path: root/sample_notebooks/NeerajBaunthiyal/chapter1_1.ipynb

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  {
   "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": 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HKRoXE8dxHKdoXEwcx3GconExcRzHcYrGxcRxHMcpGhcTx3Ecp2hcTBzHcZyi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\nx4QZ8hsay7D64i0TpyWSmSN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HeR9wk5nNzZPttPjDPwI4QtIZMb13fDt7A7gEyKx3YMAT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\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": 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kTiIdxMV0M2uGdiaSIm5KNSoXXFB0BK0RAQcdlIrpvk+ImY1W6Y2tWqnraiL/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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": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fe6f58bc190>"
       ]
      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}