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   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Spyder Editor\n",
    "\n",
    "This is a temporary script file.\n",
    "\"\"\"\n",
    "\n",
    "from numpy import arange\n",
    "\n",
    "import highspy\n",
    "import linopy\n",
    "import openpyxl\n",
    "from linopy import Model, EQUAL\n",
    "import pandas as pd\n",
    "import mercury as mr\n",
    "## import gurobipy\n",
    "import plotly.express as px\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
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       "code_uid": "Slider.0.40.34.2-rand9e15e39b",
       "disabled": false,
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       "code_uid": "Slider.0.40.34.5-rand9b30b717",
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       "hidden": false,
       "label": "CO2 price [€/t CO2eq]",
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       "hidden": false,
       "label": "Hydrogen price [€/MWh]",
       "max": 300,
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       "step": 10,
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    "# Slider for gas price [€/MWh_th]\n",
    "price_gas = mr.Slider(value=10, min=0, max=400, label=\"Natural gas price [€/MWh]\", step=10)\n",
    "\n",
    "# Slider for CO2 price [€/t]\n",
    "price_co2 = mr.Slider(value=80, min=0, max=400, label=\"CO2 price [€/t CO2eq]\", step=10)\n",
    "\n",
    "# Slider for CO2 limit [mio. t]\n",
    "limit_co2 = mr.Slider(value=400, min=0, max=750, label=\"CO2 limit [mio. t]\", step=50)\n",
    "\n",
    "# Slider for H2 price / usevalue [€/MWH_th]\n",
    "price_h2 = mr.Slider(value=100, min=0, max=300, label=\"Hydrogen price [€/MWh]\", step=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set input file\n",
    "url_excel = r'Input_Jahr_2021.xlsx'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Read Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Define all sets for the model\n",
    "# Timesteps\n",
    "df_excel= pd.read_excel(url_excel, sheet_name = 'Timesteps', header=None)\n",
    "#t = pd.Index(df_excel.iloc[:,0], name = 't')[1:167]\n",
    "t = pd.Index(df_excel.iloc[:,0], name = 't')[0:168]\n",
    "\n",
    "# Technologies\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')\n",
    "i = pd.Index(df_excel.iloc[:,0], name = 'i')\n",
    "i\n",
    "\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')\n",
    "iConv = pd.Index(df_excel.iloc[0:7,2], name = 'iConv')\n",
    "\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')\n",
    "iRes = pd.Index(df_excel.iloc[0:4,4], name = 'iRes')\n",
    "\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')\n",
    "iSto = pd.Index(df_excel.iloc[0:2,6], name = 'iSto')\n",
    "\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')\n",
    "iPtG = pd.Index(df_excel.iloc[0:1,8], name = 'iPtG')\n",
    "\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')\n",
    "iHyRes = pd.Index(df_excel.iloc[0:1,10], name = 'iHyRes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Parameters\n",
    "# CO2 limit (from slider)\n",
    "l_co2 = limit_co2.value\n",
    "p_co2 = price_co2.value\n",
    "\n",
    "# length of timesteps\n",
    "dt = 1\n",
    "\n",
    "# Demand\n",
    "df_excel= pd.read_excel(url_excel, sheet_name = 'Demand')\n",
    "#df_melt = pd.melt(df_excel, id_vars='Zeit')\n",
    "df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Unnamed: 1':'Demand'})\n",
    "#df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('t')\n",
    "D_t = df_excel.iloc[:,0].to_xarray()\n",
    "\n",
    "## Efficiencies\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'Efficiency')\n",
    "df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'Efficiency'})\n",
    "df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('i')\n",
    "eff_i = df_excel.iloc[:,0].to_xarray()\n",
    "\n",
    "## Variable costs\n",
    "# Fuel costs\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'FuelCosts')\n",
    "df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'FuelCosts'})\n",
    "df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('i')\n",
    "c_fuel_i = df_excel.iloc[:,0].to_xarray()\n",
    "# Apply slider value\n",
    "c_fuel_i.loc[dict(i = 'Fossil Gas')]  = price_gas.value\n",
    "c_fuel_i.loc[dict(i = 'H2')]  = price_h2.value\n",
    "\n",
    "# Other var. costs\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'OtherVarCosts')\n",
    "df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'OtherVarCosts'})\n",
    "df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('i')\n",
    "c_other_i = df_excel.iloc[:,0].to_xarray()\n",
    "\n",
    "# Investment costs\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'InvCosts')\n",
    "df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'InvCosts'})\n",
    "df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('i')\n",
    "c_inv_i = df_excel.iloc[:,0].to_xarray()\n",
    "\n",
    "# Emission factor\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'EmFactor')\n",
    "df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'EmFactor'})\n",
    "df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('i')\n",
    "co2_factor_i = df_excel.iloc[:,0].to_xarray()\n",
    "\n",
    "## Calculation of variable costs\n",
    "c_var_i = (c_fuel_i.sel(i = iConv) + p_co2 * co2_factor_i.sel(i = iConv)) / eff_i.sel(i = iConv) + c_other_i.sel(i = iConv)\n",
    "\n",
    "# RES capacity factors\n",
    "#df_excel = pd.read_excel(url_excel, sheet_name = 'RES',header=[0,1])\n",
    "#df_excel = pd.read_excel(url_excel, sheet_name = 'RES', index_col=['Timesteps'], columns=['PV', 'WindOn', 'WindOff', 'RoR'])\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'RES')\n",
    "df_excel = df_excel.set_index(['Timesteps'])\n",
    "df_test = df_excel\n",
    "df_excel = df_excel.stack()\n",
    "#df_excel = df_excel.rename(columns={'PV', 'WindOn', 'WindOff', 'RoR'})\n",
    "df_test2 = df_excel\n",
    "#df_excel = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "#df_excel = df_excel.fillna(0)\n",
    "\n",
    "#df_test = df_excel.set_index(['Timesteps', 'PV', 'WindOn', 'WindOff', 'RoR']).stack([0])\n",
    "#df_test.index = df_test.index.set_names(['t','i'])\n",
    "s_t_r_iRes = df_excel.to_xarray().rename({'level_1': 'i','Timesteps':'t'})\n",
    "#s_t_r_iRes = df_excel.iloc[:,0].to_xarray()\n",
    "\n",
    "# Base capacities\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'InstalledCap')\n",
    "df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'InstalledCap'})\n",
    "df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('i')\n",
    "K_0_i = df_excel.iloc[:,0].to_xarray()\n",
    "\n",
    "# Energy-to-power ratio storages\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'E2P')  \n",
    "df_excel = df_excel.rename(columns = {'Storage':'i', 'Unnamed: 1':'E2P ratio'})\n",
    "#df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('i')\n",
    "e2p_iSto = df_excel.iloc[:,0].to_xarray()\n",
    "\n",
    "# Inflow for hydro reservoir\n",
    "df_excel = pd.read_excel(url_excel, sheet_name = 'HydroInflow')\n",
    "df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Hydro Water Reservoir':'Inflow'})\n",
    "df_excel = df_excel.fillna(0)\n",
    "df_excel = df_excel.set_index('t')\n",
    "h_t = df_excel.iloc[:,0].to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
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       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
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       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "dl.xr-attrs {\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (t: 8760, i: 4)&gt;\n",
       "array([[0.        , 0.09415617, 0.05353259, 0.52617329],\n",
       "       [0.        , 0.08576801, 0.05504104, 0.52301444],\n",
       "       [0.        , 0.07759342, 0.04262261, 0.51568141],\n",
       "       ...,\n",
       "       [0.        , 0.66534576, 0.86073107, 0.65433213],\n",
       "       [0.        , 0.64106519, 0.84792675, 0.65049639],\n",
       "       [0.        , 0.60996257, 0.88616432, 0.64541968]])\n",
       "Coordinates:\n",
       "  * t        (t) object &quot;&#x27;2021-01-01 00:00 +0100&#x27;&quot; ... &quot;&#x27;2021-12-31 23:00 +01...\n",
       "  * i        (i) object &#x27;PV&#x27; &#x27;WindOn&#x27; &#x27;WindOff&#x27; &#x27;RoR&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>t</span>: 8760</li><li><span class='xr-has-index'>i</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c04efc6a-2fb8-464b-878f-4c8519d57e74' class='xr-array-in' type='checkbox' checked><label for='section-c04efc6a-2fb8-464b-878f-4c8519d57e74' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.09416 0.05353 0.5262 0.0 ... 0.6505 0.0 0.61 0.8862 0.6454</span></div><div class='xr-array-data'><pre>array([[0.        , 0.09415617, 0.05353259, 0.52617329],\n",
       "       [0.        , 0.08576801, 0.05504104, 0.52301444],\n",
       "       [0.        , 0.07759342, 0.04262261, 0.51568141],\n",
       "       ...,\n",
       "       [0.        , 0.66534576, 0.86073107, 0.65433213],\n",
       "       [0.        , 0.64106519, 0.84792675, 0.65049639],\n",
       "       [0.        , 0.60996257, 0.88616432, 0.64541968]])</pre></div></div></li><li class='xr-section-item'><input id='section-5b5490a7-04a1-41ce-b2ce-3bde95f2a97f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5b5490a7-04a1-41ce-b2ce-3bde95f2a97f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&quot;&#x27;2021-01-01 00:00 +0100&#x27;&quot; ... &quot;...</div><input id='attrs-3b06e58d-6903-41aa-8418-891f851c9cc6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b06e58d-6903-41aa-8418-891f851c9cc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7548ad5f-c77e-4773-968d-cbd5d8833149' class='xr-var-data-in' type='checkbox'><label for='data-7548ad5f-c77e-4773-968d-cbd5d8833149' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&quot;&#x27;2021-01-01 00:00 +0100&#x27;&quot;, &quot;&#x27;2021-01-01 01:00 +0100&#x27;&quot;,\n",
       "       &quot;&#x27;2021-01-01 02:00 +0100&#x27;&quot;, ..., &quot;&#x27;2021-12-31 21:00 +0100&#x27;&quot;,\n",
       "       &quot;&#x27;2021-12-31 22:00 +0100&#x27;&quot;, &quot;&#x27;2021-12-31 23:00 +0100&#x27;&quot;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;PV&#x27; &#x27;WindOn&#x27; &#x27;WindOff&#x27; &#x27;RoR&#x27;</div><input id='attrs-3be482c1-f7e8-495c-a01c-add4e3e83365' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3be482c1-f7e8-495c-a01c-add4e3e83365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5726aef0-d96d-4b51-a9ed-00314c7284d8' class='xr-var-data-in' type='checkbox'><label for='data-5726aef0-d96d-4b51-a9ed-00314c7284d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;PV&#x27;, &#x27;WindOn&#x27;, &#x27;WindOff&#x27;, &#x27;RoR&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a95adaf-691d-4ca3-b032-e30a5f6654d0' class='xr-section-summary-in' type='checkbox'  ><label for='section-1a95adaf-691d-4ca3-b032-e30a5f6654d0' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-82eb7b74-8511-466f-85e4-cfdcdd9ba01c' class='xr-index-data-in' type='checkbox'/><label for='index-82eb7b74-8511-466f-85e4-cfdcdd9ba01c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;&#x27;2021-01-01 00:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-01-01 01:00 +0100&#x27;&#x27;,\n",
       "       &#x27;&#x27;2021-01-01 02:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-01-01 03:00 +0100&#x27;&#x27;,\n",
       "       &#x27;&#x27;2021-01-01 04:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-01-01 05:00 +0100&#x27;&#x27;,\n",
       "       &#x27;&#x27;2021-01-01 06:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-01-01 07:00 +0100&#x27;&#x27;,\n",
       "       &#x27;&#x27;2021-01-01 08:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-01-01 09:00 +0100&#x27;&#x27;,\n",
       "       ...\n",
       "       &#x27;&#x27;2021-12-31 14:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-12-31 15:00 +0100&#x27;&#x27;,\n",
       "       &#x27;&#x27;2021-12-31 16:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-12-31 17:00 +0100&#x27;&#x27;,\n",
       "       &#x27;&#x27;2021-12-31 18:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-12-31 19:00 +0100&#x27;&#x27;,\n",
       "       &#x27;&#x27;2021-12-31 20:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-12-31 21:00 +0100&#x27;&#x27;,\n",
       "       &#x27;&#x27;2021-12-31 22:00 +0100&#x27;&#x27;, &#x27;&#x27;2021-12-31 23:00 +0100&#x27;&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;t&#x27;, length=8760))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d170afde-24b6-46eb-9d0d-d7fd22c9691e' class='xr-index-data-in' type='checkbox'/><label for='index-d170afde-24b6-46eb-9d0d-d7fd22c9691e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;PV&#x27;, &#x27;WindOn&#x27;, &#x27;WindOff&#x27;, &#x27;RoR&#x27;], dtype=&#x27;object&#x27;, name=&#x27;i&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3765b71-16bf-4c5b-974e-c6b5e958e4b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3765b71-16bf-4c5b-974e-c6b5e958e4b4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (t: 8760, i: 4)>\n",
       "array([[0.        , 0.09415617, 0.05353259, 0.52617329],\n",
       "       [0.        , 0.08576801, 0.05504104, 0.52301444],\n",
       "       [0.        , 0.07759342, 0.04262261, 0.51568141],\n",
       "       ...,\n",
       "       [0.        , 0.66534576, 0.86073107, 0.65433213],\n",
       "       [0.        , 0.64106519, 0.84792675, 0.65049639],\n",
       "       [0.        , 0.60996257, 0.88616432, 0.64541968]])\n",
       "Coordinates:\n",
       "  * t        (t) object \"'2021-01-01 00:00 +0100'\" ... \"'2021-12-31 23:00 +01...\n",
       "  * i        (i) object 'PV' 'WindOn' 'WindOff' 'RoR'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_t_r_iRes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Variables\n",
    "m = Model()\n",
    "\n",
    "C_tot = m.add_variables(name = 'C_tot')     # Total costs\n",
    "C_op = m.add_variables(name = 'C_op')       # Operational costs\n",
    "C_inv = m.add_variables(name = 'C_inv')     # Investment costs\n",
    "\n",
    "K = m.add_variables(coords = [i], name = 'K', lower = 0)          # Endogenous capacity\n",
    "y = m.add_variables(coords = [t,i], name = 'y', lower = 0)          # Electricity production --> für Elektrolyseure ausschließen\n",
    "y_ch = m.add_variables(coords = [t,i], name = 'y_ch', lower = 0)    # Electricity consumption --> für alles außer Elektrolyseure und Speicher ausschließen\n",
    "l = m.add_variables(coords = [t,i], name = 'l', lower = 0)          # Storage filling level\n",
    "w = m.add_variables(coords = [t], name = 'w', lower = 0)          # RES curtailment\n",
    "\n",
    "### Model\n",
    "\n",
    "## Objective function\n",
    "C_tot = C_op + C_inv\n",
    "m.add_objective(C_tot)\n",
    "\n",
    "## Costs terms for objective function\n",
    "# Operational costs minus revenue for produced hydrogen\n",
    "C_op_sum = m.add_constraints((y * c_var_i * dt).sum() - ((y_ch.sel(i = iPtG) / eff_i.sel(i = iPtG)) * price_h2.value * dt).sum() == C_op, name = 'C_op_sum')\n",
    "\n",
    "# Investment costs\n",
    "C_inv_sum = m.add_constraints((K * c_inv_i).sum() == C_inv, name = 'C_inv_sum')\n",
    "\n",
    "## Load serving\n",
    "loadserve_t = m.add_constraints(((y * dt).sum(dims = 'i') - (w * dt) == D_t.sel(t = t) * dt), name =  'load')\n",
    "\n",
    "## Maximum capacity limit\n",
    "maxcap_i_t = m.add_constraints((y - K <= K_0_i), name =  'max_cap')\n",
    "\n",
    "## Maximum storage charging and discharging\n",
    "maxcha_iSto_t = m.add_constraints((y.sel(i = iSto) + y_ch.sel(i = iSto)  - K.sel(i = iSto) <= K_0_i.sel(i = iSto)), name =  'max_cha')\n",
    "\n",
    "## Maximum electrolyzer capacity\n",
    "ptg_prod_iPtG_t = m.add_constraints((y_ch.sel(i = iPtG) - K.sel(i = iPtG)<= K_0_i.sel(i = iPtG)), name = 'max_cha_ptg')\n",
    "\n",
    "## Infeed of renewables\n",
    "infeed_iRes_t = m.add_constraints((y.sel(i = iRes) - s_t_r_iRes.sel(i = iRes).sel(t = t) * K.sel(i = iRes) <= s_t_r_iRes.sel(i = iRes).sel(t = t) * K_0_i.sel(i = iRes)), name =  'infeed')\n",
    "\n",
    "## Maximum filling level restriction storage power plant --> Energy-to-Power-Ratio eingeführt. (JR)\n",
    "maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iSto.sel(i = iSto) <= K_0_i.sel(i = iSto) * e2p_iSto.sel(i = iSto)), name = 'max_sto_filling')\n",
    "\n",
    "## Filling level restriction hydro reservoir --> Ist Kreisbedingung erfüllt? (JR)\n",
    "filling_iHydro_t = m.add_constraints(l.sel(i = iHyRes) - l.sel(i = iHyRes).roll(t = -1) + y.sel(i = iHyRes) * dt == h_t.sel(t = t) * dt, name = 'filling_level_hydro')\n",
    "\n",
    "## Filling level restriction other storages --> Ist Kreisbedingung erfüllt? (JR)\n",
    "filling_iSto_t = m.add_constraints(l.sel(i = iSto) - (l.sel(i = iSto).roll(t = -1) - (y.sel(i = iSto) / eff_i.sel(i = iSto)) * dt + y_ch.sel(i = iSto) * eff_i.sel(i = iSto) * dt) == 0, name = 'filling_level')\n",
    "\n",
    "## CO2 limit --> ggf. hier auch mit Subset arbeiten (Technologien, die Brennstoff verbrauchen). (JR)\n",
    "CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2, name = 'CO2_limit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Constraint `CO2_limit`\n",
       "----------------------\n",
       "+0 y['2021-01-01 00:00 +0100', Biomass] + 0 y['2021-01-01 01:00 +0100', Biomass] + 0 y['2021-01-01 02:00 +0100', Biomass] ... +0 y['2021-01-07 21:00 +0100', Electrolyzer] + 0 y['2021-01-07 22:00 +0100', Electrolyzer] + 0 y['2021-01-07 23:00 +0100', Electrolyzer] ≤ 400.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CO2_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;InstalledCap&#x27; (i: 15)&gt;\n",
       "array([ 9000, 18600, 34800, 18900,  4700,  4900,  1000,     0, 79200,\n",
       "        8400, 60400,     0,  9700,  7300,     0], dtype=int64)\n",
       "Coordinates:\n",
       "  * i        (i) object &#x27;Biomass&#x27; &#x27;Lignite&#x27; ... &#x27;Electrolyzer&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'InstalledCap'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>i</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d43dc91a-e1db-4df7-a2af-4c87ad773fea' class='xr-array-in' type='checkbox' checked><label for='section-d43dc91a-e1db-4df7-a2af-4c87ad773fea' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>9000 18600 34800 18900 4700 4900 1000 0 79200 8400 60400 0 9700 7300 0</span></div><div class='xr-array-data'><pre>array([ 9000, 18600, 34800, 18900,  4700,  4900,  1000,     0, 79200,\n",
       "        8400, 60400,     0,  9700,  7300,     0], dtype=int64)</pre></div></div></li><li class='xr-section-item'><input id='section-14cedeab-b3cc-4698-a2a0-3750ce52f31e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14cedeab-b3cc-4698-a2a0-3750ce52f31e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Biomass&#x27; ... &#x27;Electrolyzer&#x27;</div><input id='attrs-bb658a3b-0b10-4b0d-8d4e-b0b7fe55bf72' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb658a3b-0b10-4b0d-8d4e-b0b7fe55bf72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cae41ff-6fa1-4fd5-94df-9d8ee381456b' class='xr-var-data-in' type='checkbox'><label for='data-9cae41ff-6fa1-4fd5-94df-9d8ee381456b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Biomass&#x27;, &#x27;Lignite&#x27;, &#x27;Fossil Gas&#x27;, &#x27;Fossil Hard coal&#x27;, &#x27;Fossil Oil&#x27;,\n",
       "       &#x27;RoR&#x27;, &#x27;Hydro Water Reservoir&#x27;, &#x27;Nuclear&#x27;, &#x27;PV&#x27;, &#x27;WindOff&#x27;, &#x27;WindOn&#x27;,\n",
       "       &#x27;H2&#x27;, &#x27;Pumped Hydro Storage&#x27;, &#x27;Battery storages&#x27;, &#x27;Electrolyzer&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b604b8c6-34d2-4695-b6b5-15e35f524a7e' class='xr-section-summary-in' type='checkbox'  ><label for='section-b604b8c6-34d2-4695-b6b5-15e35f524a7e' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f5bffcfb-3887-4a48-9cc5-faef2fa0e2bd' class='xr-index-data-in' type='checkbox'/><label for='index-f5bffcfb-3887-4a48-9cc5-faef2fa0e2bd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Biomass&#x27;, &#x27;Lignite&#x27;, &#x27;Fossil Gas&#x27;, &#x27;Fossil Hard coal&#x27;, &#x27;Fossil Oil&#x27;,\n",
       "       &#x27;RoR&#x27;, &#x27;Hydro Water Reservoir&#x27;, &#x27;Nuclear&#x27;, &#x27;PV&#x27;, &#x27;WindOff&#x27;, &#x27;WindOn&#x27;,\n",
       "       &#x27;H2&#x27;, &#x27;Pumped Hydro Storage&#x27;, &#x27;Battery storages&#x27;, &#x27;Electrolyzer&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;i&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af6c1e8a-5d2c-45d6-bf9c-396bd0253f52' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af6c1e8a-5d2c-45d6-bf9c-396bd0253f52' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "<xarray.DataArray 'InstalledCap' (i: 15)>\n",
       "array([ 9000, 18600, 34800, 18900,  4700,  4900,  1000,     0, 79200,\n",
       "        8400, 60400,     0,  9700,  7300,     0], dtype=int64)\n",
       "Coordinates:\n",
       "  * i        (i) object 'Biomass' 'Lignite' ... 'Electrolyzer'"
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     "execution_count": 105,
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    "\n",
    "m.solve(solver_name = 'highs',log_fn =r'C:\\Users\\christopher.jahns\\Documents\\GIT_Local_Repos\\ERd_model\\ERD_Peak_Load_Model\\Mercury\\log.txt')"
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  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
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     "execution_count": 107,
     "metadata": {},
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'2021-01-01 00:00 +0100'</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'2021-01-01 01:00 +0100'</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'2021-01-01 02:00 +0100'</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'2021-01-01 03:00 +0100'</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'2021-01-01 04:00 +0100'</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          t  dual\n",
       "0  '2021-01-01 00:00 +0100'  -0.0\n",
       "1  '2021-01-01 01:00 +0100'  -0.0\n",
       "2  '2021-01-01 02:00 +0100'  -0.0\n",
       "3  '2021-01-01 03:00 +0100'  -0.0\n",
       "4  '2021-01-01 04:00 +0100'  -0.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Objective from solution\n",
    "m.objective_value\n",
    "\n",
    "pd.options.plotting.backend = \"plotly\"\n",
    "# Read dual values and plot \n",
    "df = loadserve_t.dual.to_dataframe().reset_index()\n",
    "#df['t'] = pd.to_datetime(df['t'])\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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            "linecolor": "white",
            "ticks": ""
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            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
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           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
          }
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        },
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          "text": "y"
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    "#pandas gui\n",
    "# Create Line plot\n",
    "fig = px.line(df, x=df['t'], y=df['y'], color = df['i'])\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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