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conv_ana_2.py
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import numpy as np
import utility as util
import GetTransPower
import RunPF
import DSSStartup
from tqdm import tqdm
from matplotlib import pyplot as plt
result_path = 'result/500_mix_f.txt'
base_load_path = 'base_load/10_min/'
env_path = 'env/500_mix.txt'
DSSObj = DSSStartup.dssstartup('master33Full.dss')
tol = 0.05
result = util.load_dict(result_path)
env = util.load_dict(env_path)
slot = 109
connected = result['central'][slot]['connected']
n_slot_per_hr = 6
h = slot//n_slot_per_hr
bl_scale = 1.3
PQ_dict = util.load_dict(base_load_path+'h'+str(h)+'.txt')
P = bl_scale*np.array(PQ_dict['P'])
Q = bl_scale*np.array(PQ_dict['Q'])
def get_driver_type():
driver_type = []
for c in connected:
driver_type.append(env['evDriverType'][c])
return np.array(driver_type)
#w = util.f(get_driver_type())
w = result['central'][slot]['w']
def get_trans_load(ev_power): # In kVA
DSSCircuit = RunPF.runPF(DSSObj, P[:, h%n_slot_per_hr], Q[:, h%n_slot_per_hr], env['evNodeNumber'], ev_power)
# get the transformer power magnitudes
trans_loads = GetTransPower.getTransPower(DSSCircuit)
trans_loads = np.ravel(trans_loads)
trans_loads = [np.sqrt(trans_loads[i]**2+trans_loads[i+1]**2) for i in range(0,len(trans_loads),2)]
return(np.array(trans_loads))
def get_UB():
return 8.0*np.ones(len(connected))
def get_available(trans_loads):
return 0.9*np.maximum(0.0, np.array(env['transRating']) - trans_loads)
def get_TAU(whole=0):
if whole==1:
connected_ = np.array(range(0,env['evNumber']))
T = []
A = []
U = []
trans_number = [env['evNodeNumber'][e]//55+1 for e in connected]
phase_number = [env['loadPhase'][env['evNodeNumber'][e]%55]-1 for e in connected]
available = get_available(get_trans_load(np.zeros(env['evNumber'])))
# For primary tranformers
for i in range(0,3):
temp = np.zeros(len(connected))
for j in range(0, len(connected)):
if phase_number[j]==i:
temp[j] = 1
if np.sum(temp)>0 or whole==1:
T.append(temp)
A.append(available[i])
U.append(i)
# For secondary transformers
for i in range(3, len(available)):
temp = np.zeros(len(connected))
for j in range(0, len(connected)):
if trans_number[j]==(i//3) and phase_number[j]==(i%3):
temp[j] = 1
if np.sum(temp)>0 or whole==1:
T.append(temp)
A.append(available[i])
U.append(i)
A = np.array(A)
#A = np.maximum(util.tol, A)
return (np.array(T), A, np.array(U))
T, A, U = get_TAU()
scale = 1e-4
legend = []
m = 10
for tol in [0.05, 0.01]:
gammas = []
iters = []
for gamma in tqdm(range(5, 100)):
gammas.append(gamma)
n_iter = 0
lamda = np.ones(len(A))
x = np.zeros(len(connected))
LB = np.zeros(len(connected))
if tol == 0.05:
##################################################################
history = []
lamda_k = np.copy( lamda )
x = np.minimum(np.maximum(LB, w/np.dot(T.T, lamda**2)), get_UB())
ev_power = np.zeros(env['evNumber'])
for j in range(0, len(connected)):
ev_power[connected[j]] = x[j]
g_k = ( np.array(env['transRating']) - get_trans_load(ev_power) )
g_k = 2 * lamda_k * np.array([g_k[e] for e in U])
###################################################################
for i in range(0, 200):
n_iter = i+1
if tol == 0.05:
lamda = lamda - util.lbfgs_get_direction(history, g_k)
x = np.minimum(np.maximum(LB, w/np.dot(T.T, lamda**2)), get_UB())
else:
x = np.minimum(np.maximum(LB, w/np.dot(T.T, lamda)), get_UB())
ev_power = np.zeros(env['evNumber'])
for j in range(0, len(connected)):
ev_power[connected[j]] = x[j]
g = ( np.array(env['transRating']) - get_trans_load(ev_power) )
if tol == 0.05:
g = 2 * lamda * np.array([g[e] for e in U])
s_k = lamda - lamda_k
y_k = g - g_k
history = util.lbfgs_update_history(history, m, s_k, y_k)
lamda_k = lamda
g_k = g
else:
g = np.array([g[e] for e in U])
lamda = np.maximum(0.0, lamda - gamma*scale*g)
#if np.allclose(self.get_trans_load(ev_power,P,Q), central['trans_load'], atol=0.0, rtol=self.params['tol'])==True:
# break
#print(ev_power)
#print(central['ev_power'])
'''
sub = [0,0]
temp = get_trans_load(ev_power)
sub[0] = result['central'][slot]['trans_load'][0]+result['central'][slot]['trans_load'][1]+result['central'][slot]['trans_load'][2]
sub[1] = temp[0]+temp[1]+temp[2]
#print(abs(sub[1]-sub[0])/sub[0])
if abs(sub[1]-sub[0]) <= tol*sub[0]:
break
'''
c = sum(result['central'][slot]['ev_power'])
d = sum(ev_power)
if abs(d-c) <= tol*c:
break
#if np.allclose(ev_power, central['ev_power'], atol=0.0, rtol=self.params['tol'])==True:
# break
print(n_iter)
iters.append(n_iter)
#legend.append(str(100-tol*100)+'%')
if tol == 0.05:
legend.append('l-bfgs')
else:
legend.append('gpa')
plt.plot(gammas, iters)
plt.legend(legend)
plt.title('99% Convergence Analysis of Decentral Algo')
plt.xlabel('step-size ($x10^{-5}$)')
plt.ylabel('# of iterations')
plt.show()