Solar_PHS_gasifier.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_PHS_gasifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OGvudMCvnWwk"
      },
      "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": 1,
      "metadata": {
        "id": "LQwk36zbnvea"
      },
      "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": 4,
      "metadata": {
        "id": "NE_GcFuGnzP3"
      },
      "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[0:100]\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": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "y8ruf5cln0Hn",
        "outputId": "a9f92407-2673-42b5-b36b-34b7f794d789"
      },
      "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": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "dY1_UIlAn2yU"
      },
      "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\n",
        "\n",
        "pump_cost_unit = 1075 #usd/kW\n",
        "pump_OM = 0.01075 #usd/kW.year\n",
        "\n",
        "reservoir_cost_unit = 170 #usd/m2\n",
        "reservoir_OM = 1.7 #usd/m3.year\n",
        "\n",
        "turbine_cost_unit = 1000 #usd/kw\n",
        "turbine_OM = 0.01 #usd/kw\n",
        "\n",
        "#single site phs(1GWh)\n",
        "reservoir_max = 10000000 #m3\n",
        "reservoir_area = 5000000 #m2\n",
        "h3 = 50 #m(minimum head)\n",
        "h2_max = reservoir_max/reservoir_area #maximum reservior head\n",
        "density = 1000 #kg/m3(water)\n",
        "g = 9.8 #m/s2\n",
        "ntur = 0.7 #efficiency of turbine\n",
        "npum = 0.7 #efficiency of pump"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "2cfAh9F0eG81"
      },
      "outputs": [],
      "source": [
        "Q_res = reservoir_max/3600\n",
        "turbine_max = Q_res*density*g*(h3+h2_max)*ntur/1000\n",
        "pump_max = Q_res*density*g*(h3+h2_max)*npum/1000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "uBv9oq_pesPk"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "    Calculating the cost of the hybrid energy system.\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 cost_pump(pump):\n",
        "  pump_cost_woelec = pump*((CRF*pump_cost_unit)+pump_OM)\n",
        "  # pump_elec_op = pump_elec*0.04*CRF\n",
        "  pump_cost = pump_cost_woelec#+pump_elec_op\n",
        "  return pump_cost\n",
        "\n",
        "def cost_reservoir(reservoir_vol):\n",
        "  reservoir_cost = reservoir_area*reservoir_cost_unit*CRF + reservoir_vol*reservoir_OM\n",
        "  return reservoir_cost\n",
        "\n",
        "def cost_turbine(turbine):\n",
        "  turbine_cost = turbine*((CRF*turbine_cost_unit)+turbine_OM)\n",
        "  return turbine_cost\n",
        "\n",
        "def LCOE(total_energy_prod, solar, gasifier, pump, reservoir, turbine):\n",
        "  ASC = cost_pv(solar)+cost_gasifier(gasifier)+cost_pump(pump)+cost_reservoir(reservoir)+cost_turbine(turbine)\n",
        "  energy_prod_per_year = total_energy_prod/N\n",
        "  LCOE = ASC/energy_prod_per_year\n",
        "  return ASC, LCOE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "lIsZjP0ZsFjb"
      },
      "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": 10,
      "metadata": {
        "id": "_MFDjpZg02mT"
      },
      "outputs": [],
      "source": [
        "def turbine_energy(turbine, reservior_lev):\n",
        "  'maximum energy produced by phs turbine'\n",
        "  Qt_rated = turbine*1000/(density*g*(h3+h2_max)*ntur) # turbine throughput\n",
        "  res_Qt = reservior_lev/3600 #maximum Qt available from reservior\n",
        "  Qt = min(Qt_rated, res_Qt)\n",
        "  energy_prod = Qt*ntur*density*g*(h3+(reservior_lev/reservoir_area))\n",
        "  return energy_prod/1000\n",
        "\n",
        "def turbine_discharge(energy, reservior_lev):\n",
        "  Qdis = energy*1000/(ntur*density*g*(h3+(reservior_lev/reservoir_area)))\n",
        "  Qdis_hr = Qdis*3600\n",
        "  return Qdis_hr\n",
        "\n",
        "def pump_energy(pump, Pch_t, reservior_lev):\n",
        "  Qp_rated = pump*1000/(density*g*(h3+h2_max)*npum) # pump throughput\n",
        "  res_Qp = (reservoir_max-reservior_lev)/3600 #maximum Qp available from reservior\n",
        "  Qp = min(Qp_rated, res_Qp)\n",
        "  Ep = Qp*density*g*(h3+reservior_lev/reservoir_area)/npum\n",
        "  Edump = 0\n",
        "  Ep = Ep/1000\n",
        "  if Ep>=Pch_t:\n",
        "    Ep = Pch_t\n",
        "    Edump = 0\n",
        "  else:\n",
        "    Ep = Ep\n",
        "    Edump = Pch_t-Ep\n",
        "  return Ep, Edump\n",
        "\n",
        "def pump_discharge(energy, reservoir_lev):\n",
        "  energy = energy*1000\n",
        "  Qdis = npum*energy/(density*g*(h3+(reservoir_lev/reservoir_area)))\n",
        "  Qdis_hr = Qdis*3600\n",
        "  return Qdis_hr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "layJpDHCyBnW"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "    Hybrid Energy system operational strategy\n",
        "'''\n",
        "def Energy_prod_(energy_def, reservoir_lev, reservoir_max, pump, turbine,  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",
        "  reservoir_lev_up = reservoir_lev\n",
        "  turbine_supply = 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 PHS 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 reservoir_lev>0:\n",
        "        if req_energy<=(turbine_energy(turbine, reservoir_lev)):\n",
        "          '''\n",
        "          req_energy met by only fuelcell\n",
        "          '''\n",
        "          turbine_supply = req_energy\n",
        "          energy_served+=turbine_supply\n",
        "          reservoir_lev_up = reservoir_lev-turbine_discharge(turbine_supply, reservoir_lev)\n",
        "\n",
        "        else:\n",
        "          '''\n",
        "          req_energy met by fuelcell and gasifier\n",
        "          '''\n",
        "          gasifier_E_req = req_energy-(turbine_energy(turbine, reservoir_lev))\n",
        "          turbine_supply = turbine_energy(turbine, reservoir_lev)\n",
        "          energy_served+=turbine_supply\n",
        "          reservoir_lev_up = reservoir_lev-turbine_discharge(turbine_supply, reservoir_lev)\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",
        "      Pch_t = solar_energy-energy_def  # extra energy from solar used for PHS charge\n",
        "\n",
        "\n",
        "      Ep, Edump = pump_energy(pump, Pch_t, reservoir_lev)\n",
        "      reservoir_lev_up = reservoir_lev+pump_discharge(Ep, reservoir_lev)\n",
        "      energy_served+=Edump\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",
        "    Pch_t = solar_energy-energy_def  # extra energy from solar used for PHS charge\n",
        "\n",
        "\n",
        "    Ep, Edump = pump_energy(pump, Pch_t, reservoir_lev)\n",
        "\n",
        "    reservoir_lev_up = reservoir_lev+pump_discharge(Ep, reservoir_lev)\n",
        "    energy_served+=Edump\n",
        "\n",
        "\n",
        "  return energy_served, turbine_supply, reservoir_lev_up, solar_energy, gasifier_energy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "oaoBfZU8RN8n"
      },
      "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, pump, turbine = design\n",
        "\n",
        "      count = 0\n",
        "      Energy_prod_N = 0 #total energy throughout the project life produced\n",
        "      reservoir_lev = reservoir_max\n",
        "\n",
        "      for i in range(len(df)):\n",
        "        Energy_served, turbine_supply, reservoir_lev, solar_ene, gasifier_ene = Energy_prod_(energy_def=df['deficit_energy'][i], reservoir_lev=reservoir_lev, reservoir_max=reservoir_max, pump=pump, turbine=turbine, solar_rate=solar, solar_rad=df['ghi'][i], gasifier_rate=gasifier, temp=df['air_temperature'][i])\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",
        "      asc, lcoe = LCOE(Energy_prod_N, solar, gasifier, pump, reservoir_max, turbine)\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=4, n_obj=1, n_eq_constr=1, xl=[0, 0, 0, 0], xu=[7440937, 2360000, int(pump_max), int(turbine_max)],vtype=int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Ck478aNHTdaw"
      },
      "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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxwPFhpHThiw",
        "outputId": "1ba02659-1c1c-42f3-9c40-8e064452241d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "=================================================================================\n",
            "n_gen  |  n_eval  |     cv_min    |     cv_avg    |     f_avg     |     f_min    \n",
            "=================================================================================\n",
            "     1 |       20 |  0.9999000000 |  0.9999000000 |             - |             -\n",
            "     2 |       40 |  0.9999000000 |  0.9999000000 |             - |             -\n",
            "     3 |       60 |  0.9999000000 |  0.9999000000 |             - |             -\n",
            "     4 |       80 |  0.000000E+00 |  0.8499150000 |  0.0858709099 |  0.0720186279\n",
            "     5 |      100 |  0.000000E+00 |  0.6499350000 |  0.0867720265 |  0.0720186279\n",
            "     6 |      120 |  0.000000E+00 |  0.4499550000 |  0.0861434301 |  0.0720186279\n",
            "     7 |      140 |  0.000000E+00 |  0.000000E+00 |  0.0780356748 |  0.0712260143\n",
            "     8 |      160 |  0.000000E+00 |  0.000000E+00 |  0.0730748099 |  0.0712260143\n",
            "     9 |      180 |  0.000000E+00 |  0.000000E+00 |  0.0716840998 |  0.0699447466\n",
            "    10 |      200 |  0.000000E+00 |  0.000000E+00 |  0.0713275426 |  0.0691129092\n",
            "    11 |      220 |  0.000000E+00 |  0.000000E+00 |  0.0709072701 |  0.0691129092\n",
            "    12 |      240 |  0.000000E+00 |  0.000000E+00 |  0.0704209263 |  0.0691129092\n",
            "    13 |      260 |  0.000000E+00 |  0.000000E+00 |  0.0698493789 |  0.0689576159\n",
            "    14 |      280 |  0.000000E+00 |  0.000000E+00 |  0.0693650153 |  0.0689342125\n",
            "    15 |      300 |  0.000000E+00 |  0.000000E+00 |  0.0692261866 |  0.0689342125\n",
            "    16 |      320 |  0.000000E+00 |  0.000000E+00 |  0.0691314540 |  0.0687928500\n",
            "    17 |      340 |  0.000000E+00 |  0.000000E+00 |  0.0690231672 |  0.0687928500\n",
            "    18 |      360 |  0.000000E+00 |  0.000000E+00 |  0.0689577123 |  0.0687928500\n",
            "    19 |      380 |  0.000000E+00 |  0.000000E+00 |  0.0689153820 |  0.0687920036\n",
            "    20 |      400 |  0.000000E+00 |  0.000000E+00 |  0.0688737032 |  0.0686836908\n",
            "    21 |      420 |  0.000000E+00 |  0.000000E+00 |  0.0688224572 |  0.0685705025\n",
            "    22 |      440 |  0.000000E+00 |  0.000000E+00 |  0.0687538752 |  0.0685232884\n",
            "    23 |      460 |  0.000000E+00 |  0.000000E+00 |  0.0686879813 |  0.0685232884\n",
            "    24 |      480 |  0.000000E+00 |  0.000000E+00 |  0.0686286562 |  0.0685232884\n",
            "    25 |      500 |  0.000000E+00 |  0.000000E+00 |  0.0685866938 |  0.0685201636\n",
            "    26 |      520 |  0.000000E+00 |  0.000000E+00 |  0.0685627740 |  0.0684899789\n",
            "    27 |      540 |  0.000000E+00 |  0.000000E+00 |  0.0685553532 |  0.0684899789\n",
            "    28 |      560 |  0.000000E+00 |  0.000000E+00 |  0.0685429090 |  0.0684899789\n",
            "    29 |      580 |  0.000000E+00 |  0.000000E+00 |  0.0685300113 |  0.0684899789\n",
            "    30 |      600 |  0.000000E+00 |  0.000000E+00 |  0.0685187954 |  0.0684851152\n",
            "    31 |      620 |  0.000000E+00 |  0.000000E+00 |  0.0685116517 |  0.0684832877\n",
            "    32 |      640 |  0.000000E+00 |  0.000000E+00 |  0.0685067267 |  0.0684832877\n",
            "    33 |      660 |  0.000000E+00 |  0.000000E+00 |  0.0685013610 |  0.0684832877\n",
            "    34 |      680 |  0.000000E+00 |  0.000000E+00 |  0.0684948250 |  0.0684831182\n",
            "    35 |      700 |  0.000000E+00 |  0.000000E+00 |  0.0684930183 |  0.0684831182\n",
            "    36 |      720 |  0.000000E+00 |  0.000000E+00 |  0.0684892519 |  0.0684831182\n",
            "    37 |      740 |  0.000000E+00 |  0.000000E+00 |  0.0684875468 |  0.0684831182\n",
            "    38 |      760 |  0.000000E+00 |  0.000000E+00 |  0.0684850289 |  0.0684793431\n",
            "    39 |      780 |  0.000000E+00 |  0.000000E+00 |  0.0684829196 |  0.0684727690\n",
            "    40 |      800 |  0.000000E+00 |  0.000000E+00 |  0.0684819098 |  0.0684727690\n",
            "    41 |      820 |  0.000000E+00 |  0.000000E+00 |  0.0684764487 |  0.0683805354\n",
            "    42 |      840 |  0.000000E+00 |  0.000000E+00 |  0.0684752261 |  0.0683805354\n",
            "    43 |      860 |  0.000000E+00 |  0.000000E+00 |  0.0684741668 |  0.0683805354\n",
            "    44 |      880 |  0.000000E+00 |  0.000000E+00 |  0.0684669113 |  0.0683789982\n",
            "    45 |      900 |  0.000000E+00 |  0.000000E+00 |  0.0684657191 |  0.0683789982\n",
            "    46 |      920 |  0.000000E+00 |  0.000000E+00 |  0.0684642159 |  0.0683789982\n",
            "    47 |      940 |  0.000000E+00 |  0.000000E+00 |  0.0684570468 |  0.0683789982\n",
            "    48 |      960 |  0.000000E+00 |  0.000000E+00 |  0.0684406274 |  0.0683789982\n",
            "    49 |      980 |  0.000000E+00 |  0.000000E+00 |  0.0684261998 |  0.0683787106\n",
            "    50 |     1000 |  0.000000E+00 |  0.000000E+00 |  0.0684131359 |  0.0683786241\n",
            "CPU times: total: 27min 3s\n",
            "Wall time: 1h 21min 4s\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": 16,
      "metadata": {
        "id": "xfd3JvcaThrg"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Solar:7440910KW, Gasifier: 2119935KW, Pump: 90895KW, Turbine: 141180KW || LCOE: [0.06837862]USD/KWh\n",
            "990888.888888889 990888.888888889\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "Optimized results for SOO.\n",
        "'''\n",
        "solar, gasifier, pump, turbine = results.X\n",
        "print(f\"Solar:{solar}KW, Gasifier: {gasifier}KW, Pump: {pump}KW, Turbine: {turbine}KW || LCOE: {results.F}USD/KWh\")\n",
        "print(pump_max,turbine_max)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "qBP30mkYWJQM"
      },
      "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[3:], opt[3:], \"--\")\n",
        "plt.xlabel(\"number of function evaluations\")\n",
        "plt.ylabel(\"LCOE(USD/KWh)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5j-0k9SWMWI",
        "outputId": "b9ab289f-4f2f-427f-f6f6-540dff1d01bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1042667827.391128 1.5029303263014617\n",
            "141180\n",
            "2507\n",
            "962689937.5699722\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "Viewing the operation of the optimized hybrid energy system.\n",
        "'''\n",
        "solar, gasifier, pump, turbine = results.X\n",
        "# solar, gasifier, battery = 3717620.230751795, 2209925.35022830, 4446649.505009017\n",
        "\n",
        "# Solar:3717620.230751795KW, Gasifier: 2209925.350228307KW, Battery: 4446649.505009017KWh || LCOE: [0.12784646]USD/KWh\n",
        "\n",
        "solar_array = []\n",
        "gasifier_array = []\n",
        "turbine_array = []\n",
        "deficit_array = []\n",
        "reservoir_array = []\n",
        "\n",
        "total_supply = []\n",
        "\n",
        "count = 0\n",
        "gasifier_up_hr = 0\n",
        "gasifier_energy = 0\n",
        "Energy_prod_N = 0\n",
        "reservoir_lev = reservoir_max\n",
        "count = 0\n",
        "Energy_prod_N = 0 #total energy throughout the project life produced\n",
        "reservoir_lev = reservoir_max\n",
        "\n",
        "# for i in range(len(df)):\n",
        "for i in range(8760):\n",
        "\n",
        "  Energy_served, turbine_supply, reservoir_lev, solar_ene, gasifier_ene = Energy_prod_(energy_def=df['deficit_energy'][i], reservoir_lev=reservoir_lev, reservoir_max=reservoir_max, pump=pump, turbine=turbine, solar_rate=solar, solar_rad=df['ghi'][i], gasifier_rate=gasifier, temp=df['air_temperature'][i])\n",
        "  Energy_prod_N += Energy_served\n",
        "  gasifier_energy+=gasifier_ene\n",
        "\n",
        "  if round(gasifier_ene, 2)>0:\n",
        "    gasifier_up_hr+=1\n",
        "\n",
        "  energy_tot = df['deficit_energy'][i]+Energy_served\n",
        "  energy_tot = round(energy_tot, 4)\n",
        "  deficit_array.append(df['deficit_energy'][i])\n",
        "  solar_array.append(solar_ene)\n",
        "  gasifier_array.append(gasifier_ene)\n",
        "  turbine_array.append(turbine_supply)\n",
        "  reservoir_array.append(reservoir_lev)\n",
        "  total_supply.append(Energy_served)\n",
        "asc, lcoe = LCOE(Energy_prod_N, solar, gasifier, pump, reservoir_max, turbine)\n",
        "print(asc, lcoe)\n",
        "print(np.array(turbine).max())\n",
        "print(gasifier_up_hr)\n",
        "print(gasifier_energy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.3500690682072626"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gasifier_energy/(5*0.55*1000*1000000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "OR4AHxieXuBj",
        "outputId": "fb74fc06-fe3f-4191-8d60-40056eb90e37"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x1b6453c3550>"
            ]
          },
          "execution_count": 26,
          "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": [
        "'''\n",
        "Viewing the operation of the optimized hybrid energy system.\n",
        "'''\n",
        "i = 0 # start hour\n",
        "j = 100# end hour\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,turbine_array[i:j], label = 'turbine')\n",
        "plt.plot(x,-np.array(deficit_array[i:j]), label = 'deficit')\n",
        "# plt.plot(x, reservoir_array[i:j], label='reservoir')\n",
        "# plt.plot(x,np.array(deficit_array[i:j]), label = 'surplus')\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))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "VwpVBgyey0XU",
        "outputId": "38d058dc-fff6-41a7-c629-04e32bc82c6a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x1b646fbba10>]"
            ]
          },
          "execution_count": 24,
          "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": [
        "plt.plot(x, reservoir_array[i:j], label='reservoir')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyNvsGJDdM1WUsCGEsJtZhr3",
      "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
}