Solar_P2H2P_gaisifer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/phantom-balance/HybridEnergy/blob/master/Solar_P2H2P_gaisifer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZRClomt1j_AG"
      },
      "outputs": [],
      "source": [
        "# pymoo used for optimization\n",
        "from IPython.display import clear_output\n",
        "!pip install pymoo\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "_erlvqwNkL03"
      },
      "outputs": [],
      "source": [
        "# import necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pymoo\n",
        "from pymoo.optimize import minimize\n",
        "from pymoo.core.problem import Problem"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ceko7VdekSH0"
      },
      "outputs": [],
      "source": [
        "# reading the hourly energy csv file \"contains hourly data for deficits, solar radiation and ambient air temperature(Jumla)\"\n",
        "df = pd.read_csv(r'C:\\Users\\Asus\\Desktop\\Gasification files\\GA\\HOURLY_DATA_new.csv')\n",
        "# df = df[0:500]\n",
        "'''\n",
        "     csv file has deficit energy in MWh, solar radiation(ghi) in W/m2, air temperature in celcius.\n",
        "    We perform the analysis in terms of KW.\n",
        "'''\n",
        "df['deficit_energy']=df['deficit_energy']*1000\n",
        "df['deficit_energy'] = df['deficit_energy'].apply(lambda x: round(x, 6))\n",
        "df['ghi']=df['ghi']/1000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "04gFW8IokWIG",
        "outputId": "5d4062f4-48ac-43ff-d525-25f471e074f1"
      },
      "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>Unnamed: 0</th>\n",
              "      <th>ghi</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>deficit_energy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>183960.000000</td>\n",
              "      <td>183960.000000</td>\n",
              "      <td>183960.000000</td>\n",
              "      <td>1.839600e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>91979.500000</td>\n",
              "      <td>0.218271</td>\n",
              "      <td>11.596737</td>\n",
              "      <td>2.363686e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>53104.822097</td>\n",
              "      <td>0.311458</td>\n",
              "      <td>8.223487</td>\n",
              "      <td>4.957133e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-8.630000</td>\n",
              "      <td>-1.302164e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>45989.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.888750</td>\n",
              "      <td>-1.272546e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>91979.500000</td>\n",
              "      <td>0.005384</td>\n",
              "      <td>12.802500</td>\n",
              "      <td>1.943938e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>137969.250000</td>\n",
              "      <td>0.403645</td>\n",
              "      <td>17.676667</td>\n",
              "      <td>5.229730e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>183959.000000</td>\n",
              "      <td>1.147850</td>\n",
              "      <td>29.931667</td>\n",
              "      <td>2.140979e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Unnamed: 0            ghi  air_temperature  deficit_energy\n",
              "count  183960.000000  183960.000000    183960.000000    1.839600e+05\n",
              "mean    91979.500000       0.218271        11.596737    2.363686e+05\n",
              "std     53104.822097       0.311458         8.223487    4.957133e+05\n",
              "min         0.000000       0.000000        -8.630000   -1.302164e+06\n",
              "25%     45989.750000       0.000000         4.888750   -1.272546e+05\n",
              "50%     91979.500000       0.005384        12.802500    1.943938e+05\n",
              "75%    137969.250000       0.403645        17.676667    5.229730e+05\n",
              "max    183959.000000       1.147850        29.931667    2.140979e+06"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "IqOThH-PkYFf"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "    All the necessary constants required for modeling.\n",
        "'''\n",
        "N = 20 # project_life\n",
        "i = 0.06 # interest_rate\n",
        "\n",
        "solar_cost_unit = 727 # usd/kW(capex)\n",
        "solar_OM = 4 # usd/kW.year(opex)\n",
        "\n",
        "CRF = (i*((1+i)**N))/(((1+i)**N)-1) # capital recovery factor\n",
        "d_f = 0.88 #derating_factor\n",
        "Gt_stc = 1 #kw/m2\n",
        "ap = -0.0035 #solar mod temp coeff of power(%/oC)\n",
        "T_cstc = 25 #oC standard temp\n",
        "NOCT = 45 #oC\n",
        "n_bat = 0.92 #round trip efficieny\n",
        "\n",
        "gasifier_cost_unit = 2300 # usd/kW(capex)\n",
        "gasifier_OM = 2 # usd/kW.year(opex)\n",
        "gasifier_CUF = 0.55 # capacity utilization factor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "_TegXdgQkgW3"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "    Calculating the Annualized system cost(ASC) and the levelised cost of energy(LCOE)\n",
        "'''\n",
        "def cost_electrolyser(electrolyser, electrolyser_elec):\n",
        "  electrolyser_cap = ((585.85+(9458.2/electrolyser)*electrolyser**0.622)*((2024/2020)**(-158.9)))\n",
        "  electrolyser_elec_op = electrolyser_elec*0.04*CRF\n",
        "  electrolyser_op = 0.04*CRF*electrolyser_cap + electrolyser_elec_op\n",
        "  electrolyser_cost = CRF*electrolyser_cap+electrolyser_op\n",
        "  return electrolyser_cost\n",
        "\n",
        "def cost_tank(tank):\n",
        "  tank_cap = tank*350\n",
        "  tank_op = tank_cap*CRF*0.01\n",
        "  tank_cost = CRF*tank_cap + tank_op\n",
        "  return tank_cost\n",
        "\n",
        "def cost_fuelcell(fuellcell):\n",
        "  fuellcell_cap = fuellcell*1600\n",
        "  fuellcell_op = fuellcell_cap*CRF*0.04\n",
        "  fuellcell_cost = CRF*fuellcell_cap + fuellcell_op\n",
        "  return fuellcell_cost\n",
        "\n",
        "def cost_pv(solar):\n",
        "  solar_cost = solar*((CRF*solar_cost_unit)+solar_OM)\n",
        "  # print(solar_cost)\n",
        "  return solar_cost\n",
        "\n",
        "def cost_gasifier(gasifier):\n",
        "  gasifier_cost = gasifier*((CRF*gasifier_cost_unit)+gasifier_OM)\n",
        "  # print(gasifier_cost)\n",
        "  return gasifier_cost\n",
        "\n",
        "def LCOE(total_energy_prod, solar, gasifier, electrolyser_elec, electrolyser, tank, fuelcell):\n",
        "  ASC = cost_pv(solar)+cost_gasifier(gasifier)+cost_electrolyser(electrolyser, electrolyser_elec)+cost_tank(tank)+cost_fuelcell(fuelcell)\n",
        "  energy_prod_per_year = total_energy_prod/N\n",
        "  lcoe = ASC/(energy_prod_per_year)\n",
        "  return ASC, lcoe\n",
        "\n",
        "def LCOH(total_hydrogen_prod, solar, electrolyser_elec, electrolyser, tank, fuelcell):\n",
        "  ASC = cost_pv(solar)+cost_electrolyser(electrolyser, electrolyser_elec)+cost_tank(tank)+cost_fuelcell(fuelcell)\n",
        "  hydrogen_prod_per_year = total_hydrogen_prod/N\n",
        "  lcoh = ASC/(hydrogen_prod_per_year)\n",
        "  return lcoh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Hd1ewRlDk_jF"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "    Calculating the energy produced by the solar panel and gasifier.\n",
        "'''\n",
        "def solar_ene_prod(solar_rate, solar_rad, temp):\n",
        "  T_cell = temp+Gt_stc*((NOCT-20)/0.8)\n",
        "  p_op = solar_rate*d_f*(solar_rad/Gt_stc)*(1+ap*(T_cell-T_cstc))\n",
        "  return p_op\n",
        "\n",
        "def gasifier_ene_prod(gasifier_rate):\n",
        "  return gasifier_rate*gasifier_CUF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "NliAx41tlCO5"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "Calculation required for P2H2P.\n",
        "'''\n",
        "def fuel_cell_energy(fuellcell, hydrogen_lev):\n",
        "  'maximum energy produced by fuelcell'\n",
        "  rated_hr_energy = fuellcell\n",
        "  rated_h2_consumption = rated_hr_energy/(33.33*0.4)\n",
        "  if rated_h2_consumption>hydrogen_lev:\n",
        "    energy_output = rated_hr_energy*(hydrogen_lev/rated_h2_consumption)\n",
        "  else:\n",
        "    energy_output = rated_hr_energy\n",
        "\n",
        "  return energy_output\n",
        "\n",
        "def electrolyser_hydrogen(electrolyser, energy):\n",
        "  h2_prod_rated = electrolyser*0.7/39.4\n",
        "  h2_made = 0\n",
        "  if energy >= electrolyser:\n",
        "      h2_made = h2_prod_rated\n",
        "  else:\n",
        "      h2_made = (energy/electrolyser)*h2_prod_rated\n",
        "  return h2_made\n",
        "\n",
        "def fuel_cell_h2_used(energy, fuellcell):\n",
        "  rated_h2_consumed = fuellcell/(33.33*0.4)\n",
        "  if energy==fuellcell:\n",
        "    h2_consumed = rated_h2_consumed\n",
        "  else:\n",
        "    h2_consumed = (energy*rated_h2_consumed)/fuellcell\n",
        "  return h2_consumed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "I4Thy8r9lXRA"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "    Hybrid Energy system operational strategy\n",
        "'''\n",
        "def Energy_prod_(energy_def, tank_lev, tank_max, electrolyser, fuelcell,  solar_rate, solar_rad, gasifier_rate, temp):\n",
        "  energy_def=-energy_def\n",
        "  energy_prod = 0\n",
        "  energy_served = 0\n",
        "  solar_energy = 0\n",
        "  gasifier_energy = 0\n",
        "  tank_lev_up = tank_lev\n",
        "  fuelcell_supply = 0\n",
        "  hydrogen_excess = 0\n",
        "  electrolyser_ene = 0\n",
        "\n",
        "\n",
        "  solar_energy = solar_ene_prod(solar_rate, solar_rad, temp)\n",
        "  if energy_def>0:\n",
        "    '''\n",
        "    Energy deficit strategy\n",
        "    '''\n",
        "    if solar_energy-energy_def<0:\n",
        "      '''\n",
        "      Solar cannot fulfill the energy deficit.\n",
        "      Run fuelcell and gasifier.\n",
        "      '''\n",
        "      # req_energy needs to be addressed by gasifier and fuelcell\n",
        "      req_energy = energy_def-solar_energy\n",
        "      energy_served+=solar_energy\n",
        "\n",
        "      if tank_lev>0:\n",
        "        if req_energy<=(fuel_cell_energy(fuelcell, tank_lev)):\n",
        "          '''\n",
        "          req_energy met by only fuelcell\n",
        "          '''\n",
        "          fuelcell_supply = req_energy\n",
        "          energy_served+=fuelcell_supply\n",
        "          tank_lev_up = tank_lev-fuel_cell_h2_used(req_energy, fuelcell)\n",
        "\n",
        "        else:\n",
        "          '''\n",
        "          req_energy met by fuelcell and gasifier\n",
        "          '''\n",
        "          gasifier_E_req = req_energy-(fuel_cell_energy(fuelcell, tank_lev))\n",
        "          fuelcell_supply = fuel_cell_energy(fuelcell, tank_lev)\n",
        "          energy_served+=fuelcell_supply\n",
        "          tank_lev_up = tank_lev-fuel_cell_h2_used(fuelcell_supply, fuelcell)\n",
        "          gasifier_energy_op = gasifier_ene_prod(gasifier_rate)\n",
        "\n",
        "          if gasifier_energy_op>gasifier_E_req:\n",
        "            gasifier_energy = gasifier_E_req\n",
        "            energy_served+=gasifier_energy\n",
        "\n",
        "          else:\n",
        "            pass\n",
        "            '''\n",
        "            Hybrid energy system cannot fulfill the energy deficit.\n",
        "            New hybrid combination required.\n",
        "            '''\n",
        "            # print(\"# THE CURRENT SYSTEM RATING ISN'T ABLE TO FULLFIL THE DEFECIT, NEW HYBRID COMBINATION REQUIRED\")\n",
        "\n",
        "      else:\n",
        "        '''\n",
        "        req_energy met only by gasifier\n",
        "        '''\n",
        "        gasifier_energy_op = gasifier_ene_prod(gasifier_rate)\n",
        "\n",
        "        if req_energy<gasifier_energy_op:\n",
        "          gasifier_energy = req_energy\n",
        "          energy_served+=gasifier_energy\n",
        "        else:\n",
        "          pass\n",
        "          '''\n",
        "          Hybrid energy system cannot fulfill the energy deficit.\n",
        "          New hybrid combination required.\n",
        "          '''\n",
        "          # print(\"THE CURRENT SYSTEM RATING ISN'T ABLE TO FULLFIL THE DEFECIT, NEW HYBRID COMBINATION REQUIRED\")\n",
        "    else:\n",
        "      '''\n",
        "      Solar can fulfill the energy deficit.\n",
        "      Extra energy from the solar panel is used in electrolyser for hydrogen production.\n",
        "      '''\n",
        "      energy_served+=energy_def  # solar panel served the entire deficit energy\n",
        "      Ech_t = solar_energy-energy_def  # extra energy from solar used for electrolyser\n",
        "      h2_made = electrolyser_hydrogen(electrolyser, Ech_t)\n",
        "\n",
        "      if h2_made<=tank_max-tank_lev:\n",
        "        tank_lev_up = tank_lev+h2_made\n",
        "\n",
        "      else:\n",
        "        tank_lev_up = tank_max\n",
        "        hydrogen_excess = h2_made - (tank_max-tank_lev)\n",
        "\n",
        "    energy_prod = solar_energy + gasifier_energy\n",
        "\n",
        "  else :\n",
        "    '''\n",
        "    Energy surplus strategy\n",
        "    '''\n",
        "    energy_prod=solar_energy+gasifier_energy #solar energy already calculated outside loop\n",
        "    surplus_ene = abs(energy_def)#make hydrogen from this\n",
        "\n",
        "    Ech_t = 0\n",
        "    if solar_energy>=electrolyser:\n",
        "      Ech_t = solar_energy\n",
        "    else:\n",
        "      if surplus_ene+solar_energy<electrolyser:\n",
        "        Ech_t = surplus_ene+solar_energy\n",
        "        electrolyser_ene = surplus_ene\n",
        "      else:\n",
        "        Ech_t = electrolyser\n",
        "        electrolyser_ene = electrolyser-solar_energy\n",
        "\n",
        "    h2_made = electrolyser_hydrogen(electrolyser, Ech_t)\n",
        "\n",
        "    if h2_made<=tank_max-tank_lev:\n",
        "      tank_lev_up = tank_lev+h2_made\n",
        "\n",
        "    else:\n",
        "      tank_lev_up = tank_max\n",
        "      hydrogen_excess = h2_made - (tank_max-tank_lev)\n",
        "\n",
        "  return energy_served, fuelcell_supply, tank_lev_up, electrolyser_ene, solar_energy, gasifier_energy, hydrogen_excess"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "2tv9kfcFo5CC"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "    Optimization for minimizing the LCOE.\n",
        "'''\n",
        "class ProblemWrapper(Problem):\n",
        "  def _evaluate(self, designs, out, *args, **kwargs):\n",
        "    res = []\n",
        "    res1 =[]\n",
        "    for design in designs:\n",
        "      # print(design)\n",
        "      solar, gasifier, electrolyser, tank, fuelcell = design\n",
        "\n",
        "      count = 0\n",
        "      Energy_prod_N = 0 #total energy throughout the project life produced\n",
        "      tank_lev = 0\n",
        "      electrolyser_elec = 0\n",
        "      hydrogen_tot = 0\n",
        "\n",
        "      for i in range(len(df)):\n",
        "        Energy_served ,fuelcell_supply, tank_lev, electrolyser_ene, solar_ene, gasifier_ene, hydrogen_sold = Energy_prod_(energy_def=df['deficit_energy'][i] , tank_lev=tank_lev ,tank_max=tank, electrolyser=electrolyser, fuelcell=fuelcell, solar_rate=solar, solar_rad=df['ghi'][i], gasifier_rate=gasifier, temp=df['air_temperature'][i])\n",
        "        electrolyser_elec+=electrolyser_ene\n",
        "        hydrogen_tot+=hydrogen_sold\n",
        "        Energy_prod_N += Energy_served\n",
        "        energy_tot = df['deficit_energy'][i]+Energy_served\n",
        "        energy_tot = round(energy_tot, 4)\n",
        "\n",
        "        if energy_tot<0 and count!=1:\n",
        "          count = 1\n",
        "          break\n",
        "\n",
        "      asc, lcoe = LCOE(Energy_prod_N, solar, gasifier, electrolyser_elec, electrolyser, tank, fuelcell)\n",
        "      res.append(lcoe)\n",
        "      res1.append(count)\n",
        "    out['F']=np.array(res)\n",
        "    out['H']=np.array(res1)\n",
        "\n",
        "problem = ProblemWrapper(n_var=5, n_obj=1, n_eq_constr=1, xl=[0,  0, 0, 0, 0], xu=[7440937, 2360000, 5469333, 293000, 1302164],vtype=int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "rEgGkB4jsTTE"
      },
      "outputs": [],
      "source": [
        "from pymoo.algorithms.soo.nonconvex.ga import GA\n",
        "from pymoo.operators.sampling.rnd import IntegerRandomSampling\n",
        "from pymoo.operators.crossover.sbx import SBX\n",
        "from pymoo.operators.mutation.pm import PM\n",
        "from pymoo.operators.repair.rounding import RoundingRepair\n",
        "algorithm = GA(pop_size=20,\n",
        "               sampling=IntegerRandomSampling(),\n",
        "               crossover=SBX(prob=1.0, eta=3.0, vtype=int, repair=RoundingRepair()),\n",
        "               mutation=PM(prob=1.0, eta=3.0, vtype=int, repair=RoundingRepair()),\n",
        "               eliminate_duplicate=True)\n",
        "stop_criteria = ('n_gen', 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0dOVoAasWr-",
        "outputId": "bfeb85ff-5098-43cd-a99a-d01264029f9c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_70024\\880116094.py:36: RuntimeWarning: divide by zero encountered in scalar divide\n",
            "  lcoe = ASC/(energy_prod_per_year)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "=================================================================================\n",
            "n_gen  |  n_eval  |     cv_min    |     cv_avg    |     f_avg     |     f_min    \n",
            "=================================================================================\n",
            "     1 |       20 |  0.000000E+00 |  0.9499050000 |  1.4139164521 |  1.4139164521\n",
            "     2 |       40 |  0.000000E+00 |  0.7999200000 |  1.3728072821 |  1.2751716299\n",
            "     3 |       60 |  0.000000E+00 |  0.4499550000 |  1.3281350846 |  1.0980707348\n",
            "     4 |       80 |  0.000000E+00 |  0.000000E+00 |  1.3101044132 |  1.0980707348\n",
            "     5 |      100 |  0.000000E+00 |  0.000000E+00 |  1.2360993342 |  1.0130872081\n",
            "     6 |      120 |  0.000000E+00 |  0.000000E+00 |  1.1585593119 |  1.0130872081\n",
            "     7 |      140 |  0.000000E+00 |  0.000000E+00 |  1.1077443760 |  1.0130872081\n",
            "     8 |      160 |  0.000000E+00 |  0.000000E+00 |  1.0708102094 |  1.0130872081\n",
            "     9 |      180 |  0.000000E+00 |  0.000000E+00 |  1.0454242319 |  0.9566791951\n",
            "    10 |      200 |  0.000000E+00 |  0.000000E+00 |  1.0127866021 |  0.9554692905\n",
            "    11 |      220 |  0.000000E+00 |  0.000000E+00 |  0.9761604382 |  0.9461098603\n",
            "    12 |      240 |  0.000000E+00 |  0.000000E+00 |  0.9623229029 |  0.9461098603\n",
            "    13 |      260 |  0.000000E+00 |  0.000000E+00 |  0.9552507015 |  0.9258355234\n",
            "    14 |      280 |  0.000000E+00 |  0.000000E+00 |  0.9483017592 |  0.9120663730\n",
            "    15 |      300 |  0.000000E+00 |  0.000000E+00 |  0.9436872630 |  0.9120663730\n",
            "    16 |      320 |  0.000000E+00 |  0.000000E+00 |  0.9394698329 |  0.9120663730\n",
            "    17 |      340 |  0.000000E+00 |  0.000000E+00 |  0.9305401862 |  0.9120663730\n",
            "    18 |      360 |  0.000000E+00 |  0.000000E+00 |  0.9264109321 |  0.9119659510\n",
            "    19 |      380 |  0.000000E+00 |  0.000000E+00 |  0.9239578129 |  0.9119659510\n",
            "    20 |      400 |  0.000000E+00 |  0.000000E+00 |  0.9206560498 |  0.9119659510\n",
            "    21 |      420 |  0.000000E+00 |  0.000000E+00 |  0.9167190292 |  0.9119323808\n",
            "    22 |      440 |  0.000000E+00 |  0.000000E+00 |  0.9152825792 |  0.9119323808\n",
            "    23 |      460 |  0.000000E+00 |  0.000000E+00 |  0.9136432018 |  0.9119323808\n",
            "    24 |      480 |  0.000000E+00 |  0.000000E+00 |  0.9126566358 |  0.9106233918\n",
            "    25 |      500 |  0.000000E+00 |  0.000000E+00 |  0.9121274215 |  0.9106233918\n",
            "    26 |      520 |  0.000000E+00 |  0.000000E+00 |  0.9116901096 |  0.9104996484\n",
            "    27 |      540 |  0.000000E+00 |  0.000000E+00 |  0.9105054143 |  0.8902327691\n",
            "    28 |      560 |  0.000000E+00 |  0.000000E+00 |  0.9097892203 |  0.8902327691\n",
            "    29 |      580 |  0.000000E+00 |  0.000000E+00 |  0.9088458627 |  0.8902327691\n",
            "    30 |      600 |  0.000000E+00 |  0.000000E+00 |  0.9056254747 |  0.8826873096\n",
            "    31 |      620 |  0.000000E+00 |  0.000000E+00 |  0.9022634527 |  0.8826873096\n",
            "    32 |      640 |  0.000000E+00 |  0.000000E+00 |  0.8986088229 |  0.8826873096\n",
            "    33 |      660 |  0.000000E+00 |  0.000000E+00 |  0.8932657930 |  0.8826842295\n",
            "    34 |      680 |  0.000000E+00 |  0.000000E+00 |  0.8866671682 |  0.8826842295\n",
            "    35 |      700 |  0.000000E+00 |  0.000000E+00 |  0.8845736973 |  0.8826842295\n",
            "    36 |      720 |  0.000000E+00 |  0.000000E+00 |  0.8829573781 |  0.8785123155\n",
            "    37 |      740 |  0.000000E+00 |  0.000000E+00 |  0.8824181860 |  0.8785123155\n",
            "    38 |      760 |  0.000000E+00 |  0.000000E+00 |  0.8822900216 |  0.8785123155\n",
            "    39 |      780 |  0.000000E+00 |  0.000000E+00 |  0.8813724735 |  0.8784904933\n",
            "    40 |      800 |  0.000000E+00 |  0.000000E+00 |  0.8807386574 |  0.8776165513\n",
            "    41 |      820 |  0.000000E+00 |  0.000000E+00 |  0.8796364273 |  0.8776165513\n",
            "    42 |      840 |  0.000000E+00 |  0.000000E+00 |  0.8790107351 |  0.8772542579\n",
            "    43 |      860 |  0.000000E+00 |  0.000000E+00 |  0.8783685029 |  0.8772542579\n",
            "    44 |      880 |  0.000000E+00 |  0.000000E+00 |  0.8781400976 |  0.8770748233\n",
            "    45 |      900 |  0.000000E+00 |  0.000000E+00 |  0.8779162216 |  0.8770748233\n",
            "    46 |      920 |  0.000000E+00 |  0.000000E+00 |  0.8777168456 |  0.8770748233\n",
            "    47 |      940 |  0.000000E+00 |  0.000000E+00 |  0.8774222678 |  0.8769588933\n",
            "    48 |      960 |  0.000000E+00 |  0.000000E+00 |  0.8772650111 |  0.8768695472\n",
            "    49 |      980 |  0.000000E+00 |  0.000000E+00 |  0.8771356137 |  0.8768426968\n",
            "    50 |     1000 |  0.000000E+00 |  0.000000E+00 |  0.8770805171 |  0.8768426968\n",
            "CPU times: total: 31min 40s\n",
            "Wall time: 1h 29min 19s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "results = minimize(problem=problem,\n",
        "                   algorithm=algorithm,\n",
        "                   termination=stop_criteria,\n",
        "                   verbose=True,\n",
        "                   seed=42,\n",
        "                   save_history=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVVNpek4sboE",
        "outputId": "045ded41-cd94-494d-968a-ea961c7def43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Solar:2485227KW, Gasifier: 2258275KW, Electrolyser: 448286KW, Tank: 29048Kg, Fuelcell: 62857 || LCOE: [0.8768427]USD/KWh\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "Optimized results for SOO.\n",
        "'''\n",
        "solar, gasifier, electrolyser, tank, fuelcell = results.X\n",
        "print(f\"Solar:{solar}KW, Gasifier: {gasifier}KW, Electrolyser: {electrolyser}KW, Tank: {tank}Kg, Fuelcell: {fuelcell} || LCOE: {results.F}USD/KWh\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "PmmQyZKMsf8P",
        "outputId": "f0362f56-5363-4a4f-f0c5-ae1760488bad"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "'''\n",
        "Convergence plot for SOO.\n",
        "'''\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "n_evals = np.array([e.evaluator.n_eval for e in results.history])\n",
        "opt = np.array([e.opt[0].F for e in results.history])\n",
        "\n",
        "plt.title(\"Convergence\")\n",
        "plt.plot(n_evals[4:], opt[4:], \"--\")\n",
        "plt.xlabel(\"number of function evaluations\")\n",
        "plt.ylabel(\"LCOE(USD/KWh)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emShuwtNsjLr",
        "outputId": "775aaf8b-d66e-4ece-f044-23a75e2e4550"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "719436440.075555 0.8768426967808973 5.769805554786776\n",
            "908455112.2256052\n",
            "36935\n",
            "9172128938.02561\n"
          ]
        }
      ],
      "source": [
        "solar, gasifier, electrolyser, tank, fuelcell  = results.X\n",
        "\n",
        "solar_array = []\n",
        "gasifier_array = []\n",
        "fuelcell_array = []\n",
        "tank_array = []\n",
        "deficit_array = []\n",
        "\n",
        "total_supply = []\n",
        "\n",
        "\n",
        "count = 0\n",
        "Energy_prod_N = 0 #total energy throughout the project life produced\n",
        "tank_lev = 0\n",
        "gasifier_up_hr = 0\n",
        "gasifier_energy = 0\n",
        "electrolyser_elec = 0\n",
        "hydrogen_tot = 0\n",
        "\n",
        "for i in range(len(df)):\n",
        "# for i in range(8760):\n",
        "  Energy_served ,fuelcell_supply, tank_lev, electrolyser_ene, solar_ene, gasifier_ene, hydrogen_sold = Energy_prod_(energy_def=df['deficit_energy'][i] , tank_lev=tank_lev ,tank_max=tank, electrolyser=electrolyser, fuelcell=fuelcell, solar_rate=solar, solar_rad=df['ghi'][i], gasifier_rate=gasifier, temp=df['air_temperature'][i])\n",
        "  electrolyser_elec+=electrolyser_ene\n",
        "  hydrogen_tot+=hydrogen_sold\n",
        "  Energy_prod_N += Energy_served\n",
        "  gasifier_energy+=gasifier_ene\n",
        "  \n",
        "  if round(gasifier_ene, 2)>0:\n",
        "    # print(gasifier_ene)\n",
        "    # print(i)\n",
        "    \n",
        "\n",
        "    gasifier_up_hr+=1\n",
        "  deficit_array.append(df['deficit_energy'][i])\n",
        "  solar_array.append(solar_ene)\n",
        "  gasifier_array.append(gasifier_ene)\n",
        "  fuelcell_array.append(fuelcell_supply)\n",
        "  tank_array.append(tank_lev)\n",
        "  total_supply.append(fuelcell_supply+gasifier_ene+solar_ene)\n",
        "\n",
        "asc, lcoe = LCOE(Energy_prod_N, solar, gasifier, electrolyser_elec, electrolyser, tank, fuelcell)\n",
        "lcoh = LCOH(hydrogen_tot, solar, electrolyser_elec, electrolyser, tank, fuelcell)\n",
        "print(asc, lcoe, lcoh)\n",
        "print(hydrogen_tot)\n",
        "print(gasifier_up_hr)\n",
        "print(gasifier_energy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.4324325158403582"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gasifier_energy/(5*0.55*1000*1000000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "Af-qESyitAQS",
        "outputId": "33065d11-6c81-431e-afd2-344f32c6263f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x17b0cee6a90>"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "i = 0\n",
        "j = 100\n",
        "x = np.arange(i,j)\n",
        "plt.plot(x,solar_array[i:j], label='solar')\n",
        "plt.plot(x,gasifier_array[i:j], label='gasifier')\n",
        "plt.plot(x,fuelcell_array[i:j], label = 'fuelcellsupply')\n",
        "plt.plot(x,tank_array[i:j], label = 'tank')\n",
        "# plt.plot(x,np.add(np.array(battery_array[i:j]),-973117), label = 'battery')\n",
        "plt.plot(x,-np.array(deficit_array[i:j]), label = 'deficit/surplus')\n",
        "# plt.plot(x,np.array(deficit_array[i:j]), label = 'surplus')\n",
        "\n",
        "plt.xlabel(\"hour\")\n",
        "plt.ylabel(\"KW\")\n",
        "# plt.plot(x,total_supply[i:j], label = 'total')\n",
        "plt.grid()\n",
        "plt.legend(loc='upper right',bbox_to_anchor=(1.2,0.5))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyN41aB6pBQBjJAC6nfSASTQ",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}