run_me.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "<h1> 5XWA0 Power System Analysis and Optimization</h1>\n",
    "<h2> Assignment 4: Multi-period optimal power flow with energy storage </h2> <br>\n",
    "<h3> Group number: 11</h3>\n",
    "<h3> Student names: Filip Forro & N. K. Panda</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>INSTRUCTIONS</h4>\n",
    "\n",
    "The first two code blocks are already filled-in for you. You do not need to make any changes there. \n",
    "\n",
    "Your code, according to the requirements of the assignment, needs to be completed in the third block. \n",
    "\n",
    "After that point, feel free to add as many blocks as you need (e.g., to solve different instances of the model, extract or plot results). Make sure that you sufficiently document your code. \n",
    "\n",
    "<b>IMPORTANT: The report or the notebook should demonstrate how you have achieved your results.</b>\n",
    "\n",
    "In case you need to import additional packages (e.g., for plotting) it is advisable that you import them in the first block and make sure that it is the one that runs first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "from pyomo.environ import *\n",
    "from pyomo.opt import SolverStatus, TerminationCondition\n",
    "import pandas, numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "#This is a custom function that writes all the decision variables to an excel file.\n",
    "from Helper_Functions.exportDecisionVariables import exportDecisionVariables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def readInputFile(filename):\n",
    "    \n",
    "    # This method loads all the data from the .xlsx file. You do not need to modify anything here.\n",
    "    \n",
    "    NetworkData = pandas.read_excel(filename, sheet_name= 'NetworkData', index_col=0)\n",
    "    SystemDemand= pandas.read_excel(filename, sheet_name = 'SystemDemand', index_col= 0)\n",
    "    PVGeneration = pandas.read_excel(filename, sheet_name='PVGeneration', index_col=0)\n",
    "    WindGeneration = pandas.read_excel(filename, sheet_name='WindGeneration', index_col=0) \n",
    "    LoadData = pandas.read_excel(filename, sheet_name = 'Loads', index_col= 0)\n",
    "    PVData = pandas.read_excel(filename, sheet_name='PVParks', index_col=0)\n",
    "    WPData = pandas.read_excel(filename, sheet_name='WindParks', index_col=0)\n",
    "    StorageData = pandas.read_excel(filename, sheet_name= 'StorageSystems', index_col=0)\n",
    "    UnitData = pandas.read_excel(filename, sheet_name = 'Generators', index_col= 0)\n",
    "    GeneratorStepSizeData = pandas.read_excel(filename, sheet_name = 'GeneratorStepSize', index_col= 0)\n",
    "    GeneratorStepCostData = pandas.read_excel(filename, sheet_name = 'GeneratorStepCost', index_col= 0)\n",
    "    \n",
    "    # We could return multiple items but it is preferable to return a structure, in this case a dictionary \n",
    "    return {'NetworkData':NetworkData, 'SystemDemand':SystemDemand, 'PVGeneration':PVGeneration, 'WindGeneration':WindGeneration,\n",
    "            'LoadData':LoadData, 'PVData':PVData, 'WPData':WPData,'StorageData':StorageData,'UnitData':UnitData,\n",
    "            'GeneratorStepSizeData':GeneratorStepSizeData, 'GeneratorStepCostData':GeneratorStepCostData}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
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   "outputs": [],
   "source": [
    "def optimizationModel(inputData, modelType):\n",
    "    \n",
    "    # This method receives as an input the Data (the dictionary returned by readInputFile) and returns an optimization model\n",
    "    # according to the value of modelType argument. \n",
    "    # Be careful: there is not a solve statement in this function! This means you should solve the model that is returned\n",
    "    # after the method has been called!\n",
    "    \n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    #Unpack the data from the dictionary\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    NetworkData = inputData['NetworkData']\n",
    "    SystemDemand = inputData['SystemDemand']\n",
    "    PVGeneration = inputData['PVGeneration']\n",
    "    WindGeneration = inputData['WindGeneration']\n",
    "    LoadData = inputData['LoadData']\n",
    "    PVData = inputData['PVData']\n",
    "    WPData = inputData['WPData']\n",
    "    StorageData = inputData['StorageData']\n",
    "    UnitData = inputData['UnitData']\n",
    "    GeneratorStepSizeData = inputData['GeneratorStepSizeData']\n",
    "    GeneratorStepCostData = inputData['GeneratorStepCostData']   \n",
    "    \n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    #Define the Model\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    \n",
    "    model = ConcreteModel()\n",
    "    \n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    #Define Sets\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    \n",
    "    model.T = Set(ordered = True, initialize = SystemDemand.index) #Set for time steps\n",
    "    \n",
    "    model.I = Set(ordered = True, initialize = UnitData.index) #Set for generators\n",
    "    model.F = Set(ordered = True, initialize = GeneratorStepSizeData.columns) #Set for generator cost blocks\n",
    "\n",
    "    model.S = Set(ordered = True, initialize = StorageData.index) #Set for storage\n",
    "\n",
    "    model.J = Set(ordered = True, initialize = LoadData.index) #Set for loads\n",
    "\n",
    "    model.L = Set(ordered=True, initialize=NetworkData.index)  # Set for lines\n",
    "    Buses = list(set(list(NetworkData.FROM.unique()) + list(NetworkData.TO.unique())))  # extract buses from NetworkData\n",
    "    model.N = Set(ordered=True, initialize=Buses) #Set for buses. Be careful when interpreting results. This is not a sorted list.\n",
    "\n",
    "    model.K = Set(ordered=True, initialize=PVData.index) # Set for PV\n",
    "    model.Q = Set(ordered=True, initialize=WPData.index) # Set for Wind Park\n",
    "    \n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    #Define Parameters\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    \n",
    "    #Question I.1: Define the technical and economic parameters of the generators\n",
    "        #The parameters that you have to define should be named: Pmin, Pmax, RU, RD, SUC, SDC, Pini, uini,C, B. \n",
    "        #IMPORTANT: you need to identify the correct indices! \n",
    "    model.Pmin = Param(model.I, within = NonNegativeReals, mutable=True)\n",
    "    model.Pmax = Param(model.I, within = NonNegativeReals, mutable=True)\n",
    "    model.RU = Param(model.I, within = NonNegativeReals, mutable=True)\n",
    "    model.RD = Param(model.I, within = NonNegativeReals, mutable=True)\n",
    "    model.SUC = Param(model.I, within = NonNegativeReals, mutable=True)\n",
    "    model.SDC = Param(model.I, within = NonNegativeReals, mutable=True)\n",
    "    model.Pini = Param(model.I, within = NonNegativeReals, mutable=True)\n",
    "    model.uini = Param(model.I, within = Binary, mutable=True)\n",
    "    model.C = Param(model.I, model.F, within = NonNegativeReals, mutable=True)\n",
    "    model.B = Param(model.I, model.F, within = NonNegativeReals, mutable=True)\n",
    "\n",
    "    #PV parks\n",
    "    model.PV = Param(model.K, model.T, within=NonNegativeReals, mutable=True) #Production of PV system k\n",
    "    \n",
    "    #Wind parks\n",
    "    model.W = Param(model.Q, model.T, within=NonNegativeReals, mutable=True) #Production of Wind Park q\n",
    "\n",
    "    #Load\n",
    "    model.D = Param(model.J, model.T, within=NonNegativeReals, mutable = True) #Consumption of load j\n",
    "\n",
    "    #Energy storage system\n",
    "    model.ESS_Pmax = Param(model.S, within=NonNegativeReals, mutable=True)\n",
    "    model.ESS_SOEmax = Param(model.S, within=NonNegativeReals, mutable=True)\n",
    "    model.ESS_SOEini = Param(model.S, within=NonNegativeReals, mutable=True)\n",
    "    model.ESS_Eff = Param(model.S, within=NonNegativeReals, mutable=True)\n",
    "\n",
    "    # Transmission lines\n",
    "    model.X = Param(model.L, within=NonNegativeReals, mutable=True)\n",
    "    model.P_line_max = Param(model.L, within=NonNegativeReals, mutable=True)\n",
    "\n",
    "    # Power flow parameters\n",
    "    model.Bmat = Param(model.L, model.N, within=Reals, mutable=True)\n",
    "    \n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    #Initialize Parameters\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "\n",
    "    #Question I.2: Give values to the parameters of the generators\n",
    "    for i in model.I:\n",
    "        model.Pmin[i] = UnitData.loc[i,'Pmin']\n",
    "        model.Pmax[i] = UnitData.loc[i,'Pmax']\n",
    "        model.RU[i] = UnitData.loc[i,'RU']\n",
    "        model.RD[i] = UnitData.loc[i,'RD']\n",
    "        model.SUC[i] = UnitData.loc[i,'SUC']\n",
    "        model.SDC[i] = UnitData.loc[i,'SDC']\n",
    "        model.Pini[i] = UnitData.loc[i,'Pini']\n",
    "        model.uini[i] = UnitData.loc[i,'uini']\n",
    "        for col in GeneratorStepCostData.columns:\n",
    "            model.C[i,col] = GeneratorStepCostData.loc[i,col]\n",
    "            model.B[i,col] = GeneratorStepSizeData.loc[i,col]\n",
    "        \n",
    "    #Question I.2: Allocate the total system demand to buses\n",
    "    for j in model.J:\n",
    "        for t in model.T:\n",
    "            #print(j)\n",
    "            #print(LoadData[LoadData['Location']==d]['Percentage'].item())\n",
    "            #model.D[d,t] = SystemDemand.loc[t,'SystemDemand']*(LoadData[LoadData['Location']==d]['Percentage'].item())\n",
    "            model.D[j,t] = SystemDemand.loc[t,'SystemDemand']*LoadData.loc[j,'Percentage']\n",
    "            #print(SystemDemand.loc[t,'SystemDemand'])\n",
    "            #print(LoadData.loc[j,'Percentage'])\n",
    "            #print(model.D[j,t])\n",
    "            \n",
    "    #Give values to ESS parameters\n",
    "    for s in model.S:\n",
    "        model.ESS_Pmax[s] = StorageData.loc[s,'Power']\n",
    "        model.ESS_SOEmax[s] = StorageData.loc[s,'Energy']\n",
    "        model.ESS_SOEini[s] = StorageData.loc[s,'SOEini']\n",
    "        model.ESS_Eff[s] = StorageData.loc[s,'Eff']\n",
    "\n",
    "    # Give values to line parameters\n",
    "    for l in model.L:\n",
    "        model.X[l] = NetworkData.loc[l, 'X']\n",
    "        model.P_line_max[l] = NetworkData.loc[l, 'Capacity']\n",
    "\n",
    "    # Construct B- matrix\n",
    "    for l in model.L:\n",
    "        for n in model.N:\n",
    "            if NetworkData.loc[l, 'FROM'] == n:\n",
    "                model.Bmat[l, n] = 1 / model.X[l]\n",
    "            elif NetworkData.loc[l, 'TO'] == n:\n",
    "                model.Bmat[l, n] = - 1 / model.X[l]\n",
    "            else:\n",
    "                model.Bmat[l, n] = 0\n",
    "    \n",
    "    #Allocate the total PV generation to buses\n",
    "    for k in model.K:\n",
    "        for t in model.T:\n",
    "            model.PV[k,t] = PVGeneration.loc[t, 'PVGeneration']*PVData.loc[k, 'Percentage']\n",
    "            \n",
    "    #Allocate the total Wind generation to buses\n",
    "    for q in model.Q:\n",
    "        for t in model.T:\n",
    "            model.W[q,t] = WindGeneration.loc[t, 'WindGeneration']*WPData.loc[q, 'Percentage']        \n",
    "    \n",
    "\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    #Define the Decision Variables\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    \n",
    "    #Generators\n",
    "    model.P = Var(model.I, model.T, within=NonNegativeReals)\n",
    "    model.b = Var(model.I, model.F, model.T, within=NonNegativeReals)\n",
    "    model.u = Var(model.I, model.T, within=Binary)\n",
    "    model.CSU = Var(model.I, model.T, within=NonNegativeReals)\n",
    "    model.CSD = Var(model.I, model.T, within=NonNegativeReals)\n",
    "    \n",
    "    #Network \n",
    "    model.Flow = Var(model.L, model.T, within=Reals) # Flow through transmission line\n",
    "    model.theta = Var(model.N, model.T, within=Reals) # Bus angle\n",
    "    \n",
    "    #Question I.3: Define the decision variables related to energy storage systems\n",
    "        #The variables that you have to define should be named: SOE, Pch, Pdis, u_ess \n",
    "        #IMPORTANT: you need to identify the correct indices and the domain of the variables! \n",
    "    #ESS\n",
    "    model.SOE = Var(model.S, model.T, within=NonNegativeReals)\n",
    "    model.Pch = Var(model.S, model.T, within=NonNegativeReals)\n",
    "    model.Pdis = Var(model.S, model.T, within=NonNegativeReals)\n",
    "    model.u_ess = Var(model.S, model.T, within=Binary)\n",
    "\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    #Define Constraints\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    # Question I.4: Complete the definition of constraints RampDownConstraint(), ShutDownCost(),ESS_SOEupdate(), Balance()\n",
    "    \n",
    "    def ObjectiveCost(model):   #Eq. (1)\n",
    "        return sum(sum(sum(model.C[i,f]*model.b[i,f,t] for f in model.F) + model.CSU[i,t] + model.CSD[i,t] for i in model.I) for t in model.T)\n",
    "\n",
    "    def PowerDecomposition1(model, i, t):  #Eq. (2)\n",
    "        return model.P[i,t] == sum(model.b[i,f,t] for f in model.F)\n",
    "\n",
    "    def PowerDecomposition2(model, i, f, t):  #Eq. (3)\n",
    "        return model.b[i,f,t] <= model.B[i,f]\n",
    "\n",
    "    def PowerMin(model, i , t): #Eq. (4)\n",
    "        return model.P[i,t] >= model.Pmin[i]* model.u[i,t]\n",
    "\n",
    "    def PowerMax(model, i, t): #Eq. (4)\n",
    "        return model.P[i,t] <= model.Pmax[i]* model.u[i,t]\n",
    "\n",
    "    def RampUpConstraint(model, i, t): #Eq. (5) and Eq. (6) \n",
    "\n",
    "        if model.T.ord(t) == 1:\n",
    "            return model.P[i,t] - model.Pini[i] <= 60*model.RU[i]\n",
    "\n",
    "        if model.T.ord(t) > 1: #model.T.prev(t) gives us t-1!\n",
    "            return model.P[i,t] - model.P[i,model.T.prev(t)] <= 60*model.RU[i]\n",
    "\n",
    "    def RampDownConstraint(model, i, t):  #Eq. (7) and Eq. (8)\n",
    "        \n",
    "        if model.T.ord(t) == 1:\n",
    "            return model.Pini[i] - model.P[i,t] <= 60*model.RD[i]\n",
    "        \n",
    "        if model.T.ord(t) > 1:\n",
    "            return model.P[i,model.T.prev(t)] - model.P[i,t] <= 60*model.RD[i]\n",
    "\n",
    "    def StartUpCost(model, i, t): #Eq. (9) and Eq. (10)\n",
    "        if model.T.ord(t) == 1:\n",
    "            return model.CSU[i, t] >= model.SUC[i] * (model.u[i, t] - model.uini[i])\n",
    "\n",
    "        if model.T.ord(t) > 1:\n",
    "            return model.CSU[i, t] >= model.SUC[i] * (model.u[i, t] - model.u[i, model.T.prev(t)])\n",
    "\n",
    "    def ShutDownCost(model, i, t): #Eq. (11) and Eq. (12)\n",
    "        if model.T.ord(t) == 1:\n",
    "            return model.CSD[i,t] >= model.SDC[i] * (model.uini[i] - model.u[i,t])\n",
    "        if model.T.ord(t) > 1:\n",
    "            return model.CSD[i,t] >= model.SDC[i] * (model.u[i,model.T.prev(t)] - model.u[i,t])\n",
    "\n",
    "\n",
    "    def ESS_SOEupdate(model, s, t): #Eq. (13) and Eq. (14)\n",
    "\n",
    "        if model.T.ord(t) == 1:\n",
    "            return model.SOE[s,t] == model.ESS_SOEini[s] + model.ESS_Eff[s] * model.Pch[s,t] - model.Pdis[s,t]/model.ESS_Eff[s]\n",
    "\n",
    "        if model.T.ord(t) > 1:\n",
    "            return model.SOE[s,t] == model.SOE[s,model.T.prev(t)] + model.ESS_Eff[s] * model.Pch[s,t] - model.Pdis[s,t]/model.ESS_Eff[s]\n",
    "    print(model.T.ord(t))    \n",
    "    def ESS_SOElimit(model, s, t): #Eq. (15)\n",
    "        return model.SOE[s,t] <= model.ESS_SOEmax[s]\n",
    "    \n",
    "    def ESS_Charging(model, s, t): #Eq. (16)\n",
    "        return model.Pch[s,t] <= model.ESS_Pmax[s] * model.u_ess[s,t]\n",
    "\n",
    "    def ESS_Discharging(model, s, t): #Eq. (16)\n",
    "        return model.Pdis[s, t] <= model.ESS_Pmax[s] * (1-model.u_ess[s, t])\n",
    "\n",
    "    def angle_ini_rule(model, t): #Eq. (18)\n",
    "        return model.theta['Bus1', t] == 0\n",
    "\n",
    "    def flow_limit_rule1(model, l, t): #Eq. (19)\n",
    "        return -model.P_line_max[l] <= model.Flow[l,t]\n",
    "    \n",
    "    def flow_limit_rule2(model, l, t): #Eq. (19)\n",
    "        return model.Flow[l,t] <= model.P_line_max[l]\n",
    "\n",
    "    def line_flow_rule(model, l, n1, n2 ,t): #Eq. (20)\n",
    "        if (n1 == NetworkData.loc[l, 'FROM']) and (n2 == NetworkData.loc[l, 'TO']):\n",
    "            return model.Flow[l, t] == model.Bmat[l, n1] * (model.theta[n1, t] - model.theta[n2, t])\n",
    "        else:\n",
    "            return Constraint.Skip\n",
    "\n",
    "    def pf_rule(model, n, t): #Eq. (21)\n",
    "        return \\\n",
    "            sum(model.PV[k,t] for k in model.K if n==PVData.loc[k,'Location'])+\\\n",
    "            sum(model.W[q,t] for q in model.Q if n==WPData.loc[q,'Location'])+\\\n",
    "            sum(model.P[i, t] for i in model.I if n == UnitData.loc[i, 'Location']) + sum(model.Pdis[s, t] for s in model.S if n == StorageData.loc[s, 'Location'])+ sum(model.Flow[l, t] for l in model.L if n == NetworkData.loc[l, 'TO']) \\\n",
    "            == sum(model.D[j, t] for j in model.J if n == LoadData.loc[j, 'Location']) + sum(model.Pch[s, t] for s in model.S if n == StorageData.loc[s, 'Location']) + sum(model.Flow[l, t] for l in model.L if n == NetworkData.loc[l, 'FROM'])\n",
    "\n",
    "    def balance(model, t): #Eq. (21) alternative according to Assignment 3, Question VI.\n",
    "        return \\\n",
    "            sum(model.PV[k,t] for k in model.K)+\\\n",
    "            sum(model.W[q,t] for q in model.Q)+\\\n",
    "            sum(model.P[i,t] for i in model.I) + sum(model.Pdis[s,t] for s in model.S) \\\n",
    "            == sum(model.D[j,t] for j in model.J) + sum(model.Pch[s,t] for s in model.S)\n",
    "\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    #Add Constraints to the Model\n",
    "    #---------------------------------------------------------------------------------------------------------\n",
    "    \n",
    "    #There are constraints that are common to all the 4 Cases\n",
    "    model.ObjCost = Objective(rule=ObjectiveCost)\n",
    "    model.ConPowerDecomposition1 = Constraint(model.I, model.T, rule=PowerDecomposition1)\n",
    "    model.ConPowerDecomposition2 = Constraint(model.I, model.F, model.T, rule=PowerDecomposition2)\n",
    "    model.ConPowerMin = Constraint(model.I, model.T, rule=PowerMin)\n",
    "    model.ConPowerMax = Constraint(model.I, model.T, rule=PowerMax)\n",
    "    model.ConSUC = Constraint(model.I, model.T, rule=StartUpCost)\n",
    "    model.ConSDC = Constraint(model.I, model.T, rule=ShutDownCost)\n",
    "    model.ConSOEUpdate = Constraint(model.S, model.T, rule=ESS_SOEupdate)\n",
    "    model.ConCharging = Constraint(model.S, model.T, rule=ESS_Charging)\n",
    "    model.ConDischarging = Constraint(model.S, model.T, rule=ESS_Discharging)\n",
    "    model.ConSOElimit = Constraint(model.S, model.T, rule=ESS_SOElimit)\n",
    "    \n",
    "    #There are constraints specific to the case you are solving\n",
    "    \n",
    "    # Question I.4: Complete the conditions in order to add only the constraints that are relevant to the 4 Cases (Assignment 4, Question II.1)\n",
    "    \n",
    "    # NO network constarints/NO ramping constraints/NO transmission line limits\n",
    "    if  modelType == 'Case1':\n",
    "        model.Bal = Constraint(model.T, rule=balance)\n",
    "        \n",
    "    # YES network constraints/YES generator ramping contraints/YES transmission line limits\n",
    "    elif  modelType == 'Case2':\n",
    "        #generator ramping contraints           \n",
    "        model.ConRampUP = Constraint(model.I, model.T, rule=RampUpConstraint)\n",
    "        model.ConRampDWN = Constraint(model.I, model.T, rule=RampDownConstraint)\n",
    "                   \n",
    "        #transmission line limits\n",
    "        model.flowlim1 = Constraint(model.L, model.T, rule = flow_limit_rule1)\n",
    "        model.flowlim2 = Constraint(model.L, model.T, rule = flow_limit_rule2)\n",
    "        \n",
    "        #network constraints\n",
    "        model.angleini = Constraint(model.T, rule = angle_ini_rule)\n",
    "        model.lfrule = Constraint(model.L,model.N,model.N,model.T,rule=line_flow_rule)\n",
    "        model.pfrule = Constraint(model.N,model.T, rule = pf_rule)\n",
    "\n",
    "    # YES network constraints/YES generator ramping contraints/NO transmission line limits\n",
    "    elif  modelType == 'Case3':\n",
    "        #generator ramping contraints\n",
    "        model.ConRampUP = Constraint(model.I, model.T, rule=RampUpConstraint)\n",
    "        model.ConRampDWN = Constraint(model.I, model.T, rule=RampDownConstraint)\n",
    "        \n",
    "        #network constraints           \n",
    "        model.angleini = Constraint(model.T, rule = angle_ini_rule)\n",
    "        model.lfrule = Constraint(model.L,model.N,model.N,model.T,rule=line_flow_rule)\n",
    "        model.pfrule = Constraint(model.N,model.T, rule = pf_rule)\n",
    "    else:\n",
    "        print('This is not a valid model type!')\n",
    "        \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Test file</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Load1</td>\n",
       "      <td>Bus2</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Load2</td>\n",
       "      <td>Bus4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Location  Percentage\n",
       "Load1     Bus2         0.6\n",
       "Load2     Bus4         0.4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'Assignment3_ValidationSystem.xlsx'\n",
    "data = readInputFile(filename)\n",
    "data['LoadData']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "source": [
    "# Run the test case and export data to confirm correct implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "20117.4\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 't' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-26d501015512>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mObjCost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mexportDecisionVariables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'TestExportCase1.xlsx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 't' is not defined"
     ]
    }
   ],
   "source": [
    "model = optimizationModel(data, 'Case1')\n",
    "opt=SolverFactory('gurobi')\n",
    "opt.options[\"MIPGap\"] = 0.0\n",
    "results=opt.solve(model)\n",
    "#print(results)\n",
    "\n",
    "print(model.ObjCost().round(3))\n",
    "print(model.T.ord(t))\n",
    "\n",
    "\n",
    "exportDecisionVariables(model, 'TestExportCase1.xlsx')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33244.94\n"
     ]
    }
   ],
   "source": [
    "model = optimizationModel(data, 'Case2')\n",
    "results=opt.solve(model)\n",
    "#print(results)\n",
    "\n",
    "print(model.ObjCost().round(3))\n",
    "exportDecisionVariables(model, 'TestExportCase2.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32052.94\n"
     ]
    }
   ],
   "source": [
    "model = optimizationModel(data, 'Case3')\n",
    "results=opt.solve(model)\n",
    "#print(results)\n",
    "\n",
    "print(model.ObjCost().round(3))\n",
    "exportDecisionVariables(model, 'TestExportCase3.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Part II -- Basic simulations</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Question II.1 </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "filename = 'Input_Files\\Assignment3_InputData.xlsx'\n",
    "data = readInputFile(filename)\n",
    "# SET RES TO 0 FOR THE FIRST TASK\n",
    "data['PVGeneration'].loc[:] = 0 \n",
    "data['WindGeneration'].loc[:] = 0  \n",
    "data['StorageData']['Power'].loc[:] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2655566.701\n",
      "3406743.352\n",
      "2675324.913\n"
     ]
    }
   ],
   "source": [
    "#A - Report and compare the total operating cost of the system in the following cases. \n",
    "model1 = optimizationModel(data,'Case1')\n",
    "results1 = opt.solve(model1)\n",
    "print(model1.ObjCost().round(3))\n",
    "\n",
    "model2 = optimizationModel(data,'Case2')\n",
    "results2 = opt.solve(model2)\n",
    "print(model2.ObjCost().round(3))\n",
    "\n",
    "model3 = optimizationModel(data,'Case3')\n",
    "results3 = opt.solve(model3)\n",
    "print(model3.ObjCost().round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Total Power [MW]</th>\n",
       "      <th>Total Energy [MWd]</th>\n",
       "      <th>Total Cost [Euro/day]</th>\n",
       "      <th>Daily Commitment Cost [Euro]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>U100</th>\n",
       "      <td>2792.6</td>\n",
       "      <td>2792.6</td>\n",
       "      <td>4.795346e+05</td>\n",
       "      <td>15432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>751.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U155</th>\n",
       "      <td>14880.0</td>\n",
       "      <td>14880.0</td>\n",
       "      <td>3.107926e+05</td>\n",
       "      <td>11470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U197</th>\n",
       "      <td>6175.4</td>\n",
       "      <td>6175.4</td>\n",
       "      <td>1.076367e+06</td>\n",
       "      <td>20156.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U350</th>\n",
       "      <td>8318.4</td>\n",
       "      <td>8318.4</td>\n",
       "      <td>1.783953e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U400</th>\n",
       "      <td>19200.0</td>\n",
       "      <td>19200.0</td>\n",
       "      <td>3.964301e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U50</th>\n",
       "      <td>7200.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U76</th>\n",
       "      <td>6333.6</td>\n",
       "      <td>6333.6</td>\n",
       "      <td>1.661906e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Total Power [MW] Total Energy [MWd] Total Cost [Euro/day]  \\\n",
       "                  sum                sum                   sum   \n",
       "Type                                                             \n",
       "U100           2792.6             2792.6          4.795346e+05   \n",
       "U12               0.0                0.0          0.000000e+00   \n",
       "U155          14880.0            14880.0          3.107926e+05   \n",
       "U197           6175.4             6175.4          1.076367e+06   \n",
       "U20               0.0                0.0          0.000000e+00   \n",
       "U350           8318.4             8318.4          1.783953e+05   \n",
       "U400          19200.0            19200.0          3.964301e+05   \n",
       "U50            7200.0             7200.0          0.000000e+00   \n",
       "U76            6333.6             6333.6          1.661906e+05   \n",
       "\n",
       "     Daily Commitment Cost [Euro]  \n",
       "                              sum  \n",
       "Type                               \n",
       "U100                      15432.0  \n",
       "U12                         751.5  \n",
       "U155                      11470.0  \n",
       "U197                      20156.5  \n",
       "U20                          47.0  \n",
       "U350                          0.0  \n",
       "U400                          0.0  \n",
       "U50                           0.0  \n",
       "U76                           0.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pandas.DataFrame(index=data['UnitData'].index, columns=['Type', 'Total Power [MW]', 'Total Energy [MWd]', 'Total Cost [Euro/day]', 'Daily Commitment Cost [Euro]'], dtype=float)\n",
    "#B - For each generator type (per type code) compute and comment on: \n",
    "for i in model1.I:\n",
    "    results_df.loc[i, 'Type'] = data['UnitData'].loc[i,'Type']\n",
    "    results_df.loc[i, 'Total Power [MW]'] = sum(model1.P[i, t].value for t in model1.T)\n",
    "    # the total daily energy generation\n",
    "    results_df.loc[i, 'Total Energy [MWd]'] = results_df.loc[i, 'Total Power [MW]']\n",
    "    # the total daily energy costs \n",
    "    results_df.loc[i, 'Total Cost [Euro/day]'] = sum((sum(model1.C[i,f].value*model1.b[i,f,t1].value for f in model1.F)) for t1 in model1.T)\n",
    "     # the total daily commitment costs (i.e., start-up and shut-down costs) \n",
    "    results_df.loc[i, 'Daily Commitment Cost [Euro]'] = sum(model1.CSU[i, t2].value for t2 in model1.T) + sum(model1.CSD[i, t3].value for t3 in model1.T)\n",
    "grouped = results_df.groupby('Type')\n",
    "grouped['Total Power [MW]','Total Energy [MWd]','Total Cost [Euro/day]','Daily Commitment Cost [Euro]'].agg([np.sum])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Total Power [MW]</th>\n",
       "      <th>Total Energy [MWd]</th>\n",
       "      <th>Total Cost [Euro/day]</th>\n",
       "      <th>Daily Commitment Cost [Euro]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>U100</th>\n",
       "      <td>5337.249446</td>\n",
       "      <td>5337.249446</td>\n",
       "      <td>9.473797e+05</td>\n",
       "      <td>7716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>751.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U155</th>\n",
       "      <td>14562.000000</td>\n",
       "      <td>14562.000000</td>\n",
       "      <td>3.037703e+05</td>\n",
       "      <td>11470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U197</th>\n",
       "      <td>8300.872048</td>\n",
       "      <td>8300.872048</td>\n",
       "      <td>1.453849e+06</td>\n",
       "      <td>11518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U20</th>\n",
       "      <td>63.200000</td>\n",
       "      <td>63.200000</td>\n",
       "      <td>1.472320e+04</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U350</th>\n",
       "      <td>8361.000000</td>\n",
       "      <td>8361.000000</td>\n",
       "      <td>1.793961e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U400</th>\n",
       "      <td>13780.164306</td>\n",
       "      <td>13780.164306</td>\n",
       "      <td>2.823655e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U50</th>\n",
       "      <td>7200.000000</td>\n",
       "      <td>7200.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U76</th>\n",
       "      <td>7295.514200</td>\n",
       "      <td>7295.514200</td>\n",
       "      <td>1.936159e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Total Power [MW] Total Energy [MWd] Total Cost [Euro/day]  \\\n",
       "                  sum                sum                   sum   \n",
       "Type                                                             \n",
       "U100      5337.249446        5337.249446          9.473797e+05   \n",
       "U12          0.000000           0.000000          0.000000e+00   \n",
       "U155     14562.000000       14562.000000          3.037703e+05   \n",
       "U197      8300.872048        8300.872048          1.453849e+06   \n",
       "U20         63.200000          63.200000          1.472320e+04   \n",
       "U350      8361.000000        8361.000000          1.793961e+05   \n",
       "U400     13780.164306       13780.164306          2.823655e+05   \n",
       "U50       7200.000000        7200.000000          0.000000e+00   \n",
       "U76       7295.514200        7295.514200          1.936159e+05   \n",
       "\n",
       "     Daily Commitment Cost [Euro]  \n",
       "                              sum  \n",
       "Type                               \n",
       "U100                       7716.0  \n",
       "U12                         751.5  \n",
       "U155                      11470.0  \n",
       "U197                      11518.0  \n",
       "U20                         188.0  \n",
       "U350                          0.0  \n",
       "U400                          0.0  \n",
       "U50                           0.0  \n",
       "U76                           0.0  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pandas.DataFrame(index=data['UnitData'].index, columns=['Type', 'Total Power [MW]', 'Total Energy [MWd]', 'Total Cost [Euro/day]', 'Daily Commitment Cost [Euro]'], dtype=float)\n",
    "#B - For each generator type (per type code) compute and comment on: \n",
    "for i in model2.I:\n",
    "    results_df.loc[i, 'Type'] = data['UnitData'].loc[i,'Type']\n",
    "    results_df.loc[i, 'Total Power [MW]'] = sum(model2.P[i, t].value for t in model2.T)\n",
    "    # the total daily energy generation\n",
    "    results_df.loc[i, 'Total Energy [MWd]'] = results_df.loc[i, 'Total Power [MW]']\n",
    "    # the total daily energy costs \n",
    "    results_df.loc[i, 'Total Cost [Euro/day]'] = sum((sum(model2.C[i,f].value*model2.b[i,f,t1].value for f in model2.F)) for t1 in model2.T)\n",
    "     # the total daily commitment costs (i.e., start-up and shut-down costs) \n",
    "    results_df.loc[i, 'Daily Commitment Cost [Euro]'] = sum(model2.CSU[i, t2].value for t2 in model2.T) + sum(model2.CSD[i, t3].value for t3 in model2.T)\n",
    "grouped = results_df.groupby('Type')\n",
    "grouped['Total Power [MW]','Total Energy [MWd]','Total Cost [Euro/day]','Daily Commitment Cost [Euro]'].agg([np.sum])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Total Power [MW]</th>\n",
       "      <th>Total Energy [MWd]</th>\n",
       "      <th>Total Cost [Euro/day]</th>\n",
       "      <th>Daily Commitment Cost [Euro]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>U100</th>\n",
       "      <td>2842.6</td>\n",
       "      <td>2842.6</td>\n",
       "      <td>4.892187e+05</td>\n",
       "      <td>15432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U12</th>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6.323321e+03</td>\n",
       "      <td>751.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U155</th>\n",
       "      <td>14810.0</td>\n",
       "      <td>14810.0</td>\n",
       "      <td>3.092225e+05</td>\n",
       "      <td>11470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U197</th>\n",
       "      <td>6189.4</td>\n",
       "      <td>6189.4</td>\n",
       "      <td>1.078875e+06</td>\n",
       "      <td>20156.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U20</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.728715e+03</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U350</th>\n",
       "      <td>8279.4</td>\n",
       "      <td>8279.4</td>\n",
       "      <td>1.774790e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U400</th>\n",
       "      <td>19200.0</td>\n",
       "      <td>19200.0</td>\n",
       "      <td>3.964301e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U50</th>\n",
       "      <td>7200.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U76</th>\n",
       "      <td>6333.6</td>\n",
       "      <td>6333.6</td>\n",
       "      <td>1.661906e+05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Total Power [MW] Total Energy [MWd] Total Cost [Euro/day]  \\\n",
       "                  sum                sum                   sum   \n",
       "Type                                                             \n",
       "U100           2842.6             2842.6          4.892187e+05   \n",
       "U12              29.0               29.0          6.323321e+03   \n",
       "U155          14810.0            14810.0          3.092225e+05   \n",
       "U197           6189.4             6189.4          1.078875e+06   \n",
       "U20              16.0               16.0          3.728715e+03   \n",
       "U350           8279.4             8279.4          1.774790e+05   \n",
       "U400          19200.0            19200.0          3.964301e+05   \n",
       "U50            7200.0             7200.0          0.000000e+00   \n",
       "U76            6333.6             6333.6          1.661906e+05   \n",
       "\n",
       "     Daily Commitment Cost [Euro]  \n",
       "                              sum  \n",
       "Type                               \n",
       "U100                      15432.0  \n",
       "U12                         751.5  \n",
       "U155                      11470.0  \n",
       "U197                      20156.5  \n",
       "U20                          47.0  \n",
       "U350                          0.0  \n",
       "U400                          0.0  \n",
       "U50                           0.0  \n",
       "U76                           0.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pandas.DataFrame(index=data['UnitData'].index, columns=['Type', 'Total Power [MW]', 'Total Energy [MWd]', 'Total Cost [Euro/day]', 'Daily Commitment Cost [Euro]'], dtype=float)\n",
    "#B - For each generator type (per type code) compute and comment on: \n",
    "for i in model3.I:\n",
    "    results_df.loc[i, 'Type'] = data['UnitData'].loc[i,'Type']\n",
    "    results_df.loc[i, 'Total Power [MW]'] = sum(model3.P[i, t].value for t in model3.T)\n",
    "    # the total daily energy generation\n",
    "    results_df.loc[i, 'Total Energy [MWd]'] = results_df.loc[i, 'Total Power [MW]']\n",
    "    # the total daily energy costs \n",
    "    results_df.loc[i, 'Total Cost [Euro/day]'] = sum((sum(model3.C[i,f].value*model3.b[i,f,t1].value for f in model3.F)) for t1 in model3.T)\n",
    "     # the total daily commitment costs (i.e., start-up and shut-down costs) \n",
    "    results_df.loc[i, 'Daily Commitment Cost [Euro]'] = sum(model3.CSU[i, t2].value for t2 in model3.T) + sum(model3.CSD[i, t3].value for t3 in model3.T)\n",
    "grouped = results_df.groupby('Type')\n",
    "grouped['Total Power [MW]','Total Energy [MWd]','Total Cost [Euro/day]','Daily Commitment Cost [Euro]'].agg([np.sum])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Question II.2</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of Loading of transmission lines for Case 2 and 3\n",
    "# visualize 0-50%/50-80%/80-100%/100+\n",
    "loading_case2 = pandas.DataFrame(index=data['NetworkData'].index,columns=model2.T)\n",
    "for l in model2.L:\n",
    "    for t in model2.T:\n",
    "        loading_case2.loc[l,t] = abs(model2.Flow[l,t].value/model2.P_line_max[l].value)*100\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "cmap, norm = mcolors.from_levels_and_colors([0, 50, 80, 100.01, 150, 180,200], ['green', 'yellow', 'orange','red', 'darkred','black'])\n",
    "plt.pcolor(loading_case2, cmap=cmap, norm=norm, edgecolors='k', linewidths=1)\n",
    "plt.yticks(np.arange(0.5, len(loading_case2.index), 1), loading_case2.index)\n",
    "plt.xticks(np.arange(0.5, len(loading_case2.columns), 1), loading_case2.columns)\n",
    "plt.title(\"Case 2 Line Loading\",fontsize=18)\n",
    "plt.xlabel(\"Time Instance\",fontsize=15)\n",
    "cb = plt.colorbar()\n",
    "cb.set_label(\"Percentage of Loading\",fontsize=15)\n",
    "plt.ylabel(\"Line\",fontsize=15)\n",
    "cb.ax.tick_params(labelsize=15)\n",
    "plt.savefig(\"out_case2.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of Loading of transmission lines for Case 2 and 3\n",
    "# visualize 0-50%/50-80%/80-100%/100+\n",
    "loading_case3 = pandas.DataFrame(index=data['NetworkData'].index,columns=model3.T)\n",
    "for l in model3.L:\n",
    "    for t in model3.T:\n",
    "        loading_case3.loc[l,t] = abs(model3.Flow[l,t].value/model3.P_line_max[l].value)*100\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.pcolor(loading_case3, cmap=cmap, norm=norm, edgecolors='k', linewidths=1)\n",
    "plt.yticks(np.arange(0.5, len(loading_case3.index), 1), loading_case3.index)\n",
    "plt.xticks(np.arange(0.5, len(loading_case3.columns), 1), loading_case3.columns)\n",
    "plt.title(\"Case 3 Line Loading\",fontsize=18)\n",
    "plt.xlabel(\"Time Instance\",fontsize=15)\n",
    "cb = plt.colorbar()\n",
    "cb.set_label(\"Percentage of Loading\",fontsize=15)\n",
    "plt.ylabel(\"Line\",fontsize=15)\n",
    "cb.ax.tick_params(labelsize=15)\n",
    "plt.savefig(\"out_case3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Part III --The effect of renewable generation and the ESS</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Data1 without Wind Generation (PV PLUS STORAGE)\n",
    "data1 = readInputFile(filename)\n",
    "data1['WindGeneration'].loc[:] = 0  \n",
    "# Data2 for case 2 with no PV Generation (WIND PLUS STORAGE)\n",
    "data2 = readInputFile(filename)\n",
    "data2['PVGeneration'].loc[:] = 0 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Question III.1 </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#solar case 3\n",
    "modelC3_1 = optimizationModel(data1,'Case3')\n",
    "resultsC3_1 = opt.solve(modelC3_1)\n",
    "#wind case 3\n",
    "modelC3_2 = optimizationModel(data2,'Case3')\n",
    "resultsC3_2 = opt.solve(modelC3_2)\n",
    "#solar case 2\n",
    "modelC2_1 = optimizationModel(data1,'Case2')\n",
    "resultsC2_1 = opt.solve(modelC2_1)\n",
    "#wind case 3\n",
    "modelC2_2 = optimizationModel(data2,'Case2')\n",
    "resultsC2_2 = opt.solve(modelC2_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Section III task A for both scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "original_total_demand = pandas.DataFrame(columns=model3.T)\n",
    "for t in model3.T:\n",
    "    original_total_demand.loc['Total Demand',t] = sum(model3.D[j,t].value for j in model3.J)\n",
    "StateOfEnergyPVC3 = pandas.DataFrame(columns=model3.T)\n",
    "for t in modelC3_1.T:\n",
    "    StateOfEnergyPVC3.loc['SoE',t] = sum(modelC3_1.SOE[s,t].value for s in modelC3_1.S)\n",
    "StateOfEnergyWC3 = pandas.DataFrame(columns=model3.T)\n",
    "for t in modelC3_2.T:\n",
    "    StateOfEnergyWC3.loc['SoE',t] = sum(modelC3_2.SOE[s,t].value for s in modelC3_2.S)\n",
    "    \n",
    "fig, ax = plt.subplots(2,1,figsize=(12,10),sharex=True)\n",
    "fig.suptitle('Case 3 - PV/Wind Parks and EES',fontsize=18)\n",
    "ax[0].set_title('State-of-Energy of EES',fontsize=16)\n",
    "ax[1].set_title('Original Total System Load Demand',fontsize=16)\n",
    "ax[0].plot(modelC3_2.T, StateOfEnergyPVC3.loc['SoE',:])\n",
    "ax[0].plot(modelC3_2.T, StateOfEnergyWC3.loc['SoE',:])\n",
    "ax[0].plot(modelC3_2.T, np.ones(len(modelC2_2.T))*490)\n",
    "ax[0].set_yticks(np.arange(0, 550, 50))\n",
    "ax[0].legend(('PV Park', 'Wind Park','Max'), bbox_to_anchor=(0.85, 0.9), loc=2, shadow=True)\n",
    "ax[0].set_ylabel(\"State-of-Energy [MWh]\",fontsize=15)\n",
    "ax[1].plot(modelC3_1.T, original_total_demand.loc['Total Demand',:])\n",
    "ax[1].set_xlabel(\"Time Instance [h]\",fontsize=15)\n",
    "ax[1].set_ylabel(\"Total Demand [MW]\",fontsize=15)\n",
    "ax[0].grid(True)\n",
    "ax[1].set_xlim(['t1','t24'])\n",
    "ax[1].grid(True)\n",
    "plt.savefig(\"PVorWind_plus_EES_A.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#original_total_demand.to_csv('Case3.csv')\n",
    "new1 = original_total_demand.T\n",
    "new1['EES_PV'] = StateOfEnergyPVC3.T\n",
    "new1['EES_Wind'] = StateOfEnergyWC3.T\n",
    "new1.to_csv('Case3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "Section III task B for both scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "original_total_demand = pandas.DataFrame(columns=model2.T)\n",
    "for t in model2.T:\n",
    "    original_total_demand.loc['Total Demand',t] = sum(model2.D[j,t].value for j in model2.J)\n",
    "StateOfEnergyPVC2 = pandas.DataFrame(columns=model2.T)\n",
    "for t in modelC2_1.T:\n",
    "    StateOfEnergyPVC2.loc['SoE',t] = sum(modelC2_1.SOE[s,t].value for s in modelC2_1.S)\n",
    "StateOfEnergyWC2 = pandas.DataFrame(columns=model2.T)\n",
    "for t in modelC2_2.T:\n",
    "    StateOfEnergyWC2.loc['SoE',t] = sum(modelC2_2.SOE[s,t].value for s in modelC2_2.S)\n",
    "    \n",
    "fig, ax = plt.subplots(2,1,figsize=(12,10),sharex=True)\n",
    "fig.suptitle('Case 2 - PV/Wind Parks and EES',fontsize=18)\n",
    "ax[0].set_title('State-of-Energy of EES',fontsize=16)\n",
    "ax[1].set_title('Original Total System Load Demand',fontsize=16)\n",
    "ax[0].plot(modelC2_2.T, StateOfEnergyPVC2.loc['SoE',:])\n",
    "ax[0].plot(modelC2_2.T, StateOfEnergyWC2.loc['SoE',:])\n",
    "ax[0].plot(modelC2_2.T, np.ones(len(modelC2_2.T))*490)\n",
    "ax[0].set_yticks(np.arange(0, 550, 50))\n",
    "ax[0].legend(('PV Park', 'Wind Park','Max'), bbox_to_anchor=(0.85, 0.9), loc=2, shadow=True)\n",
    "ax[0].set_ylabel(\"State-of-Energy [MWh]\",fontsize=15)\n",
    "ax[1].plot(modelC2_1.T, original_total_demand.loc['Total Demand',:])\n",
    "ax[1].set_xlabel(\"Time Instance [h]\",fontsize=15)\n",
    "ax[1].set_ylabel(\"Total Demand [MW]\",fontsize=15)\n",
    "ax[0].grid(True)\n",
    "ax[1].set_xlim(['t1','t24'])\n",
    "ax[1].grid(True)\n",
    "plt.savefig(\"PVorWind_plus_EES_B.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,10))\n",
    "fig.suptitle('Case 2 - PV/Wind Parks and EES',fontsize=18)\n",
    "color = 'tab:red'\n",
    "lns1 = ax.plot(modelC2_2.T, StateOfEnergyPVC2.loc['SoE',:],color=color,label='Wind Park')\n",
    "lns2 = ax.plot(modelC2_2.T, StateOfEnergyWC2.loc['SoE',:],'--',color=color,label='PV Park')\n",
    "lns3 = ax.plot(modelC2_2.T, np.ones(len(modelC2_2.T))*490,'-.',color=color,label='Max')\n",
    "ax.set_yticks(np.arange(0, 550, 50))\n",
    "ax.set_ylabel(\"State-of-Energy [MWh]\",fontsize=15,color = 'tab:red')\n",
    "ax.set_xlabel(\"Time Instance [h]\",fontsize=15)\n",
    "ax.grid(True)\n",
    "ax.set_xlim(['t1','t24'])\n",
    "ax2 = ax.twinx()\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel(\"Total Demand [MW]\",fontsize=15,color=color)\n",
    "lns4 = ax2.plot(modelC2_1.T, original_total_demand.loc['Total Demand',:],color=color,label='Load Demand')\n",
    "\n",
    "lns = lns1+lns2+lns3+lns4\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax.legend(lns, labs,bbox_to_anchor=(0.5, -0.1), loc=8, shadow=True,ncol=4)\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.savefig(\"Try.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new2 = original_total_demand.T\n",
    "new2['EES_PV'] = StateOfEnergyPVC2.T\n",
    "new2['EES_Wind'] = StateOfEnergyWC2.T\n",
    "new2.to_csv('Case2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Question III.2</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 7 hours we have same rating as before (70 MW) <br/> \n",
    "In 1 hour the power rating of energy storage is 490 MW <br/>\n",
    "The way this data are collected can be compared to the reply on Canvas: <br/>\n",
    "<img src=\"table.png\" style=\"height:250px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%% \n"
    }
   },
   "outputs": [],
   "source": [
    "#what is already calculated\n",
    "case2_no_res = model2.ObjCost().round(3)\n",
    "case3_no_res = model3.ObjCost().round(3)\n",
    "PV_plus_EES_7h_case2 = modelC2_1.ObjCost().round(3)\n",
    "PV_plus_EES_7h_case3 = modelC3_1.ObjCost().round(3)\n",
    "Wind_plus_EES_7h_case2 = modelC2_2.ObjCost().round(3)\n",
    "Wind_plus_EES_7h_case3 = modelC3_2.ObjCost().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def select_data(data, PV, WIND, EES, Disch):\n",
    "    if PV == 0:\n",
    "        data['PVGeneration'].loc[:] = 0 \n",
    "    if WIND == 0:\n",
    "        data['WindGeneration'].loc[:] = 0  \n",
    "    if EES == 0:\n",
    "        data['StorageData']['Power'].loc[:] = 0\n",
    "    if Disch == 1:\n",
    "        data['StorageData']['Power'].loc[:] = 490\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "data = readInputFile(filename)\n",
    "#PV No ESS Case 2\n",
    "dat = select_data(data, 1, 0, 0, 7)\n",
    "model = optimizationModel(dat,'Case2')\n",
    "results = opt.solve(model)\n",
    "PV_no_ees_case2 = model.ObjCost().round(3)\n",
    "#PV No ESS Case 3\n",
    "model = optimizationModel(dat,'Case3')\n",
    "results = opt.solve(model)\n",
    "PV_no_ees_case3 = model.ObjCost().round(3)\n",
    "#Wind No ESS Case 2\n",
    "data = readInputFile(filename)\n",
    "dat = select_data(data, 0, 1, 0, 7)\n",
    "model = optimizationModel(dat,'Case2')\n",
    "results = opt.solve(model)\n",
    "Wind_no_ees_case2 = model.ObjCost().round(3)\n",
    "#Wind No ESS Case 3\n",
    "model = optimizationModel(dat,'Case3')\n",
    "results = opt.solve(model)\n",
    "Wind_no_ees_case3 = model.ObjCost().round(3)\n",
    "#PV +ESS 1h discharge case 2\n",
    "data = readInputFile(filename)\n",
    "dat = select_data(data, 1, 0, 1, 1)\n",
    "model = optimizationModel(dat,'Case2')\n",
    "results = opt.solve(model)\n",
    "PV_plus_EES_1h_case2 = model.ObjCost().round(3)\n",
    "#PV +ESS 1h discharge case 3\n",
    "model = optimizationModel(dat,'Case3')\n",
    "results = opt.solve(model)\n",
    "PV_plus_EES_1h_case3 = model.ObjCost().round(3)\n",
    "#Wind +ESS 1h discharge case 2\n",
    "data = readInputFile(filename)\n",
    "dat = select_data(data, 0, 1, 1, 1)\n",
    "model = optimizationModel(dat,'Case2')\n",
    "results = opt.solve(model)\n",
    "Wind_plus_EES_1h_case2 = model.ObjCost().round(3)\n",
    "#Wind +ESS 1h discharge case 3\n",
    "model = optimizationModel(dat,'Case3')\n",
    "results = opt.solve(model)\n",
    "Wind_plus_EES_1h_case3 = model.ObjCost().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No renewables</th>\n",
       "      <th>PV No ESS</th>\n",
       "      <th>PV + ESS 7h discharge</th>\n",
       "      <th>PV + ESS 1h discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>System Costs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case 2</th>\n",
       "      <td>3406743.352</td>\n",
       "      <td>2714416.944</td>\n",
       "      <td>2628670.513</td>\n",
       "      <td>2621953.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Case 3</th>\n",
       "      <td>2675324.913</td>\n",
       "      <td>2225935.791</td>\n",
       "      <td>2155231.547</td>\n",
       "      <td>2135396.990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              No renewables    PV No ESS  PV + ESS 7h discharge  \\\n",
       "System Costs                                                      \n",
       "Case 2          3406743.352  2714416.944            2628670.513   \n",
       "Case 3          2675324.913  2225935.791            2155231.547   \n",
       "\n",
       "              PV + ESS 1h discharge  \n",
       "System Costs                         \n",
       "Case 2                  2621953.933  \n",
       "Case 3                  2135396.990  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Duplicate the table described on Canvas with results\n",
    "\n",
    "results_IIIa = pandas.DataFrame(index=['Case 2', 'Case 3'], columns=['No renewables','PV No ESS','PV + ESS 7h discharge','PV + ESS 1h discharge'], dtype=float)\n",
    "results_IIIa.index.name = 'System Costs'\n",
    "results_IIIa.loc[:,'No renewables'] = [case2_no_res, case3_no_res]\n",
    "results_IIIa.loc[:,'PV No ESS'] = [PV_no_ees_case2, PV_no_ees_case3]\n",
    "results_IIIa.loc[:,'PV + ESS 7h discharge'] = [PV_plus_EES_7h_case2, PV_plus_EES_7h_case3]\n",
    "results_IIIa.loc[:,'PV + ESS 1h discharge'] = [PV_plus_EES_1h_case2, PV_plus_EES_1h_case3]\n",
    "results_IIIa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No renewables</th>\n",
       "      <th>WP No ESS</th>\n",
       "      <th>WP + ESS 7h discharge</th>\n",
       "      <th>WP + ESS 1h discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>System Costs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Case 2</th>\n",
       "      <td>3406743.352</td>\n",
       "      <td>2251186.642</td>\n",
       "      <td>2159255.453</td>\n",
       "      <td>2140127.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Case 3</th>\n",
       "      <td>2675324.913</td>\n",
       "      <td>2084572.144</td>\n",
       "      <td>2006599.888</td>\n",
       "      <td>1991522.549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              No renewables    WP No ESS  WP + ESS 7h discharge  \\\n",
       "System Costs                                                      \n",
       "Case 2          3406743.352  2251186.642            2159255.453   \n",
       "Case 3          2675324.913  2084572.144            2006599.888   \n",
       "\n",
       "              WP + ESS 1h discharge  \n",
       "System Costs                         \n",
       "Case 2                  2140127.551  \n",
       "Case 3                  1991522.549  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_IIIb = pandas.DataFrame(index=['Case 2', 'Case 3'], columns=['No renewables','WP No ESS','WP + ESS 7h discharge','WP + ESS 1h discharge'], dtype=float)\n",
    "results_IIIb.index.name = 'System Costs'\n",
    "results_IIIb.loc[:,'No renewables'] = [case2_no_res, case3_no_res]\n",
    "results_IIIb.loc[:,'WP No ESS'] = [Wind_no_ees_case2, Wind_no_ees_case3]\n",
    "results_IIIb.loc[:,'WP + ESS 7h discharge'] = [Wind_plus_EES_7h_case2, Wind_plus_EES_7h_case3]\n",
    "results_IIIb.loc[:,'WP + ESS 1h discharge'] = [Wind_plus_EES_1h_case2, Wind_plus_EES_1h_case3]\n",
    "results_IIIb\n"
   ]
  }
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