Bottom-Up Energy Model.

Bottom-Up Energy Model

{
 "metadata": {
  "name": "Generic Bottom-Up"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 298
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PUE = 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 299
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Edge\n",
      "\n",
      "The edge network contains the B-RAS.\n",
      "\n",
      "$$I_E = R \\cdot PUE \u22c5 \\eta_{E} \\cdot  \\left( I_{E_S} + I_{E_R} \\right) $$\n",
      "\n",
      "- $R$ is redundancy (1+1).\n",
      "- $PUE$ is PUE (2)\n",
      "- $\\eta_{R}$ edge overcapacity\n",
      "- $I_{E_S}$ edge switch energy intensity\n",
      "- $I_{E_R}$ edge router energy intensity\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_E = R * PUE *eta_E * ( I_E_S + I_E_R )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Metro Transmission Bottom-Up \n",
      "\n",
      "Here we estimate the cumulated energy intensity for metro transmission from bottom-up parameters.\n",
      "This is then used in the metro model as a single parameter.\n",
      "\n",
      "$$ I_{M_{TM}} = R \\cdot n_{M_R} \\left( c_{ON} \\cdot I_{ON} + n_{M_{OA}} I_{OA} \\right) $$\n",
      "\n",
      "- $R$ is redundancy (1+1).\n",
      "- $n_{M_R}$ number metro routers\n",
      "- $c_{ON}$ ratio of WDM systems\n",
      "- $I_{ON}$ energy intensity per WDM system\n",
      "- $I_{OA}$ energy intensity per optical amplifier\n",
      "- $n_{M_{OA}}$ number metro network optical amplifiers per hop\n",
      "\n",
      "The number of optical amplifiers depends on the distance between routers s_R. If amplifiers are placed each s_A km then $ n_{OA}= \\lceil s_R/s_A \\rceil  $\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_M_TM_BU = R * n_M_R * c_ON * ( I_ON + n_M_OA  * I_OA)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Combining energy intensity from bottom-up and empirical\n",
      "\n",
      "Given the cumulative energy intensity from the bottom-up model, we can factor in the empirical results from Coroama et al.\n",
      "\n",
      "$$ I_{M_{TM}} = u ( I_{M_{TM_{BU}}}, I_{M_{TM_{EMP}}} ) $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_M_TM = np.random.uniform(I_M_TM_BU,I_M_TM_EMP)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Metro With Blackbox transmission\n",
      "\n",
      "Here we model transmission as one component. We still apply PUE and allocate overcapacity but not redundancy as these are in the bottom-up model and in the empirical values.\n",
      "\n",
      "$$I_M = PUE \u22c5 \\eta_{M} ( R \\cdot n_{M_R} \\cdot I_{M_R} + I_{M_{TM}} ) $$\n",
      "\n",
      "- $R$ is redundancy (1+1).\n",
      "- $PUE$ is PUE (2)\n",
      "- $n_{M_R}$ number metro routers\n",
      "- $\\eta_{M}$ metro overcapacity\n",
      "- $I_{M_R}$ metro energy intensity per router\n",
      "- $I_{M_{TR}}$ - transmission intensity metro (cumulative)\n",
      "- $I_{M_O}$ - energy intensity fibre optic transmission metro"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_M_IP = PUE * eta_M * (R * n_M_R * I_M_R)\n",
      "I_M_O = PUE * eta_M * I_M_TM\n",
      "I_M = I_M_IP + I_M_O\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Core Transmission Bottom-Up \n",
      "\n",
      "Analog to metro: we estimate the cumulated energy intensity for core transmission from bottom-up parameters.\n",
      "This is then used in the core model as a single parameter.\n",
      "\n",
      "$$ I_{C_{TM_{BU}}} = R \\cdot n_{C_R} \\left( I_{ON} + n_{C_{OA}} I_{OA} \\right) $$\n",
      "\n",
      "- $R$ is redundancy (1+1).\n",
      "- $n_{C_R}$ number core routers\n",
      "- $I_{ON}$ energy intensity per WDM system\n",
      "- $I_{OA}$ energy intensity per optical amplifier\n",
      "- $n_{C_{OA}}$ number core network optical amplifiers per hop\n",
      "\n",
      "The number of optical amplifiers depends on the distance between routers s_R. If amplifiers are placed each s_A km then $ n_{OA}= \\lceil s_R/s_A \\rceil  $\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_C_TM_BU = R * n_C_R * ( I_ON + n_C_OA  * I_OA)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Combining bottom-up and empirical energy intensity in the Data by Coroama et al.\n",
      "\n",
      "Given the cumulative energy intensity from the bottom-up model, we can factor in the empirical results from Coroama et al.\n",
      "\n",
      "$$ I_{C_{TM}} = u ( I_{C_{TM_{BU}}}, I_{C_{TM_{EMP}}} ) $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_C_TM = np.random.uniform(I_C_TM_BU,I_C_TM_EMP)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Core With Blackbox transmission\n",
      "\n",
      "Analog to core: we model transmission as one component. We still apply PUE and allocate overcapacity but not redundancy as these are in the bottom-up model and in the empirical values.\n",
      "\n",
      "$$I_C = PUE \u22c5 \\eta_{C} ( R \\cdot n_{C_R} \\cdot I_{C_R} + I_{C_{TM}} ) $$\n",
      "\n",
      "- $R$ is redundancy (1+1).\n",
      "- $PUE$ is PUE (2)\n",
      "- $n_{C_R}$ number core routers\n",
      "- $\\eta_{M}$ core overcapacity\n",
      "- $I_{C_R}$ core energy intensity per router\n",
      "- $I_{C_{TR}}$ - transmission intensity core (cumulative)n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_C_IP = PUE * eta_C * ( R * n_C_R * I_C_R)\n",
      "I_C_O = PUE * eta_C *  I_C_TM\n",
      "I_C= I_C_IP + I_C_O"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Transport Undersea\n",
      "$$ I_S= \\left\\lceil s_T/s_{SA} \\right\\rceil I_{OSA} + R \\cdot PUE \\cdot 2 I_{ST} + s_T / C_U $$\n",
      "\n",
      "- $s_T$ is distance between landing point terminals\n",
      "- $s_{SA}$ distance between undersea amplifiers\n",
      "- $I_{OSA}$ optical line amplifier energy intensity\n",
      "- R is redundancy (2x)\n",
      "- PUE - power utilisation\n",
      "- 2 is for landing points each side\n",
      "- $I_{ST}$ landing point sea terminal energy intensity\n",
      "- the final $s_T$ term is for cable resistance of one ohm/km apportioned to total capacity $C_U$ \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I_S= s_T/s_SA * I_OSA + R * PUE * 2 I_ST + s_T / C_U"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "265.234375\n"
       ]
      }
     ],
     "prompt_number": 304
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Overall Energy Intensity\n",
      "\n",
      "$$ I = I_M + I_C + cI_S $$\n",
      "\n",
      "- c fraction of traffic that is inter-continental\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}