def __init__(self):
self.action_space = []
self.ppopt = ppoption(VERBOSE=0,
OUT_ALL=0,
OUT_SYS_SUM=False,
OUT_BUS=False,
OUT_BRANCH=False) #output format control
self.state = {'loss': None, 'Vmin': None, 'Vmax': None}
self.bus_list = []
self.genmap = {} #{bus number: gen number}
self.actmap = {} #{0~n_actions: act number}
# assign case
self.ppc = case()
g = nx.Graph()
for bus in self.ppc['bus']:
self.bus_list.append(int(bus[0]))
g.add_node(int(bus[0]))
for brch in self.ppc['branch']:
g.add_edge(int(brch[0]), int(brch[1]))
self.pos = nx.kamada_kawai_layout(g)
red = '#ff0000' #red
green = '#00ff00' #green
blue = '#0000ff' #blue
self.cmap = col.LinearSegmentedColormap.from_list(
'cmap', [blue, green, red])
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 case_iteration():
ppc = mycase()
ppopt = ppoption(PF_ALG=2)
r = runopf(ppc, ppopt)
myprintpf(r)
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 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 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 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 __init__(self): self.ppc = case30() self.ppopt = ppoption(PF_ALG=1, RETURN_RAW_DER=True, OPF_FLOW_LIM=0) self.ppn = case30_panda() self.results = None self.sol_opt = None self.alg = None self.idx_mode = None self.res_struct = None self.load_list = None
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 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 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 proportional_sim(grid, a, attack_set, verbose=False, saveIterations=False):
"""Runs a cascading failure simulation, with capacities proportional to
initial load (i.e. C = (1+a)*L).
See run_simulation() for more details.
"""
ppopt = pp.ppoption(VERBOSE=0, OUT_ALL=0)
initial_grid = pp.rundcpf(grid, ppopt)[0]
capacities = abs(initial_grid['branch'][:, idx_brch.PF]) * (1 + a)
return run_simulation(grid,
capacities,
attack_set,
verbose=verbose,
saveIterations=saveIterations)
def iid_sim(grid, dist, attack_set, verbose=False, saveIterations=False):
"""Runs a cascading failure simulation, with capacities given by C = L + S,
where S is a random variable drawn from a given distribution.
See run_simulation() for more details.
"""
ppopt = pp.ppoption(VERBOSE=0, OUT_ALL=0)
initial_grid = pp.rundcpf(grid, ppopt)[0]
capacities = abs(initial_grid['branch'][:, idx_brch.PF]) + dist()
return run_simulation(grid,
capacities,
attack_set,
verbose=verbose,
saveIterations=saveIterations)
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 dcpf(self, time=0, mpc=None, save2network=True):
""" Run DC power flow """
if type(time) is int:
time = self.flow_modelled.index[time]
if mpc is None:
mpc = self.make_mpc()
mpc = rundcpf(
mpc,
ppoption(VERBOSE=0,
OUT_ALL=-1,
OUT_BUS=0,
OUT_ALL_LIM=0,
OUT_BRANCH=0,
OUT_SYS_SUM=0))
self.solved_mpc.append(mpc)
# AC exchange between areas
br = mpc[0]['branch']
ac_flow = pd.DataFrame(0,
index=[time],
columns=list(self.flow_modelled))
bid0 = arr(self.bus.loc[br[:, 0], 'bidz'])
bid1 = arr(self.bus.loc[br[:, 1], 'bidz'])
for n, a0 in enumerate(self.bidz):
for a1 in self.bidz[n + 1:]:
ind0 = (bid0 == a0) & (bid1 == a1) # find lines between areas
ind1 = (bid0 == a1) & (bid1 == a0
) # -"- but reverse flow direction
if np.sum(ind0) + np.sum(ind1) > 0:
exch = -np.sum(br[ind0, 13]) + np.sum(br[ind1, 13])
ac_flow.loc[time, '%s-%s' % (a0, a1)] = exch
for i in list(ac_flow):
self.flow_modelled.at[time, i] = ac_flow.at[time, i]
if save2network:
self.mpc2network(
) # add parameters to network (flows, voltage angle etc.)
def __init__(self, Time):
'''
Constructs a pypower case and inits a load profile
'''
#data for the power flow
self.Time = Time ## might be a useless variable since the laod profile has been moved to the agent
##options
self.ppopt = ppoption(PF_ALG=2, VERBOSE= 0,OUT_ALL=0)
self.ppc = {"version": '2'}
self.loadprofile = 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])
## system MVA base
self.ppc["baseMVA"] = 0.144
#self.ppc["baseMVA"] = 7
## bus data
# bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin
self.ppc["bus"] = array([
[1,3,0,0,0,0,0,1,1,0,1,1.1,0.94],
[2,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[3,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[4,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[5,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[6,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[7,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[8,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[9,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[10,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[11,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[12,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[13,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[14,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[15,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[16,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[17,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[18,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[19,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[20,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[21,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[22,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[23,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[24,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[25,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[26,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[27,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[28,1,0,0,0,0,0,1,1,0,1,1.1,0.94],
[29,1,0,0,0,0,0,1,1,0,1,1.1,0.94]
])
self.ppc["bus"][:,12] = 0.96
#self.ppc["bus"][:,12]=0.90 # relaxes the lower voltage constraint.
## generator data
# bus, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, Pc1, Pc2,
# Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf
self.ppc["branch"] = array([
[2,1,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[3,2,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[4,3,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[5,4,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[6,5,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[7,6,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[8,7,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[9,1,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[10,9,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[11,10,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[12,11,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[13,12,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[14,13,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[15,14,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[16,1,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[17,16,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[18,17,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[19,18,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[20,19,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[21,20,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[22,21,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[23,1,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[24,23,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[25,24,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[26,25,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[27,26,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[28,27,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360],
[29,28,0.001590, 0.000814,0,0,0,0,0,0,1,-360,360]
])
self.ppc["branch"][:,2]= (0.494 * 0.097 / 1.5)
self.ppc["branch"][:,3]= (0.0883 * 0.097 / 1.5)
## generator data
# bus, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, Pc1, Pc2,
# Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf
self.ppc["gen"] = array([
[1, 1, 0, 0, -1, 1.0, 100, 1, 5,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[2, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[4, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[6, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[7, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[8, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[9, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[10, 0, 0,0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[11, 0, 0,0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[12, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[13, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[14, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[15, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[16, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[17, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[18, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[19, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[20, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[21, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[22, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[23, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[24, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[25, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[26, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[27, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[28, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[29, 0, 0, 0, -1, 1.0, 100, 1,0, -20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
])
self.ppc["gen"][:,9] = -1
self.ppc["gen"][:,4]=1
self.power_slopes = array([-0.00022311,
-0.00044879,
-0.00067714,
-0.00090823,
-0.00114216,
-0.00137904,
-0.00161895]
)
self.dispatch_load = zeros(29)
"case9Q", \
"case14", \
"case24_ieee_rts", \
"case30", \
"case30pwl", \
"case30Q",\
"case39",\
"case57",\
"case118"]
cc = 0 #select test case
casea = __import__(testdata[cc])
###########################
ppopt = ppoption(OUT_ALL=0) #0=do not print output on screen
outage = imout.imout() #outage data
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
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 run_simulation(grid,
capacities,
attack_set,
verbose=False,
saveIterations=False):
"""Runs a cascading failure simulation.
addition documentation goes here
INPUT: grid: dict (representing a PYPOWER case file),
capacities: list (of the same length as grid['branch']),
attack_set: list (of line indices),
verbose: bool,
step_limit: int
OUTPUT: dict (containing data about the simulation)
"""
# initialization
if 'areas' in grid:
del grid['areas']
ppopt = pp.ppoption(VERBOSE=0, OUT_ALL=0)
grid = pp.rundcpf(grid, ppopt)[0]
# record initial data
initial_power = sum(grid['bus'][:, idx_bus.PD])
initial_size = len(grid['branch'])
# initialize data structures
failed_lines = []
failure_history = []
new_failed_lines = attack_set
components = []
if saveIterations:
grid_history = [copy.deepcopy(grid)]
if verbose:
counter = 0
print("Initial system summary:")
print(system_summary(grid, [grid], capacities))
print("Lines to fail: %s" % new_failed_lines)
while len(new_failed_lines) > 0:
if verbose:
print()
temp = input("About to run loop %d. Press enter to continue." %
counter)
# keep track of failed lines
failure_history.append(new_failed_lines)
failed_lines.extend(new_failed_lines)
# fail lines
for line in new_failed_lines:
grid['branch'][line][idx_brch.BR_R] = np.inf
grid['branch'][line][idx_brch.BR_X] = np.inf
# rescale power and run DC power flow in each component
components = get_components(grid)
for i, component in enumerate(components):
rescale_power_gen(component)
if len(component['branch']) > 0:
components[i] = pp.rundcpf(component, ppopt)[0]
# recombine components back to grid
grid = combine_components(components, grid)
# find failed lines
new_failed_lines = []
for i in range(len(grid['branch'])):
if abs(grid['branch'][i][idx_brch.PF]) > capacities[i]:
new_failed_lines.append(i)
if verbose:
print(system_summary(grid, components, capacities))
print("Lines to fail: %s" % new_failed_lines)
counter += 1
if saveIterations:
grid_history.append(copy.deepcopy(grid))
# compute power loss
final_power = sum(grid['bus'][:, idx_bus.PD])
power_loss = (initial_power - final_power) / initial_power
#compute system size
final_size = initial_size - len(failed_lines)
system_size = final_size / initial_size
# find isolated (no power generated) components and buses
isolated_components = []
isolated_buses = []
for component in components:
component_gen = sum(component['gen'][:, idx_gen.PG])
if component_gen == 0:
isolated_components.append(component)
for bus in component['bus']:
isolated_buses.append(bus)
output_data = {
"failure_history": failure_history,
"failed_lines": failed_lines,
"system_size": system_size,
"power_loss": power_loss,
"components": components,
"isolated_components": isolated_components,
"isolated_buses": isolated_buses,
"grid": grid,
"capacities": capacities
}
if saveIterations:
output_data["grid_history"] = grid_history
return output_data
def avg_line_flow(ppc):
ppc = pp.rundcpf(ppc, pp.ppoption(VERBOSE=0, OUT_ALL=0))[0]
return np.mean(abs(ppc['branch'][:, idx_brch.PF]))
fname = "Net1_UKGDS_60_subgrid.m"
fnameWhole = "Net1_UKGDS_60.m"
convert_mcase(fname)
from Net1_UKGDS_60_subgrid import Net1_UKGDS_60_ as net
from Net1_UKGDS_60 import Net1_UKGDS_60_ as netWhole
t_f = 96
# time variation
t_ges = 1440 # all time in min
delta_t = 15 # time intervals in min
time = np.arange(delta_t, t_ges + delta_t, delta_t)
casedataWhole = netWhole()
convert_to_python_indices(casedataWhole)
ppc = casedataWhole
ppopt = ppoption(PF_ALG=2)
ppc = ext2int(ppc)
baseMVA, bus, gen, branch = ppc["baseMVA"], ppc["bus"], ppc["gen"], ppc[
"branch"]
baseMVA = 1000
Ybus, Yf, Yt = makeYbus(baseMVA, bus, branch) # nodal admittance matrix Ybus
condition = np.hstack((1, np.arange(16, 27, 1)))
Yws = Ybus[np.ix_(condition, condition)]
#Yws = Y
t_f = 96
slack_idx = 0
p0 = 1e-3
iterstop = 50
accuracy = 1e-9
roundY = None
'{:.2f}'.format(unresp), '{:.2f}'.format(resp_max),
'{:.8f}'.format(c2), '{:.8f}'.format(c1), '{:.1f}'.format(deg))
x = np.array(range(25))
y = np.array(load_shape)
l = len(x)
t = np.linspace(0, 1, l - 2, endpoint=True)
t = np.append([0, 0, 0], t)
t = np.append(t, [1, 1, 1])
tck_load = [t, [x, y], 3]
u3 = np.linspace(0, 1, num=86400 / 300 + 1, endpoint=True)
newpts = ip.splev(u3, tck_load)
ppc = tesp.load_json_case(casename + '.json')
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'],
PF_MAX_IT=20,
PF_ALG=1)
StartTime = ppc['StartTime']
tmax = int(ppc['Tmax'])
period = int(ppc['Period'])
dt = int(ppc['dt'])
swing_bus = int(ppc['swing_bus'])
#period = 300
#dt = 15
# initialize for metrics collection
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)
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)))
############################# Subscribing to Feeder Load from to GridLAB-D ##############################################
for i in range(0, subkeys_count):
sub = subid["m{}".format(i)]
rload, iload = h.helicsInputGetComplex(sub)
logger.info("Python Federate grantedtime = {}".format(grantedtime))
logger.info("Load value = {} kW".format(complex(rload, iload) / 1000))
#print(voltage_plot,real_demand)
actual_demand = peak_demand * bus_profiles[x, :]
ppc['bus'][:, 2] = actual_demand
ppc['bus'][:, 3] = actual_demand * math.tan(math.acos(.85))
ppc['bus'][cosim_bus, 2] = rload * load_amplification_factor / 1000000
ppc['bus'][cosim_bus, 3] = iload * load_amplification_factor / 1000000
ppopt = ppoption(PF_ALG=1)
print('PF TIme is {} and ACOPF time is {}'.format(
time_pf[x], time_opf[k]))
############################ Running OPF For optimal power flow intervals ##############################
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(
print('bus #, Pd, Qd, Vm, LMP_P, LMP_Q, MU_VMAX, MU_VMIN')
for row in bus:
print(int(row[0]), row[2], row[3], row[7], row[13], row[14], row[15],
row[16])
print('gen #, bus, Pg, Qg, MU_PMAX, MU_PMIN, MU_PMAX, MU_PMIN')
idx = 1
for row in gen:
print(idx, int(row[0]), row[1], row[2], row[21], row[22], row[23],
row[24])
++idx
with warnings.catch_warnings():
ppc = ppcasefile()
ppopt = pp.ppoption(VERBOSE=0, OUT_ALL=0, PF_DC=1)
gencost = ppc['gencost']
bus = ppc['bus']
gen = ppc['gen']
fncsBus = ppc['FNCS']
outage = ppc['UnitsOut'][0]
bus[4, 2] = 97.124 # column 2, bus loads at 165000 from the TXT file
bus[8, 2] = 134.89 # column 3
csv_load = 110.28 # column 1
# gen[outage[0],7] = 0 # unit 2 is out
scale = float(fncsBus[0][2])
# mimic the FNCS messages coming in at 165000 for MW, prep and scale for OPF
ppc['branch'][-1, F_BUS] = 30
ppc['branch'][-1, T_BUS] = 33
ppc['branch'] = np.concatenate(
(ppc['branch'], ppc['branch'][nbrch - 1:nbrch, :]), axis=0)
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()
'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']
ppopt = pp.ppoption(
VERBOSE=0, OUT_ALL=0) # TODO - the PF_DC option doesn't seem to work
loads = np.loadtxt('NonGLDLoad.txt', delimiter=',')
outage = ppc['UnitsOut'][0]
print('unit', outage[0], 'off from', outage[1], 'to', outage[2])
nloads = loads.shape[0]
ts = 0
tnext = 0
op = open(rootname + '.csv', 'w')
print('t[s],Converged,Pload,P7,V7,LMP_P7,LMP_Q7,Pgen1,Pgen2,Pgen3,Pgen4',
file=op)
fncs.initialize()
# ts = -dt
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.")
"""
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:
dlt_vec = [0.05, -0.05, 0.10, -0.10, 0.50, -0.50]
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 = False,
# OUT_ALL_LIM = False,
# OUT_AREA_SUM = False,
# OUT_BRANCH = False,
# OUT_BUS = False,
# OUT_GEN = False,
# OUT_LINE_LIM = False,
# OUT_PG_LIM = False,
# OUT_QG_LIM = False,
# OUT_SYS_SUM = False,
# OUT_V_LIM = False) # Careful: have to use Newton Method!!!
ppopt0 = pypo.ppoption(PF_ALG=2, VERBOSE=0, OUT_ALL=0) # Careful: have to use Newton Method!!!
r0 = pypo.runpf(ppc0, ppopt0)
m1 = r0[0]['gen'][:,2][1:]
p0 = [m1 for i in range(0,8)]
mtr = zeros([8,4,len(dlt_vec)])
j = 1
for j in range(0,len(dlt_vec)):
tmp = 0
for i in range(0,13):
if r0[0]['bus'][i,1] == 1:
ppc = case14_mod.case14_mod(busN = i,dlt = dlt_vec[j], op_change=1)
ppopt = pypo.ppoption(PF_ALG=2, VERBOSE=0, OUT_ALL=0) # Careful: have to use Newton Method!!!
r = pypo.runpf(ppc, ppopt)
#plt.plot(r[0]['bus'][:,2])
pwr = r[0]['gen'][:,2][1:] # voltage 7, type 2
def parse_options(args, usage, opf=False):
"""Parse command line options.
@param opf: Include OPF options?
"""
v = ppver('all')
parser = OptionParser(
usage="""usage: %%prog [options] [casedata]
%s
If 'casedata' is provided it specifies the name of the input data file
containing the case data.""" % usage,
version='PYPOWER (%%prog) Version %s, %s' % (v["Version"], v["Date"])
)
parser.add_option("-t", "--test", action="store_true", dest="test",
default=False, help="run tests and exit")
parser.add_option("-c", "--testcase", default='case9',
choices=list(CASES.keys()),
help="built-in test case, choose from: %s" % str(
list(CASES.keys()))[1:-1])
parser.add_option("-o", "--outfile", dest='fname', default='',
type='string', help="""pretty printed output will be
appended to a file with the name specified. Defaults to stdout.""")
parser.add_option("-s", "--solvedcase", default='', type='string',
help="""the solved case will be written
to a case file with the specified name in PYPOWER format. If solvedcase ends
with '.mat' the case is saves as a MAT-file otherwise it saves it as a Python
file.""")
ppopt = ppoption()
if opf:
opf_options = OptionGroup(parser, 'OPF Options')
pdipm_options = OptionGroup(parser, 'PDIPM Options')
opf_options.add_option("-u", "--uopf", action="store_true",
help="""runs an optimal power flow with the unit-decommitment
heuristic""")
opf_options.add_option("-r", "--w_res", action="store_true",
help="""runs an optimal power flow with fixed zonal reserves""")
add_options(opf_options, OPF_OPTIONS, ppopt)
add_options(pdipm_options, PDIPM_OPTIONS, ppopt)
parser.add_option_group(opf_options)
parser.add_option_group(pdipm_options)
else:
pf_options = OptionGroup(parser, 'Power Flow Options')
add_options(pf_options, PF_OPTIONS, ppopt)
parser.add_option_group(pf_options)
output_options = OptionGroup(parser, 'Output Options')
add_options(output_options, OUTPUT_OPTIONS, ppopt)
parser.add_option_group(output_options)
options, args = parser.parse_args(args)
# casedata, fname, solvedcase = case9(), '', '' # defaults
nargs = len(args)
if nargs > 1:
stderr.write('Too many arguments')
parser.print_help()
sys.exit(2)
elif nargs == 1:
casedata = args[0]
else:
try:
casedata = CASES[options.testcase]()
except KeyError:
stderr.write("Invalid case choice: %r (choose from %s)\n" % \
(options.testcase, list(CASES.keys())))
sys.exit(2)
return options, casedata, ppopt, options.fname, options.solvedcase
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 _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) ---> 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()
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 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 solveOpf(caseName, config):
bxObj = ptbx.InitCases(caseName)
sysN, sysopt = opf_args.opf_args2(bxObj.System)
sysopt = pyp.ppoption(sysopt, VERBOSE=3)
results = pyp.runopf(bxObj.System, sysopt)
print("Success")
def parse_options(args, usage, opf=False):
"""Parse command line options.
@param opf: Include OPF options?
"""
v = ppver('all')
parser = OptionParser(
usage="""usage: %%prog [options] [casedata]
%s
If 'casedata' is provided it specifies the name of the input data file
containing the case data.""" % usage,
version='PYPOWER (%%prog) Version %s, %s' % (v["Version"], v["Date"])
)
parser.add_option("-t", "--test", action="store_true", dest="test",
default=False, help="run tests and exit")
parser.add_option("-c", "--testcase", default='case9', choices=CASES.keys(),
help="built-in test case, choose from: %s" % str(CASES.keys())[1:-1])
parser.add_option("-o", "--outfile", dest='fname', default='',
type='string', help="""pretty printed output will be
appended to a file with the name specified. Defaults to stdout.""")
parser.add_option("-s", "--solvedcase", default='', type='string',
help="""the solved case will be written
to a case file with the specified name in PYPOWER format. If solvedcase ends
with '.mat' the case is saves as a MAT-file otherwise it saves it as a Python
file.""")
ppopt = ppoption()
if opf:
opf_options = OptionGroup(parser, 'OPF Options')
pdipm_options = OptionGroup(parser, 'PDIPM Options')
opf_options.add_option("-u", "--uopf", action="store_true",
help="""runs an optimal power flow with the unit-decommitment
heuristic""")
opf_options.add_option("-r", "--w_res", action="store_true",
help="""runs an optimal power flow with fixed zonal reserves""")
add_options(opf_options, OPF_OPTIONS, ppopt)
add_options(pdipm_options, PDIPM_OPTIONS, ppopt)
parser.add_option_group(opf_options)
parser.add_option_group(pdipm_options)
else:
pf_options = OptionGroup(parser, 'Power Flow Options')
add_options(pf_options, PF_OPTIONS, ppopt)
parser.add_option_group(pf_options)
output_options = OptionGroup(parser, 'Output Options')
add_options(output_options, OUTPUT_OPTIONS, ppopt)
parser.add_option_group(output_options)
options, args = parser.parse_args(args)
# casedata, fname, solvedcase = case9(), '', '' # defaults
nargs = len(args)
if nargs > 1:
stderr.write('Too many arguments')
parser.print_help()
sys.exit(2)
elif nargs == 1:
casedata = args[0]
else:
try:
casedata = CASES[options.testcase]()
except KeyError:
stderr.write("Invalid case choice: %r (choose from %s)\n" % \
(options.testcase, CASES.keys()))
sys.exit(2)
return options, casedata, ppopt, options.fname, options.solvedcase
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']
ppopt = 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
op = open(rootname + '.csv', 'w')
print(
't[s],Converged,Pload,P7 (csv), GLD Unresp, P7 (opf), Resp (opf), GLD Pub, BID?, P7 Min, V7,LMP_P7,LMP_Q7,Pgen1,Pgen2,Pgen3,Pgen4,Pdisp, gencost2, gencost1, gencost0',
file=op,
flush=True)
# print ('t[s], ppc-Pd5, ppc-Pd9, ppc-Pd7, bus-Pd7, ppc-Pg1, gen-Pg1, ppc-Pg2, gen-Pg2, ppc-Pg3, gen-Pg3, ppc-Pg4, gen-Pg4, ppc-Pg5, gen-Pg5, ppc-Cost2, gencost-Cost2, ppc-Cost1, gencost-Cost1, ppc-Cost0, gencost-Cost0', file=op, flush=True)
fncs.initialize()
# transactive load components
csv_load = 0
scaled_unresp = 0
scaled_resp = 0
resp_c0 = 0
resp_c1 = 0
resp_c2 = 0
resp_max = 0
gld_load = 0 # this is the actual
# ==================================
# Laurentiu Marinovici - 2017-12-14
actual_load = 0
new_bid = False
# saveInd = 0
# saveDataDict = {}
# ===================================
while ts <= tmax:
if ts >= tnext_opf: # expecting to solve opf one dt before the market clearing period ends, so GridLAB-D has time to use it
idx = int((ts + dt) / period) % nloads
bus = ppc['bus']
print(
'<<<<< ts = {}, ppc-Pd5 = {}, bus-Pd5 = {}, ppc-Pd7 = {}, bus-Pd7 = {}, ppc-Pd9 = {}, bus-Pd9 = {} >>>>>>>'
.format(ts, ppc["bus"][4, 2], bus[4, 2], ppc["bus"][6, 2],
bus[6, 2], ppc["bus"][8, 2], bus[8, 2]))
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]
print(
'<<<<< ts = {}, ppc-Pd5 = {}, bus-Pd5 = {}, ppc-Pd7 = {}, bus-Pd7 = {}, ppc-Pd9 = {}, bus-Pd9 = {} >>>>>>>'
.format(ts, ppc["bus"][4, 2], bus[4, 2], ppc["bus"][6, 2],
bus[6, 2], ppc["bus"][8, 2], bus[8, 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
bus[6, 2] = csv_load
# =================================
# Laurentiu Marinovici - 2017-12-14
# bus[6,2] = csv_load + actual_load
# =================================
for row in ppc['FNCS']:
scaled_unresp = float(row[2]) * float(row[3])
newidx = int(row[0]) - 1
bus[newidx, 2] += scaled_unresp
print(
'<<<<< ts = {}, ppc-Pd5 = {}, bus-Pd5 = {}, ppc-Pd7 = {}, bus-Pd7 = {}, ppc-Pd9 = {}, bus-Pd9 = {} >>>>>>>'
.format(ts, ppc["bus"][4, 2], bus[4, 2], ppc["bus"][6, 2],
bus[6, 2], ppc["bus"][8, 2], bus[8, 2]))
gen[4][9] = -resp_max * float(fncsBus[0][2])
gencost[4][3] = 3
gencost[4][4] = resp_c2
gencost[4][5] = resp_c1
gencost[4][6] = resp_c0
# =================================
# Laurentiu Marinovici - 2017-12-14
# print('Before running OPF:')
# print('Disp load/neg gen: Pg = ', gen[4][1], ', Pmax = ', gen[4][8], ', Pmin = ', gen[4][9], ', status = ', gen[4][7])
# print('Disp load/neg gen cost coefficients: ', gencost[4][4], ', ', gencost[4][5], ', ', gencost[4][6])
# gen[4, 7] = 1 # turn on dispatchable load
#ppc['gen'] = gen
#ppc['bus'] = bus
#ppc['branch'] = branch
#ppc['gencost'] = gencost
# print (ts, ppc["bus"][4, 2], ppc["bus"][8, 2], ppc["bus"][6, 2], bus[6, 2], ppc["gen"][0, 1], gen[0, 1], ppc["gen"][1, 1], gen[1, 1], ppc["gen"][2, 1], gen[2, 1], ppc["gen"][3, 1], gen[3, 1], ppc["gen"][4, 1], gen[4, 1], ppc["gencost"][4, 4], gencost[4, 4], ppc["gencost"][4, 5], gencost[4, 5], ppc["gencost"][4, 6], gencost[4, 6], sep=',', file=op, flush=True)
# =====================================================================================================================
res = pp.runopf(ppc, ppopt)
# =================================
# Laurentiu Marinovici - 2017-12-21
# mpcKey = 'mpc' + str(saveInd)
# resKey = 'res' + str(saveInd)
# saveDataDict[mpcKey] = copy.deepcopy(ppc)
# saveDataDict[resKey] = copy.deepcopy(res)
# saveInd += 1
# =================================
bus = res['bus']
gen = res['gen']
Pload = bus[:, 2].sum()
Pgen = gen[:, 1].sum()
Ploss = Pgen - Pload
scaled_resp = -1.0 * gen[4, 1]
# CSV file output
print(ts,
res['success'],
'{:.3f}'.format(bus[:, 2].sum()),
'{:.3f}'.format(csv_load),
'{:.3f}'.format(scaled_unresp),
'{:.3f}'.format(bus[6, 2]),
'{:.3f}'.format(scaled_resp),
'{:.3f}'.format(actual_load),
new_bid,
'{:.3f}'.format(gen[4, 9]),
'{:.3f}'.format(bus[6, 7]),
'{:.3f}'.format(bus[6, 13]),
'{:.3f}'.format(bus[6, 14]),
'{:.2f}'.format(gen[0, 1]),
'{:.2f}'.format(gen[1, 1]),
'{:.2f}'.format(gen[2, 1]),
'{:.2f}'.format(gen[3, 1]),
'{:.2f}'.format(res['gen'][4, 1]),
'{:.6f}'.format(ppc['gencost'][4, 4]),
'{:.4f}'.format(ppc['gencost'][4, 5]),
'{:.4f}'.format(ppc['gencost'][4, 6]),
sep=',',
file=op,
flush=True)
fncs.publish('LMP_B7', 0.001 * bus[6, 13])
fncs.publish('three_phase_voltage_B7',
1000.0 * bus[6, 7] * bus[6, 9])
print('**OPF', ts, csv_load, scaled_unresp, gen[4][9], scaled_resp,
bus[6, 2], 'LMP', 0.001 * bus[6, 13])
# update the metrics
sys_metrics[str(ts)] = {rootname: [Ploss, res['success']]}
bus_metrics[str(ts)] = {}
for i in range(fncsBus.shape[0]):
busnum = int(fncsBus[i, 0])
busidx = busnum - 1
row = bus[busidx].tolist()
bus_metrics[str(ts)][str(busnum)] = [
row[13] * 0.001, row[14] * 0.001, row[2], row[3], row[8],
row[7], row[11], row[12]
]
gen_metrics[str(ts)] = {}
for i in range(gen.shape[0]):
row = gen[i].tolist()
busidx = int(row[0] - 1)
gen_metrics[str(ts)][str(i + 1)] = [
row[1], row[2],
float(bus[busidx, 13]) * 0.001
]
tnext_opf += period
if tnext_opf > tmax:
print('breaking out at', tnext_opf, flush=True)
break
# apart from the OPF, keep loads updated
ts = fncs.time_request(ts + dt)
events = fncs.get_events()
new_bid = False
for key in events:
topic = key.decode()
# ==================================
# Laurentiu Marinovici - 2017-12-14l
# print('The event is: ........ ', key)
# print('The topic is: ........ ', topic)
# print('The value is: ........ ', fncs.get_value(key).decode())
# =============================================================
if topic == 'UNRESPONSIVE_KW':
unresp_load = 0.001 * float(fncs.get_value(key).decode())
fncsBus[0][
3] = unresp_load # poke unresponsive estimate into the bus load slot
new_bid = True
elif topic == 'RESPONSIVE_MAX_KW':
resp_max = 0.001 * float(fncs.get_value(key).decode()) # in MW
new_bid = True
elif topic == 'RESPONSIVE_M':
# resp_c2 = 1000.0 * 0.5 * float(fncs.get_value(key).decode())
resp_c2 = -1e6 * float(fncs.get_value(key).decode())
new_bid = True
elif topic == 'RESPONSIVE_B':
# resp_c1 = 1000.0 * float(fncs.get_value(key).decode())
resp_c1 = 1e3 * float(fncs.get_value(key).decode())
new_bid = True
# ============================================
# Laurentiu Marinovici
elif topic == 'RESPONSIVE_BB':
resp_c0 = -float(fncs.get_value(key).decode())
new_bid = True
# ============================================
elif topic == 'UNRESPONSIVE_PRICE': # not actually used
unresp_price = float(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
# ==================================
# Laurentiu Marinovici - 2017-12-14
# print('GLD real = ', float(gld_load[0]), '; GLD imag = ', float(gld_load[1]))
# print('Amp factor = ', float(fncsBus[0][2]))
# ==================================================================
actual_load = float(gld_load[0]) * float(fncsBus[0][2])
print(' Time = ', ts, '; actual load real = ', actual_load)
if new_bid == True:
print('**Bid', ts, unresp_load, resp_max, resp_c2, resp_c1,
resp_c0)
# Laurentiu Marinovici - 2017-12-21
# spio.savemat('matFile.mat', saveDataDict)
# ===================================
print('writing metrics', flush=True)
print(json.dumps(bus_metrics), file=bus_mp, flush=True)
print(json.dumps(gen_metrics), file=gen_mp, flush=True)
print(json.dumps(sys_metrics), file=sys_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()