wind_addition_ener_mix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dc38108a-2bf5-4c88-b385-21379dd9ceaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver set: cplex_direct\n"
     ]
    }
   ],
   "source": [
    "#basic imports\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from unyt import kW, minute, hr, day, MW\n",
    "import sys\n",
    "\n",
    "# osier imports\n",
    "from osier import DispatchModel\n",
    "from osier import CapacityExpansion\n",
    "import osier.tech_library as lib\n",
    "\n",
    "# pymoo imports\n",
    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
    "from pymoo.optimize import minimize\n",
    "from pymoo.termination.ftol import MultiObjectiveSpaceTermination\n",
    "from pymoo.termination.robust import RobustTermination\n",
    "\n",
    "# automatically set the solver\n",
    "if \"darwin\" in sys.platform:\n",
    "    solver = 'cplex_direct'\n",
    "elif \"linux\" in sys.platform:\n",
    "    solver = \"cbc\"\n",
    "else:\n",
    "    solver = \"cbc\"\n",
    "\n",
    "print(f\"Solver set: {solver}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b990a9e-5224-43ad-ab03-3299855139f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_hours = 24  # hours per day\n",
    "n_days = 1  # days to model\n",
    "N = n_hours*n_days  # total number of time steps\n",
    "phase_shift = 0  # horizontal shift [radians]\n",
    "base_shift = 2  # vertical shift [units of demand]\n",
    "hours = np.linspace(0,N,N)\n",
    "total_demand = 328.16  # [MWh], sets the total demand [units of energy]\n",
    "\n",
    "demand = (np.sin((hours*np.pi/n_hours*2+phase_shift))*-1+np.ones(N)*(base_shift+1))\n",
    "\n",
    "np.random.seed(1234) # sets the seed for repeatability\n",
    "\n",
    "noise = np.random.random(N)\n",
    "demand += noise\n",
    "\n",
    "demand = demand/demand.sum() * total_demand  # rescale\n",
    "\n",
    "with plt.style.context(\"dark_background\"):\n",
    "    plt.plot(hours, demand, color='cyan')\n",
    "    plt.ylabel('Demand [MW]')\n",
    "    plt.grid(alpha=0.2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "030afa02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing wind data\n",
    "url = \"https://raw.githubusercontent.com/samgdotson/2021-dotson-ms/refs/heads/master/data/nsrdb_data/windHistory_2020.csv\"\n",
    "wind_power_data = pd.read_csv(url, usecols=['time', 'kw_wind'], parse_dates=True,index_col='time')\n",
    "wind_power = wind_power_data.loc['2020-01-01']#'2020-01-02']\n",
    "#wind_power2 = wind_power_data.loc['2020-01-02']\n",
    "wind_power.plot();plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa23f27c-9b89-449d-8d02-8529d6c6eea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/samgdotson/2021-dotson-ms/refs/heads/master/data/solarfarm_data.csv\"\n",
    "solar_power = pd.read_csv(url, usecols=['time', 'kw'], parse_dates=True, index_col='time')\n",
    "\n",
    "# choose a single day\n",
    "solar_day = solar_power.loc['2016-07-01']\n",
    "solar_day.plot();plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88f61c7d-3bfe-4d80-a920-96c6c95989bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the energy production values to run in the model\n",
    "\n",
    "#solar power\n",
    "solar_day_ener = solar_day['kw'].values\n",
    "df_solar = pd.DataFrame(solar_day_ener, columns=['Energy'])\n",
    "solar_ener = df_solar['Energy'].values\n",
    "\n",
    "#wind power\n",
    "df_wind = pd.DataFrame(wind_power,columns=['Energy'])\n",
    "wind_ener = df_wind['Energy'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bdc944b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[NaturalGas_Conv: 8375.1331 MW,\n",
       " Coal_Conv: 0.0 MW,\n",
       " SolarPanel: 2810.3015 MW,\n",
       " WindTurbine: 0.0 MW]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating energy mix\n",
    "\n",
    "#based off osier documentation, we need to include geothermal and add the capacity for coal\n",
    "#adding capacity of coal, based on research\n",
    "from osier.tech_library import coal\n",
    "\n",
    "#finally, create the necessary energy mix\n",
    "UIUC_mix = [lib.natural_gas,lib.coal,lib.solar,lib.wind]\n",
    "display(UIUC_mix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "180060f8-964c-4bea-84ba-f8c3ea63b9ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now wanting to do a multi-objective run\n",
    "#considered total cost, lets consider limitng CO2 emissions\n",
    "from osier import annual_emission, total_cost\n",
    "problem2 = CapacityExpansion(technology_list = UIUC_mix,\n",
    "                            demand=demand*MW,\n",
    "                            solar=solar_ener,\n",
    "                            wind=wind_ener,\n",
    "                            upper_bound= 1 / lib.solar.capacity_credit,\n",
    "                            objectives = [total_cost,annual_emission],\n",
    "                            solver=solver)  # the objectives must be passed as a LIST of functions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44551574-11e9-41a0-9188-81ee9f6f11e0",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Constraint 'oversupply[1]' created with an invalid non-finite upper bound (nan).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb Cell 8\u001b[0m line \u001b[0;36m5\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtime\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m start \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mperf_counter()\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m res \u001b[39m=\u001b[39m minimize(problem2,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m                algorithm,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m                termination,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m                seed\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m                save_history\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m                verbose\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m end \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mperf_counter()\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/cesarzambrano/2025-ans-abstract-ceser/notebooks/wind_addition_ener_mix.ipynb#X10sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThe simulation took \u001b[39m\u001b[39m{\u001b[39;00m(end\u001b[39m-\u001b[39mstart)\u001b[39m/\u001b[39m\u001b[39m60\u001b[39m\u001b[39m:\u001b[39;00m\u001b[39m.3f\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m minutes.\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/optimize.py:67\u001b[0m, in \u001b[0;36mminimize\u001b[0;34m(problem, algorithm, termination, copy_algorithm, copy_termination, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m     algorithm\u001b[39m.\u001b[39msetup(problem, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m     66\u001b[0m \u001b[39m# actually execute the algorithm\u001b[39;00m\n\u001b[0;32m---> 67\u001b[0m res \u001b[39m=\u001b[39m algorithm\u001b[39m.\u001b[39;49mrun()\n\u001b[1;32m     69\u001b[0m \u001b[39m# store the deep copied algorithm in the result object\u001b[39;00m\n\u001b[1;32m     70\u001b[0m res\u001b[39m.\u001b[39malgorithm \u001b[39m=\u001b[39m algorithm\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/algorithm.py:138\u001b[0m, in \u001b[0;36mAlgorithm.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    137\u001b[0m     \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhas_next():\n\u001b[0;32m--> 138\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext()\n\u001b[1;32m    139\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresult()\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/algorithm.py:158\u001b[0m, in \u001b[0;36mAlgorithm.next\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    156\u001b[0m \u001b[39m# call the advance with them after evaluation\u001b[39;00m\n\u001b[1;32m    157\u001b[0m \u001b[39mif\u001b[39;00m infills \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 158\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mevaluator\u001b[39m.\u001b[39;49meval(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mproblem, infills, algorithm\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    159\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madvance(infills\u001b[39m=\u001b[39minfills)\n\u001b[1;32m    161\u001b[0m \u001b[39m# if the algorithm does not follow the infill-advance scheme just call advance\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/evaluator.py:69\u001b[0m, in \u001b[0;36mEvaluator.eval\u001b[0;34m(self, problem, pop, skip_already_evaluated, evaluate_values_of, count_evals, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[39m# evaluate the solutions (if there are any)\u001b[39;00m\n\u001b[1;32m     66\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(I) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m     67\u001b[0m \n\u001b[1;32m     68\u001b[0m     \u001b[39m# do the actual evaluation - call the sub-function to set the corresponding values to the population\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_eval(problem, pop[I], evaluate_values_of, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     71\u001b[0m \u001b[39m# update the function evaluation counter\u001b[39;00m\n\u001b[1;32m     72\u001b[0m \u001b[39mif\u001b[39;00m count_evals:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/evaluator.py:90\u001b[0m, in \u001b[0;36mEvaluator._eval\u001b[0;34m(self, problem, pop, evaluate_values_of, **kwargs)\u001b[0m\n\u001b[1;32m     87\u001b[0m X \u001b[39m=\u001b[39m pop\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mX\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     89\u001b[0m \u001b[39m# call the problem to evaluate the solutions\u001b[39;00m\n\u001b[0;32m---> 90\u001b[0m out \u001b[39m=\u001b[39m problem\u001b[39m.\u001b[39;49mevaluate(X, return_values_of\u001b[39m=\u001b[39;49mevaluate_values_of, return_as_dictionary\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     92\u001b[0m \u001b[39m# for each of the attributes set it to the problem\u001b[39;00m\n\u001b[1;32m     93\u001b[0m \u001b[39mfor\u001b[39;00m key, val \u001b[39min\u001b[39;00m out\u001b[39m.\u001b[39mitems():\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/problem.py:257\u001b[0m, in \u001b[0;36mProblem.evaluate\u001b[0;34m(self, X, return_values_of, return_as_dictionary, *args, **kwargs)\u001b[0m\n\u001b[1;32m    254\u001b[0m     only_single_value \u001b[39m=\u001b[39m \u001b[39mnot\u001b[39;00m (\u001b[39misinstance\u001b[39m(X, \u001b[39mlist\u001b[39m) \u001b[39mor\u001b[39;00m \u001b[39misinstance\u001b[39m(X, np\u001b[39m.\u001b[39mndarray))\n\u001b[1;32m    256\u001b[0m \u001b[39m# this is where the actual evaluation takes place\u001b[39;00m\n\u001b[0;32m--> 257\u001b[0m _out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo(X, return_values_of, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    259\u001b[0m out \u001b[39m=\u001b[39m {}\n\u001b[1;32m    260\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m _out\u001b[39m.\u001b[39mitems():\n\u001b[1;32m    261\u001b[0m \n\u001b[1;32m    262\u001b[0m     \u001b[39m# copy it to a numpy array (it might be one of jax at this point)\u001b[39;00m\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/problem.py:297\u001b[0m, in \u001b[0;36mProblem.do\u001b[0;34m(self, X, return_values_of, *args, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m \u001b[39m# do the function evaluation\u001b[39;00m\n\u001b[1;32m    296\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39melementwise:\n\u001b[0;32m--> 297\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_evaluate_elementwise(X, out, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    298\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    299\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_evaluate_vectorized(X, out, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/problem.py:315\u001b[0m, in \u001b[0;36mProblem._evaluate_elementwise\u001b[0;34m(self, X, out, *args, **kwargs)\u001b[0m\n\u001b[1;32m    312\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39melementwise_func(\u001b[39mself\u001b[39m, args, kwargs)\n\u001b[1;32m    314\u001b[0m \u001b[39m# execute the runner\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m elems \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49melementwise_runner(f, X)\n\u001b[1;32m    317\u001b[0m \u001b[39m# for each evaluation call\u001b[39;00m\n\u001b[1;32m    318\u001b[0m \u001b[39mfor\u001b[39;00m elem \u001b[39min\u001b[39;00m elems:\n\u001b[1;32m    319\u001b[0m \n\u001b[1;32m    320\u001b[0m     \u001b[39m# for each key stored for this evaluation\u001b[39;00m\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/problem.py:32\u001b[0m, in \u001b[0;36mLoopedElementwiseEvaluation.__call__\u001b[0;34m(self, f, X)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, f, X):\n\u001b[0;32m---> 32\u001b[0m     \u001b[39mreturn\u001b[39;00m [f(x) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m X]\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/problem.py:32\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, f, X):\n\u001b[0;32m---> 32\u001b[0m     \u001b[39mreturn\u001b[39;00m [f(x) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m X]\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pymoo/core/problem.py:25\u001b[0m, in \u001b[0;36mElementwiseEvaluationFunction.__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m     24\u001b[0m     out \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m()\n\u001b[0;32m---> 25\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mproblem\u001b[39m.\u001b[39;49m_evaluate(x, out, \u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mkwargs)\n\u001b[1;32m     26\u001b[0m     \u001b[39mreturn\u001b[39;00m out\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/osier/models/capacity_expansion.py:196\u001b[0m, in \u001b[0;36mCapacityExpansion._evaluate\u001b[0;34m(self, x, out, *args, **kwargs)\u001b[0m\n\u001b[1;32m    185\u001b[0m net_demand \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdemand \\\n\u001b[1;32m    186\u001b[0m     \u001b[39m-\u001b[39m wind_gen \\\n\u001b[1;32m    187\u001b[0m     \u001b[39m-\u001b[39m solar_gen\n\u001b[1;32m    189\u001b[0m model \u001b[39m=\u001b[39m DispatchModel(technology_list\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdispatchable_techs,\n\u001b[1;32m    190\u001b[0m                       net_demand\u001b[39m=\u001b[39mnet_demand,\n\u001b[1;32m    191\u001b[0m                       power_units\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpower_units,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    194\u001b[0m                       solver\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msolver,\n\u001b[1;32m    195\u001b[0m                       verbosity\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbosity)\n\u001b[0;32m--> 196\u001b[0m model\u001b[39m.\u001b[39;49msolve()\n\u001b[1;32m    198\u001b[0m \u001b[39mif\u001b[39;00m model\u001b[39m.\u001b[39mresults \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    200\u001b[0m     model\u001b[39m.\u001b[39mresults \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([model\u001b[39m.\u001b[39mresults, renewable_df], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/osier/models/dispatch.py:601\u001b[0m, in \u001b[0;36mDispatchModel.solve\u001b[0;34m(self, solver)\u001b[0m\n\u001b[1;32m    598\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    599\u001b[0m     optimizer \u001b[39m=\u001b[39m po\u001b[39m.\u001b[39mSolverFactory(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msolver)\n\u001b[0;32m--> 601\u001b[0m results \u001b[39m=\u001b[39m optimizer\u001b[39m.\u001b[39;49msolve(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel, tee\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose)\n\u001b[1;32m    602\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    603\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobjective \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mobjective()\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pyomo/solvers/plugins/solvers/direct_solver.py:130\u001b[0m, in \u001b[0;36mDirectSolver.solve\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    127\u001b[0m     \u001b[39m# we're good to go.\u001b[39;00m\n\u001b[1;32m    128\u001b[0m     initial_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[0;32m--> 130\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_presolve(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    132\u001b[0m     presolve_completion_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[1;32m    133\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_report_timing:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pyomo/solvers/plugins/solvers/direct_solver.py:68\u001b[0m, in \u001b[0;36mDirectSolver._presolve\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m     62\u001b[0m     msg \u001b[39m=\u001b[39m (\n\u001b[1;32m     63\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mThe \u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m plugin method \u001b[39m\u001b[39m'\u001b[39m\u001b[39m_presolve\u001b[39m\u001b[39m'\u001b[39m\u001b[39m must be supplied a single problem instance - \u001b[39m\u001b[39m{1}\u001b[39;00m\u001b[39m were \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     64\u001b[0m         \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39msupplied.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     65\u001b[0m     )\u001b[39m.\u001b[39mformat(\u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m), \u001b[39mlen\u001b[39m(args))\n\u001b[1;32m     66\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg)\n\u001b[0;32m---> 68\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_set_instance(model, kwds)\n\u001b[1;32m     70\u001b[0m DirectOrPersistentSolver\u001b[39m.\u001b[39m_presolve(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pyomo/solvers/plugins/solvers/cplex_direct.py:384\u001b[0m, in \u001b[0;36mCPLEXDirect._set_instance\u001b[0;34m(self, model, kwds)\u001b[0m\n\u001b[1;32m    377\u001b[0m     msg \u001b[39m=\u001b[39m (\n\u001b[1;32m    378\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mUnable to create CPLEX model. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    379\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mHave you installed the Python \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    380\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mbindings for CPLEX?\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\\t\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mError message: \u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(e)\n\u001b[1;32m    381\u001b[0m     )\n\u001b[1;32m    382\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mException\u001b[39;00m(msg)\n\u001b[0;32m--> 384\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_add_block(model)\n\u001b[1;32m    386\u001b[0m \u001b[39mfor\u001b[39;00m var, n_ref \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_referenced_variables\u001b[39m.\u001b[39mitems():\n\u001b[1;32m    387\u001b[0m     \u001b[39mif\u001b[39;00m n_ref \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pyomo/solvers/plugins/solvers/cplex_direct.py:414\u001b[0m, in \u001b[0;36mCPLEXDirect._add_block\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[39mfor\u001b[39;00m sub_block \u001b[39min\u001b[39;00m block\u001b[39m.\u001b[39mblock_data_objects(descend_into\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, active\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m    411\u001b[0m     \u001b[39mfor\u001b[39;00m con \u001b[39min\u001b[39;00m sub_block\u001b[39m.\u001b[39mcomponent_data_objects(\n\u001b[1;32m    412\u001b[0m         ctype\u001b[39m=\u001b[39mConstraint, descend_into\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, active\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, sort\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    413\u001b[0m     ):\n\u001b[0;32m--> 414\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m con\u001b[39m.\u001b[39mhas_lb() \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m con\u001b[39m.\u001b[39;49mhas_ub():\n\u001b[1;32m    415\u001b[0m             \u001b[39massert\u001b[39;00m \u001b[39mnot\u001b[39;00m con\u001b[39m.\u001b[39mequality\n\u001b[1;32m    416\u001b[0m             \u001b[39mcontinue\u001b[39;00m  \u001b[39m# non-binding, so skip\u001b[39;00m\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pyomo/core/base/constraint.py:360\u001b[0m, in \u001b[0;36mConstraintData.has_ub\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    357\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mhas_ub\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    358\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Returns :const:`False` when the upper bound is\u001b[39;00m\n\u001b[1;32m    359\u001b[0m \u001b[39m    :const:`None` or positive infinity\"\"\"\u001b[39;00m\n\u001b[0;32m--> 360\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mub \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pyomo/core/base/constraint.py:327\u001b[0m, in \u001b[0;36mConstraintData.ub\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    324\u001b[0m \u001b[39m@property\u001b[39m\n\u001b[1;32m    325\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mub\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    326\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Access the value of the upper bound of a constraint expression.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 327\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_evaluate_bound(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mto_bounded_expression()[\u001b[39m2\u001b[39;49m], \u001b[39mFalse\u001b[39;49;00m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/osier_new/lib/python3.10/site-packages/pyomo/core/base/constraint.py:261\u001b[0m, in \u001b[0;36mConstraintData._evaluate_bound\u001b[0;34m(self, bound, is_lb)\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[39mif\u001b[39;00m bound \u001b[39m==\u001b[39m (\u001b[39m-\u001b[39m_inf \u001b[39mif\u001b[39;00m is_lb \u001b[39melse\u001b[39;00m _inf):\n\u001b[1;32m    260\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 261\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    262\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mConstraint \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m created with an invalid non-finite \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    263\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39mlower\u001b[39m\u001b[39m'\u001b[39m\u001b[39m \u001b[39m\u001b[39mif\u001b[39;00m\u001b[39m \u001b[39mis_lb\u001b[39m \u001b[39m\u001b[39melse\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m\u001b[39mupper\u001b[39m\u001b[39m'\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m bound (\u001b[39m\u001b[39m{\u001b[39;00mbound\u001b[39m}\u001b[39;00m\u001b[39m).\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    264\u001b[0m     )\n\u001b[1;32m    265\u001b[0m \u001b[39mreturn\u001b[39;00m bound\n",
      "\u001b[0;31mValueError\u001b[0m: Constraint 'oversupply[1]' created with an invalid non-finite upper bound (nan)."
     ]
    }
   ],
   "source": [
    "algorithm = NSGA2(pop_size=20)\n",
    "termination = RobustTermination(MultiObjectiveSpaceTermination(tol=0.5, n_skip=5), period=20)\n",
    "import time\n",
    "start = time.perf_counter()\n",
    "res = minimize(problem2,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "end = time.perf_counter()\n",
    "print(f\"The simulation took {(end-start)/60:.3f} minutes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "58516b02-c523-4bf5-b267-517a90fb8a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.74124916e+00, 8.76424387e-06],\n",
       "       [9.85679420e-01, 1.61806909e-04],\n",
       "       [3.11532541e+00, 9.99723784e-06],\n",
       "       [1.22488876e+00, 1.27758676e-04],\n",
       "       [3.71779426e+00, 8.89000885e-06],\n",
       "       [2.29790987e+00, 3.30487164e-05],\n",
       "       [1.08221148e+00, 1.42944315e-04],\n",
       "       [1.63338180e+00, 8.76107362e-05],\n",
       "       [1.46973019e+00, 1.05896262e-04],\n",
       "       [1.89427443e+00, 6.39376026e-05],\n",
       "       [2.10647798e+00, 4.13318400e-05],\n",
       "       [2.69510543e+00, 1.70921985e-05],\n",
       "       [1.52452651e+00, 9.68048481e-05],\n",
       "       [2.04083152e+00, 4.71151930e-05],\n",
       "       [1.04746697e+00, 1.50222296e-04],\n",
       "       [2.55545814e+00, 2.57617683e-05],\n",
       "       [2.52297868e+00, 3.07126691e-05],\n",
       "       [2.85720721e+00, 1.49954449e-05],\n",
       "       [1.87091147e+00, 6.60912462e-05],\n",
       "       [3.02966187e+00, 1.45621279e-05]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[2.09170129e-01, 3.05245864e-04, 4.77298601e-01, 2.18501429e+00],\n",
       "       [9.51547850e-01, 2.66661937e-02, 4.53326443e-05, 4.56878380e-02],\n",
       "       [1.44442691e-01, 3.12532510e-04, 4.95153295e-01, 1.79513364e+00],\n",
       "       [9.91645660e-01, 1.10105947e-03, 4.59473796e-01, 8.79457701e-03],\n",
       "       [2.10232598e-01, 3.05245864e-04, 4.39451994e-01, 2.18501429e+00],\n",
       "       [5.48595735e-01, 4.29543958e-04, 4.79248991e-01, 1.00067226e+00],\n",
       "       [9.44119079e-01, 4.60731345e-04, 1.04604218e-01, 9.65647223e-02],\n",
       "       [8.42703534e-01, 2.45175189e-03, 4.64638391e-01, 3.73775596e-01],\n",
       "       [8.20609610e-01, 1.09419509e-04, 9.39181143e-02, 4.40210031e-01],\n",
       "       [7.51496795e-01, 3.61622865e-06, 4.76786070e-01, 6.03851359e-01],\n",
       "       [5.48595735e-01, 3.84079405e-04, 4.50635577e-01, 8.83849612e-01],\n",
       "       [2.95961152e-01, 1.79461038e-04, 6.93546257e-01, 1.33185775e+00],\n",
       "       [9.37728716e-01, 2.45175189e-03, 7.92522276e-01, 9.87324224e-02],\n",
       "       [5.94873345e-01, 4.61228285e-04, 4.41338276e-01, 8.14819164e-01],\n",
       "       [9.79052636e-01, 5.62047041e-04, 3.52457751e-03, 9.52501363e-02],\n",
       "       [5.97405619e-01, 4.74746397e-04, 4.72400060e-01, 1.14735258e+00],\n",
       "       [7.34212738e-01, 4.53226882e-04, 4.57064930e-01, 1.04756356e+00],\n",
       "       [2.83142650e-01, 1.00427195e-03, 7.29656022e-01, 1.43258153e+00],\n",
       "       [7.52558351e-01, 3.61622865e-06, 4.38939464e-01, 6.03851359e-01],\n",
       "       [8.60566601e-02, 7.99626585e-02, 4.71357932e-01, 1.70369033e+00]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying multi-objective results\n",
    "display(res.F),display(res.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "91394ffe-3b06-4b65-9471-743dbf2ddda6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('dark_background'):\n",
    "    fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
    "\n",
    "    ax.scatter(res.F[:,0], res.F[:,1], edgecolors='red', facecolors='k')\n",
    "    ax.set_ylabel(r\"Lifecycle CO$_2$ Emissions [MT/MWh]\")\n",
    "    ax.set_xlabel(r\"Total Cost [M\\$]\")\n",
    "    ax.grid(alpha=0.2)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "009038f3-85e7-44e6-aae7-577b351876dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "osier_new",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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