def PF_Sim(ppc, GCtime, pDemand, rDemand, nodesStorage, U, rootV2):
"""
Uses PyPower to calculate PF to simulate node voltages after storage control
Inputs: ppc - PyPower case dictionary
GCtime - number of time steps between GC runs
pDemand/rDemand - true values of real and reactive power demanded
nodesStorage - list of storage nodes indexes
U - storage control action
rootV2 - voltage of the substation node
Outputs: runVoltage - (buses X time) array of voltages
"""
nodesNum, T = pDemand.shape
runVoltage = np.zeros((nodesNum, GCtime))
for t in range(GCtime):
pLoad = pDemand[:, t]
pLoad[nodesStorage] = pLoad[nodesStorage] + U[:, t]
rLoad = rDemand[:, t]
rootVoltage = np.sqrt(rootV2[:, t])
ppc['bus'][:, 2] = pLoad.flatten()
ppc['bus'][:, 3] = rLoad.flatten()
# ppc['bus'][rootIdx,7] = rootVoltage # Doesnt actually set PF root voltage
# for surpressing runpf output
ppopt = ppoption(VERBOSE=0, OUT_ALL=0)
ppc_out = runpf(ppc, ppopt)
rootVdiff = rootVoltage - 1
runVoltage[:, t] = ppc_out[0]['bus'][:, 7] + rootVdiff
return runVoltage
def run_pypower(case):
"""
Executes a PYPOWER power flow for *case* and writes its results back to
*case*.
If *case* is a string, :func:`transform` will be called first to create
a bus/branch model from the CIM file.
"""
basestring = (str,bytes)
from pypower.api import ppoption, runpf
from cim2busbranch import ext_pypower
if isinstance(case, basestring):
case = transform(case)
ppc = ext_pypower.create(case)
ppo = ppoption(OUT_ALL=0, VERBOSE=0)
res = runpf(ppc, ppo)
if not res[1]:
raise RuntimeError('PYPOWER power flow failed.')
ext_pypower.write_results_to_case(res[0], case)
def adapt_case(node, power, time):
#input node is the node that the agent accesses, the power is in kW , the time is the timestep [0,96]
time = time % 96 # if the time interval exceeds the number of elements in the load profile
loadprofile = 0.001 * array([
2.1632, 1.9456, 1.7568, 1.5968, 1.4784, 1.3952, 1.3408, 1.3056, 1.2832,
1.2672, 1.2608, 1.2512, 1.2416, 1.2352, 1.2256, 1.2256, 1.2288, 1.2416,
1.2576, 1.28, 1.3088, 1.3792, 1.5264, 1.7856, 2.176, 2.6496, 3.136,
3.568, 3.8912, 4.112, 4.2464, 4.3136, 4.3328, 4.3136, 4.2592, 4.1824,
4.0864, 3.9872, 3.888, 3.808, 3.7536, 3.7184, 3.7024, 3.7024, 3.7152,
3.744, 3.7984, 3.888, 4.0128, 4.1472, 4.256, 4.3136, 4.2944, 4.2144,
4.096, 3.968, 3.8464, 3.7376, 3.6384, 3.5424, 3.4528, 3.376, 3.312,
3.2768, 3.2704, 3.3024, 3.3792, 3.5168, 3.712, 3.9584, 4.2432, 4.5536,
4.8768, 5.1904, 5.4784, 5.7248, 5.9104, 6.0224, 6.0448, 5.9648, 5.7824,
5.5264, 5.2448, 4.9792, 4.7648, 4.5888, 4.4288, 4.2624, 4.0704, 3.856,
3.6256, 3.3824, 3.136, 2.8864, 2.64, 2.3968
])
q = zeros(25) #set the reactive power to zero at each point
p = loadprofile[time] * ones(
25
) # set the active power at each grid point to the value in the load profile given the time
p[0] = 0 # set the load at the transformer to 0
p[node] = p[
node] + power * 0.001 # add the power to the node that the agent controlls
# do the actual power flow simulation
ppc = casemodul(p, q)
ppopt = ppoption(PF_ALG=2, VERBOSE=False, OUT_ALL=0)
ppc_result, y = runpf(
ppc,
ppopt) #run the powerflow simulation gibven the case and the options
return ppc_result["bus"][node, 7]
def PF_Sim(ppc, pDemand, rDemand, nodesStorage, U, rootV2):
"""
Uses PyPower to calculate PF to simulate node voltages after storage action
Inputs: ppc - PyPower case dictionary
pDemand/rDemand - true values of real and reactive power demanded
nodesStorage - list of storage nodes indexes
U - storage control action
rootV2 - voltage of the substation node
Outputs: runVoltage - (buses X time) array of voltages
"""
nodesNum = 7 #pDemand.shape tuple doesnt work...
runVoltage = np.zeros((nodesNum, 1))
pLoad = np.copy(pDemand)
pLoad[nodesStorage] = pLoad[nodesStorage] + U
rLoad = rDemand
rootVoltage = np.sqrt(rootV2)
ppc['bus'][:, 2] = pLoad.flatten()
ppc['bus'][:, 3] = rLoad.flatten()
#ppc['bus'][rootIdx,7] = rootVoltage # Doesnt actually set PF root voltage
ppopt = ppoption(VERBOSE=0, OUT_ALL=0)
ppc_out = runpf(ppc, ppopt)
rootVdiff = rootVoltage - 1
runVoltage = ppc_out[0]['bus'][:, 7] + rootVdiff
return runVoltage
def reset(self):
# find Vtheta net
for b in self.ppc['bus']:
if (b[1] == 3):
self.vbus = int(b[0])
break
# for vbus generator, add 2 actions (up/down)(V)
if (self.vbus in self.ppc['gen'][:, 0]):
self.action_space.append(-1)
self.action_space.append(-2)
# for each generator, add 4 actions (up/down)(P/V)(P/Q)
for index, g in enumerate(self.ppc['gen']):
self.genmap[int(g[0])] = index
i = int(g[0])
# skip Vtheta net
if (i == self.vbus):
continue
# set uncontrollable generator
elif i in DICT1.keys():
g[1] = DICT1[i]
else:
self.action_space.append(4 * i)
self.action_space.append(4 * i + 1)
self.action_space.append(4 * i + 2)
self.action_space.append(4 * i + 3)
# build actmap
for i, act in enumerate(self.action_space):
self.actmap[i] = act
# initialize powerflow
self.results, self.success = runpf(self.ppc, self.ppopt)
self.observation = self.getobservation()
self.state = self.getstate()
return self.observation
def pf(args=sys.argv[1:]):
usage = 'Runs a power flow.'
options, casedata, ppopt, fname, solvedcase = \
parse_options(args, usage)
if options.test:
sys.exit(test_pf())
_, success = runpf(casedata, ppopt, fname, solvedcase)
exit(success)
def pf(args=sys.argv[1:]):
usage = 'Runs a power flow.'
options, casedata, ppopt, fname, solvedcase = \
parse_options(args, usage)
if options.test:
sys.exit(test_pf())
_, success = runpf(casedata, ppopt, fname, solvedcase)
exit(success)
def test_pypower_case():
#ppopt is a dictionary with the details of the optimization routine to run
ppopt = ppoption(PF_ALG=2)
#choose DC or AC
ppopt["PF_DC"] = True
#ppc is a dictionary with details about the network, including baseMVA, branches and generators
ppc = case()
results, success = runpf(ppc, ppopt)
#store results in a DataFrame for easy access
results_df = {}
#branches
columns = 'bus0, bus1, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax, p0, q0, p1, q1'.split(
", ")
results_df['branch'] = pd.DataFrame(data=results["branch"],
columns=columns)
#buses
columns = [
"bus", "type", "Pd", "Qd", "Gs", "Bs", "area", "v_mag_pu_set",
"v_ang_set", "v_nom", "zone", "Vmax", "Vmin"
]
results_df['bus'] = pd.DataFrame(data=results["bus"],
columns=columns,
index=results["bus"][:, 0])
#generators
columns = "bus, p, q, q_max, q_min, Vg, mBase, status, p_max, p_min, Pc1, Pc2, Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf".split(
", ")
results_df['gen'] = pd.DataFrame(data=results["gen"], columns=columns)
#now compute in PyPSA
network = pypsa.Network()
network.import_from_pypower_ppc(ppc)
network.lpf()
#compare generator dispatch
p_pypsa = network.generators_t.p.loc["now"].values
p_pypower = results_df['gen']["p"].values
np.testing.assert_array_almost_equal(p_pypsa, p_pypower)
#compare branch flows
for item in ["lines", "transformers"]:
df = getattr(network, item)
pnl = getattr(network, item + "_t")
for si in ["p0", "p1"]:
si_pypsa = getattr(pnl, si).loc["now"].values
si_pypower = results_df['branch'][si][df.original_index].values
np.testing.assert_array_almost_equal(si_pypsa, si_pypower)
def get_output(self,*args):
#returns the output of the load flow calculation
for k in range(1,29):
self.ppc["gen"][k,1] = self.dispatch_load[k]
ppc_result, y = runpf(self.ppc, self.ppopt)
if args:
self.add_noise(args[0])
#print ppc_result["bus"][1,12]
#print ppc_result["bus"][6,7]
#print ppc_result["bus"][12,7]
#print ppc_result["bus"][18,7]
#print ppc_result["bus"][24,7]
branch_constraints = array([(ppc_result["bus"][7,7] < (ppc_result["bus"][1,12])) ,
ppc_result["bus"][14,7] < (ppc_result["bus"][1,12]) ,
ppc_result["bus"][21,7] < (ppc_result["bus"][1,12]),
ppc_result["bus"][28,7] < (ppc_result["bus"][1,12])])
#print self.ppc["gen"][:,1]
if any(branch_constraints == True):
while(any(branch_constraints == True)):
for k in range(0,4):
if branch_constraints[k] == True:
self.adapt_branch_power(ppc_result,k)
self.adapt_main_generator()
ppc_result, y = runpf(self.ppc, self.ppopt)
#print self.ppc["gen"][:,1]
for k in range(0,4):
if branch_constraints[k] == True:
branch_constraints[k] = (abs(ppc_result["bus"][k * 7+ 7,7] - ppc_result["bus"][1,12]) > 0.001)
#print ppc_result["bus"][6,7]
#print ppc_result["bus"][12,7]
#print ppc_result["bus"][18,7]
#print ppc_result["bus"][24,7]
#print branch_constraints
return ppc_result
def test_pypower_case():
#ppopt is a dictionary with the details of the optimization routine to run
ppopt = ppoption(PF_ALG=2)
#choose DC or AC
ppopt["PF_DC"] = True
#ppc is a dictionary with details about the network, including baseMVA, branches and generators
ppc = case()
results,success = runpf(ppc, ppopt)
#store results in a DataFrame for easy access
results_df = {}
#branches
columns = 'bus0, bus1, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax, p0, q0, p1, q1'.split(", ")
results_df['branch'] = pd.DataFrame(data=results["branch"],columns=columns)
#buses
columns = ["bus","type","Pd","Qd","Gs","Bs","area","v_mag_pu_set","v_ang_set","v_nom","zone","Vmax","Vmin"]
results_df['bus'] = pd.DataFrame(data=results["bus"],columns=columns,index=results["bus"][:,0])
#generators
columns = "bus, p, q, q_max, q_min, Vg, mBase, status, p_max, p_min, Pc1, Pc2, Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf".split(", ")
results_df['gen'] = pd.DataFrame(data=results["gen"],columns=columns)
#now compute in PyPSA
network = pypsa.Network()
network.import_from_pypower_ppc(ppc)
network.lpf()
#compare generator dispatch
p_pypsa = network.generators_t.p.loc["now"].values
p_pypower = results_df['gen']["p"].values
np.testing.assert_array_almost_equal(p_pypsa,p_pypower)
#compare branch flows
for item in ["lines","transformers"]:
df = getattr(network,item)
pnl = getattr(network,item + "_t")
for si in ["p0","p1"]:
si_pypsa = getattr(pnl,si).loc["now"].values
si_pypower = results_df['branch'][si][df.original_index].values
np.testing.assert_array_almost_equal(si_pypsa,si_pypower)
def main(casefile):
ppopt = ppoption(PF_ALG=2)
r = runpf(casedata=casefile,
ppopt=ppopt)
output = {'baseMVA': r[0]['baseMVA'],
'branch': r[0]["branch"].tolist(),
'bus': r[0]["bus"].tolist(),
'gen': r[0]["gen"].tolist()
}
sys.stdout.write(json.dumps(output)+'\n')
def likelihood_ps(measur_vec,bvec):
# -- modify load buses
ppc["bus"][ind,2] = bvec
# -- estimate the transformer measurements
ppopt = pypo.ppoption(PF_ALG=2, VERBOSE=0, OUT_ALL=0)
r = pypo.runpf(ppc, ppopt)
estim = r[0]['gen'][:,2]
# -- calculate the likelihood
sig = 10.0
return np.exp(-((estim - measur_vec)**2).sum()/(2*sig**2))
def lnlike(theta, y):
# -- modify load buses
ppc["bus"][ind, 2] = theta
# -- estimate the transformer measurements
sol = pypo.runpf(ppc, pypo.ppoption(PF_ALG=2, VERBOSE=0, OUT_ALL=0))
estim = sol[0]["gen"][:, 2]
# -- calculate the likelihood
sig = 1.0
if (theta >= 0.0).all():
return -((estim - y) ** 2).sum() / (2 * sig ** 2)
else:
return -np.inf
def main():
import sys
case = sys.argv[1] if len(sys.argv) == 2 else 'a'
cases = {
'a': test_case_a,
'b': test_case_b,
}
case = cases[case]()
ppc = ext_pypower.create(case)
ppo = ppoption(OUT_ALL=0, VERBOSE=0)
res = runpf(ppc, ppo)
ext_pypower.write_results_to_case(res[0], case)
print(case)
def updateGraphFlow(self,graph,case):
solverResult = None
try:
solverResult = runpf(case, ppopt=ppoption())
# get from the results branches with counters if there is more branches between two buses
counters = []
i = 0
for branch in solverResult[0]["branch"]:
fromBusTemp = int(branch[constOut["branch"]["fromBus"]])
toBusTemp = int(branch[constOut["branch"]["toBus"]])
pIn = branch[constOut["branch"]["Pin"]]
first_or_default = next(
(x for x in counters if x["fromBus"] == fromBusTemp and x["toBus"] == toBusTemp),
None)
if first_or_default is None:
counters.append({"fromBus": fromBusTemp, "toBus": toBusTemp, "counter": 0, "Pin": pIn, "index": i})
else:
counters.append(
{"fromBus": fromBusTemp, "toBus": toBusTemp, "counter": first_or_default["counter"] + 1,
"Pin": pIn,
"index": i})
i = i + 1
# for all vertices assign 0.0 for Pin and 0.0 for Pg
for v in graph.vs:
v["Pin"] = 0.0
v["Pout"] = 0.0
v["Pg"] = 0.0
for gen in solverResult[0]["gen"]:
busName = "Bus_" + str(int(gen[0]))
graph.vs.find(busName)["Pg"] = gen[1]
for c in counters:
f = graph.vs.find("Bus_" + str(c["fromBus"]))
t = graph.vs.find("Bus_" + str(c["toBus"]))
graph.es.select(_source=f.index, _target=t.index)[c["counter"]]["Pin"] = c["Pin"]
if (c["Pin"] > 0):
t["Pin"] += c["Pin"]
f["Pout"] += c["Pin"]
else:
f["Pin"] += math.fabs(c["Pin"])
t["Pout"] += math.fabs(c["Pin"])
except ValueError as ve:
logging.error(ve)
raise SimException(ve.message)
from pypower.api import case9,runpf
from runPfOptions import ppoption
import const
succes=runpf(case9(),ppopt=ppoption());
# print(succes[0]['branch'])
print(succes[0]['bus'])
for branch in succes[0]['branch']:
print branch[const.constOut['branch']["Pin"]]
print branch[const.constOut['branch']["Pout"]]
print("done")
C = 1 * Cbest
pOption.remove(bestIdx)
pIdx.append(bestIdx)
pilotBus = [lIdx[i] for i in pIdx]
return pilotBus
if __name__ == '__main__':
print('-----------Start-----------')
ppc, success = runpf(case9())
ppc = ext2int(ppc)
baseMVA = ppc['baseMVA']
bus = ppc['bus']
branch = ppc['branch']
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch)
vm = bus[:, VM]
va = 2 * np.pi * bus[:, VA]
V = vm * np.exp(1j * va)
gIdx = [0, 1, 2]
lIdx = [3, 4, 5, 6, 7, 8]
nPilot = 2
for n in range(len(time)):
casedata['bus'][
bus_var,
2] = loadsP15to25[:, n] #Changing the values for the active power
casedata['bus'][
bus_var,
3] = loadsQ15to25[:, n] #Changing the values for the reactive power
#casedata['bus'][3,2] = LoadP[n] #Changing the values for PV values (active power), reactive==0 ???
casedata['gen'][1, 1] = LoadP[n] / (
baseMVA * 1000
) #genP19[:,n] #Changing the values for the gen-active power
casedata['gen'][
1,
2] = 0 #genQ19[:,n] #Changing the values for the gen-reactive power
ppopt = ppoption(PF_ALG=2)
resultPF, success = runpf(casedata, ppopt)
print
if success == 0:
print('ERROR in step %d', n)
slack_ang = resultPF['bus'][1, 8]
v_mag[:, n] = resultPF['bus'][:, 7] # Voltage, magnitude
v_ang[:, n] = resultPF['bus'][:, 8] - slack_ang # Voltage, angle
loadP_all[:, n] = resultPF['bus'][:, 2]
loadQ_all[:, n] = resultPF['bus'][:, 3]
genP_all[:, n] = resultPF['gen'][:, 1]
genQ_all[:, n] = resultPF['gen'][:, 2]
P_into_00[:, n] = -resultPF['branch'][0, 15]
Q_into_00[:, n] = -resultPF['branch'][0, 16]
def simulate_data(Sm, gen=None, PVdata=None, PV_idx=None, verbose = 0):
"""
Simulate data using power flow analysis
:param PVgen: data from PV generation injected at bus 6
:return: dict
"""
from scipy.io import loadmat
S=Sm.copy()
t_ges = 1440
delta_t = 15
time= np.arange(delta_t, t_ges+delta_t,delta_t)
t_f = len(time)
bus_var = np.arange(2,13,1) # buses that are varied
v_mag = np.zeros((13,t_f))
v_ang = np.zeros((13,t_f))
P = np.zeros((13,t_f))
Q = np.zeros((13,t_f))
loadP = np.zeros((13,t_f))
loadQ = np.zeros((13,t_f))
genP_all = np.zeros((2,t_f))
genQ_all = np.zeros((2,t_f))
P_into_00 = np.zeros(t_f)
Q_into_00 = np.zeros(t_f)
if (len(bus_var)==S.shape[0]):
Pdata = np.real(S)
Qdata = np.imag(S)
elif (2*len(bus_var)==S.shape[0]):
Pdata = S[:S.shape[0]/2,:]
Qdata = S[S.shape[0]/2:,:]
else:
raise ValueError("Powers have wrong dimension.")
if isinstance(PVdata,np.ndarray):
if len(PVdata.shape)==2: # P and Q for PVgen
if not PVdata.shape[0]==2:
PVdata = PVdata.T
Pdata[PV_idx,:] = -PVdata[0,:]+Pdata[PV_idx,:]
Qdata[PV_idx,:] = -PVdata[1,:]+Qdata[PV_idx,:]
else:
Pdata[PV_idx,:] = -PVdata[:] +Pdata[PV_idx,:]# with MVA
Qdata[PV_idx,:] = np.zeros_like(PVdata)
casedata = get_topology()
for n in range(len(time)):
casedata['bus'][bus_var,2] = Pdata[:,n] #Changing the values for the active power
casedata['bus'][bus_var,3] = Qdata[:,n] #Changing the values for the reactive power
if isinstance(gen,np.ndarray):
casedata['gen'][1,1] = gen[n]
casedata['gen'][1,2] = 0
ppopt = ppoption(PF_ALG=2)
ppopt["VERBOSE"] = verbose
resultPF, success = runpf(casedata, ppopt)
if success == 0:
print ('ERROR in step %d', n)
slack_ang = resultPF['bus'][1,8]
v_mag[:,n] = resultPF['bus'][:,7] # Voltage, magnitude
v_ang[:,n] = resultPF['bus'][:,8] - slack_ang # Voltage, angle
loadP[:,n] = resultPF['bus'][:,2]
loadQ[:,n] = resultPF['bus'][:,3]
genP_all[:,n] = resultPF['gen'][:,1]
genQ_all[:,n] = resultPF['gen'][:,2]
P_into_00[n]=-resultPF['branch'][0,15]
Q_into_00[n]=-resultPF['branch'][0,16]
loadP[6,:] = loadP[6,:]+genP_all[1,:]
loadQ[6,:] = loadQ[6,:]+genQ_all[1,:]
simdata = dict([])
simdata["Vm"] = (11/(np.sqrt(3)))*v_mag
simdata["Va"] = v_ang
simdata["Pk"] = -loadP[2:,:]
simdata["Qk"] = -loadQ[2:,:]
simdata["P_00"]=P_into_00
simdata["Q_00"]=Q_into_00
return simdata
Created on Wed Feb 24 15:13:38 2016
@author: saf537
"""
import case14_mod
import pandas as pd
import pypower.api as pypo
import numpy as np
load_shapes = pd.read_csv('loadShapes1.csv')
sample = 100*load_shapes.iloc[0][load_shapes.columns[9:]]
ppc0 = case14_mod.case14_mod(busN = 1,dlt = 0, op_change=1) # trivial case: original solutin
ppopt0 = pypo.ppoption(PF_ALG=2,VERBOSE=0, OUT_ALL=0)
r0 = pypo.runpf(ppc0, ppopt0)
n_trs = sum(r0[0]['bus'][:,1] == 2)
n_bldgs = sum(r0[0]['bus'][:,1] == 1)
transfs = r0[0]['bus'][:,0][r0[0]['bus'][:,1]==2]
bdgs = r0[0]['bus'][:,0][r0[0]['bus'][:,1]==1]
tm_max = len(sample)
# bus 8, power after solution
TS = np.empty((n_bldgs,n_trs,tm_max))
initial_loads_vec = r0[0]['bus'][:,2]
cont = 0
for i in bdgs:
tmp = 0
ini_load = initial_loads_vec[i-1]
for j in transfs:
for ld in sample:
nwalkers = 300
nsteps = 100
cut = 50
# -- intialize the 14-bus system
ppc0 = get_ppc14(1, 0, 1) # 0 implies no change
ppc = cp.deepcopy(ppc0)
# y = ppc0['gen'][:,2].copy() # default measured values of transformers
ind = ppc0["bus"][:, 1] == 1 # building indices
# binit = ppc0["bus"][ind,2].copy()
for val in [1.00, 2.00, 5.00, 10.00, 20.00]:
# -- Initialize sample
print("initializing sampler...")
np.random.seed(314)
ppc["bus"][ind, 2] = val * np.ones(ndim)
sol = pypo.runpf(ppc, pypo.ppoption(PF_ALG=2, VERBOSE=0, OUT_ALL=0))
y = sol[0]["gen"][:, 2]
binit = ppc["bus"][ind, 2].copy()
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnlike, args=[y])
pos = np.array([binit * (1.0 + 0.2 * np.random.randn(ndim)) for i in range(nwalkers)]).clip(min=0.0)
# -- run walkers
print("running walkers...")
sampler.run_mcmc(pos, nsteps)
print("walkers finished...")
# -- save chain
oname = "./output/random_test20percent_diffValues" + str(val) + ".npy"
print("saving chain to {0}".format(oname))
np.save(oname, sampler.chain)
def test_pypower_case():
#ppopt is a dictionary with the details of the optimization routine to run
ppopt = ppoption(PF_ALG=2)
#choose DC or AC
ppopt["PF_DC"] = False
#ppc is a dictionary with details about the network, including baseMVA, branches and generators
ppc = case()
results,success = runpf(ppc, ppopt)
#store results in a DataFrame for easy access
results_df = {}
#branches
columns = 'bus0, bus1, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax, p0, q0, p1, q1'.split(", ")
results_df['branch'] = pd.DataFrame(data=results["branch"],columns=columns)
#buses
columns = ["bus","type","Pd","Qd","Gs","Bs","area","v_mag_pu","v_ang","v_nom","zone","Vmax","Vmin"]
results_df['bus'] = pd.DataFrame(data=results["bus"],columns=columns,index=results["bus"][:,0])
#generators
columns = "bus, p, q, q_max, q_min, Vg, mBase, status, p_max, p_min, Pc1, Pc2, Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf".split(", ")
results_df['gen'] = pd.DataFrame(data=results["gen"],columns=columns)
#now compute in PyPSA
network = pypsa.Network()
network.import_from_pypower_ppc(ppc)
#PYPOWER uses PI model for transformers, whereas PyPSA defaults to
#T since version 0.8.0
network.transformers.model = "pi"
network.pf()
#compare branch flows
for c in network.iterate_components(pypsa.components.passive_branch_components):
for si in ["p0","p1","q0","q1"]:
si_pypsa = getattr(c.pnl,si).loc["now"].values
si_pypower = results_df['branch'][si][c.df.original_index].values
np.testing.assert_array_almost_equal(si_pypsa,si_pypower)
#compare generator dispatch
for s in ["p","q"]:
s_pypsa = getattr(network.generators_t,s).loc["now"].values
s_pypower = results_df["gen"][s].values
np.testing.assert_array_almost_equal(s_pypsa,s_pypower)
#compare voltages
v_mag_pypsa = network.buses_t.v_mag_pu.loc["now"]
v_mag_pypower = results_df["bus"]["v_mag_pu"]
np.testing.assert_array_almost_equal(v_mag_pypsa,v_mag_pypower)
v_ang_pypsa = network.buses_t.v_ang.loc["now"]
pypower_slack_angle = results_df["bus"]["v_ang"][results_df["bus"]["type"] == 3].values[0]
v_ang_pypower = (results_df["bus"]["v_ang"] - pypower_slack_angle)*np.pi/180.
np.testing.assert_array_almost_equal(v_ang_pypsa,v_ang_pypower)
def main_loop():
if len(sys.argv) == 2:
rootname = sys.argv[1]
else:
print('usage: python fncsPYPOWER.py rootname')
sys.exit()
ppc = ppcasefile()
StartTime = ppc['StartTime']
tmax = int(ppc['Tmax'])
period = int(ppc['Period'])
dt = int(ppc['dt'])
make_dictionary(ppc, rootname)
bus_mp = open("bus_" + rootname + "_metrics.json", "w")
gen_mp = open("gen_" + rootname + "_metrics.json", "w")
sys_mp = open("sys_" + rootname + "_metrics.json", "w")
bus_meta = {
'LMP_P': {
'units': 'USD/kwh',
'index': 0
},
'LMP_Q': {
'units': 'USD/kvarh',
'index': 1
},
'PD': {
'units': 'MW',
'index': 2
},
'QD': {
'units': 'MVAR',
'index': 3
},
'Vang': {
'units': 'deg',
'index': 4
},
'Vmag': {
'units': 'pu',
'index': 5
},
'Vmax': {
'units': 'pu',
'index': 6
},
'Vmin': {
'units': 'pu',
'index': 7
}
}
gen_meta = {
'Pgen': {
'units': 'MW',
'index': 0
},
'Qgen': {
'units': 'MVAR',
'index': 1
},
'LMP_P': {
'units': 'USD/kwh',
'index': 2
}
}
sys_meta = {
'Ploss': {
'units': 'MW',
'index': 0
},
'Converged': {
'units': 'true/false',
'index': 1
}
}
bus_metrics = {'Metadata': bus_meta, 'StartTime': StartTime}
gen_metrics = {'Metadata': gen_meta, 'StartTime': StartTime}
sys_metrics = {'Metadata': sys_meta, 'StartTime': StartTime}
gencost = ppc['gencost']
fncsBus = ppc['FNCS']
gen = ppc['gen']
ppopt_market = pp.ppoption(VERBOSE=0, OUT_ALL=0, PF_DC=1)
ppopt_regular = pp.ppoption(VERBOSE=0, OUT_ALL=0, PF_DC=1)
loads = np.loadtxt('NonGLDLoad.txt', delimiter=',')
for row in ppc['UnitsOut']:
print('unit ', row[0], 'off from', row[1], 'to', row[2], flush=True)
for row in ppc['BranchesOut']:
print('branch', row[0], 'out from', row[1], 'to', row[2], flush=True)
nloads = loads.shape[0]
ts = 0
tnext_opf = -dt
# initializing for metrics collection
tnext_metrics = 0
loss_accum = 0
conv_accum = True
n_accum = 0
bus_accum = {}
gen_accum = {}
for i in range(fncsBus.shape[0]):
busnum = int(fncsBus[i, 0])
bus_accum[str(busnum)] = [0, 0, 0, 0, 0, 0, 0, 99999.0]
for i in range(gen.shape[0]):
gen_accum[str(i + 1)] = [0, 0, 0]
op = open(rootname + '.csv', 'w')
print(
't[s],Converged,Pload,P7 (csv),Unresp (opf),P7 (rpf),Resp (opf),GLD Pub,BID?,P7 Min,V7,LMP_P7,LMP_Q7,Pgen1,Pgen2,Pgen3,Pgen4,Pdisp,Deg,c2,c1',
file=op,
flush=True)
fncs.initialize()
# transactive load components
csv_load = 0 # from the file
unresp = 0 # unresponsive load estimate from the auction agent
resp = 0 # will be the responsive load as dispatched by OPF
resp_deg = 0 # RESPONSIVE_DEG from FNCS
resp_c1 = 0 # RESPONSIVE_C1 from FNCS
resp_c2 = 0 # RESPONSIVE_C2 from FNCS
resp_max = 0 # RESPONSIVE_MAX_MW from FNCS
feeder_load = 0 # amplified feeder MW
while ts <= tmax:
# start by getting the latest inputs from GridLAB-D and the auction
events = fncs.get_events()
new_bid = False
load_scale = float(fncsBus[0][2])
for key in events:
topic = key.decode()
if topic == 'UNRESPONSIVE_MW':
unresp = load_scale * float(fncs.get_value(key).decode())
fncsBus[0][
3] = unresp # to poke unresponsive estimate into the bus load slot
new_bid = True
elif topic == 'RESPONSIVE_MAX_MW':
resp_max = load_scale * float(fncs.get_value(key).decode())
new_bid = True
elif topic == 'RESPONSIVE_C2':
resp_c2 = float(fncs.get_value(key).decode()) / load_scale
new_bid = True
elif topic == 'RESPONSIVE_C1':
resp_c1 = float(fncs.get_value(key).decode())
new_bid = True
elif topic == 'RESPONSIVE_DEG':
resp_deg = int(fncs.get_value(key).decode())
new_bid = True
else:
gld_load = parse_mva(fncs.get_value(key).decode(
)) # actual value, may not match unresp + resp load
feeder_load = float(gld_load[0]) * load_scale
if new_bid == True:
dummy = 2
# print('**Bid', ts, unresp, resp_max, resp_deg, resp_c2, resp_c1)
# update the case for bids, outages and CSV loads
idx = int((ts + dt) / period) % nloads
bus = ppc['bus']
gen = ppc['gen']
branch = ppc['branch']
gencost = ppc['gencost']
csv_load = loads[idx, 0]
bus[4, 2] = loads[idx, 1]
bus[8, 2] = loads[idx, 2]
# process the generator and branch outages
for row in ppc['UnitsOut']:
if ts >= row[1] and ts <= row[2]:
gen[row[0], 7] = 0
else:
gen[row[0], 7] = 1
for row in ppc['BranchesOut']:
if ts >= row[1] and ts <= row[2]:
branch[row[0], 10] = 0
else:
branch[row[0], 10] = 1
if resp_deg == 2:
gencost[4][3] = 3
gencost[4][4] = -resp_c2
gencost[4][5] = resp_c1
elif resp_deg == 1:
gencost[4][3] = 2
gencost[4][4] = resp_c1
gencost[4][5] = 0.0
else:
gencost[4][3] = 1
gencost[4][4] = 999.0
gencost[4][5] = 0.0
gencost[4][6] = 0.0
if ts >= tnext_opf: # expecting to solve opf one dt before the market clearing period ends, so GridLAB-D has time to use it
# for OPF, the FNCS bus load is CSV + Unresponsive estimate, with Responsive separately dispatchable
bus = ppc['bus']
gen = ppc['gen']
bus[6, 2] = csv_load
for row in ppc['FNCS']:
unresp = float(row[3])
newidx = int(row[0]) - 1
if unresp >= feeder_load:
bus[newidx, 2] += unresp
else:
bus[newidx, 2] += unresp # feeder_load
gen[4][9] = -resp_max
res = pp.runopf(ppc, ppopt_market)
if res['success'] == False:
conv_accum = False
opf_bus = deepcopy(res['bus'])
opf_gen = deepcopy(res['gen'])
lmp = opf_bus[6, 13]
resp = -1.0 * opf_gen[4, 1]
fncs.publish('LMP_B7', 0.001 * lmp) # publishing $/kwh
print(' OPF', ts, csv_load, '{:.3f}'.format(unresp),
'{:.3f}'.format(resp), '{:.3f}'.format(feeder_load),
'{:.3f}'.format(opf_bus[6, 2]),
'{:.3f}'.format(opf_gen[0, 1]), '{:.3f}'.format(opf_gen[1,
1]),
'{:.3f}'.format(opf_gen[2, 1]), '{:.3f}'.format(opf_gen[3,
1]),
'{:.3f}'.format(opf_gen[4, 1]), '{:.3f}'.format(lmp))
# if unit 2 (the normal swing bus) is dispatched at max, change the swing bus to 9
if opf_gen[1, 1] >= 191.0:
ppc['bus'][1, 1] = 2
ppc['bus'][8, 1] = 3
print(' SWING Bus 9')
else:
ppc['bus'][1, 1] = 3
ppc['bus'][8, 1] = 1
print(' SWING Bus 2')
tnext_opf += period
# always update the electrical quantities with a regular power flow
bus = ppc['bus']
gen = ppc['gen']
bus[6, 13] = lmp
gen[0, 1] = opf_gen[0, 1]
gen[1, 1] = opf_gen[1, 1]
gen[2, 1] = opf_gen[2, 1]
gen[3, 1] = opf_gen[3, 1]
# during regular power flow, we use the actual CSV + feeder load, ignore dispatchable load and use actual
bus[6, 2] = csv_load + feeder_load
gen[4, 1] = 0 # opf_gen[4, 1]
gen[4, 9] = 0
rpf = pp.runpf(ppc, ppopt_regular)
if rpf[0]['success'] == False:
conv_accum = False
bus = rpf[0]['bus']
gen = rpf[0]['gen']
Pload = bus[:, 2].sum()
Pgen = gen[:, 1].sum()
Ploss = Pgen - Pload
# update the metrics
n_accum += 1
loss_accum += Ploss
for i in range(fncsBus.shape[0]):
busnum = int(fncsBus[i, 0])
busidx = busnum - 1
row = bus[busidx].tolist()
# LMP_P, LMP_Q, PD, QD, Vang, Vmag, Vmax, Vmin: row[11] and row[12] are Vmax and Vmin constraints
PD = row[
2] + resp # TODO, if more than one FNCS bus, track scaled_resp separately
Vpu = row[7]
bus_accum[str(busnum)][0] += row[13] * 0.001
bus_accum[str(busnum)][1] += row[14] * 0.001
bus_accum[str(busnum)][2] += PD
bus_accum[str(busnum)][3] += row[3]
bus_accum[str(busnum)][4] += row[8]
bus_accum[str(busnum)][5] += Vpu
if Vpu > bus_accum[str(busnum)][6]:
bus_accum[str(busnum)][6] = Vpu
if Vpu < bus_accum[str(busnum)][7]:
bus_accum[str(busnum)][7] = Vpu
for i in range(gen.shape[0]):
row = gen[i].tolist()
busidx = int(row[0] - 1)
# Pgen, Qgen, LMP_P (includes the responsive load as dispatched by OPF)
gen_accum[str(i + 1)][0] += row[1]
gen_accum[str(i + 1)][1] += row[2]
gen_accum[str(i + 1)][2] += float(opf_bus[busidx, 13]) * 0.001
# write the metrics
if ts >= tnext_metrics:
sys_metrics[str(ts)] = {
rootname: [loss_accum / n_accum, conv_accum]
}
bus_metrics[str(ts)] = {}
for i in range(fncsBus.shape[0]):
busnum = int(fncsBus[i, 0])
busidx = busnum - 1
row = bus[busidx].tolist()
met = bus_accum[str(busnum)]
bus_metrics[str(ts)][str(busnum)] = [
met[0] / n_accum, met[1] / n_accum, met[2] / n_accum,
met[3] / n_accum, met[4] / n_accum, met[5] / n_accum,
met[6], met[7]
]
bus_accum[str(busnum)] = [0, 0, 0, 0, 0, 0, 0, 99999.0]
gen_metrics[str(ts)] = {}
for i in range(gen.shape[0]):
met = gen_accum[str(i + 1)]
gen_metrics[str(ts)][str(i + 1)] = [
met[0] / n_accum, met[1] / n_accum, met[2] / n_accum
]
gen_accum[str(i + 1)] = [0, 0, 0]
tnext_metrics += period
n_accum = 0
loss_accum = 0
conv_accum = True
volts = 1000.0 * bus[6, 7] * bus[6, 9]
fncs.publish('three_phase_voltage_B7', volts)
# CSV file output
print(
ts,
res['success'],
'{:.3f}'.format(Pload), # Pload
'{:.3f}'.format(csv_load), # P7 (csv)
'{:.3f}'.format(unresp), # GLD Unresp
'{:.3f}'.format(bus[6, 2]), # P7 (rpf)
'{:.3f}'.format(resp), # Resp (opf)
'{:.3f}'.format(feeder_load), # GLD Pub
new_bid,
'{:.3f}'.format(gen[4, 9]), # P7 Min
'{:.3f}'.format(bus[6, 7]), # V7
'{:.3f}'.format(bus[6, 13]), # LMP_P7
'{:.3f}'.format(bus[6, 14]), # LMP_Q7
'{:.2f}'.format(gen[0, 1]), # Pgen1
'{:.2f}'.format(gen[1, 1]), # Pgen2
'{:.2f}'.format(gen[2, 1]), # Pgen3
'{:.2f}'.format(gen[3, 1]), # Pgen4
'{:.2f}'.format(res['gen'][4, 1]), # Pdisp
'{:.4f}'.format(resp_deg), # degree
'{:.8f}'.format(ppc['gencost'][4, 4]), # c2
'{:.8f}'.format(ppc['gencost'][4, 5]), # c1
sep=',',
file=op,
flush=True)
# request the next time step
ts = fncs.time_request(ts + dt)
if ts > tmax:
print('breaking out at', ts, flush=True)
break
# spio.savemat('matFile.mat', saveDataDict)
# ===================================
print('writing metrics', flush=True)
print(json.dumps(sys_metrics), file=sys_mp, flush=True)
print(json.dumps(bus_metrics), file=bus_mp, flush=True)
print(json.dumps(gen_metrics), file=gen_mp, flush=True)
print('closing files', flush=True)
bus_mp.close()
gen_mp.close()
sys_mp.close()
op.close()
print('finalizing FNCS', flush=True)
fncs.finalize()
from pypower.api import case9, ppoption, runpf, printpf
from constants import *
import numpy as np
# See http://rwl.github.io/PYPOWER/api/ for description of variables
'''
Slack Bus: At the slack bus, the voltage magnitude and angle are fixed and the power
injections are free. There is only one slack bus in a power system.
Load Bus: At a load bus, or PQ bus, the power injections are fixed while the voltage
magnitude and angle are free. There are M PQ buses in the system.
Voltage-Controlled Bus: At a voltage controlled bus, or PV bus, the real power
injection and voltage magnitude are fixed while the reactive power injection
and the voltage angle are free. (This corresponds to allowing a local source
of reactive power to regulate the voltage to a desired setpoint.) There are
N − M − 1 PV buses in the system.
'''
ppc = case9()
ppopt = ppoption(PF_ALG=2, VERBOSE=0, OUT_ALL=0)
r1, succeed = runpf(ppc, ppopt)
print("Success:", succeed)
ppc['gen'][2, PG] = 100000.0
r2, succeed = runpf(ppc, ppopt)
print("Success:", succeed)
from pypower.api import case9, ppoption, runpf import numpy as np temp = case9() temp = case9() ppopt = ppoption(OUT_ALL=0) (r, s) = runpf(temp, ppopt) #(r, s) = runpf(temp) voltage = r["bus"][:, 7] angle = r["bus"][:, 8] real_gen = r["gen"][:, 1] reactive_gen = r["gen"][:, 2] real_flow_fr = r["branch"][:, 13] real_flow_to = r["branch"][:, 15] reactive_flow_fr = r["branch"][:, 14] reactive_flow_to = r["branch"][:, 16] apparent_power_flow_fr = np.sqrt(real_flow_fr**2 + reactive_flow_fr**2) apparent_power_flow_to = np.sqrt(real_flow_to**2 + reactive_flow_to**2) print type(r["success"]) print(1 - 10.0 / 100)
nbus_out = len(outage["bus"])
ngen_out = len(outage["gen"])
nbr_out = len(outage["branch"])
n_tot = nbus_out + ngen_out + nbr_out #total number of outages
success = [] #intialize success of load flow
n_out = 1 #initialize outage counter
# simulate bus outages
c = 0 # start counter
while c < nbus_out:
temp = casea.casef() # initialize orignal testcase data
temp = bus_out.bus_out(temp, c) #modify data according to outage
(r, s) = runpf(temp, ppopt) #run load flow
if s == 1:
check_sol.check_sol(n_out, r) #check violations for convg case
else:
#print results if divergent case
print_sim_results.print_sim_results(s, n_out, r, 0, 0, 0, 0)
success.append(s)
del s, r, temp
c += 1
n_out += 1
# Simulate gen outages
c = 0 # start counter
while c < ngen_out:
temp = casea.casef()
temp = gen_out.gen_out(temp, c)
def _get_param(self):
# delect the disturbances not defined in the set_disturb, and renumber the new disturbance set.
for d_type in {1, 2, 3, 4}:
i_d_all = list()
for i_d in range(len(self.casedata['disturbance'][d_type])):
if (d_type, self.casedata['disturbance'][d_type][i_d, 0]
) in self.set_disturb:
i_d_all.append(i_d)
self.casedata['disturbance'][d_type] = self.casedata[
'disturbance'][d_type][i_d_all, :]
for i_d in range(len(self.casedata['disturbance'][d_type])):
self.casedata['disturbance'][d_type][i_d, 0] = i_d + 1
if len(self.casedata['disturbance'][d_type]) == 0:
del self.casedata['disturbance'][d_type]
# parameters
if len(self.casedata['bus']) >= 200 or len(self.casedata['bus']) == 59:
opt = ppoption(PF_TOL=1e-12, PF_MAX_IT=20)
else:
opt = ppoption(PF_TOL=1e-13, PF_MAX_IT=20)
s_pf = runpf(self.casedata, opt)
pcc_y = ext2int(self.casedata)
Ybus = makeYbus(pcc_y["baseMVA"], pcc_y["bus"],
pcc_y["branch"])[0].toarray()
n_bus = len(Ybus)
B_0 = np.imag(Ybus)
for i in range(len(B_0)):
B_0[i, i] = 0
pg_0 = np.zeros(n_bus)
pg_0[(s_pf[0]['gen'][:, 0] - 1).astype(int)] = s_pf[0]['gen'][:, 1]
i_gen = self.casedata['gen'][:, 0].astype(int).tolist()
i_non_0 = (np.where(self.casedata['bus'][:, 2] == 0)[0] + 1).tolist()
i_non = list(set(i_non_0).difference(set(i_gen)))
i_load_0 = list(
set(self.casedata['bus'][:, 0].astype(int).tolist()).difference(
set(i_gen)))
i_load = list(set(i_load_0).difference(set(i_non)))
self.Ybus = Ybus
self.n_bus = n_bus
self.B_0 = B_0
self.v_0 = s_pf[0]['bus'][:, 7]
self.theta_0 = np.radians(s_pf[0]['bus'][:, 8])
self.pl_0 = s_pf[0]['bus'][:, 2] / 100.0
self.pg_0 = pg_0 / 100.0
self.w_0 = np.zeros(self.n_bus)
self.i_gen = i_gen
self.i_non = i_non
self.i_load = i_load
self.i_gl = i_gen + i_load
self.i_all = self.casedata['bus'][:, 0].astype(int).tolist()
self.theta_gl_0 = self.theta_0[(np.array(self.i_gl) - 1).tolist()]
# initial values and bounds of optimization variables
self.m_0, self.m_l, self.m_u = dict(), dict(), dict()
self.d_0, self.d_l, self.d_u = dict(), dict(), dict()
self.M = self.casedata['bus'][:, 13].tolist()
self.D = self.casedata['bus'][:, 14].tolist()
for i in self.i_gen:
i_gc = np.where(self.casedata['gencontrol'][:, 0] == i)[0]
i_bus = np.where(self.casedata['bus'][:, 0] == i)[0][0]
self.m_0[i] = self.M[i_bus]
self.m_l[i] = self.casedata['gencontrol'][i_gc, 2][0]
self.m_u[i] = self.casedata['gencontrol'][i_gc, 3][0]
self.d_0[i] = self.D[i_bus]
self.d_l[i] = self.casedata['gencontrol'][i_gc, 4][0]
self.d_u[i] = self.casedata['gencontrol'][i_gc, 5][0]
# set of index for each kind of branch, used for computing the objective function
self.ind_branch = list(
) # index set [(ind_bus_from, ind_bus_to)] of all branch
for i_b in self.casedata['branch'][:, [0, 1]]:
self.ind_branch.append((int(i_b[0]), int(i_b[1])))
set_k_i = list() # list of (k,i) of all disturbances and time elements
for k0 in self.casedata['disturbance'].keys():
if k0 != 9:
for k1 in self.casedata['disturbance'][k0][:, 0]:
for i in range(
1,
self.casedata['param_disc']['time_ele'][k0] + 1):
set_k_i.append(((int(k0), int(k1)), i))
self.set_k_i = set_k_i
def mainJAPowerFlow(baseMVAName, busName, genName, branchName, splitCharacter,
outputBusName, outputBranchName, outputGenName,
printOutput, optimal, areasName, genCostName):
#Variables (by for testing)
#baseMVAName = "baseMVA.txt"
#busName = "bus.txt"
#genName = "gen.txt"
#branchName = "branch.txt"
#splitCharacter = ' '
#outputBusName = "outputBus.txt"
#outputBranchName = "outputBranch.txt"
#outputBranchName = "outputGen.txt"
#printOutput = 0 #printOutput = 0 or 1, 0 for no stdout printed output, 1 if it is wanted. Note that both still output to the text files.
#optimal = 0 #optimal = 0 or 1, 0 for power flow, 1 for optimal power flow. NOTE: All following inputs are only used in optimal power flow, OPF, analysis (optimal = 1), but some values are still required as inputs, even if they are not used in the event of PF (optimal = 0).
#areasName = "areas.txt"
#genCostName = "genCost.txt"
#Assign ppc
ppc = readText(baseMVAName, busName, genName, branchName, splitCharacter,
optimal, areasName, genCostName)
#ppc = casetest()
#Set pf test type
#ppopt = ppoption(PF_ALG=1) #Includes printing output (of standard pf)
#ppopt = ppoption(OUT_ALL=0, VERBOSE=0) #These options prevent printing output
#ppopt = ppoption(PF_ALG=1, OUT_ALL=0, VERBOSE=0)
if (printOutput == 1):
ppopt = ppoption(OUT_ALL=1, VERBOSE=1)
elif (printOutput == 0):
ppopt = ppoption(OUT_ALL=0, VERBOSE=0)
else:
print(
"printOutput must be 0 or 1, 0 for no stdout printed output, and 1 if that is desired. Both still output to text files."
)
#Run pf or opf test
if (optimal == 0):
r = runpf(ppc, ppopt)
elif (optimal == 1):
r = runopf(ppc, ppopt)
else:
print("optimal must be 0 or 1, 0 for pf and 1 for opf.")
#Now clear the output files (method of writing to them might be altered, so doing this is to be sure
open(outputBusName, 'w').close()
open(outputBranchName, 'w').close()
open(outputGenName, 'w').close()
#For Optimal Power Flow
if (optimal == 1):
#Establish lengths
busCount = len(r['bus'])
branchCount = len(r['branch'])
genCount = len(r['gen'])
#Find Generator Per Bus Output
busGenP = numpy.zeros(busCount, dtype=numpy.float)
busGenQ = numpy.zeros(busCount, dtype=numpy.float)
f = open(outputGenName, 'w')
i = 0
while (i < genCount):
#For Gen Output
f.write(
str(i + 1) + splitCharacter + str(r['gen'][i][1]) +
splitCharacter + str(r['gen'][i][2]) + '\n')
#For Bus Output
busGenP[int(r['gen'][i][0])] += r['gen'][i][1]
busGenQ[int(r['gen'][i][0])] += r['gen'][i][2]
i += 1
f.close()
#Print Bus Output
f = open(outputBusName, 'w')
i = 0
while (i < busCount):
f.write(
str(i + 1) + splitCharacter + str(r['bus'][i][7]) +
splitCharacter + str(r['bus'][i][8]) + splitCharacter +
str(busGenP[i]) + splitCharacter + str(busGenQ[i]) +
splitCharacter + str(r['bus'][i][2]) + splitCharacter +
str(r['bus'][i][3]) + '\n')
i += 1
f.close()
#Print Branch Output
f = open(outputBranchName, 'w')
i = 0
dic = {}
while (i < branchCount):
f.write(
str(i + 1) + splitCharacter +
str(absDiff(r['branch'][i][15], r['branch'][i][13])) +
splitCharacter +
str(absDiff(r['branch'][i][16], r['branch'][i][14])) + '\n')
dic[i] = [
str(absDiff(r['branch'][i][15], r['branch'][i][13])),
str(absDiff(r['branch'][i][16], r['branch'][i][14]))
]
i += 1
f.close()
with open(
'C:\\Users\\LONG01\\TOMCAT\\webapps\\ROOT\OntoEN\\outputOPF.json',
'w') as fp:
json.dump(dic, fp)
#For Standard Power Flow
elif (optimal == 0):
#Establish lengths
busCount = len(r[0]['bus'])
branchCount = len(r[0]['branch'])
#print(branchCount)
genCount = len(r[0]['gen'])
#Find Generator Per Bus Output
busGenP = numpy.zeros(busCount, dtype=numpy.float)
busGenQ = numpy.zeros(busCount, dtype=numpy.float)
f = open(outputGenName, 'w')
i = 0
while (i < genCount):
#For Gen Output
f.write(
str(i + 1) + splitCharacter + str(r[0]['gen'][i][1]) +
splitCharacter + str(r[0]['gen'][i][2]) + '\n')
#For Bus Output
busGenP[int(r[0]['gen'][i][0])] += r[0]['gen'][i][1]
busGenQ[int(r[0]['gen'][i][0])] += r[0]['gen'][i][2]
i += 1
f.close()
#Print Bus Output
f = open(outputBusName, 'w')
i = 0
while (i < busCount):
f.write(
str(i + 1) + splitCharacter + str(r[0]['bus'][i][7]) +
splitCharacter + str(r[0]['bus'][i][8]) + splitCharacter +
str(busGenP[i]) + splitCharacter + str(busGenQ[i]) +
splitCharacter + str(r[0]['bus'][i][2]) + splitCharacter +
str(r[0]['bus'][i][3]) + '\n')
i += 1
f.close()
#Print Branch Output
f = open(outputBranchName, 'w')
i = 0
while (i < branchCount):
#P, Q and S (average)
PAve = (r[0]['branch'][i][15] + r[0]['branch'][i][13]) / 2.0
QAve = (r[0]['branch'][i][14] + r[0]['branch'][i][16]) / 2.0
SAve = numpy.sqrt(((PAve * PAve) + (QAve * QAve)))
#Print P loss, Q loss, aveP, aveQ and aveS.
f.write(
str(i + 1) + splitCharacter +
str(absDiff(r[0]['branch'][i][15], r[0]['branch'][i][13])) +
splitCharacter +
str(absDiff(r[0]['branch'][i][16], r[0]['branch'][i][14])) +
splitCharacter + str(PAve) + splitCharacter + str(QAve) +
splitCharacter + str(SAve) + '\n')
i += 1
f.close()
else:
print("optimal must be 0 or 1, 0 for pf and 1 for opf.")
ppc['branch'][-1, F_BUS] = 22
ppc['branch'][-1, T_BUS] = 32
# from pypower.case24_ieee_rts import case24_ieee_rts
# ppc = case24_ieee_rts()
####
## LF by pypower (Newton-Raphson) using PYPOWER
###
import pypower.api as pypow
ppopt = pypow.ppoption(VERBOSE=0,
OUT_ALL=0) #prevents results printing in each iteration
start_time_LF = timeit.default_timer()
results, success = pypow.runpf(ppc, ppopt=ppopt) #ppopt=ppopt
time_LF = timeit.default_timer() - start_time_LF
print("\n\t N-R converged in {0} s".format(time_LF))
if not success:
print("\n powerflow did not converge")
# ####
# Direct load flow solution (a matrix back/fwd sweep)
# ####
start_time = timeit.default_timer()
V_DDLF = run_ddlf(ppc, epsilon=1.e-5)
time_DDLF = timeit.default_timer() - start_time
print("\n\t Direct PF converged in {0} s".format(time_DDLF))
@author: saur
'''
from cases_second import cases
from pypower.api import ppoption, runopf, runpf
from numpy import linspace
from scipy import array,ones , zeros
import matplotlib.pyplot as plt
from scipy.stats import linregress
time = 72
test_object = cases(time)
#test_object.set_base(time)
result , y = runpf(test_object.ppc,test_object.ppopt)
power = linspace(0,25,num=25)
slopes = zeros(6)
power_data_matrix = zeros((25,6))
voltage_data_matrix = zeros((25,6))
for index in range(2,8):
count = 0
voltages = zeros(25)
powers = zeros(25)
for p in power:
test_object = cases(time)
test_object.ppc["gen"][index-1,1] = - p /1000.0
result , y = runpf(test_object.ppc,test_object.ppopt)
#print result
power_data_matrix[count, index - 2] = p
ppc['bus'][:, 13] = opf_bus[:, 13] # set the lmp
ppc['gen'][:, 1] = opf_gen[:, 1] # set the economic dispatch
# add the actual scaled GridLAB-D loads to the baseline curve loads, turn off dispatchable loads
for row in fncs_bus:
busnum = int(row[0])
gld_scale = float(row[2])
Pgld = gld_load[busnum]['p'] * gld_scale
Qgld = gld_load[busnum]['q'] * gld_scale
ppc['bus'][busnum - 1, 2] = gld_load[busnum]['pcrv'] + Pgld
ppc['bus'][busnum - 1, 3] = gld_load[busnum]['qcrv'] + Qgld
genidx = gld_load[busnum]['genidx']
ppc['gen'][genidx, 1] = 0 # p
ppc['gen'][genidx, 2] = 0 # q
ppc['gen'][genidx, 9] = 0 # pmin
# print_gld_load (ppc, gld_load, 'RPF', ts)
rpf = pp.runpf(ppc, ppopt_regular)
if rpf[0]['success'] == False:
conv_accum = False
print('rpf did not converge at', ts)
# pp.printpf (100.0,
# bus=rpf[0]['bus'],
# gen=rpf[0]['gen'],
# branch=rpf[0]['branch'],
# fd=sys.stdout,
# et=rpf[0]['et'],
# success=rpf[0]['success'])
bus = rpf[0]['bus']
gen = rpf[0]['gen']
fncsBus = ppc['FNCS']
Pload = bus[:, 2].sum()
Pgen = gen[:, 1].sum()
if (time_pf[x] == time_opf[k]):
results_opf = runopf(ppc, ppopt)
if (results_opf['success']):
ppc['bus'] = results_opf['bus']
ppc['gen'] = results_opf['gen']
if (k == 0):
LMP_solved = results_opf['bus'][:, 13]
else:
LMP_solved = numpy.vstack(
(LMP_solved, results_opf['bus'][:, 13]))
opf_time = time_opf[0:k + 1] / 3600
k = k + 1
################################ Running PF For optimal power flow intervals ##############################
solved_pf = runpf(ppc, ppopt)
results_pf = solved_pf[0]
ppc['bus'] = results_pf['bus']
ppc['gen'] = results_pf['gen']
if (results_pf['success'] == 1):
if (x == 0):
voltages = results_pf['bus'][:, 7]
real_demand = results_pf['bus'][:, 2]
distribution_load = [rload / 1000000]
else:
voltages = numpy.vstack((voltages, results_pf['bus'][:, 7]))
real_demand = numpy.vstack((real_demand, results_pf['bus'][:,
2]))
distribution_load.append(rload / 1000000)
pf_time = time_pf[0:x + 1] / 3600
def mainJAPowerFlow(baseMVAName, busName, genName, branchName, splitCharacter,
outputBusName, outputBranchName, outputGenName, optimal,
printOutput, areasName, genCostName):
#Variables (by for testing)
#baseMVAName = "baseMVA.txt"
#busName = "bus.txt"
#genName = "gen.txt"
#branchName = "branch.txt"
#splitCharacter = ' '
#outputBusName = "outputBus.txt"
#outputBranchName = "outputBranch.txt"
#outputBranchName = "outputGen.txt"
#optimal = 0 #optimal = 0 or 1, 0 for power flow, 1 for optimal power flow.
#printOutput = 0 #printOutput = 0 or 1, 0 for no stdout printed output, 1 if it is wanted. Note that both still output to the text files.
#areasName = "areas.txt"
#genCostName = "genCost.txt"
#Assign ppc
ppc = readText(baseMVAName, busName, genName, branchName, splitCharacter,
optimal, areasName, genCostName)
#ppc = casetest()
#Set pf test type
#ppopt = ppoption(PF_ALG=1) #Includes printing output (of standard pf)
#ppopt = ppoption(OUT_ALL=0, VERBOSE=0) #These options prevent printing output
#ppopt = ppoption(PF_ALG=1, OUT_ALL=0, VERBOSE=0)
if (printOutput == 1):
ppopt = ppoption(OUT_ALL=1, VERBOSE=1)
elif (printOutput == 0):
ppopt = ppoption(OUT_ALL=0, VERBOSE=0)
else:
print(
"printOutput must be 0 or 1, 0 for no stdout printed output, and 1 if that is desired. Both still output to text files."
)
#Run pf or opf test
if (optimal == 0):
r = runpf(ppc, ppopt)
elif (optimal == 1):
r = runopf(ppc, ppopt)
else:
print("optimal must be 0 or 1, 0 for pf and 1 for opf.")
#Now clear the output files (method of writing to them might be altered, so doing this is to be sure
open(outputBusName, 'w').close()
open(outputBranchName, 'w').close()
open(outputGenName, 'w').close()
#For Optimal Power Flow
if (optimal == 1):
#Establish lengths
busCount = len(r['bus'])
branchCount = len(r['branch'])
genCount = len(r['gen'])
#Find Generator Per Bus Output
busGenP = numpy.zeros(busCount, dtype=numpy.float)
busGenQ = numpy.zeros(busCount, dtype=numpy.float)
f = open(outputGenName, 'w')
i = 0
while (i < genCount):
#For Gen Output
f.write(
str(i + 1) + splitCharacter + str(r['gen'][i][1]) +
splitCharacter + str(r['gen'][i][2]) + '\n')
#For Bus Output
busGenP[int(r['gen'][i][0])] += r['gen'][i][1]
busGenQ[int(r['gen'][i][0])] += r['gen'][i][2]
i += 1
f.close()
#Print Bus Output
f = open(outputBusName, 'w')
i = 0
while (i < busCount):
f.write(
str(i + 1) + splitCharacter + str(r['bus'][i][7]) +
splitCharacter + str(r['bus'][i][8]) + splitCharacter +
str(busGenP[i]) + splitCharacter + str(busGenQ[i]) +
splitCharacter + str(r['bus'][i][2]) + splitCharacter +
str(r['bus'][i][3]) + '\n')
i += 1
f.close()
#Print Branch Output
f = open(outputBranchName, 'w')
i = 0
while (i < branchCount):
f.write(
str(i + 1) + splitCharacter +
str(absDiff(r['branch'][i][15], r['branch'][i][13])) +
splitCharacter +
str(absDiff(r['branch'][i][16], r['branch'][i][14])) + '\n')
i += 1
f.close()
#For Standard Power Flow
elif (optimal == 0):
#Establish lengths
busCount = len(r[0]['bus'])
branchCount = len(r[0]['branch'])
#print(branchCount)
genCount = len(r[0]['gen'])
#Find Generator Per Bus Output
busGenP = numpy.zeros(busCount, dtype=numpy.float)
busGenQ = numpy.zeros(busCount, dtype=numpy.float)
f = open(outputGenName, 'w')
i = 0
while (i < genCount):
#For Gen Output
f.write(
str(i + 1) + splitCharacter + str(r[0]['gen'][i][1]) +
splitCharacter + str(r[0]['gen'][i][2]) + '\n')
#For Bus Output
busGenP[int(r[0]['gen'][i][0])] += r[0]['gen'][i][1]
busGenQ[int(r[0]['gen'][i][0])] += r[0]['gen'][i][2]
i += 1
f.close()
#Print Bus Output
f = open(outputBusName, 'w')
i = 0
while (i < busCount):
f.write(
str(i + 1) + splitCharacter + str(r[0]['bus'][i][7]) +
splitCharacter + str(r[0]['bus'][i][8]) + splitCharacter +
str(busGenP[i]) + splitCharacter + str(busGenQ[i]) +
splitCharacter + str(r[0]['bus'][i][2]) + splitCharacter +
str(r[0]['bus'][i][3]) + '\n')
i += 1
f.close()
#Print Branch Output
f = open(outputBranchName, 'w')
i = 0
while (i < branchCount):
f.write(
str(i + 1) + splitCharacter +
str(absDiff(r[0]['branch'][i][15], r[0]['branch'][i][13])) +
splitCharacter +
str(absDiff(r[0]['branch'][i][16], r[0]['branch'][i][14])) +
'\n')
i += 1
f.close()
else:
print("optimal must be 0 or 1, 0 for pf and 1 for opf.")
def pypower_loop (casefile, rootname):
""" Public function to start PYPOWER solutions under control of FNCS
The time step, maximum time, and other data must be set up in a JSON file.
This function will run the case under FNCS, manage the FNCS message traffic,
and shutdown FNCS upon completion. Five files are written:
- *rootname.csv*; intermediate solution results during simulation
- *rootname_m_dict.json*; metadata for post-processing
- *bus_rootname_metrics.json*; bus metrics for GridLAB-D connections, upon completion
- *gen_rootname_metrics.json*; bulk system generator metrics, upon completion
- *sys_rootname_metrics.json*; bulk system-level metrics, upon completion
Args:
casefile (str): the configuring JSON file name, without extension
rootname (str): the root filename for metrics output, without extension
"""
# if len(sys.argv) == 3:
# rootname = sys.argv[1]
# casefile = sys.argv[2]
# else:
# print ('usage: python fncsPYPOWER.py metrics_rootname casedata.json')
# sys.exit()
ppc = load_json_case (casefile)
StartTime = ppc['StartTime']
tmax = int(ppc['Tmax'])
period = int(ppc['Period'])
dt = int(ppc['dt'])
make_dictionary (ppc, rootname)
bus_mp = open ("bus_" + rootname + "_metrics.json", "w")
gen_mp = open ("gen_" + rootname + "_metrics.json", "w")
sys_mp = open ("sys_" + rootname + "_metrics.json", "w")
bus_meta = {'LMP_P':{'units':'USD/kwh','index':0},'LMP_Q':{'units':'USD/kvarh','index':1},
'PD':{'units':'MW','index':2},'QD':{'units':'MVAR','index':3},'Vang':{'units':'deg','index':4},
'Vmag':{'units':'pu','index':5},'Vmax':{'units':'pu','index':6},'Vmin':{'units':'pu','index':7}}
gen_meta = {'Pgen':{'units':'MW','index':0},'Qgen':{'units':'MVAR','index':1},'LMP_P':{'units':'USD/kwh','index':2}}
sys_meta = {'Ploss':{'units':'MW','index':0},'Converged':{'units':'true/false','index':1}}
bus_metrics = {'Metadata':bus_meta,'StartTime':StartTime}
gen_metrics = {'Metadata':gen_meta,'StartTime':StartTime}
sys_metrics = {'Metadata':sys_meta,'StartTime':StartTime}
gencost = ppc['gencost']
fncsBus = ppc['FNCS']
gen = ppc['gen']
ppopt_market = pp.ppoption(VERBOSE=0, OUT_ALL=0, PF_DC=ppc['opf_dc'])
ppopt_regular = pp.ppoption(VERBOSE=0, OUT_ALL=0, PF_DC=ppc['pf_dc'])
loads = np.loadtxt(ppc['CSVFile'], delimiter=',')
for row in ppc['UnitsOut']:
print ('unit ', row[0], 'off from', row[1], 'to', row[2], flush=True)
for row in ppc['BranchesOut']:
print ('branch', row[0], 'out from', row[1], 'to', row[2], flush=True)
nloads = loads.shape[0]
ts = 0
tnext_opf = -dt
# initializing for metrics collection
tnext_metrics = 0
loss_accum = 0
conv_accum = True
n_accum = 0
bus_accum = {}
gen_accum = {}
for i in range (fncsBus.shape[0]):
busnum = int(fncsBus[i,0])
bus_accum[str(busnum)] = [0,0,0,0,0,0,0,99999.0]
for i in range (gen.shape[0]):
gen_accum[str(i+1)] = [0,0,0]
op = open (rootname + '.csv', 'w')
print ('t[s],Converged,Pload,P7 (csv),Unresp (opf),P7 (rpf),Resp (opf),GLD Pub,BID?,P7 Min,V7,LMP_P7,LMP_Q7,Pgen1,Pgen2,Pgen3,Pgen4,Pdisp,Deg,c2,c1', file=op, flush=True)
fncs.initialize()
# transactive load components
csv_load = 0 # from the file
unresp = 0 # unresponsive load estimate from the auction agent
resp = 0 # will be the responsive load as dispatched by OPF
resp_deg = 0 # RESPONSIVE_DEG from FNCS
resp_c1 = 0 # RESPONSIVE_C1 from FNCS
resp_c2 = 0 # RESPONSIVE_C2 from FNCS
resp_max = 0 # RESPONSIVE_MAX_MW from FNCS
feeder_load = 0 # amplified feeder MW
while ts <= tmax:
# start by getting the latest inputs from GridLAB-D and the auction
events = fncs.get_events()
new_bid = False
load_scale = float (fncsBus[0][2])
for topic in events:
value = fncs.get_value(topic)
if topic == 'UNRESPONSIVE_MW':
unresp = load_scale * float(value)
fncsBus[0][3] = unresp # to poke unresponsive estimate into the bus load slot
new_bid = True
elif topic == 'RESPONSIVE_MAX_MW':
resp_max = load_scale * float(value)
new_bid = True
elif topic == 'RESPONSIVE_C2':
resp_c2 = float(value) / load_scale
new_bid = True
elif topic == 'RESPONSIVE_C1':
resp_c1 = float(value)
new_bid = True
elif topic == 'RESPONSIVE_DEG':
resp_deg = int(value)
new_bid = True
else:
gld_load = parse_mva (value) # actual value, may not match unresp + resp load
feeder_load = float(gld_load[0]) * load_scale
if new_bid == True:
dummy = 2
# print('**Bid', ts, unresp, resp_max, resp_deg, resp_c2, resp_c1)
# update the case for bids, outages and CSV loads
idx = int ((ts + dt) / period) % nloads
bus = ppc['bus']
gen = ppc['gen']
branch = ppc['branch']
gencost = ppc['gencost']
csv_load = loads[idx,0]
bus[4,2] = loads[idx,1]
bus[8,2] = loads[idx,2]
# process the generator and branch outages
for row in ppc['UnitsOut']:
if ts >= row[1] and ts <= row[2]:
gen[row[0],7] = 0
else:
gen[row[0],7] = 1
for row in ppc['BranchesOut']:
if ts >= row[1] and ts <= row[2]:
branch[row[0],10] = 0
else:
branch[row[0],10] = 1
if resp_deg == 2:
gencost[4][3] = 3
gencost[4][4] = -resp_c2
gencost[4][5] = resp_c1
elif resp_deg == 1:
gencost[4][3] = 2
gencost[4][4] = resp_c1
gencost[4][5] = 0.0
else:
gencost[4][3] = 1
gencost[4][4] = 999.0
gencost[4][5] = 0.0
gencost[4][6] = 0.0
if ts >= tnext_opf: # expecting to solve opf one dt before the market clearing period ends, so GridLAB-D has time to use it
# for OPF, the FNCS bus load is CSV + Unresponsive estimate, with Responsive separately dispatchable
bus = ppc['bus']
gen = ppc['gen']
bus[6,2] = csv_load
for row in ppc['FNCS']:
unresp = float(row[3])
newidx = int(row[0]) - 1
if unresp >= feeder_load:
bus[newidx,2] += unresp
else:
bus[newidx,2] += feeder_load
gen[4][9] = -resp_max
res = pp.runopf(ppc, ppopt_market)
if res['success'] == False:
conv_accum = False
opf_bus = deepcopy (res['bus'])
opf_gen = deepcopy (res['gen'])
lmp = opf_bus[6,13]
resp = -1.0 * opf_gen[4,1]
fncs.publish('LMP_B7', 0.001 * lmp) # publishing $/kwh
# print (' OPF', ts, csv_load, '{:.3f}'.format(unresp), '{:.3f}'.format(resp),
# '{:.3f}'.format(feeder_load), '{:.3f}'.format(opf_bus[6,2]),
# '{:.3f}'.format(opf_gen[0,1]), '{:.3f}'.format(opf_gen[1,1]), '{:.3f}'.format(opf_gen[2,1]),
# '{:.3f}'.format(opf_gen[3,1]), '{:.3f}'.format(opf_gen[4,1]), '{:.3f}'.format(lmp))
# if unit 2 (the normal swing bus) is dispatched at max, change the swing bus to 9
if opf_gen[1,1] >= 191.0:
ppc['bus'][1,1] = 2
ppc['bus'][8,1] = 3
print (' SWING Bus 9')
else:
ppc['bus'][1,1] = 3
ppc['bus'][8,1] = 1
print (' SWING Bus 2')
tnext_opf += period
# always update the electrical quantities with a regular power flow
bus = ppc['bus']
gen = ppc['gen']
bus[6,13] = lmp
gen[0,1] = opf_gen[0, 1]
gen[1,1] = opf_gen[1, 1]
gen[2,1] = opf_gen[2, 1]
gen[3,1] = opf_gen[3, 1]
# during regular power flow, we use the actual CSV + feeder load, ignore dispatchable load and use actual
bus[6,2] = csv_load + feeder_load
gen[4,1] = 0 # opf_gen[4, 1]
gen[4,9] = 0
rpf = pp.runpf(ppc, ppopt_regular)
if rpf[0]['success'] == False:
conv_accum = False
bus = rpf[0]['bus']
gen = rpf[0]['gen']
Pload = bus[:,2].sum()
Pgen = gen[:,1].sum()
Ploss = Pgen - Pload
# update the metrics
n_accum += 1
loss_accum += Ploss
for i in range (fncsBus.shape[0]):
busnum = int(fncsBus[i,0])
busidx = busnum - 1
row = bus[busidx].tolist()
# LMP_P, LMP_Q, PD, QD, Vang, Vmag, Vmax, Vmin: row[11] and row[12] are Vmax and Vmin constraints
PD = row[2] + resp # the ERCOT version shows how to track scaled_resp separately for each FNCS bus
Vpu = row[7]
bus_accum[str(busnum)][0] += row[13]*0.001
bus_accum[str(busnum)][1] += row[14]*0.001
bus_accum[str(busnum)][2] += PD
bus_accum[str(busnum)][3] += row[3]
bus_accum[str(busnum)][4] += row[8]
bus_accum[str(busnum)][5] += Vpu
if Vpu > bus_accum[str(busnum)][6]:
bus_accum[str(busnum)][6] = Vpu
if Vpu < bus_accum[str(busnum)][7]:
bus_accum[str(busnum)][7] = Vpu
for i in range (gen.shape[0]):
row = gen[i].tolist()
busidx = int(row[0] - 1)
# Pgen, Qgen, LMP_P (includes the responsive load as dispatched by OPF)
gen_accum[str(i+1)][0] += row[1]
gen_accum[str(i+1)][1] += row[2]
gen_accum[str(i+1)][2] += float(opf_bus[busidx,13])*0.001
# write the metrics
if ts >= tnext_metrics:
sys_metrics[str(ts)] = {rootname:[loss_accum / n_accum,conv_accum]}
bus_metrics[str(ts)] = {}
for i in range (fncsBus.shape[0]):
busnum = int(fncsBus[i,0])
busidx = busnum - 1
row = bus[busidx].tolist()
met = bus_accum[str(busnum)]
bus_metrics[str(ts)][str(busnum)] = [met[0]/n_accum, met[1]/n_accum, met[2]/n_accum, met[3]/n_accum,
met[4]/n_accum, met[5]/n_accum, met[6], met[7]]
bus_accum[str(busnum)] = [0,0,0,0,0,0,0,99999.0]
gen_metrics[str(ts)] = {}
for i in range (gen.shape[0]):
met = gen_accum[str(i+1)]
gen_metrics[str(ts)][str(i+1)] = [met[0]/n_accum, met[1]/n_accum, met[2]/n_accum]
gen_accum[str(i+1)] = [0,0,0]
tnext_metrics += period
n_accum = 0
loss_accum = 0
conv_accum = True
volts = 1000.0 * bus[6,7] * bus[6,9] / sqrt(3.0) # VLN for GridLAB-D
fncs.publish('three_phase_voltage_B7', volts)
# CSV file output
print (ts, res['success'],
'{:.3f}'.format(Pload), # Pload
'{:.3f}'.format(csv_load), # P7 (csv)
'{:.3f}'.format(unresp), # GLD Unresp
'{:.3f}'.format(bus[6,2]), # P7 (rpf)
'{:.3f}'.format(resp), # Resp (opf)
'{:.3f}'.format(feeder_load), # GLD Pub
new_bid,
'{:.3f}'.format(gen[4,9]), # P7 Min
'{:.3f}'.format(bus[6,7]), # V7
'{:.3f}'.format(bus[6,13]), # LMP_P7
'{:.3f}'.format(bus[6,14]), # LMP_Q7
'{:.2f}'.format(gen[0,1]), # Pgen1
'{:.2f}'.format(gen[1,1]), # Pgen2
'{:.2f}'.format(gen[2,1]), # Pgen3
'{:.2f}'.format(gen[3,1]), # Pgen4
'{:.2f}'.format(res['gen'][4, 1]), # Pdisp
'{:.4f}'.format(resp_deg), # degree
'{:.8f}'.format(ppc['gencost'][4, 4]), # c2
'{:.8f}'.format(ppc['gencost'][4, 5]), # c1
sep=',', file=op, flush=True)
# request the next time step, if necessary
if ts >= tmax:
print ('breaking out at',ts,flush=True)
break
ts = fncs.time_request(min(ts + dt, tmax))
# ===================================
print ('writing metrics', flush=True)
print (json.dumps(sys_metrics), file=sys_mp, flush=True)
print (json.dumps(bus_metrics), file=bus_mp, flush=True)
print (json.dumps(gen_metrics), file=gen_mp, flush=True)
print ('closing files', flush=True)
bus_mp.close()
gen_mp.close()
sys_mp.close()
op.close()
print ('finalizing FNCS', flush=True)
fncs.finalize()
if sys.platform != 'win32':
usage = resource.getrusage(resource.RUSAGE_SELF)
RESOURCES = [
('ru_utime', 'User time'),
('ru_stime', 'System time'),
('ru_maxrss', 'Max. Resident Set Size'),
('ru_ixrss', 'Shared Memory Size'),
('ru_idrss', 'Unshared Memory Size'),
('ru_isrss', 'Stack Size'),
('ru_inblock', 'Block inputs'),
('ru_oublock', 'Block outputs')]
print('Resource usage:')
for name, desc in RESOURCES:
print(' {:<25} ({:<10}) = {}'.format(desc, name, getattr(usage, name)))
def mainJAPowerFlow(baseMVAName, busName, genName, branchName, splitCharacter,
outputBusName, outputBranchName, outputGenName,
printOutput, optimal, areasName, genCostName):
# Variables (by for testing)
# baseMVAName = "baseMVA.txt"
# busName = "bus.txt"
# genName = "gen.txt"
# branchName = "branch.txt"
# splitCharacter = ' '
# outputBusName = "outputBus.txt"
# outputBranchName = "outputBranch.txt"
# outputBranchName = "outputGen.txt"
# printOutput = 0
##### printOutput = 0 or 1, 0 for no stdout printed output, 1 if it is wanted.
##### Note that both still output to the text files.
# optimal = 0
##### optimal = 0 or 1, 0 for power flow, 1 for optimal power flow.
##### NOTE: All following inputs are only used in optimal power flow, OPF, analysis (optimal = 1),
##### but some values are still required as inputs, even if they are not used in the event of PF (optimal = 0).
# areasName = "areas.txt"
# genCostName = "genCost.txt"
# Assign ppc
ppc = readText(baseMVAName, busName, genName, branchName, splitCharacter,
optimal, areasName, genCostName)
#ppc = casetest()
# Set pf test type
# ppopt = ppoption(PF_ALG=1) ---> Power Flow algorithm: 1- NR Method
# ppopt = ppoption(OUT_ALL=0, VERBOSE=0) ---> These options prevent printing output
# ppopt = ppoption(PF_ALG=1, OUT_ALL=0, VERBOSE=0)
if (printOutput == 1):
ppopt = ppoption(OUT_ALL=1, VERBOSE=1)
elif (printOutput == 0):
ppopt = ppoption(OUT_ALL=0, VERBOSE=0)
else:
print(
"printOutput must be 0 or 1, 0 for no stdout printed output, and 1 if that is desired. Both still output to text files."
)
# (A) --pf_alg = PF_ALG # power flow algorithm :
# 1 - Newton’s method,
# 2 - FastDecoupled (XB version),
# 3 - Fast-Decoupled (BX
# version),
# 4 - Gauss Seidel
# [default: 1]
# (B) --verbose = VERBOSE # amount of progress info printed:
# 0 - print no progress info,
# 1 - print a little progress info,
# 2 - print a lot of progress info,
# 3 - print all progress info
# [default: 1]
# (C) --out_all = OUT_ALL # controls printing of results:
# -1 - individual flags control what prints,
# 0 - don’t print anything
# (overrides individual flags, except OUT_RAW),
# 1 - print everything (overrides individual flags, except OUT_RAW)
# [default: -1]
# Run pf or opf test
if (optimal == 0):
r = runpf(ppc, ppopt)
elif (optimal == 1):
r = runopf(ppc, ppopt)
else:
print("optimal must be 0 or 1, 0 for pf and 1 for opf.")
# Check if voltage constraints are met?
# Check if solution converged?
if (optimal == 1):
genCount = len(r['gen']) #check no. of generators = 16
busCount = len(r['bus']) #check no. of buses = 15
branchCount = len(r['branch']) #check no. of branches = 25
print("\nNo. of gen= ", genCount)
print("\nbus= ", busCount)
print("\nbranch=", branchCount)
busGenP = numpy.zeros(
genCount, dtype=numpy.float
) #create a zero column vector (16x1) for P generation
busLoadP = numpy.zeros(
busCount,
dtype=numpy.float) #create a zero column vector (15x1) for P load
branchLoss = numpy.zeros(
branchCount, dtype=numpy.float
) #create a zero column vector (25x1) for branch losses
i = 0
while (i < genCount):
#busGenP[int(r['gen'][i][1])] += r['gen'][i][1]
busGenP[i] += r['gen'][i][1]
i += 1
j = 0
while (j < busCount):
busLoadP[j] += r['bus'][j][2]
j += 1
k = 0
while (k < branchCount):
branchLoss[k] += absDiff(r['branch'][k][15], r['branch'][k][13])
k += 1
# print("\nReal power generated= \n",busGenP)
# print("\nReal power demand= \n", busLoadP)
# print ("\nBranch Losses= \n", branchLoss)
sumGen = round(busGenP.sum(), 2) # round off up to 2 decimal points.
sumLoad = round(busLoadP.sum(), 2)
sumLoss = round(branchLoss.sum(), 2)
print("\n")
print("\nTotal Real Power Generation= ", sumGen)
print("\nTotal Real Power Load= ", sumLoad)
print("\nTotal Real Power Losses= ", sumLoss)
print("\n")
# Clears the output files (method of writing to them might be altered, so doing this is to be sure
open(outputBusName, 'w').close()
open(outputBranchName, 'w').close()
open(outputGenName, 'w').close()
# For Optimal Power Flow
#Establish lengths
busCount = len(r['bus'])
branchCount = len(r['branch'])
genCount = len(r['gen'])
#Find Generator Per Bus Output
busGenP = numpy.zeros(busCount, dtype=numpy.float)
busGenQ = numpy.zeros(busCount, dtype=numpy.float)
f = open(outputGenName, 'w')
i = 0
while (i < genCount):
#Print Gen Output
f.write(
str(i + 1) + splitCharacter + str(r['gen'][i][1]) +
splitCharacter + str(r['gen'][i][2]) + '\n')
#For Bus Output --> assign values for busGenP and busGenQ
busGenP[int(r['gen'][i][0])] += r['gen'][i][1]
busGenQ[int(r['gen'][i][0])] += r['gen'][i][2]
i += 1
f.close()
#Print Bus Output
f = open(outputBusName, 'w')
i = 0
while (i < busCount):
f.write(
str(i + 1) + splitCharacter + str(r['bus'][i][7]) +
splitCharacter + str(r['bus'][i][8]) + splitCharacter +
str(busGenP[i]) + splitCharacter + str(busGenQ[i]) +
splitCharacter + str(r['bus'][i][2]) + splitCharacter +
str(r['bus'][i][3]) + '\n')
i += 1
f.close()
#Print Branch Output
f = open(outputBranchName, 'w')
i = 0
while (i < branchCount):
#P, Q and S (average)
PAve = (r['branch'][i][15] + r['branch'][i][13]) / 2.0
QAve = (r['branch'][i][14] + r['branch'][i][16]) / 2.0
SAve = numpy.sqrt(((PAve * PAve) + (QAve * QAve)))
f.write(
str(i + 1) + splitCharacter +
str(absDiff(r['branch'][i][15], r['branch'][i][13])) +
splitCharacter +
str(absDiff(r['branch'][i][16], r['branch'][i][14])) +
splitCharacter + str(PAve) + splitCharacter + str(QAve) +
splitCharacter + str(SAve) + '\n')
i += 1
f.close()
'''
Created on Mar 16, 2015
@author: saur
'''
from cases_alternative_model import cases
from agent import agent
from pypower.api import ppoption, runopf, runpf
min_value_voltage = 0.99
for time in range(0, 96):
testcase = cases(time)
testcase.set_base(time)
result, y = runpf(testcase.ppc, testcase.ppopt)
print min(result["bus"][:, 7])
if min(result["bus"][:, 7]) < min_value_voltage:
min_value_voltage = min(result["bus"][:, 7])
print min_value_voltage
"""
#create the agents
SOC_initial = 5
agents = dict()
for k in range(2,26):
agents[k]= agent(SOC_initial,k,time_init)
##here is what has to be repeated over and over again.
## each agent does something to the environment
testcase.set_base(time_init)
#for i in agents:
# agents[i].do_interaction(testcase)
from pypower.api import case14, ppoption, runpf, printpf ppc = case14() ppopt = ppoption(PF_ALG=2) r = runpf(ppc,ppopt) printpf(r)
def step(self, action):
action = self.actmap[action] # transfer act
if (action > 0):
num = self.genmap[int(action / 4)] # gen number
move = int(action % 4) # type of move
reward = 0 # Default reward and done state
done = False
if action == -1:
self.ppc['gen'][self.genmap[self.vbus]][5] += VUNIT
elif action == -2:
self.ppc['gen'][self.genmap[self.vbus]][5] -= VUNIT
elif move == 0: # P up by UNIT
self.ppc['gen'][num][1] += PUNIT
elif move == 1: # P down by UNIT
self.ppc['gen'][num][1] -= PUNIT
elif move == 2 and self.ppc['bus'][int(action / 4)][1] == 2:
self.ppc['gen'][num][5] += VUNIT
elif move == 2 and self.ppc['bus'][int(action / 4)][1] == 1:
self.ppc['gen'][1][2] += QUNIT
elif move == 3 and self.ppc['bus'][int(action / 4)][1] == 2:
self.ppc['gen'][num][5] -= VUNIT
elif move == 3 and self.ppc['bus'][int(action / 4)][1] == 1:
self.ppc['gen'][1][2] -= QUNIT
# limit PQ of generator
for g in self.ppc['gen']:
if g[1] > g[8] > 0:
g[1] = g[8]
elif g[1] < g[9]:
g[1] = g[9]
elif g[2] > g[3]:
g[2] = g[3]
elif g[2] < g[4]:
g[2] = g[4]
# randomnize the output of uncontrolled generator
# for g in self.ppc['gen']:
# i=int(g[0])
# if i in DICT1.keys():
# g[1] += 10*np.random.rand()-5
#Use pypower to calculate pf
self.results, self.success = runpf(self.ppc, self.ppopt)
#get the next state
s_ = self.getstate()
#get observation
self.observation = self.getobservation()
# reward function
# if voltage is out of range, reward = -1
if s_['Vmin'] <= LOW or s_['Vmax'] >= HIGH:
reward = 0
done = True
# if loss is lower than before, reward = 1
elif s_['loss'] < self.state['loss']:
reward = 1
done = False
self.state = s_
return self.observation, reward, done
def test_pypower_case():
#ppopt is a dictionary with the details of the optimization routine to run
ppopt = ppoption(PF_ALG=2)
#choose DC or AC
ppopt["PF_DC"] = False
#ppc is a dictionary with details about the network, including baseMVA, branches and generators
ppc = case()
results, success = runpf(ppc, ppopt)
#store results in a DataFrame for easy access
results_df = {}
#branches
columns = 'bus0, bus1, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax, p0, q0, p1, q1'.split(
", ")
results_df['branch'] = pd.DataFrame(data=results["branch"],
columns=columns)
#buses
columns = [
"bus", "type", "Pd", "Qd", "Gs", "Bs", "area", "v_mag_pu", "v_ang",
"v_nom", "zone", "Vmax", "Vmin"
]
results_df['bus'] = pd.DataFrame(data=results["bus"],
columns=columns,
index=results["bus"][:, 0])
#generators
columns = "bus, p, q, q_max, q_min, Vg, mBase, status, p_max, p_min, Pc1, Pc2, Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf".split(
", ")
results_df['gen'] = pd.DataFrame(data=results["gen"], columns=columns)
#now compute in PyPSA
network = pypsa.Network()
network.import_from_pypower_ppc(ppc)
#PYPOWER uses PI model for transformers, whereas PyPSA defaults to
#T since version 0.8.0
network.transformers.model = "pi"
network.pf()
#compare branch flows
for c in network.iterate_components(network.passive_branch_components):
for si in ["p0", "p1", "q0", "q1"]:
si_pypsa = getattr(c.pnl, si).loc["now"].values
si_pypower = results_df['branch'][si][c.df.original_index].values
equal(si_pypsa, si_pypower)
#compare generator dispatch
for s in ["p", "q"]:
s_pypsa = getattr(network.generators_t, s).loc["now"].values
s_pypower = results_df["gen"][s].values
equal(s_pypsa, s_pypower)
#compare voltages
v_mag_pypsa = network.buses_t.v_mag_pu.loc["now"]
v_mag_pypower = results_df["bus"]["v_mag_pu"]
equal(v_mag_pypsa, v_mag_pypower)
v_ang_pypsa = network.buses_t.v_ang.loc["now"]
pypower_slack_angle = results_df["bus"]["v_ang"][results_df["bus"]["type"]
== 3].values[0]
v_ang_pypower = (results_df["bus"]["v_ang"] -
pypower_slack_angle) * np.pi / 180.
equal(v_ang_pypsa, v_ang_pypower)
def get_voltage(self, node):
#returns the voltage at specific node after running power flow simulation
ppc_result, y = runpf(self.ppc, self.ppopt)
#print ppc_result["bus"][node,7]
return ppc_result["bus"][node, 7]
def simulate_data(Sm, gen=None, PVdata=None, PV_idx=None, verbose=0):
"""
Simulate data using power flow analysis
:param PVgen: data from PV generation injected at bus 6
:return: dict
"""
from scipy.io import loadmat
S = Sm.copy()
t_ges = 1440
delta_t = 15
time = np.arange(delta_t, t_ges + delta_t, delta_t)
t_f = len(time)
bus_var = np.arange(2, 13, 1) # buses that are varied
v_mag = np.zeros((13, t_f))
v_ang = np.zeros((13, t_f))
P = np.zeros((13, t_f))
Q = np.zeros((13, t_f))
loadP = np.zeros((13, t_f))
loadQ = np.zeros((13, t_f))
genP_all = np.zeros((2, t_f))
genQ_all = np.zeros((2, t_f))
P_into_00 = np.zeros(t_f)
Q_into_00 = np.zeros(t_f)
if (len(bus_var) == S.shape[0]):
Pdata = np.real(S)
Qdata = np.imag(S)
elif (2 * len(bus_var) == S.shape[0]):
Pdata = S[:S.shape[0] / 2, :]
Qdata = S[S.shape[0] / 2:, :]
else:
raise ValueError("Powers have wrong dimension.")
if isinstance(PVdata, np.ndarray):
if len(PVdata.shape) == 2: # P and Q for PVgen
if not PVdata.shape[0] == 2:
PVdata = PVdata.T
Pdata[PV_idx, :] = -PVdata[0, :] + Pdata[PV_idx, :]
Qdata[PV_idx, :] = -PVdata[1, :] + Qdata[PV_idx, :]
else:
Pdata[PV_idx, :] = -PVdata[:] + Pdata[PV_idx, :] # with MVA
Qdata[PV_idx, :] = np.zeros_like(PVdata)
casedata = get_topology()
for n in range(len(time)):
casedata['bus'][
bus_var, 2] = Pdata[:,
n] #Changing the values for the active power
casedata['bus'][
bus_var, 3] = Qdata[:,
n] #Changing the values for the reactive power
if isinstance(gen, np.ndarray):
casedata['gen'][1, 1] = gen[n]
casedata['gen'][1, 2] = 0
ppopt = ppoption(PF_ALG=2)
ppopt["VERBOSE"] = verbose
resultPF, success = runpf(casedata, ppopt)
if success == 0:
print('ERROR in step %d', n)
slack_ang = resultPF['bus'][1, 8]
v_mag[:, n] = resultPF['bus'][:, 7] # Voltage, magnitude
v_ang[:, n] = resultPF['bus'][:, 8] - slack_ang # Voltage, angle
loadP[:, n] = resultPF['bus'][:, 2]
loadQ[:, n] = resultPF['bus'][:, 3]
genP_all[:, n] = resultPF['gen'][:, 1]
genQ_all[:, n] = resultPF['gen'][:, 2]
P_into_00[n] = -resultPF['branch'][0, 15]
Q_into_00[n] = -resultPF['branch'][0, 16]
loadP[6, :] = loadP[6, :] + genP_all[1, :]
loadQ[6, :] = loadQ[6, :] + genQ_all[1, :]
simdata = dict([])
simdata["Vm"] = (11 / (np.sqrt(3))) * v_mag
simdata["Va"] = v_ang
simdata["Pk"] = -loadP[2:, :]
simdata["Qk"] = -loadQ[2:, :]
simdata["P_00"] = P_into_00
simdata["Q_00"] = Q_into_00
return simdata
def mainJAPowerFlow(baseMVAName, busName, genName, branchName, splitCharacter,
outputBusName, outputBranchName, outputGenName,
printOutput, optimal, areasName, genCostName,
convergedOutputName):
#Variables (by for testing)
#baseMVAName = "baseMVA.txt"
#busName = "bus.txt"
#genName = "gen.txt"
#branchName = "branch.txt"
#splitCharacter = ' '
#outputBusName = "outputBus.txt"
#outputBranchName = "outputBranch.txt"
#outputBranchName = "outputGen.txt"
#printOutput = 0 #printOutput = 0 or 1, 0 for no stdout printed output, 1 if it is wanted. Note that both still output to the text files.
#optimal = 0 #optimal = 0 or 1, 0 for power flow, 1 for optimal power flow. NOTE: All following inputs are only used in optimal power flow, OPF, analysis (optimal = 1), but some values are still required as inputs, even if they are not used in the event of PF (optimal = 0).
#areasName = "areas.txt"
#genCostName = "genCost.txt"
#convergedOutputName = "outputStatus.txt", which will have three lines. The first will just be the inputs variables given (inputs to mainJAPowerFLow). The second of which will state "0" (if it did not converge) or "1" (if it converged), while the third line will state this in text as "Converged!" (if it converged) or "Diverged!" (if it did not). Note that the "'s are not printed, just to show the contents. Also note that the second and third lines show the same information in different formats.
#Assign ppc
ppc = readText(baseMVAName, busName, genName, branchName, splitCharacter,
optimal, areasName, genCostName)
#ppc = casetest()
#Set pf test type
#ppopt = ppoption(PF_ALG=1) #Includes printing output (of standard pf)
#ppopt = ppoption(OUT_ALL=0, VERBOSE=0) #These options prevent printing output (so prints to terminal if 1 and does not if 0)
#ppopt = ppoption(PF_ALG=1, OUT_ALL=0, VERBOSE=0)
if (printOutput == 1):
ppopt = ppoption(OUT_ALL=1, VERBOSE=1)
elif (printOutput == 0):
ppopt = ppoption(OUT_ALL=0, VERBOSE=0)
else:
print(
"printOutput must be 0 or 1, 0 for no stdout printed output, and 1 if that is desired. Both still output to text files."
)
#Run pf or opf test
if (optimal == 0):
r = runpf(ppc, ppopt)
elif (optimal == 1):
r = runopf(ppc, ppopt)
else:
print("optimal must be 0 or 1, 0 for pf and 1 for opf.")
#Output Metadata, which outputs the input variables to the function as well as if the model converged as described in the convergedOutputName description above.
out = open(convergedOutputName, 'w')
out.write(baseMVAName + splitCharacter + busName + splitCharacter +
genName + splitCharacter + branchName + splitCharacter +
splitCharacter + splitCharacter + outputBusName +
splitCharacter + outputBranchName + splitCharacter +
outputGenName + splitCharacter + str(printOutput) +
splitCharacter + str(optimal) + splitCharacter + areasName +
splitCharacter + genCostName + splitCharacter +
convergedOutputName + '\n')
if (optimal == 1):
#If this is OPF the convergence can be found as follows.
if (r['success'] == True):
print("Converged.")
out.write("1\n")
out.write("Converged!\n")
else:
print("Did not converge.")
out.write("0\n")
out.write("Diverged!\n")
elif (optimal == 0):
#For some reason this doesn't work for just PF, so will check in a more crude mannor.
if ("'success': 1" in str(r)):
print("Converged.")
out.write("1\n")
out.write("Converged!\n")
else:
print("Did not converge.")
out.write("0\n")
out.write("Diverged!\n")
out.close()
#Now clear the output files (method of writing to them might be altered, so doing this is to be sure
open(outputBusName, 'w').close()
open(outputBranchName, 'w').close()
open(outputGenName, 'w').close()
#For Optimal Power Flow
if (optimal == 1):
#Establish lengths
busCount = len(r['bus'])
branchCount = len(r['branch'])
genCount = len(r['gen'])
#Find Generator Per Bus Output
busGenP = numpy.zeros(busCount, dtype=numpy.float)
busGenQ = numpy.zeros(busCount, dtype=numpy.float)
f = open(outputGenName, 'w')
i = 0
while (i < genCount):
#For Gen Output
f.write(
str(i + 1) + splitCharacter + str(r['gen'][i][1]) +
splitCharacter + str(r['gen'][i][2]) + '\n')
#For Bus Output
busGenP[int(r['gen'][i][0])] += r['gen'][i][1]
busGenQ[int(r['gen'][i][0])] += r['gen'][i][2]
i += 1
f.close()
#Print Bus Output
f = open(outputBusName, 'w')
i = 0
while (i < busCount):
f.write(
str(i + 1) + splitCharacter + str(r['bus'][i][7]) +
splitCharacter + str(r['bus'][i][8]) + splitCharacter +
str(busGenP[i]) + splitCharacter + str(busGenQ[i]) +
splitCharacter + str(r['bus'][i][2]) + splitCharacter +
str(r['bus'][i][3]) + '\n')
i += 1
f.close()
#Print Branch Output
f = open(outputBranchName, 'w')
i = 0
while (i < branchCount):
PAve = (r['branch'][i][15] + r['branch'][i][13]) / 2.0
QAve = (r['branch'][i][14] + r['branch'][i][16]) / 2.0
SAve = numpy.sqrt(((PAve * PAve) + (QAve * QAve)))
f.write(
str(i + 1) + splitCharacter +
str(absDiff(r['branch'][i][15], r['branch'][i][13])) +
splitCharacter +
str(absDiff(r['branch'][i][16], r['branch'][i][14])) +
splitCharacter + str(PAve) + splitCharacter + str(QAve) +
splitCharacter + str(SAve) + '\n')
i += 1
f.close()
#For Standard Power Flow
elif (optimal == 0):
#Establish lengths
busCount = len(r[0]['bus'])
branchCount = len(r[0]['branch'])
#print(branchCount)
genCount = len(r[0]['gen'])
#Find Generator Per Bus Output
busGenP = numpy.zeros(busCount, dtype=numpy.float)
busGenQ = numpy.zeros(busCount, dtype=numpy.float)
f = open(outputGenName, 'w')
i = 0
while (i < genCount):
#For Gen Output
f.write(
str(i + 1) + splitCharacter + str(r[0]['gen'][i][1]) +
splitCharacter + str(r[0]['gen'][i][2]) + '\n')
#For Bus Output
busGenP[int(r[0]['gen'][i][0])] += r[0]['gen'][i][1]
busGenQ[int(r[0]['gen'][i][0])] += r[0]['gen'][i][2]
i += 1
f.close()
#Print Bus Output
f = open(outputBusName, 'w')
i = 0
while (i < busCount):
f.write(
str(i + 1) + splitCharacter + str(r[0]['bus'][i][7]) +
splitCharacter + str(r[0]['bus'][i][8]) + splitCharacter +
str(busGenP[i]) + splitCharacter + str(busGenQ[i]) +
splitCharacter + str(r[0]['bus'][i][2]) + splitCharacter +
str(r[0]['bus'][i][3]) + '\n')
i += 1
f.close()
#Print Branch Output
f = open(outputBranchName, 'w')
i = 0
while (i < branchCount):
#P, Q and S (average)
PAve = (r[0]['branch'][i][15] + r[0]['branch'][i][13]) / 2.0
QAve = (r[0]['branch'][i][14] + r[0]['branch'][i][16]) / 2.0
SAve = numpy.sqrt(((PAve * PAve) + (QAve * QAve)))
#Print P loss, Q loss, aveP, aveQ and aveS.
f.write(
str(i + 1) + splitCharacter +
str(absDiff(r[0]['branch'][i][15], r[0]['branch'][i][13])) +
splitCharacter +
str(absDiff(r[0]['branch'][i][16], r[0]['branch'][i][14])) +
splitCharacter + str(PAve) + splitCharacter + str(QAve) +
splitCharacter + str(SAve) + '\n')
i += 1
f.close()
else:
print("optimal must be 0 or 1, 0 for pf and 1 for opf.")
