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SDDiP20221010_v1.py
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SDDiP20221010_v1.py
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import gurobipy as gp
from gurobipy import GRB
import numpy as np
# waterNet 4-stage schedule: SDDiP
# use sB and Lag cut, convergent after 20 iterations (see the bottom)
# speed is satisfied
# gap is closed
# 20221010
# @doorvanbei
def gsum(a):
return gp.quicksum(a)
def wNet(dR,ite,t,htoulast,s,pai):
m = gp.Model('water_t')
if t != 0:
zl = len(htoulast) # length of copy vector = length of lastAction
z = m.addVars(zl, ub=1)
if s != 2:
m.addConstrs(z[i] == htoulast[i] for i in range(zl))
# -----model part-----
s_pump = m.addVars(pumpNum,4,vtype=GRB.CONTINUOUS if s == 1 else GRB.BINARY,ub=1) # represent 4 finite states
q_pump,h_pump,p_pump = m.addVars(pumpNum,lb=100,ub=1100),m.addVars(pumpNum,lb=86,ub=229),m.addVars(pumpNum,lb=160,ub=556)
l_pump = m.addVars(pumpNum, ub=1)
s_pipe = m.addVars(pipesNum,vtype=GRB.CONTINUOUS if s == 1 else GRB.BINARY,ub=1) # represent 3 finite states
q_pipe,h_pipe = m.addVars(pipesNum,ub=1500),m.addVars(pipesNum,ub=704)
l_pipe = m.addVars(pipesNum)
hrd = m.addVars(hrdNum) # RDV
h0, h1, h2, h3, h4 = m.addVar(),m.addVar(),m.addVar(),m.addVar(),m.addVar()
htin = m.addVar()
htou = m.addVars(Bhtou,vtype=GRB.CONTINUOUS if s == 1 else GRB.BINARY,ub=1)
if t == 0:
m.addConstr(gsum(fbhtou[i]*htou[i] for i in range(Bhtou)) == 100 + tkCoef * (q_pipe[2] - q_pipe[3]))
else:
m.addConstr(gsum(fbhtou[i]*htou[i] for i in range(Bhtou)) == gsum(fbhtou[i]*z[i] for i in range(Bhtou)) + tkCoef * (q_pipe[2] - q_pipe[3]))
for n in range(pumpNum):
m.addConstr(gsum(s_pump[n, i] for i in range(4)) == 1)
m.addConstr(l_pump[n] * qt_pump[0, 0] + (1 - l_pump[n]) * qt_pump[0, 1] - q_pump[n] >= -1000*(1-s_pump[n, 0]))
m.addConstr(l_pump[n] * qt_pump[0, 0] + (1 - l_pump[n]) * qt_pump[0, 1] - q_pump[n] <= 1000*(1-s_pump[n, 0]))
m.addConstr(l_pump[n] * qt_pump[0, 2] + (1 - l_pump[n]) * qt_pump[0, 1] - q_pump[n] >= -1000*(1-s_pump[n, 1]))
m.addConstr(l_pump[n] * qt_pump[0, 2] + (1 - l_pump[n]) * qt_pump[0, 1] - q_pump[n] <= 1000*(1-s_pump[n, 1]))
m.addConstr(l_pump[n] * qt_pump[1, 0] + (1 - l_pump[n]) * qt_pump[1, 1] - q_pump[n] >= -1000*(1-s_pump[n, 2]))
m.addConstr(l_pump[n] * qt_pump[1, 0] + (1 - l_pump[n]) * qt_pump[1, 1] - q_pump[n] <= 1000*(1-s_pump[n, 2]))
m.addConstr(l_pump[n] * qt_pump[1, 2] + (1 - l_pump[n]) * qt_pump[1, 1] - q_pump[n] >= -1000*(1-s_pump[n, 3]))
m.addConstr(l_pump[n] * qt_pump[1, 2] + (1 - l_pump[n]) * qt_pump[1, 1] - q_pump[n] <= 1000*(1-s_pump[n, 3]))
m.addConstr(l_pump[n] * ht_pump[0, 0] + (1 - l_pump[n]) * ht_pump[0, 1] - h_pump[n] >= -143*(1-s_pump[n, 0]))
m.addConstr(l_pump[n] * ht_pump[0, 0] + (1 - l_pump[n]) * ht_pump[0, 1] - h_pump[n] <= 143*(1-s_pump[n, 0]))
m.addConstr(l_pump[n] * ht_pump[0, 2] + (1 - l_pump[n]) * ht_pump[0, 1] - h_pump[n] >= -143*(1-s_pump[n, 1]))
m.addConstr(l_pump[n] * ht_pump[0, 2] + (1 - l_pump[n]) * ht_pump[0, 1] - h_pump[n] <= 143*(1-s_pump[n, 1]))
m.addConstr(l_pump[n] * ht_pump[1, 0] + (1 - l_pump[n]) * ht_pump[1, 1] - h_pump[n] >= -143*(1-s_pump[n, 2]))
m.addConstr(l_pump[n] * ht_pump[1, 0] + (1 - l_pump[n]) * ht_pump[1, 1] - h_pump[n] <= 143*(1-s_pump[n, 2]))
m.addConstr(l_pump[n] * ht_pump[1, 2] + (1 - l_pump[n]) * ht_pump[1, 1] - h_pump[n] >= -143*(1-s_pump[n, 3]))
m.addConstr(l_pump[n] * ht_pump[1, 2] + (1 - l_pump[n]) * ht_pump[1, 1] - h_pump[n] <= 143*(1-s_pump[n, 3]))
m.addConstr(l_pump[n] * pt_pump[0, 0] + (1 - l_pump[n]) * pt_pump[0, 1] - p_pump[n] >= -396*(1-s_pump[n, 0]))
m.addConstr(l_pump[n] * pt_pump[0, 0] + (1 - l_pump[n]) * pt_pump[0, 1] - p_pump[n] <= 396*(1-s_pump[n, 0]))
m.addConstr(l_pump[n] * pt_pump[0, 2] + (1 - l_pump[n]) * pt_pump[0, 1] - p_pump[n] >= -396*(1-s_pump[n, 1]))
m.addConstr(l_pump[n] * pt_pump[0, 2] + (1 - l_pump[n]) * pt_pump[0, 1] - p_pump[n] <= 396*(1-s_pump[n, 1]))
m.addConstr(l_pump[n] * pt_pump[1, 0] + (1 - l_pump[n]) * pt_pump[1, 1] - p_pump[n] >= -396*(1-s_pump[n, 2]))
m.addConstr(l_pump[n] * pt_pump[1, 0] + (1 - l_pump[n]) * pt_pump[1, 1] - p_pump[n] <= 396*(1-s_pump[n, 2]))
m.addConstr(l_pump[n] * pt_pump[1, 2] + (1 - l_pump[n]) * pt_pump[1, 1] - p_pump[n] >= -396*(1-s_pump[n, 3]))
m.addConstr(l_pump[n] * pt_pump[1, 2] + (1 - l_pump[n]) * pt_pump[1, 1] - p_pump[n] <= 396*(1-s_pump[n, 3]))
for n in range(pipesNum):
m.addConstr(l_pipe[n] * qt_pipe[0] + (1 - l_pipe[n]) * qt_pipe[1] - q_pipe[n] >= -1500*s_pipe[n])
m.addConstr(l_pipe[n] * qt_pipe[0] + (1 - l_pipe[n]) * qt_pipe[1] - q_pipe[n] <= 1500*s_pipe[n])
m.addConstr(l_pipe[n]*qt_pipe[2]+(1-l_pipe[n])*qt_pipe[1] - q_pipe[n] >= -1500*(1-s_pipe[n]))
m.addConstr(l_pipe[n]*qt_pipe[2]+(1-l_pipe[n])*qt_pipe[1] - q_pipe[n] <= 1500*(1-s_pipe[n]))
m.addConstr(l_pipe[n] * ht_pipe[n, 0] + (1 - l_pipe[n]) * ht_pipe[n, 1] - h_pipe[n] >= -704 * s_pipe[n])
m.addConstr(l_pipe[n] * ht_pipe[n, 0] + (1 - l_pipe[n]) * ht_pipe[n, 1] - h_pipe[n] <= 704 * s_pipe[n])
m.addConstr(l_pipe[n]*ht_pipe[n,2]+(1-l_pipe[n])*ht_pipe[n,1] - h_pipe[n] >= -704*(1-s_pipe[n]))
m.addConstr(l_pipe[n]*ht_pipe[n,2]+(1-l_pipe[n])*ht_pipe[n,1] - h_pipe[n] <= 704*(1-s_pipe[n]))
# flow constrs
m.addConstr(q_pump[0] == q_pipe[2] + q_pipe[0]) # node 0
m.addConstr(q_pipe[0]+q_pump[1] == q_pipe[1]) # node 1
m.addConstr(q_pipe[1] == q_pipe[5]) # node 2
m.addConstr(q_pipe[4]+q_pipe[5] == lW[t]) # node 3: load level: 140~1000
m.addConstr(q_pipe[4] == q_pipe[3]) # node 4
# pressure constrs
m.addConstr(h_pump[0] == h0) # node 0_with pump
m.addConstr(h_pump[1] == h1) # node 1_with pump
m.addConstr(h0-hrd[0]-h_pipe[0] == h1)# node 1
m.addConstr(h1-h_pipe[1] == h2)# node 2
m.addConstr(h2 - h_pipe[5] - hrd[1] == h3)# node 5
m.addConstr(h0-h_pipe[2] == htin)# node tank
m.addConstr(gsum(fbhtou[i]*htou[i] for i in range(Bhtou)) - h_pipe[3] == h4)# node 6
m.addConstr(h4 - h_pipe[4] - hrd[2] == h3)# node 5
activePM = 1e-3*gsum(p_pump[p] for p in range(pumpNum))
theObj = 10 * gsum(hrd[i] for i in range(hrdNum)) + Pe[t]*activePM*(1 + np.tan(np.arccos(.85)))
theObj = 1e-5*Pe[t]*(q_pump[0] + q_pump[1])
if t == nss-1: # penalty for not-satisfying daily balance
endPen = m.addVar()
m.addConstr(endPen >= 100-gsum(fbhtou[i]*htou[i] for i in range(Bhtou)))
theObj += .3*endPen
tha = m.addVar()
if t < nss-1:
for its in range(ite+dR):
m.addConstr(tha>=cut[its,t,0,-1]+gsum(cut[its,t,0,i]*htou[i] for i in range(Bhtou)))
m.addConstr(tha>=cut[its,t,1,-1]+gsum(cut[its,t,1,i]*htou[i] for i in range(Bhtou)))
theObj += tha
# -----model part-----
if s == 2:
theObj -= gsum(pai[i]*z[i] for i in range(zl))
m.setObjective(theObj)
m.setParam('OutputFlag', 0)
m.optimize()
if m.status != GRB.OPTIMAL:
print('opt Fail >>>>>>>>>>>>>')
exit(3)
# print('\nThe ObjVal is %g' % m.ObjVal)
# print('\nThe tank water level')
# print('%8d|%8g' % (t, htou.X))
# print('\nThe lower|upper flow')
# print('%8d|%8g|%8g' % (t,q_pipe[4].X,q_pipe[5].X))
# print('\nThe pump0|pump1 w level')
# print('%8d:%3d|%3d|%3d|%3d| |%3d|%3d|%3d|%3d' % (
# t, s_pump[ 0, 0].X, s_pump[ 0, 1].X, s_pump[ 0, 2].X, s_pump[ 0, 3].X, s_pump[ 1, 0].X,
# s_pump[ 1, 1].X, s_pump[ 1, 2].X, s_pump[ 1, 3].X))
# print('%8d:%8g|%8g (kW)' % (t, p_pump[ 0].X, p_pump[ 1].X))
# print('%8d:%8g|%8g (m)' % (t, h_pump[ 0].X, h_pump[ 1].X))
# print('%8d:%8g|%8g (m3/h)' % (t, q_pump[ 0].X, q_pump[ 1].X))
# print('\nThe hrd')
# print('%8d|%12g|%12g|%12g' % (t, hrd[ 0].X, hrd[ 1].X, hrd[ 2].X))
if dR == 0:
a = np.zeros(Bhtou)
for i in range(Bhtou):
a[i] = htou[i].X
return m.ObjVal-tha.X,a,m.ObjVal
else:
if s == 0:
return m.ObjVal
elif s == 1:
pai = np.zeros(zl)
l = m.getConstrs()
for i in range(zl):
pai[i] = l[i].Pi
return pai
elif s == 2:
subVal = m.ObjVal
tailVal = np.dot(pai,htoulast)
maxerVal = subVal + tailVal
cpv = np.zeros(zl)
for i in range(zl):
cpv[i] = z[i].X
drc = htoulast - cpv
return maxerVal,subVal,tailVal,drc,cpv
def fbgen(lb,ub):
fb = np.zeros(40)
for i in range(-20,20):
fb[i+20] = 2**i
fb = fb[(fb<ub)&(fb>lb)]
return fb,len(fb),fb[0]
fbhtou,Bhtou,SCLhtou = fbgen(.5,1000)
vP, fP, pP = 308.9, 626.8, 1044 # valley,flat,peak price of ele (yuan/MWh)
lW = np.array([368,395,395,401,380,358,357,319,314,343,357,335,334,324,316,319,331,341,338,331,398,422,385,352],dtype=np.float64)
Pe = np.array([vP,vP,vP,vP,vP,vP,vP,vP,pP,pP,pP,pP,fP,fP,fP,fP,fP,pP,pP,pP,pP,fP,fP,fP])
lW = np.array([324,395,432,373])
Pe = np.array([vP,vP,fP,pP])
tkCoef = 1/20
qt_pump = np.array([[1,7,11],[1,5.5,8.8]],dtype=np.float64)*100 # 3 points: 2 segments
ht_pump = np.array([[229,210,149],[138,122,86]],dtype=np.float64)
pt_pump = np.array([[330,500,556],[160,233,262]],dtype=np.float64) # kW
qt_pipe = np.array([100,500,1000],dtype=np.float64)/3600
ppK = np.array([1547.2653,1041.4285,833.1428,5207.1425,3644.99975,2395.28555],dtype=np.float64)# h=Kq**2, q in (m3/s), h in (m)
ht_pipe = np.zeros((len(ppK),3),dtype=np.float64)
for i,e in enumerate(ppK):
ht_pipe[i,:] = e * qt_pipe**2
qt_pipe = np.array([100,500,1000],dtype=np.float64)
pipesNum = 6
pumpNum = 2
hrdNum = 3
nss = 4
MaxIte = 50000
cut = np.zeros((MaxIte,nss-1,2,Bhtou+1)) # last stage do not need a cut
act = np.zeros((nss,Bhtou))
val = np.zeros(nss)
vlb = np.zeros(nss)
vldCut = 0
for ite in range(MaxIte):
dR = 0
print('htouLevel:',end='')
for t in range(nss):
val[t],act[t],vlb[t] = wNet(dR,ite,t,act[t-1],0,0)
print('%3d:%8g'%(t,np.dot(fbhtou,act[t])),end='|')
lb = vlb[0]
ub = sum(val)
print('ite=%8d\lb=%8g<%8g='%(ite,lb,ub),end='')
for i in range(nss-1):
print('%8g + ' % (val[i]),end='')
print('%8g' % (val[nss-1]))
dR = 1
for t in range(nss-1,0,-1):
valt = wNet(dR,ite,t,act[t-1],0,0)
# print(act[t-1])
pai = wNet(dR,ite,t,act[t-1],1,0)
maxerVal, subVal, tailVal, drc, cpv = wNet(dR, ite, t, act[t - 1], 2, pai)
cut[ite, t - 1, 0] = np.append(pai, subVal) # sB cut
while 1:
maxerVal, subVal, tailVal, drc, cpv = wNet(dR,ite,t,act[t-1],2,pai)
# print('\nf(pai)=%8g=%8g+%8g' % (maxerVal,subVal,tailVal))
# print('drc is:',drc,'\ncpv is:',cpv)
# print('t=%8d: maxer = %8g|stdVal = %8g' % (t,maxerVal,valt))
if sum(abs(drc)) < SCLhtou / 2 ** 10:# or maxerVal + 1e-4 >= valt:
# print('subGrad = 0, opt Attained!')
cut[ite,t-1,1] = np.append(pai, subVal)
# check tightness
# print('t=%8d: maxer = %8g|stdVal = %8g' % (t,maxerVal,valt))
if maxerVal + 1e-4 <= valt:
print('gap = %8g' % (valt-maxerVal))
break
pai += 19000*drc
# 190 is perfect
# if vldCut != (nss-1)*ite + nss - t: # for lag cuts
# print('one invalid cut may be added ______>______>______>______>')
# htouLevel:Set parameter Username
# Academic license - for non-commercial use only - expires 2023-07-26
# 0: 98| 1: 92| 2: 84| 3: 100|ite= 0\lb=0.877276< 10.6666=0.877276 + 0.849475 + 1.7049 + 7.23492
# htouLevel: 0: 99| 1: 93| 2: 99| 3: 100|ite= 1\lb= 3.43426< 9.35139=0.939056 + 0.849475 + 3.45994 + 4.10292
# htouLevel: 0: 98| 1: 94| 2: 112| 3: 107|ite= 2\lb= 3.46< 9.66469=0.877276 + 0.973035 + 4.96426 + 2.85012
# gap = 0.061457
# htouLevel: 0: 98| 1: 92| 2: 100| 3: 100|ite= 3\lb= 6.18222< 9.33153=0.877276 + 0.849475 + 3.71066 + 3.89412
# htouLevel: 0: 99| 1: 95| 2: 104| 3: 100|ite= 4\lb= 6.21826< 8.80703=0.939056 + 0.973035 + 3.83602 + 3.05892
# htouLevel: 0: 100| 1: 96| 2: 105| 3: 100|ite= 5\lb= 6.28004< 8.66001= 1.00084 + 0.973035 + 3.83602 + 2.85012
# htouLevel: 0: 101| 1: 97| 2: 105| 3: 100|ite= 6\lb= 6.34182< 8.59643= 1.06262 + 0.973035 + 3.71066 + 2.85012
# htouLevel: 0: 98| 1: 98| 2: 105| 3: 100|ite= 7\lb= 6.36756< 8.53285=0.877276 + 1.22016 + 3.5853 + 2.85012
# htouLevel: 0: 102| 1: 99| 2: 105| 3: 100|ite= 8\lb= 6.4036< 8.46927= 1.1244 + 1.03482 + 3.45994 + 2.85012
# htouLevel: 0: 99| 1: 100| 2: 105| 3: 100|ite= 9\lb= 6.42934< 8.40569=0.939056 + 1.28193 + 3.33458 + 2.85012
# htouLevel: 0: 103| 1: 101| 2: 105| 3: 100|ite= 10\lb= 6.46538< 8.34211= 1.18618 + 1.09659 + 3.20922 + 2.85012
# htouLevel: 0: 100| 1: 102| 2: 105| 3: 100|ite= 11\lb= 6.49112< 8.27853= 1.00084 + 1.34372 + 3.08386 + 2.85012
# htouLevel: 0: 104| 1: 103| 2: 105| 3: 100|ite= 12\lb= 6.52716< 8.21495= 1.24796 + 1.15838 + 2.9585 + 2.85012
# htouLevel: 0: 101| 1: 104| 2: 105| 3: 100|ite= 13\lb= 6.5529< 8.15137= 1.06262 + 1.40549 + 2.83314 + 2.85012
# htouLevel: 0: 105| 1: 105| 2: 105| 3: 100|ite= 14\lb= 6.58894< 8.08779= 1.30974 + 1.22016 + 2.70778 + 2.85012
# htouLevel: 0: 98| 1: 106| 2: 105| 3: 100|ite= 15\lb= 6.61468< 8.02421=0.877276 + 1.7144 + 2.58242 + 2.85012
# htouLevel: 0: 106| 1: 107| 2: 105| 3: 100|ite= 16\lb= 6.65072< 7.96063= 1.37152 + 1.28193 + 2.45706 + 2.85012
# htouLevel: 0: 102| 1: 108| 2: 105| 3: 100|ite= 17\lb= 6.67646< 7.89705= 1.1244 + 1.59084 + 2.3317 + 2.85012
# htouLevel: 0: 107| 1: 109| 2: 105| 3: 100|ite= 18\lb= 6.7125< 7.83347= 1.4333 + 1.34372 + 2.20634 + 2.85012
# htouLevel: 0: 99| 1: 110| 2: 105| 3: 100|ite= 19\lb= 6.73824< 7.76989=0.939056 + 1.89974 + 2.08098 + 2.85012
# htouLevel: 0: 108| 1: 111| 2: 105| 3: 100|ite= 20\lb= 6.77428< 7.70631= 1.49508 + 1.4055 + 1.95562 + 2.85012
# htouLevel: 0: 103| 1: 112| 2: 105| 3: 100|ite= 21\lb= 6.80002< 7.64273= 1.18618 + 1.77618 + 1.83026 + 2.85012
# htouLevel: 0: 109| 1: 113| 2: 105| 3: 100|ite= 22\lb= 6.83606< 7.57915= 1.55686 + 1.46728 + 1.7049 + 2.85012
# htouLevel: 0: 100| 1: 114| 2: 106| 3: 101|ite= 23\lb= 6.8618< 7.64093= 1.00084 + 2.08507 + 1.7049 + 2.85012
# htouLevel: 0: 110| 1: 113| 2: 105| 3: 100|ite= 24\lb= 6.89784< 7.57915= 1.61864 + 1.4055 + 1.7049 + 2.85012
# htouLevel: 0: 104| 1: 113| 2: 105| 3: 100|ite= 25\lb= 6.92358< 7.57915= 1.24796 + 1.77618 + 1.7049 + 2.85012
# htouLevel: 0: 111| 1: 113| 2: 105| 3: 100|ite= 26\lb= 6.95962< 7.57915= 1.68042 + 1.34371 + 1.7049 + 2.85012
# htouLevel: 0: 101| 1: 113| 2: 105| 3: 100|ite= 27\lb= 6.98536< 7.57915= 1.06262 + 1.96152 + 1.7049 + 2.85012
# htouLevel: 0: 112| 1: 113| 2: 105| 3: 100|ite= 28\lb= 7.0214< 7.57915= 1.7422 + 1.28193 + 1.7049 + 2.85012
# htouLevel: 0: 105| 1: 113| 2: 105| 3: 100|ite= 29\lb= 7.04714< 7.57915= 1.30974 + 1.71439 + 1.7049 + 2.85012
# htouLevel: 0: 113| 1: 113| 2: 105| 3: 100|ite= 30\lb= 7.08318< 7.57915= 1.80398 + 1.22016 + 1.7049 + 2.85012
# htouLevel: 0: 98| 1: 113| 2: 105| 3: 100|ite= 31\lb= 7.10892< 7.57915=0.877276 + 2.14685 + 1.7049 + 2.85012
# htouLevel: 0: 114| 1: 113| 2: 105| 3: 100|ite= 32\lb= 7.14496< 7.57915= 1.86576 + 1.15837 + 1.7049 + 2.85012
# htouLevel: 0: 106| 1: 113| 2: 105| 3: 100|ite= 33\lb= 7.1707< 7.57915= 1.37152 + 1.65261 + 1.7049 + 2.85012
# htouLevel: 0: 115| 1: 113| 2: 105| 3: 100|ite= 34\lb= 7.20674< 7.57915= 1.92754 + 1.09659 + 1.7049 + 2.85012
# htouLevel: 0: 102| 1: 113| 2: 105| 3: 100|ite= 35\lb= 7.23248< 7.57915= 1.1244 + 1.89973 + 1.7049 + 2.85012
# htouLevel: 0: 116| 1: 113| 2: 105| 3: 100|ite= 36\lb= 7.26852< 7.57915= 1.98932 + 1.03482 + 1.7049 + 2.85012
# htouLevel: 0: 107| 1: 113| 2: 105| 3: 100|ite= 37\lb= 7.29426< 7.57915= 1.4333 + 1.59084 + 1.7049 + 2.85012
# htouLevel: 0: 117| 1: 113| 2: 105| 3: 100|ite= 38\lb= 7.3303< 7.57915= 2.0511 + 0.973035 + 1.7049 + 2.85012
# htouLevel: 0: 99| 1: 113| 2: 105| 3: 100|ite= 39\lb= 7.35604< 7.57915=0.939056 + 2.08507 + 1.7049 + 2.85012
# htouLevel: 0: 118| 1: 113| 2: 105| 3: 100|ite= 40\lb= 7.39208< 7.57915= 2.11288 + 0.911255 + 1.7049 + 2.85012
# htouLevel: 0: 108| 1: 113| 2: 105| 3: 100|ite= 41\lb= 7.41782< 7.57915= 1.49508 + 1.52905 + 1.7049 + 2.85012
# htouLevel: 0: 119| 1: 113| 2: 105| 3: 100|ite= 42\lb= 7.45386< 7.57915= 2.17466 + 0.849475 + 1.7049 + 2.85012
# htouLevel: 0: 103| 1: 113| 2: 105| 3: 100|ite= 43\lb= 7.4796< 7.57915= 1.18618 + 1.83796 + 1.7049 + 2.85012
# htouLevel: 0: 120| 1: 115| 2: 107| 3: 102|ite= 44\lb= 7.51564< 7.70271= 2.23644 + 0.911255 + 1.7049 + 2.85012
# htouLevel: 0: 121| 1: 116| 2: 108| 3: 103|ite= 45\lb= 7.57742< 7.76449= 2.29822 + 0.911255 + 1.7049 + 2.85012
# htouLevel: 0: 100| 1: 113| 2: 105| 3: 100|ite= 46\lb= 7.57915< 7.57915= 1.00084 + 2.0233 + 1.7049 + 2.85012
# htouLevel: 0: 100| 1: 113| 2: 105| 3: 100|ite= 47\lb= 7.57915< 7.57915= 1.00084 + 2.0233 + 1.7049 + 2.85012
# htouLevel: 0: 100| 1: 113| 2: 105| 3: 100|ite= 48\lb= 7.57915< 7.57915= 1.00084 + 2.0233 + 1.7049 + 2.85012