol = 1e-4 ## tolerance for displaying shadow prices
## create map of external bus numbers to bus indices
i2e = bus[:, BUS_I].astype(int)
e2i = zeros(max(i2e) + 1, int)
e2i[i2e] = arange(bus.shape[0])
## sizes of things
nb = bus.shape[0] ## number of buses
nl = branch.shape[0] ## number of branches
ng = gen.shape[0] ## number of generators
## zero out some data to make printout consistent for DC case
if isDC:
bus[:, r_[QD, BS]] = zeros((nb, 2))
gen[:, r_[QG, QMAX, QMIN]] = zeros((ng, 3))
branch[:, r_[BR_R, BR_B]] = zeros((nl, 2))
## parameters
ties = find(
bus[e2i[branch[:, F_BUS].real.astype(int)],
BUS_AREA] != bus[e2i[branch[:, T_BUS].real.astype(int)], BUS_AREA])
## area inter-ties
tap = ones(nl) ## default tap ratio = 1 for lines
xfmr = find(branch[:, TAP]).real ## indices of transformers
tap[xfmr] = branch[xfmr, TAP].real ## include transformer tap ratios
tap = tap * exp(-1j * pi / 180 * branch[:, SHIFT]) ## add phase shifters
nzld = find((bus[:, PD] != 0.0) | (bus[:, QD] != 0.0))
sorted_areas = sort(bus[:, BUS_AREA])
## area numbers
s_areas = sorted_areas[r_[1, find(diff(sorted_areas)) + 1]]
nzsh = find((bus[:, GS] != 0.0) | (bus[:, BS] != 0.0))
allg = find(~isload(gen))
ong = find((gen[:, GEN_STATUS] > 0) & ~isload(gen))
onld = find((gen[:, GEN_STATUS] > 0) & isload(gen))
V = bus[:, VM] * exp(-1j * pi / 180 * bus[:, VA])
out = find(branch[:, BR_STATUS] == 0) ## out-of-service branches
nout = len(out)
if isDC:
loss = zeros(nl)
else:
loss = baseMVA * abs(V[e2i[ branch[:, F_BUS].real.astype(int) ]] / tap -
V[e2i[ branch[:, T_BUS].real.astype(int) ]])**2 / \
(branch[:, BR_R] - 1j * branch[:, BR_X])
fchg = abs(V[e2i[branch[:, F_BUS].real.astype(int)]] /
tap)**2 * branch[:, BR_B].real * baseMVA / 2
tchg = abs(V[e2i[branch[:, T_BUS].real.astype(
int)]])**2 * branch[:, BR_B].real * baseMVA / 2
loss[out] = zeros(nout)
fchg[out] = zeros(nout)
tchg[out] = zeros(nout)
##----- print the stuff -----
if OUT_ANY:
## convergence & elapsed time
if success:
fd.write('\nConverged in %.2f seconds' % et)
else:
fd.write('\nDid not converge (%.2f seconds)\n' % et)
## objective function value
if isOPF:
fd.write('\nObjective Function Value = %.2f $/hr' % f)
if OUT_SYS_SUM:
fd.write(
'\n================================================================================'
)
fd.write(
'\n| System Summary |'
)
fd.write(
'\n================================================================================'
)
fd.write(
'\n\nHow many? How much? P (MW) Q (MVAr)'
)
fd.write(
'\n--------------------- ------------------- ------------- -----------------'
)
fd.write(
'\nBuses %6d Total Gen Capacity %7.1f %7.1f to %.1f'
% (nb, sum(gen[allg, PMAX]), sum(gen[allg,
QMIN]), sum(gen[allg, QMAX])))
fd.write(
'\nGenerators %5d On-line Capacity %7.1f %7.1f to %.1f'
% (len(allg), sum(gen[ong, PMAX]), sum(
gen[ong, QMIN]), sum(gen[ong, QMAX])))
fd.write(
'\nCommitted Gens %5d Generation (actual) %7.1f %7.1f'
% (len(ong), sum(gen[ong, PG]), sum(gen[ong, QG])))
fd.write(
'\nLoads %5d Load %7.1f %7.1f'
% (len(nzld) + len(onld), sum(bus[nzld, PD]) - sum(gen[onld, PG]),
sum(bus[nzld, QD]) - sum(gen[onld, QG])))
fd.write(
'\n Fixed %5d Fixed %7.1f %7.1f'
% (len(nzld), sum(bus[nzld, PD]), sum(bus[nzld, QD])))
fd.write(
'\n Dispatchable %5d Dispatchable %7.1f of %-7.1f%7.1f'
% (len(onld), -sum(gen[onld, PG]), -sum(gen[onld, PMIN]),
-sum(gen[onld, QG])))
fd.write(
'\nShunts %5d Shunt (inj) %7.1f %7.1f'
% (len(nzsh), -sum(bus[nzsh, VM]**2 * bus[nzsh, GS]),
sum(bus[nzsh, VM]**2 * bus[nzsh, BS])))
fd.write(
'\nBranches %5d Losses (I^2 * Z) %8.2f %8.2f'
% (nl, sum(loss.real), sum(loss.imag)))
fd.write(
'\nTransformers %5d Branch Charging (inj) - %7.1f'
% (len(xfmr), sum(fchg) + sum(tchg)))
fd.write(
'\nInter-ties %5d Total Inter-tie Flow %7.1f %7.1f'
% (len(ties), sum(abs(branch[ties, PF] - branch[ties, PT])) / 2,
sum(abs(branch[ties, QF] - branch[ties, QT])) / 2))
fd.write('\nAreas %5d' % len(s_areas))
fd.write('\n')
fd.write(
'\n Minimum Maximum')
fd.write(
'\n ------------------------- --------------------------------'
)
minv = min(bus[:, VM])
mini = argmin(bus[:, VM])
maxv = max(bus[:, VM])
maxi = argmax(bus[:, VM])
fd.write(
'\nVoltage Magnitude %7.3f p.u. @ bus %-4d %7.3f p.u. @ bus %-4d'
% (minv, bus[mini, BUS_I], maxv, bus[maxi, BUS_I]))
minv = min(bus[:, VA])
mini = argmin(bus[:, VA])
maxv = max(bus[:, VA])
maxi = argmax(bus[:, VA])
fd.write(
'\nVoltage Angle %8.2f deg @ bus %-4d %8.2f deg @ bus %-4d'
% (minv, bus[mini, BUS_I], maxv, bus[maxi, BUS_I]))
if not isDC:
maxv = max(loss.real)
maxi = argmax(loss.real)
fd.write(
'\nP Losses (I^2*R) - %8.2f MW @ line %d-%d'
% (maxv, branch[maxi, F_BUS].real, branch[maxi, T_BUS].real))
maxv = max(loss.imag)
maxi = argmax(loss.imag)
fd.write(
'\nQ Losses (I^2*X) - %8.2f MVAr @ line %d-%d'
% (maxv, branch[maxi, F_BUS].real, branch[maxi, T_BUS].real))
if isOPF:
minv = min(bus[:, LAM_P])
mini = argmin(bus[:, LAM_P])
maxv = max(bus[:, LAM_P])
maxi = argmax(bus[:, LAM_P])
fd.write(
'\nLambda P %8.2f $/MWh @ bus %-4d %8.2f $/MWh @ bus %-4d'
% (minv, bus[mini, BUS_I], maxv, bus[maxi, BUS_I]))
minv = min(bus[:, LAM_Q])
mini = argmin(bus[:, LAM_Q])
maxv = max(bus[:, LAM_Q])
maxi = argmax(bus[:, LAM_Q])
fd.write(
'\nLambda Q %8.2f $/MWh @ bus %-4d %8.2f $/MWh @ bus %-4d'
% (minv, bus[mini, BUS_I], maxv, bus[maxi, BUS_I]))
fd.write('\n')
if OUT_AREA_SUM:
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Area Summary |'
)
fd.write(
'\n================================================================================'
)
fd.write(
'\nArea # of # of Gens # of Loads # of # of # of # of'
)
fd.write(
'\n Num Buses Total Online Total Fixed Disp Shunt Brchs Xfmrs Ties'
)
fd.write(
'\n---- ----- ----- ------ ----- ----- ----- ----- ----- ----- -----'
)
for i in range(len(s_areas)):
a = s_areas[i]
ib = find(bus[:, BUS_AREA] == a)
ig = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a)
& ~isload(gen))
igon = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a)
& (gen[:, GEN_STATUS] > 0) & ~isload(gen))
ildon = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a)
& (gen[:, GEN_STATUS] > 0) & isload(gen))
inzld = find((bus[:, BUS_AREA] == a)
& logical_or(bus[:, PD], bus[:, QD]))
inzsh = find((bus[:, BUS_AREA] == a)
& logical_or(bus[:, GS], bus[:, BS]))
ibrch = find(
(bus[e2i[branch[:, F_BUS].real.astype(int)], BUS_AREA] == a)
& (bus[e2i[branch[:, T_BUS].real.astype(int)], BUS_AREA] == a))
in_tie = find(
(bus[e2i[branch[:, F_BUS].real.astype(int)], BUS_AREA] == a)
& (bus[e2i[branch[:, T_BUS].real.astype(int)], BUS_AREA] != a))
out_tie = find(
(bus[e2i[branch[:, F_BUS].real.astype(int)], BUS_AREA] != a)
& (bus[e2i[branch[:, T_BUS].real.astype(int)], BUS_AREA] == a))
if not any(xfmr + 1):
nxfmr = 0
else:
nxfmr = len(
find((bus[e2i[branch[xfmr, F_BUS].real.astype(int)],
BUS_AREA] == a)
& (bus[e2i[branch[xfmr, T_BUS].real.astype(int)],
BUS_AREA] == a)))
fd.write('\n%3d %6d %5d %5d %5d %5d %5d %5d %5d %5d %5d' %
(a, len(ib), len(ig), len(igon), \
len(inzld)+len(ildon), len(inzld), len(ildon), \
len(inzsh), len(ibrch), nxfmr, len(in_tie)+len(out_tie)))
fd.write(
'\n---- ----- ----- ------ ----- ----- ----- ----- ----- ----- -----'
)
fd.write(
'\nTot: %6d %5d %5d %5d %5d %5d %5d %5d %5d %5d' %
(nb, len(allg), len(ong), len(nzld) + len(onld), len(nzld),
len(onld), len(nzsh), nl, len(xfmr), len(ties)))
fd.write('\n')
fd.write(
'\nArea Total Gen Capacity On-line Gen Capacity Generation'
)
fd.write(
'\n Num MW MVAr MW MVAr MW MVAr'
)
fd.write(
'\n---- ------ ------------------ ------ ------------------ ------ ------'
)
for i in range(len(s_areas)):
a = s_areas[i]
ig = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a)
& ~isload(gen))
igon = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a)
& (gen[:, GEN_STATUS] > 0) & ~isload(gen))
fd.write(
'\n%3d %7.1f %7.1f to %-.1f %7.1f %7.1f to %-7.1f %7.1f %7.1f'
%
(a, sum(gen[ig, PMAX]), sum(gen[ig, QMIN]), sum(
gen[ig, QMAX]), sum(gen[igon, PMAX]), sum(gen[igon, QMIN]),
sum(gen[igon, QMAX]), sum(gen[igon, PG]), sum(gen[igon, QG])))
fd.write(
'\n---- ------ ------------------ ------ ------------------ ------ ------'
)
# fd.write('\nTot: %7.1f %7.1f to %-7.1f %7.1f %7.1f to %-7.1f %7.1f %7.1f' %
# (sum(gen[allg, PMAX]), sum(gen[allg, QMIN]), sum(gen[allg, QMAX]),
# sum(gen[ong, PMAX]), sum(gen[ong, QMIN]), sum(gen[ong, QMAX]),
# sum(gen[ong, PG]), sum(gen[ong, QG]) ))
fd.write('\n')
fd.write(
'\nArea Disp Load Cap Disp Load Fixed Load Total Load'
)
fd.write(
'\n Num MW MVAr MW MVAr MW MVAr MW MVAr'
)
fd.write(
'\n---- ------ ------ ------ ------ ------ ------ ------ ------'
)
Qlim = (gen[:, QMIN] == 0) * gen[:, QMAX] + (gen[:, QMAX]
== 0) * gen[:, QMIN]
for i in range(len(s_areas)):
a = s_areas[i]
ildon = find((bus[e2i[gen[:, GEN_BUS].astype(int)], BUS_AREA] == a)
& (gen[:, GEN_STATUS] > 0) & isload(gen))
inzld = find((bus[:, BUS_AREA] == a)
& logical_or(bus[:, PD], bus[:, QD]))
fd.write(
'\n%3d %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f'
% (a, -sum(gen[ildon, PMIN]), -sum(Qlim[ildon]),
-sum(gen[ildon, PG]), -sum(gen[ildon, QG]),
sum(bus[inzld, PD]), sum(bus[inzld, QD]),
-sum(gen[ildon, PG]) + sum(bus[inzld, PD]),
-sum(gen[ildon, QG]) + sum(bus[inzld, QD])))
fd.write(
'\n---- ------ ------ ------ ------ ------ ------ ------ ------'
)
fd.write(
'\nTot: %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f' %
(-sum(gen[onld, PMIN]), -sum(Qlim[onld]), -sum(gen[onld, PG]),
-sum(gen[onld, QG]), sum(bus[nzld, PD]), sum(
bus[nzld, QD]), -sum(gen[onld, PG]) + sum(bus[nzld, PD]),
-sum(gen[onld, QG]) + sum(bus[nzld, QD])))
fd.write('\n')
fd.write(
'\nArea Shunt Inj Branch Series Losses Net Export'
)
fd.write(
'\n Num MW MVAr Charging MW MVAr MW MVAr'
)
fd.write(
'\n---- ------ ------ -------- ------ ------ ------ ------'
)
for i in range(len(s_areas)):
a = s_areas[i]
inzsh = find((bus[:, BUS_AREA] == a)
& logical_or(bus[:, GS], bus[:, BS]))
ibrch = find(
(bus[e2i[branch[:, F_BUS].real.astype(int)], BUS_AREA] == a)
& (bus[e2i[branch[:, T_BUS].real.astype(int)], BUS_AREA] == a)
& branch[:, BR_STATUS].astype(bool))
in_tie = find(
(bus[e2i[branch[:, F_BUS].real.astype(int)], BUS_AREA] != a)
& (bus[e2i[branch[:, T_BUS].real.astype(int)], BUS_AREA] == a)
& branch[:, BR_STATUS].astype(bool))
out_tie = find(
(bus[e2i[branch[:, F_BUS].real.astype(int)], BUS_AREA] == a)
& (bus[e2i[branch[:, T_BUS].real.astype(int)], BUS_AREA] != a)
& branch[:, BR_STATUS].astype(bool))
fd.write(
'\n%3d %7.1f %7.1f %7.1f %7.2f %7.2f %7.1f %7.1f' %
(a, -sum(bus[inzsh, VM]**2 * bus[inzsh, GS]),
sum(bus[inzsh, VM]**2 * bus[inzsh, BS]), sum(fchg[ibrch]) +
sum(tchg[ibrch]) + sum(fchg[out_tie]) + sum(tchg[in_tie]),
sum(real(loss[ibrch])) +
sum(real(loss[r_[in_tie, out_tie]])) / 2,
sum(imag(loss[ibrch])) +
sum(imag(loss[r_[in_tie, out_tie]])) / 2,
sum(branch[in_tie, PT]) + sum(branch[out_tie, PF]) -
sum(real(loss[r_[in_tie, out_tie]])) / 2,
sum(branch[in_tie, QT]) + sum(branch[out_tie, QF]) -
sum(imag(loss[r_[in_tie, out_tie]])) / 2))
fd.write(
'\n---- ------ ------ -------- ------ ------ ------ ------'
)
fd.write(
'\nTot: %7.1f %7.1f %7.1f %7.2f %7.2f - -' %
(-sum(bus[nzsh, VM]**2 * bus[nzsh, GS]),
sum(bus[nzsh, VM]**2 * bus[nzsh, BS]), sum(fchg) + sum(tchg),
sum(real(loss)), sum(imag(loss))))
fd.write('\n')
## generator data
if OUT_GEN:
if isOPF:
genlamP = bus[e2i[gen[:, GEN_BUS].astype(int)], LAM_P]
genlamQ = bus[e2i[gen[:, GEN_BUS].astype(int)], LAM_Q]
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Generator Data |'
)
fd.write(
'\n================================================================================'
)
fd.write('\n Gen Bus Status Pg Qg ')
if isOPF: fd.write(' Lambda ($/MVA-hr)')
fd.write('\n # # (MW) (MVAr) ')
if isOPF: fd.write(' P Q ')
fd.write('\n---- ----- ------ -------- --------')
if isOPF: fd.write(' -------- --------')
for k in range(len(ong)):
i = ong[k]
fd.write('\n%3d %6d %2d ' %
(i, gen[i, GEN_BUS], gen[i, GEN_STATUS]))
if (gen[i, GEN_STATUS] > 0) & logical_or(gen[i, PG], gen[i, QG]):
fd.write('%10.2f%10.2f' % (gen[i, PG], gen[i, QG]))
else:
fd.write(' - - ')
if isOPF: fd.write('%10.2f%10.2f' % (genlamP[i], genlamQ[i]))
fd.write('\n -------- --------')
fd.write('\n Total: %9.2f%10.2f' %
(sum(gen[ong, PG]), sum(gen[ong, QG])))
fd.write('\n')
if any(onld + 1):
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Dispatchable Load Data |'
)
fd.write(
'\n================================================================================'
)
fd.write('\n Gen Bus Status Pd Qd ')
if isOPF: fd.write(' Lambda ($/MVA-hr)')
fd.write('\n # # (MW) (MVAr) ')
if isOPF: fd.write(' P Q ')
fd.write('\n---- ----- ------ -------- --------')
if isOPF: fd.write(' -------- --------')
for k in range(len(onld)):
i = onld[k]
fd.write('\n%3d %6d %2d ' %
(i, gen[i, GEN_BUS], gen[i, GEN_STATUS]))
if (gen[i, GEN_STATUS] > 0) & logical_or(
gen[i, PG], gen[i, QG]):
fd.write('%10.2f%10.2f' % (-gen[i, PG], -gen[i, QG]))
else:
fd.write(' - - ')
if isOPF: fd.write('%10.2f%10.2f' % (genlamP[i], genlamQ[i]))
fd.write('\n -------- --------')
fd.write('\n Total: %9.2f%10.2f' %
(-sum(gen[onld, PG]), -sum(gen[onld, QG])))
fd.write('\n')
## bus data
if OUT_BUS:
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Bus Data |'
)
fd.write(
'\n================================================================================'
)
fd.write(
'\n Bus Voltage Generation Load ')
if isOPF: fd.write(' Lambda($/MVA-hr)')
fd.write(
'\n # Mag(pu) Ang(deg) P (MW) Q (MVAr) P (MW) Q (MVAr)')
if isOPF: fd.write(' P Q ')
fd.write(
'\n----- ------- -------- -------- -------- -------- --------')
if isOPF: fd.write(' ------- -------')
for i in range(nb):
fd.write('\n%5d%7.3f%9.3f' % tuple(bus[i, [BUS_I, VM, VA]]))
if bus[i, BUS_TYPE] == REF:
fd.write('*')
else:
fd.write(' ')
g = find((gen[:, GEN_STATUS] > 0)
& (gen[:, GEN_BUS] == bus[i, BUS_I]) & ~isload(gen))
ld = find((gen[:, GEN_STATUS] > 0)
& (gen[:, GEN_BUS] == bus[i, BUS_I]) & isload(gen))
if any(g + 1):
fd.write('%9.2f%10.2f' % (sum(gen[g, PG]), sum(gen[g, QG])))
else:
fd.write(' - - ')
if logical_or(bus[i, PD], bus[i, QD]) | any(ld + 1):
if any(ld + 1):
fd.write('%10.2f*%9.2f*' % (bus[i, PD] - sum(gen[ld, PG]),
bus[i, QD] - sum(gen[ld, QG])))
else:
fd.write('%10.2f%10.2f ' % tuple(bus[i, [PD, QD]]))
else:
fd.write(' - - ')
if isOPF:
fd.write('%9.3f' % bus[i, LAM_P])
if abs(bus[i, LAM_Q]) > ptol:
fd.write('%8.3f' % bus[i, LAM_Q])
else:
fd.write(' -')
fd.write(
'\n -------- -------- -------- --------')
fd.write('\n Total: %9.2f %9.2f %9.2f %9.2f' %
(sum(gen[ong, PG]), sum(gen[ong, QG]), sum(bus[nzld, PD]) -
sum(gen[onld, PG]), sum(bus[nzld, QD]) - sum(gen[onld, QG])))
fd.write('\n')
## branch data
if OUT_BRANCH:
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Branch Data |'
)
fd.write(
'\n================================================================================'
)
fd.write(
'\nBrnch From To From Bus Injection To Bus Injection Loss (I^2 * Z) '
)
fd.write(
'\n # Bus Bus P (MW) Q (MVAr) P (MW) Q (MVAr) P (MW) Q (MVAr)'
)
fd.write(
'\n----- ----- ----- -------- -------- -------- -------- -------- --------'
)
for i in range(nl):
fd.write(
'\n%4d%7d%7d%10.2f%10.2f%10.2f%10.2f%10.3f%10.2f' %
(i, branch[i, F_BUS].real, branch[i, T_BUS].real,
branch[i, PF].real, branch[i, QF].real, branch[i, PT].real,
branch[i, QT].real, loss[i].real, loss[i].imag))
fd.write(
'\n -------- --------'
)
fd.write(
'\n Total:%10.3f%10.2f'
% (sum(real(loss)), sum(imag(loss))))
fd.write('\n')
##----- constraint data -----
if isOPF:
ctol = ppopt['OPF_VIOLATION'] ## constraint violation tolerance
## voltage constraints
if (not isDC) & (
OUT_V_LIM == 2 |
(OUT_V_LIM == 1 &
(any(bus[:, VM] < bus[:, VMIN] + ctol)
| any(bus[:, VM] > bus[:, VMAX] - ctol)
| any(bus[:, MU_VMIN] > ptol) | any(bus[:, MU_VMAX] > ptol)))):
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Voltage Constraints |'
)
fd.write(
'\n================================================================================'
)
fd.write('\nBus # Vmin mu Vmin |V| Vmax Vmax mu')
fd.write('\n----- -------- ----- ----- ----- --------')
for i in range(nb):
if (OUT_V_LIM == 2) | (OUT_V_LIM == 1 &
((bus[i, VM] < bus[i, VMIN] + ctol) |
(bus[i, VM] > bus[i, VMAX] - ctol) |
(bus[i, MU_VMIN] > ptol) |
(bus[i, MU_VMAX] > ptol))):
fd.write('\n%5d' % bus[i, BUS_I])
if ((bus[i, VM] < bus[i, VMIN] + ctol) |
(bus[i, MU_VMIN] > ptol)):
fd.write('%10.3f' % bus[i, MU_VMIN])
else:
fd.write(' - ')
fd.write('%8.3f%7.3f%7.3f' %
tuple(bus[i, [VMIN, VM, VMAX]]))
if (bus[i, VM] > bus[i, VMAX] - ctol) | (bus[i, MU_VMAX] >
ptol):
fd.write('%10.3f' % bus[i, MU_VMAX])
else:
fd.write(' - ')
fd.write('\n')
## generator P constraints
if (OUT_PG_LIM == 2) | \
((OUT_PG_LIM == 1) & (any(gen[ong, PG] < gen[ong, PMIN] + ctol) |
any(gen[ong, PG] > gen[ong, PMAX] - ctol) |
any(gen[ong, MU_PMIN] > ptol) |
any(gen[ong, MU_PMAX] > ptol))) | \
((not isDC) & ((OUT_QG_LIM == 2) |
((OUT_QG_LIM == 1) & (any(gen[ong, QG] < gen[ong, QMIN] + ctol) |
any(gen[ong, QG] > gen[ong, QMAX] - ctol) |
any(gen[ong, MU_QMIN] > ptol) |
any(gen[ong, MU_QMAX] > ptol))))):
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Generation Constraints |'
)
fd.write(
'\n================================================================================'
)
if (OUT_PG_LIM == 2) | (
(OUT_PG_LIM == 1) &
(any(gen[ong, PG] < gen[ong, PMIN] + ctol)
| any(gen[ong, PG] > gen[ong, PMAX] - ctol)
| any(gen[ong, MU_PMIN] > ptol) | any(gen[ong, MU_PMAX] > ptol))):
fd.write('\n Gen Bus Active Power Limits')
fd.write(
'\n # # Pmin mu Pmin Pg Pmax Pmax mu'
)
fd.write(
'\n---- ----- ------- -------- -------- -------- -------'
)
for k in range(len(ong)):
i = ong[k]
if (OUT_PG_LIM == 2) | ((OUT_PG_LIM == 1) &
((gen[i, PG] < gen[i, PMIN] + ctol) |
(gen[i, PG] > gen[i, PMAX] - ctol) |
(gen[i, MU_PMIN] > ptol) |
(gen[i, MU_PMAX] > ptol))):
fd.write('\n%4d%6d ' % (i, gen[i, GEN_BUS]))
if (gen[i, PG] < gen[i, PMIN] + ctol) | (gen[i, MU_PMIN] >
ptol):
fd.write('%8.3f' % gen[i, MU_PMIN])
else:
fd.write(' - ')
if gen[i, PG]:
fd.write('%10.2f%10.2f%10.2f' %
tuple(gen[i, [PMIN, PG, PMAX]]))
else:
fd.write('%10.2f - %10.2f' %
tuple(gen[i, [PMIN, PMAX]]))
if (gen[i, PG] > gen[i, PMAX] - ctol) | (gen[i, MU_PMAX] >
ptol):
fd.write('%9.3f' % gen[i, MU_PMAX])
else:
fd.write(' - ')
fd.write('\n')
## generator Q constraints
if (not isDC) & ((OUT_QG_LIM == 2) | (
(OUT_QG_LIM == 1) &
(any(gen[ong, QG] < gen[ong, QMIN] + ctol)
| any(gen[ong, QG] > gen[ong, QMAX] - ctol) |
any(gen[ong, MU_QMIN] > ptol) | any(gen[ong, MU_QMAX] > ptol)))):
fd.write('\nGen Bus Reactive Power Limits')
fd.write(
'\n # # Qmin mu Qmin Qg Qmax Qmax mu')
fd.write(
'\n--- --- ------- -------- -------- -------- -------')
for k in range(len(ong)):
i = ong[k]
if (OUT_QG_LIM == 2) | ((OUT_QG_LIM == 1) &
((gen[i, QG] < gen[i, QMIN] + ctol) |
(gen[i, QG] > gen[i, QMAX] - ctol) |
(gen[i, MU_QMIN] > ptol) |
(gen[i, MU_QMAX] > ptol))):
fd.write('\n%3d%5d' % (i, gen[i, GEN_BUS]))
if (gen[i, QG] < gen[i, QMIN] + ctol) | (gen[i, MU_QMIN] >
ptol):
fd.write('%8.3f' % gen[i, MU_QMIN])
else:
fd.write(' - ')
if gen[i, QG]:
fd.write('%10.2f%10.2f%10.2f' %
tuple(gen[i, [QMIN, QG, QMAX]]))
else:
fd.write('%10.2f - %10.2f' %
tuple(gen[i, [QMIN, QMAX]]))
if (gen[i, QG] > gen[i, QMAX] - ctol) | (gen[i, MU_QMAX] >
ptol):
fd.write('%9.3f' % gen[i, MU_QMAX])
else:
fd.write(' - ')
fd.write('\n')
## dispatchable load P constraints
if (OUT_PG_LIM == 2) | (OUT_QG_LIM == 2) | \
((OUT_PG_LIM == 1) & (any(gen[onld, PG] < gen[onld, PMIN] + ctol) |
any(gen[onld, PG] > gen[onld, PMAX] - ctol) |
any(gen[onld, MU_PMIN] > ptol) |
any(gen[onld, MU_PMAX] > ptol))) | \
((OUT_QG_LIM == 1) & (any(gen[onld, QG] < gen[onld, QMIN] + ctol) |
any(gen[onld, QG] > gen[onld, QMAX] - ctol) |
any(gen[onld, MU_QMIN] > ptol) |
any(gen[onld, MU_QMAX] > ptol))):
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Dispatchable Load Constraints |'
)
fd.write(
'\n================================================================================'
)
if (OUT_PG_LIM == 2) | (
(OUT_PG_LIM == 1) &
(any(gen[onld, PG] < gen[onld, PMIN] + ctol)
| any(gen[onld, PG] > gen[onld, PMAX] - ctol) |
any(gen[onld, MU_PMIN] > ptol) | any(gen[onld, MU_PMAX] > ptol))):
fd.write('\nGen Bus Active Power Limits')
fd.write(
'\n # # Pmin mu Pmin Pg Pmax Pmax mu')
fd.write(
'\n--- --- ------- -------- -------- -------- -------')
for k in range(len(onld)):
i = onld[k]
if (OUT_PG_LIM == 2) | ((OUT_PG_LIM == 1) &
((gen[i, PG] < gen[i, PMIN] + ctol) |
(gen[i, PG] > gen[i, PMAX] - ctol) |
(gen[i, MU_PMIN] > ptol) |
(gen[i, MU_PMAX] > ptol))):
fd.write('\n%3d%5d' % (i, gen[i, GEN_BUS]))
if (gen[i, PG] < gen[i, PMIN] + ctol) | (gen[i, MU_PMIN] >
ptol):
fd.write('%8.3f' % gen[i, MU_PMIN])
else:
fd.write(' - ')
if gen[i, PG]:
fd.write('%10.2f%10.2f%10.2f' %
gen[i, [PMIN, PG, PMAX]])
else:
fd.write('%10.2f - %10.2f' %
gen[i, [PMIN, PMAX]])
if (gen[i, PG] > gen[i, PMAX] - ctol) | (gen[i, MU_PMAX] >
ptol):
fd.write('%9.3f' % gen[i, MU_PMAX])
else:
fd.write(' - ')
fd.write('\n')
## dispatchable load Q constraints
if (not isDC) & ((OUT_QG_LIM == 2) |
((OUT_QG_LIM == 1) &
(any(gen[onld, QG] < gen[onld, QMIN] + ctol)
| any(gen[onld, QG] > gen[onld, QMAX] - ctol)
| any(gen[onld, MU_QMIN] > ptol)
| any(gen[onld, MU_QMAX] > ptol)))):
fd.write('\nGen Bus Reactive Power Limits')
fd.write(
'\n # # Qmin mu Qmin Qg Qmax Qmax mu')
fd.write(
'\n--- --- ------- -------- -------- -------- -------')
for k in range(len(onld)):
i = onld[k]
if (OUT_QG_LIM == 2) | ((OUT_QG_LIM == 1) &
((gen[i, QG] < gen[i, QMIN] + ctol) |
(gen[i, QG] > gen[i, QMAX] - ctol) |
(gen[i, MU_QMIN] > ptol) |
(gen[i, MU_QMAX] > ptol))):
fd.write('\n%3d%5d' % (i, gen(i, GEN_BUS)))
if (gen[i, QG] < gen[i, QMIN] + ctol) | (gen[i, MU_QMIN] >
ptol):
fd.write('%8.3f' % gen[i, MU_QMIN])
else:
fd.write(' - ')
if gen[i, QG]:
fd.write('%10.2f%10.2f%10.2f' %
gen[i, [QMIN, QG, QMAX]])
else:
fd.write('%10.2f - %10.2f' %
gen[i, [QMIN, QMAX]])
if (gen[i, QG] > gen[i, QMAX] - ctol) | (gen[i, MU_QMAX] >
ptol):
fd.write('%9.3f' % gen[i, MU_QMAX])
else:
fd.write(' - ')
fd.write('\n')
## line flow constraints
if (ppopt['OPF_FLOW_LIM'] == 1) | isDC: ## P limit
Ff = branch[:, PF]
Ft = branch[:, PT]
strg = '\n # Bus Pf mu Pf |Pmax| Pt Pt mu Bus'
elif ppopt['OPF_FLOW_LIM'] == 2: ## |I| limit
Ff = abs((branch[:, PF] + 1j * branch[:, QF]) /
V[e2i[branch[:, F_BUS].astype(int)]])
Ft = abs((branch[:, PT] + 1j * branch[:, QT]) /
V[e2i[branch[:, T_BUS].astype(int)]])
strg = '\n # Bus |If| mu |If| |Imax| |It| |It| mu Bus'
else: ## |S| limit
Ff = abs(branch[:, PF] + 1j * branch[:, QF])
Ft = abs(branch[:, PT] + 1j * branch[:, QT])
strg = '\n # Bus |Sf| mu |Sf| |Smax| |St| |St| mu Bus'
if (OUT_LINE_LIM == 2) | (
(OUT_LINE_LIM == 1) &
(any((branch[:, RATE_A] != 0) &
(abs(Ff) > branch[:, RATE_A] - ctol)) | any(
(branch[:, RATE_A] != 0) &
(abs(Ft) > branch[:, RATE_A] - ctol))
| any(branch[:, MU_SF] > ptol) | any(branch[:, MU_ST] > ptol))):
fd.write(
'\n================================================================================'
)
fd.write(
'\n| Branch Flow Constraints |'
)
fd.write(
'\n================================================================================'
)
fd.write(
'\nBrnch From "From" End Limit "To" End To'
)
fd.write(strg)
fd.write(
'\n----- ----- ------- -------- -------- -------- ------- -----'
)
for i in range(nl):
if (OUT_LINE_LIM == 2) | ((OUT_LINE_LIM == 1) & (
((branch[i, RATE_A] != 0) &
(abs(Ff[i]) > branch[i, RATE_A] - ctol)) |
((branch[i, RATE_A] != 0) &
(abs(Ft[i]) > branch[i, RATE_A] - ctol)) |
(branch[i, MU_SF] > ptol) | (branch[i, MU_ST] > ptol))):
fd.write('\n%4d%7d' % (i, branch[i, F_BUS]))
if (Ff[i] > branch[i, RATE_A] - ctol) | (branch[i, MU_SF] >
ptol):
fd.write('%10.3f' % branch[i, MU_SF])
else:
fd.write(' - ')
fd.write('%9.2f%10.2f%10.2f' %
(Ff[i], branch[i, RATE_A], Ft[i]))
if (Ft[i] > branch[i, RATE_A] - ctol) | (branch[i, MU_ST] >
ptol):
fd.write('%10.3f' % branch[i, MU_ST])
else:
fd.write(' - ')
fd.write('%6d' % branch[i, T_BUS])
fd.write('\n')
## execute userfcn callbacks for 'printpf' stage
if have_results_struct and 'userfcn' in results:
if not isOPF: ## turn off option for all constraints if it isn't an OPF
ppopt = ppoption(ppopt, 'OUT_ALL_LIM', 0)
run_userfcn(results["userfcn"], 'printpf', results, fd, ppopt)
def gausspf(Ybus, Sbus, V0, ref, pv, pq, ppopt=None):
"""Solves the power flow using a Gauss-Seidel method.
Solves for bus voltages given the full system admittance matrix (for
all buses), the complex bus power injection vector (for all buses),
the initial vector of complex bus voltages, and column vectors with
the lists of bus indices for the swing bus, PV buses, and PQ buses,
respectively. The bus voltage vector contains the set point for
generator (including ref bus) buses, and the reference angle of the
swing bus, as well as an initial guess for remaining magnitudes and
angles. C{ppopt} is a PYPOWER options vector which can be used to
set the termination tolerance, maximum number of iterations, and
output options (see C{ppoption} for details). Uses default options
if this parameter is not given. Returns the final complex voltages,
a flag which indicates whether it converged or not, and the number
of iterations performed.
@see: L{runpf}
@author: Ray Zimmerman (PSERC Cornell)
@author: Alberto Borghetti (University of Bologna, Italy)
"""
## default arguments
if ppopt is None:
ppopt = ppoption()
## options
tol = ppopt['PF_TOL']
max_it = ppopt['PF_MAX_IT_GS']
verbose = ppopt['VERBOSE']
## initialize
converged = 0
i = 0
V = V0.copy()
#Va = angle(V)
Vm = abs(V)
## set up indexing for updating V
npv = len(pv)
npq = len(pq)
pvpq = r_[pv, pq]
## evaluate F(x0)
mis = V * conj(Ybus * V) - Sbus
F = r_[ mis[pvpq].real,
mis[pq].imag ]
## check tolerance
normF = linalg.norm(F, Inf)
if verbose > 1:
sys.stdout.write('\n it max P & Q mismatch (p.u.)')
sys.stdout.write('\n---- ---------------------------')
sys.stdout.write('\n%3d %10.3e' % (i, normF))
if normF < tol:
converged = 1
if verbose > 1:
sys.stdout.write('\nConverged!\n')
## do Gauss-Seidel iterations
while (not converged and i < max_it):
## update iteration counter
i = i + 1
## update voltage
## at PQ buses
for k in pq[list(range(npq))]:
tmp = (conj(Sbus[k] / V[k]) - Ybus[k, :] * V) / Ybus[k, k]
V[k] = V[k] + asscalar(tmp)
## at PV buses
if npv:
for k in pv[list(range(npv))]:
tmp = (V[k] * conj(Ybus[k,:] * V)).imag
Sbus[k] = Sbus[k].real + 1j * asscalar(tmp)
tmp = (conj(Sbus[k] / V[k]) - Ybus[k, :] * V) / Ybus[k, k]
V[k] = V[k] + asscalar(tmp)
# V[k] = Vm[k] * V[k] / abs(V[k])
V[pv] = Vm[pv] * V[pv] / abs(V[pv])
## evalute F(x)
mis = V * conj(Ybus * V) - Sbus
F = r_[ mis[pv].real,
mis[pq].real,
mis[pq].imag ]
## check for convergence
normF = linalg.norm(F, Inf)
if verbose > 1:
sys.stdout.write('\n%3d %10.3e' % (i, normF))
if normF < tol:
converged = 1
if verbose:
sys.stdout.write('\nGauss-Seidel power flow converged in '
'%d iterations.\n' % i)
if verbose:
if not converged:
sys.stdout.write('Gauss-Seidel power did not converge in %d '
'iterations.' % i)
return V, converged, i
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 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_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 fdpf(Ybus, Sbus, V0, Bp, Bpp, ref, pv, pq, ppopt=None):
"""Solves the power flow using a fast decoupled method.
Solves for bus voltages given the full system admittance matrix (for
all buses), the complex bus power injection vector (for all buses),
the initial vector of complex bus voltages, the FDPF matrices B prime
and B double prime, and column vectors with the lists of bus indices
for the swing bus, PV buses, and PQ buses, respectively. The bus voltage
vector contains the set point for generator (including ref bus)
buses, and the reference angle of the swing bus, as well as an initial
guess for remaining magnitudes and angles. C{ppopt} is a PYPOWER options
vector which can be used to set the termination tolerance, maximum
number of iterations, and output options (see L{ppoption} for details).
Uses default options if this parameter is not given. Returns the
final complex voltages, a flag which indicates whether it converged
or not, and the number of iterations performed.
@see: L{runpf}
@author: Ray Zimmerman (PSERC Cornell)
"""
if ppopt is None:
ppopt = ppoption()
## options
tol = ppopt['PF_TOL']
max_it = ppopt['PF_MAX_IT_FD']
verbose = ppopt['VERBOSE']
## initialize
converged = 0
i = 0
V = V0
Va = angle(V)
Vm = abs(V)
## set up indexing for updating V
#npv = len(pv)
#npq = len(pq)
pvpq = r_[pv, pq]
## evaluate initial mismatch
mis = (V * conj(Ybus * V) - Sbus) / Vm
P = mis[pvpq].real
Q = mis[pq].imag
## check tolerance
normP = linalg.norm(P, Inf)
normQ = linalg.norm(Q, Inf)
if verbose > 1:
sys.stdout.write('\niteration max mismatch (p.u.) ')
sys.stdout.write('\ntype # P Q ')
sys.stdout.write('\n---- ---- ----------- -----------')
sys.stdout.write('\n - %3d %10.3e %10.3e' % (i, normP, normQ))
if normP < tol and normQ < tol:
converged = 1
if verbose > 1:
sys.stdout.write('\nConverged!\n')
## reduce B matrices
Bp = Bp[array([pvpq]).T, pvpq].tocsc() # splu requires a CSC matrix
Bpp = Bpp[array([pq]).T, pq].tocsc()
## factor B matrices
Bp_solver = splu(Bp)
Bpp_solver = splu(Bpp)
## do P and Q iterations
while (not converged and i < max_it):
## update iteration counter
i = i + 1
##----- do P iteration, update Va -----
dVa = -Bp_solver.solve(P)
## update voltage
Va[pvpq] = Va[pvpq] + dVa
V = Vm * exp(1j * Va)
## evalute mismatch
mis = (V * conj(Ybus * V) - Sbus) / Vm
P = mis[pvpq].real
Q = mis[pq].imag
## check tolerance
normP = linalg.norm(P, Inf)
normQ = linalg.norm(Q, Inf)
if verbose > 1:
sys.stdout.write("\n %s %3d %10.3e %10.3e" %
(type,i, normP, normQ))
if normP < tol and normQ < tol:
converged = 1
if verbose:
sys.stdout.write('\nFast-decoupled power flow converged in %d '
'P-iterations and %d Q-iterations.\n' % (i, i - 1))
break
##----- do Q iteration, update Vm -----
dVm = -Bpp_solver.solve(Q)
## update voltage
Vm[pq] = Vm[pq] + dVm
V = Vm * exp(1j * Va)
## evalute mismatch
mis = (V * conj(Ybus * V) - Sbus) / Vm
P = mis[pvpq].real
Q = mis[pq].imag
## check tolerance
normP = linalg.norm(P, Inf)
normQ = linalg.norm(Q, Inf)
if verbose > 1:
sys.stdout.write('\n Q %3d %10.3e %10.3e' % (i, normP, normQ))
if normP < tol and normQ < tol:
converged = 1
if verbose:
sys.stdout.write('\nFast-decoupled power flow converged in %d '
'P-iterations and %d Q-iterations.\n' % (i, i))
break
if verbose:
if not converged:
sys.stdout.write('\nFast-decoupled power flow did not converge in '
'%d iterations.' % i)
return V, converged, i
def t_runmarket(quiet=False):
"""Tests for code in C{runmkt}, C{smartmkt} and C{auction}.
@author: Ray Zimmerman (PSERC Cornell)
"""
n_tests = 20
t_begin(n_tests, quiet)
try:
from pypower.extras.smartmarket import runmarket
except ImportError:
t_skip(n_tests, 'smartmarket code not available')
t_end;
return
ppc = loadcase('t_auction_case')
ppopt = ppoption(OPF_ALG=560, OUT_ALL_LIM=1,
OUT_BRANCH=0, OUT_SYS_SUM=0)
ppopt = ppoption(ppopt, OUT_ALL=0, VERBOSE=1)
#ppopt = ppoption(ppopt, OUT_GEN=1, OUT_BRANCH=0, OUT_SYS_SUM=0)
offers = {'P': {}, 'Q': {}}
bids = {'P': {}, 'Q': {}}
offers['P']['qty'] = array([
[12, 24, 24],
[12, 24, 24],
[12, 24, 24],
[12, 24, 24],
[12, 24, 24],
[12, 24, 24]
])
offers['P']['prc'] = array([
[20, 50, 60],
[20, 40, 70],
[20, 42, 80],
[20, 44, 90],
[20, 46, 75],
[20, 48, 60]
])
bids['P']['qty'] = array([
[10, 10, 10],
[10, 10, 10],
[10, 10, 10]
])
bids['P']['prc'] = array([
[100, 70, 60],
# [100, 64.3, 20],
# [100, 30.64545, 0],
[100, 50, 20],
[100, 60, 50]
])
offers['Q']['qty'] = [ 60, 60, 60, 60, 60, 60, 0, 0, 0 ]
offers['Q']['prc'] = [ 0, 0, 0, 0, 0, 3, 0, 0, 0 ]
bids.Q['qty'] = [ 15, 15, 15, 15, 15, 15, 15, 12, 7.5 ]
# bids.Q['prc'] = [ 0, 0, 0, 0, 0, 0, 0, 83.9056, 0 ]
bids.Q['prc'] = [ 0, 0, 0, 0, 0, 0, 0, 20, 0 ]
t = 'marginal Q offer, marginal PQ bid, auction_type = 5'
mkt = {'auction_type': 5,
't': [],
'u0': [],
'lim': []}
r, co, cb, _, _, _, _ = runmarket(ppc, offers, bids, mkt, ppopt)
co5 = co.copy()
cb5 = cb.copy()
# [ co['P']['qty'] co['P']['prc'] ]
# [ cb['P']['qty'] cb['P']['prc'] ]
# [ co['Q']['qty'] co['Q']['prc'] ]
# [ cb['Q']['qty'] cb['Q']['prc'] ]
i2e = r['bus'][:, BUS_I]
e2i = sparse((max(i2e), 1))
e2i[i2e] = range(r['bus'].size)
G = find( isload(r['gen']) == 0 ) ## real generators
L = find( isload(r['gen']) ) ## dispatchable loads
Gbus = e2i[r['gen'][G, GEN_BUS]]
Lbus = e2i[r['gen'][L, GEN_BUS]]
t_is( co['P']['qty'], ones((6, 1)) * [12, 24, 0], 2, [t, ' : gen P quantities'] )
t_is( co['P']['prc'][0, :], 50.1578, 3, [t, ' : gen 1 P prices'] )
t_is( cb['P']['qty'], [[10, 10, 10], [10, 0.196, 0], [10, 10, 0]], 2, [t, ' : load P quantities'] )
t_is( cb['P']['prc'][1, :], 56.9853, 4, [t, ' : load 2 P price'] )
t_is( co['P']['prc'][:, 0], r['bus'][Gbus, LAM_P], 8, [t, ' : gen P prices'] )
t_is( cb['P']['prc'][:, 0], r['bus'][Lbus, LAM_P], 8, [t, ' : load P prices'] )
t_is( co['Q']['qty'], [4.2722, 11.3723, 14.1472, 22.8939, 36.7886, 12.3375, 0, 0, 0], 2, [t, ' : Q offer quantities'] )
t_is( co['Q']['prc'], [0, 0, 0, 0, 0, 3, 0.4861, 2.5367, 1.3763], 4, [t, ' : Q offer prices'] )
t_is( cb['Q']['qty'], [0, 0, 0, 0, 0, 0, 15, 4.0785, 5], 2, [t, ' : Q bid quantities'] )
t_is( cb['Q']['prc'], [0, 0, 0, 0, 0, 3, 0.4861, 2.5367, 1.3763], 4, [t, ' : Q bid prices'] )
t_is( co['Q']['prc'], r['bus'][[Gbus, Lbus], LAM_Q], 8, [t, ' : Q offer prices'] )
t_is( cb['Q']['prc'], co['Q']['prc'], 8, [t, ' : Q bid prices'] )
t = 'marginal Q offer, marginal PQ bid, auction_type = 0'
mkt['auction_type'] = 0
r, co, cb, _, _, _, _ = runmarket(ppc, offers, bids, mkt, ppopt)
t_is( co['P']['qty'], co5['P']['qty'], 8, [t, ' : gen P quantities'] )
t_is( cb['P']['qty'], cb5['P']['qty'], 8, [t, ' : load P quantities'] )
t_is( co['P']['prc'], offers['P']['prc'], 8, [t, ' : gen P prices'] )
t_is( cb['P']['prc'], bids['P']['prc'], 8, [t, ' : load P prices'] )
t_is( co['Q']['qty'], co5['Q']['qty'], 8, [t, ' : gen Q quantities'] )
t_is( cb['Q']['qty'], cb5['Q']['qty'], 8, [t, ' : load Q quantities'] )
t_is( co['Q']['prc'], offers['Q']['prc'], 8, [t, ' : gen Q prices'] )
t_is( cb['Q']['prc'], bids['Q']['prc'], 8, [t, ' : load Q prices'] )
t_end
