DE_coalPhaseOut.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demand scalling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The demand data has been scalled based on annual electricity consumption available at [AGEB](https://ag-energiebilanzen.de/7-0-Bilanzen-1990-2016.html) for the year 2009 to 2016. \n",
    "\n",
    "For 2017, since the data is not updated at AGEB, it has been scalled based on ENTSO-E [factsheet](https://www.entsoe.eu/publications/statistics-and-data/#statistical-factsheet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dem_unscalled = pd.read_csv(os.path.abspath('Load/dem_unscalled.csv')).rename(columns={'Unnamed: 0': 'h'}).set_index('h').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "demTotENTSOE_powerStat = (dem_unscalled.sum(axis=0)/1000000).values\n",
    "demTotENTSOE_factsheet = [495.2, 527.4, 521.2, 523.3, 523.2, 512.8, 514.7, 517.6, 538.7]\n",
    "demScalled = dem_unscalled*(demTotENTSOE_factsheet/demTotENTSOE_powerStat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "demScalled.to_csv('dem_scalled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "demandAll = pd.read_csv('Load/dem_scalled.csv').rename(columns={'Unnamed: 0':'h'}).set_index('h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capacity Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pvCF = pd.read_csv(os.path.abspath('Res_CF/pv_EMIHRES.csv'))\n",
    "wonCF = pd.read_csv(os.path.abspath('Res_CF/won_EMIHRES.csv'))\n",
    "woffCF = pd.read_csv(os.path.abspath('Res_CF/woff_EMIHRES.csv'))\n",
    "\n",
    "pvCFMeera = pd.read_csv(os.path.abspath('Res_CF/pv_NinjaMeera.csv'))\n",
    "wonCFNinja = pd.read_csv(os.path.abspath('Res_CF/won_Ninja.csv'))\n",
    "woffCFNinja = pd.read_csv(os.path.abspath('Res_CF/woff_Ninja.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scenarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renewables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Best Case\n",
    "pvCap_b   = 50334 \n",
    "wonCap_b  = 64691 \n",
    "woffCap_b =  9446\n",
    "\n",
    "# Worst Case\n",
    "pvCap_w   = 50334 \n",
    "wonCap_w  = 54691 \n",
    "woffCap_w =  7127"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reliable Capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# including PSP (only local)\n",
    "reliableCap_pumpLocalOnly = 75547\n",
    "\n",
    "# including PSP (local + cross border capacity connnected to Germany)\n",
    "reliableCap_pump = 78547\n",
    "\n",
    "# exluding PSP\n",
    "reliableCap_noPump = 68621"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# colors for scenarios\n",
    "c_noPump = '#C0392B'\n",
    "c_pump = '#16A085'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "outLoc ='/Users/samarth/Desktop/Work/24_coalPhaseOut/draft/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getResLoad(pvCap, wonCap, woffCap):\n",
    "    resLoad = pd.DataFrame()\n",
    "    for demYear in demandAll:\n",
    "        for wYear in range(1986,2016,1):\n",
    "            insRESLoad = pd.DataFrame(demandAll[str(demYear)] - \n",
    "                                      pvCF[str(wYear)]*pvCap-\n",
    "                                      wonCF[str(wYear)]*wonCap-\n",
    "                                      woffCF[str(wYear)]*woffCap, \n",
    "                                      columns=[str(demYear)+'_'+str(wYear)])\n",
    "            resLoad = resLoad.join(insRESLoad, how='outer')\n",
    "    \n",
    "    for demYear in demandAll:\n",
    "        for wYear in range(1986,2016,1):\n",
    "            insRESLoad = pd.DataFrame(demandAll[str(demYear)] - \n",
    "                                      pvCFMeera[str(wYear)]*pvCap-\n",
    "                                      wonCFNinja[str(wYear)]*wonCap-\n",
    "                                      woffCFNinja[str(wYear)]*woffCap, \n",
    "                                      columns=[str(demYear)+'_'+str(wYear)+'_ninja'])\n",
    "            resLoad = resLoad.join(insRESLoad, how='outer')\n",
    "    return resLoad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "resLoad_b  = getResLoad(pvCap_b,wonCap_b,woffCap_b)\n",
    "resLoad_w  = getResLoad(pvCap_w,wonCap_w,woffCap_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotResLoad(resLoad, reliableCap):\n",
    "    resLoadSort = resLoad.values\n",
    "    resLoadSort.sort(axis=0)\n",
    "    plt.plot(resLoadSort, color='b',alpha=0.01);\n",
    "    plt.hlines(reliableCap, xmin=0, xmax=8760)\n",
    "    plt.xlim([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMaxResLoads(resLoad):\n",
    "    maxLoads = []\n",
    "    for col in resLoad.columns:\n",
    "        maxLoads.append(resLoad[col].max())\n",
    "        \n",
    "    return maxLoads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of peak residual load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "maxValues_w = getMaxResLoads(resLoad_w)\n",
    "(pd.DataFrame(maxValues_w)/1000).plot(kind='hist', bins=14, range=(72,86), \n",
    "                                      ax=ax,\n",
    "                                     # label='highRes', \n",
    "                                    legend=False,\n",
    "                                      align ='mid',\n",
    "                                     edgecolor='k', color='red', alpha=0.5)\n",
    "\n",
    "maxValues_b = getMaxResLoads(resLoad_b)\n",
    "(pd.DataFrame(maxValues_b)/1000).plot(kind='hist', bins=14, range=(72,86), \n",
    "                                      ax=ax,\n",
    "                                      #label='lowRes',\n",
    "                                      legend=False,\n",
    "                                      edgecolor='k', color='green', alpha=0.5)\n",
    "plt.ylabel('Frequency', size=12)\n",
    "plt.xlabel('Peak Residual Load [GW]', size=12)\n",
    "plt.xticks(size=12)\n",
    "plt.grid( axis='y', linestyle = '--')\n",
    "plt.tight_layout()\n",
    "#plt.savefig(outLoc+'/PeakLoadDiss.pdf')\n",
    "\n",
    "t = plt.text(0.8, 0.90, 'LowRes', transform=ax.transAxes, fontsize=12)\n",
    "t.set_bbox(dict(facecolor='red', alpha=0.5, edgecolor='black'))\n",
    "\n",
    "t = plt.text(0.8, 0.80, 'HighRes', transform=ax.transAxes, fontsize=12)\n",
    "t.set_bbox(dict(facecolor='green', alpha=0.5, edgecolor='black'))\n",
    "\n",
    "plt.savefig(outLoc+'/PeakResLoadDiss_2.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation for EAR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EAR (Energy at Risk): Energy at risk is calculated as the sum of the LAR over the stress duration combined with the same calculation for the previous stress instances if the previous instance happened within the last 12h. The reason for combining the EAR is to take into account the incapability of the pump storage to charge itself in case of stress situations occurring close to each other. Since the energy/power for the PSP in Germany is around 6, it would take 6 hours to charge to if charging at full power. However, for charging at full capacity the power should be available in the market and the price incentive should be there for charging, hence 12h approximation was taken to combine the stress instances.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getContStressStat(resLoad, reliableCap):\n",
    "    resLoadStress = resLoad.copy()\n",
    "    resLoadStress[resLoad < reliableCap] = 0\n",
    "    \n",
    "    conStat = []\n",
    "   \n",
    "    # calculating the hours of stress and the hours since last stress\n",
    "    for col in resLoadStress.columns:\n",
    "        resLoadIns = resLoadStress[col].values\n",
    "        counter_noStress = 0\n",
    "        for i in range(0,len(resLoadIns),1):\n",
    "\n",
    "            if resLoadIns[i]==0:\n",
    "                counter_stress = 0\n",
    "                excessEnergy = 0\n",
    "                counter_noStress = counter_noStress + 1 \n",
    "                \n",
    "            else:\n",
    "                counter_stress = counter_stress + 1\n",
    "                excessEnergy = excessEnergy + resLoadIns[i] - reliableCap\n",
    "                conStat.append([counter_stress, excessEnergy,resLoadIns[i] - reliableCap, i, counter_noStress+1])\n",
    "                counter_noStress=0\n",
    "                \n",
    "    conStressHoursStat = pd.DataFrame(conStat, \n",
    "                                      columns=['conNumHours','excessLoad', \n",
    "                                               'excessCap','hour',\n",
    "                                               'hoursSinceLastStress'])\n",
    "    instanceCount = 0\n",
    "    instanceList = []\n",
    "\n",
    "    # adding the stress counter to be able to \n",
    "    # combine different stress hours into stress instances\n",
    "    \n",
    "    for row in conStressHoursStat.iterrows():\n",
    "        if row[1][0]==1:\n",
    "            instanceCount=instanceCount+1\n",
    "        instanceList.append(instanceCount)\n",
    "        \n",
    "    conStressHoursStat['instanceCount'] = instanceList\n",
    "    \n",
    "    maxInfoStressInstance = []\n",
    "    for instance in range(1,conStressHoursStat['instanceCount'].max(), 1):\n",
    "        instanceInfo = conStressHoursStat.loc[conStressHoursStat['instanceCount']==instance]\n",
    "        maxInfoStressInstance.append([instanceInfo['conNumHours'].max(), \n",
    "                                      instanceInfo['excessLoad'].max()/1000, \n",
    "                                      instanceInfo['hoursSinceLastStress'].max()])\n",
    "\n",
    "    stressHMaxChar = pd.DataFrame(maxInfoStressInstance, columns=['hours','TotEnergy','hoursSinceLast'])\n",
    "    \n",
    "    # adding LAR_max and LAR_mean\n",
    "    stressHMaxChar['LAR_max [GW]'] = conStressHoursStat[['instanceCount','excessCap']].groupby('instanceCount').max()['excessCap']/1000\n",
    "    stressHMaxChar['LAR_mean [GW]'] = conStressHoursStat[['instanceCount','excessCap']].groupby('instanceCount').mean()['excessCap']/1000\n",
    "    \n",
    "    # combining different stress instances to calculate EAR (12h definition)\n",
    "    totEnergy2 =[]\n",
    "    hours2 = []\n",
    "    for index, row in stressHMaxChar.iterrows():\n",
    "        if row[2]<=12:\n",
    "            totEnergy2.append(row[1]+stressHMaxChar.xs(index-1)[1])\n",
    "            hours2.append(row[0]+stressHMaxChar.xs(index-1)[0])\n",
    "        else:\n",
    "            totEnergy2.append(row[1])\n",
    "            hours2.append(row[0])\n",
    "\n",
    "    stressHMaxChar['TotEnergy_2'] = totEnergy2\n",
    "    stressHMaxChar['hours_2'] = hours2\n",
    "    \n",
    "    return conStressHoursStat, stressHMaxChar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "stressHourChar_pump, stressHourMaxChar_pump = getContStressStat(resLoad=resLoad_b.dropna(axis=1), \n",
    "                                                          reliableCap=reliableCap_pump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "stressHourChar_noPump, stressHourMaxChar_noPump = getContStressStat(resLoad=resLoad_b.dropna(axis=1), \n",
    "                                                          reliableCap=reliableCap_noPump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pltStrHours_hoursSinceLast(stressHMaxChar):\n",
    "    \n",
    "    fig, ax1 = plt.subplots()\n",
    "    ax2 = ax1.twinx()\n",
    "    stressHMaxChar.plot(kind='scatter', x='hours', \n",
    "                          y='hoursSinceLast',\n",
    "                        ax=ax1,\n",
    "                        color='k')\n",
    "    \n",
    "    meanHoursLast = stressHMaxChar[['hours', 'hoursSinceLast']].groupby('hours').mean()\n",
    "    \n",
    "    stressHMaxChar[['hours', \n",
    "                    'hoursSinceLast']].groupby('hours').mean().reset_index().plot(kind='scatter', \n",
    "                                                                                  x='hours',y='hoursSinceLast', \n",
    "                                                                                  ax=ax2)\n",
    "\n",
    "    ax1.set_ylim([0,5000\n",
    "                 ])\n",
    "    ax1.set_xlim([0,19])\n",
    "    ax2.set_ylim([0,250])\n",
    "    plt.xticks(np.arange(0,21,1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pltHourScatter(stressHMaxChar):\n",
    "    \n",
    "    fig, ax1 = plt.subplots(sharex=True)\n",
    "\n",
    "#    ax2 = ax1.twinx()\n",
    "#    stressHMaxChar.groupby('hours').count().rename(columns={'hoursSinceLast':'count'}).plot(kind='bar', y='count',\n",
    "#                                                                                           ax=ax1\n",
    "#                                                                                           )\n",
    "#    ax1.set_xlim([0,19])\n",
    "    # for plotting days since last in color map\n",
    "    \n",
    "    days = (stressHMaxChar.hoursSinceLast/24).astype(int)\n",
    "    days[days>7]=7\n",
    "    \n",
    "    \n",
    "    stressHMaxChar.plot.scatter(x='hours_2', y='TotEnergy_2', colormap='Reds',\n",
    "                        c='LAR_max [GW]', ax=ax1, \n",
    "                        alpha=0.5)\n",
    "    \n",
    "    #ax1.set_ylim([0,200000])\n",
    "    ax1.axhline(y=58, xmin=0, xmax=19,\n",
    "               linewidth=2, color='k')\n",
    "    #ax1.set_xlim([0,19])\n",
    "    plt.ylabel('Energy At Risk (EAR) [GWh]')\n",
    "    plt.xlabel('Consecutive Load At Risk (LAR) Hours')\n",
    "    #plt.xticks(np.arange(0,20,1));\n",
    "    plt.tight_layout()\n",
    "    #plt.savefig(outLoc+'/energyHours_dis.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.898406211213922"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(stressHourMaxChar_noPump.loc[stressHourMaxChar_noPump['TotEnergy_2']>58])['LAR_max [GW]'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pltHourScatter( stressHourMaxChar_noPump)\n",
    "\n",
    "plt.text(0.1* stressHourMaxChar_noPump['hours_2'].max(), \n",
    "         0.8* stressHourMaxChar_noPump['TotEnergy_2'].max(),\n",
    "         s='EAR>58GWh: 1083 instances\\nmcYears: 540',\n",
    "         fontweight='bold'\n",
    "        )\n",
    "\n",
    "plt.grid(axis='y',linestyle='--')\n",
    "#plt.savefig(outLoc+'/hours_energyAtRisk_noPump_4.pdf')\n",
    "#pltHourScatter(stressHMaxChar_b_ne)\n",
    "#plt.text(x=22.5, y=28,s= 'Days since last stress', rotation=90)\n",
    "#plt.grid( axis='y', linestyle='--')\n",
    "#plt.savefig(outLoc+'/hours_energyAtRisk_Pump.pdf')\n",
    "#pltHourScatter(stressHMaxChar_b_ne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conNumHours</th>\n",
       "      <th>excessLoad</th>\n",
       "      <th>excessCap</th>\n",
       "      <th>hour</th>\n",
       "      <th>hoursSinceLastStress</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1217.402269</td>\n",
       "      <td>1217.402269</td>\n",
       "      <td>113</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1470.618459</td>\n",
       "      <td>253.216190</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3876.309852</td>\n",
       "      <td>3876.309852</td>\n",
       "      <td>137</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>8785.497318</td>\n",
       "      <td>4909.187466</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>10731.551839</td>\n",
       "      <td>1946.054520</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   conNumHours    excessLoad    excessCap  hour  hoursSinceLastStress\n",
       "0            1   1217.402269  1217.402269   113                   114\n",
       "1            2   1470.618459   253.216190   114                     1\n",
       "2            1   3876.309852  3876.309852   137                    23\n",
       "3            2   8785.497318  4909.187466   138                     1\n",
       "4            3  10731.551839  1946.054520   139                     1"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conStressHour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatterHours(conStressHour):\n",
    "    fig, ax1 = plt.subplots()\n",
    "    ax2 = ax1.twinx()\n",
    "\n",
    "\n",
    "    conStressHour.plot(kind='scatter', \n",
    "                       x='conNumHours',y='excessLoad',\n",
    "                    Valpha=0.1, ax=ax2, color='k')\n",
    "    plt.savefig('frey_excessEnergy.pdf')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hours at Risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hoursAtRisk(resLoad, reliableCap, saveFig, figName, color, case):\n",
    "    hoursAtRisk = (resLoad>reliableCap).sum()\n",
    "    stat = case + ' \\nmean:%.2f \\nstd: %.2f'% (hoursAtRisk.mean(), hoursAtRisk.std())\n",
    "    plt.hist(hoursAtRisk, bins=15, label=stat, \n",
    "             edgecolor='black', color=color, alpha=0.7)\n",
    "    plt.xlabel('Hours at Risk [h]', size=12)\n",
    "    plt.ylabel('Frequency', size=12)\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    if saveFig:\n",
    "        plt.savefig(outLoc+'/'+figName)\n",
    "    return hoursAtRisk\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "har_np = hoursAtRisk(resLoad_b, reliableCap_noPump, saveFig=False, \n",
    "            figName='HoursAtRisk_withoutPump.pdf', color=c_pump, case='NoPSP: ')\n",
    "plt.grid(linestyle='--')\n",
    "plt.subplot(1,2,2)\n",
    "har_p = hoursAtRisk(resLoad_b, reliableCap_pump, saveFig=False, \n",
    "            figName='HoursAtRisk_withPump.pdf', color=c_noPump, case='WithPSP: ')\n",
    "plt.grid(linestyle='--')\n",
    "plt.savefig(outLoc+'/'+'HoursAtRisk_2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maximum Load at Risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def maxLoadRisk(resLoad, reliableCap, saveFig, figName, color, case):\n",
    "    loadAtRisk = ((resLoad[resLoad>reliableCap] - reliableCap)/1000).max()\n",
    "    stat = case+'\\nmean:%.2f\\nstd: %.2f'% (loadAtRisk.mean(), loadAtRisk.std())\n",
    "    plt.hist(loadAtRisk, bins=16, \n",
    "             label=stat, range=(0,16), \n",
    "             edgecolor='black', \n",
    "             color=color, alpha=0.7)\n",
    "    plt.xlabel('Maximum Load At Risk [GW]', fontsize=12)\n",
    "    plt.ylabel('Frequency', fontsize=12)\n",
    "    #plt.xlim([0,20])\n",
    "    #plt.ylim([0,60])\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    if saveFig:\n",
    "        plt.savefig(outLoc+\"/\"+figName)\n",
    "    \n",
    "    \n",
    "maxLoadRisk(resLoad_b, reliableCap_w, saveFig=False, figName='as', \n",
    "            color=c_withoutPump, case='NoPSP')\n",
    "maxLoadRisk(resLoad_b, reliableCap_b_ne, saveFig=False, figName='as', \n",
    "            color=c_withPump, case='WithPSP')\n",
    "plt.grid(linestyle='--')\n",
    "plt.savefig(outLoc+'/MaxLoadAtRisk_2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load at Risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadRisk(resLoad, reliableCap, saveFig, figName, color, case):\n",
    "    loadAtRisk = [] \n",
    "    loadAtRiskDF = ((resLoad[resLoad>reliableCap] - reliableCap)/1000)\n",
    "    count = 1\n",
    "    for col in loadAtRiskDF.columns:\n",
    "        loadIns = loadAtRiskDF[[col]]\n",
    "        loadVals = loadIns[loadIns[col].notnull()].values.flatten()\n",
    "        loadAtRisk.append(list(loadVals))\n",
    "        count= count+1\n",
    "        \n",
    "    loadAtRisk = np.hstack(loadAtRisk)\n",
    "    \n",
    "    stat = case+'\\nmean:%.2f\\nstd: %.2f'% (loadAtRisk.mean(), loadAtRisk.std())\n",
    "    plt.hist(loadAtRisk, bins=20, label=stat,  \n",
    "             edgecolor='black', color=color, alpha=0.7)\n",
    "    plt.xlabel('Load At Risk [GW]', fontsize=12)\n",
    "    plt.ylabel('Frequency', fontsize=12)\n",
    "    #plt.xlim([0,20])\n",
    "    #plt.ylim([0,60])\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    if saveFig:\n",
    "        plt.savefig(outLoc+\"/\"+figName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y5sKwMGvttw72tMPzzz9PZmYm2dnZTJ482eeD2I8//pi0tDRGjRrFY4895k6/7rrrSElJITs7m+zsbNauXduleu3Inv37zJYQFOzU1u3V5bPPPiMnJ4eIiAjeece3d5xf//rXDB06tM0zuPnz5zNgwAC33bz00ktB0e2J6YskQpFl//qYmVNOMVtGh2n9YPaaSy4OqseLYLt36tmzZ9CuBXDFFVdwyy3a75vFixdzxx138PHHLTdbNzU1ceutt7JkyRKSkpI45ZRTmDlzJuPGaVvpnnjiCS6++OI21+4KvXbkn4X/YcypY8yWERA/ueqKoHq+MNvNU3v9dtiwYcyfP9+9T8oXF154IT//+c/bLEwBuOyyy3jmmWcC0ukPZ4DywsXXXgc7rOP8svV+oH27dvKHIA6wdy5f1W6eiooKzjnnHCZPnszKlSvJyspizpw5zJs3jwMHDvD666+Tnp7OL37xC9auXYtSioceeohZs2ZRUVHB1Vdf7V7W+8wzz3DqqadSWFjIQw89RP/+/d37L1577bU2Lvw9Q1sfO3bMq4v/L7/8klGjRjFixAgAZs+ezfvvv+8eoPxhduA3K3D9pbP5ov1sIc03e/cw6p6b3KvjAmXzo8+2m8eI3bi8qnTUbvr27cvmzZt92k1ycjLQ/gzMpEmTOvcFBAFrzQ11EyLO19IZtm3bxi9/+Uu+/vprNm/ezN///neKiop48sknefTRR3nkkUc444wzWLZsGZ9++il33303x44dY+DAgSxZsoQ1a9awcOFCbrvtNvc1fblzefDBB91elwH+/Oc/M3LkSO655x63bzNPdu/ezdChQ93HSUlJ7r00oE1jjB8/nrlz53b7bnk74NhM5+mo3axatcqQ3Tz22GPt2k2gLFq0iPHjx3PxxRe797cFE6dXeUGp0PYOHqqkpKSQmZlJWFgY6enpTJ8+HREhMzOTiooKPvnkEx577DFOO+00CgoKOHHiBDt37qShoYEbb7yRzMxMLrnkkhbPkHy5c/nNb37DzJkz3fluvfVWtm/fzuOPP85vf/vbNtq8badw/aL83e9+x+bNm1m1ahWHDh3i8ccfD/I3Y38cm+k8HbWb7OxsQ3YzZMiQdu0mEC688EIqKir4+uuvOfPMM7n22muDcl1PnCk+L7yzYD75FnoG5dpIZzaerlfCwsLcx2FhYTQ2NhIeHs6iRYvaeFF+6KGHGDRoEKWlpTQ3N7eojz93Lt6YPXs2//Vf/9UmPSkpqcUvvMrKShITEwHcmwijoqKYM2dOmzl5Z3qvfV5+603G/KpjoSBCmWBN7xmhq+zGc5N6V7hDc3lGB7jxxhu59957g/4Zzh2UF6bNaO3XNrSxyj6oGTNm8Kc//ck9Z/7VV18B8N1335GQkEBYWBh/+9vfDDvsLCv73sXchx9+6PVh7imnnEJZWRnl5eXU19fz5ptvun9J7t27F9Dust577z0yMjJalLXDfpKu5tyCM8yWEBRC0TGwy25cswAdtZuu7rcuuwFtcVJ7vgM7gzNAeWFgQqLZEgxhtjfzjvLAAw/Q0NDAxIkTycjI4IEHHgDgZz/7GQsWLGDSpEls3bq1jcsUb3jOpT/zzDOkp6eTnZ3N//7v/7JgwQIA9uzZw3nnnQdov4yfeeYZZsyYwdixY7n00kvd3jeuvPJKtxPNgwcP8t///d8tPisU/2mFGomDBpstISh0l2cdI7jsZvz48Ybsxlu/9bSbVatWkZSUxNtvv83NN9/cwhtNdna2+/0999xDUlISx48fJykpiYceegiAp59+mvT0dLKysnj66afdjmeDSbe5OgpFfLluefH555nkw5t5KLo6+uqrrzj55JPdx6G+zNxqq+Ja6w1VV0ddjT9XR8+98AKfj+zbJt1Kro5+ctUVlFfuIixIe6HMXmYeKnYWiL04z6C8sPTDfzDJhy++UKT1PqhQC0nfGqvtK7KaXjP4x7//RfzItr74rMSi1/4eMv7rgoEd+q0zxeeFwUOS2s8UQlhlis+F2fGrjGI1vWYwZLC1psV9Yae2tkNdnAHKC+nZJ7efKYSwWke0yqIOF1bTawY5GZlmSwgKdmprO9TFGaAcuow//vGPPlcSzZ8/n5///Od+y69du5b8/HzS09MZP348Cxcu9Jv/nXfeQURo/Zxk586dxMbGtuvSxcEhFAjUbgAWLFhAdnY2qamp7kVDrSktLSU/P5/MzEwuvPBCd0DE+vp65syZQ2ZmJllZWe6Ak2bgDFBe+Hp1+659Qgkz9m50BF+G1vqZmS969uzJq6++yoYNG/j444+5/fbbOXLkiNe81dXVPP30020C04EWzvrcc881Jr4Ten/IrCpd234mCxAKbe1vgOoIhw4d4uGHH6aoqIgvv/yShx9+mMOHD7fJ99Of/pTHHnuMdevW8aMf/YgnnngCgBdffBHQAqEuWbKEO++807THCM4A5YXDB6vMlmAIs72ZHzt2jPPPP5+srCwyMjJYuHAhTz/9NHv27OH000/n9NNPB+CVV15h9OjRnHXWWW7XK/4YPXq0e09TYmIiAwcO5Ntvv/Wa94EHHuCee+5ps2n5vffeY8SIEQEFdLTLQ/Ou5OBha9mML7qzrY3azbRp0zpkN//6178466yzGDBgAHFxcZx11lltnCcDbNmyhalTpwJw1llnsWiRFoFl48aNTJ8+HdAi4Pbt27fNrER34QxQXnA26hrj448/JjExkdLSUtavX88555zDbbfdRmJiIp9++imffvope/fuZd68eXz++ee8++67LdyyLF68mAcffNDvZ3z55ZfU19e3CRcO2jL7Xbt2ccEFF7RIP3bsGI8//jjz5s0LqH61tbUBlf8hcG7BdLMlBIXubGujdrNkyZIO2Y3L76SrLq39TrrIyMhw74l6++233Z5WsrKyeP/992lsbKS8vJzVq1d3iZ+9jhCac0MhzPYdOzh70kSv54K9X8gqZGZmctddd3HvvfdywQUXMGXKlDZ5iouLKSgoYMCAAVRXV3PZZZexdetWAGbOnOnXP9jevXu5+uqrWbBgQZu7xebmZubOnet1k+C8efOYO3dumzg2Dg6hgFG7ATpkN/78Tnry8ssvc9ttt7n987mmN6+//no2bdpEbm4uw4cP59RTTzXtMYIzQHlhj59NrqqhwWcoi46EpegKzJ7iGz16NKtXr+ajjz7i/vvv5+yzz/b6y85lJEamUY4ePcr555/Pb3/7W69u/6urq1m/fj0FBQUA7Nu3j5kzZ7J48WKKi4t55513uOeeezhy5AhhYWFER0d36CGzJ84UX/vs3LMbvGzUtRrd2dZG7aajJCUlUVhY6K5LZWWl2z48GTNmDJ988gkAW7du5cMPPwS0Z9pPPfWUO9+pp57q1X1Yd+BM8Xnhi/8sNVuCISIjI039/D179tCzZ0+uuuoq7rrrLtasWQNoTlarq6sByMvLo7CwkKqqKiIiInj77bfbvW59fT0/+tGPuOaaa7jkkku85jnppJM4ePAgFRUVVFRUMGnSJBYvXkxubi7Lly93p99+++386le/Mjw4AcTExBgu80Pj30WfmS0hKHRnWxu1m4aGhg7ZzYwZM/jkk084ceIEhw8f5pNPPmHGjBlt8h04cADQZiF++9vfuoN+Hj9+3O0vc8mSJURERHQoblpX4NxBeeHia+eAD1dHoYjZ8YvWrVvH3XffTVhYGJGRkTz33HMA3HTTTZx77rkkJCTw6aef8tBDD5Gfn8/AgQOZMGGC21fY4sWLKSkp4Te/+U2L67711lt89tlnVFVVuafw5s+fT3Z2Ng8++CC5ublBCx3gj5qampBwGRPKXH/p5bT/+D706c62Nmo3CQkJ5OTktGs38fHxPPDAA+Tm5hIWFsaDDz5IfHw8oK3cu+WWW8jNzeWNN97gz3/+MwA//vGPmTNnDqANXDNmzCAsLIwhQ4bwt7/9rVu+D284vvgM+uKb9dJrvP/Tq7yeM8tPX2tffKFOqPgI6yiOLz4Nu/viA+v1TX+ESl0CsRdnis8LDQ3W34Ht4NCdNDQ2mC3BwYY4A5QX3nv9NbMlGCJUAhZ2lFD4VWcEK+gVkZdF5ICIrPdIe0hEdovIWv11nse5+0Vkm4hsEZG2DygM8uqi9p+NWAErtHVHsUNdnAHKC2ecf0H7mUKI+vr6kIxj4wvXA1ir4Kk3hL/n+YC3DXxPKaWy9ddHACIyDpgNpOtlnhWRgJavzTzz7ECKm0br9rRa3/RHKNQlUHtxBigv9Bsw0GwJhmhoaKCqqiqU/3m2wGre1116lVJUVVWF5B2rUuoz4FAHs88C3lRK1SmlyoFtgPfNfR1kQL/+gRQ3hejo6DZ2Y7W+6Q+z6xIMe3FW8dmAY8eOUV1d7dMNUKhx4sSJkPwn7wtPvdHR0SQlWSocy89F5BqgBLhTKXUYGAKs9MhTqae1QURuAm4Czd2Uy3HoiBEj6N27N6WlpQD0iY1FFJxRq215aAIKezYw88yz3WVyc3PZv3+/2ytBamoqUVFRrF+vzUoOHDiQ0aNHU1RUBEBUVBT5+fmUlJRQU1MDaMuuKysr3Z4R0tLSCA8Pd3tYGDx4MCkpKaxYsQLQlo3n5eVRXFzs9qyQn59PeXk5+/fvp1evXuzbtw/QfugppYiIiCAiIsK9OlZEiIqKoq6uzj2YRUVF0djY6F5RFxkZiVLKHVkgPDyc8PBwt5eXsLAwevTowYkTJ9zfbVRUFA0NDe6BpEePHjQ3N7uvERERQVhYmN9rREdHU19f7/UaSikiIyPbXCMyMrLFyl9v12hqanLXLSIiAhGhoaHBXbeOfj/19fUcO3aM6OjoFu3UUZxVfF5WJV1y7gwezBjltUworuKrqamxlLcEO+oNhVV8IpIMfKCUytCPBwEHAQX8D5CglLpeRP4MrFBKvabn+yvwkVJqkb/r+1vFd/aPL2LgrVe0SQ/lVXzesFrf9Eco18VZxRcAw734ewtl9u/fb7YEQzh6uwel1H6lVJNSqhl4ke+n8SqBoR5Zk4A9gXzWyOTkQIqHDFZta2/YoS7dMkD5WGEULyJLRKRM/xunp4uIPK2vMPpaRHI8ylyr5y8TkWs90ieIyDq9zNNi1DdIK9IyxgdSvNsxy5FjZ3H0dg8ikuBx+CPAZX+LgdkiEiUiKUAqENCt//gx5ngaCDZWbWtv2KEu3XUHNZ+2K4zuA5YqpVKBpfoxwLloBpOKNvf9HGgDGjAPyEP7JTjPNajpeW7yKGctd+QODgEiIm8AK4A0EakUkRuA3+s/3L4GTgfmAiilNgBvARuBj4FblVJNJkl3cPBJtyySUEp9ps+PezILKNDfLwAKgXv19FeV9nBspYj01X8JFgBLlFKHAERkCXCOiBQCfZRSK/T0V4GLgH92Vu9XxSuY1D805269YZYjx87i6A0+SqnLvST/1U/+R4BHgvX5K9aUwMgzg3U507BCW3cUO9TFzFV8g5RSewGUUntFxLW2ewjgeW/qWmHkL73SS7pXOrIqaeKUqRw8cYx+5VvZnaVN24c1N5G4roTLf3ojlVnaFOCgLes4Htef6oHaTMqogzUcPHiw21clpaWlsXnzZvdqpHHjxtHU1MSWLVu0L3TIEJKSkiguLgYgNjaW3NxcVqxY4V6JM3nyZLZu3ep2IJmRkUFdXR1lZWUADB06lEGDBrkDl/Xp04ecnByKiorcq46mTp3Khg0bqKrSgtdlZWVRXV3Njh07AEhOTiY+Pp5NmzZRVlZGXFwcWVlZLFu2DKUUIsK0adMoLS11RwDNycnh0KFDVFRUtGkngH79+pGens5nn2nOSiMiIpg8eTJr1qxxh7AOdPVYY2Mj8fHxftvph07NsWP09JK+Y/t2cqcXeC0zPCGRRa/9vUt1GSUqKspsCUHDDnXptlV8XlYYHVFK9fU4f1gpFSciHwK/U0oV6elLgXuAM4AopdRv9fQHgOPAZ3r+M/X0KcA9SqkL29NkF198hYWFXt3phyp21BsKq/i6ms744ntvzlwueuUpLyVCc4Wf1fqmP0K5LlZYxbff9RBX/3tAT/e1wshfepKXdAcHBwcHC2PmALUYcK3EuxZ43yP9Gn013yTgO30q8F/A2SISpy+OOBv4l36uWkQm6av3rvFBmxwlAAAgAElEQVS4VqfYVb4jkOLdzsCB1vJ84ei1H+W7vjFbQlCwU1vboS7dtczc2wqjx4CzRKQMOEs/BvgI2IHmfuVF4GcA+uKI/wFW6a/fuBZMAP8FvKSX2U4ACyQAVn9hrcg2o0ePNluCIRy99mP5qu6f2u4K7NTWdqhLtwxQSqnLlVIJSqlIpVSSUuqvSqkqpdR0pVSq/veQnlcppW5VSo1USmUqpUo8rvOyUmqU/nrFI71EKZWhl/m5CvDB2kVXXh1I8W7H9XDfKjh67cc1P/Ye8dhq2Kmt7VAXx5OEg4ODg0NI4gxQXqitPW62BENYbTmpo9d+HD9Ra7aEoGCntrZDXZwBygsfLHzTbAmGyM/PN1uCIRy99uON9981W0JQsFNb26EuzgDlhbNnXWS2BEP42psSqjh67cePzzmv/UwWwE5tbYe6OAOUF06KizdbgiFcXimsgqPXfsSd1HaTrhWxU1vboS7OAOXg4ODgEJI4EXW98M9FbzMpe4zhctt37ODsSW0jZw8eOoxX334nGNK8kpeX12XX7gocvfbj7Q8Xk3jbNWbLCBg7tbUd6uIMUF5IHZeOFrTaGKqhgT9MOaVN+p3LVwVBlW8qKyst5bnY0Ws/0keP4bDZIoKAndraDnVxpvi8MGqstYKvuTxsWwVHr/0Yl2p9rwVgr7a2Q12cAcrBwcHBISRxBigvrCpabrYEQ6SlpZktwRCOXvux/MuVZksICnZqazvUxRmgvNDU1Gi2BEOEh4ebLcEQjl770Wgxm/GFndraDnVxBigvTJp2utkSDGG1qK6OXvtxev5ksyUEBTu1tR3q4gxQDg4ODg4hiTNAeaFiW5nZEgwxePBgsyUYwtFrP8osFuTTF3ZqazvUxRmgvLBu9WqzJRgiJSXFbAmGcPQGHxF5WUQOiMh6j7QnRGSziHwtIu+KSF89PVlEakVkrf56PtDPL1lXGuglQgIrtHVHsUNdnAHKCxdeNttsCYZYsWKF2RIM4ejtEuYD57RKWwJkKKXGA1uB+z3ObVdKZeuvWwL98Mtn/ijQS4QEFmnrDmGHujgDlIODDVBKfQYcapX2iVLKtbxuJZDU7cIcHALAGaC8UFN91GwJhoiJiTFbgiEcvaZwPfBPj+MUEflKRJaJyJRAL15dUx3oJUICm7Q1YI+6OL74vPDPRe9wphefeqGK1ZxCOnq7FxH5NdAIvK4n7QWGKaWqRGQC8J6IpCul2vwyE5GbgJsAEhMTKSwsBGDEiBH07t2b0lLt2dPho0cRBWfURgKaJ8vCng3cfNXVZBzX0oqjGxncGMbwRu13cVjqaA4ePMj69dpjs4EDBzJ69GiKiooALSJsfn4+JSUl7tAReXl5VFZWut34pKWlER4e7l5SPXjwYFJSUtzTWzExMeTl5VFcXExtrRb1Nz8/n/Lycvbt2wfAuHHjaGpqYsuWLQCUlZWRlJREcXExALGxseTm5rJixQrq6uoAmDx5Mlu3buXAgQMAZGRkUFdXR1mZtsBq6NChDBo0yB2TqU+fPuTk5FBUVERjo3ZTO3XqVDZs2EBVVRUAWVlZVFdXs2OHtuAkOTmZ+Ph41qxZA0BcXBxZWVksW7YMpRQiwrRp0ygtLeXwYc0TYk5ODocOHaKiogKAnTt3tminfv36kZ6ezmeffQZAREQEkydPZs2aNRw9qjV/bm4u+/fvZ9euXQCkpqYSFRUV1HbqKKKUMlTATuTm5ipvQb2eeuL3nHngG69lZr30Gu//9CpD5+5cvopPVn4ZmFg/FBcXW+qfqB31ishqpVRuN0nypSEZ+EApleGRdi1wCzBdKXXcR7lC4C6llN8Id77sBeD3//sHvs4a2ib9vTlzueiVp7yW2fzos5QsLfT3kd2O1fqmP0K5Lh21F2eKzwuxvfuYLcEQrl+GVsHR2z2IyDnAvcBMz8FJRAaISLj+fgSQCgS0Trx3bO9AiocMVm1rb9ihLs4Un4ODDRCRN4ACoL+IVALz0FbtRQFLRARgpb5ibyrwGxFpRJuNu0UpdcjrhR0cTMQZoLzwj4VvMik33WwZHSY/P99sCYZw9AYfpdTlXpL/6iPvImBRMD//jcXvMmzunGBe0hSs0NYdxQ51cab4vJA5YYLZEgxRXl5utgRDOHrtR25mltkSgoKd2toOdXEGKC8kj7JWFErXaiSr4Oi1H6kpI8yWEBTs1NZ2qIvpA5SIzBWRDSKyXkTeEJFoEUkRkWIRKRORhSLSQ88bpR9v088ne1znfj19i4jMMKs+Dg4ODg7BwdQBSkSGALcBufrS2HBgNvA48JRSKhU4DNygF7kBOKyUGgU8pedDRMbp5dLR3L0861ql1BlWLvu0s0VNYdw4a4Wod/Taj09XFJktISjYqa3tUBfT76DQFmrEiEgE0BNtE+EZwDv6+QXARfr7Wfox+vnpoi1PmgW8qZSqU0qVA9uAiZ0VFB5urbUjTU1NZkswhKPXfkRYzGZ8Yae2tkNdOtyrROQ24O9KqYPB+nCl1G4ReRLYCdQCnwCrgSMePsQqgSH6+yHALr1so4h8B/TT0z1jTnuWaV2PdnfGnz3rIg5+u5d+5VvZnaWNc2HNTSSuK+Hyn95IZdZ4AAZtWcfxuP5UD0wAIHtiGbV94qhKGQ1AzyNV9N1VziXXXU9hYWGX7Yxvbm7mu+++87kzfsiQISG1M3716tXExsZ2amd8aw8G3bEzvqamhunTpwdlZ7w/usLGuospEyfxudkigsCWLVtISEgwW0ZQsENdOuxJQkQWo93ZFAJ/A95TStUF9OEicWjLXS8DjgBv68fz9Gk8RGQo8JFSKlNENgAzlFKV+rntaHdKvwFWKKVe09P/qpfxu5TW1874F59/nknb13kt0xlPEhctWMjIEd4fIg8eOoxX337H67mOUlhYSEFBQUDX6E7sqDcYniS6wsaCiT9PEs+98AKfj+zbJt1qniSs1jf9Ecp16ai9dPgOSik1U0T6oT3ruR14XkQWAa/qnpQ7w5lAuVLqW130/wGnAn1FJEK/i0oC9uj5K4GhQKU+JXgSmgdnV7oLzzKG2bZpI5N6dLZ0W1RDA3/w4dvvzuWrAr7+kCFebxZDFkevd7rIxrqFjWVbYWSnZ9VDBqv1TX/YoS6GnkEppaqUUn9WSuUD04BTgE9FpEJEfi0isQY/fycwSUR66s+SpgMbgU+Bi/U81wLv6+8X68fo5/+jtFvAxcBsfZVfCprrlk47vyvbuKGzRU0hKclaURQcvb7pAhvrFjZs3Wy2hKBgtb7pDzvUxfAiCRGZLiKvoE1D7AeuAa4GTqalO/92UUoVoy12WAOs0/W8gOY/7A4R2Yb2jMm1I/6vQD89/Q7gPv06G4C30Aa3j4FblVKdfkJ47k8u6WxRU3A9W7IKjl7/BNPGuotLzp9ptoSgYLW+6Q871MXIIokn0aYevgNeBf5bKbXb4/xKtCXhhlBKzUPzG+bJDryswlNKnQC8jh5KqUeAR4x+voNDqNBVNubgYFWMrA2NBn6klPL60EQp1SAipoYbCBbfHbaW38zY2JCc9fGJo9cnlrWxw98dAdoukrAaVuub/rBDXYxM8f0ObX+RGxGJE5FE17FSyhYT0Z+8/57ZEgyRmxuS/7N84uj1iWVt7P8+/shsCUHBan3TH3aoi5EB6j201XGeJAHvBk9OaHDBZbPNlmAI1x4pq+Do9YllbezyWT8yXGbH9u3kTi/w+vrJVVd0gcr2sVrf9Icd6mJkii9NKdVic5BSap2IjAmyJtOJielptgRDuDbbWgVHr08sa2M9o2MMl6lvbmbMr37m9dzmR58NVFKnsFrf9Icd6mLkDuqAiIzyTNCPq4IrycHhB4tjYw4OHhgZoF4GFonIBSIyTkQuRFsi/lLXSDOP917/m9kSDDF58mSzJRjC0esTy9rYq//3ttkSgoLV+qY/7FAXIwPUY8BrwJPAKuAJ/fixLtBlKhNOPc1sCYbYunWr2RIM4ej1iWVtbMop1vciAdbrm/6wQ106PEAppZqVUk8opcYopXrpf59USjV3pUAzGGqx4GsuB69WwdHrHSvbWMrQ4WZLCApW65v+sENdDPnIF5E0IAtoscBeKfVyMEU5OPxQcWzMweF7jHiS+BXwIFAKHPc4pdDmzm1D0dIlTEoebLaMDpORkWG2BEM4er1jZRtbsnwZPUfOCtr1XEvQvTE8IZFFr/09aJ/lidX6pj/sUBcjd1C3AxOVUl93lZhQoZfFdmBbbTmpo9cnlrWx2F69COY8pFlL0K3WN/1hh7oYWSRRC4TkLvZgc3JevtkSDOEKKmgVHL0+6bSNicjLInJARNZ7pMWLyBIRKdP/xunpIiJPi8g2EflaRHICFZ6fY32vBWC9vukPO9TFyAD1APAnEUkQkTDPV1eJc3D4gRGIjc0HzmmVdh+wVCmVCizVjwHORQtJk4oWXfq5oKh3cAgyRgaX+cCNaMEBG/RXo/7XVmxZb60ZlqFDh7afKYRw9PpkPp20MT2gYWsvx7OABfr7BcBFHumvKo2VaAFCA4oN/vXmjYEUDxms1jf9YYe6GHkGldJlKkKMb7Zvh4xR7WcMEQYNGmS2BEM4en0SbBsbpJTaC6CU2isiA/X0IcAuj3yVetre1hcQkZvQ7rJITEyksLAQgBEjRtC7d29KS0sBGDxgIDUKzqiNBKAJKOzZwM1XXU3GcS2tOLqRwY1hDG/Ufhfvy5lA/yYhq077N7Q/vJlNPZooqI1kwt330rM2kuUxDeSdiCC2WQD4IrqB/Jxct460tDTCw8PZuFEbIAcPHkxKSorbD11MTAx5eXkUFxdTW1sLQH5+PuXl5ezbtw+AcePG0dTUxJYtW2hubqa5uZmkpCR3PKXY2Fhyc3NZsWKF+7nO5MmT2bp1q3spd0ZGBnV1de5ptaFDhzJo0CBKSkoA6NOnDzk5ORQVFdHY2AjA1KlT2bBhA1VVmqOQrKwsqqur2bFjBwDJycnEx8ezZs0aAOLi4sjKymLZsmUopRARpk2bRmlpKYcPa1FYcnJyOHToEBUVFTQ3NxMZGdminfr160d6ejqffaYFaI6IiGDy5MmsWbOGo0ePApqT2f3797Nrl9ZFUlNTiYqKYv16bfZ44MCBjB49mqKiIgCioqLIz8+npKSEmpoaAPLy8qisrGT37t1e26mjiBaQ1kABbbrB3fGtTG5urnJ1IE9efP55Jm1f56UEzHrpNd7/6VWGzvkrc+fyVXyystPBfwEoLCykoKAgoGt0J3bUKyKrlVJBeRDTWRsTkWTgA6VUhn58RCnV1+P8YaVUnIh8CPxOKVWkpy8F7lFKrfZ3fV/2AvDcCy/w+ci24TbemzOXi155ymuZzp7b/OizlCwt9Ce101itb/ojlOvSUXsxssy8L/AsWqj1BqCXiMxEW3X0351W+gNn+44dnD3J+y78wUOH8erb73SzIgez6AIb2y8iCfrdUwLg2rlZCXjO/yQBewKQ7uDQJRiZ4nseLZrncLTQ6gArgD8Athqgqr7tvh3YqqGBP0w5xeu5O5d7jVvXhj59+gRTUpfj6PVJsG1sMXAtmquka4H3PdJ/LiJvAnnAd4HOiHxbdRC83EFZDav1TX/YoS5GFklMB27TO7ICUEp9Cwz0W8qC/OfDD8yWYIicnIBXCXcrjl6fdNrGROQNtMEsTUQqReQGtIHpLBEpA87ie59+HwE70IIjvgh433BkgMX//iTQS4QEVuub/rBDXYwMUN8B/T0TRGQYXh6sWp2LrvT+vChUcT2stAqOXp902saUUpcrpRKUUpFKqSSl1F+VUlVKqelKqVT97yE9r1JK3aqUGqmUylRKeX+wZIBrfnJJoJcICazWN/1hh7oYGaBeQgsFcDoQJiL5aEtXn+8SZSYSGdnDbAmGcK0KsgqOXp9Y1sYiIyLNlhAUrNY3/WGHuhh5BvU4cAL4MxCJ5hvsL8D/6wJdDg4/RBwbc3DwwEi4DaWU+qNSapweCmCsfmxsnboFeGfBK2ZLMMTUqVPNlmAIR693rGxjL7/1htkSgoLV+qY/7FCXDg9QInKGr1dXCjSDU8+YbrYEQ2zYsMFsCYZw9HrHyjZ25mTr/zME6/VNf9ihLkam+P7a6ngA0ANtT4W1Ivy1Q+LQYeBjo24o4tqJbhUcvT6xrI0NSxzSwjWFVbFa3/SHHerS4QFKKdXCDYuIhKPtzagOtigHhx8ijo05OLSk057IlVJNwCPAPYEIEJG+IvKOiGwWkU0ikt+ZMAEicq2ev0xErg1E07J/fRxI8W4nKyvLbAmGcPR2jGDZWHfwz8KlZksIClbrm/6wQ10CDZVxFgQcp+z/AR8rpcaghbrehMEwASISD8xD2xU/EZjnGtQ6Q1z/fp0tagrV1db6ge3oNUQwbKzL6R9nLZvxhdX6pj/sUBcjiyR2ichOj9dB4G2+HzwMIyJ9gKnoc+9KqXql1BGMhwmYASxRSh1SSh0GltA2Nk6HGT/Bu+uhUMXl/dgqOHq90xU21l2ckpVttoSgYLW+6Q871MXIIonW7hWOAVuVUkcD+PwRwLfAKyKSBawGfonxMAG+0h0crERX2JiDg2UxskhiWRd9fg7wC6VUsYj8P/z/WhRv0vykt71AB+LbRPfsycGUNPqVb2V3luZpPKy5icR1JVz+0xupzBoPwKAt6zge15/qgVqst+yJZdT2iaMqZTQAPY9U0XdXOXfMe5jK/vGENzaQsGEN+9MyaYjuCUDf+I84MiSZmv5azKG4XTuQ5mYODR/FJSMz2bx5c7vxbZKSkti8ebPX+DYAQ4YMCan4NvX19RQWFhqOb9O6naB74tvU19dTW1sblPg2/ugiG+sW1qxfByOnmC0jYJKTk82WEDTsUJcOx4MSkb/h45++J0qpazr84SKDgZVKqWT9eAraADUKKPAIE1ColEoTkb/o79/Q828BClwvpdTNenqLfL7wFd9m9oXn8+sxyV7LBDseVDBiRR09etRSnovtqDcY8aC6wsaCib94UOdd+hPib76sTXpXxIP66IY7GTFyZJv04QmJLHrt777kdwir9U1/hHJdOmovRhZJHEF7FhSONoUWhvZM6Aiw3ePVYZRS+4BdIpKmJ01HCzPgChMAbcMEXKOv5pvE92EC/gWcLSJx+uKIs/W0TjH9/As7W9QUXBE3rYKj1ydBt7Hu4sIzZ3TbZ9U3NzPmVz9r8/pmb+AhrazWN/1hh7oYeQY1GjhfKbXclSAik4EHlFKB9M5fAK+LSA+0EABz0AzzLT1kwE7A5Sr5I+A8tDABx/W8KKUOicj/AK4ASr9xeW52cLAQXWVjDg6WxMgANQlY2SqtGMgPRIBSai3g7Vavjb8h3SfZrT6u8zKac82AORCEX2LdSVxcp1fUm4Kj1yddYmPdwZ79+2wRsNBqfdMfdqiLkSm+r4BHRSQGQP/7CLC2K4SZibNRt2tx9PrEsjb2z8L/mC0hKFitb/rDDnUxMkBdB5wGfCci+9GCq03m+2dFtuHia68zW4Ihli2z1uIvR69PrsOiNnb9pbPNlhAUrNY3/WGHuhhZZl4BnCoiQ4FEYK9SamdXCTMTkUAdbASH7Tt2cPakiW3SBw8dxqtvv+M+tkA0hhY4en1+TgUWtbFQsZlAsVrf9Icd6mLkGRQi0g9tSXeCUur3IpIIhCmlKrtCnFkoFRqeZVRDA3+Y0tarxZ3LV7U4FvG2DSx0cfT6/SxL2lio2EygWK1v+sMOdTHi6mgasAW4EnhAT05F94dnJ95ZMN9sCYaYNm2a2RIM4ej1jpVt7OW33jRbQlCwWt/0hx3qYuS+/I/AZUqpcwBXsPtiNOestmLajE678TMFl1cFq+Do9YllbezcgpCPqdghrNY3/WGHuhgZoJKVUi6f+q7JzXoMThNagYEJiWZLMITLLZBVcPT6JOg2JiJpIrLW43VURG4XkYdEZLdH+nmBCE8cNDiQ4iGD1fqmP+xQFyMD1EYRab1Z8EzAOqFnHRxCm6DbmFJqi1IqWymVDUxA2+D+rn76Kdc5pdRHnf0MB4euwsgAdSeax4cFQIzu724+cHdXCDOTpR/+w2wJhsjJyWk/Uwjh6PVJV9vYdGC7UuqbIF3PzT/+3WnPYiGF1fqmP+xQFyPLzFeKyHi0kAAvo4W3mBjqq4s6w+AhSWZLMMShQ4dC1imkNxy93ukGG5sNeDpQ/rmIXAOUAHfqsdRa0BHv/wBnTSlgjYIzaiMBaAIKezZw81VXk3FcSyuObmRwYxjDG7XfxftyJtC/Sciq0/4N7Q9vZlOPJgpqI5lw9730rI1keUwDeSciiG3WVqR9Ed3ABdPPYrp+zY09mmhGkVEfwWmXXdEh7//5+fmUl5d79f5fX19PSkpKSHn/d/nUM+r9v76+njFjxnS793+AvLy8oHj/75A3cxEJR4tsO0MpVWfoE0IYX96ZX3z+eSZt9z6r0p3ezH2da+3lvLCwkIKCAq/XCEXsqDdQb+ZdbWO6r8s9QLpSar+IDAIOoj3r+h+0Ze3X+7uGP2/mz73wAp97cXXUFd7MfZ3b/OizlCwt9KG+Y1itb/ojlOsSVG/mSqkmIKWj+R0cHIzRDTZ2LrBGKbVf/7z9SqkmpW1gehELrBR0+OFhxBgeBp4TkeEiEi4iYa5XV4kzi69Xr2o/UwgxYsQIsyUYwtHrk660scvxmN7T46y5+BGwPpCLryoNeXeBHcJqfdMfdqiLkeWrL+l/r+H7JbCivw8PpiizOXywCvpaJ2J87969zZZgCEevT7rExkSkJ3AWcLNH8u9FJFu/dkWrc4Y5eLgK6zxV9I3V+qY/7FCXdn+Z6VFvQZt+cL1G6C/Xe1vhbNTtWhy9LelqG1NKHVdK9VNKfeeRdrVSKlMpNV4pNVMP/Nlpzi1oEx3Hklitb/rDDnXpyB3UVqCPa2mqiPyfUurHXSvLweEHhWNjDg5e6MjcdmuPgwVdoCOk2LPLEg6k3fTr189sCYZw9LbB8ja2c89usyUEBav1TX/YoS4dGaCs77PdIF/8Z2n7mUKI9PR0syUYwtHbBsvb2L+LPjNbQlCwWt/0hx3q0pEBKkJETheRM0TkjNbHepqtuPjaOWZLMIRr051VcPS2wfI2dv2ll5stIShYrW/6ww516cgzqANou9pdVLU6VthwoYSDQzfi2JiDgxfaHaCUUsndoCOkaGioN1uCISIirOVQ3tHbEjvYWENjg9kSgoLV+qY/7FAX69egC3jv9deY4iWSbajgKxQ8tA0HH4pMnjzZbAmGsJpeM3h10duM+dXPzJYRMHZqazvUxRmgvHDG+RfA0f1my/BJ61DwB1LTGVi2AWgbDj4UWbNmjaU8LVtNrxnMPPNstpotIgjYqa3tUBfbuSkKBv0GDDRbgiHqe8aaLcEQLq/JVsFqes1gQL/+ZksICnZqazvUxbmDcnBwsD0/ueoKvtm7x+u54QmJLHrt792syKEjOAOUFz55/10mZYwyW0aHGbTFWkGNc3M7HZXCFKym1wze/fgjBt56hdkyfPLN3j0+n5FtfvRZ93s7tbUd6uJM8Xlh+MiRZkswxPE4a02v7N8fus/3vGE1vWYwMjnZbAlBwU5tbYe6hMQApYcW+EpEPtCPU0SkWETKRGShHmwNEYnSj7fp55M9rnG/nr5FRGYEoictY3wgxbud6oEJ7WcKIVyROq2C1fSawfgx48yWwI7t28mdXuD1taOiokPXsFNb26EuoTLF90tgE7g99j8OPKWUelNEngduAJ7T/x5WSo0Skdl6vstEZBxaOOt0IBH4t4iM1oPAOTg4/ACob272PY03Z243q3EIBqbfQYlIEnA+eiwcERHgDMC1mWcBcJH+fpZ+jH5+up5/FvCmUqpOKVUObCOACKFfFa/obFFT6Lu7wmwJhkhNTTVbgiGsptcMVqzxHgreatipre1Ql1C4g/ojcA/giq7VDziilGrUjysBV/TAIcAuAKVUo4h8p+cfAqz0uKZnmRaIyE3ATQCJiYkUFhYCWvTJ3r17U1paysQpUzl44hj9yreyO0sb58Kam0hcV8LlP72RyixtCnDQlnUcj+vvnmLLnlhGbZ84qlJGA9DzSBV9d5Vzx7yHqewfT3hjAwkb1rA/LZOG6J4A9I3/iCNDkqnpPwiAuF07kOZmDg0fxR3zUjkc1kSfvbvYm67tZ4iorwNeY9/YbBp7RGnX2LmDw0NHcCx+AJeMzOTAgQM0NTWxZcsW7UsbMoSkpCSKi4sBiI2NJTc3lxUrVlBXVwdom/q2bt3KgQMHAMjIyKCuro6ysjIAhg4dyqBBgygp0f4R9enTh5ycHIqKimhs1Jpq6tSpbNiwgaqqKgCysrKorq5mx44dACQnJxMfH8+mTZsoKysjLi6OrKwsli1bhlIKEWHatGmUlpZy+PBhAHJycjh06BAV+hSNZzuB5rE5PT3d7XcsIiKCyZMns2bNGvcy29zcXPbv3++e8khNTSUqKor167UgsgMHDmT06NEUFRUBEBUVRX5+PiUlJdTU1NDY2Eh8fDyVlZXs3q157U5LSyM8PJyNGzd662Y/OGqOHaOn2SKCQFRUlNkSgoYd6mLqACUiFwAHlFKrRaTAlewlq2rnnL8yLROVegF4ASA3N1cVFBS0OF9QUMCLmzfTv1z7555UWtzi/Bsvvcjsn17lPj5p705O2quF51j7ZTEx41PblPnfh+fxvkcZz1V3Rw4dou/uijZ3QT2PVHHrS6+5y7W+5uBN34fYrszKI6m0mLhdO3hq+SpuvOUWABISWj6bal3X/Pz8Fsfjxo1j3LiWzxKGDGk5zre+Ruvd6pmZmS2O4+LiGDZsWIu0iIiIFteZNm1ai/NZWVktjvv06UNyq4fw3trNk9YbFGNjYxnZavFLe9dwrYIqLCwkJiaG1NTUNr9KBw601p65ruKsKdP43GwRQWD9+vVt+oFVsUNdzL6DOg2YKSLnAdFoz6D+CBhDnukAAByJSURBVPQVkQj9LioJcG1gqASGApUiEgGcBBzySHfhWcbBwcHBwYKY+gxKKXW/UipJd5Y5G/iPUupK4FPgYj3btcD7+vvF+jH6+f8opZSePltf5ZcCpAJfdlbXrvIdnS1qCj2PVJktwRBWu+uwml4zKN/1jdkSgoKd2toOdTF9kYQP7gXuEJFtaM+Y/qqn/xXop6ffAdwHoJTaALwFbAQ+Bm4NZAXf6i+sNVnRd1e52RIMMXr0aLMlGMJqes1g+apO/x4MKezU1naoS8gMUEqpQqXUBfr7HUqpiUqpUUqpS5RSdXr6Cf14lH5+h0f5R5RSI5VSaUqpfwai5aIrrw6sMt3Mnkxr7Rh3LUawClbTawbX/PgSsyUEBTu1tR3qEjIDlIODg4ODgydmL5IISWprj5stwRDhFgsWZ7Xlr1bT2xoRqQCqgSagUSmVKyLxwEIgGagALlVKHe7sZxw/UQv0DVir2Vi9rT2xQ12cOygvfLDwTbMlGCJhwxqzJRii9fL2UMdqen1wulIqWynlmg++D1iqlEoFlurHneaN998NVF9IYJO2BuxRF2eA8sLZsy5qP1MIsT8ts/1MIYRrs69VsJreDuLplcXTW0un+PE55wUsKBSwU1vboS7OFJ8XToqLh0O7zZbRYVxeKcAa4eBramrMlmAIq+n1ggI+EREF/EXfrD5IKbUXQCm1V0S8rknuiOcVgOFDktik4IzaSECbSyzs2cDNV11NxnEtrTi6kcGNYQxv1H4X78uZQP8mIatO+ze0P7yZTT2aKKiNZMLd99KzNpLlMQ3knYggtlnbi/9FdAMXTD+L6fo1N/ZoohlFRn0EE+6+l9r6cLZFNjFF11Erii9iGpl7482M0Mssj2lgVEM4CbqO+mHD2Lt3L1u2bKGmpoaysrKQ8ryyZo02Q2LU80pNTQ07d+7sds8rAHl5eUHxvOIMUDajdTh4T6wQDt6hSzhNKbVHH4SWiMjmjhbsiOcVgOdeeAE1qC9Le7Z8HvqX1/7GRdO/9+qxrUcT23poO0BWrlnN4PCr2pRZ2rOB9554nIteeQrQBjZPPli6hIirWt6x7Y9oWab1NZ968S/uc6ANbBt1HTt27iQhIYGEhAQKCwvd3kJCxfNK62t01PNKYWGh+1rd6XnFRTA8rzhTfF7456K3zZZgCE+3R1YgLy/PbAmGsJre1iil9uh/DwDvojlS3i8iCQD63wOBfMbbHy4OVGZIYPW29sQOdXEGKC+kjks3W4IhagZYKx5UZWWl2RIMYTW9nohILxHp7XoPnA2sp6VXFk9vLZ0iffSYQIqHDFZu69bYoS7OAOWFUWPND75mBJcndKvgmpe2ClbT24pBQJGIlKK5//pQKfUx8BhwloiUAWfpx51mXKr1vRaA5du6BXaoi/MMysHBxujeVrK8pFcB07tfUejhisQLcMNlV3DX/zwEwPCERBa99nfzhDk4A5Q3VhUtZ1KCdTYdxu2ylnPbtLQ0syUYwmp6zWD5lysJG3mO2TI6hWck3h2NYe73mx991kxZAWOHfutM8Xmhqamx/UwhhDQ3my3BEOHh4WZLMITV9JpBo8VsxhfN3sPIWRI79FtngPLCpGmnmy3BEIeGjzJbgiGsFoXWanrN4PT8ye1nsgAZ9faZVLJDv3UGKAcHBweHkMQZoLxQsa3MbAmG6HXoW7MlGGLw4MFmSzCE1fSaQZnFgnz6Ym+EtabL/WGHfusMUF5Yt3q12RIM0WfvLrMlGCIlJcVsCYawml4zKFlXaraEoLAtstNxTkMOO/RbZ4DywoWXzTZbgiH2pue0nymEWLFihdkSDGE1vWZw+cwfmS0hKLh8+MH3y8+9vX5y1RUmquwYdui39nki6NAuVnAk6+AQKnguP2+N1ZegWwVngPJCTfVRsyUYIqK+rkP5QsWRbExMTLd9VjCwml4zqK6pxg4BC2vFPsvM7dBvnSk+L/xzkbXuJBxnsV2L1fSawVsf/sNsCUHhixh77OcCe/RbZ4Dywrk/udhsCYbYNzbbbAmGcMXZsQpW02sGl55/odkSgsKptfaZVLJDv3UGKC/E9u5jtgRDNPaIMluCIWpra82WYAir6TWD3rG9zZYQFGKUmC0haNih3zoDlIODg4NDSOIMUF74x8I3zZZgiIQNa8yWYIjWkUlDHavpNYM3Fr9rtoSgsDymof1MFsEO/dYZoLyQOWGC2RIMcTRhqNkSDFFeXm62BENYTa8Z5Ga2iehhSUY1WN/Bqgs79FtngPJC8qhUsyUY4lj8ALMlGGLfvn1mSzCE1fSaQWrKCLMlBIWERvv8S7RDvzW1NURkqIh8KiKbRGSDiPxST48XkSUiUqb/jdPTRUSeFpFtIvK1iOR4XOtaPX+ZiFzr6zMdHBwcHKyB2T8XGoE7lVJjgUnArSIyDrgPWKqUSgWW6scA5wKp+usm4DnQBjRgHpAHTATmuQa1zrBy2aedLWoK8d9sM1uCIcaNG2e2BENYTa8ZfLqiyGwJQWF9D/vsg7JDvzV1gFJK7VVKrdHfVwObgCHALGCBnm0BcJH+fhbwqtJYCfQVkQRgBrBEKXVIKXUYWAJ0OrxneLi19kKoMLN/ZxijqclaDjmtptcMIixmM74Iwz7LzO3Qb0OmV4lIMnAyUAwMUkrtBW0QE5GBerYhgKfr7ko9zVe6t8+5Ce3ui8TERAoLCwEYMWIEvXv3prS0lLNnXcTBb/fSr3wru7M033VhzU0krivh8p/eSGXWeAAGbVnH8bj+VA9MACB7Yhm1feKoShkNQM8jVfTdVc4d8x6msn884Y0NJGxYw/60TBqiewLQN/4jjgxJpqb/IEAL3y7NzRwaPoo75qVyOKyJPnt3uR3Cam6NXmPf2Gz3/iclQn2v3hyLH8Ad81I5fuwQKiyMw0O15wKxB/cT++1et47IE8cZtGUde9NzaIrQnGNGrlzLxo0bOXDgAAAZGRnU1dVRVqaFHhk6dCiDBg2ipKQEgD59+pCTk0NRURGNjdqvzqlTp7JhwwaqqqoAyMrKorq6mh07tFAMycnJxMfHs3r1amJjY4mLiyMrK4tly5ahlEJEmDZtGqWlpRw+fBiAnJwcDh06REVFRZt2Auj3/9s78yip6iuPf67dDTS2LS0EZEcBcQFBRG1ksSPqUfS4jMvoEVziNol6YuLgdiYTMyaOTo4aE03iEqPGbZxo1OMYl2AIoIbYMir70ojQLI2CiH1Yervzx3uFVdWvuruqq/r33vN+zqnTVb+3fV/1u3Xf+/3u797evTniiCOYO3eu9/0UFzN58mQWLlzIjh1eyqoJEyZQV1fH+vXeJTJy5Ei6d+/O4sWLAejbty+HHHII8+d7TwHdu3dn4sSJVFdXU19fT319PdOmTaO2tpYNGzYAXjntoqKiWBSFywdTjq3kHdci8sDhDUWxKbmxYsUK+vfv71pGpwiFgxKRMuAF4AZV3SGS8S4maIG20d66UfVh4GGACRMmaFVVVcryqqoqHvntbxn1yQoABn2UOhv72Ucf4cIrZ+z9vP+mdey/aR0AH/5jAaVHjmy1zb0/+TEvJ23Tb8Wive+3b9tGrw1r6bVhbco2Pbdv5dpHn9q7Xfo+k9Mb1Y49jor1a6hYvyZlm/Q6Uek6ksPTl69YwQ3fuYx00pPIpn9fkyenVlIdM2ZMyueKigqGDBmS0lZWVpaynxNOOCFl+dixqRFh5eXlDBs2LKUt6P+WzPjxqRney8rKGD58eFb7mDBhAgBz5syhtLSUkSNHMnJkagBN3759MQyjMDh3UCJSguecnlbVF/3mOhHp7z899Qe2+O21QHJM9SBgo99eldY+J1dNq5ctpbJbrlt3PWWf13V6H5kSyRYiiezAgYEPt6ElanqTEZHBwJPAgUAL8LCq3i8itwNXAYm7mNtU9bVcj7N01UoYHpwpP0rUxuTpCaJ93SZwHcUnwO+AZap6b9KiV4BEJN6lwMtJ7Zf40XyVwJd+V+AbwCkiUuEHR5zit+XEqqVLct3UCWWfbXItISsGDRrkWkJWRE1vGpkCkQDuU9Vx/itn5wSwZOXyzuoMBeuKoz9ukyDi1y3g/glqEjATWCQiiT6r24C7gOdF5ApgHXC+v+w1YDqwGtgJXA6gqttE5A4gcbv/H6q6LVdRp517PtQsan/FkLD5sHGtugDDzIIFC1p1p4WZqOlNxr+BS4znfiUiiUCkvHL+6WfGYgzq+N0lzO7ZfjaJRDHDIIb2H8ALTz2TZ2XZE+XrNoFTB6Wq8wkePwKYFrC+Atdm2NdjwGP5U2cY8SItEGkScJ2IXAJU4z1lfRGwTbtBRQDlZWWIwol+RdpmYE7PRq6ZMZPRO722BT2aOLBpH4b6k2E3jz+aPs3C2D3ez1BdUQvLujVTtauEo2fdTM9dJcwrbeS43cWUtXg/E+/2aOSMaSczzd/n0m7NtKCMbijm6Fk3s6uhiNUlzXsr4+4S5d3SJn5w1TUc7G8zr7SREY1Feyfl1hx6GP2b9uHwhiLKW4RRDUWsK27mjlk302tnCfX7KAt6NDFlVwnd/JHtPxeXcN6N19Ov2dvHR92b6NEijGosYvemz6ipqclbUNHChd5YcbZBRfX19axbt67Lg4rAK/WRj6Ai109QoeTLL3J++HJCye6driVkRVlZmWsJWRE1vUEEBCL9BrgDL5joDuAe4Dvp23UkqAjgzp//F9pvWKunj4ee+gNnT/s6YGV1t2ZWd/O60f6+8AMOLJrRapvZPRt56ed3c/bv7wM8x5bMq7PfonjG9JS2uuLUbdL3ed8jD+1dBp5jW+rrWLR8GcOLW9hU3MJxu4tZ4bf/KGl/kJqnb0/DHhZ3b2YxSV2CRUptSQvL//sZvnv11SnfT4JcgorS99HRoKLq6uq9++rKoKIE+QgqitYEmi7izZdfci0hK5KjAqNA+oUcdqKmN52gQCRVrVPVZlVtAR7Bm+CeMy++3qkhrNCQ7gyjTNSvWzAHFcgZ/3yhawlZkZgjFRXee+891xKyImp6k8kUiORHxyY4B1jcmeNcdNY5ndk8NCS6BuNAlK/bBNbFF0BpaU/XErIiMdk2KuzZs8e1hKyImt40MgUiXSQi4/C6+NYC13TmID17lHZm89DQLXD2ZHZkCqDo6uCJiF+3gDkoox1q1qzhlMrg3p/0SbxG+GgjECkefXIhpKGlhUNv+16r9uV3/tqBmmhjDiqAl57+A5WV41zL6DADFlUXbN+ZJvBC7pN40weKw07U9LrgyRf/h+GzrnIto9PMiVHBwjhctzYGFcDRx09yLSErtg8+yLWErFi5cqVrCVkRNb0umHJM9LNIABzWEJ+ChXG4bs1BBTA4YsXXdvbq7VpCViQS0kaFqOl1wUGDh7qWkBcS85riQByuW+viMwzD6AKikH0ibJiDCmD+7LeoHHagaxkdpvcn0XqUHz16tGsJWRE1vS54a97f6Dn8LNcyOs1H3Qs3DypT8AQUJoAiDtdtfJ5n88i+Ecsc0NwtQqnXiV74a9T0uqBs331dS8gLPVriU7AwDtetOagAjjpuomsJWbF94DDXErIiUQQxKkRNrwsmjo9+1gKAUY3xCZKIw3VrXXxGztgcKcMwCok5qABWLP6YytLoPOrvt8VNPahc50gNHjw447IwEjW9Lvh4+VIYfrxrGZ3mU0cFCwsRQBGH69YcVACf1tTA6BGuZXSYnl987lpCVvTr18+1hKyIml4X1KxdS1+i76A2O3JQhQigiMN1a2NQAZwSscSXdaPGtL9SiEjUyIkKUdPrgnNOnd7+ShHguN3xuWePw3Ubn/+GYRhGDMnU/bd5w0YOHDggcJuh/Qdw/ZVXF1hZ4TEHFcDWz6I1A7vbznrXErKivLzctYSsiJpeF3y29XMY3su1jE6zY588pDPPMxmTz17+A6ra6BaMw3VrXXwBvP2/r7qWkBV9Vy1xLSEr0qt3hp2o6XXBK39507WEvPB+jAoWxuG6NQcVwNkXz3AtISs2jgnfHJRECHr665Lzz2P+/Pmu5WVF1PS64JJzz3ctIS9U7YxWbbW2iMN1a118AZSURCszQ8s+4ZtcmCkE/cZ579PUFK271KjpdUFJxIpmZiJ8lpQ7cbhu7QnKMAzDCCX2BBXAH5/4PZWTw9dtlomBH/3DtYQOU7NmDf95683cqa0Ho8OafWLq1KmuJYSex55/llG3fte1jE7zdkwKFq6pqWHWT3+C3nF7q2VRypxuDiqA40+cBg1fupbRYbYedAh9PlnhWkaH0MZGbpl5caDeXCv0FpolS5YwZky05pp1NSdNnsp61yLywJENRXzUvdm1jE7T0NLCBT+8LvBcolR63rr4AhgweIhrCVmxuzxa4b1R07t161bXEkLPkAEDXUvIC31iVLAwDudiT1BGaLDks4ZhJGMOKoC/vfE6lSOic0fYp2aZawlZkUlvrslnC83YsWOdHTsq/HnObMqHn+taRqdZWMCChV1NpnPJlJkijGNTsXJQInIqcD9etOijqnpXLvup6NM7r7oKTWPPMnrU73Ato8PkojfT01VXPFl99dVXVFRUFPQYLsiXvQD0qehNQ96UuaO8RfiiKHzZJHIh07lkykzx2hU3Zsyo3l5apUI5ttg4KBEpAh4ETgZqgfdF5BVVXZrtvo48+hioWZRviQXjy/6D2W/LRtcyOkwuetuaV1Vo1qxZw5Ah0RqXbI982gvAMWPH8U4+BTpiRGMRn5a4yWieb7I9lzYzqreTVqlQxMZBAccCq1V1DYCIPAecBeRkcEY0aGvcasOmTQzs3z+rZd+gsS6zFyMvFLLLUDRgPkoUEZHzgFNV9Ur/80zgOFW9Lm29q4FEmt9RQFB8dh8gSkWWTG9h6Yjeoar6ra4Qkw/ybC8Qvf9pJuJyHhDuc+mQvcTpCSqoBG4r76uqDwMPt7kjkWpVjcxMXdNbWKKmt4PkzV4gPt9RXM4D4nEu0Q+U/5paILnG8SAgOgMzhtG1mL0YoSdODup9YKSIHCQi3YALgVccazKMsGL2YoSe2HTxqWqTiFwHvIEXNvuYquZaKKndLo2QYXoLS9T0tkue7QXi8x3F5TwgBucSmyAJwzAMI17EqYvPMAzDiBHmoAzDMIxQYg4qDRE5VURWiMhqEbnFtZ72EJG1IrJIRD4UkWrXetIRkcdEZIuILE5qO0BE3hKRVf7f0OQRyqD3dhHZ4H/HH4rIdJcaw0TU7CUTIjJYRP4qIstEZImIfN+1ps4iIkUi8n8i8qprLbliDiqJpPQvpwGHAxeJyOFuVXWIb6vquJDOeXgcODWt7RZgtqqOBGb7n8PC47TWC3Cf/x2PU9XXulhTKImwvQTRBNyoqocBlcC1ET6XBN8HopVJOg1zUKnsTf+iqg1AIv2LkSOqOhfYltZ8FvCE//4J4OwuFdUGGfQawcTGXlR1k6ou9N9/hffDHp2SBmmIyCDgdOBR11o6gzmoVAZCSmHQWsJ/kSrwpoh84KeliQL9VHUTeD8MQF/HejrCdSLysd8FGJouScdE0V7aRUSGAUcBC9wq6RS/AG4CIp351hxUKh1K/xIyJqnqeLxulmtFZKprQTHkN8BwYBywCbjHrZzQEEV7aRMRKQNeAG5Q1ejUsElCRM4AtqjqB661dBZzUKlELv2Lqm70/24B/oTX7RJ26kSkP4D/d4tjPW2iqnWq2qyqLcAjROM77goiZy9tISIleM7paVV90bWeTjAJOFNE1uJ1u54oIk+5lZQb5qBSiVT6FxHZV0T2S7wHTgEWt71VKHgFuNR/fynwskMt7ZJwpj7nEI3vuCuIlL20hYgI8Dtgmare61pPZ1DVW1V1kKoOw/ufvK2qMxzLyonYpDrKBwVI/1Jo+gF/8myLYuAZVX3draRURORZoAroIyK1wI+Bu4DnReQKYB1wvjuFqWTQWyUi4/C6r9YC1zgTGCIiaC9tMQmYCSwSkQ/9ttssYtMtlurIMAzDCCXWxWcYhmGEEnNQhmEYRigxB2UYhmGEEnNQhmEYRigxB2UYhmGEEnNQhmEYRigxBxUxRKTKn5/jUsMSEalqZ51hIqIi0u5cOxG5TESaRaReRA7Lm9DWxznJP0aLiJxUqOMY4cHsJXfCYC/moPKIX5vJ+Q+feKwRkaUBy+aIyJVtbJswlHr/tTa9zo+qHqGqc/Is+z1VLVPVveUBRGSkiDwnIp+JyA6/ftSv/EzN+HWILkhaf5KvPb2tXkSKVfUvqlqGNznYcIzZS6f4RtiLOah4MhUvQ/jBInJMjvvo5V+c5wE/EpGT86auA4jICLxs0huBo1S1HG+2fw0w2V9tLnBC0mZTgeUBbe+qalPBRRtRxewltS009mIOqosQkavEqzq6TUReEZEBScvuF5H1/l3PByIyJWlZqYg8LiJf+Hd4HTGgRH671/g65x0i8jNgCvCAf5f0QHs7UtVqYAleJu/Efvbe+YrIsSJS7WuvE5HAPGYicq6/3egO6Ae4HXhHVX+oqrW+li2q+gtVfc5fZy6eQSWYAtwd0Da3g8c0QoLZi9kLAKpqrzy98PK0nRTQfiLwOTAe6A78CpibtHwG0Bsvn96NwGagh7/sLmAecABe5ujFQG0bGnoCO4DpwLn+cbslLZ8DXNnG9sPwcs4V+58rgZ3AOUHnCbwHzPTflwGV6fsBLgdWAyMyHPMyYH5a22bgsna+7yF49W4OwLvZ2gKU4tUoSrRtB6Z25P9kL7MXs5dw2Ys9QXUNF+Ml0lyoqnuAW4GJ4hVGQ1WfUtWtqtqkqvfgGeUof9sLgJ+p6jZVXQ/8sp1j/ROwB3gTeBXvgj89B82fi8guPIP6NfBShvUagREi0kdV61X172nLbwBmAVWqujqL4/fBMzoAROQ6Ednu38k+AqCq6/D6x6cAY4FVqroLeCeprQfRLjz3TcTsxewFsC6+rmIA8Gnig6rWA1vxq4+KyI0iskxEvhSR7cD+eBdcYtvkqqWf0jaXAs/7xrsHeJGkboss6IN3h/eveNm9SzKsdwVwCLBcRN4Xr1haMrOAB9XvdsiCrcDeMheq+oCq9sKrFJqsJdFtMRXvzhlgflLbAv97MKKD2YvZC2AOqqvYCAxNfBCvdlNvYIPff34z3p1fhX9RfcnX1Uo3kVoUbkimg/jROicCM0Rks4hsxhu0nS4iCQPucPp69Yr03QPsBr6XYZ1VqnoR3iDz3cAf/fNLcArwbyJybkeP6zMb7+62PRIGN4WvDW5eUlt4+tONjmL2YvYCmIMqBCUi0iPpVQw8A1wuIuNEpDtwJ96dylpgP6AJ+AwoFpF/B8qT9vc8cKuIVPgGdX0bx54JrMTr7hjnvw7Bq3x6kb9OHXBwlud0F3CTiPRIXyAiM0TkW+pVm93uNzcnrbIEOBV4UETOzOKYtwNTROReEUncOfcB0ud9zAWOwotEesdvWwQcBHybkBmc0QqzF7OXzLgY+IrrC28wUdNeP/WX/QteyOc2vL7uQX57EV4lzx14d383kTqo2hN4Eu9iXorXBRA46IsXMnp9QPtNQLX/fiKeUX4B/DJg3WEkDfr6bYJnONcnnWdC31N4g631/jpnB+0HmIBn7KcFHPMy0gZ9/fZD8X5wPge+AlbgDZgPTltvI/BxWttreP39+2b4P1mQhOOX2YvZS3svK1hoOEdEZgIPAQ3ARE2afJjn40wDXsAbVJ+uqn8txHEMo5B8k+zFHJRhGIYRSmwMyjAMwwgl5qAMwzCMUGIOyjAMwwgl5qAMwzCMUGIOyjAMwwgl5qAMwzCMUPL/2F91IVkUaq0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.grid(linestyle='--')\n",
    "loadRisk(resLoad_b, reliableCap_w, saveFig=False, \n",
    "         figName='asas', color=c_withoutPump, case='withoutPSP')\n",
    "plt.subplot(1,2,2)\n",
    "loadRisk(resLoad_b, reliableCap_b_ne, saveFig=False, \n",
    "         figName='asas', color=c_withPump, case='withPSPS')\n",
    "plt.grid(linestyle='--')\n",
    "plt.tight_layout()\n",
    "#plt.savefig(outLoc+'/LoadAtRisk_2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Energy at risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def energyAtRisk(resLoad, reliableCap, color, case, saveFig=False, figName=False):\n",
    "    energyAtRisk=((resLoad[resLoad>reliableCap] - reliableCap)/1000000).sum()\n",
    "    stat = case+'\\nmean:%.3f\\nstd: %.3f'% (energyAtRisk.mean(), energyAtRisk.std())\n",
    "    plt.hist(energyAtRisk, bins=20, label=stat,  \n",
    "             edgecolor='black', color=color, alpha=0.7)\n",
    "    plt.xlabel('Energy [TWh]', fontsize=12)\n",
    "    plt.ylabel('Frequency', fontsize=12)\n",
    "    plt.legend()\n",
    "    #plt.show()\n",
    "    \n",
    "energyAtRisk(resLoad_b, reliableCap_w, color=c_withoutPump, case='NoPSP')\n",
    "energyAtRisk(resLoad_b, reliableCap_b_ne, color=c_withPump, case='WithPSP')\n",
    "\n",
    "plt.savefig(outLoc+'/EnergyAtRisk.pdf')"
   ]
  }
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