Multi-regional_Energy_System_Workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflow for a multi-regional energy system\n",
    "\n",
    "In this application of the FINE framework, a multi-regional energy system is modeled and optimized.\n",
    "\n",
    "All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n",
    "\n",
    "The workflow is structures as follows:\n",
    "1. Required packages are imported and the input data path is set\n",
    "2. An energy system model instance is created\n",
    "3. Commodity sources are added to the energy system model\n",
    "4. Commodity conversion components are added to the energy system model\n",
    "5. Commodity storages are added to the energy system model\n",
    "6. Commodity transmission components are added to the energy system model\n",
    "7. Commodity sinks are added to the energy system model\n",
    "8. The energy system model is optimized\n",
    "9. Selected optimization results are presented\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import required packages and set input data path\n",
    "\n",
    "The FINE framework is imported which provides the required classes and functions for modeling the energy system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import FINE as fn\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from getData import getData\n",
    "\n",
    "cwd = os.getcwd()\n",
    "data = getData()\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Create an energy system model instance \n",
    "\n",
    "The structure of the energy system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n",
    "\n",
    "The commodities are specified by a unit (i.e. 'GW_electric', 'GW_H2lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = {'cluster_0', 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5', 'cluster_6', 'cluster_7'}\n",
    "commodityUnitDict = {'electricity': r'GW$_{el}$', 'methane': r'GW$_{CH_{4},LHV}$', 'biogas': r'GW$_{biogas,LHV}$',\n",
    "                     'CO2': r'Mio. t$_{CO_2}$/h', 'hydrogen': r'GW$_{H_{2},LHV}$'}\n",
    "commodities = {'electricity', 'hydrogen', 'methane', 'biogas', 'CO2'}\n",
    "numberOfTimeSteps = 8760\n",
    "hoursPerTimeStep = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM = fn.EnergySystemModel(locations=locations, commodities=commodities, numberOfTimeSteps=8760,\n",
    "                           commodityUnitsDict=commodityUnitDict,\n",
    "                           hoursPerTimeStep=1, costUnit='1e9 Euro', lengthUnit='km', verboseLogLevel=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Values are [0.75, 0.8, 0.85, 0.9, 0.95, 1]\n",
    "CO2_reductionTarget = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Add commodity sources to the energy system model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Electricity sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wind onshore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Source(esM=esM, name='Wind (onshore)', commodity='electricity', hasCapacityVariable=True,\n",
    "                  operationRateMax=data['Wind (onshore), operationRateMax'],\n",
    "                  capacityMax=data['Wind (onshore), capacityMax'],\n",
    "                  investPerCapacity=1.1, opexPerCapacity=1.1 * 0.02, interestRate=0.08,\n",
    "                  economicLifetime=20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Full load hours:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "cluster_0    1572.003960\ncluster_1    2350.292663\ncluster_2    2374.507270\ncluster_3    2186.572278\ncluster_4    1572.650655\ncluster_5    1767.840650\ncluster_6    2719.564564\ncluster_7    1553.045964\ndtype: float64"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Wind (onshore), operationRateMax'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wind offshore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Source(esM=esM, name='Wind (offshore)', commodity='electricity', hasCapacityVariable=True,\n",
    "                  operationRateMax=data['Wind (offshore), operationRateMax'],\n",
    "                  capacityMax=data['Wind (offshore), capacityMax'],\n",
    "                  investPerCapacity=2.3, opexPerCapacity=2.3 * 0.02, interestRate=0.08,\n",
    "                  economicLifetime=20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Full load hours:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "cluster_0       0.000000\ncluster_1    4435.420314\ncluster_2    4301.655834\ncluster_3    3902.391858\ncluster_4       0.000000\ncluster_5       0.000000\ncluster_6    4609.508396\ncluster_7       0.000000\ndtype: float64"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Wind (offshore), operationRateMax'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Source(esM=esM, name='PV', commodity='electricity', hasCapacityVariable=True,\n",
    "                  operationRateMax=data['PV, operationRateMax'], capacityMax=data['PV, capacityMax'],\n",
    "                  investPerCapacity=0.65, opexPerCapacity=0.65 * 0.02, interestRate=0.08,\n",
    "                  economicLifetime=25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Full load hours:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "cluster_0    1113.216464\ncluster_1    1053.579422\ncluster_2    1058.005181\ncluster_3    1079.872237\ncluster_4    1140.407380\ncluster_5    1051.848141\ncluster_6    1069.843344\ncluster_7    1085.697466\ndtype: float64"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PV, operationRateMax'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exisisting run-of-river hydroelectricity plants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Source(esM=esM, name='Existing run-of-river plants', commodity='electricity',\n",
    "                  hasCapacityVariable=True,\n",
    "                  operationRateFix=data['Existing run-of-river plants, operationRateFix'], tsaWeight=0.01,\n",
    "                  capacityFix=data['Existing run-of-river plants, capacityFix'],\n",
    "                  investPerCapacity=0, opexPerCapacity=0.208))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Methane (natural gas and biogas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Natural gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Source(esM=esM, name='Natural gas purchase', commodity='methane',\n",
    "                  hasCapacityVariable=False, commodityCost=0.0331 * 1e-3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biogas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Source(esM=esM, name='Biogas purchase', commodity='biogas',\n",
    "                  operationRateMax=data['Biogas, operationRateMax'], hasCapacityVariable=False,\n",
    "                  commodityCost=0.05409 * 1e-3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Add conversion components to the energy system model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combined cycle gas turbine plants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Conversion(esM=esM, name='CCGT plants (methane)', physicalUnit=r'GW$_{el}$',\n",
    "                      commodityConversionFactors={'electricity': 1, 'methane': -1 / 0.6, 'CO2': 201 * 1e-6 / 0.6},\n",
    "                      hasCapacityVariable=True,\n",
    "                      investPerCapacity=0.65, opexPerCapacity=0.021, interestRate=0.08,\n",
    "                      economicLifetime=33))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New combined cycle gas turbine plants for biogas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Conversion(esM=esM, name='New CCGT plants (biogas)', physicalUnit=r'GW$_{el}$',\n",
    "                      commodityConversionFactors={'electricity': 1, 'biogas': -1 / 0.63},\n",
    "                      hasCapacityVariable=True,\n",
    "                      investPerCapacity=0.7, opexPerCapacity=0.021, interestRate=0.08,\n",
    "                      economicLifetime=33))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New combined cycly gas turbines for hydrogen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Conversion(esM=esM, name='New CCGT plants (hydrogen)', physicalUnit=r'GW$_{el}$',\n",
    "                      commodityConversionFactors={'electricity': 1, 'hydrogen': -1 / 0.63},\n",
    "                      hasCapacityVariable=True,\n",
    "                      investPerCapacity=0.7, opexPerCapacity=0.021, interestRate=0.08,\n",
    "                      economicLifetime=33))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Electrolyzers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Conversion(esM=esM, name='Electrolyzer', physicalUnit=r'GW$_{el}$',\n",
    "                      commodityConversionFactors={'electricity': -1, 'hydrogen': 0.7},\n",
    "                      hasCapacityVariable=True,\n",
    "                      investPerCapacity=0.5, opexPerCapacity=0.5 * 0.025, interestRate=0.08,\n",
    "                      economicLifetime=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rSOC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "capexRSOC = 1.5\n",
    "\n",
    "esM.add(fn.Conversion(esM=esM, name='rSOEC', physicalUnit=r'GW$_{el}$', linkedConversionCapacityID='rSOC',\n",
    "                      commodityConversionFactors={'electricity': -1, 'hydrogen': 0.6},\n",
    "                      hasCapacityVariable=True,\n",
    "                      investPerCapacity=capexRSOC / 2, opexPerCapacity=capexRSOC * 0.02 / 2, interestRate=0.08,\n",
    "                      economicLifetime=10))\n",
    "\n",
    "esM.add(fn.Conversion(esM=esM, name='rSOFC', physicalUnit=r'GW$_{el}$', linkedConversionCapacityID='rSOC',\n",
    "                      commodityConversionFactors={'electricity': 1, 'hydrogen': -1 / 0.6},\n",
    "                      hasCapacityVariable=True,\n",
    "                      investPerCapacity=capexRSOC / 2, opexPerCapacity=capexRSOC * 0.02 / 2, interestRate=0.08,\n",
    "                      economicLifetime=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Add commodity storages to the energy system model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1. Electricity storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lithium ion batteries\n",
    "\n",
    "The self discharge of a lithium ion battery is here described as 3% per month. The self discharge per hours is obtained using the equation (1-$\\text{selfDischarge}_\\text{hour})^{30*24\\text{h}} = 1-\\text{selfDischarge}_\\text{month}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Storage(esM=esM, name='Li-ion batteries', commodity='electricity',\n",
    "                   hasCapacityVariable=True, chargeEfficiency=0.95,\n",
    "                   cyclicLifetime=10000, dischargeEfficiency=0.95, selfDischarge=1 - (1 - 0.03) ** (1 / (30 * 24)),\n",
    "                   chargeRate=1, dischargeRate=1, doPreciseTsaModeling=False,\n",
    "                   investPerCapacity=0.151, opexPerCapacity=0.002, interestRate=0.08,\n",
    "                   economicLifetime=22))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2. Hydrogen storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hydrogen filled salt caverns\n",
    "The maximum capacity is here obtained by: dividing the given capacity (which is given for methane) by the lower heating value of methane and then multiplying it with the lower heating value of hydrogen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Storage(esM=esM, name='Salt caverns (hydrogen)', commodity='hydrogen',\n",
    "                   hasCapacityVariable=True, capacityVariableDomain='continuous',\n",
    "                   capacityPerPlantUnit=133,\n",
    "                   chargeRate=1 / 470.37, dischargeRate=1 / 470.37, sharedPotentialID='Existing salt caverns',\n",
    "                   stateOfChargeMin=0.33, stateOfChargeMax=1, capacityMax=data['Salt caverns (hydrogen), capacityMax'],\n",
    "                   investPerCapacity=0.00011, opexPerCapacity=0.00057, interestRate=0.08,\n",
    "                   economicLifetime=30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3. Methane storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methane filled salt caverns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Storage(esM=esM, name='Salt caverns (biogas)', commodity='biogas',\n",
    "                   hasCapacityVariable=True, capacityVariableDomain='continuous',\n",
    "                   capacityPerPlantUnit=443,\n",
    "                   chargeRate=1 / 470.37, dischargeRate=1 / 470.37, sharedPotentialID='Existing salt caverns',\n",
    "                   stateOfChargeMin=0.33, stateOfChargeMax=1, capacityMax=data['Salt caverns (methane), capacityMax'],\n",
    "                   investPerCapacity=0.00004, opexPerCapacity=0.00001, interestRate=0.08,\n",
    "                   economicLifetime=30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Pumped hydro storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pumped hydro storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Storage(esM=esM, name='Pumped hydro storage', commodity='electricity',\n",
    "                   chargeEfficiency=0.88, dischargeEfficiency=0.88,\n",
    "                   hasCapacityVariable=True, selfDischarge=1 - (1 - 0.00375) ** (1 / (30 * 24)),\n",
    "                   chargeRate=0.16, dischargeRate=0.12, capacityFix=data['Pumped hydro storage, capacityFix'],\n",
    "                   investPerCapacity=0, opexPerCapacity=0.000153))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Add commodity transmission components to the energy system model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1. Electricity transmission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AC cables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "esM.add(fn.LinearOptimalPowerFlow(esM=esM, name='AC cables', commodity='electricity',\n",
    "                                  hasCapacityVariable=True, capacityFix=data['AC cables, capacityFix'],\n",
    "                                  reactances=data['AC cables, reactances']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distances of a component are set to a normalized value of 1.\n"
     ]
    }
   ],
   "source": [
    "esM.add(fn.Transmission(esM=esM, name='AC cables', commodity='electricity',\n",
    "                        hasCapacityVariable=True, capacityFix=data['AC cables, capacityFix']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DC cables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Transmission(esM=esM, name='DC cables', commodity='electricity', losses=data['DC cables, losses'],\n",
    "                        distances=data['DC cables, distances'],\n",
    "                        hasCapacityVariable=True, capacityFix=data['DC cables, capacityFix']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Methane transmission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methane pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "esM.add(fn.Transmission(esM=esM, name='Pipelines (biogas)', commodity='biogas',\n",
    "                        distances=data['Pipelines, distances'],\n",
    "                        hasCapacityVariable=True, hasIsBuiltBinaryVariable=True, bigM=300,\n",
    "                        locationalEligibility=data['Pipelines, eligibility'],\n",
    "                        capacityMax=data['Pipelines, eligibility'] * 15, sharedPotentialID='pipelines',\n",
    "                        investPerCapacity=0.000037, investIfBuilt=0.000314,\n",
    "                        interestRate=0.08, economicLifetime=40))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Hydrogen transmission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hydrogen pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "esM.add(fn.Transmission(esM=esM, name='Pipelines (hydrogen)', commodity='hydrogen',\n",
    "                        distances=data['Pipelines, distances'],\n",
    "                        hasCapacityVariable=True, hasIsBuiltBinaryVariable=True, bigM=300,\n",
    "                        locationalEligibility=data['Pipelines, eligibility'],\n",
    "                        capacityMax=data['Pipelines, eligibility'] * 15, sharedPotentialID='pipelines',\n",
    "                        investPerCapacity=0.000177, investIfBuilt=0.00033,\n",
    "                        interestRate=0.08, economicLifetime=40))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Add commodity sinks to the energy system model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1. Electricity sinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Electricity demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Sink(esM=esM, name='Electricity demand', commodity='electricity',\n",
    "                hasCapacityVariable=False, operationRateFix=data['Electricity demand, operationRateFix']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2. Hydrogen sinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fuel cell electric vehicle (FCEV) demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "FCEV_penetration = 0.5\n",
    "esM.add(fn.Sink(esM=esM, name='Hydrogen demand', commodity='hydrogen', hasCapacityVariable=False,\n",
    "                operationRateFix=data['Hydrogen demand, operationRateFix'] * FCEV_penetration))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3. CO2 sinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CO2 exiting the system's boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "esM.add(fn.Sink(esM=esM, name='CO2 to enviroment', commodity='CO2',\n",
    "                hasCapacityVariable=False, commodityLimitID='CO2 limit', yearlyLimit=366 * (1 - CO2_reductionTarget)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Optimize energy system model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clustering time series data with 7 typical periods and 24 time steps per period...\n",
      "\t\t(1.5455 sec)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\tsam\\timeseriesaggregation.py:897: UserWarning:Something went wrong: At least one maximal value of the aggregated time series exceeds the maximal value the input time series\n"
     ]
    }
   ],
   "source": [
    "esM.cluster(numberOfTypicalPeriods=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true,
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time series aggregation specifications:\n",
      "Number of typical periods:7, number of time steps per period:24\n",
      "\n",
      "Declaring sets, variables and constraints for SourceSinkModel\n",
      "\tdeclaring sets... \n",
      "\tdeclaring variables... \n",
      "\tdeclaring constraints... \n",
      "\t\t(0.6290 sec)\n",
      "\n",
      "Declaring sets, variables and constraints for ConversionModel\n",
      "\tdeclaring sets... \n",
      "\tdeclaring variables... \n",
      "\tdeclaring constraints... \n",
      "\t\t(0.2670 sec)\n",
      "\n",
      "Declaring sets, variables and constraints for StorageModel\n",
      "\tdeclaring sets... \n",
      "\tdeclaring variables... \n",
      "\tdeclaring constraints... \n",
      "\t\t(2.1780 sec)\n",
      "\n",
      "Declaring sets, variables and constraints for TransmissionModel\n",
      "\tdeclaring sets... \n",
      "\tdeclaring variables... \n",
      "\tdeclaring constraints... \n",
      "\t\t(0.4505 sec)\n",
      "\n",
      "Declaring shared potential constraint...\n",
      "\t\t(0.0030 sec)\n",
      "\n",
      "Declaring linked component quantity constraint...\n",
      "\t\t(0.0000 sec)\n",
      "\n",
      "Declaring commodity balances...\n",
      "\t\t(0.9870 sec)\n",
      "\n",
      "\t\t(0.0005 sec)\n",
      "\n",
      "Declaring objective function...\n",
      "\t\t(0.4310 sec)\n",
      "\n",
      "Academic license - for non-commercial use only - expires 2022-03-05\n",
      "Using license file C:\\Users\\Paul\\gurobi.lic\n",
      "Read LP format model from file C:\\Users\\Paul\\AppData\\Local\\Temp\\tmpakee2wso.pyomo.lp\n",
      "Reading time = 0.46 seconds\n",
      "x96946: 85942 rows, 55618 columns, 264787 nonzeros\n",
      "Changed value of parameter QCPDual to 1\n",
      "   Prev: 0  Min: 0  Max: 1  Default: 0\n",
      "Changed value of parameter Threads to 3\n",
      "   Prev: 0  Min: 0  Max: 1024  Default: 0\n",
      "Parameter logfile unchanged\n",
      "   Value:   Default: \n",
      "Changed value of parameter OptimalityTol to 0.001\n",
      "   Prev: 1e-06  Min: 1e-09  Max: 0.01  Default: 1e-06\n",
      "Changed value of parameter method to 2\n",
      "   Prev: -1  Min: -1  Max: 5  Default: -1\n",
      "Changed value of parameter cuts to 0\n",
      "   Prev: -1  Min: -1  Max: 3  Default: -1\n",
      "Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 3 threads\n",
      "Optimize a model with 85942 rows, 55618 columns and 264787 nonzeros\n",
      "Model fingerprint: 0x8e13af19\n",
      "Variable types: 55566 continuous, 52 integer (52 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [2e-06, 5e+02]\n",
      "  Objective range  [1e-05, 3e-01]\n",
      "  Bounds range     [1e+00, 1e+05]\n",
      "  RHS range        [4e-02, 4e+02]\n",
      "Presolve removed 24794 rows and 13791 columns\n",
      "Presolve time: 0.41s\n",
      "Presolved: 61148 rows, 41827 columns, 212766 nonzeros\n",
      "Variable types: 41801 continuous, 26 integer (26 binary)\n",
      "Root barrier log...\n",
      "\n",
      "Ordering time: 0.90s\n",
      "\n",
      "Barrier statistics:\n",
      " Dense cols : 196\n",
      " Free vars  : 1288\n",
      " AA' NZ     : 1.235e+06\n",
      " Factor NZ  : 6.968e+06 (roughly 100 MBytes of memory)\n",
      " Factor Ops : 3.826e+09 (less than 1 second per iteration)\n",
      " Threads    : 3\n",
      "\n",
      "                  Objective                Residual\n",
      "Iter       Primal          Dual         Primal    Dual     Compl     Time\n",
      "   0   4.67093515e+05 -2.42037113e+06  1.97e+06 1.38e-02  2.28e+03     2s\n",
      "   1   4.02143755e+05 -2.33446866e+06  1.38e+06 2.05e-01  1.48e+03     2s\n",
      "   2   2.97488119e+05 -2.08138006e+06  9.49e+05 9.33e-02  9.74e+02     2s\n",
      "   3   1.21000591e+05 -1.55018433e+06  2.42e+05 2.76e-02  2.75e+02     2s\n",
      "   4   5.90249866e+04 -6.84434731e+05  4.05e+04 1.62e-03  4.40e+01     2s\n",
      "   5   2.99366137e+04 -2.07376759e+05  1.04e+04 8.74e-04  1.08e+01     3s\n",
      "   6   1.03574450e+04 -1.19349138e+05  1.36e+03 3.39e-04  2.30e+00     3s\n",
      "   7   3.86482174e+03 -2.92606139e+04  1.85e+02 4.03e-04  3.74e-01     3s\n",
      "   8   1.49053940e+03 -6.75219428e+03  4.55e+01 1.34e-04  7.85e-02     3s\n",
      "   9   1.07896978e+03 -2.51669111e+03  2.63e+01 1.48e-04  3.33e-02     3s\n",
      "  10   6.10094564e+02 -1.69662726e+03  1.69e-01 1.57e-04  2.02e-02     3s\n",
      "  11   2.52646239e+02 -6.17151725e+02  4.80e-02 8.10e-05  7.31e-03     3s\n",
      "  12   1.25432647e+02 -2.54760744e+02  1.65e-02 4.74e-05  3.15e-03     3s\n",
      "  13   7.07925128e+01 -7.16740752e+01  4.02e-03 2.03e-05  1.17e-03     4s\n",
      "  14   5.28746091e+01  1.23997896e+00  1.04e-03 8.34e-06  4.24e-04     4s\n",
      "  15   4.52151992e+01  2.41161804e+01  1.88e-04 3.90e-06  1.73e-04     4s\n",
      "  16   4.38405812e+01  3.32778837e+01  1.06e-04 1.76e-06  8.67e-05     4s\n",
      "  17   4.24602831e+01  3.55505612e+01  4.53e-05 1.24e-06  5.67e-05     4s\n",
      "  18   4.18410737e+01  3.69866627e+01  2.35e-05 9.08e-07  3.98e-05     4s\n",
      "  19   4.14554066e+01  3.82585243e+01  1.36e-05 5.84e-07  2.62e-05     4s\n",
      "  20   4.09977966e+01  3.89475656e+01  5.14e-06 3.92e-07  1.68e-05     5s\n",
      "  21   4.07472280e+01  3.96525951e+01  1.65e-06 2.06e-07  8.98e-06     5s\n",
      "  22   4.06153697e+01  4.01833176e+01  4.88e-07 9.06e-08  3.55e-06     5s\n",
      "  23   4.05756628e+01  4.03291527e+01  2.54e-07 5.88e-08  2.02e-06     5s\n",
      "  24   4.05543494e+01  4.04331934e+01  1.44e-07 3.23e-08  9.94e-07     5s\n",
      "  25   4.05426271e+01  4.04507659e+01  9.20e-08 2.70e-08  7.54e-07     5s\n",
      "  26   4.05336715e+01  4.04810372e+01  5.62e-08 1.68e-08  4.32e-07     6s\n",
      "  27   4.05289443e+01  4.04985777e+01  3.36e-08 1.04e-08  2.49e-07     6s\n",
      "  28   4.05245010e+01  4.05081030e+01  1.78e-08 6.69e-09  1.35e-07     6s\n",
      "  29   4.05230524e+01  4.05163686e+01  1.26e-08 2.27e-09  5.49e-08     6s\n",
      "  30   4.05214537e+01  4.05167255e+01  6.88e-09 2.04e-09  3.88e-08     6s\n",
      "  31   4.05210275e+01  4.05175036e+01  5.34e-09 1.46e-09  2.89e-08     6s\n",
      "  32   4.05200402e+01  4.05189735e+01  1.69e-09 3.36e-10  8.75e-09     7s\n",
      "  33   4.05196460e+01  4.05193278e+01  2.23e-09 7.55e-11  2.61e-09     7s\n",
      "  34   4.05194744e+01  4.05194400e+01  9.36e-08 1.39e-11  2.83e-10     7s\n",
      "  35   4.05194608e+01  4.05194516e+01  2.15e-08 8.47e-11  7.54e-11     7s\n",
      "  36   4.05194574e+01  4.05194550e+01  5.63e-09 1.99e-11  1.97e-11     7s\n",
      "  37   4.05194572e+01  4.05193904e+01  1.33e-08 1.13e-06  1.56e-11     8s\n",
      "  38   4.05194568e+01  4.05194070e+01  3.30e-08 8.49e-07  1.07e-11     8s\n",
      "  39   4.05194568e+01  4.05194082e+01  4.64e-08 8.23e-07  1.04e-11     8s\n",
      "  40   4.05194568e+01  4.05194860e+01  5.35e-08 1.25e-06  1.04e-11     8s\n",
      "  41   4.05194568e+01  4.05194863e+01  6.83e-08 1.25e-06  1.04e-11     8s\n",
      "  42   4.05194568e+01  4.05193669e+01  7.18e-08 4.01e-07  1.03e-11     9s\n",
      "  43   4.05194568e+01  4.05193477e+01  7.15e-08 7.27e-07  1.03e-11     9s\n",
      "  44   4.05194568e+01  4.05193481e+01  7.15e-08 3.94e-07  1.03e-11     9s\n",
      "\n",
      "Barrier solved model in 44 iterations and 8.94 seconds\n",
      "Optimal objective 4.05194608e+01\n",
      "\n",
      "\n",
      "Root crossover log...\n",
      "\n",
      "    4910 DPushes remaining with DInf 7.9371735e+00                 9s\n",
      "       0 DPushes remaining with DInf 1.0925773e+00                 9s\n",
      "\n",
      "   11846 PPushes remaining with PInf 1.1312306e-01                 9s\n",
      "       0 PPushes remaining with PInf 0.0000000e+00                 9s\n",
      "\n",
      "  Push phase complete: Pinf 0.0000000e+00, Dinf 3.9693653e+01      9s\n",
      "\n",
      "\n",
      "Root simplex log...\n",
      "\n",
      "Iteration    Objective       Primal Inf.    Dual Inf.      Time\n",
      "   15091    4.0519456e+01   0.000000e+00   3.864676e+01      9s\n",
      "   15943    4.0519456e+01   0.000000e+00   9.831234e+00     10s\n",
      "   16482    4.0519456e+01   0.000000e+00   0.000000e+00     10s\n",
      "   16482    4.0519456e+01   0.000000e+00   0.000000e+00     11s\n",
      "\n",
      "Root relaxation: objective 4.051946e+01, 16482 iterations, 10.01 seconds\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0   40.51946    0    7          -   40.51946      -     -   10s\n",
      "H    0     0                      40.5680199   40.51946  0.12%     -   11s\n",
      "H    0     0                      40.5680181   40.51946  0.12%     -   11s\n",
      "     0     0   40.51946    0    7   40.56802   40.51946  0.12%     -   18s\n",
      "     0     2   40.51972    0    7   40.56802   40.51972  0.12%     -   23s\n",
      "     1     2     cutoff    1        40.56802   40.51972  0.12%  3449   25s\n",
      "    14    14   40.55775    6    5   40.56802   40.53726  0.08%   776   33s\n",
      "    17    16     cutoff    7        40.56802   40.53726  0.08%  1147   38s\n",
      "    32    13   40.55253    5    4   40.56802   40.54038  0.07%   713   40s\n",
      "    63     3     cutoff    6        40.56802   40.55432  0.03%   459   49s\n",
      "    71     1     cutoff    6        40.56802   40.56253  0.01%   484   50s\n",
      "\n",
      "Explored 73 nodes (50939 simplex iterations) in 50.26 seconds\n",
      "Thread count was 3 (of 8 available processors)\n",
      "\n",
      "Solution count 1: 40.568 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Warning: max constraint violation (6.2533e-06) exceeds tolerance\n",
      "Best objective 4.056801809336e+01, best bound 4.056799072155e+01, gap 0.0001%\n",
      "\n",
      "Status: ok\n",
      "Return code: 0\n",
      "Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n",
      "Termination condition: optimal\n",
      "Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n",
      "Wall time: 50.261003494262695\n",
      "Error rc: 0\n",
      "Time: 51.499999046325684\n",
      "\n",
      "\n",
      "Name: x96946\n",
      "Lower bound: 40.56799072154567\n",
      "Upper bound: 40.568018093361246\n",
      "Number of objectives: 1\n",
      "Number of constraints: 85942\n",
      "Number of variables: 55618\n",
      "Number of binary variables: 52\n",
      "Number of integer variables: 52\n",
      "Number of continuous variables: 55566\n",
      "Number of nonzeros: 264787\n",
      "Sense: minimize\n",
      "\n",
      "Solve time: 58.06550216674805 sec.\n",
      "\n",
      "Processing optimization output...\n",
      "for SourceSinkModel ...    (0.5120sec)\n",
      "for ConversionModel ...    (0.5310sec)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\storage.py:1170: UserWarning:Charge and discharge at the same time for component Pumped hydro storage\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for StorageModel ...       (1.6090sec)\n",
      "for TransmissionModel ...  (1.4350sec)\n",
      "\t\t(4.1110 sec)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "esM.optimize(timeSeriesAggregation=True, optimizationSpecs='OptimalityTol=1e-3 method=2 cuts=0', solver=\"gurobi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Create output file\n",
    "Creates for a specific `CO2_reductionTarget` an output file in folder `./results`."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Writing output to Excel... \n",
      "\tProcessing SourceSinkModel ...\n",
      "\tProcessing ConversionModel ...\n",
      "\tProcessing StorageModel ...\n",
      "\tProcessing TransmissionModel ...\n",
      "\tSaving file...\n",
      "Done. (19.8415 sec)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df1 = esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2)\n",
    "df2 = esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)\n",
    "df3 = esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)\n",
    "df4 = esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=2).loc['Pipelines (hydrogen)']\n",
    "\n",
    "# Create a Pandas Excel writer using XlsxWriter as the engine.\n",
    "if not os.path.exists(\"./results\"):\n",
    "    os.mkdir(\"./results\")\n",
    "filename = \"./results/CO2_reduction_{}_percent.xlsx\".format(int(CO2_reductionTarget * 100))\n",
    "writer = pd.ExcelWriter(filename, engine='xlsxwriter')\n",
    "\n",
    "# Write each dataframe to a different worksheet.\n",
    "df1.to_excel(writer, sheet_name='Sources and Sink')\n",
    "df2.to_excel(writer, sheet_name='Conversion')\n",
    "df3.to_excel(writer, sheet_name='Storage')\n",
    "df4.to_excel(writer, sheet_name='Transmission')\n",
    "\n",
    "# Close the Pandas Excel writer and output the Excel file.\n",
    "writer.save()\n",
    "\n",
    "fn.writeOptimizationOutputToExcel(esM=esM,\n",
    "                                  outputFileName=\"./results/ESM_Output_CO2_reduction_{}_percent\".format(\n",
    "                                      int(CO2_reductionTarget * 100)),\n",
    "                                  optSumOutputLevel=2, optValOutputLevel=1)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Selected results output\n",
    "\n",
    "Plot locations (GeoPandas required)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [],
   "source": [
    "# Import the geopandas package for plotting the locations\n",
    "import geopandas as gpd\n",
    "import os\n",
    "\n",
    "\n",
    "def save_figure(fig, file_name):\n",
    "    folder = \"./images/\"\n",
    "    if not os.path.exists(folder):\n",
    "        os.makedirs(folder)\n",
    "    fig.savefig(folder + file_name + f\"_{CO2_reductionTarget}.png\", dpi=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\IOManagement\\standardIO.py:693: MatplotlibDeprecationWarning:The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\IOManagement\\standardIO.py:693: MatplotlibDeprecationWarning:The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\IOManagement\\standardIO.py:693: MatplotlibDeprecationWarning:The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\IOManagement\\standardIO.py:693: MatplotlibDeprecationWarning:The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\IOManagement\\standardIO.py:693: MatplotlibDeprecationWarning:The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\IOManagement\\standardIO.py:693: MatplotlibDeprecationWarning:The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\IOManagement\\standardIO.py:693: MatplotlibDeprecationWarning:The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      " C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\IOManagement\\standardIO.py:693: MatplotlibDeprecationWarning:The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locFilePath = os.path.join(cwd, 'InputData', 'SpatialData', 'ShapeFiles', 'clusteredRegions.shp')\n",
    "fig, ax = fn.plotLocations(locFilePath, plotLocNames=True, indexColumn='index')\n",
    "save_figure(fig, 'Locations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sources and Sink\n",
    "\n",
    "Show optimization summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                                                      cluster_0  \\\nComponent                    Property    Unit                                     \nCO2 to enviroment            operation   [Mio. t$_{CO_2}$/h*h/a]      30.231486   \n                                         [Mio. t$_{CO_2}$/h*h]        30.231486   \nElectricity demand           operation   [GW$_{el}$*h/a]          133963.451433   \n                                         [GW$_{el}$*h]            133963.451433   \nExisting run-of-river plants TAC         [1e9 Euro/a]                  0.144316   \n                             capacity    [GW$_{el}$]                   0.693828   \n                             operation   [GW$_{el}$*h/a]            3167.329183   \n                                         [GW$_{el}$*h]              3167.329183   \n                             opexCap     [1e9 Euro/a]                  0.144316   \nHydrogen demand              operation   [GW$_{H_{2},LHV}$*h/a]    15855.229091   \n                                         [GW$_{H_{2},LHV}$*h]      15855.229091   \nNatural gas purchase         TAC         [1e9 Euro/a]                  4.978419   \n                             commodCosts [1e9 Euro/a]                  4.978419   \n                             operation   [GW$_{CH_{4},LHV}$*h/a]  150405.404924   \n                                         [GW$_{CH_{4},LHV}$*h]    150405.404924   \nPV                           TAC         [1e9 Euro/a]                       0.0   \n                             capacity    [GW$_{el}$]                        0.0   \n                             capexCap    [1e9 Euro/a]                       0.0   \n                             invest      [1e9 Euro]                         0.0   \n                             operation   [GW$_{el}$*h/a]                    0.0   \n                                         [GW$_{el}$*h]                      0.0   \n                             opexCap     [1e9 Euro/a]                       0.0   \nWind (offshore)              TAC         [1e9 Euro/a]                       0.0   \n                             capacity    [GW$_{el}$]                        NaN   \n                             capexCap    [1e9 Euro/a]                       NaN   \n                             invest      [1e9 Euro]                         NaN   \n                             operation   [GW$_{el}$*h/a]                    NaN   \n                                         [GW$_{el}$*h]                      NaN   \n                             opexCap     [1e9 Euro/a]                       NaN   \nWind (onshore)               TAC         [1e9 Euro/a]                       0.0   \n                             capacity    [GW$_{el}$]                        0.0   \n                             capexCap    [1e9 Euro/a]                       0.0   \n                             invest      [1e9 Euro]                         0.0   \n                             operation   [GW$_{el}$*h/a]                    0.0   \n                                         [GW$_{el}$*h]                      0.0   \n                             opexCap     [1e9 Euro/a]                       0.0   \n\n                                                                     cluster_1  \\\nComponent                    Property    Unit                                    \nCO2 to enviroment            operation   [Mio. t$_{CO_2}$/h*h/a]     15.160913   \n                                         [Mio. t$_{CO_2}$/h*h]       15.160913   \nElectricity demand           operation   [GW$_{el}$*h/a]          66115.987455   \n                                         [GW$_{el}$*h]            66115.987455   \nExisting run-of-river plants TAC         [1e9 Euro/a]                 0.007791   \n                             capacity    [GW$_{el}$]                  0.037456   \n                             operation   [GW$_{el}$*h/a]            170.988002   \n                                         [GW$_{el}$*h]              170.988002   \n                             opexCap     [1e9 Euro/a]                 0.007791   \nHydrogen demand              operation   [GW$_{H_{2},LHV}$*h/a]   11007.056882   \n                                         [GW$_{H_{2},LHV}$*h]     11007.056882   \nNatural gas purchase         TAC         [1e9 Euro/a]                 2.496648   \n                             commodCosts [1e9 Euro/a]                 2.496648   \n                             operation   [GW$_{CH_{4},LHV}$*h/a]  75427.427228   \n                                         [GW$_{CH_{4},LHV}$*h]    75427.427228   \nPV                           TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       0.0   \n                             capexCap    [1e9 Euro/a]                      0.0   \n                             invest      [1e9 Euro]                        0.0   \n                             operation   [GW$_{el}$*h/a]                   0.0   \n                                         [GW$_{el}$*h]                     0.0   \n                             opexCap     [1e9 Euro/a]                      0.0   \nWind (offshore)              TAC         [1e9 Euro/a]                  1.12104   \n                             capacity    [GW$_{el}$]                       4.0   \n                             capexCap    [1e9 Euro/a]                  0.93704   \n                             invest      [1e9 Euro]                        9.2   \n                             operation   [GW$_{el}$*h/a]          17741.681254   \n                                         [GW$_{el}$*h]            17741.681254   \n                             opexCap     [1e9 Euro/a]                    0.184   \nWind (onshore)               TAC         [1e9 Euro/a]                 0.505739   \n                             capacity    [GW$_{el}$]                  3.773114   \n                             capexCap    [1e9 Euro/a]                  0.42273   \n                             invest      [1e9 Euro]                   4.150426   \n                             operation   [GW$_{el}$*h/a]           8867.917789   \n                                         [GW$_{el}$*h]             8867.917789   \n                             opexCap     [1e9 Euro/a]                 0.083009   \n\n                                                                      cluster_2  \\\nComponent                    Property    Unit                                     \nCO2 to enviroment            operation   [Mio. t$_{CO_2}$/h*h/a]      22.306461   \n                                         [Mio. t$_{CO_2}$/h*h]        22.306461   \nElectricity demand           operation   [GW$_{el}$*h/a]          124796.759468   \n                                         [GW$_{el}$*h]            124796.759468   \nExisting run-of-river plants TAC         [1e9 Euro/a]                   0.00795   \n                             capacity    [GW$_{el}$]                   0.038221   \n                             operation   [GW$_{el}$*h/a]             174.477553   \n                                         [GW$_{el}$*h]               174.477553   \n                             opexCap     [1e9 Euro/a]                   0.00795   \nHydrogen demand              operation   [GW$_{H_{2},LHV}$*h/a]    16452.594944   \n                                         [GW$_{H_{2},LHV}$*h]      16452.594944   \nNatural gas purchase         TAC         [1e9 Euro/a]                  3.673353   \n                             commodCosts [1e9 Euro/a]                  3.673353   \n                             operation   [GW$_{CH_{4},LHV}$*h/a]   110977.41832   \n                                         [GW$_{CH_{4},LHV}$*h]     110977.41832   \nPV                           TAC         [1e9 Euro/a]                       0.0   \n                             capacity    [GW$_{el}$]                        0.0   \n                             capexCap    [1e9 Euro/a]                       0.0   \n                             invest      [1e9 Euro]                         0.0   \n                             operation   [GW$_{el}$*h/a]                    0.0   \n                                         [GW$_{el}$*h]                      0.0   \n                             opexCap     [1e9 Euro/a]                       0.0   \nWind (offshore)              TAC         [1e9 Euro/a]                       0.0   \n                             capacity    [GW$_{el}$]                        0.0   \n                             capexCap    [1e9 Euro/a]                       0.0   \n                             invest      [1e9 Euro]                         0.0   \n                             operation   [GW$_{el}$*h/a]                    0.0   \n                                         [GW$_{el}$*h]                      0.0   \n                             opexCap     [1e9 Euro/a]                       0.0   \nWind (onshore)               TAC         [1e9 Euro/a]                  3.336771   \n                             capacity    [GW$_{el}$]                  24.894326   \n                             capexCap    [1e9 Euro/a]                  2.789096   \n                             invest      [1e9 Euro]                   27.383759   \n                             operation   [GW$_{el}$*h/a]           59111.743288   \n                                         [GW$_{el}$*h]             59111.743288   \n                             opexCap     [1e9 Euro/a]                  0.547675   \n\n                                                                     cluster_3  \\\nComponent                    Property    Unit                                    \nCO2 to enviroment            operation   [Mio. t$_{CO_2}$/h*h/a]      11.71189   \n                                         [Mio. t$_{CO_2}$/h*h]        11.71189   \nElectricity demand           operation   [GW$_{el}$*h/a]          40441.203266   \n                                         [GW$_{el}$*h]            40441.203266   \nExisting run-of-river plants TAC         [1e9 Euro/a]                 0.009063   \n                             capacity    [GW$_{el}$]                  0.043572   \n                             operation   [GW$_{el}$*h/a]             198.90441   \n                                         [GW$_{el}$*h]               198.90441   \n                             opexCap     [1e9 Euro/a]                 0.009063   \nHydrogen demand              operation   [GW$_{H_{2},LHV}$*h/a]    5264.131864   \n                                         [GW$_{H_{2},LHV}$*h]      5264.131864   \nNatural gas purchase         TAC         [1e9 Euro/a]                 1.928674   \n                             commodCosts [1e9 Euro/a]                 1.928674   \n                             operation   [GW$_{CH_{4},LHV}$*h/a]  58268.107686   \n                                         [GW$_{CH_{4},LHV}$*h]    58268.107686   \nPV                           TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       0.0   \n                             capexCap    [1e9 Euro/a]                      0.0   \n                             invest      [1e9 Euro]                        0.0   \n                             operation   [GW$_{el}$*h/a]                   0.0   \n                                         [GW$_{el}$*h]                     0.0   \n                             opexCap     [1e9 Euro/a]                      0.0   \nWind (offshore)              TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       0.0   \n                             capexCap    [1e9 Euro/a]                      0.0   \n                             invest      [1e9 Euro]                        0.0   \n                             operation   [GW$_{el}$*h/a]                   0.0   \n                                         [GW$_{el}$*h]                     0.0   \n                             opexCap     [1e9 Euro/a]                      0.0   \nWind (onshore)               TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       0.0   \n                             capexCap    [1e9 Euro/a]                      0.0   \n                             invest      [1e9 Euro]                        0.0   \n                             operation   [GW$_{el}$*h/a]                   0.0   \n                                         [GW$_{el}$*h]                     0.0   \n                             opexCap     [1e9 Euro/a]                      0.0   \n\n                                                                      cluster_4  \\\nComponent                    Property    Unit                                     \nCO2 to enviroment            operation   [Mio. t$_{CO_2}$/h*h/a]      23.107098   \n                                         [Mio. t$_{CO_2}$/h*h]        23.107098   \nElectricity demand           operation   [GW$_{el}$*h/a]           77229.281165   \n                                         [GW$_{el}$*h]             77229.281165   \nExisting run-of-river plants TAC         [1e9 Euro/a]                  0.556096   \n                             capacity    [GW$_{el}$]                   2.673537   \n                             operation   [GW$_{el}$*h/a]           12204.710271   \n                                         [GW$_{el}$*h]             12204.710271   \n                             opexCap     [1e9 Euro/a]                  0.556096   \nHydrogen demand              operation   [GW$_{H_{2},LHV}$*h/a]    10078.267525   \n                                         [GW$_{H_{2},LHV}$*h]      10078.267525   \nNatural gas purchase         TAC         [1e9 Euro/a]                  3.805199   \n                             commodCosts [1e9 Euro/a]                  3.805199   \n                             operation   [GW$_{CH_{4},LHV}$*h/a]  114960.686843   \n                                         [GW$_{CH_{4},LHV}$*h]    114960.686843   \nPV                           TAC         [1e9 Euro/a]                  0.053425   \n                             capacity    [GW$_{el}$]                   0.723019   \n                             capexCap    [1e9 Euro/a]                  0.044025   \n                             invest      [1e9 Euro]                    0.469962   \n                             operation   [GW$_{el}$*h/a]             824.536128   \n                                         [GW$_{el}$*h]               824.536128   \n                             opexCap     [1e9 Euro/a]                  0.009399   \nWind (offshore)              TAC         [1e9 Euro/a]                       0.0   \n                             capacity    [GW$_{el}$]                        NaN   \n                             capexCap    [1e9 Euro/a]                       NaN   \n                             invest      [1e9 Euro]                         NaN   \n                             operation   [GW$_{el}$*h/a]                    NaN   \n                                         [GW$_{el}$*h]                      NaN   \n                             opexCap     [1e9 Euro/a]                       NaN   \nWind (onshore)               TAC         [1e9 Euro/a]                       0.0   \n                             capacity    [GW$_{el}$]                        0.0   \n                             capexCap    [1e9 Euro/a]                       0.0   \n                             invest      [1e9 Euro]                         0.0   \n                             operation   [GW$_{el}$*h/a]                    0.0   \n                                         [GW$_{el}$*h]                      0.0   \n                             opexCap     [1e9 Euro/a]                       0.0   \n\n                                                                     cluster_5  \\\nComponent                    Property    Unit                                    \nCO2 to enviroment            operation   [Mio. t$_{CO_2}$/h*h/a]     16.907707   \n                                         [Mio. t$_{CO_2}$/h*h]       16.907707   \nElectricity demand           operation   [GW$_{el}$*h/a]          41413.409155   \n                                         [GW$_{el}$*h]            41413.409155   \nExisting run-of-river plants TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       NaN   \n                             operation   [GW$_{el}$*h/a]                   NaN   \n                                         [GW$_{el}$*h]                     NaN   \n                             opexCap     [1e9 Euro/a]                      NaN   \nHydrogen demand              operation   [GW$_{H_{2},LHV}$*h/a]    5747.423572   \n                                         [GW$_{H_{2},LHV}$*h]      5747.423572   \nNatural gas purchase         TAC         [1e9 Euro/a]                 2.784304   \n                             commodCosts [1e9 Euro/a]                 2.784304   \n                             operation   [GW$_{CH_{4},LHV}$*h/a]  84117.946148   \n                                         [GW$_{CH_{4},LHV}$*h]    84117.946148   \nPV                           TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       0.0   \n                             capexCap    [1e9 Euro/a]                      0.0   \n                             invest      [1e9 Euro]                        0.0   \n                             operation   [GW$_{el}$*h/a]                   0.0   \n                                         [GW$_{el}$*h]                     0.0   \n                             opexCap     [1e9 Euro/a]                      0.0   \nWind (offshore)              TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       NaN   \n                             capexCap    [1e9 Euro/a]                      NaN   \n                             invest      [1e9 Euro]                        NaN   \n                             operation   [GW$_{el}$*h/a]                   NaN   \n                                         [GW$_{el}$*h]                     NaN   \n                             opexCap     [1e9 Euro/a]                      NaN   \nWind (onshore)               TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       0.0   \n                             capexCap    [1e9 Euro/a]                      0.0   \n                             invest      [1e9 Euro]                        0.0   \n                             operation   [GW$_{el}$*h/a]                   0.0   \n                                         [GW$_{el}$*h]                     0.0   \n                             opexCap     [1e9 Euro/a]                      0.0   \n\n                                                                     cluster_6  \\\nComponent                    Property    Unit                                    \nCO2 to enviroment            operation   [Mio. t$_{CO_2}$/h*h/a]     14.992398   \n                                         [Mio. t$_{CO_2}$/h*h]       14.992398   \nElectricity demand           operation   [GW$_{el}$*h/a]          28088.766223   \n                                         [GW$_{el}$*h]            28088.766223   \nExisting run-of-river plants TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       NaN   \n                             operation   [GW$_{el}$*h/a]                   NaN   \n                                         [GW$_{el}$*h]                     NaN   \n                             opexCap     [1e9 Euro/a]                      NaN   \nHydrogen demand              operation   [GW$_{H_{2},LHV}$*h/a]    3710.050514   \n                                         [GW$_{H_{2},LHV}$*h]      3710.050514   \nNatural gas purchase         TAC         [1e9 Euro/a]                 2.468897   \n                             commodCosts [1e9 Euro/a]                 2.468897   \n                             operation   [GW$_{CH_{4},LHV}$*h/a]  74589.045437   \n                                         [GW$_{CH_{4},LHV}$*h]    74589.045437   \nPV                           TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       0.0   \n                             capexCap    [1e9 Euro/a]                      0.0   \n                             invest      [1e9 Euro]                        0.0   \n                             operation   [GW$_{el}$*h/a]                   0.0   \n                                         [GW$_{el}$*h]                     0.0   \n                             opexCap     [1e9 Euro/a]                      0.0   \nWind (offshore)              TAC         [1e9 Euro/a]                      0.0   \n                             capacity    [GW$_{el}$]                       0.0   \n                             capexCap    [1e9 Euro/a]                      0.0   \n                             invest      [1e9 Euro]                        0.0   \n                             operation   [GW$_{el}$*h/a]                   0.0   \n                                         [GW$_{el}$*h]                     0.0   \n                             opexCap     [1e9 Euro/a]                      0.0   \nWind (onshore)               TAC         [1e9 Euro/a]                 4.804654   \n                             capacity    [GW$_{el}$]                 35.845616   \n                             capexCap    [1e9 Euro/a]                 4.016051   \n                             invest      [1e9 Euro]                  39.430178   \n                             operation   [GW$_{el}$*h/a]          95743.992731   \n                                         [GW$_{el}$*h]            95743.992731   \n                             opexCap     [1e9 Euro/a]                 0.788604   \n\n                                                                     cluster_7  \nComponent                    Property    Unit                                   \nCO2 to enviroment            operation   [Mio. t$_{CO_2}$/h*h/a]      9.792601  \n                                         [Mio. t$_{CO_2}$/h*h]        9.792601  \nElectricity demand           operation   [GW$_{el}$*h/a]          15851.141245  \n                                         [GW$_{el}$*h]            15851.141245  \nExisting run-of-river plants TAC         [1e9 Euro/a]                 0.067472  \n                             capacity    [GW$_{el}$]                  0.324386  \n                             operation   [GW$_{el}$*h/a]           1480.823705  \n                                         [GW$_{el}$*h]             1480.823705  \n                             opexCap     [1e9 Euro/a]                 0.067472  \nHydrogen demand              operation   [GW$_{H_{2},LHV}$*h/a]    1453.124166  \n                                         [GW$_{H_{2},LHV}$*h]      1453.124166  \nNatural gas purchase         TAC         [1e9 Euro/a]                 1.612612  \n                             commodCosts [1e9 Euro/a]                 1.612612  \n                             operation   [GW$_{CH_{4},LHV}$*h/a]  48719.406234  \n                                         [GW$_{CH_{4},LHV}$*h]    48719.406234  \nPV                           TAC         [1e9 Euro/a]                      0.0  \n                             capacity    [GW$_{el}$]                       0.0  \n                             capexCap    [1e9 Euro/a]                      0.0  \n                             invest      [1e9 Euro]                        0.0  \n                             operation   [GW$_{el}$*h/a]                   0.0  \n                                         [GW$_{el}$*h]                     0.0  \n                             opexCap     [1e9 Euro/a]                      0.0  \nWind (offshore)              TAC         [1e9 Euro/a]                      0.0  \n                             capacity    [GW$_{el}$]                       NaN  \n                             capexCap    [1e9 Euro/a]                      NaN  \n                             invest      [1e9 Euro]                        NaN  \n                             operation   [GW$_{el}$*h/a]                   NaN  \n                                         [GW$_{el}$*h]                     NaN  \n                             opexCap     [1e9 Euro/a]                      NaN  \nWind (onshore)               TAC         [1e9 Euro/a]                      0.0  \n                             capacity    [GW$_{el}$]                       0.0  \n                             capexCap    [1e9 Euro/a]                      0.0  \n                             invest      [1e9 Euro]                        0.0  \n                             operation   [GW$_{el}$*h/a]                   0.0  \n                                         [GW$_{el}$*h]                     0.0  \n                             opexCap     [1e9 Euro/a]                      0.0  ",
      "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></th>\n      <th></th>\n      <th>cluster_0</th>\n      <th>cluster_1</th>\n      <th>cluster_2</th>\n      <th>cluster_3</th>\n      <th>cluster_4</th>\n      <th>cluster_5</th>\n      <th>cluster_6</th>\n      <th>cluster_7</th>\n    </tr>\n    <tr>\n      <th>Component</th>\n      <th>Property</th>\n      <th>Unit</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">CO2 to enviroment</th>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[Mio. t$_{CO_2}$/h*h/a]</th>\n      <td>30.231486</td>\n      <td>15.160913</td>\n      <td>22.306461</td>\n      <td>11.71189</td>\n      <td>23.107098</td>\n      <td>16.907707</td>\n      <td>14.992398</td>\n      <td>9.792601</td>\n    </tr>\n    <tr>\n      <th>[Mio. t$_{CO_2}$/h*h]</th>\n      <td>30.231486</td>\n      <td>15.160913</td>\n      <td>22.306461</td>\n      <td>11.71189</td>\n      <td>23.107098</td>\n      <td>16.907707</td>\n      <td>14.992398</td>\n      <td>9.792601</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Electricity demand</th>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>133963.451433</td>\n      <td>66115.987455</td>\n      <td>124796.759468</td>\n      <td>40441.203266</td>\n      <td>77229.281165</td>\n      <td>41413.409155</td>\n      <td>28088.766223</td>\n      <td>15851.141245</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>133963.451433</td>\n      <td>66115.987455</td>\n      <td>124796.759468</td>\n      <td>40441.203266</td>\n      <td>77229.281165</td>\n      <td>41413.409155</td>\n      <td>28088.766223</td>\n      <td>15851.141245</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">Existing run-of-river plants</th>\n      <th>TAC</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.144316</td>\n      <td>0.007791</td>\n      <td>0.00795</td>\n      <td>0.009063</td>\n      <td>0.556096</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.067472</td>\n    </tr>\n    <tr>\n      <th>capacity</th>\n      <th>[GW$_{el}$]</th>\n      <td>0.693828</td>\n      <td>0.037456</td>\n      <td>0.038221</td>\n      <td>0.043572</td>\n      <td>2.673537</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.324386</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>3167.329183</td>\n      <td>170.988002</td>\n      <td>174.477553</td>\n      <td>198.90441</td>\n      <td>12204.710271</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1480.823705</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>3167.329183</td>\n      <td>170.988002</td>\n      <td>174.477553</td>\n      <td>198.90441</td>\n      <td>12204.710271</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1480.823705</td>\n    </tr>\n    <tr>\n      <th>opexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.144316</td>\n      <td>0.007791</td>\n      <td>0.00795</td>\n      <td>0.009063</td>\n      <td>0.556096</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.067472</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Hydrogen demand</th>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{H_{2},LHV}$*h/a]</th>\n      <td>15855.229091</td>\n      <td>11007.056882</td>\n      <td>16452.594944</td>\n      <td>5264.131864</td>\n      <td>10078.267525</td>\n      <td>5747.423572</td>\n      <td>3710.050514</td>\n      <td>1453.124166</td>\n    </tr>\n    <tr>\n      <th>[GW$_{H_{2},LHV}$*h]</th>\n      <td>15855.229091</td>\n      <td>11007.056882</td>\n      <td>16452.594944</td>\n      <td>5264.131864</td>\n      <td>10078.267525</td>\n      <td>5747.423572</td>\n      <td>3710.050514</td>\n      <td>1453.124166</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">Natural gas purchase</th>\n      <th>TAC</th>\n      <th>[1e9 Euro/a]</th>\n      <td>4.978419</td>\n      <td>2.496648</td>\n      <td>3.673353</td>\n      <td>1.928674</td>\n      <td>3.805199</td>\n      <td>2.784304</td>\n      <td>2.468897</td>\n      <td>1.612612</td>\n    </tr>\n    <tr>\n      <th>commodCosts</th>\n      <th>[1e9 Euro/a]</th>\n      <td>4.978419</td>\n      <td>2.496648</td>\n      <td>3.673353</td>\n      <td>1.928674</td>\n      <td>3.805199</td>\n      <td>2.784304</td>\n      <td>2.468897</td>\n      <td>1.612612</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{CH_{4},LHV}$*h/a]</th>\n      <td>150405.404924</td>\n      <td>75427.427228</td>\n      <td>110977.41832</td>\n      <td>58268.107686</td>\n      <td>114960.686843</td>\n      <td>84117.946148</td>\n      <td>74589.045437</td>\n      <td>48719.406234</td>\n    </tr>\n    <tr>\n      <th>[GW$_{CH_{4},LHV}$*h]</th>\n      <td>150405.404924</td>\n      <td>75427.427228</td>\n      <td>110977.41832</td>\n      <td>58268.107686</td>\n      <td>114960.686843</td>\n      <td>84117.946148</td>\n      <td>74589.045437</td>\n      <td>48719.406234</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">PV</th>\n      <th>TAC</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.053425</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>capacity</th>\n      <th>[GW$_{el}$]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.723019</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>capexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.044025</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>invest</th>\n      <th>[1e9 Euro]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.469962</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>824.536128</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>824.536128</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>opexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.009399</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">Wind (offshore)</th>\n      <th>TAC</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>1.12104</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>capacity</th>\n      <th>[GW$_{el}$]</th>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>capexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>NaN</td>\n      <td>0.93704</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>invest</th>\n      <th>[1e9 Euro]</th>\n      <td>NaN</td>\n      <td>9.2</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>NaN</td>\n      <td>17741.681254</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>NaN</td>\n      <td>17741.681254</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>opexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>NaN</td>\n      <td>0.184</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">Wind (onshore)</th>\n      <th>TAC</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.505739</td>\n      <td>3.336771</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.804654</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>capacity</th>\n      <th>[GW$_{el}$]</th>\n      <td>0.0</td>\n      <td>3.773114</td>\n      <td>24.894326</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>35.845616</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>capexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.42273</td>\n      <td>2.789096</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.016051</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>invest</th>\n      <th>[1e9 Euro]</th>\n      <td>0.0</td>\n      <td>4.150426</td>\n      <td>27.383759</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>39.430178</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>0.0</td>\n      <td>8867.917789</td>\n      <td>59111.743288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>95743.992731</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>0.0</td>\n      <td>8867.917789</td>\n      <td>59111.743288</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>95743.992731</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>opexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.083009</td>\n      <td>0.547675</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.788604</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot installed capacities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = fn.plotLocationalColorMap(esM, 'Wind (offshore)', locFilePath, 'index', perArea=False)\n",
    "save_figure(fig, 'Capacity_wind_offshore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = fn.plotLocationalColorMap(esM, 'Wind (onshore)', locFilePath, 'index', perArea=False)\n",
    "save_figure(fig, 'Capacity_wind_onshore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = fn.plotLocationalColorMap(esM, 'PV', locFilePath, 'index', perArea=False)\n",
    "save_figure(fig, 'Capacity_PV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot operation time series (either one or two dimensional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = fn.plotOperation(esM, 'Electricity demand', 'cluster_0')\n",
    "save_figure(fig, 'Demand_electricity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = fn.plotOperationColorMap(esM, 'Electricity demand', 'cluster_0')\n",
    "save_figure(fig, 'Demand_electricity_C0_operation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversion\n",
    "\n",
    "Show optimization summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                                    cluster_0     cluster_1  \\\nComponent             Property  Unit                                          \nCCGT plants (methane) TAC       [1e9 Euro/a]         0.927685      0.642583   \n                      capacity  [GW$_{el}$]           11.9773      8.296368   \n                      capexCap  [1e9 Euro/a]         0.676161       0.46836   \n                      invest    [1e9 Euro]           7.785245      5.392639   \n                      operation [GW$_{el}$*h/a]  90243.242955  45256.456337   \n                                [GW$_{el}$*h]    90243.242955  45256.456337   \n                      opexCap   [1e9 Euro/a]         0.251523      0.174224   \nElectrolyzer          TAC       [1e9 Euro/a]              0.0           0.0   \n                      capacity  [GW$_{el}$]               0.0           0.0   \n                      capexCap  [1e9 Euro/a]              0.0           0.0   \n                      invest    [1e9 Euro]                0.0           0.0   \n                      operation [GW$_{el}$*h/a]           0.0           0.0   \n                                [GW$_{el}$*h]             0.0           0.0   \n                      opexCap   [1e9 Euro/a]              0.0           0.0   \n\n                                                    cluster_2     cluster_3  \\\nComponent             Property  Unit                                          \nCCGT plants (methane) TAC       [1e9 Euro/a]         0.811177      0.467613   \n                      capacity  [GW$_{el}$]         10.473074      6.037338   \n                      capexCap  [1e9 Euro/a]         0.591242      0.340829   \n                      invest    [1e9 Euro]           6.807498       3.92427   \n                      operation [GW$_{el}$*h/a]  66586.450992  34960.864611   \n                                [GW$_{el}$*h]    66586.450992  34960.864611   \n                      opexCap   [1e9 Euro/a]         0.219935      0.126784   \nElectrolyzer          TAC       [1e9 Euro/a]              0.0           0.0   \n                      capacity  [GW$_{el}$]               0.0           0.0   \n                      capexCap  [1e9 Euro/a]              0.0           0.0   \n                      invest    [1e9 Euro]                0.0           0.0   \n                      operation [GW$_{el}$*h/a]           0.0           0.0   \n                                [GW$_{el}$*h]             0.0           0.0   \n                      opexCap   [1e9 Euro/a]              0.0           0.0   \n\n                                                    cluster_4     cluster_5  \\\nComponent             Property  Unit                                          \nCCGT plants (methane) TAC       [1e9 Euro/a]         0.823105      0.471151   \n                      capacity  [GW$_{el}$]         10.627083      6.083015   \n                      capexCap  [1e9 Euro/a]         0.599937      0.343408   \n                      invest    [1e9 Euro]           6.907604       3.95396   \n                      operation [GW$_{el}$*h/a]  68976.412106  50470.767689   \n                                [GW$_{el}$*h]    68976.412106  50470.767689   \n                      opexCap   [1e9 Euro/a]         0.223169      0.127743   \nElectrolyzer          TAC       [1e9 Euro/a]              0.0           0.0   \n                      capacity  [GW$_{el}$]               0.0           0.0   \n                      capexCap  [1e9 Euro/a]              0.0           0.0   \n                      invest    [1e9 Euro]                0.0           0.0   \n                      operation [GW$_{el}$*h/a]           0.0           0.0   \n                                [GW$_{el}$*h]             0.0           0.0   \n                      opexCap   [1e9 Euro/a]              0.0           0.0   \n\n                                                    cluster_6    cluster_7  \nComponent             Property  Unit                                        \nCCGT plants (methane) TAC       [1e9 Euro/a]         0.647333     0.292278  \n                      capacity  [GW$_{el}$]          8.357697     3.773586  \n                      capexCap  [1e9 Euro/a]         0.471822     0.213032  \n                      invest    [1e9 Euro]           5.432503     2.452831  \n                      operation [GW$_{el}$*h/a]  44753.427262  29231.64374  \n                                [GW$_{el}$*h]    44753.427262  29231.64374  \n                      opexCap   [1e9 Euro/a]         0.175512     0.079245  \nElectrolyzer          TAC       [1e9 Euro/a]         0.987187          0.0  \n                      capacity  [GW$_{el}$]         11.345055          0.0  \n                      capexCap  [1e9 Euro/a]         0.845374          0.0  \n                      invest    [1e9 Euro]           5.672528          0.0  \n                      operation [GW$_{el}$*h/a]  99382.683656          0.0  \n                                [GW$_{el}$*h]    99382.683656          0.0  \n                      opexCap   [1e9 Euro/a]         0.141813          0.0  ",
      "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></th>\n      <th></th>\n      <th>cluster_0</th>\n      <th>cluster_1</th>\n      <th>cluster_2</th>\n      <th>cluster_3</th>\n      <th>cluster_4</th>\n      <th>cluster_5</th>\n      <th>cluster_6</th>\n      <th>cluster_7</th>\n    </tr>\n    <tr>\n      <th>Component</th>\n      <th>Property</th>\n      <th>Unit</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">CCGT plants (methane)</th>\n      <th>TAC</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.927685</td>\n      <td>0.642583</td>\n      <td>0.811177</td>\n      <td>0.467613</td>\n      <td>0.823105</td>\n      <td>0.471151</td>\n      <td>0.647333</td>\n      <td>0.292278</td>\n    </tr>\n    <tr>\n      <th>capacity</th>\n      <th>[GW$_{el}$]</th>\n      <td>11.9773</td>\n      <td>8.296368</td>\n      <td>10.473074</td>\n      <td>6.037338</td>\n      <td>10.627083</td>\n      <td>6.083015</td>\n      <td>8.357697</td>\n      <td>3.773586</td>\n    </tr>\n    <tr>\n      <th>capexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.676161</td>\n      <td>0.46836</td>\n      <td>0.591242</td>\n      <td>0.340829</td>\n      <td>0.599937</td>\n      <td>0.343408</td>\n      <td>0.471822</td>\n      <td>0.213032</td>\n    </tr>\n    <tr>\n      <th>invest</th>\n      <th>[1e9 Euro]</th>\n      <td>7.785245</td>\n      <td>5.392639</td>\n      <td>6.807498</td>\n      <td>3.92427</td>\n      <td>6.907604</td>\n      <td>3.95396</td>\n      <td>5.432503</td>\n      <td>2.452831</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>90243.242955</td>\n      <td>45256.456337</td>\n      <td>66586.450992</td>\n      <td>34960.864611</td>\n      <td>68976.412106</td>\n      <td>50470.767689</td>\n      <td>44753.427262</td>\n      <td>29231.64374</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>90243.242955</td>\n      <td>45256.456337</td>\n      <td>66586.450992</td>\n      <td>34960.864611</td>\n      <td>68976.412106</td>\n      <td>50470.767689</td>\n      <td>44753.427262</td>\n      <td>29231.64374</td>\n    </tr>\n    <tr>\n      <th>opexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.251523</td>\n      <td>0.174224</td>\n      <td>0.219935</td>\n      <td>0.126784</td>\n      <td>0.223169</td>\n      <td>0.127743</td>\n      <td>0.175512</td>\n      <td>0.079245</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">Electrolyzer</th>\n      <th>TAC</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.987187</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>capacity</th>\n      <th>[GW$_{el}$]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.345055</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>capexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.845374</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>invest</th>\n      <th>[1e9 Euro]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.672528</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operation</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>99382.683656</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>99382.683656</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>opexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.141813</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = fn.plotLocationalColorMap(esM, 'Electrolyzer', locFilePath, 'index', perArea=False,\n",
    "                                    zlabel='Installed capacity\\nin GW\\n')\n",
    "save_figure(fig, 'Capacity_Elektrolyzer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for num in range(0, 8):\n",
    "    try:\n",
    "        fig, ax = fn.plotOperationColorMap(esM, 'New CCGT plants (biogas)', f'cluster_{num}')\n",
    "        save_figure(fig, f'Operation_CCGT_biogas_C{num}')\n",
    "    except KeyError as err:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "for num in range(0, 8):\n",
    "    try:\n",
    "        fig, ax = fn.plotOperationColorMap(esM, 'CCGT plants (methane)', f'cluster_{num}')\n",
    "        save_figure(fig, f'Operation_CCGT_methan_C{num}')\n",
    "    except KeyError as err:\n",
    "        pass"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storage\n",
    "\n",
    "Show optimization summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                                           cluster_0  \\\nComponent            Property           Unit                           \nPumped hydro storage TAC                [1e9 Euro/a]        0.002436   \n                     capacity           [GW$_{el}$*h]         15.924   \n                     operationCharge    [GW$_{el}$*h/a]  2727.320491   \n                                        [GW$_{el}$*h]    2727.320491   \n                     operationDischarge [GW$_{el}$*h/a]  2111.887075   \n                                        [GW$_{el}$*h]    2111.887075   \n                     opexCap            [1e9 Euro/a]        0.002436   \n\n                                                           cluster_1  \\\nComponent            Property           Unit                           \nPumped hydro storage TAC                [1e9 Euro/a]        0.001186   \n                     capacity           [GW$_{el}$*h]          7.751   \n                     operationCharge    [GW$_{el}$*h/a]  1320.616322   \n                                        [GW$_{el}$*h]    1320.616322   \n                     operationDischarge [GW$_{el}$*h/a]  1022.599291   \n                                        [GW$_{el}$*h]    1022.599291   \n                     opexCap            [1e9 Euro/a]        0.001186   \n\n                                                          cluster_2  \\\nComponent            Property           Unit                          \nPumped hydro storage TAC                [1e9 Euro/a]       0.000125   \n                     capacity           [GW$_{el}$*h]         0.818   \n                     operationCharge    [GW$_{el}$*h/a]  134.728777   \n                                        [GW$_{el}$*h]    134.728777   \n                     operationDischarge [GW$_{el}$*h/a]  104.318102   \n                                        [GW$_{el}$*h]    104.318102   \n                     opexCap            [1e9 Euro/a]       0.000125   \n\n                                                           cluster_3  \\\nComponent            Property           Unit                           \nPumped hydro storage TAC                [1e9 Euro/a]        0.003002   \n                     capacity           [GW$_{el}$*h]          19.62   \n                     operationCharge    [GW$_{el}$*h/a]  3181.765767   \n                                        [GW$_{el}$*h]    3181.765767   \n                     operationDischarge [GW$_{el}$*h/a]  2463.795997   \n                                        [GW$_{el}$*h]    2463.795997   \n                     opexCap            [1e9 Euro/a]        0.003002   \n\n                                                          cluster_4  \\\nComponent            Property           Unit                          \nPumped hydro storage TAC                [1e9 Euro/a]         0.0011   \n                     capacity           [GW$_{el}$*h]          7.19   \n                     operationCharge    [GW$_{el}$*h/a]  1224.77343   \n                                        [GW$_{el}$*h]    1224.77343   \n                     operationDischarge [GW$_{el}$*h/a]  948.391244   \n                                        [GW$_{el}$*h]    948.391244   \n                     opexCap            [1e9 Euro/a]         0.0011   \n\n                                                           cluster_5  \\\nComponent            Property           Unit                           \nPumped hydro storage TAC                [1e9 Euro/a]         0.00383   \n                     capacity           [GW$_{el}$*h]         25.034   \n                     operationCharge    [GW$_{el}$*h/a]  3959.480031   \n                                        [GW$_{el}$*h]    3959.480031   \n                     operationDischarge [GW$_{el}$*h/a]  3066.020578   \n                                        [GW$_{el}$*h]    3066.020578   \n                     opexCap            [1e9 Euro/a]         0.00383   \n\n                                                         cluster_6 cluster_7  \nComponent            Property           Unit                                  \nPumped hydro storage TAC                [1e9 Euro/a]      0.000092       0.0  \n                     capacity           [GW$_{el}$*h]          0.6       NaN  \n                     operationCharge    [GW$_{el}$*h/a]  118.67999       NaN  \n                                        [GW$_{el}$*h]    118.67999       NaN  \n                     operationDischarge [GW$_{el}$*h/a]  91.885687       NaN  \n                                        [GW$_{el}$*h]    91.885687       NaN  \n                     opexCap            [1e9 Euro/a]      0.000092       NaN  ",
      "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></th>\n      <th></th>\n      <th>cluster_0</th>\n      <th>cluster_1</th>\n      <th>cluster_2</th>\n      <th>cluster_3</th>\n      <th>cluster_4</th>\n      <th>cluster_5</th>\n      <th>cluster_6</th>\n      <th>cluster_7</th>\n    </tr>\n    <tr>\n      <th>Component</th>\n      <th>Property</th>\n      <th>Unit</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">Pumped hydro storage</th>\n      <th>TAC</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.002436</td>\n      <td>0.001186</td>\n      <td>0.000125</td>\n      <td>0.003002</td>\n      <td>0.0011</td>\n      <td>0.00383</td>\n      <td>0.000092</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>capacity</th>\n      <th>[GW$_{el}$*h]</th>\n      <td>15.924</td>\n      <td>7.751</td>\n      <td>0.818</td>\n      <td>19.62</td>\n      <td>7.19</td>\n      <td>25.034</td>\n      <td>0.6</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operationCharge</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>2727.320491</td>\n      <td>1320.616322</td>\n      <td>134.728777</td>\n      <td>3181.765767</td>\n      <td>1224.77343</td>\n      <td>3959.480031</td>\n      <td>118.67999</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>2727.320491</td>\n      <td>1320.616322</td>\n      <td>134.728777</td>\n      <td>3181.765767</td>\n      <td>1224.77343</td>\n      <td>3959.480031</td>\n      <td>118.67999</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">operationDischarge</th>\n      <th>[GW$_{el}$*h/a]</th>\n      <td>2111.887075</td>\n      <td>1022.599291</td>\n      <td>104.318102</td>\n      <td>2463.795997</td>\n      <td>948.391244</td>\n      <td>3066.020578</td>\n      <td>91.885687</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>[GW$_{el}$*h]</th>\n      <td>2111.887075</td>\n      <td>1022.599291</td>\n      <td>104.318102</td>\n      <td>2463.795997</td>\n      <td>948.391244</td>\n      <td>3066.020578</td>\n      <td>91.885687</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>opexCap</th>\n      <th>[1e9 Euro/a]</th>\n      <td>0.002436</td>\n      <td>0.001186</td>\n      <td>0.000125</td>\n      <td>0.003002</td>\n      <td>0.0011</td>\n      <td>0.00383</td>\n      <td>0.000092</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for num in range(0, 8):\n",
    "    try:\n",
    "        fig, ax = fn.plotOperationColorMap(esM, 'Li-ion batteries', f'cluster_{num}',\n",
    "                                           variableName='stateOfChargeOperationVariablesOptimum')\n",
    "        save_figure(fig, f'Operation_Li_battery_storage_C{num}')\n",
    "    except KeyError as err:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for num in range(0, 8):\n",
    "    try:\n",
    "        fig, ax = fn.plotOperationColorMap(esM, 'Pumped hydro storage', f'cluster_{num}',\n",
    "                                           variableName='stateOfChargeOperationVariablesOptimum')\n",
    "        save_figure(fig, f'Operation_Pump_hydro_storage_C{num}')\n",
    "    except KeyError as err:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for num in range(0, 8):\n",
    "    try:\n",
    "        fig, ax = fn.plotOperationColorMap(esM, 'Salt caverns (biogas)', f'cluster_{num}',\n",
    "                                           variableName='stateOfChargeOperationVariablesOptimum')\n",
    "        save_figure(fig, f'Operation_Salt_biogas_storage_C{num}')\n",
    "    except KeyError as err:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for num in range(0, 8):\n",
    "    try:\n",
    "        fig, ax = fn.plotOperationColorMap(esM, 'Salt caverns (hydrogen)', f'cluster_{num}',\n",
    "                                           variableName='stateOfChargeOperationVariablesOptimum')\n",
    "        save_figure(fig, f'Operation_Salt_hydro_storage_C{num}')\n",
    "    except KeyError as err:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transmission\n",
    "\n",
    "Show optimization summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true,
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                                   cluster_0     cluster_1  \\\nProperty     Unit                   LocationIn                               \nTAC          [1e9 Euro/a]           cluster_0            0.0      0.013705   \n                                    cluster_1       0.013705           0.0   \n                                    cluster_2            0.0      0.005555   \n                                    cluster_3            0.0           0.0   \n                                    cluster_4       0.006892           0.0   \n                                    cluster_5            0.0           0.0   \n                                    cluster_6            0.0      0.018393   \n                                    cluster_7       0.002953           0.0   \ncapacity     [GW$_{H_{2},LHV}$]     cluster_0            NaN      3.126327   \n                                    cluster_1       3.126327           NaN   \n                                    cluster_2            0.0       1.87815   \n                                    cluster_3            NaN           0.0   \n                                    cluster_4       1.150487           NaN   \n                                    cluster_5            0.0           0.0   \n                                    cluster_6            NaN       6.26099   \n                                    cluster_7       0.165882           NaN   \ncapexCap     [1e9 Euro/a]           cluster_0            NaN      0.008585   \n                                    cluster_1       0.008585           NaN   \n                                    cluster_2            0.0      0.002788   \n                                    cluster_3            NaN           0.0   \n                                    cluster_4        0.00263           NaN   \n                                    cluster_5            0.0           0.0   \n                                    cluster_6            NaN      0.014172   \n                                    cluster_7       0.000241           NaN   \ncapexIfBuilt [1e9 Euro/a]           cluster_0            NaN       0.00512   \n                                    cluster_1        0.00512           NaN   \n                                    cluster_2            0.0      0.002767   \n                                    cluster_3            NaN           0.0   \n                                    cluster_4       0.004262           NaN   \n                                    cluster_5            0.0           0.0   \n                                    cluster_6            NaN       0.00422   \n                                    cluster_7       0.002712           NaN   \ninvest       [1e9 Euro]             cluster_0            NaN      0.163422   \n                                    cluster_1       0.163422           NaN   \n                                    cluster_2            0.0      0.066243   \n                                    cluster_3            NaN           0.0   \n                                    cluster_4        0.08218           NaN   \n                                    cluster_5            0.0           0.0   \n                                    cluster_6            NaN      0.219325   \n                                    cluster_7       0.035217           NaN   \nisBuilt      [-]                    cluster_0            NaN           1.0   \n                                    cluster_1            1.0           NaN   \n                                    cluster_2            0.0           1.0   \n                                    cluster_3            NaN           0.0   \n                                    cluster_4            1.0           NaN   \n                                    cluster_5            0.0           0.0   \n                                    cluster_6            NaN           1.0   \n                                    cluster_7            1.0           NaN   \noperation    [GW$_{H_{2},LHV}$*h/a] cluster_0            NaN           0.0   \n                                    cluster_1   27386.620783           NaN   \n                                    cluster_3            NaN           0.0   \n                                    cluster_6            NaN  54846.272609   \n             [GW$_{H_{2},LHV}$*h]   cluster_0            NaN           0.0   \n                                    cluster_1   27386.620783           NaN   \n                                    cluster_3            NaN           0.0   \n                                    cluster_6            NaN  54846.272609   \n\n                                                   cluster_2     cluster_3  \\\nProperty     Unit                   LocationIn                               \nTAC          [1e9 Euro/a]           cluster_0            0.0           0.0   \n                                    cluster_1       0.005555           0.0   \n                                    cluster_2            0.0           0.0   \n                                    cluster_3            0.0           0.0   \n                                    cluster_4            0.0           0.0   \n                                    cluster_5            0.0      0.005462   \n                                    cluster_6            0.0      0.008896   \n                                    cluster_7            0.0           0.0   \ncapacity     [GW$_{H_{2},LHV}$]     cluster_0            0.0           NaN   \n                                    cluster_1        1.87815           0.0   \n                                    cluster_2            NaN           NaN   \n                                    cluster_3            NaN           NaN   \n                                    cluster_4            NaN           NaN   \n                                    cluster_5            NaN      0.656099   \n                                    cluster_6            NaN      1.257027   \n                                    cluster_7            0.0           NaN   \ncapexCap     [1e9 Euro/a]           cluster_0            0.0           NaN   \n                                    cluster_1       0.002788           0.0   \n                                    cluster_2            NaN           NaN   \n                                    cluster_3            NaN           NaN   \n                                    cluster_4            NaN           NaN   \n                                    cluster_5            NaN      0.001422   \n                                    cluster_6            NaN      0.003582   \n                                    cluster_7            0.0           NaN   \ncapexIfBuilt [1e9 Euro/a]           cluster_0            0.0           NaN   \n                                    cluster_1       0.002767           0.0   \n                                    cluster_2            NaN           NaN   \n                                    cluster_3            NaN           NaN   \n                                    cluster_4            NaN           NaN   \n                                    cluster_5            NaN       0.00404   \n                                    cluster_6            NaN      0.005313   \n                                    cluster_7            0.0           NaN   \ninvest       [1e9 Euro]             cluster_0            0.0           NaN   \n                                    cluster_1       0.066243           0.0   \n                                    cluster_2            NaN           NaN   \n                                    cluster_3            NaN           NaN   \n                                    cluster_4            NaN           NaN   \n                                    cluster_5            NaN      0.065135   \n                                    cluster_6            NaN      0.106079   \n                                    cluster_7            0.0           NaN   \nisBuilt      [-]                    cluster_0            0.0           NaN   \n                                    cluster_1            1.0           0.0   \n                                    cluster_2            NaN           NaN   \n                                    cluster_3            NaN           NaN   \n                                    cluster_4            NaN           NaN   \n                                    cluster_5            NaN           1.0   \n                                    cluster_6            NaN           1.0   \n                                    cluster_7            0.0           NaN   \noperation    [GW$_{H_{2},LHV}$*h/a] cluster_0            0.0           NaN   \n                                    cluster_1   16452.594944           0.0   \n                                    cluster_3            NaN           NaN   \n                                    cluster_6            NaN  11011.555436   \n             [GW$_{H_{2},LHV}$*h]   cluster_0            0.0           NaN   \n                                    cluster_1   16452.594944           0.0   \n                                    cluster_3            NaN           NaN   \n                                    cluster_6            NaN  11011.555436   \n\n                                                   cluster_4    cluster_5  \\\nProperty     Unit                   LocationIn                              \nTAC          [1e9 Euro/a]           cluster_0       0.006892          0.0   \n                                    cluster_1            0.0          0.0   \n                                    cluster_2            0.0          0.0   \n                                    cluster_3            0.0     0.005462   \n                                    cluster_4            0.0          0.0   \n                                    cluster_5            0.0          0.0   \n                                    cluster_6            0.0          0.0   \n                                    cluster_7            0.0          0.0   \ncapacity     [GW$_{H_{2},LHV}$]     cluster_0       1.150487          0.0   \n                                    cluster_1            NaN          0.0   \n                                    cluster_2            NaN          NaN   \n                                    cluster_3            NaN     0.656099   \n                                    cluster_4            NaN          0.0   \n                                    cluster_5            0.0          NaN   \n                                    cluster_6            NaN          NaN   \n                                    cluster_7            NaN          NaN   \ncapexCap     [1e9 Euro/a]           cluster_0        0.00263          0.0   \n                                    cluster_1            NaN          0.0   \n                                    cluster_2            NaN          NaN   \n                                    cluster_3            NaN     0.001422   \n                                    cluster_4            NaN          0.0   \n                                    cluster_5            0.0          NaN   \n                                    cluster_6            NaN          NaN   \n                                    cluster_7            NaN          NaN   \ncapexIfBuilt [1e9 Euro/a]           cluster_0       0.004262          0.0   \n                                    cluster_1            NaN          0.0   \n                                    cluster_2            NaN          NaN   \n                                    cluster_3            NaN      0.00404   \n                                    cluster_4            NaN          0.0   \n                                    cluster_5            0.0          NaN   \n                                    cluster_6            NaN          NaN   \n                                    cluster_7            NaN          NaN   \ninvest       [1e9 Euro]             cluster_0        0.08218          0.0   \n                                    cluster_1            NaN          0.0   \n                                    cluster_2            NaN          NaN   \n                                    cluster_3            NaN     0.065135   \n                                    cluster_4            NaN          0.0   \n                                    cluster_5            0.0          NaN   \n                                    cluster_6            NaN          NaN   \n                                    cluster_7            NaN          NaN   \nisBuilt      [-]                    cluster_0            1.0          0.0   \n                                    cluster_1            NaN          0.0   \n                                    cluster_2            NaN          NaN   \n                                    cluster_3            NaN          1.0   \n                                    cluster_4            NaN          0.0   \n                                    cluster_5            0.0          NaN   \n                                    cluster_6            NaN          NaN   \n                                    cluster_7            NaN          NaN   \noperation    [GW$_{H_{2},LHV}$*h/a] cluster_0   10078.267525          0.0   \n                                    cluster_1            NaN          0.0   \n                                    cluster_3            NaN  5747.423572   \n                                    cluster_6            NaN          NaN   \n             [GW$_{H_{2},LHV}$*h]   cluster_0   10078.267525          0.0   \n                                    cluster_1            NaN          0.0   \n                                    cluster_3            NaN  5747.423572   \n                                    cluster_6            NaN          NaN   \n\n                                               cluster_6    cluster_7  \nProperty     Unit                   LocationIn                         \nTAC          [1e9 Euro/a]           cluster_0        0.0     0.002953  \n                                    cluster_1   0.018393          0.0  \n                                    cluster_2        0.0          0.0  \n                                    cluster_3   0.008896          0.0  \n                                    cluster_4        0.0          0.0  \n                                    cluster_5        0.0          0.0  \n                                    cluster_6        0.0          0.0  \n                                    cluster_7        0.0          0.0  \ncapacity     [GW$_{H_{2},LHV}$]     cluster_0        NaN     0.165882  \n                                    cluster_1    6.26099          NaN  \n                                    cluster_2        NaN          0.0  \n                                    cluster_3   1.257027          NaN  \n                                    cluster_4        NaN          NaN  \n                                    cluster_5        NaN          NaN  \n                                    cluster_6        NaN          NaN  \n                                    cluster_7        NaN          NaN  \ncapexCap     [1e9 Euro/a]           cluster_0        NaN     0.000241  \n                                    cluster_1   0.014172          NaN  \n                                    cluster_2        NaN          0.0  \n                                    cluster_3   0.003582          NaN  \n                                    cluster_4        NaN          NaN  \n                                    cluster_5        NaN          NaN  \n                                    cluster_6        NaN          NaN  \n                                    cluster_7        NaN          NaN  \ncapexIfBuilt [1e9 Euro/a]           cluster_0        NaN     0.002712  \n                                    cluster_1    0.00422          NaN  \n                                    cluster_2        NaN          0.0  \n                                    cluster_3   0.005313          NaN  \n                                    cluster_4        NaN          NaN  \n                                    cluster_5        NaN          NaN  \n                                    cluster_6        NaN          NaN  \n                                    cluster_7        NaN          NaN  \ninvest       [1e9 Euro]             cluster_0        NaN     0.035217  \n                                    cluster_1   0.219325          NaN  \n                                    cluster_2        NaN          0.0  \n                                    cluster_3   0.106079          NaN  \n                                    cluster_4        NaN          NaN  \n                                    cluster_5        NaN          NaN  \n                                    cluster_6        NaN          NaN  \n                                    cluster_7        NaN          NaN  \nisBuilt      [-]                    cluster_0        NaN          1.0  \n                                    cluster_1        1.0          NaN  \n                                    cluster_2        NaN          0.0  \n                                    cluster_3        1.0          NaN  \n                                    cluster_4        NaN          NaN  \n                                    cluster_5        NaN          NaN  \n                                    cluster_6        NaN          NaN  \n                                    cluster_7        NaN          NaN  \noperation    [GW$_{H_{2},LHV}$*h/a] cluster_0        NaN  1453.124166  \n                                    cluster_1        0.0          NaN  \n                                    cluster_3        0.0          NaN  \n                                    cluster_6        NaN          NaN  \n             [GW$_{H_{2},LHV}$*h]   cluster_0        NaN  1453.124166  \n                                    cluster_1        0.0          NaN  \n                                    cluster_3        0.0          NaN  \n                                    cluster_6        NaN          NaN  ",
      "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></th>\n      <th></th>\n      <th>cluster_0</th>\n      <th>cluster_1</th>\n      <th>cluster_2</th>\n      <th>cluster_3</th>\n      <th>cluster_4</th>\n      <th>cluster_5</th>\n      <th>cluster_6</th>\n      <th>cluster_7</th>\n    </tr>\n    <tr>\n      <th>Property</th>\n      <th>Unit</th>\n      <th>LocationIn</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">TAC</th>\n      <th rowspan=\"8\" valign=\"top\">[1e9 Euro/a]</th>\n      <th>cluster_0</th>\n      <td>0.0</td>\n      <td>0.013705</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.006892</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.002953</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>0.013705</td>\n      <td>0.0</td>\n      <td>0.005555</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.018393</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_2</th>\n      <td>0.0</td>\n      <td>0.005555</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.005462</td>\n      <td>0.008896</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_4</th>\n      <td>0.006892</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_5</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.005462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>0.0</td>\n      <td>0.018393</td>\n      <td>0.0</td>\n      <td>0.008896</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_7</th>\n      <td>0.002953</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">capacity</th>\n      <th rowspan=\"8\" valign=\"top\">[GW$_{H_{2},LHV}$]</th>\n      <th>cluster_0</th>\n      <td>NaN</td>\n      <td>3.126327</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>1.150487</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.165882</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>3.126327</td>\n      <td>NaN</td>\n      <td>1.87815</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>6.26099</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_2</th>\n      <td>0.0</td>\n      <td>1.87815</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.656099</td>\n      <td>1.257027</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_4</th>\n      <td>1.150487</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_5</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.656099</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>NaN</td>\n      <td>6.26099</td>\n      <td>NaN</td>\n      <td>1.257027</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_7</th>\n      <td>0.165882</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">capexCap</th>\n      <th rowspan=\"8\" valign=\"top\">[1e9 Euro/a]</th>\n      <th>cluster_0</th>\n      <td>NaN</td>\n      <td>0.008585</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.00263</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.000241</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>0.008585</td>\n      <td>NaN</td>\n      <td>0.002788</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.014172</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_2</th>\n      <td>0.0</td>\n      <td>0.002788</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.001422</td>\n      <td>0.003582</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_4</th>\n      <td>0.00263</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_5</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.001422</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>NaN</td>\n      <td>0.014172</td>\n      <td>NaN</td>\n      <td>0.003582</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_7</th>\n      <td>0.000241</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">capexIfBuilt</th>\n      <th rowspan=\"8\" valign=\"top\">[1e9 Euro/a]</th>\n      <th>cluster_0</th>\n      <td>NaN</td>\n      <td>0.00512</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.004262</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.002712</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>0.00512</td>\n      <td>NaN</td>\n      <td>0.002767</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.00422</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_2</th>\n      <td>0.0</td>\n      <td>0.002767</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.00404</td>\n      <td>0.005313</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_4</th>\n      <td>0.004262</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_5</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.00404</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>NaN</td>\n      <td>0.00422</td>\n      <td>NaN</td>\n      <td>0.005313</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_7</th>\n      <td>0.002712</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">invest</th>\n      <th rowspan=\"8\" valign=\"top\">[1e9 Euro]</th>\n      <th>cluster_0</th>\n      <td>NaN</td>\n      <td>0.163422</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.08218</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.035217</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>0.163422</td>\n      <td>NaN</td>\n      <td>0.066243</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.219325</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_2</th>\n      <td>0.0</td>\n      <td>0.066243</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.065135</td>\n      <td>0.106079</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_4</th>\n      <td>0.08218</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_5</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.065135</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>NaN</td>\n      <td>0.219325</td>\n      <td>NaN</td>\n      <td>0.106079</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_7</th>\n      <td>0.035217</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">isBuilt</th>\n      <th rowspan=\"8\" valign=\"top\">[-]</th>\n      <th>cluster_0</th>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_2</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_4</th>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_5</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_7</th>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">operation</th>\n      <th rowspan=\"4\" valign=\"top\">[GW$_{H_{2},LHV}$*h/a]</th>\n      <th>cluster_0</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>10078.267525</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>1453.124166</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>27386.620783</td>\n      <td>NaN</td>\n      <td>16452.594944</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5747.423572</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>NaN</td>\n      <td>54846.272609</td>\n      <td>NaN</td>\n      <td>11011.555436</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">[GW$_{H_{2},LHV}$*h]</th>\n      <th>cluster_0</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>10078.267525</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>1453.124166</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>27386.620783</td>\n      <td>NaN</td>\n      <td>16452.594944</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5747.423572</td>\n      <td>0.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>NaN</td>\n      <td>54846.272609</td>\n      <td>NaN</td>\n      <td>11011.555436</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=2).loc['Pipelines (hydrogen)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the shared capacity of the pipelines are not exceeded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "           cluster_0  cluster_1  cluster_2  cluster_3  cluster_4  cluster_5  \\\ncluster_0        NaN   3.126327    0.00000        NaN   1.150487   0.000000   \ncluster_1   3.126327        NaN    1.87815   0.000000        NaN   0.000000   \ncluster_2   0.000000   1.878150        NaN        NaN        NaN        NaN   \ncluster_3        NaN   0.000000        NaN        NaN        NaN   0.656099   \ncluster_4   1.150487        NaN        NaN        NaN        NaN   0.000000   \ncluster_5   0.000000   0.000000        NaN   0.656099   0.000000        NaN   \ncluster_6        NaN   6.260990        NaN   1.257027        NaN        NaN   \ncluster_7   0.165882        NaN    0.00000        NaN        NaN        NaN   \n\n           cluster_6  cluster_7  \ncluster_0        NaN   0.165882  \ncluster_1   6.260990        NaN  \ncluster_2        NaN   0.000000  \ncluster_3   1.257027        NaN  \ncluster_4        NaN        NaN  \ncluster_5        NaN        NaN  \ncluster_6        NaN        NaN  \ncluster_7        NaN        NaN  ",
      "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>cluster_0</th>\n      <th>cluster_1</th>\n      <th>cluster_2</th>\n      <th>cluster_3</th>\n      <th>cluster_4</th>\n      <th>cluster_5</th>\n      <th>cluster_6</th>\n      <th>cluster_7</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>cluster_0</th>\n      <td>NaN</td>\n      <td>3.126327</td>\n      <td>0.00000</td>\n      <td>NaN</td>\n      <td>1.150487</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.165882</td>\n    </tr>\n    <tr>\n      <th>cluster_1</th>\n      <td>3.126327</td>\n      <td>NaN</td>\n      <td>1.87815</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>6.260990</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_2</th>\n      <td>0.000000</td>\n      <td>1.878150</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>cluster_3</th>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.656099</td>\n      <td>1.257027</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_4</th>\n      <td>1.150487</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_5</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.656099</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_6</th>\n      <td>NaN</td>\n      <td>6.260990</td>\n      <td>NaN</td>\n      <td>1.257027</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>cluster_7</th>\n      <td>0.165882</td>\n      <td>NaN</td>\n      <td>0.00000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = esM.componentModelingDict[\"TransmissionModel\"].capacityVariablesOptimum\n",
    "df.loc['Pipelines (biogas)'] + df.loc['Pipelines (hydrogen)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot installed transmission capacities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transFilePath = os.path.join(cwd, 'InputData', 'SpatialData', 'ShapeFiles', 'AClines.shp')\n",
    "\n",
    "fig, ax = fn.plotLocations(locFilePath, indexColumn='index')\n",
    "fig, ax = fn.plotTransmission(esM, 'AC cables', transFilePath, loc0='bus0', loc1='bus1', fig=fig, ax=ax)\n",
    "save_figure(fig, 'Installed_ac_cables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transFilePath = os.path.join(cwd, 'InputData', 'SpatialData', 'ShapeFiles', 'DClines.shp')\n",
    "\n",
    "fig, ax = fn.plotLocations(locFilePath, indexColumn='index')\n",
    "fig, ax = fn.plotTransmission(esM, 'DC cables', transFilePath, loc0='cluster0', loc1='cluster1', fig=fig, ax=ax)\n",
    "save_figure(fig, 'Installed_dc_cables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transFilePath = os.path.join(cwd, 'InputData', 'SpatialData', 'ShapeFiles', 'transmissionPipeline.shp')\n",
    "\n",
    "fig, ax = fn.plotLocations(locFilePath, indexColumn='index')\n",
    "fig, ax = fn.plotTransmission(esM, 'Pipelines (hydrogen)', transFilePath, loc0='loc1', loc1='loc2',\n",
    "                              fig=fig, ax=ax)\n",
    "save_figure(fig, 'Installed_hydro_pipelines')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transFilePath = os.path.join(cwd, 'InputData', 'SpatialData', 'ShapeFiles', 'transmissionPipeline.shp')\n",
    "\n",
    "fig, ax = fn.plotLocations(locFilePath, indexColumn='index')\n",
    "fig, ax = fn.plotTransmission(esM, 'Pipelines (biogas)', transFilePath, loc0='loc1', loc1='loc2',\n",
    "                              fig=fig, ax=ax)\n",
    "save_figure(fig, 'Installed_biogas_pipelines')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Postprocessing: Determine robust pipeline design"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "# Import module expansion \"robustPipelineSizing\"\n",
    "from FINE.expansionModules import robustPipelineSizing"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "   cluster_3  cluster_6  cluster_7  cluster_1  cluster_5  cluster_4  \\\n0    5.00974 -62.675211   1.382901  10.475135   5.469676   9.591229   \n1    5.00974 -62.675211   1.382901  10.475135   5.469676   9.591229   \n2    5.00974 -62.675211   1.382901  10.475135   5.469676   9.591229   \n3    5.00974 -62.675211   1.382901  10.475135   5.469676   9.591229   \n4    5.00974 -62.675211   1.382901  10.475135   5.469676   9.591229   \n\n   cluster_0  cluster_2  \n0  15.089016  15.657514  \n1  15.089016  15.657514  \n2  15.089016  15.657514  \n3  15.089016  15.657514  \n4  15.089016  15.657514  ",
      "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>cluster_3</th>\n      <th>cluster_6</th>\n      <th>cluster_7</th>\n      <th>cluster_1</th>\n      <th>cluster_5</th>\n      <th>cluster_4</th>\n      <th>cluster_0</th>\n      <th>cluster_2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n      <td>1.382901</td>\n      <td>10.475135</td>\n      <td>5.469676</td>\n      <td>9.591229</td>\n      <td>15.089016</td>\n      <td>15.657514</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n      <td>1.382901</td>\n      <td>10.475135</td>\n      <td>5.469676</td>\n      <td>9.591229</td>\n      <td>15.089016</td>\n      <td>15.657514</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n      <td>1.382901</td>\n      <td>10.475135</td>\n      <td>5.469676</td>\n      <td>9.591229</td>\n      <td>15.089016</td>\n      <td>15.657514</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n      <td>1.382901</td>\n      <td>10.475135</td>\n      <td>5.469676</td>\n      <td>9.591229</td>\n      <td>15.089016</td>\n      <td>15.657514</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n      <td>1.382901</td>\n      <td>10.475135</td>\n      <td>5.469676</td>\n      <td>9.591229</td>\n      <td>15.089016</td>\n      <td>15.657514</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Option to get the injection and withdrawal rates for the pipeline sizing (in kg/s)\n",
    "rates = robustPipelineSizing.getInjectionWithdrawalRates(componentName='Pipelines (hydrogen)', esM=esM)  # in GWh\n",
    "# Convert GWh to kg/s: GWh * (kWh/GWh) * (kg/kWh) * (1/ timestepLengthInSeconds) with timestepLengthInSeconds\n",
    "# being 3600 seconds for the present example\n",
    "rates = rates * (10 ** 6) * (1 / 33.32) * (1 / 3600)\n",
    "rates.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "   cluster_0  cluster_1  cluster_2  cluster_4  cluster_5  cluster_7  \\\n0  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n1  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n2  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n3  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n4  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n\n   cluster_3  cluster_6  \n0    5.00974 -62.675211  \n1    5.00974 -62.675211  \n2    5.00974 -62.675211  \n3    5.00974 -62.675211  \n4    5.00974 -62.675211  ",
      "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>cluster_0</th>\n      <th>cluster_1</th>\n      <th>cluster_2</th>\n      <th>cluster_4</th>\n      <th>cluster_5</th>\n      <th>cluster_7</th>\n      <th>cluster_3</th>\n      <th>cluster_6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Option to get the injection and withdrawal rates for the pipeline sizing (in kg/s)\n",
    "op = esM.componentModelingDict[esM.componentNames['Pipelines (hydrogen)']].getOptimalValues('operationVariablesOptimum')['values'].loc['Pipelines (hydrogen)']\n",
    "rates = robustPipelineSizing.getInjectionWithdrawalRates(operationVariablesOptimumData=op)  # in GWh\n",
    "# Convert GWh to kg/s: GWh * (kWh/GWh) * (kg/kWh) * (1/ timestepLengthInSeconds) with timestepLengthInSeconds\n",
    "# being 3600 seconds for the present example\n",
    "rates = rates * (10 ** 6) * (1 / 33.32) * (1 / 3600)\n",
    "rates.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "   cluster_0  cluster_1  cluster_2  cluster_4  cluster_5  cluster_7  \\\n0  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n1  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n2  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n3  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n4  15.089016  10.475135  15.657514   9.591229   5.469676   1.382901   \n\n   cluster_3  cluster_6  \n0    5.00974 -62.675211  \n1    5.00974 -62.675211  \n2    5.00974 -62.675211  \n3    5.00974 -62.675211  \n4    5.00974 -62.675211  ",
      "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>cluster_0</th>\n      <th>cluster_1</th>\n      <th>cluster_2</th>\n      <th>cluster_4</th>\n      <th>cluster_5</th>\n      <th>cluster_7</th>\n      <th>cluster_3</th>\n      <th>cluster_6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>15.089016</td>\n      <td>10.475135</td>\n      <td>15.657514</td>\n      <td>9.591229</td>\n      <td>5.469676</td>\n      <td>1.382901</td>\n      <td>5.00974</td>\n      <td>-62.675211</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine unique withdrawal and injection scenarios to save computation time\n",
    "rates = rates.drop_duplicates()\n",
    "rates.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "(cluster_0, cluster_1)    370000.0\n(cluster_0, cluster_2)    336000.0\n(cluster_0, cluster_4)    308000.0\n(cluster_0, cluster_5)    501000.0\n(cluster_0, cluster_7)    196000.0\ndtype: float64"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the lengths of the pipeline (in m)\n",
    "lengths = robustPipelineSizing.getNetworkLengthsFromESM('Pipelines (hydrogen)', esM)\n",
    "lengths = lengths * 1e3\n",
    "lengths.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "# Specify minimum and maximum pressure levels for all injection and withdrawal nodes (in bar)\n",
    "p_min_nodes = {'cluster_5': 60, 'cluster_3': 50, 'cluster_7': 50, 'cluster_1': 60, 'cluster_6': 50,\n",
    "               'cluster_4': 50, 'cluster_0': 50, 'cluster_2': 50}\n",
    "\n",
    "p_max_nodes = {'cluster_5': 100, 'cluster_3': 100, 'cluster_7': 100, 'cluster_1': 90, 'cluster_6': 100,\n",
    "               'cluster_4': 100, 'cluster_0': 100, 'cluster_2': 100}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "# Specify the investment cost of the available diameter classes in €/m\n",
    "\n",
    "# For single pipes\n",
    "dic_diameter_costs = {0.1063: 37.51, 0.1307: 38.45, 0.1593: 39.64, 0.2065: 42.12,\n",
    "                      0.2588: 45.26, 0.3063: 48.69, 0.3356: 51.07, 0.3844: 55.24,\n",
    "                      0.432: 59.86, 0.4796: 64.98, 0.527: 70.56, 0.578: 76.61,\n",
    "                      0.625: 82.99, 0.671: 89.95, 0.722: 97.38, 0.7686: 105.28,\n",
    "                      0.814: 113.63, 0.864: 122.28, 0.915: 131.56, 0.96: 141.3,\n",
    "                      1.011: 151.5, 1.058: 162.17, 1.104: 173.08, 1.155: 184.67,\n",
    "                      1.249: 209.24, 1.342: 235.4, 1.444: 263.66, 1.536: 293.78}\n",
    "\n",
    "# For parallel pipes\n",
    "dic_candidateMergedDiam_costs = {1.342: 235.4, 1.444: 263.66, 1.536: 293.78}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [],
   "source": [
    "# Choose if a robust pipeline desin should be determined or the design should be optimized based on the\n",
    "# given injection and withdrawal rates only\n",
    "robust = True"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Determine design for simple network structure"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating graph with respect to given distances\n",
      "Original Network Graph:\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating a Steiner treee\n",
      "Steiner tree:\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute robust scenario set for tree network (based on 56 node combinations). Threads: 7\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"Cannot treat the scalar component 'Nodes' as an indexed component\"",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mRemoteTraceback\u001B[0m                           Traceback (most recent call last)",
      "\u001B[1;31mRemoteTraceback\u001B[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\multiprocessing\\pool.py\", line 121, in worker\n    result = (True, func(*args, **kwds))\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\expansionModules\\robustPipelineSizing.py\", line 507, in _generateRobustScenarios\n    return startNode_endNode, computeSingleSpecialScenario(startNode=startNode, endNode=endNode, **kwargs)\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\expansionModules\\robustPipelineSizing.py\", line 673, in computeSingleSpecialScenario\n    model.Nodes = py.Set(initialize=graph.nodes)\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\pyomo\\core\\base\\block.py\", line 541, in __setattr__\n    self.add_component(name, val)\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\pyomo\\core\\base\\block.py\", line 1086, in add_component\n    val.construct(data)\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\pyomo\\core\\base\\disable_methods.py\", line 115, in construct\n    return base.construct(self, data)\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\pyomo\\core\\base\\set.py\", line 1990, in construct\n    IndexedComponent.__getitem__(self, index)\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 511, in __getitem__\n    validated_index = self._validate_index(index)\n  File \"C:\\Users\\Paul\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 732, in _validate_index\n    \"as an indexed component\" % ( self.name, ))\nKeyError: \"Cannot treat the scalar component 'Nodes' as an indexed component\"\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_57116/3803589960.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      6\u001B[0m     \u001B[0mdistances\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mlengths\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdic_node_minPress\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mp_min_nodes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdic_node_maxPress\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mp_max_nodes\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      7\u001B[0m     \u001B[0mdic_diameter_costs\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mdic_diameter_costs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdic_candidateMergedDiam_costs\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mdic_candidateMergedDiam_costs\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 8\u001B[1;33m     threads=7)\n\u001B[0m\u001B[0;32m      9\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\expansionModules\\robustPipelineSizing.py\u001B[0m in \u001B[0;36mdetermineDiscretePipelineDesign\u001B[1;34m(robust, injectionWithdrawalRates, distances, dic_node_minPress, dic_node_maxPress, dic_diameter_costs, dic_candidateMergedDiam_costs, gdfEdges, regColumn1, regColumn2, solver, opexForDiameters, economicLifetime, interestRate, costUnit, ir, rho_n, T_m, T_n, p_n, Z_n, originalFluidFlows, nDigits, verbose, threads)\u001B[0m\n\u001B[0;32m   2106\u001B[0m     \u001B[0mtimeStart\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtime\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtime\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   2107\u001B[0m     dic_nodePair_flows, entries, exits = generateRobustScenarios(injectionWithdrawalRates, graph, distances,\n\u001B[1;32m-> 2108\u001B[1;33m         dic_node_minPress, dic_node_maxPress, solver=solver, threads=threads, verbose=verbose)\n\u001B[0m\u001B[0;32m   2109\u001B[0m     \u001B[0mutils\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0moutput\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"Number of robust scenarios: \"\u001B[0m \u001B[1;33m+\u001B[0m \u001B[0mstr\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdic_nodePair_flows\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mkeys\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m,\u001B[0m \u001B[0mverbose\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m0\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   2110\u001B[0m     \u001B[0mutils\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0moutput\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"\\t\\t(%.4f\"\u001B[0m \u001B[1;33m%\u001B[0m \u001B[1;33m(\u001B[0m\u001B[0mtime\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtime\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m-\u001B[0m \u001B[0mtimeStart\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m+\u001B[0m \u001B[1;34m\" sec)\\n\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mverbose\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m0\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\.conda\\envs\\gas-wasserstoff3\\lib\\site-packages\\FINE\\expansionModules\\robustPipelineSizing.py\u001B[0m in \u001B[0;36mgenerateRobustScenarios\u001B[1;34m(injectionWithdrawalRates, graph, distances, dic_node_minPress, dic_node_maxPress, solver, threads, verbose)\u001B[0m\n\u001B[0;32m    595\u001B[0m                                                 \u001B[0mentries\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mentries\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mexits\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mexits\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdic_nodes_MinCapacity\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mdic_nodes_MinCapacity\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    596\u001B[0m                                                 dic_nodes_MaxCapacity=dic_nodes_MaxCapacity, solver=solver),\n\u001B[1;32m--> 597\u001B[1;33m                                nodes), 1):\n\u001B[0m\u001B[0;32m    598\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mverbose\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;36m0\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    599\u001B[0m             \u001B[0msys\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstderr\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwrite\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m'\\rPercentage simulated: {:d}%'\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mformat\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mi\u001B[0m \u001B[1;33m/\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnodes\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m*\u001B[0m \u001B[1;36m100\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\.conda\\envs\\gas-wasserstoff3\\lib\\multiprocessing\\pool.py\u001B[0m in \u001B[0;36mnext\u001B[1;34m(self, timeout)\u001B[0m\n\u001B[0;32m    746\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0msuccess\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    747\u001B[0m             \u001B[1;32mreturn\u001B[0m \u001B[0mvalue\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 748\u001B[1;33m         \u001B[1;32mraise\u001B[0m \u001B[0mvalue\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    749\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    750\u001B[0m     \u001B[0m__next__\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnext\u001B[0m                    \u001B[1;31m# XXX\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: \"Cannot treat the scalar component 'Nodes' as an indexed component\""
     ]
    }
   ],
   "source": [
    "# Compute with 7 threads and simple network structure\n",
    "\n",
    "dic_arc_optimalDiameters, dic_scen_PressLevels, dic_scen_MaxViolPress, dic_timeStep_PressLevels, \\\n",
    "dic_timeStep_MaxViolPress, _ = robustPipelineSizing.determineDiscretePipelineDesign(\n",
    "    robust=robust, injectionWithdrawalRates=rates,\n",
    "    distances=lengths, dic_node_minPress=p_min_nodes, dic_node_maxPress=p_max_nodes,\n",
    "    dic_diameter_costs=dic_diameter_costs, dic_candidateMergedDiam_costs=dic_candidateMergedDiam_costs,\n",
    "    threads=7)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Compute with 2 threads and simple network structure\n",
    "\n",
    "dic_arc_optimalDiameters, dic_scen_PressLevels, dic_scen_MaxViolPress, dic_timeStep_PressLevels,\n",
    "dic_timeStep_MaxViolPress, _ = robustPipelineSizing.determineDiscretePipelineDesign(\n",
    "    robust=robust, injectionWithdrawalRates=rates,\n",
    "    distances=lengths, dic_node_minPress=p_min_nodes, dic_node_maxPress=p_max_nodes,\n",
    "    dic_diameter_costs=dic_diameter_costs, dic_candidateMergedDiam_costs=dic_candidateMergedDiam_costs,\n",
    "    threads=2)\n",
    "\n",
    "print(\"Finished Discrete Pipeline Optimization\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Determine design for more complex network structure"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Redfine minimum and maximum pressure levels to reduce robust scenario computation time\n",
    "p_min_nodes = {'cluster_5': 50, 'cluster_3': 50, 'cluster_7': 50, 'cluster_1': 50, 'cluster_6': 50,\n",
    "               'cluster_4': 50, 'cluster_0': 50, 'cluster_2': 50}\n",
    "\n",
    "p_max_nodes = {'cluster_5': 100, 'cluster_3': 100, 'cluster_7': 100, 'cluster_1': 100, 'cluster_6': 100,\n",
    "               'cluster_4': 100, 'cluster_0': 100, 'cluster_2': 100}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Get more complex network structure\n",
    "regColumn1 = 'loc1'\n",
    "regColumn2 = 'loc2'\n",
    "\n",
    "dic_node_minPress = p_min_nodes\n",
    "dic_node_maxPress = p_max_nodes\n",
    "\n",
    "maxPipeLength = 35 * 1e3\n",
    "minPipeLength = 1 * 1e3\n",
    "\n",
    "distances_new, dic_node_minPress_new, dic_node_maxPress_new, gdfNodes, gdfEdges = robustPipelineSizing.getRefinedShapeFile(transFilePath, regColumn1, regColumn2, dic_node_minPress,\n",
    "                                             dic_node_maxPress, minPipeLength, maxPipeLength)\n",
    "\n",
    "fig, ax = fn.plotLocations(locFilePath, indexColumn='index')\n",
    "\n",
    "gdfEdges.plot(ax=ax, color='grey')\n",
    "gdfNodes.plot(ax=ax, color='red', markersize=2)\n",
    "ax.axis('off')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Determine optimal pipeline design with seven threads\n",
    "dic_arc_optimalDiameters, dic_scen_PressLevels, dic_scen_MaxViolPress, dic_timeStep_PressLevels, dic_timeStep_MaxViolPress, gdfEdges = robustPipelineSizing.determineDiscretePipelineDesign(\n",
    "    robust=robust, injectionWithdrawalRates=rates,\n",
    "    distances=distances_new, dic_node_minPress=dic_node_minPress_new, dic_node_maxPress=dic_node_maxPress_new,\n",
    "    dic_diameter_costs=dic_diameter_costs, dic_candidateMergedDiam_costs=dic_candidateMergedDiam_costs,\n",
    "    gdfEdges=gdfEdges, regColumn1='nodeIn', regColumn2='nodeOut', threads=7, solver='gurobi')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plot scenario output"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Get regions shapefile as geopandas GeoDataFrame\n",
    "gdf_regions = gpd.read_file(locFilePath)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Visualize pipeline diameters\n",
    "fig, ax = robustPipelineSizing.plotOptimizedNetwork(gdfEdges, gdf_regions=gdf_regions, figsize=(5, 5),\n",
    "                                                    line_scaling=0.8)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Get a minimum and maximum pressure scenario\n",
    "dic_timeStep_PressLevels = pd.DataFrame.from_dict(dic_scen_PressLevels)\n",
    "\n",
    "scen_min = dic_timeStep_PressLevels.mean().min()\n",
    "scen_min = dic_timeStep_PressLevels.loc[:, dic_timeStep_PressLevels.mean() == scen_min].iloc[:, 0]\n",
    "scen_max = dic_timeStep_PressLevels.mean().max()\n",
    "scen_max = dic_timeStep_PressLevels.loc[:, dic_timeStep_PressLevels.mean() == scen_max].iloc[:, 0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fig, ax = robustPipelineSizing.plotOptimizedNetwork(gdfEdges, gdf_regions=gdf_regions, figsize=(5, 5),\n",
    "                                                    line_scaling=0.9, pressureLevels=scen_min)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fig, ax = robustPipelineSizing.plotOptimizedNetwork(gdfEdges, gdf_regions=gdf_regions, figsize=(5, 5),\n",
    "                                                    line_scaling=0.9, pressureLevels=scen_max)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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