VMS_IE_contour.ipynb

{
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>tech</th>\n",
       "      <th>RO</th>\n",
       "      <th>W</th>\n",
       "      <th>WONA2</th>\n",
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       "      <th>electrolyser</th>\n",
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       "      <th>H2store</th>\n",
       "      <th>bat</th>\n",
       "      <th>bat_cap</th>\n",
       "      <th>PIT_heat</th>\n",
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       "<p>8760 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "tech    RO    W     WONA2     WONA3     WONA4     WONA5     WONB5      WOFF  \\\n",
       "d001a  0.0  0.0  0.000000  0.000000  0.000000  5.588046  0.000000  0.000000   \n",
       "d001b  0.0  0.0  0.000000  0.000000  0.000000  5.795631  0.000000  0.023358   \n",
       "d001c  0.0  0.0  0.000000  0.000000  0.000000  5.472462  0.000000  0.000000   \n",
       "d001d  0.0  0.0  0.004317  0.213797  2.442577  2.480103  0.000000  0.023340   \n",
       "d001e  0.0  0.0  0.000000  0.000000  0.000000  4.943122  0.000000  0.000000   \n",
       "...    ...  ...       ...       ...       ...       ...       ...       ...   \n",
       "d365t  0.0  0.0  0.006398  0.242556  2.565611  4.091756  0.000000  0.000000   \n",
       "d365u  0.0  0.0  0.006206  0.243185  0.589794  6.080462  0.000558  0.023074   \n",
       "d365v  0.0  0.0  0.006354  0.244235  2.572517  3.704910  0.000399  0.000000   \n",
       "d365x  0.0  0.0  0.000000  0.000000  0.000000  6.070893  0.000439  0.021756   \n",
       "d365y  0.0  0.0  0.000000  0.000000  0.000000  6.073016  0.000000  0.000000   \n",
       "\n",
       "tech   PVPA1   WG  WG_peak  electrolyser        EB    H2store       bat  \\\n",
       "d001a    0.0  0.0      0.0      1.661385  0.358916  38.584895  0.421779   \n",
       "d001b    0.0  0.0      0.0      1.661385  0.358916  39.106148  0.000000   \n",
       "d001c    0.0  0.0      0.0      1.661385  0.358916  39.627401  0.000000   \n",
       "d001d    0.0  0.0      0.0      1.661385  0.358916  40.148654  0.000000   \n",
       "d001e    0.0  0.0      0.0      1.661385  0.358916  40.669907  0.000000   \n",
       "...      ...  ...      ...           ...       ...        ...       ...   \n",
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       "d365y    0.0  0.0      0.0      1.661385  0.000000  38.063642  0.000000   \n",
       "\n",
       "tech    bat_cap   PIT_heat  Tank_heat  \n",
       "d001a  0.420579  53.590104   0.461219  \n",
       "d001b  0.000000  53.577414   0.461110  \n",
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       "d001e  0.000000  53.539365   0.460783  \n",
       "...         ...        ...        ...  \n",
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       "d365x  0.199306  53.106936   0.398195  \n",
       "d365y -0.420579  53.347973   0.398101  \n",
       "\n",
       "[8760 rows x 18 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from os import path\n",
    "import pandas as pd\n",
    "from pandas.io.excel import read_excel\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as mpatch\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "from pandas import ExcelWriter\n",
    "import itertools\n",
    "\n",
    "data_dir='/Users/edqvist/Documents/VMSpaper/results2021'\n",
    "file = 'IE_generation.xlsx'\n",
    "\n",
    "xls_file = pd.ExcelFile(path.join(data_dir, file))\n",
    "df_gen = xls_file.parse('Sheet2')\n",
    "df_gen = df_gen.replace('Eps', 0)\n",
    "df_gen = df_gen.replace('NaN', 0)\n",
    "df_gen = df_gen.replace('Undf', 0)\n",
    "\n",
    "#df_gen = df_gen.dropindex\n",
    "df_gen.set_index('tech', inplace=True)\n",
    "df_gen = df_gen.fillna(0)\n",
    "df_disp = df_gen.T\n",
    "df_disp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_disp['Wind'] = df_disp['WONA2']+df_disp['WONA3']+df_disp['WONA4']+df_disp['WONA5']+df_disp['WONB5']+df_disp['WOFF']\n",
    "df_disp['bat_ch'] = df_disp['bat'].diff().shift(-1)\n",
    "\n",
    "\n",
    "df_disp['Demand El'] = df_disp['Wind']+df_disp['W']+df_disp['WG']+df_disp['WG_peak']+df_disp['PVPA1']-df_disp['EB']-df_disp['electrolyser']-df_disp['bat_ch']\n",
    "df_disp['netload'] = df_disp['W']+df_disp['WG']+df_disp['WG_peak']-df_disp['EB']-df_disp['electrolyser']-df_disp['bat_ch']\n",
    "df_disp['netload H2']= df_disp['W']+df_disp['WG']+df_disp['WG_peak']-df_disp['EB']-df_disp['bat_ch']\n",
    "df_disp['netload BAT']= df_disp['W']+df_disp['WG']+df_disp['WG_peak']-df_disp['EB']-df_disp['electrolyser']\n",
    "df_disp['netload CCGT']= df_disp['W']+df_disp['WG_peak']-df_disp['EB']-df_disp['electrolyser']-df_disp['bat_ch']\n",
    "df_disp['netload HEAT']= df_disp['W']+df_disp['WG']+df_disp['WG_peak']-df_disp['electrolyser']-df_disp['bat_ch']\n",
    "df_disp['netload GT']= df_disp['W']+df_disp['WG']-df_disp['EB']-df_disp['electrolyser']-df_disp['bat_ch']\n",
    "\n",
    "df_disp.to_excel(path.join(data_dir,\"dataIE.xlsx\"),columns=['netload','bat','electrolyser','bat_ch','RO', 'Demand El','Wind','PVPA1'], sheet_name = \"netload\")\n",
    "\n",
    "#df_disp['x1'] = range(0,len(df_disp))\n",
    "#df_disp.plot(kind='scatter', x='x1', y='netload BAT')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "net_load=((-1)*df_disp['bat_ch'])\n",
    "#net_load=(df_disp['WG_peak'])\n",
    "d = {'net load': net_load,'count1':0,'count2':0}\n",
    "df_netload = pd.DataFrame(data=d)\n",
    "#maxind, maxval = max(net_load, key=lambda item: item[1])\n",
    "maxval=int(math.ceil(df_disp['netload'].max()))\n",
    "minval=int(math.floor(df_disp['netload'].min()))\n",
    "amps = np.arange(minval, maxval, 0.1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#kolla på diffen av detta\n",
    "df_freq = pd.DataFrame()\n",
    "output = {}\n",
    "for amp in amps:\n",
    "    d = {'net load': net_load,'count1':0,'count2':0}\n",
    "    df_netload = pd.DataFrame(data=d)\n",
    "    s=[]\n",
    "    \n",
    "    dfrows=df_netload.index\n",
    "    rowbefore='d001b'\n",
    "#ökande siffra när värde över amp\n",
    "    for row in dfrows:\n",
    "        if amp>0 and df_netload.at[row,'net load']>amp:\n",
    "#            df_netload.set_value(row, 'count1', df_netload.at[rowbefore,'count1']+1)\n",
    "            df_netload.at[row, 'count1']= df_netload.at[rowbefore,'count1']+1\n",
    "        elif amp<0 and df_netload.at[row,'net load']<amp:\n",
    "#            df_netload.set_value(row, 'count1', df_netload.at[rowbefore,'count1']+1)\n",
    "            df_netload.at[row, 'count1']= df_netload.at[rowbefore,'count1']+1\n",
    "        rowbefore=row\n",
    "\n",
    "    rowbefore='d001b'\n",
    "#varje periods längd vid sluttillfället\n",
    "    for row in dfrows:\n",
    "        if amp >0 and df_netload.at[rowbefore,'net load']>amp and df_netload.at[row,'net load']<amp:\n",
    "#            df_netload.set_value(rowbefore, 'count2', df_netload.at[rowbefore,'count1'])\n",
    "            df_netload.at[rowbefore, 'count2']= df_netload.at[rowbefore,'count1']\n",
    "        elif amp<0 and df_netload.at[rowbefore,'net load']<amp and df_netload.at[row,'net load']>amp:\n",
    "#            df_netload.set_value(rowbefore, 'count2', df_netload.at[rowbefore,'count1'])\n",
    "            df_netload.at[rowbefore, 'count2']= df_netload.at[rowbefore,'count1']\n",
    "        rowbefore=row\n",
    "        \n",
    "    s=df_netload.count2.value_counts() \n",
    "    df_freq=pd.DataFrame(data=s)\n",
    "#    s_form=list(s)\n",
    "    output[amp]=df_freq\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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       "    .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></th>\n",
       "      <th>Occurences</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amplitude</th>\n",
       "      <th>Duration</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">-3.4</th>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3.4</th>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>818 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Occurences\n",
       "Amplitude Duration            \n",
       "-3.4      1                 64\n",
       "          2                 31\n",
       "          3                 25\n",
       "          5                 18\n",
       "          4                 16\n",
       "...                        ...\n",
       " 3.4      2                 20\n",
       "          4                 10\n",
       "          3                  8\n",
       "          5                  2\n",
       "          6                  1\n",
       "\n",
       "[818 rows x 1 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_out=pd.DataFrame(data=output, index=[amp])\n",
    "df_out = pd.DataFrame()\n",
    "df_out_tot = pd.DataFrame()\n",
    "output2 = {}\n",
    "for amp in amps:\n",
    "    df_out = output[amp] \n",
    "    df_out = df_out.iloc[1:]\n",
    "    df_out.index.name = 'Duration'\n",
    "    df_out = pd.concat([df_out], keys=[amp], names=['Amplitude'])\n",
    "    df_out.rename(columns={'count2':'Occurences'}, inplace=True)\n",
    "    df_out_tot=df_out_tot.append(df_out)\n",
    "#    output2[amp] = df_out\n",
    "#df_out[0]\n",
    "df_out_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reset = df_out_tot.reset_index()\n",
    "df_reset.columns = ['Amplitude', 'Duration', 'Occurrence']\n",
    "df_pivot=df_reset.pivot('Amplitude', 'Duration')\n",
    "\n",
    "X=df_pivot.columns.levels[1].values\n",
    "Y=df_pivot.index.values\n",
    "Z=df_pivot.values\n",
    "#df1['A'] = df1['A'].apply(lambda x: [y if y <= 9 else 11 for y in x])\n",
    "Z_BAT=np.where(Z > 50, 50, Z)\n",
    "X_BAT,Y_BAT = np.meshgrid(X, Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "plt.contourf(Ynetload, Xnetload, Znetload, alpha=0.7, cmap=plt.cm.jet);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 850.394x566.929 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Znetload, Xnetload, Ynetload\n",
    "#Z_H2diff, X_H2diff, Y_H2diff\n",
    "fig,([ax1,ax2,ax3],[ax4,ax5,ax6]) = plt.subplots(2,3,figsize=(30/2.54,20/2.54),sharey=False, sharex=True)\n",
    "\n",
    "\n",
    "ax1.contourf(Ynetload, Xnetload, Znetload, alpha=0.7, cmap=plt.cm.jet);\n",
    "ax2.contourf(Y_H2, X_H2, Z_H2, alpha=0.7, cmap=plt.cm.jet);\n",
    "ax3.contourf(Y_BAT, X_BAT, Z_BAT, alpha=0.7, cmap=plt.cm.jet);\n",
    "ax4.contourf(Y_HEAT, X_HEAT, Z_HEAT, alpha=0.7, cmap=plt.cm.jet);\n",
    "ax5.contourf(Y_CCGT, X_CCGT, Z_CCGT, alpha=0.7, cmap=plt.cm.jet);\n",
    "ax6.contourf(Y_GT, X_GT, Z_GT, alpha=0.7, cmap=plt.cm.jet);\n",
    "#ax2.contourf(Y_BAT, X_B, Z_BAT, alpha=0.7, cmap=plt.cm.jet);\n",
    "#ax1.contourf(Y_BATdiff, X_BATdiff, Z_BATdiff, alpha=0.7, cmap=plt.cm.jet);\n",
    "\n",
    "ax3.set_ylim(0,70)\n",
    "ax1.set_ylim(0,70)\n",
    "ax4.set_ylim(0,70)\n",
    "\n",
    "ax4.set_ylim(0,70)\n",
    "#ax5.set_ylim(0,70)\n",
    "ax6.set_ylim(0,70)\n",
    "#ax1.set_ylim(0,50)\n",
    "#plt.zlim(0,100)\n",
    "cmap = plt.cm.jet\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=50)\n",
    "\n",
    "#fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),\n",
    "#             orientation='vertical', label='Occurences [#]')\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([0.85, 0.15, 0.02, 0.7])\n",
    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax,label='Occurences [#]')\n",
    "\n",
    "#X=df_out_tot.index.levels[0].values\n",
    "#Y=df_out_tot.index.levels[1].values\n",
    "#Z=df_out_tot['Occurences']\n",
    "#Zi = np.reshape(Z, (X, Y))\n",
    "#Xi,Yi = np.meshgrid(X, Y)\n",
    "#plt.contourf(Yi, Xi, Z, alpha=0.7, cmap=plt.cm.jet);\n",
    "#Z\n",
    "ax1.title.set_text('a) Original')\n",
    "ax2.title.set_text('b) Electrolyzer')\n",
    "ax3.title.set_text('c) Battery')\n",
    "ax4.title.set_text('d) Electric boiler')\n",
    "ax5.title.set_text('e) Bio CCGT')\n",
    "ax6.title.set_text('f) Bio GT')\n",
    "ax1.set_ylabel('Duration [h]')\n",
    "ax4.set_ylabel('Duration [h]')\n",
    "#ax3.set_ylabel('[100 recurrences]')\n",
    "ax4.set_xlabel('Amplitude [GW]')\n",
    "ax5.set_xlabel('Amplitude [GW]')\n",
    "ax6.set_xlabel('Amplitude [GW]')\n",
    "\n",
    "fig.savefig(path.join(data_dir,'IE_contour_VMS2.png'), dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}