add books

2 files changed, 1141 insertions, 0 deletions

diff --git a/sample_notebooks/NeerajBaunthiyal/chapter1.ipynb b/sample_notebooks/NeerajBaunthiyal/chapter1.ipynb
new file mode 100755
index 00000000..2d6f6ded
--- /dev/null
+++ b/sample_notebooks/NeerajBaunthiyal/chapter1.ipynb
@@ -0,0 +1,570 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chapter1 : Introduction"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.1 : 25"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "from numpy import *\n",
+      "B=100 #W(8Bulb)\n",
+      "F=60 #W(2Fan)\n",
+      "L=100 #W(2Light)\n",
+      "LoadConnected=8*B+2*F+2*L #W\n",
+      "print \"(a) Connected Load =\",LoadConnected,\"W\"\n",
+      "#12 midnight to 5am\n",
+      "demand1=1*F #W\n",
+      "#5am to 7am\n",
+      "demand2=2*F+1*L #W\n",
+      "#7am to 9am\n",
+      "demand3=0 #W\n",
+      "#9am to 6pm\n",
+      "demand4=2*F #W\n",
+      "#6pm to midnight\n",
+      "demand5=2*F+4*B #W\n",
+      "DEMAND=array([demand1, demand2, demand3, demand4, demand5])\n",
+      "max_demand=max(DEMAND) \n",
+      "print \"(b) Maximum demand =\",max_demand,\"W\" \n",
+      "df=max_demand/LoadConnected #demand factor\n",
+      "print \"(c) Demand factor =\",df \n",
+      "E=demand1*5+demand2*2+demand3*2+demand4*9+demand5*6 #Wh\n",
+      "E=E/1000 #kWh\n",
+      "print \"(d) Energy consumed during 24 hours =\",E,\"kWh \"\n",
+      "Edash=LoadConnected*24/1000 #kWh\n",
+      "print \"(e) Energy consumed during 24 hours if all devices are used =\",Edash,\"kWh\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(a) Connected Load = 1120 W\n",
+        "(b) Maximum demand = 520 W\n",
+        "(c) Demand factor = 0.464285714286\n",
+        "(d) Energy consumed during 24 hours = 4.94 kWh \n",
+        "(e) Energy consumed during 24 hours if all devices are used = 26.88 kWh\n"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.2 : page 26"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "LoadA=2.5*1000 #W\n",
+      "#12 midnight to 5am\n",
+      "d1A=100 #W\n",
+      "#5am to 6am\n",
+      "d2A=1.1*1000 #W\n",
+      "#6am to 8am\n",
+      "d3A=200 #W\n",
+      "#8am to 5pm\n",
+      "d4A=0 #W\n",
+      "#5pm to 12 midnight\n",
+      "d5A=500 #W\n",
+      "LoadB=3*1000 #W\n",
+      "#11 pm to 7am\n",
+      "d1B=0 #W\n",
+      "#7 am to 8 am\n",
+      "d2B=300 #W\n",
+      "#8 am to 10 am\n",
+      "d3B=1*1000 #W\n",
+      "#10 am to 6 pm\n",
+      "d4B=200 #W\n",
+      "#6 pm to 11 pm\n",
+      "d5B=600 #W\n",
+      "DEMAND_A=array([d1A, d2A, d3A, d4A, d5A]) #W\n",
+      "DEMAND_B=array([d1B, d2B, d3B, d4B, d5B]) #W\n",
+      "max_demand_A=max(DEMAND_A) #W\n",
+      "max_demand_B=max(DEMAND_B) #W\n",
+      "df_A=max_demand_A/LoadA #demand factor\n",
+      "df_B=max_demand_B/LoadB #demand factor\n",
+      "print \"Demand factor of consumer A & B are :\",round(df_A,2),\"&\",round(df_B ,2)\n",
+      "gd_factor=(max_demand_A+max_demand_B)/max_demand_A \n",
+      "print \"Group diversity factor :\",round(gd_factor,2)\n",
+      "E_A=d1A*5+d2A*1+d3A*2+d4A*9+d5A*7 #Wh\n",
+      "E_B=d1B*8+d2B*1+d3B*2+d4B*8+d5B*5 #Wh\n",
+      "E_A=E_A/1000 #kWh\n",
+      "E_B=E_B/1000 #kWh\n",
+      "print \"Energy consumed by A & B during 24 hours =\",E_A,\"&\",E_B,\"kWh\"\n",
+      "Emax_A=max_demand_A*24/1000 #kWh\n",
+      "Emax_B=max_demand_B*24/1000 #kWh\n",
+      "print \"Maximum energy consumer A & B can consume during 24 hours =\",Emax_B,Emax_A,\"kWh \"\n",
+      "ratio_A=E_A/Emax_A \n",
+      "ratio_B=E_B/Emax_B \n",
+      "print \"Ratio of actual energy to maximum energy of consumer A & B :\",round(ratio_A,2),\"&\",round(ratio_B,2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Demand factor of consumer A & B are : 0.44 & 0.33\n",
+        "Group diversity factor : 1.91\n",
+        "Energy consumed by A & B during 24 hours = 5.5 & 6.9 kWh\n",
+        "Maximum energy consumer A & B can consume during 24 hours = 24.0 26.4 kWh \n",
+        "Ratio of actual energy to maximum energy of consumer A & B : 0.21 & 0.29\n"
+       ]
+      }
+     ],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.3 : page 31"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "n1=600 #No. of apartments\n",
+      "L1=5 #kW#Each Apartment Load\n",
+      "n2=20 #No. of general purpose shops\n",
+      "L2=2 #kW#Each Shop Load\n",
+      "df=0.8 #demand factor\n",
+      "#1 Floor mill\n",
+      "L3=10 #kW#Load\n",
+      "df3=0.7 #demand factor\n",
+      "#1 Saw mill\n",
+      "L4=5 #kW#Load\n",
+      "df4=0.8 #demand factor\n",
+      "#1 Laundry\n",
+      "L5=20 #kW#Load\n",
+      "df5=0.65 #demand factor\n",
+      "#1 Cinema\n",
+      "L6=80 #kW#Load\n",
+      "df6=0.5 #demand factor\n",
+      "#Street lights\n",
+      "n7=200 #no. of tube lights\n",
+      "L7=40 #W#Load of each light\n",
+      "#Residential Load\n",
+      "df8=0.5 #demand factor\n",
+      "gdf_r=3 #group diversity factor\n",
+      "pdf_r=1.25 #peak diversity factor\n",
+      "#Commertial Load\n",
+      "gdf_c=2 #group diversity factor\n",
+      "pdf_c=1.6 #peak diversity factor\n",
+      "#Solution :\n",
+      "#Maximum demand of each apartment\n",
+      "dmax_1a=L1*df8 #kW\n",
+      "#Maximum demand of 600 apartment\n",
+      "dmax_a=n1*dmax_1a/gdf_r #kW\n",
+      "#demand of apartments at system peak time\n",
+      "d_a_sp=dmax_a/pdf_r #kW\n",
+      "#Maximum Commercial demand\n",
+      "dmax_c=(n2*L2*df+L3*df3+L4*df4+L5*df5+L6*df6)/gdf_c #kW\n",
+      "#Commercial demand at system peak time\n",
+      "d_c_sp=dmax_c/pdf_c #kW\n",
+      "#demand of street light at system peak time\n",
+      "d_sl_sp=n7*L7/1000 #kW\n",
+      "#Increase in system peak demand\n",
+      "DI=d_a_sp+d_c_sp+d_sl_sp #kW\n",
+      "print \"Increase in system peak demand =\",DI,\"kW \" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Increase in system peak demand = 438.0 kW \n"
+       ]
+      }
+     ],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.4 : page 35"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#12 to 5 am\n",
+      "L1=20 #MW\n",
+      "t1=5 #hours\n",
+      "#5 to 9 am\n",
+      "L2=40 #MW\n",
+      "t2=4 #hours\n",
+      "#9 to 6 pm\n",
+      "L3=80 #MW\n",
+      "t3=9 #hours\n",
+      "#6 to 10 pm\n",
+      "L4=100 #MW\n",
+      "t4=4 #hours\n",
+      "#10 to 12 am\n",
+      "L5=20 #MW\n",
+      "t5=2 #hours\n",
+      "#Energy Poduced in 24 hours\n",
+      "E=L1*t1+L2*t2+L3*t3+L4*t4+L5*t5 #MWh\n",
+      "print \"Energy Supplied by the plant in 24 hours =\",E,\"MWh \" \n",
+      "LF=E/24 #%#Load Factor\n",
+      "print \"Load Factor =\",round(LF,2),\"% \" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Energy Supplied by the plant in 24 hours = 1420 MWh \n",
+        "Load Factor = 59.17 % \n"
+       ]
+      }
+     ],
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.5 : page 39"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "C=125 #MW#Installed Capacity\n",
+      "#12 to 5 am\n",
+      "L1=20 #MW\n",
+      "t1=5 #hours\n",
+      "#5 to 9 am\n",
+      "L2=40 #MW\n",
+      "t2=4 #hours\n",
+      "#9 to 6 pm\n",
+      "L3=80 #MW\n",
+      "t3=9 #hours\n",
+      "#6 to 10 pm\n",
+      "L4=100 #MW\n",
+      "t4=4 #hours\n",
+      "#10 to 12 am\n",
+      "L5=20 #MW\n",
+      "t5=2 #hours\n",
+      "#Energy Poduced in 24 hours\n",
+      "E=L1*t1+L2*t2+L3*t3+L4*t4+L5*t5 #MWh\n",
+      "LF=E/24 #%#Load Factor\n",
+      "CF=LF/C #%#Capacity Factor\n",
+      "print \"Capacity Factor =\",round(CF,2),\"% \" \n",
+      "UF=100/C #%#Utilisation Factor\n",
+      "print \"Utilisation Factor =\",UF,\"% \" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Capacity Factor = 0.47 % \n",
+        "Utilisation Factor = 0.8 % \n"
+       ]
+      }
+     ],
+     "prompt_number": 35
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.6 : page 40"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#12 to 5 am & 10 to 12 am\n",
+      "L1=20 #MW\n",
+      "E1=L1*24 #MWh\n",
+      "#5 to 9 am\n",
+      "L2=40 #MW\n",
+      "E2=E1+(L2-L1)*17 #MWh\n",
+      "#9 to 6 pm\n",
+      "L3=80 #MW\n",
+      "E3=E2+(L3-L2)*13 #MWh\n",
+      "#6 to 10 pm\n",
+      "L4=100 #MW\n",
+      "E4=E3+(L4-L3)*4 #MWh\n",
+      "#Plotting Energy load curve\n",
+      "%matplotlib inline\n",
+      "from matplotlib.pyplot import *\n",
+      "L=array([0,L1,L2,L3,L4]) #MW\n",
+      "E=array([0,E1,E2,E3,E4]) #Mwh\n",
+      "subplot(3,1,1)\n",
+      "plot(E,L)\n",
+      "xlabel('Energy(MWh)') \n",
+      "ylabel('Load(MW)') \n",
+      "title('Energy Load Curve') \n",
+      "#Energy Supplied\n",
+      "#Upto 5am\n",
+      "t1=5 #hours\n",
+      "E1=L1*t1 #MWh\n",
+      "#Upto 9am\n",
+      "t2=4 #hours\n",
+      "E2=E1+L2*t2 #MWh\n",
+      "#Upto 6pm\n",
+      "t3=9 #hours\n",
+      "E3=E2+L3*t3 #MWh\n",
+      "#Upto 10pm\n",
+      "t4=4 #hours\n",
+      "E4=E3+L4*t4 #MWh\n",
+      "#Upto 12pm\n",
+      "t4=2 #hours\n",
+      "E4=E3+L4*t4 #MWh\n",
+      "#Plotting Mass curve\n",
+      "T=[0,1,2,3,4] #MW\n",
+      "E=[0,E1,E2,E3,E4] #Mwh\n",
+      "subplot(3,1,3)\n",
+      "plot(T,E)\n",
+      "ylabel('Energy(MWh)') \n",
+      "xlabel('0-1: 12-5am 1-2: 5-9am 2-3: 9-6pm 3-4: 6-10pm above4: 10-12pm') \n",
+      "title('Mass Curve') \n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEZCAYAAABSN8jfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUZu5Oj0hAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbc05b82a10>"
+       ]
+      }
+     ],
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.7 : page 45"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dmax=40 #MW#Maximum demand\n",
+      "CF=0.5 #Capacity Factor\n",
+      "UF=0.8 #Utilisation Factor\n",
+      "LF=CF/UF #/Load Factor\n",
+      "print \"(a) Load Factor =\",LF \n",
+      "C=dmax/UF #MW#Plant Capacity\n",
+      "print \"(b) Plant Capacity =\",C,\"MW \" \n",
+      "RC=C-dmax #MW#Reserve Capacity\n",
+      "print \"(c) Reserve Capacity =\",RC,\"MW \" \n",
+      "p=dmax*LF*24*365 #MWh#Annual Energy Production\n",
+      "print \"(d) Annual Energy Production =\",p,\"MWh\" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(a) Load Factor = 0.625\n",
+        "(b) Plant Capacity = 50.0 MW \n",
+        "(c) Reserve Capacity = 10.0 MW \n",
+        "(d) Annual Energy Production = 219000.0 MWh\n"
+       ]
+      }
+     ],
+     "prompt_number": 37
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.8 : page 52"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "L1=50 #MW#Initial\n",
+      "t1=5 #hours\n",
+      "L2=50 #MW#5am\n",
+      "t2=4 #hours\n",
+      "L3=100 #MW#9am\n",
+      "t3=9 #hours\n",
+      "L4=100 #MW#6pm\n",
+      "t4=2 #hours\n",
+      "L5=150 #MW#8pm\n",
+      "t5=2 #hours\n",
+      "L6=80 #MW#10pm\n",
+      "t6=2 #hours\n",
+      "L7=50 #MW\n",
+      "#Energy Required in 24 hours\n",
+      "E=L1*t1+(L2+L3)/2*t2+(L3+L4)/2*t3+(L4+L5)/2*t4+(L5+L6)/2*t5+(L6+L1)/2*t6 #MWh\n",
+      "print \"Energy required in one day =\",E,\"MWh\" \n",
+      "DLF=E/L5/24*100 #%#Daily Load Factor\n",
+      "print \"Daily Load Factor =\",round(DLF,2),\"%\" \n",
+      "#Plotting load curve\n",
+      "% matplotlib inline\n",
+      "T=arange(0,7,1) #Slots\n",
+      "L=array([L1,L2,L3,L4,L5,L6,L7]) #MW\n",
+      "plot(T,L)\n",
+      "ylabel('Load(MW)') \n",
+      "xlabel('0-1: 12-5am 1-2: 5-9am 2-3: 9-6pm 3-4: 6-8pm 4-5:8-10pm 5-6 :10-12pm') \n",
+      "title('Chronological Load Curve') \n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Energy required in one day = 2060.0 MWh\n",
+        "Daily Load Factor = 57.22 %\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEZCAYAAABWwhjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbc0577c050>"
+       ]
+      }
+     ],
+     "prompt_number": 38
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.9 : page 55"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "L1=50 #MW#Initial\n",
+      "t1=5 #hours\n",
+      "L2=50 #MW#5am\n",
+      "t2=4 #hours\n",
+      "L3=100 #MW#9am\n",
+      "t3=9 #hours\n",
+      "L4=100 #MW#6pm\n",
+      "t4=2 #hours\n",
+      "L5=150 #MW#8pm\n",
+      "t5=2 #hours\n",
+      "L6=80 #MW#10pm\n",
+      "t6=2 #hours\n",
+      "L7=50 #MW\n",
+      "#Load Duration Curve\n",
+      "l1=L5 #Mw\n",
+      "l2=L4 #MW\n",
+      "l3=L1 #MW\n",
+      "L=array([l1,l2,l2,l3,l3])\n",
+      "T=arange(0,30,6) #Duration in hours\n",
+      "subplot(3,1,1)\n",
+      "plot(T,L)\n",
+      "ylabel('Load(MW)') \n",
+      "xlabel('Hours') \n",
+      "title('Load Duration Curve') \n",
+      "#Energy Consumed\n",
+      "#Upto 5am\n",
+      "t1=5 #hours\n",
+      "E1=L1*t1 #MWh\n",
+      "#Upto 9am\n",
+      "t2=4 #hours\n",
+      "E2=E1+L2*t2 #MWh\n",
+      "#Upto 6pm\n",
+      "t3=9 #hours\n",
+      "E3=E2+L3*t3 #MWh\n",
+      "#Upto 10pm\n",
+      "t4=4 #hours\n",
+      "E4=E3+L4*t4 #MWh\n",
+      "#Upto 12pm\n",
+      "t4=2 #hours\n",
+      "E4=E3+L4*t4 #MWh\n",
+      "#Plotting Mass curve\n",
+      "T=arange(0,5,1) #MW\n",
+      "E=[0,E1,E2,E3,E4] #Mwh\n",
+      "subplot(3,1,3)\n",
+      "plot(T,E)\n",
+      "ylabel('Energy(MWh)') \n",
+      "xlabel('0-1: 12-5am 1-2: 5-9am 2-3: 9-6pm 3-4: 6-10pm above4: 10-12pm') \n",
+      "title('Mass Curve') \n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEZCAYAAABvpam5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbc0576d9d0>"
+       ]
+      }
+     ],
+     "prompt_number": 39
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
diff --git a/sample_notebooks/NeerajBaunthiyal/chapter1_1.ipynb b/sample_notebooks/NeerajBaunthiyal/chapter1_1.ipynb
new file mode 100755
index 00000000..3adc7469
--- /dev/null
+++ b/sample_notebooks/NeerajBaunthiyal/chapter1_1.ipynb
@@ -0,0 +1,571 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:a43ad834b520d63f186663d7cd92f123a2eb2f923f95a0f00e0d45a5a563ba8b"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chapter1 : Introduction"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.1 : 25"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "from numpy import array\n",
+      "B=100 #W(8Bulb)\n",
+      "F=60 #W(2Fan)\n",
+      "L=100 #W(2Light)\n",
+      "LoadConnected=8*B+2*F+2*L #W\n",
+      "print \"(a) Connected Load =\",LoadConnected,\"W\"\n",
+      "#12 midnight to 5am\n",
+      "demand1=1*F #W\n",
+      "#5am to 7am\n",
+      "demand2=2*F+1*L #W\n",
+      "#7am to 9am\n",
+      "demand3=0 #W\n",
+      "#9am to 6pm\n",
+      "demand4=2*F #W\n",
+      "#6pm to midnight\n",
+      "demand5=2*F+4*B #W\n",
+      "DEMAND=array([demand1, demand2, demand3, demand4, demand5])\n",
+      "max_demand=max(DEMAND) \n",
+      "print \"(b) Maximum demand =\",max_demand,\"W\" \n",
+      "df=max_demand/LoadConnected #demand factor\n",
+      "print \"(c) Demand factor =\",df \n",
+      "E=demand1*5+demand2*2+demand3*2+demand4*9+demand5*6 #Wh\n",
+      "E=E/1000 #kWh\n",
+      "print \"(d) Energy consumed during 24 hours =\",E,\"kWh \"\n",
+      "Edash=LoadConnected*24/1000 #kWh\n",
+      "print \"(e) Energy consumed during 24 hours if all devices are used =\",Edash,\"kWh\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(a) Connected Load = 1120 W\n",
+        "(b) Maximum demand = 520 W\n",
+        "(c) Demand factor = 0.464285714286\n",
+        "(d) Energy consumed during 24 hours = 4.94 kWh \n",
+        "(e) Energy consumed during 24 hours if all devices are used = 26.88 kWh\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.2 : page 26"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "LoadA=2.5*1000 #W\n",
+      "#12 midnight to 5am\n",
+      "d1A=100 #W\n",
+      "#5am to 6am\n",
+      "d2A=1.1*1000 #W\n",
+      "#6am to 8am\n",
+      "d3A=200 #W\n",
+      "#8am to 5pm\n",
+      "d4A=0 #W\n",
+      "#5pm to 12 midnight\n",
+      "d5A=500 #W\n",
+      "LoadB=3*1000 #W\n",
+      "#11 pm to 7am\n",
+      "d1B=0 #W\n",
+      "#7 am to 8 am\n",
+      "d2B=300 #W\n",
+      "#8 am to 10 am\n",
+      "d3B=1*1000 #W\n",
+      "#10 am to 6 pm\n",
+      "d4B=200 #W\n",
+      "#6 pm to 11 pm\n",
+      "d5B=600 #W\n",
+      "DEMAND_A=array([d1A, d2A, d3A, d4A, d5A]) #W\n",
+      "DEMAND_B=array([d1B, d2B, d3B, d4B, d5B]) #W\n",
+      "max_demand_A=max(DEMAND_A) #W\n",
+      "max_demand_B=max(DEMAND_B) #W\n",
+      "df_A=max_demand_A/LoadA #demand factor\n",
+      "df_B=max_demand_B/LoadB #demand factor\n",
+      "print \"Demand factor of consumer A & B are :\",round(df_A,2),\"&\",round(df_B ,2)\n",
+      "gd_factor=(max_demand_A+max_demand_B)/max_demand_A \n",
+      "print \"Group diversity factor :\",round(gd_factor,2)\n",
+      "E_A=d1A*5+d2A*1+d3A*2+d4A*9+d5A*7 #Wh\n",
+      "E_B=d1B*8+d2B*1+d3B*2+d4B*8+d5B*5 #Wh\n",
+      "E_A=E_A/1000 #kWh\n",
+      "E_B=E_B/1000 #kWh\n",
+      "print \"Energy consumed by A & B during 24 hours =\",E_A,\"&\",E_B,\"kWh\"\n",
+      "Emax_A=max_demand_A*24/1000 #kWh\n",
+      "Emax_B=max_demand_B*24/1000 #kWh\n",
+      "print \"Maximum energy consumer A & B can consume during 24 hours =\",Emax_B,Emax_A,\"kWh \"\n",
+      "ratio_A=E_A/Emax_A \n",
+      "ratio_B=E_B/Emax_B \n",
+      "print \"Ratio of actual energy to maximum energy of consumer A & B :\",round(ratio_A,2),\"&\",round(ratio_B,2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Demand factor of consumer A & B are : 0.44 & 0.33\n",
+        "Group diversity factor : 1.91\n",
+        "Energy consumed by A & B during 24 hours = 5.5 & 6.9 kWh\n",
+        "Maximum energy consumer A & B can consume during 24 hours = 24.0 26.4 kWh \n",
+        "Ratio of actual energy to maximum energy of consumer A & B : 0.21 & 0.29\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.3 : page 31"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "n1=600 #No. of apartments\n",
+      "L1=5 #kW#Each Apartment Load\n",
+      "n2=20 #No. of general purpose shops\n",
+      "L2=2 #kW#Each Shop Load\n",
+      "df=0.8 #demand factor\n",
+      "#1 Floor mill\n",
+      "L3=10 #kW#Load\n",
+      "df3=0.7 #demand factor\n",
+      "#1 Saw mill\n",
+      "L4=5 #kW#Load\n",
+      "df4=0.8 #demand factor\n",
+      "#1 Laundry\n",
+      "L5=20 #kW#Load\n",
+      "df5=0.65 #demand factor\n",
+      "#1 Cinema\n",
+      "L6=80 #kW#Load\n",
+      "df6=0.5 #demand factor\n",
+      "#Street lights\n",
+      "n7=200 #no. of tube lights\n",
+      "L7=40 #W#Load of each light\n",
+      "#Residential Load\n",
+      "df8=0.5 #demand factor\n",
+      "gdf_r=3 #group diversity factor\n",
+      "pdf_r=1.25 #peak diversity factor\n",
+      "#Commertial Load\n",
+      "gdf_c=2 #group diversity factor\n",
+      "pdf_c=1.6 #peak diversity factor\n",
+      "#Solution :\n",
+      "#Maximum demand of each apartment\n",
+      "dmax_1a=L1*df8 #kW\n",
+      "#Maximum demand of 600 apartment\n",
+      "dmax_a=n1*dmax_1a/gdf_r #kW\n",
+      "#demand of apartments at system peak time\n",
+      "d_a_sp=dmax_a/pdf_r #kW\n",
+      "#Maximum Commercial demand\n",
+      "dmax_c=(n2*L2*df+L3*df3+L4*df4+L5*df5+L6*df6)/gdf_c #kW\n",
+      "#Commercial demand at system peak time\n",
+      "d_c_sp=dmax_c/pdf_c #kW\n",
+      "#demand of street light at system peak time\n",
+      "d_sl_sp=n7*L7/1000 #kW\n",
+      "#Increase in system peak demand\n",
+      "DI=d_a_sp+d_c_sp+d_sl_sp #kW\n",
+      "print \"Increase in system peak demand =\",DI,\"kW \" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Increase in system peak demand = 438.0 kW \n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.4 : page 35"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#12 to 5 am\n",
+      "L1=20 #MW\n",
+      "t1=5 #hours\n",
+      "#5 to 9 am\n",
+      "L2=40 #MW\n",
+      "t2=4 #hours\n",
+      "#9 to 6 pm\n",
+      "L3=80 #MW\n",
+      "t3=9 #hours\n",
+      "#6 to 10 pm\n",
+      "L4=100 #MW\n",
+      "t4=4 #hours\n",
+      "#10 to 12 am\n",
+      "L5=20 #MW\n",
+      "t5=2 #hours\n",
+      "#Energy Poduced in 24 hours\n",
+      "E=L1*t1+L2*t2+L3*t3+L4*t4+L5*t5 #MWh\n",
+      "print \"Energy Supplied by the plant in 24 hours =\",E,\"MWh \" \n",
+      "LF=E/24 #%#Load Factor\n",
+      "print \"Load Factor =\",round(LF,2),\"% \" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Energy Supplied by the plant in 24 hours = 1420 MWh \n",
+        "Load Factor = 59.17 % \n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.5 : page 39"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "C=125 #MW#Installed Capacity\n",
+      "#12 to 5 am\n",
+      "L1=20 #MW\n",
+      "t1=5 #hours\n",
+      "#5 to 9 am\n",
+      "L2=40 #MW\n",
+      "t2=4 #hours\n",
+      "#9 to 6 pm\n",
+      "L3=80 #MW\n",
+      "t3=9 #hours\n",
+      "#6 to 10 pm\n",
+      "L4=100 #MW\n",
+      "t4=4 #hours\n",
+      "#10 to 12 am\n",
+      "L5=20 #MW\n",
+      "t5=2 #hours\n",
+      "#Energy Poduced in 24 hours\n",
+      "E=L1*t1+L2*t2+L3*t3+L4*t4+L5*t5 #MWh\n",
+      "LF=E/24 #%#Load Factor\n",
+      "CF=LF/C #%#Capacity Factor\n",
+      "print \"Capacity Factor =\",round(CF,2),\"% \" \n",
+      "UF=100/C #%#Utilisation Factor\n",
+      "print \"Utilisation Factor =\",UF,\"% \" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Capacity Factor = 0.47 % \n",
+        "Utilisation Factor = 0.8 % \n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.6 : page 40"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#12 to 5 am & 10 to 12 am\n",
+      "L1=20 #MW\n",
+      "E1=L1*24 #MWh\n",
+      "#5 to 9 am\n",
+      "L2=40 #MW\n",
+      "E2=E1+(L2-L1)*17 #MWh\n",
+      "#9 to 6 pm\n",
+      "L3=80 #MW\n",
+      "E3=E2+(L3-L2)*13 #MWh\n",
+      "#6 to 10 pm\n",
+      "L4=100 #MW\n",
+      "E4=E3+(L4-L3)*4 #MWh\n",
+      "#Plotting Energy load curve\n",
+      "%matplotlib inline\n",
+      "import matplotlib.pyplot as plt\n",
+      "L=array([0,L1,L2,L3,L4]) #MW\n",
+      "E=array([0,E1,E2,E3,E4]) #Mwh\n",
+      "plt.subplot(3,1,1)\n",
+      "plt.plot(E,L)\n",
+      "plt.xlabel('Energy(MWh)') \n",
+      "plt.ylabel('Load(MW)') \n",
+      "plt.title('Energy Load Curve') \n",
+      "#Energy Supplied\n",
+      "#Upto 5am\n",
+      "t1=5 #hours\n",
+      "E1=L1*t1 #MWh\n",
+      "#Upto 9am\n",
+      "t2=4 #hours\n",
+      "E2=E1+L2*t2 #MWh\n",
+      "#Upto 6pm\n",
+      "t3=9 #hours\n",
+      "E3=E2+L3*t3 #MWh\n",
+      "#Upto 10pm\n",
+      "t4=4 #hours\n",
+      "E4=E3+L4*t4 #MWh\n",
+      "#Upto 12pm\n",
+      "t4=2 #hours\n",
+      "E4=E3+L4*t4 #MWh\n",
+      "#Plotting Mass curve\n",
+      "T=[0,1,2,3,4] #MW\n",
+      "E=[0,E1,E2,E3,E4] #Mwh\n",
+      "plt.subplot(3,1,3)\n",
+      "plt.plot(T,E)\n",
+      "plt.ylabel('Energy(MWh)') \n",
+      "plt.xlabel('0-1: 12-5am 1-2: 5-9am 2-3: 9-6pm 3-4: 6-10pm above4: 10-12pm') \n",
+      "plt.title('Mass Curve') \n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEZCAYAAABSN8jfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXmYVMXV/z9fERFlFwVZZHHXoCjuUSBmMxo1b3zdoiZq\nNmNcE/NGNG8kMRrFxC2JiYkat6Ci8fWHxo2oRNwAFZDVjUUQAWURRFnn/P6oauZO0z3TM90zfXvm\nfJ6nn763bt1b59b03O+tqlOnZGY4juM4TjFsUW4DHMdxnMrHxcRxHMcpGhcTx3Ecp2hcTBzHcZyi\ncTFxHMdxisbFxHEcxykaFxPHSTmS7pR0ZbntcJzacDFxUoGkuZI+lbQq8bm53HbVhaQqSf0buRiL\nn3w27CjpdkkLJa2UNFPScEnbNLJdjrMJFxMnLRjwdTNrn/hcUOpCJLUq9TWbCOVMlLoALwNtgEPM\nrAPwZaAjsHO9C6nc+nHKjIuJk3oknSnpBUnXSVomabakoxLHOybezBdIulLSFolzX5R0vaSPgCsk\ndZH0qKSPJU2Q9BtJ42L+P0n6XVb5oyVdVE+bO0q6W9KS2Oq6XJLisZ0lPSvpI0kfSrpXUsfEuftJ\nej22Mu4Htq6lqJ8AH5vZ6Wb2HoCZLTCzi81sqqS+sfW06X9d0lhJ381TP1dKWi5p70T+7WOrsWvc\n/7qkyTHfi5IG1KdunOaJi4mTJnK+fUcOAmYB2wEjgNsTx+4E1hHexPcDvgJ8L+vcd4EdgKuBW4BV\nQDfgO8C3qe5GuhM4NfHg7wp8EfhHPe/lD0B7oB8wJJZxVuL4VcCOwJ5Ab2B4LG8r4BHgLqAz8CBw\nAvm7ub4EPFxP27K7zZL18+t4vVMTx08CxprZR5L2I9T994EuwK3A6Gi304JxMXHSgoBH4ttu5vPd\nxPF5Zna7hWBydwM7StpBUjfga8DFZvaZmX0I3Aickjh3oZn9ycyqgPXAN4ErzGyNmc0kPLgFYGYT\ngY8JAkK8znPxuoXdSOgqOhkYZmarzWwe8HvgjFjGu2b2jJmtN7OPgBsIggNwCLClmd1kZhvN7J/A\nxFqK6wJ8UKhtedhUP2a2BhhJzfr7VkwD+AFwq5lNtMDdwNpot9OC2bLcBjhOxIDjzezZPMcXbcpo\n9mlsOLQDugKtgQ9iGoSXpPcS585PbG9P+N0n0xZklXU3cDrw7/h9Q31uJGHTvETae0BPgCiANwGH\nE1ovWwDLYr4ewPtZ15tH/lbb0nhOMczP2h8LbCPpIGAJsC/wf/FYH+Dbks5P5G9NaGU5LRhvmTiV\nznzCm/F2ZtY5fjqaWbIfP9ml8yGwgdC1lCG5DXAvcLykfYE9CN1O9eEjQguobyJtJ6pF62pgI/A5\nM+tIaLFk/hc/IIpOgj7k7+b6N/BfSihpFqvjd9Kzq3tWnhrXNrONwChCV9epwKNmlrnOe8BVibru\nbGbtzOyBPOU7LQQXEydN1DZmkhMz+wB4GrheUntJW8QB7sF58m8kjAkMl9RW0h6Eh7kl8iwAXiW0\nUB4ys7V1mNFG0taZT0wbBVwlqZ2kPsDFBJGC0KJaDayU1BP4WeJaLwMbJF0gqbWkbwIH1lL29UAH\n4C5JOwFI6inp95I+F7vn3gfOkNRK0tkU5uWV6epKdnEB/A04R9JBCmwr6RhJ7Qq4ptOMcTFx0sSj\nqjnP5J8xPdc8i+T+t4GtgBmE7qIHqX77znXueQTX2UWE8ZL7CAP4Se4CBgD3FGD3dODTxOc7wPkE\nwZgNjCMM4P895v8VsD9hbOZR4J8ZG81sHWFM50xCF9ZJ8XhOzGw5cBihJTRe0kpCa2UFYVAdwmD5\nzwgtpr2AF5OXIEerx8wmAJ8Quq+eSKS/Fq/3R0Jdv02of6eFo3IsjiXpDuAYYEmmOyL6yz9AaNLP\nBU4ysxXx2DDgbELXwAVm9nSTG+00WyRdC+xgZmcl0o4A7jWzPuWzzHEqh3K1TP4OHJWVdikwxsx2\nA56J+0jai+AZs1c855akz7zj1BdJu0vaJ3bTHER4Ufm/xPHWwEWELh3HcQqgLA9lMxsHLM9KPo7Q\ntUD8/kbcPh64L7pRzgXeIfjFO05DaU/oOvoEuB/4nZmNBpC0J+G32Y3gYuw4TgGkyTW4m5ktjtuL\nCf/MENweX0nkW8Dm3i6OUzBm9iqwa55jMwkD5I7j1INUdhfFiWm1DeY0/UCP4ziOk5c0tUwWS+pu\nZosk7UiYLAXBrTE5D6AXm0/qQpILjOM4TgMws3q75WeTppbJaIJLJfH7kUT6KZK2ktSP0D0xIdcF\nzCz1nyuuuKLsNjQXOyvBRrez5dp54olX0LWrcccd5beltk+pKEvLRNJ9hFhEXSXNB34JXAOMivGY\n5hL86zGzGZJGEeYQbADOtVLWgOM4TgnZsAEuuwyefhqeeQYGDSq3RU1DWcTEzE7Nc+hLefJfTQhB\n4TiOk1qWLIFTToEtt4Qf/KDlCAmkq5urRTB06NBym1AQlWBnJdgIbmepSaud48fDAQfAYYfBE0/A\n0UcPLbdJTUpZZsA3BpK898txnCbHDG69FX75S/jb3+D448ttUf2QhJVgAD5N3lyO4zgVxWefwbnn\nwsSJ8MILsNtu5baofHg3l+M4TgOYOxcOPxzWrAldXC1ZSCBlYiJpmKTpkqZKGimpjcJ63WMkvSXp\naUmdym2n4zgtm6eegoMPhjPOgJEjYdtty21R+Sl6zCTGMuoLVBGWVp3VwOv0BZ4F9jSztZIeAB4H\n9gY+MrMRkn4OdDazS3Oc72MmjuM0KlVVcPXVcMstcP/9MDjnqjmVRVnHTOLkwYuBowmz0RcSFjba\nUVIv4DHgBguBGQtlJWFNhm0kbSSsDLcQGEb1+th3EZYU3UxMHMdxGpMVK+Db34alS+HVV6FHsYsl\nNzMa2s11LWFRnz3NbIiZnWpmp5jZEMIyp/8CRtTngma2DPg9YVnQhcAKMxtD/gCQjuM4TcLUqXDg\ngdCnDzz3nAtJLlLjGixpZ4JAHUFYge5BQpjwP5hZ50S+ZWbWJcf53s3lOE5JMAsD7M8/Hz6jR8MN\nN8Dpp5fbstJT7m6uKYSlP18EXjKzOcUaAhwQr7U0lvEwcCiwKE8AyM0YPnz4pu2hQ4emdnKT4zjp\nwgxmzqwWj+efD+MjgweHzy9+Af36ldvK0jB27FjGjh1b8us2qGUiaQBh3enDCA/8dsBLVIvL+AZc\nc1/COtkHAmuAOwkBHfsAS83sWkmXAp18AN5xnGLYsAGmTKkWjnHjoGPHavE44gjYeWdQ0e/r6adU\nLZOSdHNJ6gqcQljqtJ+ZtWrgdf6HEDG4Cngd+B5hVbxRwE5krQ2fda6LieM4OVm7NkwszIjHyy9D\n7941xaNnC11yr6xiIqkVsD/VrZNdCCsgvgy8bGb/KdawBtjkYuI4DgCffBIEIyMer70Ge+5ZLR6f\n/zx07VpuK9NBucXkU0JI+D8B/zGz2cUaUiwuJo7Tclm2LIQzyYjHjBmw//7VrY7DDoP27cttZTop\nt5icSmiR7E/okppAdatks1UQmwIXE8dpOSxcGMY5MuMdc+fCoYdWi8dBB8HWW5fbysogNWMmkrYB\nDgI+D5wFbGVmOxVrWAPscDFxnGaIGcyZUy0czz8fWiJHHBE+gwfDfvuFNUSc+lP2qMGStgUOoXrc\n5EDCuMkLxRrlOE7Lpaqq2k03Ix5JN92LL4a99oItUhVZ0GloN9dkoDfwKtEdGBhvZquKNigEcryN\nEJPLCK2dt4EHCG7Cc8nh0eUtE8epTDZsgMmTa3ZbdepU3eoYPBj6928ZbrrloNxjJvsAUxvj6S3p\nLsKg/h2StgS2BS6njmCPLiaOUxm4m266KLeY/JTQashlgJnZ9Q0yRuoITDKz/lnps4AhZrZYUndg\nrJntkZXHxcRxUkhdbrqHHw7bbVduK1su5RaTKmAK8ASwNvu4mf2qQcZIA4FbCW7H+wKvESZCLsjE\n55IkYFkyXldMdzFxnBRQm5vu4MHB68rddNNDucVkIHAq8FXCTPX7gGfMrKooY6QDCC7Gh5nZREk3\nAquA8+oK9uhi4jjlIemm+/zzMG9etZvu4MEh2q676aaXsnpzmdlkYHKMlXUoQVhulvRzMxtdhD0L\nCK2QiXH/IcJ6JgUFe/RAj47TuCTddDOf5curB8vPOgsGDnQ33TSTqkCPm06WdgBOBE4C1gG/NLOX\nizJIeh74npm9JWk4YZEsqCPYo7dMHKf0JN10M55WVVUwZEj1YLm76VY25e7m+i5BQNoQWg8PJhaw\nKs6gED34NmAr4F2Ca3Ar6gj26GLiOMWTcdPNiMcLLwQ33YxwuJtu86PcYlIFTAPm5ThsZnZcsYbV\nFxcTx6k/a9ZUu+mOGxe8rnbaqVo83E23+VNuMRkaNzMnJw0xjxrsOOlk1apqN91x44Kb7l57Vbc6\n3E235ZGa2FxpwcXEcTZn6dLQVZXxtnI3XSebcrdM/kVYCfFfZvZp1rFtgGOB75jZ0cUaWA+bXEyc\nFo+76Tr1pdxisgNwHvDfwEbgA0JXV3eCu/EDwJ/M7MNiDayHTS4mTovCDGbPrikeSTfdwYPdTdep\nm9R0c8XwJn3i7jwzW1SsUQ20w8XEadZUVYVuqqR4QLVwDB4cwpS4m65TH1IjJqUmLgn8KmHy4rGS\nulBHxOB4nouJ06zIdtMdNw46d64pHv36uZuuUxzl7ub6hGpPrmzMzDo02CDpJ8AgoL2ZHSdpBHVE\nDI7nuZg4FU3STTcTTbdPn5rRdHv0KLeVTnMjFS0TSb8BFgL3xqTTgB5m9r8NvF4vwsD+VcBPYsuk\nzojB8VwXE6eiSLrpPv88vP56cNPNCIe76TpNQVrE5A0z26eutHpc70HgaqADcEkUk+V1RQyOx1xM\nnNSyfj28+SZMmRJEY9y4MP4xaFC1eLibrlMOyr5sb2S1pNMJUYMBTgE+aciFJH0dWGJmkxKTImtg\nZiYpr2J4oEcnDSxfDm+8EcY7pkwJn5kzwwJQ++4b1iv//e/dTdcpD2kN9NgPuImwBjyEJXwvNLO5\nDbjW1cAZwAZga0Lr5GHC2vJDExGDn/NuLicNVFUF19yMYGTEY9ky2GefIByZz4ABsO225bbYcTYn\nFd1cjYWkIVR3c42gjojB8RwXE6fRWL0apk6tFo4pU0Lro0uXIBYDB1YLR//+7p7rVA6p6OaS1Bb4\nLrAXoTUBgJmdXaRdUO0tdg0wKkYqnkuIVuw4jYIZvP9+zS6qKVNg/vwwhyMjGCefHFofnTcbvXOc\nlkmx3VwPATMJXly/Ak4HZprZBaUxr162eMvEqRfr1oVB8GQX1ZQpYcZ4sqWx776w++7QunW5LXac\n0pOKbi5Jk81sYMaDS1Jr4AUzO7hYwxpgi4uJk5cPP6zZ0pg8Gd5+O3RJZXdTde9ebmsdp+lIRTcX\nYXVFgI8lDQAWAdsXeU3HaTAbNwaRyO6mWr26WiyGDIELL4S993ZvKscpFcWKyd9iuJNfAKOBdkCD\nJiw6Tn1ZuTIMgie7qaZPDy2LTEvjhz8M3336eNgRx2lMUunN1RC8m6v5YgZz527eTbV4MXzuczW7\nqQYMgA4NDubjOC2PtIyZdAKuAAbHpLHAr83s4wZerzdwN7ADwZvrr2Z2cyHBHl1MmgeffRZaF8lu\nqjfegHbtag6IDxwIu+wCrVqV22LHqWzSIiYPA1OBuwjrmZwB7GNm32zg9boD3c1ssqR2wGvAN4Cz\nqCPYo4tJZWEGixZtPuFvzhzYbbfNvam6di23xY7TPEmLmEwxs33rSivi+o8Af4yfWoM9upikl/Xr\nYdaszbupqqpqtjT23TfM5dhqq3Jb7Dgth7R4c30m6QgzGxeNOhz4tI5zCkJSX2A/YDzQzcwWx0OL\ngW6lKMMpLWvWhLGN2bPhrbeqhWPWLNhpp2rhuPDCIB49eviguOM0F4oVk3OAuyV1jPvLge8UeU1i\nF9c/CXG+VinxxKkt2KMHemxcNm4Ma4zPmRMEI/k9Zw4sXRpEo1+/MJ5x6KFwzjlhkNzjUjlOOkhl\noMdNF4liYmYfS7rIzG4s4lqtgceAJzLXiWua1Brs0bu5SsPy5TUFIika770XYlH16xcm+2V/9+jh\nA+KOU2mkYswk5wWl+WbWu4HnijCYv9TMLk6k1xns0cWkMNasgXnzcrcuZs8O4xi5hKJfP+jbF9q2\nLfcdOI5TSpqrmBwOPA+8QXWgx2HABGAUsBPuGlwrVVXwwQe5u6Fmzw5hRXr3zt+66NLFxzEcpyXR\nLMWkyHJbjJisWJG7G2rOnNDq6Ngxf+uiZ88QyNBxHAfKLCaSPqG65ZDNNmbW5D3nzUlM1q0LopCr\nG2rOnOBqmxSI7K4oH+x2HKdQUtsyKReVJCZVVWHCXj6vqMWLQwsiX+uia1fvinIcpzS4mGSRNjFZ\nuTL/IPfcuSF+VL5xi169vCvKcZymwcUki6YWk3XrgqtsPsFYsyZ3N1T//qErql27JjPVcRwnLy1O\nTCQdBdwItAJuM7Nrs46XVEzMQndTPq+oRYvCvIp8rYvtt8/dFTV27NiKmExZCXZWgo3gdpYat7O0\npCWcSpMgqRUhPteXgPeBiZJGm9nMYq67alV+r6g5c8JAdlIgDj0UTjst7Pfu3bBlXCvlB1YJdlaC\njeB2lhq3M51UhJgABwHvmNlcAEn3A8cT1p/Py/r1MH9+fq+o1as374b64her09q3b/wbcxzHaQ5U\nipj0BOYn9hcAm60zf9VVNcVi4cKw6l6ydXHssdX73bq5V5TjOE4pqIgxE0knAEeZ2ffj/unAwWZ2\nfiJP+m/EcRwnhbSYMRPCOElyVn1vQutkE6WoDMdxHKdhbFFuAwrkVWBXSX0lbQWcDIwus02O4zhO\npCJaJma2QdJ5wFME1+Dbi/XkchzHcUpHRYyZOI7jOOmmUrq58iLpKEmzJL0t6edltqW3pOckTZc0\nTdIFMb2LpDGS3pL0tKROiXOGRdtnSfpKE9vbStIkSY+m1U5JnSQ9JGmmpBmSDk6bnbHM6ZKmShop\nqU0abJR0h6TFkqYm0uptl6RB8d7elnRTE9l5XfybT5H0cGI111TZmTj2U0lVkrqk1U5J58c6nSbp\n2kR6aew0s4r9ELq83gH6Aq2BycCeZbSnOzAwbrcD3gT2BEYA/xPTfw5cE7f3ija3jvfwDrBFE9r7\nE+AfwOi4nzo7CYulnR23twQ6psnOWM5soE3cf4CwdHXZbQSOAPYDpibS6mNXpudiAnBQ3H6c4FnZ\n2HZ+OVMvwDVptTOm9waeBOYAXdJoJ/AFYAzQOu5vX2o7K71lsmkyo5mtBzKTGcuCmS0ys8lx+xPC\npMqewHGEhyLx+xtx+3jgPjNbb2FC5juEe2p0JPUCjgZuAzKecKmyM76NHmFmd0AYOzOzj1Nm50pg\nPbCNpC2BbYCFabDRzMYBy7OS62PXwQrLZLc3swkx392JcxrNTjMbY2ZVcXc80CuNdkauB/4nKy1t\ndv4I+G18TmJmH5bazkoXk1yTGXuWyZYaSOpLeDsYD3Qzs8Xx0GKgW9zuQU0X56a0/wbgZ0BVIi1t\ndvYDPpT0d0mvS/qbpG3TZKeZLQN+D7xHEJEVZjYmTTZmUV+7stPfp+n/x84mvBmTw56y2inpeGCB\nmb2RdShVdgK7AoMlvSJprKQDSm1npYtJKr0HJLUD/glcaGarkscstBlrs7vR70nS14ElZjaJ6lZJ\nTSNSYCehW2t/4BYz2x9YDVxaw4gy2ylpZ+AiQhdBD6CdwqTaagPSUZebF1q3XWVH0uXAOjMbWW5b\nspG0DXAZcEUyuUzm1MWWQGczO4TwEjmq1AVUupjUOZmxqZHUmiAk95jZIzF5saTu8fiOwJKYnm1/\nr5jW2BwGHCdpDnAfcKSke1Jo5wLCW9/EuP8QQVwWpcjOA4CXzGypmW0AHgYOTZmNSerzN14Q03tl\npTeJvZLOJHTFnpZITpOdOxNeIqbE/6VewGuSuqXMTmLZDwPE/6cqSV1LaWeli0mqJjNKEnA7MMPM\nbkwcGk0YlCV+P5JIP0XSVpL6EZqiE2hkzOwyM+ttZv2AU4BnzeyMFNq5CJgvabeY9CVgOvBoiuyc\nBRwiqW38+38JmJEyG5PU628c/wYrFbzoBJyROKfRUFhy4mfA8Wa2Jsv+VNhpZlPNrJuZ9Yv/SwuA\n/WM3YmrsjDwCHAkQ/5+2MrOPSmpnKb0IyvEBvkbwmnoHGFZmWw4njEFMBibFz1FAF+DfwFvA00Cn\nxDmXRdtnAV8tg81DqPbmSp2dwL7ARGAK4c2qY9rsJAy+TgemEga1W6fBRkKrcyGwjjC2eFZD7AIG\nxXt7B7i5Cew8G3gbmJf4P7olRXauzdRn1vHZRG+utNkZf5P3xHJfA4aW2k6ftOg4juMUTaV3czmO\n4zgpwMXEcRzHKRoXE8dxHKdoXEwcx3GconExcRzHcYrGxcRxHMcpGhcTp1kjaaNCmP3MJzsgX1Pa\n0kbSfxToG0OWX5k43lXSekk3S+ooaWni2KExf4+431HS0nitsZIG5ShvH0m3N83dOS0dFxOnufOp\nme2X+Iwo9oIxOnBDOA14zKond80hhAvJcCIwDcBCdOSFkvaMxw4DXgc+H/cPAcbHa+WcLGYh+ODO\nknZooL2OUzAuJk6LRNJcScMlvSbpDUm7x/RtFRYXGh8jFR8X08+UNFrSM8CYGD5llMKiWA/HaKyD\nJJ0l6YZEOd+XdH3cPRX4fwkzPgVmJloVJxEC8GWCBb5EEBEI8b5uTOwfBryYuNaJ0eY3JR2eSH+C\nIFKO06i4mDjNnbZZ3VyZB6sBH5rZIODPwCUx/XLgGTM7mBDL6LoYHRbCkgInmNkXgB8DS81sb+B/\nCaEnjCAGx0pqFc85E7g97n/OzN7Ksu9+QmykXsBGQhiMDC9SLR79gQcJgSWJ6S8l8raKNl9EzSi2\nE4DBddaS4xRJQ5vrjlMpfGZm++U59nD8fh34Ztz+CkEMMuLSBtiJIBRjzGxFTP88oaWAmU2X9Ebc\nXi3p2XiNWYSV7abHSLI1liOIPAX8hrC2yANZx14GhimsjTPXzNbGMZJtCdGTx+e5l76J9A+y9h2n\nUXAxcVoya+P3Rmr+L3zTzN5OZpR0MGE9lRrJea57G6GFMxO4o7b8ZrZe0muEJZT3IrGanZm9rbBG\n+7FUt0JeIwRCnGtmnxZwLyLla5Y4zQPv5nKcmjwFXJDZkZRp1WQLwYuEMQ4k7QUMyBywsNRpL+Bb\nhAiuAB8B7fKU+Xvg54lWT5JXgAsJrRTi90XAC4XdDjsSou86TqPiYuI0d7LHTK7OkSfpEXUl0DoO\nyk8DfpUjD8AtwPaSpsdzpgMfJ46PAl6IXlmY2UZgWmagP3FNzGyGmd2Tp5wXCcL0atx/hbCccXK8\nJNf9ZDgIeL6WvI5TEjwEveM0AElbEMZD1ios3TsG2M3CaotIehS43syeS5xzJmEN9mub0M6xwElm\ntqSuvI5TDN4ycZyGsS3wgqTJhMHvH5nZBkmdJL1JmN/yXNY5I4Fj4sp1jY6kfYB3XEicpsBbJo7j\nOE7ReMvEcRzHKRoXE8dxHKdoXEwcx3GconExcRzHcYrGxcRxHMcpGhcTx3Ecp2hcTBzHcZyicTFx\nHMdxisbFxHEcxykaFxPHcRynaFxMHMdxnKJxMXEcx3GKxsXEcRzHKRoXE8dxHKdoXEwcx3GconEx\ncRzHcYrGxcRxHMcpGhcTx3Ecp2hcTBzHcZyicTFxHMdxisbFxHEcxykaFxPHcRynaFxMHMdxnKJx\nMXEcx3GKxsXEcRzHKRoXE8dxHKdoXEwcx3GconExcRzHcYrGxcRxHMcpGhcTx3Ecp2hcTBzHcZyi\ncTFxHMdxisbFxHEcxykaFxPHcRynaFxMHMdxnKJxMXEcx3GKxsXEcRzHKRoXE8dxHKdoXEwcx3Gc\nonExcRzHcYrGxcRxHMcpGhcTx3Ecp2hcTBzHcZyicTFxHMdxisbFxHEcxykaFxPHcRynaFxMHMdx\nnKJxMXEcx3GKxsXEadZImitpraTtstInSaqStFMT2rKbpAclfShphaQpki6W5P+HTsXjP2KnuWPA\nbODUTIKkAUDbeKxJkLQzMB6YB3zOzDoBJwKDgPYNuF6r0lroOMXhYuK0BO4Fvp3Y/w5wN6BMgqRj\nYmvlY0nvSboicWxrSfdK+kjSckkTJO0Qj50p6V1JKyXNlvStPDb8CnjBzC4xs8UAZvaWmZ1uZh9L\nGippfvKE2Ko6Mm4Pl/SQpHskfQxcJulTSZ0T+feLrZ5Wcf9sSTMkLZP0ZFO2wpyWh4uJ0xJ4Begg\naY/4oD2ZIDBJPgFON7OOwDHAjyQdH499B+gA9AK6AD8EPpO0LXATcJSZdQAOBSbnseGLwEP1tDu7\n5XQc8GC08TrgZeCExPFvxeMbo+3DgP8CugLjgPvqWb7jFIyLidNSuIfQOvkyMAN4P3nQzP5jZtPj\n9lTgfmBIPLwO2A7Y1QKTzGxVPFYFDJDU1swWm9mMPOVvB3xQ5D28ZGajo41rgJHE7jtJIojkyJj3\nHOC3ZvammVUBvwUGSupdpA2OkxMXE6clYAQxOY0cXVwAkg6W9JykJZJWEFofmUH7e4CngPslvS/p\nWklbmtlqwgP8HGChpMck7Z7HhqVAjyLvY0HW/sPAoZK6A4OBKjN7IR7rA9wUu+WWx/IBehZpg+Pk\nxMXEaRGY2XuEgfivER7C2YwEHgF6xcHxvxD/P8xsg5n92sz2Bg4Dvk4cgzGzp83sK0B3YBbwtzwm\n/JuaXVLZrAa2yezE7rjts28j656WA08TBO1b1OzGeg/4gZl1Tny2NbNXarHBcRqMi4nTkvgucKSZ\nfZbjWDtguZmtk3QQ4eFsAHFwfEB8wK8C1gMbJe0g6fg4drKeIAgb85R9BXCYpBGSusXr7hIH1DsA\nbwFbSzpaUmvgF0CbAu5pJKG1dQLVXVwQxPAySXvFsjpKOrGA6zlOg3AxcVoMZjbbzF5PJiW2zwV+\nLWkl8L/AA4lj3YEHgY8J4y1jCV1fWwAXE8ZflgJHAD/KVzZhgL4vMD12pT0ETAQ+MbOPow23Ebqz\nPgGS3l1zteuwAAAeOUlEQVRGblfm0cAuwAdxrCdT3iPAtYSuuY+BqcBXc9nmOKVAZo3jai/pDoJX\nzBIzGxDTriN0EawD3gXOiv9ESBoGnE14s7vAzJ6O6YOAO4GtgcfN7MJGMdhxHMdpMI3ZMvk7cFRW\n2tPA3ma2L6FZPwwgNsVPBvaK59wSvVMA/gx818x2BXaVlH1Nx3Ecp8w0mpiY2ThgeVbamOimCGE2\ncK+4fTxwn5mtN7O5wDvAwZJ2BNqb2YSY727gG41ls+M4jtMwyjlmcjbweNzuQU23xwUEF8bs9Pdx\n10bHcZzUsWU5CpV0ObDOzEbWmbnwazZZnCXHcZzmhJmp7ly1U2vLJLo+/ljSA5LGS3olbv84E5uo\nvkg6EziaMIEsw/tAcmZuL0KL5H2qu8Iy6TVmLicxs9R/rrjiirLb0FzsrAQb3U63s1yfZcuM114z\nHnzQGDHCOOcc46tfNXbd1WjTxthxR+Oww0r3Dp63ZSLpdmBn4AmCz/oHhFnDOwIHAaMkvWNm3yu0\nsDh4/jNgiIVwEBlGAyMlXU/oxtoVmGBmFgPoHQxMAM4Abq7PDTqO4zRH1qyBefNgzhyYPXvz76oq\n6N8f+vUL33vvDcceG/b79oW2bcN1VHSbJFBbN9dNZvZGjvSZwLPANZL2yXeypPsIsY26xmioVxC8\nt7YCxkRnrZfN7FwzmyFpFMGHfwNwrpllJPNcgmtwW4Jr8JP1uUHHcZxKpKoKPvhgc6HIbH/4IfTu\nXS0Y/frBgQdWi0eXLqUTikLIKyZ5hKTgPGZ2ao7kO2rJfzVwdY7014ABddlSKQwdOrTcJhREJdhZ\nCTaC21lqmpOdK1bUFIjk97x50LFjzdbFkCFw1llhv2dP2LIso965qXPSoqTDCa2KvlSLj5lZ/8Y1\nrX5IsrruxXEcpylZty6IQq5uqDlzYP36aqFIfme6orbdtvFtlISVYAC+EDF5E7gIeJ1E3CEz+6jY\nwkuJi4njOE1NVRUsWpRbKObMgcWLQwsiWywy3127Nm1XVC6aUkzGm9nB9b5w7nAqXQgxj/oAc4GT\nzGxFPFZUOBUXE8dxGoOVK/MPcs+dCx06bN6qyGz37p2urqhcNLqYxIc4hHWqWxHCdq/NHLeaAfNy\nnX8EIVjd3QkxGQF8ZGYjJP0c6Gxml8ZwKiOBAwneXP8mLkQkaQJwnplNkPQ4cHOuQXgXE8dxGsK6\ndfDee/kFY82a3K2K/v1DV1S7duW+g+JoCjEZS+4opQCY2RfqvLjUF3g0ISazCG7Bi+OCPmPNbI/Y\nKqkys2tjvieB4cA84Fkz2zOmnwIMNbNzcpTlYuI4zmaYhe6mfF5RixZBjx75Wxc77FD+rqjGpFRi\nUlsD7AuN8HTuZmaL4/ZioFvc7kFYpztDJpzKejyciuM4dbBqVX6vqDlzwkB2UiAOPRS+9a2w37s3\ntG5d7juofGoTk48kjQdeBF4CxpvZp6UqOHZhlVSshg8fvml76NChFeNC6DhOYcyYAS++uLlgrF69\neYviyCOrt9u3L7fl6WHs2LGMHTu25NetrZurI3AIYZnSw4D9CYPmLwAvmdkDOU+seY2+bN7NNdTM\nFsWIwM/Fbq5LAczsmpjvSYI78ryYJ9PNdSqhm8y7uRynBfHii3DNNTBxInzta0EkkmMX3bo1766o\nxqTRu7ksLFr1VPwQlyY9m+AmfD41V6IrlNGEJUavjd+PJNI9nIrjOJswgyeeCCKyYAH87GcwalR1\nGBAnXdTWMukBfJ7QKjmAEJfrNeBl4BUL647kv3AinAphfOSXwP8DRgE7sblr8GUEsdoAXGhmGRHL\nuAZnwqlckKc8b5k4TjNgwwZ48MEgImZw6aVw0knpd7GtVJrCm6uKMFHxRuBBM1ubM2NKcDFxnMpm\nzRq480647jrYcUcYNgyOPtq7rxqbphCTQwmtkkOB/oSWxEuElsmraRMXFxPHqUxWroQ//xluvBEG\nDQotkcMPL7dVLYemGDN5mSAcmQL7AscCdxHWFdm62MIdx2m5LF4MN90Et94KRx0FTz0F++SNQ+6k\nnboWx9pT0nfj2iZPAJcBU4FfFFOopGGSpkuaKmmkpDaSukgaI+ktSU9L6pSV/21JsyR9pZiyHccp\nL3PmwI9/DHvsEaLmTpwI//iHC0mlU1s311JgIaFr60XC2iNvF11gaOE8C+xpZmslPUBYC35vCg+1\nspuZVWVd17u5HCfFTJ0K114bPLR+8AO48ELo3r3cVjlNMQO+f3QPLjUrCTPbt5G0EdiGIFrDCN5f\nELrSxgKXAscD95nZemCupHcIKz2+guM4qeell+C3vw0tkIsugj/9KazT4TQvahOTK+NqiLkUy/K5\n6NaFmS2T9HvgPeAz4CkzGyOpvqFWHMdJKWbw5JNBRHyOSMugNjH5ETCNMC9kYUzLCEuD+5Mk7UyY\n+NgX+Bh4UNLpyTwFhFrJeczDqThOedmwAR56KMwRqaryOSJppBzhVLoSws+fRFhj5AHCfJMVRRUo\nnQx82cy+F/fPIIRtOZIQXLKgUCtmNj7ruj5m4jhlYs0auOsuGDHC54hUGqUaM8nrzWVmH5nZn2Oo\n+TOBjsCM+PAvhlnAIZLaKvSjfQmYATxKCLECm4daOUXSVpL6EUOtFGmD4zglYOXKICD9+8OjjwZB\neeEFOOYYF5KWRp2NzxjO5BTgywT34NeKKdDMpki6G3gVyMyy/yvQHhgl6bvEUCsx/wxJowiCswE4\n15sgjlNeliypniPy1a+G8RF37W3Z1NbNdSVwNDATuJ8wUL6+CW2rF97N5TiNz9y58LvfwciRcMop\ncMkloVXiVC5NFZtrDpBrDRMzs1S9h7iYOE7jMW1aGFTPzBG56KIQ9t2pfJpknkmxF3ccp7LJzBF5\n9dUwydDniDj5qE1M5tX1qq8GNgdiqJTbCLPeDTgLeJvgMdaHzcPTDyOEp98IXGBmT9e3TMdxCsPn\niDgNobZurv8AjwH/z8zeyjq2O/AN4BgzG1zvQqW7gP+Y2R2StgS2BS7Hw6k4TtnwOSItk6YYM2kD\nnAacCnwOWEWYtNiOMJnxH8BIM1tXrwLDcsCTzKx/VvoswpK8iyV1B8bGeSbDgCozuzbmexIYbmav\nZJ3vYuI4DSA5R6RHjyAiPkek5dAUIejXAncAd0hqRVgxEULrYWMRZfYDPpT0d2BfgqvxRYCHU3Gc\nJmTlSvjLX8I6IvvvHwTF1xFxGkoh80yuB243s+klLHN/4DwzmyjpRkJAx014OBXHaTx8jkjLpsnD\nqWzKIH2fMAO+NaGlcl8x0YRjF9bLZtYv7h9OiBjcHw+n4jiNhs8RcXLR6OFUMpjZ38zs88C3CcEZ\nMwtafaEhBZrZImC+pN1i0peA6Xg4FcdpFKZNg9NPD0vitm8PM2fCLbe4kDilpSA/jThmsgewJ/Ah\nMAX4iaRzzOzkBpR7PvAPSVsB7xJcg1vh4VQcp2T4HBGnKSmkm+sGwtrvzwK3mdmExLE3zWz3xjWx\nMLyby3Gq54hccw3Mnx/miJx5ps8RcfLTFDPgM7wB/MLMVuc4dnCxBjiOUzw+R8QpN4W0TAaxuffU\nx4QZ8hsay7D64i0TpyWSmSNy3XVhHRGfI+LUl6ZsmfwJGERooQAMIAyYd5T0IzN7qiEFx3GYV4EF\nZnaspC54OBXHKYjsOSJ33ulzRJzyUqc3F2HJ3oFmNsjMBgEDgdmE9U1GFFH2hYRB9Uxz4lJgjJnt\nBjwT94nhVE4G9gKOAm6RVIjdjtPsWLIELr88eGJNmRLGRx57zIXEKT+FPJR3T05YNLMZwB5m9i4N\nXAteUi/CWim3Ub2u/HHAXXH7LkLsL4DjCXNb1pvZXOAd4KCGlOs4lcrcuXDeebDHHrB8OUyYAP/4\nh082dNJDIWIyXdKfJQ2RNFTSLYTle9sADV0s6wbgZ4SVFjPUFk5lQSKfh1NxWgzTpsEZZ/gcESf9\nFDJm8h3gx4T4WQAvApcQhOTI+hYo6evAEjObJGlorjweTsVp6bz0UvDMmjgxzBH54x99johTGsoS\nTiWGhx9jZg2a7Z7nmlcDZxAmIG4NdAAeJoSYH+rhVJyWis8RccpBo4egTxT0DHBCxrOqlEgaAlwS\nvblGAEvN7NooIJ2y1jM5iOr1THbJVg4XE6dS8TkiTjlpStfg1YR4XGPiNoSeqAuKLTxzrfh9DR5O\nxWlBZM8RueoqnyPiVC6FtEzOjJuZjCKIyV25zygP3jJxKoXsOSKXXuquvU75aLKWiZndKWkbYCcz\nm1VsgY7TUkmuI3LUUb6OiNO8qNM1WNJxwCTgybi/n6TRDS1QUm9Jz0maLmmapAtiehdJYyS9Jelp\nSZ0S5wyT9LakWZK+0tCyHaccJOeIrFgR5ojce68LidO8KKSb63WCC/BzZrZfTJtmZp9rUIFhcazu\nZjZZUjvCsr3fIISh/8jMRkj6OdA5awD+QKoH4Hczs6qs63o3l9PkrFsHH3wA778PCxeG78wns79s\nGfzgB8HFt1u3uq/pOE1JUw7ArzezFao5KliVL3NdxMWxFsXtTyTNJIjEccCQmO0uYCwhpMqmGfDA\nXEmZGfCv4DiNhFkQgWxhyN5fvjwIRM+e0KNH+O7ZEwYMqE7r08fde53mTyFiMl3SacCWknYFLgBe\nKkXhkvoC+wHjqX0GfFI4fAa8UxRr1tQUh2yhyKS1bVstDhlhGDgQjjmmen+HHaBVq3LfkeOUn0LE\n5HzgcmAtcB/wFHBlsQXHLq5/Ahea2apky6ehM+Cdlk1VFXz4Yf5WRObzySfBFTdbKAYNqrm/zTbl\nviPHqRwK8eZaDVwWPyVBUmuCkNxjZpm13hdL6p6YAb8kpr8P9E6c3iumbYaHU2m+rF5de3fT++/D\nokXQoUPN7qaePeGgg2rub7cdbOFxp50WSlnCqQBI2p0Qi6sv1eJjZlbvuFzxeiKMiSw1s4sT6T4D\nvgWycSMsXlx7d9P778PatZuPSyRbEZnvNm3KfUeOU1k0ZTiVN4A/A68TFqeCICavNahA6XDgecJi\nW5nChwETgFHATmy+ONZlhMWxNhC6xTZbkMvFJH2sXFn3uMSSJdClS91C0bmzzwx3nMagKcXktbgo\nVqpxMWk61q8PXUq1jUssXBg8onIJQ3J/xx2hdety35HjtFyaUkyGAx8SIvuuzaSb2bJiCy8lLibF\nYQaffhom1S1bVru307JlsP32dQtFhw7emnCctNOUYjKXHN5TZtav2MJLiYtJ8GZauTIIwvLl1Z/a\n9pPbW24JnTqFbqekt1O2UHTr5u6wjtNcaDIxSQuSjgJuBFoBt5nZtVnHm4WYrF8fHvD1EYTM/sqV\nsO22QRA6d67+JPfzHevUCbbeutx37zhOU9PoYiLpf8xsRNw+0cweTBy72sxK5ipcF5JaAW8CXyK4\nBU8ETjWzmYk8qRGTzz7L/8B//fWxdOo0NG8L4bPPwoO9NkHIJxAdO5ZuDYyxY8em3rW6EmwEt7PU\nuJ2lpSnCqZwKjIjblwEPJo59jRLOOymAg4B3zGwugKT7CWFWZtZ2UkMxg1WrCm8RZO9DfgF4992x\nfOMbQ9lnn9yC0L59OsYZKuEfoRJsBLez1Lid6aRS1nLrCcxP7C8ADq7thI0bqx/09R1DWLEihNKo\nrUWw++75j9cWh2n4cPjJT0pRJY7jOOmhUsSkoP6rgQOrBWH16uBNVFu3UN++uQWhY0d3V3Ucx6kP\ntY2ZbAQ+jbttgc8Sh9uaWZMJkaRDgOFmdlTcHwZUJQfh64jl5TiO4+ShxXhzSdqSMAD/RWAhYbZ8\njQF4x3Ecp3xURDeXmW2QdB4hYnEr4HYXEsdxnPRQES0Tx3EcJ91UXCBuSUfFteDfjsv75spzczw+\nRdJ+abNR0lBJH0uaFD+/KIONd0haLGlqLXnKWo/RhlrtTENdRjt6S3pO0nRJ0yRdkCdfuX+bddqZ\nhjqVtLWk8ZImS5oh6bd58pW7Puu0Mw31Ge1oFct/NM/x4urSzCrmQ+jieocQDr81MBnYMyvP0cDj\ncftg4JUU2jgUGF3mujyCsMrl1DzHy1qP9bCz7HUZ7egODIzb7QhjfKn6bdbDzrTU6Tbxe0vCaquH\np60+C7QzLfX5E+AfuWwpRV1WWstk0+RFC2vCZyYvJjmOsF4KZjYe6CSpG01HITYClHVqopmNA5bX\nkqXc9Ugsuy47ocx1CWBmi8xsctz+hDChtkdWtrLXaYF2QjrqNONNuhXhJS07uGzZ6zOWXZedUOb6\nlNSLIBi35bGl6LqsNDHJNXkxez34XHl6NbJddZWfbaMBh8Xm5ONxAbC0Ue56LJTU1aWkvoTW1Pis\nQ6mq01rsTEWdStpC0mRgMfCcmc3IypKK+izAzjTU5w3Az4CqPMeLrstKE5NCvQWylbcpvQwKKet1\noLeZ7Qv8AXikjvzlopz1WCipqktJ7YCHCIu4fZIrS9Z+Weq0DjtTUadmVmVmAwkPtcGShubIVvb6\nLMDOstanpK8DS8xsErW3kIqqy0oTk+z14HsTFLS2PHnXjG8k6rTRzFZlmsZm9gTQWlKXpjOxIMpd\njwWRprqU1Br4J3CvmeV6YKSiTuuyM011Gm34GPgXcEDWoVTUZ4Z8dqagPg8DjpM0B7gPOFLS3Vl5\niq7LShOTV4FdJfWVtBVwMjA6K89o4Nuwaeb8CjNbnCYbJXWTQjhHSQcRXLRTtdgY5a/HgkhLXUYb\nbgdmmNmNebKVvU4LsTMNdSqpq6ROcbst8GVgUla2NNRnnXaWuz7N7DIz621hDapTgGfN7NtZ2Yqu\ny4qYtJjB8kxelPTDePxWM3tc0tGS3gFWA2elzUbgv4EfSdpACFlzSlPaCCDpPmAI0FXSfOAKgvdZ\nKuqxUDtJQV1GPg+cDrwhKfMwuQzYCVJVp3XaSTrqdEfgLklbEF567zGzZ9L0v16onaSjPpMYQKnr\n0ictOo7jOEVTad1cjuM4TgpxMXEcx3GKxsXEcRzHKRoXE8dxHKdoXEwcx3GconExcRzHcYqm2YmJ\nCghRH/PVGYI9K38XhdDdqyT9IZHeVtK/JM1UCOmdM1R2zDs22pYJRd21fndXkJ0nKoQX3yhp/1ry\nXRdtniLpYUkdC7h2QfZLOjled5qka4q5n1psKch+SVfGPJMlPSOpd658WecUZL+krST9VdKb0ZZv\nFnNPeco4R9Ibsb5flrRvHflPkFRV298+K/9V0f4Zks7Pk+c8Se/E63bJOla2EPCScoWraYxyfprr\n3vPkvUrSe5JWZaW3kfRArKtXJPXJc/5gSa9LWi/phET6QEkvxd/kFEknFX9nJaYpQh831YcCwr8n\n8tYa2jxH/m0IE75+CPwhkd4WGBK3WwPPA0flucZzwP6NXAd7ALvVVRZhpu4Wcfsa4JoCrl2n/cB2\nwDxgu7h/J3BkI9xnQfYD7RPb5wO3lcp+4FfAr5PnNsJ9Ju0/Fvh3bXnj7++lQn5nhIlpdyb2t8+T\nbyDQB5gDdEmkl3u5h1VNUEZv4Mnse68l/0GEMP+rstLPBW6J2ycD9+c5vw8wgBDB94RE+q7AznF7\nR8Ly5R2asr7r+jS3lkmh4d+xwkKbJ/N/amYvAmuz0j8zs//E7fWEoG7ZUYKTbBZoTdKx8W3ldUlj\nJO0Q04dLukvS85LmSvqmpN/FN9UnJG0WwcDMZpnZWwXczxgzy0QQHU/hEULrCqXdH3jbzJbG/WeA\nExrhPguy38ySb4jtgI8aan8OzgI2tUQz50i6U9JfJE2Mb/3HxPQzJT0i6WlJc+Ib/yWxPl6W1LlI\n+68kCOtaCgt5fg7w60RZH+bKZGaTzWxejkM5w5YrhBKaJene2OJ5UCHUCPHve3Vsab0qaf9YH+8o\nzsjORtL/xbzTJH0/69j1Mf3fii3l+Bb/SqLV2knSHpLGJ87rK+mNuD1IodX9qqQnJXVPFHE98D91\nV+WmuppgZotqqytCXLQv5jl/nplNJSu6r5m9bWbvxu0PgCXA9tH+uZKujf8v4yXtHNPvlHRL/G29\nq7BI113xb/L3Qu+pUJqbmBQS/r1WJP0w3486kjdkgEKMnmMJD6B83KXNV1sbZ2aHmNn+wAPU/PH2\nA75A+DHeC4wxs32Az4Bjar+bgjkbeDzeQw9J/6qn/UneAXaX1CeKwDeoDiDXWPe5yf5cZLoegO8Q\nHra13Wdt9iev2Slu/kbSa5JGZcQxspOZHRht/4ukNjF9b+C/gAOBq4CVsT5eJsZGylHWuQphLq4H\nhuXJsz/Q08wy9WCJY9kxrTLsDJwSRe9xSbvkyZeP2v7fdgP+ZGZ7ASsJb+YZu+aZ2X6EVtSdhPo4\nhNDSy8XZZnYAoc4uSIjutsBEM/sc8B9CqB2Au4GfWYjSOxW4wsxmAVsphN2H2DqIf+M/EFoBBwB/\nJ/xdkHQ8sMDM3kgaU8D/SC421ZWZbQA+VgODPSrE99oqIy6EOl0R/1/+CCRjrnUys0OBiwnxt0YQ\nfoMDVEeXaX1pbmJSdGwYC3Fqbq3vefFHeR9wk5nNzZPttPjDPwI4QtIZMb13fDt7A7gEyKx3YMAT\nZrYRmEbo1nkqHptK6M4rCkmXA+vMbCSAmS00s3wP73z2b8LMlgM/IojF84TugY3xcMnvM9v+XJjZ\n5Wa2E+HBdUNt91mH/Um2JLSGXjSzQQQx+F3ifkbF670DzCZ0PxphvYvVZvYRsALILKGa9z7N7BYz\n24WwUt4dOepgC4LQXJJMTpyfbyyjDfBZFL2/5bp2AeRrAc03s5fj9r3A4YljmcCnU4GXE/WxVlKH\nHNe6UGG9kJcJwr5rTK8i/J02lRHP7xh7HiC0BgbH7VEEEQE4KZ67B+Hh+u8oupcDPWNL6jKqBWrT\nvdbxP9KoSNqRIJZnZh26L37fDxwat43q39c0YJGZTbfQVzadEjw/kjQ3MckZ/l1SL4UB2EmSftBI\nZf8VeNPMboYa6y1PkjQcwo8wfn8CjCR0y0F4M7o5vln8kDAOk2FdPKcKWJ9Ir6IegToVHA4mSXos\nkXYmod/7tEKukct+xYWBsu7zsdgCOQx4i7A0bMnvM5f9ue4zwUjC221d97mZ/dn3GR9+n5rZw/G0\nh4DaBr0zLzrJbtKqxH4hf88HMmXE1tYkSa8Tur/2BsYqhBk/BBitugfhFwAZ+x8B9onXfipe+691\nnF9b2PLki52y9pP3vC6RvlkdKKwN8kXgEAtrhkwCts5hS3YZyfQMDwAnSdoVsPhmL2C6me0XP/uY\n2VHALoSH7ZRYp72A17Jan/XhfWIwzfji2dHMlmX9HbOpcT9RKB8DLjOzCbWUlTwvU7/J31pmv6SB\nfisqanABbAr/ThigOhk41cwWEAYRS0GuMY/fAB2A72bS4lv2fok8rYDOZvaRwnoSxwJPx8Mdor1Q\n842j2KU+k2+nZ2fZfBRh5bUhZramzgvlsT8+/Adm5d3BzJbE7ogfASfGQyW7z3z257jPXc3s7bh7\nPJuHMc917c3sz3WfwKOSvmBmzxEeeNMT93OipLsIYzD9gVnULjY560DSLrF1A6HL7I14n5cT3qIz\nbJ845zngp2aW6wGV5BHgSELXzhCi6JvZVwu0czRwHqG7aFPY8vj/t5OkQ8zsFeBbwLjNrlTY370D\nsNzM1kjagyCUGbYg/LYeyJRhZislLZd0uJm9AJwBjI33NVvSRuB/CW/wxHvePmNr/G3vGsctNi1b\nGwVlkDU8dPxoQjfrK4Qows9Em7L/jpuKJFE/CstZ/B9wd+IFJsnJwLXx+6UG2lgUzaplEvsiM+Hf\nZwAPmNnMXHkVQpu/BOwmab6ks2J63jETSXOB3wNnxnP2UFhb+TJgT+D1+JZxdo7T2wBPSppCeKDN\nJ3QtAAwHHpT0KvAh1W8WRs23jOw3r83exCT9l0Ko9kOAf0l6Ite9EFoJ7YAx0eZb4vn5+oNrsz+b\nGyVNB14Afpt4GJbsPvPZn4PfSpoau0mGAj+t4z5rsz+bnwPDY52clrl2tPc9YAJhLOeHZraujvvM\nPpbhPIUB5kkEb7T6hwbPP2ZyDXBC7Ha8CvhenvMviL+pnoTQ9X8FiOMzsxXGc26lelwEwkP6x5Jm\nAB2BP8f02u451/0/CWwZr/NbQldXhtWE1vFUwt8240zwHeC6+HfZJ5EOQXhOo7obch3h4X5t/I1M\norqbKElyDCrvb0fSiFhXbeMz4pfx0O3AdpLeBi4CLs1z/oHx/P8GblX11IWTCN3LZyZ6PPZJnNo5\n3u/5hPGRzeymsP+rBuMh6B2nxCh4yjya5w2y2RNbJo+a2YAym9IiKEGrqSQ0q5aJ4zipwd9Sm45U\n1LW3TBzHcZyi8ZaJ4ziOUzQuJo7jOE7RuJg4juM4ReNi4jiO4xSNi4njOI5TNC4mjuM4TtH8f3Sp\nUZu5Oj0hAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe6f57defd0>"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.7 : page 45"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dmax=40 #MW#Maximum demand\n",
+      "CF=0.5 #Capacity Factor\n",
+      "UF=0.8 #Utilisation Factor\n",
+      "LF=CF/UF #/Load Factor\n",
+      "print \"(a) Load Factor =\",LF \n",
+      "C=dmax/UF #MW#Plant Capacity\n",
+      "print \"(b) Plant Capacity =\",C,\"MW \" \n",
+      "RC=C-dmax #MW#Reserve Capacity\n",
+      "print \"(c) Reserve Capacity =\",RC,\"MW \" \n",
+      "p=dmax*LF*24*365 #MWh#Annual Energy Production\n",
+      "print \"(d) Annual Energy Production =\",p,\"MWh\" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(a) Load Factor = 0.625\n",
+        "(b) Plant Capacity = 50.0 MW \n",
+        "(c) Reserve Capacity = 10.0 MW \n",
+        "(d) Annual Energy Production = 219000.0 MWh\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.8 : page 52"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from numpy import arange\n",
+      "L1=50 #MW#Initial\n",
+      "t1=5 #hours\n",
+      "L2=50 #MW#5am\n",
+      "t2=4 #hours\n",
+      "L3=100 #MW#9am\n",
+      "t3=9 #hours\n",
+      "L4=100 #MW#6pm\n",
+      "t4=2 #hours\n",
+      "L5=150 #MW#8pm\n",
+      "t5=2 #hours\n",
+      "L6=80 #MW#10pm\n",
+      "t6=2 #hours\n",
+      "L7=50 #MW\n",
+      "#Energy Required in 24 hours\n",
+      "E=L1*t1+(L2+L3)/2*t2+(L3+L4)/2*t3+(L4+L5)/2*t4+(L5+L6)/2*t5+(L6+L1)/2*t6 #MWh\n",
+      "print \"Energy required in one day =\",E,\"MWh\" \n",
+      "DLF=E/L5/24*100 #%#Daily Load Factor\n",
+      "print \"Daily Load Factor =\",round(DLF,2),\"%\" \n",
+      "#Plotting load curve\n",
+      "% matplotlib inline\n",
+      "T=arange(0,7,1) #Slots\n",
+      "L=array([L1,L2,L3,L4,L5,L6,L7]) #MW\n",
+      "plt.plot(T,L)\n",
+      "plt.ylabel('Load(MW)') \n",
+      "plt.xlabel('0-1: 12-5am 1-2: 5-9am 2-3: 9-6pm 3-4: 6-8pm 4-5:8-10pm 5-6 :10-12pm') \n",
+      "plt.title('Chronological Load Curve') \n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Energy required in one day = 2060.0 MWh\n",
+        "Daily Load Factor = 57.22 %\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEZCAYAAABWwhjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe6f5884cd0>"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "exa 1.9 : page 55"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "L1=50 #MW#Initial\n",
+      "t1=5 #hours\n",
+      "L2=50 #MW#5am\n",
+      "t2=4 #hours\n",
+      "L3=100 #MW#9am\n",
+      "t3=9 #hours\n",
+      "L4=100 #MW#6pm\n",
+      "t4=2 #hours\n",
+      "L5=150 #MW#8pm\n",
+      "t5=2 #hours\n",
+      "L6=80 #MW#10pm\n",
+      "t6=2 #hours\n",
+      "L7=50 #MW\n",
+      "#Load Duration Curve\n",
+      "l1=L5 #Mw\n",
+      "l2=L4 #MW\n",
+      "l3=L1 #MW\n",
+      "L=array([l1,l2,l2,l3,l3])\n",
+      "T=arange(0,30,6) #Duration in hours\n",
+      "plt.subplot(3,1,1)\n",
+      "plt.plot(T,L)\n",
+      "plt.ylabel('Load(MW)') \n",
+      "plt.xlabel('Hours') \n",
+      "plt.title('Load Duration Curve') \n",
+      "#Energy Consumed\n",
+      "#Upto 5am\n",
+      "t1=5 #hours\n",
+      "E1=L1*t1 #MWh\n",
+      "#Upto 9am\n",
+      "t2=4 #hours\n",
+      "E2=E1+L2*t2 #MWh\n",
+      "#Upto 6pm\n",
+      "t3=9 #hours\n",
+      "E3=E2+L3*t3 #MWh\n",
+      "#Upto 10pm\n",
+      "t4=4 #hours\n",
+      "E4=E3+L4*t4 #MWh\n",
+      "#Upto 12pm\n",
+      "t4=2 #hours\n",
+      "E4=E3+L4*t4 #MWh\n",
+      "#Plotting Mass curve\n",
+      "T=arange(0,5,1) #MW\n",
+      "E=[0,E1,E2,E3,E4] #Mwh\n",
+      "plt.subplot(3,1,3)\n",
+      "plt.plot(T,E)\n",
+      "plt.ylabel('Energy(MWh)') \n",
+      "plt.xlabel('0-1: 12-5am 1-2: 5-9am 2-3: 9-6pm 3-4: 6-10pm above4: 10-12pm') \n",
+      "plt.title('Mass Curve') \n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEZCAYAAABvpam5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe6f58bc190>"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file