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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Analysis of Demand-side Storage Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook executes several analyses to assess the feasibility of purchasing batteries for electricity storage.  Annual electricity costs are reduced by opting into a time-of-use pricing plan and purchasing electricity during less expensive off-peak hours.\n",
      "\n",
      "Created by Justin Elszasz."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Full descriptions of these models can be found in the docstrings for each module.\n",
      "\n",
      "#TOU_pricing.py contains functions that determine the season, period (peak, off-peak, intermediate), \n",
      "#and cost of electricity for each hour of a timestampd dataframe containing hourly residential \n",
      "#electricity consumption.\n",
      "import TOU_pricing\n",
      "\n",
      "\n",
      "#storage_logic.py contains functions that implement a control algorithm for the purchase of electricity\n",
      "#based on available storage and period of the day (peak, off-peak). \n",
      "import storage_logic\n",
      "\n",
      "#calculations.py contains functions which return key metrics for analyzing the model output.\n",
      "import calculations\n",
      "\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import time\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
        "  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Reload modules during development\n",
      "reload(TOU_pricing)\n",
      "reload(storage_logic)\n",
      "reload(calculations)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<module 'calculations' from 'calculations.pyc'>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Battery Size Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This section analyzes the performance of the model based on the size of batteries in the system in kilowatt-hours."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define efficiencies of inverter and battery."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def inverter_efficiency(direction):\n",
      "    if direction == 'charging':\n",
      "        eff = .85\n",
      "    elif direction == 'discharging':\n",
      "        eff = .85\n",
      "    return eff\n",
      "\n",
      "def battery_efficiency(direction):\n",
      "    if direction == 'charging':\n",
      "        eff = .85\n",
      "    elif direction == 'discharging':\n",
      "        eff = .85\n",
      "    return eff"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create array of all the battery sizes to be analyzed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "battery_sizes = [1., 2., 5., 7.5, 10., 15., 20., 25., 30.]\n",
      "max_dod = .2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Calculate the output and annual cost with normal (static) BGE electricity prices."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "R_output = TOU_pricing.main('R',False)\n",
      "R_annual_cost = np.sum(R_output['USAGE'] * R_output['cost'])\n",
      "print R_annual_cost"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "729.88896351\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This loop determines the model output for each battery size and the relevant metrics for each output.  If save_results is True, a csv for each output (all hourly flows for one year) is saved for each battery size along with a csv containing the metrics for each battery size."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "save_results = False\n",
      "\n",
      "t0 = time.time()\n",
      "\n",
      "for i, bat_size in enumerate(battery_sizes):\n",
      "\n",
      "    system_param = {\n",
      "        'Inverter Cost' : 1500., # ballpark\n",
      "        'Storage Cost' : 200. / 1.2, # cost per kilowatt-hour based on link above\n",
      "        'Storage Size' : bat_size,  # storage size in kilowatt-hours\n",
      "        'Max Charge Rate' : bat_size / 8.,\n",
      "        'Max DOD' : max_dod, # DOD\n",
      "        'Bat Depleted' : max_dod * bat_size,\n",
      "        'Inverter Efficiency' : inverter_efficiency,\n",
      "        'Battery Efficiency' : battery_efficiency,\n",
      "    }\n",
      "\n",
      "    results = storage_logic.main(TOU_pricing.main('EV', False), system_param)\n",
      "    filename = 'results/storage_size/output_%s_kWh.csv'%bat_size\n",
      "    if save_results == True: results.to_csv(filename)\n",
      "    \n",
      "    metrics = calculations.calc_metrics(results, system_param)\n",
      "\n",
      "    if i == 0: all_metrics = pd.DataFrame(index=battery_sizes, columns=metrics.keys()) \n",
      "        \n",
      "    all_metrics['Total kWh Purchased'][bat_size] = metrics['Total kWh Purchased']\n",
      "    all_metrics['% Purchased During Peak'][bat_size] = metrics['% Purchased During Peak']\n",
      "    all_metrics['% Purchased During Off-Peak'][bat_size] =  metrics['% Purchased During Off-Peak']\n",
      "    all_metrics['Hours on Battery Only'][bat_size] = metrics['Hours on Battery Only']\n",
      "    all_metrics['% Peak Demand Battery'][bat_size] = metrics['% Peak Demand Battery']\n",
      "    all_metrics['Hours Battery Full'][bat_size] = metrics['Hours Battery Full']\n",
      "    all_metrics['Hours Battery Depleted'][bat_size] = metrics['Hours Battery Depleted']\n",
      "    all_metrics['Annual System Eff'][bat_size] = metrics['Annual System Eff']\n",
      "    all_metrics['Annual Var Cost'][bat_size] = metrics['Annual Var Cost']\n",
      "    all_metrics['Initial Cost'][bat_size] = metrics['Initial Cost']\n",
      "    all_metrics['Peak kWh Shaved'][bat_size] = metrics['Peak kWh Shaved']\n",
      "    all_metrics['% Peak kWh Shaved'][bat_size] = metrics['% Peak kWh Shaved']\n",
      "    all_metrics['PBP'][bat_size] = metrics['PBP']\n",
      "    all_metrics['Annual Savings'][bat_size] = metrics['Annual Savings']\n",
      "    all_metrics['% Annual Cost Savings'][bat_size] = metrics['% Annual Cost Savings']\n",
      "\n",
      "if save_results == True: all_metrics.to_csv('results/storage_size/all_metrics_storage_size.csv')    \n",
      "    \n",
      "t1 = time.time()\n",
      "print 'Time for model run:',round(t1-t0,1),'s'\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Time for model run: 151.5 s\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We now have a Pandas DataFrame containing metrics that describe the system performance\n",
      "# indexed by battery size.\n",
      "all_metrics.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PBP</th>\n",
        "      <th>Annual Var Cost</th>\n",
        "      <th>% Peak kWh Shaved</th>\n",
        "      <th>Total kWh Purchased</th>\n",
        "      <th>% Purchased During Off-Peak</th>\n",
        "      <th>Peak kWh Shaved</th>\n",
        "      <th>% Purchased During Peak</th>\n",
        "      <th>% Annual Cost Savings</th>\n",
        "      <th>% Peak Demand Battery</th>\n",
        "      <th>Annual Savings</th>\n",
        "      <th>Initial Cost</th>\n",
        "      <th>Hours Battery Full</th>\n",
        "      <th>Annual System Eff</th>\n",
        "      <th>Hours Battery Depleted</th>\n",
        "      <th>Hours on Battery Only</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1.0 </th>\n",
        "      <td> 18.55998</td>\n",
        "      <td> 640.09</td>\n",
        "      <td> 0.1353311</td>\n",
        "      <td> 8566.259</td>\n",
        "      <td> 0.8183945</td>\n",
        "      <td> 243.4825</td>\n",
        "      <td>  0.1816055</td>\n",
        "      <td>  0.123031</td>\n",
        "      <td> 0.1957039</td>\n",
        "      <td> 89.79896</td>\n",
        "      <td> 1666.667</td>\n",
        "      <td> 3914</td>\n",
        "      <td> 0.9739731</td>\n",
        "      <td> 2224</td>\n",
        "      <td>   32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2.0 </th>\n",
        "      <td> 16.92688</td>\n",
        "      <td> 621.58</td>\n",
        "      <td> 0.2706621</td>\n",
        "      <td> 8789.213</td>\n",
        "      <td> 0.8507037</td>\n",
        "      <td>  486.965</td>\n",
        "      <td>  0.1492963</td>\n",
        "      <td>  0.148391</td>\n",
        "      <td>  0.367376</td>\n",
        "      <td>  108.309</td>\n",
        "      <td> 1833.333</td>\n",
        "      <td> 3914</td>\n",
        "      <td> 0.9492666</td>\n",
        "      <td> 1799</td>\n",
        "      <td>  457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5.0 </th>\n",
        "      <td> 14.52985</td>\n",
        "      <td>  569.3</td>\n",
        "      <td> 0.6551423</td>\n",
        "      <td> 9423.424</td>\n",
        "      <td> 0.9341582</td>\n",
        "      <td> 1178.707</td>\n",
        "      <td> 0.06584177</td>\n",
        "      <td> 0.2200183</td>\n",
        "      <td> 0.7265188</td>\n",
        "      <td>  160.589</td>\n",
        "      <td> 2333.333</td>\n",
        "      <td>    1</td>\n",
        "      <td> 0.8853795</td>\n",
        "      <td>  795</td>\n",
        "      <td> 1461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7.5 </th>\n",
        "      <td> 15.39935</td>\n",
        "      <td> 551.31</td>\n",
        "      <td> 0.8048847</td>\n",
        "      <td> 9673.588</td>\n",
        "      <td> 0.9637111</td>\n",
        "      <td> 1448.118</td>\n",
        "      <td> 0.03628891</td>\n",
        "      <td> 0.2446659</td>\n",
        "      <td> 0.8335197</td>\n",
        "      <td>  178.579</td>\n",
        "      <td>     2750</td>\n",
        "      <td>    2</td>\n",
        "      <td> 0.8624831</td>\n",
        "      <td>  382</td>\n",
        "      <td> 1874</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10.0</th>\n",
        "      <td>  17.0978</td>\n",
        "      <td> 544.68</td>\n",
        "      <td> 0.8771617</td>\n",
        "      <td> 9792.536</td>\n",
        "      <td> 0.9774312</td>\n",
        "      <td> 1578.156</td>\n",
        "      <td> 0.02256882</td>\n",
        "      <td> 0.2537495</td>\n",
        "      <td> 0.8975529</td>\n",
        "      <td>  185.209</td>\n",
        "      <td> 3166.667</td>\n",
        "      <td>    1</td>\n",
        "      <td> 0.8520066</td>\n",
        "      <td>  248</td>\n",
        "      <td> 2008</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "           PBP Annual Var Cost % Peak kWh Shaved Total kWh Purchased  \\\n",
        "1.0   18.55998          640.09         0.1353311            8566.259   \n",
        "2.0   16.92688          621.58         0.2706621            8789.213   \n",
        "5.0   14.52985           569.3         0.6551423            9423.424   \n",
        "7.5   15.39935          551.31         0.8048847            9673.588   \n",
        "10.0   17.0978          544.68         0.8771617            9792.536   \n",
        "\n",
        "     % Purchased During Off-Peak Peak kWh Shaved % Purchased During Peak  \\\n",
        "1.0                    0.8183945        243.4825               0.1816055   \n",
        "2.0                    0.8507037         486.965               0.1492963   \n",
        "5.0                    0.9341582        1178.707              0.06584177   \n",
        "7.5                    0.9637111        1448.118              0.03628891   \n",
        "10.0                   0.9774312        1578.156              0.02256882   \n",
        "\n",
        "     % Annual Cost Savings % Peak Demand Battery Annual Savings Initial Cost  \\\n",
        "1.0               0.123031             0.1957039       89.79896     1666.667   \n",
        "2.0               0.148391              0.367376        108.309     1833.333   \n",
        "5.0              0.2200183             0.7265188        160.589     2333.333   \n",
        "7.5              0.2446659             0.8335197        178.579         2750   \n",
        "10.0             0.2537495             0.8975529        185.209     3166.667   \n",
        "\n",
        "     Hours Battery Full Annual System Eff Hours Battery Depleted  \\\n",
        "1.0                3914         0.9739731                   2224   \n",
        "2.0                3914         0.9492666                   1799   \n",
        "5.0                   1         0.8853795                    795   \n",
        "7.5                   2         0.8624831                    382   \n",
        "10.0                  1         0.8520066                    248   \n",
        "\n",
        "     Hours on Battery Only  \n",
        "1.0                     32  \n",
        "2.0                    457  \n",
        "5.0                   1461  \n",
        "7.5                   1874  \n",
        "10.0                  2008  "
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Simple Payback Period"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the simple payback period as a function of battery size."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "save_fig = True\n",
      "\n",
      "a = 6.\n",
      "fig = plt.figure(figsize=[1.3*a,a])\n",
      "ax = subplot(111)\n",
      "\n",
      "# turn off square border around plot\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"bottom\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False)  \n",
      "ax.spines[\"left\"].set_visible(False)\n",
      "\n",
      "# ensure only ticks on bottom and left, not top and right (unnecessary)\n",
      "#ax.get_xaxis().tick_bottom()  \n",
      "#ax.get_yaxis().tick_left()  \n",
      "\n",
      "all_metrics['PBP'].plot(grid='off',color='#FF3300',linewidth=2,marker='.',markersize=14)\n",
      "title('Payback Period in Years',fontsize=16)\n",
      "xlabel('Storage Size (kWh)',fontsize=14)\n",
      "xticks(fontsize=14)\n",
      "yticks(fontsize=14)\n",
      "\n",
      "# turn off tick marks\n",
      "plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",\n",
      "                labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")  \n",
      "\n",
      "ylabel('Years',fontsize=14)\n",
      "\n",
      "if save_fig == True: fig.savefig('results/storage_size/PBP.png',bbox_inches='tight')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ya+d2f/+1LYP67DB4ortOEZOoE45JbIeB85yftwPXYAGeE7gwvU8uIpLE6hU2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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115631cd0>"
       ]
      }
     ],
     "prompt_number": 178
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Cost Savings"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the cost savings ($ and %) compared to a normal BGE plan without time-of-use pricing."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "save_fig = True\n",
      "\n",
      "a = 6.\n",
      "fig = plt.figure(figsize=[1.3*a,a])\n",
      "ax1 = plt.subplot(111)\n",
      "\n",
      "\n",
      "# Ticks\n",
      "ticks_left = np.array(range(10,30,2))/100.\n",
      "ticks_right_ymin = min(ticks_left) * R_annual_cost\n",
      "ticks_right_ymax = max(ticks_left) * R_annual_cost\n",
      "ticks_right = np.array(range(80,200,20))\n",
      "\n",
      "# turn off square border around plot\n",
      "ax1.spines[\"top\"].set_visible(False)  \n",
      "ax1.spines[\"bottom\"].set_visible(False)  \n",
      "ax1.spines[\"right\"].set_visible(False)  \n",
      "ax1.spines[\"left\"].set_visible(False)\n",
      "\n",
      "title('Annual Electricity Bill Cost Savings',fontsize=16)\n",
      "xlabel('Storage Size (kWh)',fontsize=14)\n",
      "xticks(fontsize=14)\n",
      "\n",
      "ax1.plot(all_metrics.index, all_metrics['% Annual Cost Savings'],color='#FF3300',linewidth=2,marker='.',markersize=14)\n",
      "ax1.set_ylabel('% Annual Savings', fontsize=14)\n",
      "\n",
      "ax2 = ax1.twinx()\n",
      "ax2.set_ylim(min(all_metrics['Annual Savings']), max(all_metrics['Annual Savings']))\n",
      "ax2.set_yticklabels(ticks_right, fontsize=14)\n",
      "ax2.set_ylabel('Annual Savings (USD)', fontsize=14)\n",
      "\n",
      "\n",
      "# turn off ticks\n",
      "ax1.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",\n",
      "               labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\",labelsize=14) \n",
      "ax2.tick_params(axis=\"y\", which=\"both\", bottom=\"off\", top=\"off\",\n",
      "               labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"off\",labelsize=14) \n",
      "\n",
      "\n",
      "\n",
      "if save_fig == True: fig.savefig('results/storage_size/cost_savings.png',bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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joEsfv1skIpK1cnX4Jt2JrlIZbdsKN51jAcnpQxWQiIjklnygDXAAthTMH8CP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\nREREREREREREJF//B+rF/Kj87/vYAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11385ff10>"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Peak Load Shaving"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Number/percent of kilowatt-hours no longer purchased during peak hours compared to normal BGE cost plan and no storage."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "save_fig = True\n",
      "\n",
      "a = 6.\n",
      "fig = plt.figure(figsize=[1.3*a,a])\n",
      "ax1 = plt.subplot(111)\n",
      "\n",
      "# Ticks\n",
      "\n",
      "ticks_right = np.array(range(0,2000,200))\n",
      "\n",
      "# turn off square border around plot\n",
      "ax1.spines[\"top\"].set_visible(False)  \n",
      "ax1.spines[\"bottom\"].set_visible(False)  \n",
      "ax1.spines[\"right\"].set_visible(False)  \n",
      "ax1.spines[\"left\"].set_visible(False)\n",
      "\n",
      "title('Demand Shaved From Peak Hours',fontsize=16)\n",
      "xlabel('Storage Size (kWh)',fontsize=14)\n",
      "xticks(fontsize=14)\n",
      "\n",
      "ax1.plot(all_metrics.index, all_metrics['% Peak kWh Shaved'],color='#FF3300',linewidth=2,marker='.',markersize=14)\n",
      "ax1.set_ylabel('% Peak Demand Shaved', fontsize=14)\n",
      "ax1.set_ylim(0,1.1)\n",
      "\n",
      "ax2 = ax1.twinx()\n",
      "ax2.set_yticks(ticks_right)\n",
      "ax2.set_yticklabels(ticks_right, fontsize=14)\n",
      "ax2.set_ylim(0,1.1*max(all_metrics['Peak kWh Shaved']))\n",
      "ax2.set_ylabel('Peak Demand Shaved (kWh)', fontsize=14)\n",
      "\n",
      "# turn off ticks\n",
      "ax1.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",\n",
      "               labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\",labelsize=14) \n",
      "ax2.tick_params(axis=\"y\", which=\"both\", bottom=\"off\", top=\"off\",\n",
      "               labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"off\",labelsize=14) \n",
      "\n",
      "if save_fig == True: fig.savefig('results/storage_size/peak_shaved_stor_cap.png',bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f2GvpcKAx0BZoh+WBDi2gXftgqwO/BCzEys0PxIqtvoIFJrsnf3t5/A7fdMYK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\n6jV+DFxCjDV5D7icuL9O7sOyd0seQcnZwLXExYG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bJJsnO0V1ONF9kw1E0vYG3iAGVk4j\nWgA2zuR5jvZdSLtTDXbmEONJNiduzNlHd0xPfmaDhIq9iK6ko2nfrfN7ovVkZp0y1GsJIUmfQ4yn\nSWshutG+QLQ23UL72UpTiKDuEGrXOLmfCFZGEGNmlmbO68rsqY2IVpc3u5BXkqReN4SYOno08Dlg\nE+ArxFol6ZkYdxPf3NejuibGTlQHun42eY0PqB1oWm8syqHAR8TA2E2BU4lWhU9SeY4gbpDHE1Nc\nzyUGZ76UyvP1JM9ZVAeZfo32AzjTfpXk34O4CY8gxq+8ScxEgtoxJa1EAHEp0S3SmjzWSZ5fN3n+\nJiJo2pQIKq6hcZAD0WWUHTMzkersmyHAU0QrTDow2YC4TnOJ1pSKQcQ1rQwqbvS6FenPWDEOZ99I\nknI0gGixeJyY7bEAeIHoSlkzlW8PYjrtImq/hR8O/IHo/nmV9uNQ6gUlAD8gApF5RKBwCu2/3Vem\nBM8jbqzjiLERaUcSrS2LkvI/QMdrbXyDGOA5m+o05uuIsRgVY5LPBBG01JsSnG6N2Rz4ZfL+C4lW\nnvFUZ8nUc3jy3mnZxdMqgcmt1AYmfyKuVXbMyiNJ+s6dvC7UfsaKZ2g/I0iSJNVxC12bydMfrEQE\nbAfkXZDELkSQ6IqukiRlrEIsa78t0TUzmmgFOCzPQvWyY2g/SyYvNwHfyrsQkiQV0UCim+Udokvp\nCaKrRpIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkqTf9P8Vwq/UKGNcqAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11151f410>"
       ]
      }
     ],
     "prompt_number": 243
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "System Efficiency"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot of total annual kilowatt-hours purchased and the resulting overall system efficiency."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "save_fig = True\n",
      "\n",
      "a = 6.\n",
      "fig = plt.figure(figsize=[1.3*a,a])\n",
      "ax1 = plt.subplot(111)\n",
      "\n",
      "# Ticks\n",
      "ticks_right = np.array(range(0,2000,200))\n",
      "\n",
      "# turn off square border around plot\n",
      "ax1.spines[\"top\"].set_visible(False)  \n",
      "ax1.spines[\"bottom\"].set_visible(False)  \n",
      "ax1.spines[\"right\"].set_visible(False)  \n",
      "ax1.spines[\"left\"].set_visible(False)\n",
      "\n",
      "title('Annual System Efficiency',fontsize=16)\n",
      "xlabel('Storage Size (kWh)',fontsize=14)\n",
      "xticks(fontsize=14)\n",
      "\n",
      "ax1.plot(all_metrics.index, all_metrics['Total kWh Purchased'],color='#FF3300',linewidth=2,marker='.',markersize=14)\n",
      "ax1.set_ylabel('Total kWh Purchased', fontsize=14, color='#FF3300')\n",
      "#ax1.set_ylim(0,1.1)\n",
      "\n",
      "ax2 = ax1.twinx()\n",
      "ax2.plot(all_metrics.index, all_metrics['Annual System Eff'],color='#00CCFF',linewidth=2,marker='.',markersize=14)\n",
      "#ax2.set_yticks([0,.25,.5,1])\n",
      "#ax2.set_yticklabels(, fontsize=14)\n",
      "ax2.set_ylim(0,1)\n",
      "ax2.set_ylabel('System Efficiency', fontsize=14, color='#00CCFF')\n",
      "\n",
      "# turn off ticks\n",
      "ax1.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",\n",
      "               labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\",labelsize=14) \n",
      "ax2.tick_params(axis=\"y\", which=\"both\", bottom=\"off\", top=\"off\",\n",
      "               labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"off\",labelsize=14) \n",
      "\n",
      "if save_fig == True: fig.savefig('results/storage_size/sys_eff_stor_cap.png',bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GT0FLI5EPQUvMp8steBn/pz1v4Vgy74XdYJ1m6V8rIiKFS0FLI5FPQUtM1AteXvSCl+YO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\nG1a/O4C/+srcirWkjEhzjBKgAvg6sH2F2+eVWRXYH3fHLUF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       "text": [
        "<matplotlib.figure.Figure at 0x113864d10>"
       ]
      }
     ],
     "prompt_number": 247
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 247
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 247
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}